CN117893038A - Intelligent decision system and method integrating statistical strategy and deep learning model - Google Patents
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Abstract
The invention relates to an intelligent decision system and method integrating a statistical strategy and a deep learning model, wherein the system comprises a variable data management module and a decision flow management module which are sequentially connected, the variable data module is used for establishing a corresponding relation between variable data and vector data, and extracting the variable data and the vector data to the decision flow management module at the same time; the decision flow management module is used for realizing the cross-category assembly of the rule set and the deep learning model and outputting and obtaining a decision result. The method comprises the following steps: establishing a corresponding relation between variable data and vector data, and simultaneously extracting the variable data and the vector data; constructing a rule set by using the extracted variable data, and training a deep learning model by using the extracted vector data; and according to the user requirements, freely assembling the rule set and the deep learning model, and outputting to obtain a decision result. Compared with the prior art, the method and the device can improve the multiplexing degree of variable data on the decision platform, improve the configurability of overall decisions and develop time efficiency.
Description
Technical Field
The invention relates to an intelligent decision system and method integrating a statistical strategy and a deep learning model.
Background
Intelligent decision making is a process of modeling, analyzing and obtaining decision making on the basis of a set target by an organization or a person comprehensively utilizing various intelligent technologies and tools. The process integrates factors such as constraint conditions, strategies, preference, uncertainty and the like, and can automatically realize optimal decision making so as to solve the increasingly complex production and living problems in the new growth era.
The current artificial intelligence technology and data analysis algorithm are continuously developed and innovated, so that the intelligent decision platform can process more and more complex data and realize faster and more efficient data analysis and decision support. In the current financial science and technology field, the intelligent decision platform mainly adopts the following decision methods:
1. decision execution under traditional statistical policies: the scene mainly uses a traditional statistical strategy, aims at specific business scenes such as wind control and the like, takes a rule set in the scene as a core, combines a statistical model, performs decision analysis based on variables, and generates a decision result under a corresponding decision.
2. Decision execution under deep learning model: typical implementations are either built in the form of code scripts, or specialized artificial intelligence platforms are built, often requiring pre-computation of vectors and storing of vectors. ( And (3) injection: the deep learning model here refers to: model for processing and calculating data under specific marketing or wind control scene by using deep learning method such as convolutional neural network and the like in order to solve specific problems )
3. Cross-platform or multi-service approach: a single type of policy type cannot meet rapidly changing business needs, either in a wind-controlled or marketing scenario. In general, the implementation mode under the scene of the cross-strategy type mostly adopts a cross-platform or multi-service calling mode, so that the consistency of data is difficult to ensure, and the analysis of the whole decision result is quite split.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide an intelligent decision system and method integrating a statistical strategy and a deep learning model, which can improve the multiplexing degree of variable data on a decision platform, the configurability of overall decision and the development timeliness.
The aim of the invention can be achieved by the following technical scheme: an intelligent decision system integrating a statistical strategy and a deep learning model comprises a variable data management module and a decision flow management module which are connected in sequence, wherein the variable data module is used for establishing a corresponding relation between variable data and vector data and extracting the variable data and the vector data to the decision flow management module at the same time;
the decision flow management module is used for realizing cross-category assembly of the rule set and the deep learning model so as to output and obtain a decision result.
Further, the variable data management module is connected with a variable center database and a vector center database.
Further, the decision flow management module comprises a rule model management unit and a decision flow unit, wherein the rule model management unit is connected with the variable data management module and is used for receiving variable data and corresponding vector data so as to construct a rule set and train a deep learning model;
the decision flow unit is used for assembling the rule set and the deep learning model and outputting a decision result.
An intelligent decision method integrating a statistical strategy and a deep learning model comprises the following steps:
s1, establishing a corresponding relation between variable data and vector data, and simultaneously extracting the variable data and the vector data;
s2, constructing a rule set by using the extracted variable data, and training a deep learning model by using the extracted vector data;
and S3, freely assembling the rule set and the deep learning model according to the user requirements, and outputting to obtain a decision result.
Further, the step S1 specifically includes the following steps:
s11, establishing a corresponding relation between variable data and vector data;
s12, variable data and corresponding vector data are extracted at the same time, and preprocessing is carried out on the variable data in the extraction process so as to form a unified variable data source.
Further, the step S12 is specifically to extract variable data and corresponding vector data from OLAP (On-Line Analytical Processing, online analysis and processing) database or external data service.
Further, in step S12, data complement or spam processing is specifically performed on the variable data in the extraction process.
Further, the free assembly rule set and the deep learning model in the step S3 specifically include the following cases:
assembling the rule sets;
the deep learning model and the deep learning model are assembled;
the rule set and the deep learning model are assembled.
Further, in the step S3, if the rule set and the deep learning model are assembled, taking the parameters of the rule set as the modeling parameters of the nodes of the next deep learning model; or taking the modulo variable of the deep learning model node as the condition parameter of the next rule set node.
Further, the step S3 is specifically to implement cross-class assembly of rule sets and deep learning models by using a unified node configuration manner, where the rule configuration is updated by page setting and priority modification, and the deep learning model is modified in version by using a PMML (Predictive Model Markup Language ) type script manner.
Compared with the prior art, the invention has the following advantages:
the invention establishes the corresponding relation between the variable data and the vector data by using the variable data module by arranging the variable data management module and the decision flow management module which are connected in sequence, and simultaneously extracts the variable data and the vector data to the decision flow management module; and (3) utilizing a decision flow management module to realize the cross-category assembly of the rule set and the deep learning model so as to output and obtain a decision result. Therefore, the aim of using a unified variable data source can be fulfilled, the multiplexing degree of variable data is greatly improved, the rule set and the deep learning model can be assembled and arranged according to requirements, and the configurability and development timeliness of overall decision are greatly improved.
The invention can form a unified variable data source by establishing the corresponding relation between the variable data and the vector data and simultaneously extracting the variable data and the corresponding vector data, adopts a mode of adding variable group data sources when the follow-up rules and models are configured, uses the variable data, reduces repeated development, can acquire the rules and the models from the data sources according to the requirements, and can reach the multiplexing degree of 100% at most under the condition of meeting the requirements.
The invention uses a unified node configuration mode to realize the cross-class assembly of the rule set and the deep learning model, and for the rule set and the rule set, the model and the model, and the data transmission is conducted among the rule and the model, the method of applying the result parameters supplies the decision result of each layer to the next layer for use, namely, the rule and the parameter of each model are conducted in a unified flow, so that the configuration can be rapidly carried out, the rule set and the deep learning model are assembled into a service to be output externally, in the on-line service docking, an external system only needs to pay attention to the specification of one service, and the rule set and the model do not need to be distinguished to be respectively used for service docking, and the unified service output can effectively improve the efficiency of the on-line service docking.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention;
fig. 2 is a schematic diagram of an application framework of an embodiment.
Detailed Description
The invention will now be described in detail with reference to the drawings and specific examples.
Examples
The intelligent decision system integrating the statistical strategy and the deep learning model comprises a variable data management module and a decision flow management module which are sequentially connected, wherein the variable data management module is connected with a variable center database and a vector center database, and is used for establishing a corresponding relation between variable data and vector data and extracting the variable data and the vector data to the decision flow management module at the same time;
the decision flow management module is used for realizing cross-category assembly of the rule set and the deep learning model so as to output and obtain a decision result, and comprises a rule model management unit and a decision flow unit, wherein the rule model management unit is connected with the variable data management module and is used for receiving variable data and corresponding vector data so as to construct the rule set and train the deep learning model; and the decision flow unit is used for assembling the rule set and the deep learning model and outputting a decision result.
Specifically, the variable data management module enables the variable and the vector corresponding to the variable to establish a corresponding relation by opening the variable center and the vector database. The data used for analysis and modeling can be extracted from an OLAP database, an external data service and the like according to requirements, and in the extraction process, some data processing, such as some data complement or spam strategies and the like, can be performed in the variables to form a unified variable data source. The variable data is used in a manner of adding variable group data sources in the rule and model configuration.
The decision flow management module (comprising a rule and model management unit and a decision flow unit) can freely combine variable-based rule and traditional machine learning with vector-based deep learning in the decision configuration process. The rule set and the deep learning model are assembled across categories by using a unified node configuration mode, but different details are arranged inside, the rule configuration is updated by page setting and priority modification, and the model is modified in version by using a PMML (Predictive Model Markup Language ) type script mode.
The data information processing process between the two functional modules is as follows: the variable data stored in the variable data management module can be accessed in the decision flow management module, the bottom layer authority is opened, and the platform configuration selects the variable data to be used. The rule set based on the traditional statistical strategy uses a variable center in a variable data module, the deep learning model uses a vector center in a variable data management module, and finally, the corresponding module is found by setting up a unified index and configuring an index ID in a decision flow management module in a form of adding file storage to a data table, and the processing and the assembly are carried out.
Based on the system, an intelligent decision method integrating a statistical strategy and a deep learning model is realized, as shown in fig. 1, and comprises the following steps:
s1, establishing a corresponding relation between variable data and vector data, and simultaneously extracting the variable data and the vector data;
s2, constructing a rule set by using the extracted variable data, and training a deep learning model by using the extracted vector data;
and S3, freely assembling the rule set and the deep learning model according to the user requirements, and outputting to obtain a decision result.
By applying the technical scheme, the embodiment builds an application framework shown in fig. 2, and the main contents are as follows:
1. and building a variable center module in the decision platform, and opening the variable center and the vector database to enable the variable and the vector corresponding to the variable to establish a corresponding relation. The method mainly provides a basis for data preparation for the fusion of the following rules, the traditional statistical model and the depth model. After the variable extraction, it can be applied to the rules and models, respectively, or both.
2. And respectively constructing a rule and model management module in the decision platform, configuring the use variables of the rule and the machine learning model in the module, mapping the result parameters and the like.
3. Within the decision platform, a decision flow module is built in which the configuration of nodes supports multiple types, such as rules and deep learning models.
4. And assembling and arranging the rules and the deep learning according to the requirements, supporting the results of the rules to be used by the deep learning model, and supporting the results of the deep learning model of the source of the entering of the rules. Supporting a single decision flow may choose to execute only rules, only models, or both rules and models.
Therefore, on one hand, by opening the variable center and the vector database, the corresponding relation between the variable and the vector corresponding to the variable is established, and rules based on the variable and traditional machine learning and deep learning based on the vector can be freely combined in the decision configuration process. The use of unified variable data sources (corresponding variable data management modules) is supported. When the rule and the deep learning model are assembled and fused in the decision flow, variable data sources with uniform sources can be selected so as to ensure the data consistency of the whole decision and reduce repeated configuration and development.
On the other hand, the deep learning model of deep integration: the decision engine can allocate resources when independently running according to configuration, automatically load a depth model to become an operable node, and form a unified decision calculation flow with the traditional rules and statistical learning and serve.
In addition, decision flow management supports upper layer assembly. Unified platform configuration: all configuration operations are performed on the platform. Result parameter delivery and cross-class assembly: and for all the rule sets, the models and the rules and the models, the data transmission is conducted, and the decision result of each layer is supplied to the next layer for use by a method of applying the result parameters. For example, the result parameters of the rule set can be used as the modulus parameters of the nodes of the next-layer deep learning model, and at this time, the result parameters of the rule set are automatically converted into input vectors of the depth model according to a function defined in advance as required. And the model-out variable of the deep learning model node can be used as the condition parameter of the next rule set node. The depth model and the depth model can be assembled and combined, so that the end-to-end and pre-training mode configuration switching, the initial model and the distillation model configuration switching and the like can be realized, and the decision flow parameters can be transmitted across categories in a wider sense. Unified service output: the rule set and the deep learning model are assembled into a service for outputting. In the online service docking, the external system only needs to pay attention to the specification of one service, and does not need to distinguish rule sets and models to respectively perform service docking.
In summary, the variable multiplexing degree can be improved by the scheme: the variable data on the platform can reach 100% multiplexing, the unified variable data source is used, repeated development is reduced, rules and model arrangement can be obtained from the data source according to requirements, and the highest multiplexing degree can reach 100% under the condition of meeting the requirements.
The decision development timeliness can be improved, the rules, the parameter entering and result parameters of various models are opened in a unified flow, the configuration can be rapidly carried out, and the configurability and the development timeliness of the whole decision are greatly improved; the unified service output is also significant for efficiency improvement of online service docking.
Claims (10)
1. The intelligent decision system integrating the statistical strategy and the deep learning model is characterized by comprising a variable data management module and a decision flow management module which are sequentially connected, wherein the variable data module is used for establishing a corresponding relation between variable data and vector data and extracting the variable data and the vector data to the decision flow management module at the same time;
the decision flow management module is used for realizing cross-category assembly of the rule set and the deep learning model so as to output and obtain a decision result.
2. The intelligent decision system integrating statistical strategies and deep learning models according to claim 1, wherein the variable data management module is connected with a variable center database and a vector center database.
3. The intelligent decision system integrating statistical strategies and deep learning models according to claim 1, wherein the decision flow management module comprises a rule model management unit and a decision flow unit, the rule model management unit is connected with the variable data management module and is used for receiving variable data and corresponding vector data to construct a rule set and train out the deep learning model;
the decision flow unit is used for assembling the rule set and the deep learning model and outputting a decision result.
4. An intelligent decision method integrating a statistical strategy and a deep learning model is characterized by comprising the following steps:
s1, establishing a corresponding relation between variable data and vector data, and simultaneously extracting the variable data and the vector data;
s2, constructing a rule set by using the extracted variable data, and training a deep learning model by using the extracted vector data;
and S3, freely assembling the rule set and the deep learning model according to the user requirements, and outputting to obtain a decision result.
5. The intelligent decision method integrating the statistical strategy and the deep learning model according to claim 4, wherein the step S1 specifically comprises the following steps:
s11, establishing a corresponding relation between variable data and vector data;
s12, variable data and corresponding vector data are extracted at the same time, and preprocessing is carried out on the variable data in the extraction process so as to form a unified variable data source.
6. The method according to claim 5, wherein the step S12 is to extract variable data and corresponding vector data from OLAP database or external data service at the same time.
7. The intelligent decision-making method integrating statistical strategies and deep learning models according to claim 4, wherein in step S12, data complement or spam is performed on variable data in the extraction process.
8. The intelligent decision method integrating statistical strategies and deep learning models according to claim 4, wherein the free assembly rule set and the deep learning model in step S3 specifically include the following cases:
assembling the rule sets;
the deep learning model and the deep learning model are assembled;
the rule set and the deep learning model are assembled.
9. The intelligent decision-making method integrating statistical strategies and deep learning models according to claim 8, wherein in the step S3, if the rule set and the deep learning model are assembled, the parameters of the rule set are taken as the modeling parameters of the nodes of the next-layer deep learning model; or taking the modulo variable of the deep learning model node as the condition parameter of the next rule set node.
10. The intelligent decision method integrating statistical strategies and deep learning models according to claim 8, wherein the step S3 is specifically to implement cross-class assembly of rule sets and deep learning models by using a unified node configuration method, wherein the rule configuration is updated by page setting and priority modification, and the deep learning model is modified in version by PMML type script method.
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