CN114359846A - Identification method for abnormal unloading behaviors of building material transport vehicle based on big data - Google Patents
Identification method for abnormal unloading behaviors of building material transport vehicle based on big data Download PDFInfo
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Abstract
The invention discloses a method for identifying abnormal unloading behaviors of a building material transport vehicle based on big data, which relates to the technical field of big data and comprises the following steps: uploading real-time online data transmitted in a mobile mode to a cloud big data platform based on a vehicle-mounted terminal; and calculating the mass of the vehicle by utilizing a big data analysis technology, and identifying the loading and unloading behavior of the vehicle according to the change of the mass of the vehicle. The vehicle-mounted terminal is used for transmitting the real-time data of the delivery information and the travel information of the vehicle, calculating the mass of the vehicle and judging the unloading behavior through cloud big data analysis and processing, so that the whole process of monitoring the delivery start and the parking of the material transport vehicle in a building site is realized, and the loading and unloading behaviors are identified, thereby effectively identifying the abnormal unloading and stealing behaviors of the transport vehicle; the method and the device perform real-time calculation on the message data uploaded by the vehicle in combination with the delivery bill data based on the kinetic energy theorem, do not depend on other external data, and are high in efficiency, simple in calculation and high in timeliness.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a method for identifying abnormal unloading behaviors of a building material transport vehicle based on big data.
Background
The real-time quality calculation of the vehicle and the identification of the loading and unloading behaviors have great significance for judging whether the delivery vehicle delivers the goods according to the regulations and the punctuality and accuracy, can effectively check the illegal goods stealing behaviors of partial drivers, and assist the supervision work of building construction owner units to be effectively carried out, thereby avoiding loss.
The existing vehicle real-time quality calculation requires excessive parameters, including: the method comprises the following steps that parameters such as road gradient, vehicle windward area and air resistance coefficient are not fixed and change along with the change of road and weather, except that the calculation process is complicated and unexpected, and the accuracy of the calculation result is difficult to guarantee due to the complicated parameter requirement; and on the basis of big data, too many uncertain parameters lead the method to be difficult to engineer.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a building material transport vehicle abnormal unloading behavior identification method based on big data.
In order to solve the technical problems, the invention provides the following technical scheme:
the method for identifying the abnormal unloading behaviors of the building material transport vehicle based on the big data comprises the following steps:
uploading real-time online data transmitted in a mobile mode to a cloud big data platform based on a vehicle-mounted terminal;
and calculating the mass of the vehicle by utilizing a big data analysis technology, and identifying the loading and unloading behavior of the vehicle according to the change of the mass of the vehicle.
The vehicle-mounted terminal based real-time online data transmission method comprises the following steps that the real-time online data transmitted in a mobile mode are uploaded to a cloud big data platform; the method specifically comprises the following steps: the building material transport vehicle is provided with a vehicle-mounted terminal, and the vehicle-mounted terminal sends vehicle information and delivery note information to the cloud big data platform through an MQTT protocol.
The vehicle information comprises a unique vehicle identification code, acquisition time, vehicle speed, the maximum torque output proportion of an engine, the rotating speed of the engine, longitude positioning data and latitude positioning data; the delivery bill information is a target value of a delivery starting address, a target address and predicted delivery time recorded aiming at the material delivery bill.
The further technical scheme of the invention is that the mass of the vehicle is calculated by utilizing a big data analysis technology, and the loading and unloading behavior of the vehicle is identified according to the change of the mass of the vehicle; the method specifically comprises the following steps:
carrying out data quality preprocessing on the vehicle information and the delivery note information uploaded by the vehicle-mounted terminal;
slicing the vehicle driving segment of the preprocessed data;
calculating the estimated load mass of the vehicle driving section;
identifying a vehicle parking point by identifying longitude and latitude continuous data frame errors and solving the final vehicle mass of the parking point;
and identifying the vehicle as an unloading point and judging the illegal unloading point according to the comparison between the vehicle mass of each parking point of the vehicle and the mass of the delivery starting point.
The invention has the further technical scheme that the vehicle information and the delivery note information uploaded by the vehicle-mounted terminal are subjected to data quality preprocessing; the method specifically comprises the following steps: deleting abnormal values and missing values of fields of the vehicle information and the delivery bill information, determining time span of the vehicle information and the delivery bill information, and performing consistency integration on data units of the vehicle volume information and the delivery bill information.
As a further technical solution of the present invention, the slicing of the vehicle driving segment of the preprocessed data specifically includes: and according to the speed change, cutting out each accelerated driving segment of the vehicle, according to the principle that the head-tail difference of 10 frame data before and after the speed exceeds a set percentage, screening the sliced original driving segments according to the limiting conditions of the acceleration starting speed, the acceleration ending speed and the acceleration time, and filtering out invalid segments with the time less than the invalid time.
The invention adopts the further technical scheme that the estimated load mass of the vehicle driving section is calculated; the method specifically comprises the following steps:
for each acceleration segment of the vehicle, according to the formula:
power-torque-rotational speed;
wherein M is power, PT is torque, V1 is initial speed of acceleration segment, and V2 is terminal speed of acceleration segment;
calculating the estimated load mass function of each driving segment in the vehicle distance as follows:
gong＝g1/9550；
Zhiliang1＝2*gong*1000/math.abs(math.pow(sd1/3.6,2)-math.pow(sd2/3.6,2)；
Zhiliang＝Zhiliang1/1000；
wherein sd1 is the start of the mileage segment, sd2 is the end of the mileage segment, g1 is the bow height, Gong is the bow, and Zhiliang is the mass.
The invention has the further technical scheme that the vehicle parking point is identified by identifying the longitude and latitude continuous data frame errors, and the final vehicle mass of the parking point is obtained; the method specifically comprises the following steps: identifying a vehicle parking point by identifying the behaviors that the frame errors of the longitude and latitude continuous data are within a set distance and exceed a set time and the last frame data is in a flameout state, and calculating the information of the time, the position and the duration of parking of the vehicle; and calculating the median according to the mass of each parking point vehicle to be used as the final vehicle mass of the parking point.
As a further technical solution of the present invention, the identifying as an unloading point and performing illegal unloading point behavior determination according to comparison between the vehicle mass at each stopping point of the vehicle and the mass at the starting point of the delivery specifically includes: according to the comparison between the vehicle mass of each parking point of the vehicle and the mass of the delivery starting point, identifying the parking point with obviously reduced mass and identifying the parking point as an unloading point; and matching the longitude and latitude and time of the unloading points with the target addresses of the delivery lists, if the total number of the unloading points is larger than the total number of the target delivery points, regarding the unloading points as suspected violation behaviors, recording the unloading addresses with errors larger than a certain distance from the non-delivery addresses as violation unloading points, and providing monitoring evidence for a building owner for management and control.
The invention has the beneficial effects that:
the vehicle-mounted terminal is used for transmitting the real-time data of the delivery information and the travel information of the vehicle, calculating the mass of the vehicle and judging the unloading behavior through cloud big data analysis and processing, so that the whole process of monitoring the delivery start and the parking of the material transport vehicle in a building site is realized, and the loading and unloading behaviors are identified, thereby effectively identifying the abnormal unloading and stealing behaviors of the transport vehicle; the method and the device perform real-time calculation on the message data uploaded by the vehicle in combination with the delivery bill data based on the kinetic energy theorem, do not depend on other external data, and are high in efficiency, simple in calculation and high in timeliness.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a flow chart of a method for identifying abnormal unloading behaviors of a construction material transportation vehicle based on big data, which is provided by the invention;
fig. 2 is a flow chart for identifying loading and unloading behaviors according to changes of vehicle mass.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
As shown in fig. 1, the present invention provides a big data-based method for identifying an abnormal unloading behavior of a construction material transportation vehicle, comprising:
100, uploading real-time online data transmitted in a mobile mode to a cloud big data platform based on a vehicle-mounted terminal;
and 200, calculating the mass of the vehicle by utilizing a big data analysis technology, and identifying the loading and unloading behavior of the vehicle according to the change of the mass of the vehicle.
According to the invention, the vehicle delivery information and the travel information are subjected to real-time data transmission through the vehicle-mounted terminal, the mass of the vehicle is calculated and the unloading behavior is judged through cloud big data analysis and processing, the whole process of monitoring the delivery start and the parking of the material transport vehicle in a building site is realized, and the loading and unloading behaviors are identified, so that the abnormal unloading and stealing behaviors of the transport vehicle are effectively identified.
In step 100, the real-time online data transmitted in a mobile mode is uploaded to a cloud big data platform based on the vehicle-mounted terminal; the method specifically comprises the following steps: the building material transport vehicle is provided with a vehicle-mounted terminal, and the vehicle-mounted terminal sends vehicle information and delivery note information to the cloud big data platform through an MQTT protocol.
The vehicle information comprises a vehicle unique identification code, acquisition time, vehicle speed, the maximum torque output proportion of an engine, the rotating speed of the engine, longitude positioning data and latitude positioning data; the delivery bill information is a target value of a delivery starting address, a target address and predicted delivery time recorded aiming at the material delivery bill.
Referring to fig. 2, in step 200, the mass of the vehicle is calculated by using a big data analysis technology, and the loading and unloading behavior of the vehicle is identified according to the change of the mass of the vehicle; the method specifically comprises the following steps:
and step 205, according to the comparison between the vehicle mass of each parking point of the vehicle and the mass of the delivery starting point, identifying the vehicle as an unloading point and judging the behavior of the illegal unloading point.
In step 201, the vehicle information and the delivery note information uploaded by the vehicle-mounted terminal are subjected to data quality preprocessing; the method specifically comprises the following steps: deleting abnormal values and missing values of fields of the vehicle information and the delivery bill information, determining time span of the vehicle information and the delivery bill information, and performing consistency integration on data units of the vehicle volume information and the delivery bill information.
In step 202, slicing the vehicle driving segment of the preprocessed data specifically includes: and according to the speed change, cutting out each accelerated driving segment of the vehicle, according to the principle that the head-tail difference of 10 frame data before and after the speed exceeds a set percentage, screening the sliced original driving segments according to the limiting conditions of the acceleration starting speed, the acceleration ending speed and the acceleration time, and filtering out invalid segments with the time less than the invalid time. The certain percentage is 5-8%, and the ineffective time is 5 seconds.
In step 203, calculating the estimated load mass of the vehicle driving section; the method specifically comprises the following steps:
for each acceleration segment of the vehicle, according to the formula:
power-torque-rotational speed;
wherein M is power, PT is torque, V1 is initial speed of acceleration segment, and V2 is terminal speed of acceleration segment;
neglecting the gradient calculation dimension, calculating the estimated load mass function of each driving segment in the vehicle distance as follows:
gong＝g1/9550；
Zhiliang1＝2*gong*1000/math.abs(math.pow(sd1/3.6,2)-math.pow(sd2/3.6,2)；
Zhiliang＝Zhiliang1/1000；
wherein sd1 is the start of the mileage segment, sd2 is the end of the mileage segment, g1 is the bow height, Gong is the bow, and Zhiliang is the mass.
In step 204, identifying a vehicle parking point by identifying longitude and latitude continuous data frame errors and solving the final vehicle mass of the parking point; the method specifically comprises the following steps: identifying a vehicle parking point by identifying the behaviors that the frame errors of the longitude and latitude continuous data are within a set distance and exceed a set time and the last frame data is in a flameout state, and calculating the information of the time, the position and the duration of parking of the vehicle; and calculating the median according to the mass of each parking point vehicle to be used as the final vehicle mass of the parking point. The set distance is 3-8 m and the set time is 3-8 s.
In step 205, the identifying of the vehicle mass at each stopping point of the vehicle and the mass at the starting point of the delivery as the unloading point and the determining of the behavior of the illegal unloading point specifically include: and identifying parking points with obviously reduced mass according to the comparison between the mass of the vehicle at each parking point of the vehicle and the mass of the delivery starting point, and identifying the parking points as unloading points. And matching the longitude and latitude and time of the unloading points with the target addresses of the delivery lists, if the total number of the unloading points is greater than the total number of the target delivery points, regarding the unloading points as suspected violation behaviors, recording the unloading addresses with errors greater than a certain distance from the non-delivery addresses as violation unloading points, and providing monitoring evidence for a building owner for management and control. Wherein, a certain distance can be set to be 20-30 meters, which is subject to the design requirement.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. The method for identifying the abnormal unloading behaviors of the building material transport vehicle based on the big data is characterized by comprising the following steps of:
uploading real-time online data transmitted in a mobile mode to a cloud big data platform based on a vehicle-mounted terminal;
and calculating the mass of the vehicle by utilizing a big data analysis technology, and identifying the loading and unloading behavior of the vehicle according to the change of the mass of the vehicle.
2. The method for identifying abnormal unloading behaviors of a building material transport vehicle based on big data as claimed in claim 1, wherein the vehicle-mounted terminal uploads the real-time online data of mobile transmission to a cloud big data platform; the method specifically comprises the following steps: the building material transport vehicle is provided with a vehicle-mounted terminal, and the vehicle-mounted terminal sends vehicle information and delivery note information to the cloud big data platform through an MQTT protocol.
3. The method for identifying abnormal unloading behavior of a big-data based construction material transportation vehicle according to claim 2, wherein the vehicle information includes a vehicle unique identification code, a collection time, a vehicle speed, an engine maximum torque output ratio, an engine speed, longitude positioning data, and latitude positioning data; the delivery bill information is a target value of a delivery starting address, a target address and predicted delivery time recorded aiming at the material delivery bill.
4. The method for identifying abnormal unloading behaviors of construction material transportation vehicles based on big data as claimed in claim 1, wherein the big data analysis technology is used to calculate the mass of the vehicle and identify the loading and unloading behaviors according to the change of the mass of the vehicle; the method specifically comprises the following steps:
carrying out data quality preprocessing on the vehicle information and the delivery note information uploaded by the vehicle-mounted terminal;
slicing the vehicle driving segment of the preprocessed data;
calculating the estimated load mass of the vehicle driving section;
identifying a vehicle parking point by identifying longitude and latitude continuous data frame errors and solving the final vehicle mass of the parking point;
and identifying the vehicle as an unloading point and judging the illegal unloading point according to the comparison between the vehicle mass of each parking point of the vehicle and the mass of the delivery starting point.
5. The method for identifying the abnormal unloading behavior of the building material transport vehicle based on the big data as claimed in claim 4, wherein the data quality preprocessing is performed on the vehicle information uploaded by the vehicle-mounted terminal and the delivery note information; the method specifically comprises the following steps: deleting abnormal values and missing values of fields of the vehicle information and the delivery bill information, determining time span of the vehicle information and the delivery bill information, and performing consistency integration on data units of the vehicle volume information and the delivery bill information.
6. The method for identifying abnormal unloading behavior of a construction material transportation vehicle based on big data as claimed in claim 4, wherein the slicing of the vehicle driving section for the preprocessed data comprises: and according to the speed change, cutting out each accelerated driving segment of the vehicle, according to the principle that the head-tail difference of 10 frame data before and after the speed exceeds a set percentage, screening the sliced original driving segments according to the limiting conditions of the acceleration starting speed, the acceleration ending speed and the acceleration time, and filtering out invalid segments with the time less than the invalid time.
7. The big-data based identification method of abnormal unloading behavior of a construction material transportation vehicle according to claim 4, wherein the estimated load mass of the vehicle driving section is calculated; the method specifically comprises the following steps:
for each acceleration segment of the vehicle, according to the formula:
power-torque-rotational speed;
wherein M is power, PT is torque, V1 is initial speed of acceleration segment, and V2 is terminal speed of acceleration segment;
neglecting the gradient calculation dimension, calculating the estimated load mass function of each driving segment in the vehicle distance as follows:
gong＝g1/9550；
Zhiliang1＝2*gong*1000/math.abs(math.pow(sd1/3.6,2)-math.pow(sd2/3.6,2)；
Zhiliang＝Zhiliang1/1000；
wherein sd1 is the start of the mileage segment, sd2 is the end of the mileage segment, g1 is the bow height, Gong is the bow, and Zhiliang is the mass.
8. The method for identifying abnormal unloading behavior of a construction material transportation vehicle based on big data as claimed in claim 4, wherein the vehicle parking point is identified by identifying longitude and latitude continuous data frame errors and the final vehicle mass of the parking point is obtained; the method specifically comprises the following steps: identifying a vehicle parking point by identifying the behaviors that the frame errors of the longitude and latitude continuous data are within a set distance and exceed a set time and the last frame data is in a flameout state, and calculating the information of the time, the position and the duration of parking of the vehicle; and calculating the median according to the mass of each parking point vehicle to be used as the final vehicle mass of the parking point.
9. The big data-based identification method for abnormal unloading behaviors of a construction material transportation vehicle according to claim 4, wherein the identifying as an unloading point and the determining as an illegal unloading point behavior according to the comparison between the vehicle mass at each parking point of the vehicle and the mass at the starting point of the delivery of goods specifically comprises: according to the comparison between the vehicle mass of each parking point of the vehicle and the mass of the delivery starting point, identifying the parking point with obviously reduced mass and identifying the parking point as an unloading point; and matching the longitude and latitude and time of the unloading points with the target addresses of the delivery lists, if the total number of the unloading points is larger than the total number of the target delivery points, regarding the unloading points as suspected violation behaviors, recording the unloading addresses with errors larger than a certain distance from the non-delivery addresses as violation unloading points, and providing monitoring evidence for a building owner for management and control.
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Application publication date: 20220415 |