CN117350520B - Automobile production optimization method and system - Google Patents

Abstract

The application provides an automobile production optimization method and system. Acquiring basic data of automobile production; establishing a business fact table and a theme fact table according to the basic data; establishing an automobile production global simulation model based on the basic data; calculating an initial real-time production schedule based on real-time data in the production schedule preview model and the business facts table; calculating a material allocation scheme corresponding to the initial real-time production schedule based on the line Bian Ku logistics scheduling model and the arrival delivery plan model; and determining an automobile production simulation result according to the theme fact table, the initial real-time production schedule and the material allocation scheme, rendering the simulation result, and realizing automobile production according to the initial real-time production schedule and the material allocation scheme. The automobile production optimization method and system provided by the application can provide more intelligent, efficient and flexible production and decision support for whole automobile manufacturing enterprises.

Description

Automobile production optimization method and system

Technical Field

The application relates to the technical field of automobile production, in particular to an automobile production optimization method and system.

Background

With the rapid development of the automobile industry, whole automobile manufacturing enterprises face complex and changeable production environments and management challenges, from upstream automobile software and hardware development and manufacture, midstream whole automobile manufacture, downstream automobile purchasing and after-market service, and the problems of production plan generation, decision optimization, total control real dispatching, production state evaluation difficulty and the like are all important factors restricting industrial development.

Currently, whole car manufacturing enterprises in China have made remarkable progress in the aspects of informatization and automation systems, such as MES, ERP, PLM, WMS and other systems. However, there are still problems of complexity of component management, complexity and cycle of production and manufacturing processes, numerous software and difficulty in integration, and challenges of accurately considering real-time production conditions.

Disclosure of Invention

In view of this, the present application provides a method and system for optimizing automobile production, which is used to provide more intelligent, efficient and flexible production and decision support for the whole automobile manufacturing enterprises.

Specifically, the application is realized by the following technical scheme:

a first aspect of the present application provides a method for optimizing production of an automobile, the method comprising:

acquiring basic data of automobile production, wherein the basic data at least comprises order information of the automobile production and layout information of an automobile production factory;

establishing a business fact table and a theme fact table according to the basic data;

the business fact table takes the business of each automobile production link as a clustering object, and stores the basic data in a form of a table; the topic fact table takes the interesting topics produced by the automobile as clustering objects, the basic data and the data of the service fact table are stored in a form, and the topic fact table at least comprises the connection identification of the service fact table;

Establishing an automobile production global simulation model based on the basic data, wherein the global simulation model at least comprises a production plan previewing model, a line Bian Ku logistics scheduling model and a delivery plan model;

the production plan previewing model is used for simulating an automobile production process, the line Bian Ku logistics scheduling model is used for simulating a material scheduling process required by an automobile generation process, and the delivery plan model is used for simulating a material purchasing condition required by the automobile production process;

calculating an initial real-time production schedule based on the real-time data in the production schedule preview model and the service fact table, wherein the real-time data at least comprises real-time to-be-produced order data and production line production order data;

calculating a material allocation scheme corresponding to the initial real-time production schedule based on the line Bian Ku logistics scheduling model and a to-delivery plan model;

and determining an automobile production simulation result according to the theme facts table, the initial real-time production schedule and the material allocation scheme, rendering the simulation result, and realizing automobile production according to the initial real-time production schedule and the material allocation scheme.

A second aspect of the present application provides an automotive production optimization system, the system comprising an acquisition module, a processing module, a modeling module, a calculation module, and a production module; wherein,

the acquisition module is used for acquiring basic data of automobile production, wherein the basic data at least comprises order information of the automobile production and layout information of an automobile production factory;

the processing module is used for establishing a business fact table and a theme fact table according to the basic data;

the business fact table takes the business of each automobile production link as a clustering object, and stores the basic data in a form of a table; the topic fact table takes the interesting topics produced by the automobile as clustering objects, the basic data and the data of the service fact table are stored in a form, and the topic fact table at least comprises the connection identification of the service fact table;

the modeling module is used for building an automobile production global simulation model based on the basic data, wherein the global simulation model at least comprises a production plan previewing model, a line Bian Ku logistics scheduling model and a delivery plan model;

the production plan previewing model is used for simulating an automobile production process, the line Bian Ku logistics scheduling model is used for simulating a material scheduling process required by an automobile generation process, and the delivery plan model is used for simulating a material purchasing condition required by the automobile production process;

The calculation module is used for calculating an initial real-time production schedule based on real-time data in the production schedule preview model and the service fact table, wherein the real-time data at least comprises real-time to-be-produced order data and production line in-production order data;

the calculation module is further used for calculating a material allocation scheme corresponding to the initial real-time production schedule based on the line Bian Ku logistics scheduling model and a delivery plan model;

and the production module is used for determining an automobile production simulation result according to the theme facts table, the initial real-time production schedule and the material allocation scheme, rendering the simulation result and realizing automobile production according to the initial real-time production schedule and the material allocation scheme.

According to the automobile production optimization method and system, basic data of automobile production are obtained, and a service fact table and a theme fact table are built according to the basic data, wherein the service fact table takes services of all automobile production links as clustering objects, and the basic data are stored in a form; the theme fact table takes the application theme of the automobile production as a clustering object, and stores the basic data and the data of the service fact table in a form; further establishing an automobile production global simulation model, wherein the global simulation model at least comprises a production plan previewing model, a line Bian Ku logistics scheduling model and a delivery plan model; the production plan previewing model is used for simulating an automobile production process, the line Bian Ku logistics scheduling model is used for simulating a material scheduling process required by an automobile generation process, and the delivery plan model is used for simulating a material purchasing condition required by the automobile production process; and further, calculating a material allocation scheme corresponding to the initial real-time production schedule based on the line Bian Ku logistics scheduling model and the delivery plan model, and realizing automobile production according to the initial real-time production schedule and the material allocation scheme. On the one hand, through the establishment of two types of fact tables, a high-efficiency data storage mode is provided, inconsistency of data formats from different sources is avoided, the calculation amount and the data utilization complexity increased by data format processing are reduced, careful modeling of an automobile production process in a data layer is facilitated, data support and business insight are provided, and the utilization efficiency of related data is further improved; meanwhile, because the association identification still exists between the theme facts table and the business facts table, the data problem found in the rendering result can be directly linked to the source of the data, and the data maintenance is convenient. On the other hand, an automobile production global simulation model is established, the whole automobile production process can be simulated and evaluated in a virtual environment, the prediction and optimization of a production plan are facilitated, the automobile production process can be completely analyzed and monitored, the evaluation of the production state is facilitated, hidden or impending problems can be found, and early warning and early treatment are achieved; in the actual production process, the production plan is adjusted according to the real-time situation, so that the flexibility and timeliness of production are ensured; the required materials can be ensured to be scheduled and purchased according to actual production requirements through simulation of the model. In the whole, the whole automobile production process is more intelligent through the establishment of the global simulation model and the application of real-time data, the data with different sources and different forms are uniformly managed according to the use mode based on the establishment of the fact table, and meanwhile, the simulation and the planning generated based on the uniformly managed data are beneficial to the prediction, the adjustment and the optimization of the system according to the actual situation, the human intervention is reduced, and the automation level of the production is improved. Thus, an efficient, flexible and intelligent solution is provided for automobile production.

Drawings

FIG. 1 is a flowchart of a first embodiment of an automobile production optimization method provided in the present application;

FIG. 2 is a data hierarchical design diagram provided in this embodiment;

FIG. 3 is a flow chart for establishing and optimizing an automobile production simulation model provided by the application;

fig. 4 is a schematic structural diagram of a first embodiment of an automobile production optimization system provided in the present application.

Detailed Description

Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the accompanying claims.

The terminology used in the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application. As used in this application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any or all possible combinations of one or more of the associated listed items.

It should be understood that although the terms first, second, third, etc. may be used herein to describe various information, these information should not be limited by these terms. These terms are only used to distinguish one type of information from another. For example, a first message may also be referred to as a second message, and similarly, a second message may also be referred to as a first message, without departing from the scope of the present application. The word "if" as used herein may be interpreted as "at … …" or "at … …" or "responsive to a determination", depending on the context.

The application provides an automobile production optimization method and system, which are used for providing more intelligent, efficient and flexible production and decision support for whole automobile manufacturing enterprises.

According to the automobile production optimization method and system, basic data of automobile production are obtained, wherein the basic data at least comprise order information of the automobile production and layout information of an automobile production factory; establishing a business fact table and a theme fact table according to the basic data; the business fact table takes the business of each automobile production link as a clustering object, and stores the basic data in a form of a table; the topic fact table takes the interesting topics produced by the automobile as clustering objects, the basic data and the data of the service fact table are stored in a form, and the topic fact table at least comprises the connection identification of the service fact table; establishing an automobile production global simulation model based on the basic data, wherein the global simulation model at least comprises a production plan previewing model, a line Bian Ku logistics scheduling model and a delivery plan model; the production plan previewing model is used for simulating an automobile production process, the line Bian Ku logistics scheduling model is used for simulating a material scheduling process required by an automobile generation process, and the delivery plan model is used for simulating a material purchasing condition required by the automobile production process; calculating an initial real-time production schedule based on the real-time data in the production schedule preview model and the service fact table, wherein the real-time data at least comprises real-time to-be-produced order data and production line production order data; calculating a material allocation scheme corresponding to the initial real-time production schedule based on the line Bian Ku logistics scheduling model and a to-delivery plan model; and determining an automobile production simulation result according to the theme facts table, the initial real-time production schedule and the material allocation scheme, rendering the simulation result, and realizing automobile production according to the initial real-time production schedule and the material allocation scheme. On one hand, by establishing two types of fact tables, a data efficient storage mode is provided, careful modeling is facilitated in the data layer facing the automobile production process, data support and business insight are provided, and the utilization efficiency of related data is further improved; on the other hand, an automobile production global simulation model is established, the whole automobile production process can be simulated and evaluated in a virtual environment, the prediction and optimization of a production plan are facilitated, the automobile production process can be completely analyzed and monitored, the evaluation of the production state is facilitated, hidden or impending problems can be found, and early warning and early treatment are achieved; in the actual production process, the production plan is adjusted according to the real-time situation, so that the flexibility and timeliness of production are ensured; the required materials can be ensured to be scheduled and purchased according to actual production requirements through simulation of the model. In the whole, the whole automobile production process is more intelligent through the establishment of the global simulation model and the application of real-time data, the data with different sources and different forms are uniformly managed according to the use mode based on the establishment of the fact table, and meanwhile, the simulation and the planning generated based on the uniformly managed data are beneficial to the prediction, the adjustment and the optimization of the system according to the actual situation, the human intervention is reduced, and the automation level of the production is improved. Thus, an efficient, flexible and intelligent solution is provided for automobile production.

Specific examples are given below to describe the technical solutions of the present application in detail.

Fig. 1 is a flowchart of an embodiment of an automobile production optimization method provided in the present application. Referring to fig. 1, the method provided in this embodiment may include:

s101, basic data of automobile production are obtained, wherein the basic data at least comprise order information of the automobile production and layout information of an automobile production factory.

It should be noted that, the basic data of the automobile production comes from the real-time automobile production process, and the basic data at least includes the order information of the automobile production and the layout information of the automobile production factory; the basic data is unprocessed original data from the automobile production process, and specifically, the basic data can include order data, resource data, logistics data, workshop layout data, process data, fault data, quality data and the like.

In order to store various data in different forms, the prior art usually prescribes a unified data storage format, but the mode can lose part of important information, is inconvenient for utilizing information, has complex link of automobile production and complex definition of the format, and the invention provides a method for storing data in a link mode by relying on two tables, namely, clustering and storing the data in association relation, thereby avoiding unification of the formats, fully preserving the information quantity of the data and improving the utilization precision of the data.

It should be further noted that, the basic data may be managed in a layered manner, and specifically, the layering includes: a base data layer, a detail data layer, a public summary layer and a data application layer; the basic data layer is used for storing the basic data, the detail data layer is used for storing the business fact table, and the public summarization layer is used for storing the theme fact table.

The data stored in the basic data layer and the detail data layer are of a first granularity, the data stored in the public summary layer are of a second granularity, the first granularity and the second granularity are the lengths of the data, and the first granularity is smaller than the second granularity.

In particular, granularity is used to determine what a row in a fact table represents, declaring granularity is an important component of the dimension design process, which must be declared before a dimension or fact is selected, because each dimension and fact must be consistent with a defined granularity.

It should be noted that layering the basic data has the following points:

(1) Explicit data structure: each layer of data hierarchy has its corresponding range, and the data required can be accurately located and processed when using the fact table.

(2) Simplifying the complex problem: the complex task is decomposed into a plurality of links to be completed, each layer only processes a single small task, and the method is simple and easy to understand and is convenient for maintaining the accuracy of data.

(3) Reducing the repeated development pressure: the data layering method is utilized to realize the general development of middle layer data, and the repeated development pressure can be greatly reduced.

(4) Masking original data errors: the influence of the original data on the service is shielded, and the re-access during each change is avoided.

(5) Tracking of data blood edges: the final result of the business is represented by a business table, and because of the wide sources, if one source table is wrong, the abnormality can be accurately and rapidly located by virtue of the blood relationship, and the hazard is estimated.

Fig. 2 is a data hierarchical design diagram provided in this embodiment, please refer to fig. 2, specifically, a base data layer includes a storage unit for storing base data, wherein the base data represents raw data that is not processed, and maintains a consistent structure with a source system; in principle, all acquired basic data are reserved in the basic data layer, but the basic data are preprocessed according to service requirements, wherein the preprocessing comprises adding partitions according to the service requirements so as to prepare for subsequent data processing.

The detail data layer comprises a storage unit for storing a service fact table, wherein the service fact table represents the detail data subjected to cleaning and arrangement and is a fact table with the finest granularity (first granularity) constructed based on the characteristics of the service process; and a processing unit for data completion and providing data quality assurance. Preferably, the detail data layer adopts a dimension degradation and detail breadth table mode so as to improve the usability and performance of the data. Specifically, the dimension degradation mode refers to that in the business fact table, other contents are not stored except for the unique identification of the business fact table row number, so that unnecessary data display is reduced, that is, the dimension has no other contents except for the main key, such as order numbers and the like. To improve the usability of the data detail layer, the layer adopts a plurality of dimension degradation methods to degrade the dimension into the fact table, and reduces the association between the fact table and the dimension table. Meanwhile, a detail wide table is adopted at the layer, and the correlation calculation is multiplexed, so that the data scanning is reduced.

The public summarization layer comprises a storage unit for storing a topic fact table, wherein the topic fact table represents a summary index fact table with public granularity (second granularity) obtained by slightly summarizing the data of the detail data layer; and a processing unit for constructing a behavior broad table to meet upper-level application and product index requirements. The public summary layer provides a behavior broad table with a certain dimension as a clue through the light level summary of the subject domain. For example: all things done by the user (merchandise, merchant) are counted in the dimension of time (daily, weekly, monthly).

The data application layer comprises a storage unit for storing data generated according to report form and thematic analysis demand calculation; and an interface unit for directly providing to the front-end application for use by business personnel. The data application layer faces to actual data requirements, and the data source provides highly customized and performance optimized data for business personnel based on the data of the data detail layer or the public summary layer.

S102, establishing a service fact table and a theme fact table according to the basic data, wherein the service fact table takes the service of each automobile production link as a clustering object, and stores the basic data in a form of a form; and the theme fact table takes the application theme of the automobile production as a clustering object, and stores the basic data and the data of the service fact table in a form.

In a specific implementation, the step of establishing a service facts table according to the basic data may include:

(1) And determining a target service of automobile production, wherein the target service belongs to the automobile production service.

It should be noted that, determining the target services of the automobile production, and ensuring that the target services are related to the core services of the automobile production, where the target services are services that need to store the related data currently, and the services may be any services in the automobile production process. For example, the target business may be accessory production, material transportation, material procurement, etc.

(2) And determining target data from the basic data based on the business process definition information of the target business, wherein the target data is at least part of the basic data.

Business process definition information associated with the target business is extracted from the underlying data, the business process definition information defining specific references to the targets in the business process and definitions of granularity, dimensions, facts, which may relate to data in terms of production planning, parts supply, production assembly, etc., for example. Based on the business process definition information of the target business, target data is determined, which data includes at least a portion of the underlying data to ensure that the business fact table can cover all aspects of the target business.

(3) And determining the target granularity of the target service based on the service process definition information of the target service.

The target granularity of the target service is the aggregation level of the data in the service fact table, the determination of the target granularity needs to consider the specific requirement of service analysis, and different service analysis may need data with different granularities. The determination of the target granularity is helpful for establishing a service fact table, so that the data in the table can meet the requirement of service analysis and can be realized under reasonable calculation and storage cost. The target granularity is the data granularity of the target traffic store. Preferably, the target granularity of the target service is consistent with the granularity of the basic data corresponding to the target service.

(4) And determining a result value of the target service based on the target data and the target granularity, wherein the data granularity of the result value of the target service is consistent with the target granularity, and the type of the result value at least comprises an additivable value, a semi-additivable value and a non-additivable value.

It should be noted that the summable value type means that the metrics may be summarized in any dimension associated with the fact table. For example, the unit price of the commodity can be summarized according to the class dimension, the store dimension, the average value, the total price, and the like.

Semi-additive numerical types refer to the fact that the metrics cannot be summarized in some dimension or do not make sense, say, the balance, and the balance do not make sense in the time dimension.

The non-additizable value type refers to the metric not being aggregated in all dimensions associated with the fact table, say, ratiometric data.

(5) And establishing the service fact table based on the result value of the target service and the service fact table row identifier.

And establishing a service fact table based on the result value of the target service and the row identification of the service fact table. It is ensured that each row of data clearly corresponds to a particular result value for the target service, whereas the target granularity of the service facts table corresponds to the result value.

The service fact table is one or more, the target granularity of a plurality of service fact tables is not identical, and the target granularity of each row of data in one service fact table is identical.

It should be noted that if there are multiple service fact tables, it is ensured that their target granularity is not exactly the same, and in one service fact table, the target granularity of each row of data should be the same to ensure consistency and comparability of the data in the tables.

The service fact table is exemplified by the service of each automobile production link, and the automobile production mainly comprises four processes of stamping, welding, coating and final assembly. The corresponding four workshops have different production beats and other data due to different production processes, various data related to corresponding services, such as production order numbers, vehicle types, vehicle body colors, work order numbers, production time and the like, are extracted from basic data according to the corresponding service process descriptions of the processes, target granularity of the data is determined according to the related data, multi-table searching is carried out according to the service process descriptions to determine result values, the result values are stored in a table according to the target granularity, and a service fact table is formed, wherein the production order numbers, the vehicle types, the vehicle body colors, the work order numbers, the production time and the like are stored in the service fact table. As shown in table 1 below. Wherein NodeName is the number of the service.

Table 1 service facts table

In specific implementation, the step of establishing the theme facts table according to the basic data may include:

(1) A topic of interest for automotive production is determined.

In particular, a determination of topics of interest in the field of automotive production may relate to, for example, production efficiency, quality control, supply chain management, and the like.

(2) One or more target dimensions are determined based on the subject of interest.

It should be noted that these target dimensions may include time, product model, production line, vendor, etc., depending on the characteristics of the subject of interest.

For example, when determining a topic of interest in automotive production as quality management, we can observe analysis from multiple dimensions of product yield, production lot reject rate, rework rate, failure rate, quality feedback rate, etc.

(3) And extracting target dimension data corresponding to each target dimension from the basic data and the business fact table based on the one or more target dimensions.

It should be noted that, data corresponding to the target dimension is extracted from the basic data, which includes each field of the target dimension, and at the same time, business data related to the target dimension is extracted from the business fact table.

(4) And summarizing the target dimension data corresponding to each target dimension, and determining the data value corresponding to each target dimension.

Specifically, the data of each target dimension are summarized, so that the aggregation level of the data is ensured to be consistent with the target granularity, and the data value corresponding to each target dimension is obtained.

(5) And constructing a theme fact table by utilizing the target dimension and the corresponding data value thereof, wherein the theme fact table at least comprises interesting theme identifications and identifications of dimensions to which the theme fact table belongs.

Wherein a target dimension includes one or more fields, each field corresponding to a data value, the correspondence facilitating the expression of diversity in different dimensions.

The structure of the topic facts table is illustratively similar to the business facts table structure mentioned above, and is not repeated here. The topic fact table also stores the basic data and the data of the business fact table in the form of a form; wherein the subject of interest is used for extraction presentation of data required for the production process. The application scene comprises production scheduling, production planning, process scheduling and the like. The service fact table and the topic fact table are associated by using a unique identifier of NodeName as a link.

It should be noted that, a topic fact table is built according to the basic data, and when the topic fact tables are multiple, whether any two topic fact tables are consistent is judged; if the existing target dimensions in any two topic fact tables and the corresponding data values are identical, the topic fact tables are identical; if the two topic facts tables are consistent, merging the two topic facts that are consistent at least includes simultaneously reserving different dimensions and corresponding data values thereof, and only reserving one same dimension and corresponding data value thereof.

Specifically, for two topic fact tables, their column names and the values corresponding to the column names are compared one by one, wherein the column names should match and the corresponding values should be the same. And if the column names and the values corresponding to the column names in any two topic fact tables are the same, judging that the two topic fact tables are consistent. If the two topic fact tables agree, they may be merged, for example, by merging the data of the two tables into one larger table, ensuring that all columns of the two tables are contained in the merged table. Such operations help to maintain consistency and integrity of data during data integration and consolidation.

It should be noted that the topic fact table at least includes a service connection identifier, where the service connection identifier is at least used to determine a service fact table associated with the topic fact table, and the service fact table associated with the topic fact table may be one or more.

Wherein the service connection identifier is used to establish a connection between the topic facts table and the service facts table, and is an identifier by which it is possible to trace back and confirm with which service facts table or tables the data of the topic facts table is associated. The design of the service connection identifier supports the association between the topic fact table and one or more service fact tables, and in particular, can be designed according to actual service requirements.

The use of service connection identifiers improves the traceability of the data. By looking up the service connection identifier, the method can know how the data in the topic fact table is related to the service fact table, so that the data source is better understood, meanwhile, tracing and problem searching of the data in the later stage of data rendering are facilitated, and the accuracy of data utilization is improved.

S103, establishing an automobile production global simulation model, wherein the global simulation model at least comprises a production plan previewing model, a line Bian Ku logistics scheduling model and a delivery plan model; the production plan previewing model is used for simulating an automobile production process, the line Bian Ku logistics scheduling model is used for simulating a material scheduling process required by an automobile generation process, and the delivery plan model is used for simulating a material purchasing condition required by the automobile production process.

And building a global simulation model of the automobile production in a simulation platform in a module mode according to a data flow route. The production simulation adopts a self-grinding digital base platform as simulation software to simulate, a work decomposition structure is determined based on the simulation software, a business process in the automobile production process is disassembled, and simulation modules of links such as welding, coating, assembly, buffer (PBS) and the like are built based on the disassembled results so as to build an automobile production global simulation model. The method comprises the steps of obtaining a production queue and generating a production calendar by setting starting conditions and data sources of an application; the data is accessed into a buffer area simulation module, a station model is built in a graphical operation interface of the module, the simulation of the automobile production process is realized, and the simulation result is transferred into a data storage module.

It should be noted that, the flow chart for establishing and optimizing the automobile production simulation model provided by the application is shown in fig. 3.

S104, calculating an initial real-time production schedule based on the real-time data in the production schedule preview model and the service fact table, wherein the real-time data are real-time to-be-produced order data and production line production order data.

It should be noted that, the process of calculating the initial real-time production schedule may include:

(1) And acquiring real-time to-be-produced order data and production line in-process order data. For example, the to-be-produced order data may include data of order number, product model number, production quantity, delivery date, order status, customer information, etc.; the production line production order data may include order number, production quantity, production status, production progress, predicted completion time, etc. data.

Preferably, the data preprocessing can be further included after the real-time to-be-produced order data and the production line to-be-produced order data are acquired, for example, data cleaning can be performed, and possible anomalies or errors can be processed.

(2) And calculating information such as task completion information in production, order sequence information to be produced and the like by combining the obtained order data to be produced, production line order data (useful data content) and a production plan previewing model.

(3) Based on the results of the production planning model previewing, an initial real-time production planning table is generated, which comprises at least the allocation of production tasks for each production line, the order scheduling order, and possibly the lead time.

As an optional embodiment, the calculating an initial real-time production schedule based on the real-time data in the production schedule preview model and the business facts table specifically includes:

Determining a first associated service fact table of the current-day order making data from the real-time data, wherein the first associated service fact table is one or more;

determining the residual workload of automobile production based on the associated business fact table and the generation plan preview model;

determining a second associated service fact table corresponding to the real-time to-be-produced order data based on the real-time to-be-produced order data, wherein one or more second associated service fact tables are provided;

and optimizing and obtaining an initial real-time production schedule based on the residual workload and the second associated business facts table, wherein the initial real-time production schedule at least comprises partial orders to be produced and the corresponding orders in the second associated business facts table.

Preferably, after calculating the initial real-time production schedule, the method further comprises:

(1) After the initial real-time production schedule is generated, constraints on actual production conditions (resources), such as constraints on labor, materials, etc., are determined.

(2) The invention provides a model optimization method to improve the accuracy of the production plan.

Specifically, the content of the optimization and iteration may include: layout reconstruction, production sequence optimization, buffer optimization, personnel optimization and the like.

(3) The corresponding real-time report is generated according to the production schedule, and for example, the real-time report can comprise indexes such as utilization rate of a production line, order delivery condition, layout condition of the production line, production sequence condition, personnel condition, fault condition, equipment condition, productivity condition and the like.

S105, calculating a material allocation scheme corresponding to the initial real-time production schedule based on the line Bian Ku logistics scheduling model and the arrival delivery plan model.

Specifically, the process of calculating the material allocation scheme may include:

(1) According to the initial real-time production schedule, obtaining a production schedule (including information such as delivery date, delivery tool, delivery quantity and the like) in the current production period, and calculating the target material demand of each production link at the current moment according to the production schedule.

(2) And acquiring inventory distribution information of the target materials from the basic data, wherein the inventory distribution information comprises inventory conditions of the materials on each warehouse or production line and the existing material quantity of the production line.

(3) Based on the existing material amount of the production line and the required amount of the target material, the difference of the target material is calculated, namely, how much target material (the amount of the material to be blended from the stock) is required to meet the current production plan.

(4) Using the line Bian Ku logistics scheduling model, logistics transportation schemes are calculated based on target material differences and initial real-time production schedules, which may include determining from which warehouse to extract material, the number of extractions, and the order of extractions to ensure that the desired material reaches the production line in time.

(5) Based on the production schedule, material supply and delivery date, a delivery schedule is calculated, which includes information that determines the time to arrival, the physical distribution path, etc. of the material needed for purchase or production.

(6) And combining the calculation results of the line side library logistics scheduling model and the delivery plan model to generate a logistics purchasing scheme, and adjusting according to actual conditions in the execution process to ensure smooth execution of the production plan.

It should be noted that, the material allocation scheme at least includes a logistics transportation scheme, and the calculating, based on the line Bian Ku logistics scheduling model and the delivery plan model, the material allocation scheme corresponding to the initial real-time production schedule specifically includes:

(1) And determining the demand of the target material produced in real time based on the initial real-time production schedule.

By analyzing the initial real-time production schedule, the production demand of each target material at the current moment is defined, and the factors such as order quantity, production period and the like need to be considered.

(2) And determining inventory distribution information of the target materials and the existing material quantity of the production line based on the basic data.

The inventory distribution information may include information such as warehouse information (information such as name, position, capacity, etc. of a warehouse), bill of materials (information such as name, specification, model, etc. of materials), status of materials (whether available or not), and in-out records.

(3) And determining the difference of the target materials based on the existing material quantity of the production line and the required quantity of the target materials.

It should be noted that the difference represents the amount of additional target material required to meet the production demand.

(4) The line Bian Ku logistics scheduling model optimally calculates logistics transportation schemes based on the differences and the initial real-time production schedule with the goal of minimizing shipping time and cost.

Based on the difference and the initial real-time production schedule, a line side library logistics scheduling model is used to calculate a logistics diversion scheme, which may include, for example, extracting target materials from suppliers or warehouses to meet the needs of each production line.

The logistics transportation scheme at least comprises one or more target libraries for extracting the target materials and target quantity corresponding to the target libraries, and the material extraction of each target library has a sequence.

Specifically, the sequence of material extraction of the target library is determined through calculation.

It should be further noted that, the material allocation scheme at least includes a logistics purchasing scheme, and the calculating, based on the line Bian Ku logistics scheduling model and the delivery plan model, the material allocation scheme corresponding to the initial real-time production schedule specifically includes:

(1) And determining the purchase amount of the target material according to the initial real-time production schedule and the inventory distribution information of the target material.

The purchase amount indicates the amount of the target material to be purchased from the supplier or the warehouse.

(2) Determining the arrival time of the target material based on the purchase amount of the target material and the initial real-time production schedule.

(3) And the arrival and delivery plan model takes the shortest supply time and the lowest cost as the optimal targets, and optimally calculates the logistics purchasing scheme of the target materials according to the arrival time of the target materials and the supply information of the target materials.

The logistics purchasing scheme at least comprises a purchasing party, a material identifier and purchasing quantity.

After calculating the material allocation scheme corresponding to the initial real-time production schedule based on the line Bian Ku logistics scheduling model and the arrival and departure plan model, the method further comprises:

(1) Judging whether the material allocation scheme is matched with the initial real-time production schedule or not; if the material arrival time of the material allocation scheme can meet the automobile production requirement of the initial real-time production schedule, matching; otherwise, the two do not match.

(2) If the two are not matched, optimizing the initial real-time production schedule according to the material arrival time of the material allocation scheme, wherein the optimization at least comprises adjustment of order production sequence and/or replacement of production orders.

And S106, determining an automobile production simulation result according to the theme facts table, the initial real-time production schedule and the material allocation scheme, rendering the simulation result, and realizing automobile production according to the initial real-time production schedule and the material allocation scheme.

The theme facts table is a data storage table taking each interested theme as a clustering object, and data corresponding to the interested theme are rendered and displayed to a user according to an automobile production simulation result; preferably, after the simulation result is rendered, if the simulation result is abnormal, determining an abnormal service fact table according to the topic fact table corresponding to the abnormal result, correcting abnormal basic data based on the abnormal service fact table, and if the abnormal basic data cannot be corrected, discarding the abnormal basic data. Preferably, if the production result data corresponding to the topic fact table of the simulation result cannot meet the preset requirement, the step S104 is returned.

Specifically, a production schedule which is generated in real time initially is executed, a production line is started according to the schedule, the production of automobiles is carried out, and required raw materials and parts are prepared from all storehouses or suppliers according to a material preparation scheme, so that materials are ensured to be delivered to the production line in time according to the schedule; each link including the running state, the production progress, the quality control and the like of the production line is monitored in real time in the production process, and real-time data is used for feeding back to a production plan previewing model so as to perform real-time optimization; according to the production requirements and the material allocation scheme, the inventory information is updated in time, so that the inventory level is ensured to meet the expectations, and production interruption and inventory backlog are avoided; according to the logistics purchasing scheme, a logistics plan is adjusted, so that timely arrival of materials is ensured, and the waiting time of a production line is reduced; and feeding the data of the actual production situation back to the production plan previewing model, performing real-time optimization and iteration, and adjusting the production plan according to the fed-back data so as to adapt to the changed production environment and requirements.

According to the automobile production optimization method, on one hand, the data is processed, the aggregation mode with the business as the center and the theme as the center is adopted, the hierarchical storage is carried out, the data utilization rate is high, the searching efficiency is high, the traceability is high, the data is efficiently utilized and stored, and the accuracy and the efficiency of the production optimization process are improved. On the other hand, the optimal initial real-time generation schedule and material allocation scheme are obtained through the simulation of the whole production line, the intelligent of the whole automobile production line is realized, the result of model calculation is further optimized through the result of model calculation and the actual production and material conditions, and the scientificity, the accuracy and the intelligence of the schedule are improved. The method comprises the steps of obtaining basic data of automobile production, and establishing a service fact table and a theme fact table according to the basic data, wherein the service fact table takes services of all automobile production links as clustering objects, and stores the basic data in a form; the theme fact table takes the application theme of the automobile production as a clustering object, and stores the basic data and the data of the service fact table in a form; further establishing an automobile production global simulation model, wherein the global simulation model at least comprises a production plan previewing model, a line Bian Ku logistics scheduling model and a delivery plan model; the production plan previewing model is used for simulating an automobile production process, the line Bian Ku logistics scheduling model is used for simulating a material scheduling process required by an automobile generation process, and the delivery plan model is used for simulating a material purchasing condition required by the automobile production process; and further, calculating a material allocation scheme corresponding to the initial real-time production schedule based on the line Bian Ku logistics scheduling model and the delivery plan model, and realizing automobile production according to the initial real-time production schedule and the material allocation scheme. On one hand, by establishing two types of fact tables, a data efficient storage mode is provided, careful modeling is facilitated in the data layer facing the automobile production process, data support and business insight are provided, and the utilization efficiency of related data is further improved; on the other hand, an automobile production global simulation model is established, the whole automobile production process can be simulated and evaluated in a virtual environment, the prediction and optimization of a production plan are facilitated, the automobile production process can be completely analyzed and monitored, the evaluation of the production state is facilitated, hidden or impending problems can be found, and early warning and early treatment are achieved; in the actual production process, the production plan is adjusted according to the real-time situation, so that the flexibility and timeliness of production are ensured; the required materials can be ensured to be scheduled and purchased according to actual production requirements through simulation of the model. In the whole, the whole automobile production process is more intelligent through the establishment of the global simulation model and the application of real-time data, the data with different sources and different forms are uniformly managed according to the use mode based on the establishment of the fact table, and meanwhile, the simulation and the planning generated based on the uniformly managed data are beneficial to the prediction, the adjustment and the optimization of the system according to the actual situation, the human intervention is reduced, and the automation level of the production is improved. Thus, an efficient, flexible and intelligent solution is provided for automobile production.

Corresponding to the foregoing embodiment of an automobile production optimization method, the present application further provides an embodiment of an automobile production optimization system.

Fig. 4 is a schematic structural diagram of an embodiment of an automotive production optimization system provided in the present application, referring to fig. 4, where the system includes an obtaining module, a processing module, a modeling module, a calculating module, and a production module; wherein,

the acquisition module is used for acquiring basic data of automobile production, wherein the basic data at least comprises order information of the automobile production and layout information of an automobile production factory;

the processing module is used for establishing a business fact table and a theme fact table according to the basic data;

the business fact table takes the business of each automobile production link as a clustering object, and stores the basic data in a form of a table; the topic fact table takes the interesting topics produced by the automobile as clustering objects, the basic data and the data of the service fact table are stored in a form, and the topic fact table at least comprises the connection identification of the service fact table;

the modeling module is used for building an automobile production global simulation model based on the basic data, wherein the global simulation model at least comprises a production plan previewing model, a line Bian Ku logistics scheduling model and a delivery plan model;

The production plan previewing model is used for simulating an automobile production process, the line Bian Ku logistics scheduling model is used for simulating a material scheduling process required by an automobile generation process, and the delivery plan model is used for simulating a material purchasing condition required by the automobile production process;

the calculation module is used for calculating an initial real-time production schedule based on real-time data in the production schedule preview model and the service fact table, wherein the real-time data at least comprises real-time to-be-produced order data and production line in-production order data;

the calculation module is further used for calculating a material allocation scheme corresponding to the initial real-time production schedule based on the line Bian Ku logistics scheduling model and a delivery plan model;

and the production module is used for determining an automobile production simulation result according to the theme facts table, the initial real-time production schedule and the material allocation scheme, rendering the simulation result and realizing automobile production according to the initial real-time production schedule and the material allocation scheme.

The automobile production optimization method and system provided by the application have wide industrial application value and significance:

The production line twin platform is used as a base, interconnection and intercommunication among OT, IT and other systems are realized, information is fused into a complete service flow, and functions of planning production scheduling, intelligent control and the like are combined with service depth, so that real-time connection of all production elements measured by a man-machine material method is realized. The problems found in the production simulation process can be solved and improved in advance, so that bottlenecks in actual production and influences on product shipment are avoided, unscheduled downtime is reduced, efficiency and productivity are maximized, and intelligent applications such as intelligent decision making of operation, intelligent scheduling of equipment and supply chain optimization are supported.

Taking the application scene of a whole automobile manufacturing enterprise in a country as an example, more than 1500 production units and more than 2000 management/operators are connected. When the production conditions change, the production elements are reconfigured and the scheduling plan is optimized through the production simulation and real-time optimization system, so that the raw material inventory funds occupy 18 percent, the storage and management cost is reduced by 22 percent, the flowing funds are increased by 2 hundred million, and the electric energy consumption is reduced by about 1000 ten thousand per year.

The foregoing description of the preferred embodiments of the present invention is not intended to limit the invention to the precise form disclosed, and any modifications, equivalents, improvements and alternatives falling within the spirit and principles of the present invention are intended to be included within the scope of the present invention.

Claims (8)

1. A method for optimizing the production of an automobile, the method comprising:

acquiring basic data of automobile production, wherein the basic data at least comprises order information of the automobile production and layout information of an automobile production factory;

establishing a business fact table and a theme fact table according to the basic data;

the business fact table takes the business of each automobile production link as a clustering object, and stores the basic data in a form of a table; the topic fact table takes the interesting topics produced by the automobile as clustering objects, the basic data and the data of the service fact table are stored in a form, and the topic fact table at least comprises the connection identification of the service fact table;

establishing an automobile production global simulation model based on the basic data, wherein the global simulation model at least comprises a production plan previewing model, a line Bian Ku logistics scheduling model and a delivery plan model;

the production plan previewing model is used for simulating an automobile production process, the line Bian Ku logistics scheduling model is used for simulating a material scheduling process required by an automobile generation process, and the delivery plan model is used for simulating a material purchasing condition required by the automobile production process;

Calculating an initial real-time production schedule based on the real-time data in the production schedule preview model and the service fact table, wherein the real-time data at least comprises real-time to-be-produced order data and production line production order data;

calculating a material allocation scheme corresponding to the initial real-time production schedule based on the line Bian Ku logistics scheduling model and a to-delivery plan model;

determining an automobile production simulation result according to the theme facts table, the initial real-time production schedule and the material allocation scheme, rendering the simulation result, and realizing automobile production according to the initial real-time production schedule and the material allocation scheme;

the establishing a service fact table according to the basic data specifically comprises the following steps:

determining a target service of automobile production, wherein the target service belongs to the automobile production service;

determining target data from the basic data based on the business process definition information of the target business, wherein the target data is at least part of the basic data;

determining a target granularity of the target service based on service process definition information of the target service;

determining a result value of the target service based on the target data and the target granularity, wherein the data granularity of the result value of the target service is consistent with the target granularity, and the type of the result value at least comprises an additivable value, a semi-additivable value and a non-additivable value;

Establishing the service fact table based on the result value of the target service and the service fact table row identification;

wherein, the service fact table is one or more, the target granularity of a plurality of service fact tables is not identical, and the target granularity of each row of data in one service fact table is identical;

the establishing a theme facts table according to the basic data specifically comprises the following steps:

determining a theme of interest for automotive production;

determining one or more target dimensions based on the topic of interest;

extracting target dimension data corresponding to each target dimension from the basic data and the business fact table based on the one or more target dimensions;

summarizing target dimension data corresponding to each target dimension, and determining a data value corresponding to each target dimension;

constructing a topic fact table by utilizing a target dimension and a data value corresponding to the target dimension, wherein the topic fact table at least comprises interesting topic identifications and identifications of dimensions to which the topic fact table belongs;

wherein a target dimension includes one or more fields, each field corresponding to a data value.

2. The method of claim 1, wherein hierarchically managing the base data comprises: a base data layer, a detail data layer, a public summary layer and a data application layer; the public summary layer is used for storing the topic fact table;

The data stored in the basic data layer and the detail data layer are of a first granularity, the data stored in the public summary layer are of a second granularity, the first granularity and the second granularity are the lengths of the data, and the first granularity is smaller than the second granularity.

3. The method of claim 1, wherein said creating a topic facts table from said base data further comprises:

the number of the topic fact tables is multiple, and whether any two topic fact tables are consistent is judged;

if the existing target dimensions in any two topic fact tables and the corresponding data values are identical, the topic fact tables are identical;

if the two topic facts tables are consistent, merging the two topic facts that are consistent at least includes simultaneously reserving different dimensions and corresponding data values thereof, and only reserving one same dimension and corresponding data value thereof.

4. The method according to claim 1, wherein said calculating an initial real-time production schedule based on real-time data in said production schedule preview model and said business facts table, in particular comprises:

determining a first associated service fact table of the current-day order making data from the real-time data, wherein the first associated service fact table is one or more;

Determining the residual workload of automobile production based on the associated business fact table and the generation plan preview model;

determining a second associated service fact table corresponding to the real-time to-be-produced order data based on the real-time to-be-produced order data, wherein one or more second associated service fact tables are provided;

and optimizing and obtaining an initial real-time production schedule based on the residual workload and the second associated business facts table, wherein the initial real-time production schedule at least comprises partial orders to be produced and the corresponding orders in the second associated business facts table.

5. The method of claim 1, wherein the material compounding regimen comprises at least a logistic transportation regimen,

the calculating a material allocation scheme corresponding to the initial real-time production schedule based on the line Bian Ku logistics scheduling model and the arrival delivery plan model specifically comprises the following steps:

determining a demand for a target material produced in real time based on the initial real-time production schedule;

determining inventory distribution information of the target materials and the existing material quantity of the production line based on the basic data;

determining a difference of the target material based on the line existing material amount and the target material demand;

The line Bian Ku logistics scheduling model aims at the shortest transportation time and the lowest cost, and optimally calculates a logistics transportation scheme based on the difference and the initial real-time production schedule;

the logistics transportation scheme at least comprises one or more target libraries for extracting the target materials and target quantity corresponding to the target libraries, and the material extraction of each target library has a sequence.

6. The method of claim 1, wherein the material distribution scheme comprises at least a logistics procurement scheme,

the calculating a material allocation scheme corresponding to the initial real-time production schedule based on the line Bian Ku logistics scheduling model and the arrival delivery plan model specifically comprises the following steps:

determining the purchase amount of the target material according to the initial real-time production schedule and the inventory distribution information of the target material;

determining an arrival time of the target material based on the purchase amount of the target material and the initial real-time production schedule;

the delivery plan arrival model takes the shortest supply time and the lowest cost as the optimal targets, and optimally calculates the logistics purchasing scheme of the target materials according to the arrival time of the target materials and the supply information of the target materials;

The logistics purchasing scheme at least comprises a purchasing party, a material identifier and purchasing quantity.

7. The method of claim 1, wherein after calculating a material allocation scheme corresponding to the initial real-time production schedule based on the line Bian Ku logistics scheduling model and the to-delivery plan model, further comprising:

judging whether the material allocation scheme is matched with the initial real-time production schedule or not; if the material arrival time of the material allocation scheme can meet the automobile production requirement of the initial real-time production schedule, matching; otherwise, the two are not matched;

if the two are not matched, optimizing the initial real-time production schedule according to the material arrival time of the material allocation scheme, wherein the optimization at least comprises adjustment of order production sequence and/or replacement of production orders.

8. An automobile production optimization system is characterized by comprising an acquisition module, a processing module, a modeling module, a calculation module and a production module; wherein,

the acquisition module is used for acquiring basic data of automobile production, wherein the basic data at least comprises order information of the automobile production and layout information of an automobile production factory;

The processing module is used for establishing a business fact table and a theme fact table according to the basic data;

the business fact table takes the business of each automobile production link as a clustering object, and stores the basic data in a form of a table; the topic fact table takes the interesting topics produced by the automobile as clustering objects, the basic data and the data of the service fact table are stored in a form, and the topic fact table at least comprises the connection identification of the service fact table;

the modeling module is used for building an automobile production global simulation model based on the basic data, wherein the global simulation model at least comprises a production plan previewing model, a line Bian Ku logistics scheduling model and a delivery plan model;

the production plan previewing model is used for simulating an automobile production process, the line Bian Ku logistics scheduling model is used for simulating a material scheduling process required by an automobile generation process, and the delivery plan model is used for simulating a material purchasing condition required by the automobile production process;

the calculation module is used for calculating an initial real-time production schedule based on real-time data in the production schedule preview model and the service fact table, wherein the real-time data at least comprises real-time to-be-produced order data and production line in-production order data;

The calculation module is further used for calculating a material allocation scheme corresponding to the initial real-time production schedule based on the line Bian Ku logistics scheduling model and a delivery plan model;

the production module is used for determining an automobile production simulation result according to the theme facts table, the initial real-time production schedule and the material allocation scheme, rendering the simulation result and realizing automobile production according to the initial real-time production schedule and the material allocation scheme;

the establishing a service fact table according to the basic data specifically comprises the following steps:

determining a target service of automobile production, wherein the target service belongs to the automobile production service;

determining target data from the basic data based on the business process definition information of the target business, wherein the target data is at least part of the basic data;

determining a target granularity of the target service based on service process definition information of the target service;

determining a result value of the target service based on the target data and the target granularity, wherein the data granularity of the result value of the target service is consistent with the target granularity, and the type of the result value at least comprises an additivable value, a semi-additivable value and a non-additivable value;

Establishing the service fact table based on the result value of the target service and the service fact table row identification;

wherein, the service fact table is one or more, the target granularity of a plurality of service fact tables is not identical, and the target granularity of each row of data in one service fact table is identical;

the establishing a theme facts table according to the basic data specifically comprises the following steps:

determining a theme of interest for automotive production;

determining one or more target dimensions based on the topic of interest;

extracting target dimension data corresponding to each target dimension from the basic data and the business fact table based on the one or more target dimensions;

summarizing target dimension data corresponding to each target dimension, and determining a data value corresponding to each target dimension;

constructing a topic fact table by utilizing a target dimension and a data value corresponding to the target dimension, wherein the topic fact table at least comprises interesting topic identifications and identifications of dimensions to which the topic fact table belongs;

wherein a target dimension includes one or more fields, each field corresponding to a data value.