Method, device and apparatus for evaluating remote monitoring data of heavy vehicles

Abstract

The present invention relates to the technical field of vehicle big data, and discloses a method, a device and equipment for evaluating remote monitoring data of heavy vehicles. The method comprises: obtaining remote monitoring data of heavy vehicles, the remote monitoring data comprising: on-board diagnostic system data and engine data; generating data evaluation indicators based on the remote monitoring data, the data evaluation indicators comprising at least one of the following: a terminal activation quality evaluation indicator for evaluating data generated by activation of an on-board terminal set in the heavy vehicle, a data signature verification quality evaluation indicator, a data transmission quality evaluation indicator, a data acquisition quality evaluation indicator, a networking status data evaluation indicator, an abnormal data evaluation indicator for evaluating abnormal data generated during the operation of the heavy vehicle, and a data jump evaluation indicator; determining the weight of each data evaluation indicator in the process of comprehensive evaluation of the heavy vehicle; and generating a comprehensive evaluation result of the remote monitoring data based on the data evaluation indicators and the weights corresponding to each data evaluation indicator.

Description

Technical Field

[0001] The present invention relates to the field of vehicle big data technology, and in particular to a method, device and equipment for evaluating remote monitoring data of heavy vehicles. Background Art

[0002] The heavy-duty vehicle remote monitoring system can obtain the vehicle's own status and positioning information in real time through satellite positioning modules, CAN bus modules, wireless communication and other technical means, such as emission system parameters, vehicle speed, acceleration, heading, and longitude and latitude coordinates when the vehicle is driving. At the same time, the above information is sent to the remote monitoring platform in real time through the wireless communication module. For the large amount of heavy-duty vehicle data obtained by the heavy-duty vehicle remote monitoring system, there is a lack of effective methods for evaluating the heavy-duty vehicle remote monitoring data in related technologies. Summary of the invention

[0003] In view of this, the present invention provides a method, device, equipment, storage medium and program product for evaluating remote monitoring data of heavy vehicles to solve the problem of lack of effective methods for evaluating remote monitoring data of heavy vehicles in related technologies.

[0004] In a first aspect, the present invention provides a method for evaluating remote monitoring data of heavy vehicles, the method comprising: acquiring remote monitoring data of a heavy vehicle, the remote monitoring data comprising: on-board diagnostic system data and engine data; generating data evaluation indicators based on the remote monitoring data, the data evaluation indicators comprising at least one of the following: a terminal activation quality evaluation indicator for evaluating data generated by activation of an on-board terminal disposed on the heavy vehicle, a data verification quality evaluation indicator for evaluating data generated by data verification of the heavy vehicle, a data transmission quality evaluation indicator for evaluating data generated by data transmission of the heavy vehicle, a data collection quality evaluation indicator for evaluating data generated by data collection of the heavy vehicle, a networking status data evaluation indicator for evaluating data generated by networking of the heavy vehicle, an abnormal data evaluation indicator for evaluating abnormal data generated during operation of the heavy vehicle, and a data jump evaluation indicator for evaluating jump data generated during operation; determining the weight of each of the data evaluation indicators in the comprehensive evaluation process of the heavy vehicle; and generating a comprehensive evaluation result of the remote monitoring data based on the data evaluation indicators and the weights corresponding to each of the data evaluation indicators.

[0005] In an optional embodiment, the data evaluation index is generated in the following manner: obtaining the number of consecutive frames in which abnormal data items are generated in the data of the heavy vehicle within a target preset period; determining the number of heavy vehicles with data abnormalities based on the number of consecutive frames in which abnormal data items are generated in the data of the heavy vehicle; obtaining the initial number of heavy vehicles that perform data generation operations within the target preset period; obtaining the data evaluation index based on the ratio of the number of heavy vehicles with data abnormalities to the initial number of heavy vehicles; wherein the data generation process includes at least one of the following: the process of activating the vehicle-mounted terminal, the process of data verification, the process of data transmission, the process of data collection, the process of networking and the operation process.

[0006] In an optional embodiment, the data collection quality evaluation index includes at least one of the following: a data cross-boundary rate evaluation index of the heavy vehicle, a data inefficiency evaluation index of the heavy vehicle, an average data collection cycle evaluation index of the heavy vehicle, and a data packet loss rate evaluation index of the heavy vehicle.

[0007] In an optional embodiment, the data out-of-bounds rate evaluation index is generated in the following manner: obtaining the cumulative out-of-bounds frame number of each data item of the first heavy vehicle within a fourth preset period, the out-of-bounds frame number being the number of data frames whose numerical range of the data item is outside the standard numerical range; obtaining the total transmission frame number of each data item of the first heavy vehicle within the fourth preset period; determining the data out-of-bounds rate of the first heavy vehicle based on the ratio of the cumulative out-of-bounds frame number to the total transmission frame number; dividing the data out-of-bounds range based on preset intervals to generate multiple data out-of-bounds intervals; obtaining the seventh number of heavy vehicles within each of the data out-of-bounds intervals, and obtaining the eighth number of heavy vehicles performing data acquisition operations within the fourth preset period; determining the data out-of-bounds rate evaluation index corresponding to each of the data items based on the ratio of the seventh number to the eighth number.

[0008] In an optional embodiment, the data cross-border rate evaluation index is also generated in the following manner: obtaining the ninth number of cross-border heavy vehicles corresponding to each of the data items within the fifth preset period, the cross-border heavy vehicles including heavy vehicles whose consecutive cross-border frames exceed the first preset threshold; obtaining the tenth number of heavy vehicles performing data collection operations within the fifth preset period; and determining the data cross-border rate evaluation index corresponding to each of the data items based on the ratio of the ninth number to the tenth number.

[0009] In an optional embodiment, the determining of the weights of each of the data evaluation indicators in the comprehensive evaluation process of the heavy vehicle includes: normalizing each indicator value in the target data evaluation indicator to obtain a normalized target data evaluation indicator value; determining a first weight of the target data evaluation indicator based on a ratio of the normalized target data evaluation indicator value to a first indicator value, the first indicator value being the sum of the normalized target data evaluation indicator values; determining an entropy value of the target data evaluation indicator based on the first weight; determining an information entropy redundancy of the target data evaluation indicator based on the entropy value of the target data evaluation indicator; determining a weight corresponding to the target data evaluation indicator in the comprehensive evaluation process based on a ratio of the information entropy redundancy of the target data evaluation indicator to the first information entropy redundancy, the first information entropy redundancy being the sum of the information entropy redundancy of each of the data evaluation indicators.

[0010] In a second aspect, the present invention provides a device for evaluating remote monitoring data of heavy vehicles, the device comprising: a data acquisition module, used to acquire remote monitoring data of heavy vehicles, the remote monitoring data comprising: on-board diagnostic system data and engine data; a data evaluation index acquisition module, used to generate data evaluation indexes based on the remote monitoring data, the data evaluation indexes comprising at least one of the following: a terminal activation quality evaluation index for evaluating data generated by activation of an on-board terminal set at the heavy vehicle, a data verification quality evaluation index for evaluating data generated by data verification of the heavy vehicle, a data transmission quality evaluation index for evaluating data generated by data transmission of the heavy vehicle, a data collection quality evaluation index for evaluating data generated by data collection of the heavy vehicle, a networking status data evaluation index for evaluating data generated by networking of the heavy vehicle, an abnormal data evaluation index for evaluating abnormal data generated during the operation of the heavy vehicle, and a data jump evaluation index for evaluating jump data generated during the operation; a weight determination module, used to determine the weight of each of the data evaluation indicators in the comprehensive evaluation process of the heavy vehicle; a comprehensive evaluation acquisition module, used to generate a comprehensive evaluation result of the remote monitoring data based on the data evaluation indicators and the weights corresponding to each of the data evaluation indicators.

[0011] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the method for evaluating remote monitoring data of heavy vehicles according to the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0012] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method for evaluating remote monitoring data of heavy vehicles according to the first aspect or any corresponding embodiment thereof.

[0013] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions for causing a computer to execute the method for evaluating remote monitoring data of heavy vehicles according to the first aspect or any corresponding embodiment thereof.

[0014] Based on national standards and data logical relationships, the remote monitoring data of heavy vehicles can be evaluated from the dimensions of data evaluation indicators such as terminal activation quality, data verification quality, data transmission quality, data collection quality, heavy vehicle networking quality, abnormal data identification quality and jump data identification quality. Based on the data evaluation indicators, weighted evaluation results are generated. The data has a wide coverage and high utilization rate. The evaluation method is scientific and accurate. It can check the data quality problems of the heavy vehicle remote monitoring platform and complete a reasonable and accurate evaluation of the data quality, help enterprises complete data governance, help the competent departments improve data quality, give full play to the role of the heavy vehicle remote monitoring platform, realize effective dynamic monitoring of heavy vehicles, and lay the foundation for big data analysis and mining and the establishment of application models. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the related technologies, the drawings required for use in the specific embodiments or the related technical descriptions will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 A schematic flow chart of a method for evaluating remote monitoring data of a heavy vehicle provided by an embodiment of the present invention is shown;

[0017] Figure 2 A schematic diagram showing the structure of a device for evaluating remote monitoring data of heavy vehicles provided by an embodiment of the present invention is shown;

[0018] Figure 3 A schematic diagram showing the hardware structure of a computer device according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0020] According to an embodiment of the present invention, a method embodiment for evaluating remote monitoring data of heavy vehicles is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0021] In this embodiment, a method for evaluating remote monitoring data of heavy vehicles is provided, which can be used in mobile terminals, such as mobile phones, tablet computers, desktop computers or servers, etc. Figure 1 FIG. 1 is a flow chart showing a method for evaluating remote monitoring data of a heavy vehicle provided by an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0022] Step S101, obtaining remote monitoring data of a heavy vehicle, the remote monitoring data including: On-Board Diagnostics (OBD) data and engine data.

[0023] In this step, heavy vehicles include M and N vehicles with a maximum total mass greater than 3,500 kg, specifically heavy tractors and heavy trailers. Heavy vehicles generally use axle tandem hydraulic suspension devices and can be used to carry large cargoes whose size and weight exceed the limits specified by highway traffic regulations.

[0024] The GB 17691-2018 "Emission Limits and Measurement Methods for Pollutants from Heavy-Duty Diesel Vehicles (China Phase VI)" and HJ1239-2021 "Technical Specifications for Remote Monitoring of Emissions from Heavy-Duty Vehicles" standards propose that newly produced heavy-duty vehicles that meet the National VI standard must be equipped with on-board terminals, and the specified parameters must be transmitted to the ecological and environmental authorities through the on-board terminals. The basic requirements for uploading data are also specified in the standards, and the Transmission Control Protocol / Internet Protocol (TCP / IP) network control protocol is used as the underlying communication bearer protocol.

[0025] The on-board terminal installed in the heavy vehicle initiates a communication connection request to the management platform. When the communication link is established, the on-board terminal should automatically send login information for identity recognition. The remote service and management platform should verify the received data. When the verification is correct, the management platform receives the data; when the verification is incorrect, the platform ignores the received data.

[0026] The data acquisition frequency of the engine data in the remote monitoring data can be 1hz, and the OBD data can be transmitted at least once every 24 hours. The specific remote monitoring data transmitted includes:

[0027] OBD data: OBD diagnostic protocol, Malfunction Indicator Lamp (MIL) status, diagnostic support status, diagnostic ready status, vehicle identification number, software calibration identification number, calibration verification code, vehicle emission actual detection frequency (In-Use Monitor Performance Ratio, IUPR) value, total number of fault codes, etc.

[0028] Engine data: vehicle speed, atmospheric pressure, engine net output torque, friction torque, engine speed, engine fuel flow, Selective Catalytic Reduction (SCR) upstream nitrogen oxide (NOx) sensor output value, SCR downstream NOx sensor output value, reactant residue, intake volume, SCR inlet temperature, SCR outlet temperature, diesel particulate filter (DPF) pressure difference, engine coolant temperature, fuel tank level, positioning status, longitude, latitude, accumulated mileage and other 19 items.

[0029] Step S102, based on the remote monitoring data, generates data evaluation indicators, and the data evaluation indicators include at least one of the following: a terminal activation quality evaluation indicator for evaluating data generated by activation of an on-board terminal installed in a heavy-duty vehicle, a data verification quality evaluation indicator for evaluating data generated by data verification of the heavy-duty vehicle, a data transmission quality evaluation indicator for evaluating data generated by data transmission of the heavy-duty vehicle, a data collection quality evaluation indicator for evaluating data generated by data collection of the heavy-duty vehicle, a networking status data evaluation indicator for evaluating data generated by networking of the heavy-duty vehicle, an abnormal data evaluation indicator for evaluating abnormal data generated during the operation of the heavy-duty vehicle, and a data jump evaluation indicator for evaluating jump data generated during the operation.

[0030] In this step, the data evaluation indicators may include: terminal activation quality evaluation indicators; data verification quality evaluation indicators; data transmission quality evaluation indicators; data collection quality evaluation indicators; networking status data evaluation indicators; abnormal data evaluation indicators; data jump evaluation indicators; terminal activation quality evaluation indicators and data verification quality evaluation indicators; terminal activation quality evaluation indicators and data transmission quality evaluation indicators; terminal activation quality evaluation indicators and data collection quality evaluation indicators; terminal activation quality evaluation indicators and networking status data evaluation indicators; terminal activation quality evaluation indicators and abnormal data evaluation indicators; terminal activation quality evaluation indicators and data jump evaluation indicators; data verification quality evaluation indicators and data transmission quality evaluation indicators; data verification Signature quality evaluation indicators and data collection quality evaluation indicators; data verification quality evaluation indicators and networking status data evaluation indicators; data verification quality evaluation indicators and abnormal data evaluation indicators; data verification quality evaluation indicators and data jump evaluation indicators; data transmission quality evaluation indicators and data collection quality evaluation indicators; data transmission quality evaluation indicators and networking status data evaluation indicators; data transmission quality evaluation indicators and abnormal data evaluation indicators; data transmission quality evaluation indicators and data jump evaluation indicators; data collection quality evaluation indicators and networking status data evaluation indicators; data collection quality evaluation indicators and abnormal data evaluation indicators; data collection quality evaluation indicators and data jump evaluation indicators; networking status data evaluation indicators and abnormal data evaluation indicators Indicators; Network status data evaluation indicators and data jump evaluation indicators; Abnormal data evaluation indicators and data jump evaluation indicators; Terminal activation quality evaluation indicators, data verification quality evaluation indicators and data transmission quality evaluation indicators; Terminal activation quality evaluation indicators, data verification quality evaluation indicators and data collection quality evaluation indicators; Terminal activation quality evaluation indicators, data verification quality evaluation indicators and network status data evaluation indicators; Terminal activation quality evaluation indicators, data verification quality evaluation indicators and abnormal data evaluation indicators; Terminal activation quality evaluation indicators, data verification quality evaluation indicators and data jump evaluation indicators; Terminal activation quality evaluation indicators, data transmission quality evaluation indicators and data collection quality evaluation indicators; Terminal activation quality evaluation indicators Indicators, data transmission quality evaluation indicators and networking status data evaluation indicators; terminal activation quality evaluation indicators, data transmission quality evaluation indicators and abnormal data evaluation indicators; terminal activation quality evaluation indicators, data transmission quality evaluation indicators and data jump evaluation indicators; terminal activation quality evaluation indicators, data collection quality evaluation indicators and networking status data evaluation indicators; terminal activation quality evaluation indicators, data collection quality evaluation indicators and abnormal data evaluation indicators; terminal activation quality evaluation indicators, data collection quality evaluation indicators and data jump evaluation indicators; terminal activation quality evaluation indicators, networking status data evaluation indicators and abnormal data evaluation indicators; terminal activation quality evaluation indicators, networking status data evaluation indicators and data jump evaluation indicators;Terminal activation quality evaluation indicators, abnormal data evaluation indicators and data jump evaluation indicators; data verification quality evaluation indicators, data transmission quality evaluation indicators and data collection quality evaluation indicators; data verification quality evaluation indicators, data transmission quality evaluation indicators and networking status data evaluation indicators; data verification quality evaluation indicators, data transmission quality evaluation indicators and abnormal data evaluation indicators; data verification quality evaluation indicators, data transmission quality evaluation indicators and data jump evaluation indicators; data verification quality evaluation indicators, data collection quality evaluation indicators and networking status data evaluation indicators; data verification quality evaluation indicators, data collection quality evaluation indicators and abnormal data evaluation indicators; data verification quality evaluation indicators, data collection quality evaluation indicators and data jump Evaluation indicators; data verification quality evaluation indicators, networking status data evaluation indicators and abnormal data evaluation indicators; data verification quality evaluation indicators, networking status data evaluation indicators and data jump evaluation indicators; data verification quality evaluation indicators, abnormal data evaluation indicators and data jump evaluation indicators; data transmission quality evaluation indicators, data collection quality evaluation indicators and networking status data evaluation indicators; data transmission quality evaluation indicators, data collection quality evaluation indicators and abnormal data evaluation indicators; data transmission quality evaluation indicators, data collection quality evaluation indicators and data jump evaluation indicators; data transmission quality evaluation indicators, networking status data evaluation indicators and abnormal data evaluation indicators; data transmission quality evaluation indicators, networking status data evaluation indicators and data jump Evaluation indicators; data transmission quality evaluation indicators, abnormal data evaluation indicators and data jump evaluation indicators; data collection quality evaluation indicators, networking status data evaluation indicators and abnormal data evaluation indicators; data collection quality evaluation indicators, networking status data evaluation indicators and data jump evaluation indicators; data collection quality evaluation indicators, abnormal data evaluation indicators and data jump evaluation indicators; networking status data evaluation indicators; abnormal data evaluation indicators and data jump evaluation indicators; terminal activation quality evaluation indicators, data verification quality evaluation indicators, data transmission quality evaluation indicators and data collection quality evaluation indicators; terminal activation quality evaluation indicators, data verification quality evaluation indicators, data transmission quality evaluation indicators and networking status data evaluation indicators; terminal activation quality evaluation indicators Evaluation indicators, data verification quality evaluation indicators, data transmission quality evaluation indicators and abnormal data evaluation indicators; terminal activation quality evaluation indicators, data verification quality evaluation indicators, data transmission quality evaluation indicators and data jump evaluation indicators; terminal activation quality evaluation indicators, data verification quality evaluation indicators, data collection quality evaluation indicators and networking status data evaluation indicators; terminal activation quality evaluation indicators, data verification quality evaluation indicators, data collection quality evaluation indicators and abnormal data evaluation indicators; terminal activation quality evaluation indicators, data verification quality evaluation indicators, data collection quality evaluation indicators and data jump evaluation indicators; terminal activation quality evaluation indicators, data verification quality evaluation indicators, networking status data evaluation indicators and abnormal data evaluation indicators;Terminal activation quality evaluation indicators, data verification quality evaluation indicators, networking status data evaluation indicators and data jump evaluation indicators; terminal activation quality evaluation indicators, data verification quality evaluation indicators, abnormal data evaluation indicators and data jump evaluation indicators; data verification quality evaluation indicators, data transmission quality evaluation indicators, data collection quality evaluation indicators and networking status data evaluation indicators; data verification quality evaluation indicators, data transmission quality evaluation indicators, data collection quality evaluation indicators and abnormal data evaluation indicators; data verification quality evaluation indicators, data transmission quality evaluation indicators, data collection quality evaluation indicators and data jump evaluation indicators; data verification quality evaluation indicators, data collection quality evaluation indicators, networking status data evaluation indicators Indicators and abnormal data evaluation indicators; data verification quality evaluation indicators, data collection quality evaluation indicators, networking status data evaluation indicators and data jump evaluation indicators; data verification quality evaluation indicators, networking status data evaluation indicators, abnormal data evaluation indicators and data jump evaluation indicators; data transmission quality evaluation indicators, data collection quality evaluation indicators, networking status data evaluation indicators and abnormal data evaluation indicators; data transmission quality evaluation indicators, data collection quality evaluation indicators, networking status data evaluation indicators and data jump evaluation indicators; data collection quality evaluation indicators, networking status data evaluation indicators, abnormal data evaluation indicators and data jump evaluation indicators; data transmission quality evaluation indicators, data collection quality evaluation indicators, abnormal data evaluation indicators and data jump evaluation indicators Normal data evaluation indicators and data jump evaluation indicators; data transmission quality evaluation indicators, networking status data evaluation indicators, abnormal data evaluation indicators and data jump evaluation indicators; terminal activation quality evaluation indicators, data verification quality evaluation indicators, data transmission quality evaluation indicators, data collection quality evaluation indicators and networking status data evaluation indicators; terminal activation quality evaluation indicators, data verification quality evaluation indicators, data transmission quality evaluation indicators, data collection quality evaluation indicators and abnormal data evaluation indicators; terminal activation quality evaluation indicators, data verification quality evaluation indicators, data transmission quality evaluation indicators, data collection quality evaluation indicators and data jump evaluation indicators; terminal activation quality evaluation indicators, data transmission quality evaluation indicators, Data collection quality evaluation indicators, networking status data evaluation indicators and abnormal data evaluation indicators; terminal activation quality evaluation indicators, data transmission quality evaluation indicators, data collection quality evaluation indicators, networking status data evaluation indicators and data jump evaluation indicators; terminal activation quality evaluation indicators, data collection quality evaluation indicators, networking status data evaluation indicators, abnormal data evaluation indicators and data jump evaluation indicators; data verification quality evaluation indicators, data transmission quality evaluation indicators, data collection quality evaluation indicators, networking status data evaluation indicators and abnormal data evaluation indicators; data verification quality evaluation indicators, data transmission quality evaluation indicators, data collection quality evaluation indicators, networking status data evaluation indicators and data jump evaluation indicators;Data verification quality evaluation index, data collection quality evaluation index, networking status data evaluation index, abnormal data evaluation index and data jump evaluation index; data transmission quality evaluation index, data collection quality evaluation index, networking status data evaluation index, abnormal data evaluation index and data jump evaluation index; terminal activation quality evaluation index, data verification quality evaluation index, data transmission quality evaluation index, data collection quality evaluation index, networking status data evaluation index and abnormal data evaluation index; terminal activation quality evaluation index, data verification quality evaluation index, data transmission quality evaluation index, data collection quality evaluation index, networking status data evaluation index and data jump evaluation index; data verification quality evaluation index, data transmission quality evaluation index, data collection quality evaluation index, networking status data evaluation index, abnormal data evaluation index and data jump evaluation index; and terminal activation quality evaluation index, data verification quality evaluation index, data transmission quality evaluation index, data collection quality evaluation index, networking status data evaluation index, abnormal data evaluation index and data jump evaluation index. ;

[0031] Step S103, determining the weight of each data evaluation index in the comprehensive evaluation process of heavy vehicles.

[0032] In this step, the standard deviation method, the coefficient of variation method or the principal component analysis method can be used to determine the weight of each data evaluation index in the comprehensive evaluation process. Among them, the standard deviation method can determine the weight corresponding to each data evaluation index by calculating the standard deviation of each data evaluation index. The larger the standard deviation, the higher the degree of discreteness of the data represented by the standard deviation, the greater the amount of information, and the higher the weight. The coefficient of variation method can be used as the weight by calculating the ratio of the standard deviation of each data evaluation index to the average value. The larger the coefficient of variation, the higher the relative degree of discreteness of the data evaluation index, and the higher the weight. The principal component analysis method can convert multiple data evaluation indicators into a few principal components through dimensionality reduction technology, and allocate weights according to the explanatory power of each principal component to reflect the relationship between data indicators and reduce information overlap.

[0033] Step S104: generating a comprehensive evaluation result of the remote monitoring data based on the data evaluation indicators and the weights corresponding to the data evaluation indicators.

[0034] In this step, a comprehensive score (S) can be generated based on the data evaluation indicators and the weights corresponding to each data evaluation indicator, and the data quality threshold of the remote monitoring data of heavy vehicles uploaded by the enterprise can be set according to the comprehensive score to perform grading.

[0035] For example, the comprehensive evaluation results may include: unqualified, qualified, good and excellent. The grading rules include: S < 60, the comprehensive evaluation result is unqualified; 60 ≤ S < 80, the comprehensive evaluation result is qualified; 80 ≤ S < 90, the comprehensive evaluation result is good; S ≥ 90, the comprehensive evaluation result is excellent. Those skilled in the art may also set the evaluation level according to actual evaluation requirements.

[0036] The method for evaluating the remote monitoring data of heavy vehicles provided in this embodiment is based on national standards and data logical relationships. It can evaluate the remote monitoring data of heavy vehicles from the dimensions of data evaluation indicators such as terminal activation quality, data verification quality, data transmission quality, data collection quality, heavy vehicle networking quality, abnormal data identification quality and jump data identification quality. On the basis of each data evaluation indicator, weighted evaluation results are generated. The data coverage is wide, the utilization rate is high, and the evaluation method is scientific and accurate. It can check the data quality problems of the remote monitoring platform of heavy vehicles, complete a reasonable and accurate evaluation of data quality, help enterprises complete data governance, help competent departments improve data quality, give full play to the role of the remote monitoring platform of heavy vehicles, realize effective dynamic monitoring of heavy vehicles, and lay the foundation for big data analysis and mining and establishment of application models.

[0037] In some optional implementations, the data evaluation index is generated by: obtaining the number of consecutive frames with abnormal data items in the heavy-duty vehicle data generation process within a target preset period; determining the number of heavy-duty vehicles with abnormal data based on the number of consecutive frames with abnormal data items in the heavy-duty vehicle data generation process; obtaining the initial number of heavy-duty vehicles that perform data generation operations within the target preset period; obtaining the data evaluation index based on the ratio of the number of heavy-duty vehicles with abnormal data to the initial number of heavy-duty vehicles; wherein the data generation process includes at least one of the following: activation process, data signature verification process, data transmission process, data collection process, networking process, and operation process. Abnormal data or jump data may be generated during the operation process.

[0038] In this way, data quality evaluation can be achieved in different time dimensions (day / month / year) and different spatial dimensions (city / province / country). It can specifically reflect the results of remote monitoring data, help enterprises to troubleshoot data quality issues, complete data governance, and upload accurate and effective data; at the same time, it can also help government departments to grasp the true situation of monitoring data, and provide technical support for completing effective data evaluation, conducting data mining analysis and application, and realizing all-round vehicle supervision.

[0039] In some optional embodiments, the terminal activation quality evaluation index during the activation process can be generated in the following manner: obtaining the number of consecutive frames in which abnormal data items exist during the heavy vehicle activation process within a first preset period; determining a first number of heavy vehicles with activation abnormalities based on the number of consecutive frames in which abnormal data items exist during the heavy vehicle activation process; obtaining a second number of heavy vehicles that perform activation operations within the first preset period; and determining the terminal activation quality evaluation index based on the ratio of the first number to the second number.

[0040] In this embodiment, before the on-board terminal uploads data to the remote monitoring platform, it is necessary to send activation information for activation. The remote monitoring platform sends a response based on the activation information. The activation information data includes data collection time, chip identifier (ID), public key, vehicle identification code (VIN) and signature information, etc. The abnormal data items existing in the activation of the on-board terminal set in heavy vehicles are shown in the following table.

[0041] Table 1 Abnormal data items during the activation process of the heavy-duty vehicle remote monitoring terminal

[0042]

[0043]

[0044] The situation of vehicles with problems that generate abnormal data items when the vehicle-mounted terminal is activated can be counted separately to determine the evaluation threshold of each abnormal data item generated by activation. Statistics can be performed based on the year / month / week / day period. The following is an exemplary description of the specific statistical rules for terminal activation quality based on a weekly time period:

[0045] First, the first preset period can be set to one week, and the number of consecutive frames in which terminal activation failed within one week is counted; secondly, if the number of consecutive failed frames exceeds a certain threshold, such as 60s, 120s, 180s, 240s, 300s, 600s or 1200s, the vehicle will be recorded as a problem vehicle, and the number of problem vehicles, i.e., the first number, will be counted; thirdly, the number of vehicles that send activation information within one week will be counted, i.e., the second number; finally, the proportion of vehicles that failed to activate is calculated, proportion = number of problem vehicles / number of vehicles that send activation information, and the terminal activation quality evaluation threshold, i.e., the terminal activation quality evaluation index, will be set according to the proportion under different thresholds.

[0046] In this way, the data generated during the activation process of the on-board terminal installed in the heavy-duty vehicle can be evaluated, the vehicle status can be monitored, potential problems of the vehicle can be discovered in time, and data support can be provided for the intelligence and interconnection of the vehicle.

[0047] In some optional embodiments, the data verification quality evaluation index can be generated in the following manner: obtaining the number of consecutive frames with abnormal data items in the heavy vehicle data verification process within a second preset period; determining the third number of heavy vehicles that failed the verification based on the number of consecutive frames with abnormal data items in the heavy vehicle data verification process; obtaining the fourth number of heavy vehicles that performed the verification operation within the second preset period; determining the data verification quality evaluation index based on the ratio of the third number to the fourth number.

[0048] In this embodiment, after the vehicle-mounted terminal successfully completes activation, the enterprise platform needs to perform data verification to pave the way for subsequent data transmission. Data verification requires sending data packet information. The data packet structure includes: start character, command unit, vehicle identification code, data encryption method, data unit length, data unit and verification code, etc. The abnormal data items in the data verification are shown in the following table.

[0049] Table 2 Statistics of abnormal data items in data verification of heavy-duty vehicle remote monitoring terminal

[0050]

[0051]

[0052] The situation of vehicles with abnormal data items in each data verification process can be counted separately to determine the evaluation threshold of each abnormal data item in the data verification process. Statistics can be performed based on the year / month / week / day period. The following is an example of the specific statistical rules for data verification quality, with a weekly time period:

[0053] First, the second preset period can be set to one week, and the number of consecutive frames in which data verification fails within one week is counted; secondly, if the number of consecutive failed frames exceeds a certain threshold, such as 60s, 120s, 180s, 240s, 300s, 600s or 1200s, the vehicle is recorded as a problem vehicle, and the number of problem vehicles, i.e., the third number, is counted; thirdly, the number of vehicles online within one week is counted, i.e., the fourth number; finally, the proportion of vehicles in which data verification fails is calculated, proportion = number of problem vehicles / number of online vehicles, and the data verification quality evaluation threshold, i.e., the data verification quality evaluation index, is set according to the proportion under different thresholds.

[0054] In this way, it can be ensured that the data transmitted by the on-board terminal installed in the heavy vehicle has not been tampered with, thereby improving the security of data transmission. Data verification, as a part of data quality control, helps to discover and correct errors in the data transmission process and improve the reliability of the entire system.

[0055] In some optional embodiments, the data transmission quality evaluation index can be generated in the following manner: obtain a target heavy-duty vehicle with abnormal data items in the heavy-duty vehicle data transmission process within a third preset period; determine the fifth number of heavy-duty vehicles with abnormal data transmission based on the duration of the abnormal data items in the target heavy-duty vehicle data transmission process; obtain the sixth number of heavy-duty vehicles that perform data transmission operations within the third preset period; and determine the data transmission quality evaluation index based on the ratio of the fifth number to the sixth number.

[0056] In this embodiment, after the data verification is passed, the enterprise platform starts data transmission. The transmitted data includes: engine data stream, OBD data stream and vehicle login and logout information. The abnormal data items in the data transmission are shown in the following table.

[0057] Table 3 Statistics of abnormal data items in remote monitoring data transmission of heavy vehicles

[0058]

[0059]

[0060] The situation of vehicles with problems that generate abnormal data items during data transmission can be counted separately, and the evaluation threshold of each abnormal data item generated by data transmission can be determined. Statistics can be performed based on the year / month / week / day period. The following is an exemplary description of the specific statistical rules for data transmission quality based on the week as the time period:

[0061] First, the third preset period can be set to one week, and the situation of the problem occurring on the day is calculated according to the definition of the data transmission abnormal data item; secondly, the number of problem vehicles with abnormal data for a certain number of consecutive days (for example, 2 days, 3 days, 4 days, 5 days, 6 days or 7 days) is counted, that is, the fifth number, and the sixth number of heavy vehicles performing data transmission operations during this week; finally, the proportion of problem vehicles on each day is calculated, proportion = fifth number / sixth number, and the evaluation threshold, that is, the data transmission quality evaluation index, is set according to the proportion on different days.

[0062] In this way, the regulatory authorities can understand the vehicle status in real time and improve regulatory efficiency. The above evaluation process can detect anomalies in data transmission and ensure the authenticity and validity of the data.

[0063] In some optional embodiments, the data collection quality evaluation index includes at least one of the following: a data cross-border rate evaluation index for heavy vehicles, a data inefficiency evaluation index for heavy vehicles, an average data collection cycle evaluation index for heavy vehicles, and a data packet loss rate evaluation index for heavy vehicles.

[0064] In this embodiment, after completing the data transmission, it is necessary to evaluate the terminal collection quality according to the GB 17691-2018 and HJ 1239-2021 standards to ensure that the terminal data collection quality installed on the heavy-duty vehicle meets the standard requirements.

[0065] In an optional embodiment, a data out-of-bounds rate evaluation index is generated in the following manner: obtaining the cumulative out-of-bounds frame number of each data item of the first heavy-duty vehicle within a fourth preset period, the out-of-bounds frame number being the number of data frames whose numerical range of the data item is outside the standard numerical range; obtaining the total transmission frame number of each data item of the first heavy-duty vehicle within the fourth preset period; determining the data out-of-bounds rate of the first heavy-duty vehicle based on the ratio of the cumulative out-of-bounds frame number to the total transmission frame number; dividing the data out-of-bounds range based on preset intervals to generate multiple data out-of-bounds intervals; obtaining the seventh number of heavy-duty vehicles within each data out-of-bounds interval, and obtaining the eighth number of heavy-duty vehicles that perform data acquisition operations within the fourth preset period; determining the data out-of-bounds rate evaluation index corresponding to each data item based on the ratio of the seventh number to the eighth number.

[0066] In this embodiment, according to the standard GB 17691-2018 "Heavy-duty diesel vehicle pollutant emission limits and measurement methods (China Phase VI)", the data out-of-bounds range is determined as shown in the following table.

[0067] Table 4 Heavy vehicle remote monitoring data out-of-bounds determination table

[0068]

[0069]

[0070] Among them, the number of out-of-bounds frames does not include invalid values. For the data out-of-bounds rate evaluation index except the output value data items of the upstream / downstream NOx sensors of the SCR, the cumulative out-of-bounds frames of each field in a week of a single vehicle can be counted separately; the total transmission frames of each data item in a week of a single vehicle can be counted separately; the data out-of-bounds rate of each data item of a single vehicle can be calculated separately = cumulative out-of-bounds frames / total transmission frames; the out-of-bounds rate interval range is divided according to the preset interval to generate multiple data out-of-bounds intervals, for example, (1%, 10%], (10%, 20%], (20%, 30%], (30%, 40%], (40%, 50%], >50%; the number of out-of-bounds vehicles for each data item in each out-of-bounds rate interval is counted, that is, the seventh number, the eighth number of heavy vehicles performing data collection operations in the fourth preset period is obtained, and the proportion of out-of-bounds vehicles is calculated, the proportion = the seventh number / the eighth number, and the out-of-bounds rate evaluation threshold of each data item is set according to the proportion in different out-of-bounds rate intervals, that is, the data out-of-bounds rate evaluation index.

[0071] For the data out-of-bounds rate evaluation index of the SCR upstream / downstream NOx sensor output value data item, the data frames with atmospheric pressure > 76kpa and engine coolant temperature > 70℃ are screened and recorded as the total number of frames; in the total number of frames, the cumulative total number of out-of-bounds frames of each field of the SCR upstream / downstream NOx sensor output value of the single vehicle within one week are counted respectively; the out-of-bounds rate of each field of the single vehicle is calculated respectively = cumulative total number of out-of-bounds frames / total number of frames; the out-of-bounds rate interval range is divided according to a certain interval, such as: (1%, 10%], (10%, 20%], (20%, 30%], (30%, 40%], (40%, 50%], > 50%; the number of out-of-bounds vehicles in each field in each interval is counted, and the proportion of out-of-bounds vehicles is calculated, the proportion = number of out-of-bounds vehicles / number of online vehicles, so as to set the out-of-bounds rate evaluation threshold of each field, that is, the data out-of-bounds rate evaluation index.

[0072] In this way, abnormal data can be discovered in time, potential safety hazards can be prevented, and driving safety can be guaranteed; by evaluating data out-of-bounds situations, the data processing logic of the vehicle terminal can be optimized to improve system stability and performance.

[0073] In some optional embodiments, the data cross-border rate evaluation index is also generated in the following manner: obtaining the ninth number of cross-border heavy vehicles corresponding to each data item within the fifth preset period, and the cross-border heavy vehicles include heavy vehicles whose consecutive cross-border frame numbers exceed the first preset threshold; obtaining the tenth number of heavy vehicles performing data collection operations within the fifth preset period; based on the ratio of the ninth number to the tenth number, determining the data cross-border rate evaluation index corresponding to each data item.

[0074] In this embodiment, for the data out-of-bounds rate evaluation index except for the SCR upstream / downstream NOx sensor output value data items, a vehicle whose continuous out-of-bounds frame number exceeds a certain threshold can be defined as an out-of-bounds vehicle, that is, the first preset threshold can be set to 30s, 60s, 120s, 180s, 240s or 300s; the number of out-of-bounds vehicles in each field within a week is counted respectively; the number of vehicles online within a week is counted respectively; the proportion of out-of-bounds vehicles is calculated, proportion = number of out-of-bounds vehicles / number of vehicles online, and the out-of-bounds rate evaluation threshold of each field, that is, the data out-of-bounds rate evaluation index, is set according to the proportion under different thresholds.

[0075] For the data out-of-bounds rate evaluation index of the SCR upstream / downstream NOx sensor output value data items, screen the data with atmospheric pressure > 76kpa and engine coolant temperature > 70℃ and record them as valid data; in the valid data, define the vehicles whose continuous out-of-bounds frames exceed a certain threshold as out-of-bounds vehicles, such as 30s, 60s, 120s, 180s, 240s or 300s; count the number of out-of-bounds vehicles in each field within a week; count the number of vehicles online within a week; calculate the proportion of out-of-bounds vehicles, proportion = number of out-of-bounds vehicles / number of vehicles online, and set the out-of-bounds rate evaluation threshold of each field according to the proportion under different thresholds, that is, the data out-of-bounds rate evaluation index.

[0076] In this way, a data out-of-bounds rate evaluation index can be generated based on the cumulative number of out-of-bounds frames or the continuous number of out-of-bounds frames. The two methods can verify each other, improve the accuracy of the data, reduce the errors that may be caused by a single method, enhance the reliability of the data, and more comprehensively reflect the characteristics of the data in terms of the out-of-bounds rate.

[0077] The data inefficiency evaluation index can be generated in the following way. According to GB 17691-2018 "Heavy-duty diesel vehicle pollutant emission limits and measurement methods (China Phase VI)", the data invalid value determination is shown in the following table.

[0078] Table 5 Heavy-duty vehicle remote monitoring data invalidity determination table

[0079]

[0080]

[0081] Among them, the above table can be converted into decimal values ​​and recorded as invalid values. The problem vehicle situation of each data item can be counted separately to determine the invalid evaluation threshold of each data item. Two statistical ideas are provided based on the cumulative value and continuous value of the number of invalid frames. The specific statistical rules are as follows (exemplified by the weekly dimension, and statistics can be performed according to the year / month / day / week as a period):

[0082] According to the statistics of the cumulative number of invalid frames, for the data inefficiency evaluation index of the data items except the output value of the NOx sensor upstream / downstream of the SCR: the cumulative total number of invalid frames of each field in a week of a single vehicle can be counted separately; the total number of transmission frames of each field in a week of a single vehicle can be counted separately; the inefficiency of each field of a single vehicle can be calculated separately = the cumulative total number of invalid frames / the total number of transmission frames; the inefficiency interval range is divided according to a certain interval, such as: (1%, 10%], (10%, 20%], (20%, 30%], (30%, 40%], (40%, 50%], >50%; the number of invalid vehicles in each field in each interval is counted, and the proportion of invalid vehicles is calculated (proportion = number of invalid vehicles / number of online vehicles). According to the proportion in different intervals, the inefficiency evaluation threshold of each field, that is, the data inefficiency evaluation index, is set.

[0083] For the data inefficiency evaluation index of the SCR upstream / downstream NOx sensor output value data item: the data frames with atmospheric pressure > 76kpa and engine coolant temperature > 70℃ can be screened and recorded as the total number of frames; in the total number of frames, the cumulative total number of invalid frames of each field of the SCR upstream / downstream NOx sensor output value of the single vehicle within one week are counted respectively; the inefficiency of each field of the single vehicle is calculated respectively = the cumulative total number of invalid frames / total number of frames; the inefficiency interval range is divided according to a certain interval, such as: (1%, 10%], (10%, 20%], (20%, 30%], (30%, 40%], (40%, 50%], >50%; the number of invalid vehicles in each field in each interval is counted, and the proportion of invalid vehicles is calculated (proportion = number of invalid vehicles / number of online vehicles), so as to set the inefficiency evaluation threshold of each field, that is, the data inefficiency evaluation index.

[0084] According to the statistics of consecutive invalid frames, for the data inefficiency evaluation index except the SCR upstream / downstream NOx sensor output value data items: vehicles with consecutive invalid frames exceeding a certain threshold (such as 30s, 60s, 120s, 180s, 240s, 300s) can be defined as invalid vehicles; the number of invalid vehicles in each field within a week is counted separately; the number of vehicles online within a week is counted separately; the proportion of invalid vehicles is calculated (proportion = number of invalid vehicles / number of vehicles online), so as to set the inefficiency evaluation threshold of each field, that is, the data inefficiency evaluation index.

[0085] For the data inefficiency evaluation index of the SCR upstream / downstream NOx sensor output value data items: the data with atmospheric pressure > 76kpa and engine coolant temperature > 70℃ can be screened; vehicles with consecutive invalid frames exceeding a certain threshold (such as 30s, 60s, 120s, 180s, 240s, 300s) are defined as invalid vehicles; the number of invalid vehicles in each field within a week is counted separately; the number of vehicles online within a week is counted separately; the proportion of invalid vehicles is calculated (proportion = number of invalid vehicles / number of vehicles online), and the inefficiency evaluation threshold of each field, that is, the data inefficiency evaluation index, is set according to the proportion under different thresholds.

[0086] Accurate data is the basis for decision-making. Evaluating invalid data can ensure the reliability of the decision-making basis and improve the accuracy of the decision.

[0087] The data average collection cycle evaluation index can be generated in the following way. According to the standard requirements of HJ1239.1-2021 "Heavy-duty vehicle emission remote monitoring technical specifications part 1 vehicle terminal": data collection frequency 1Hz, define the data average collection cycle as: based on the supplemented vehicle login and logout time as the start time and end time of each trip, calculate the average difference in seconds between the second frame of data and the previous frame of data in each trip as the average data collection cycle of the trip, and calculate the average of the average data collection cycle of each trip as the average data collection cycle of the vehicle on that day.

[0088] The current status of vehicle login and logout problems is summarized in the following table.

[0089] Table 6 Heavy-duty vehicle remote monitoring data login and logout

[0090] Serial number login Sign out Engine serial number 1 have have Consistency 2 have have Inconsistency 3 have none / 4 none have / 5 none none /

[0091] For the case where the vehicle logs in and out and the engine serial number is the same, the running time can be obtained by subtracting the vehicle log-in time from the vehicle log-out time. For the case where the vehicle logs in and out but the engine serial number is inconsistent, the vehicle offline rule can be set. If the vehicle has no data transmission for a certain period of time (such as 300s), it is considered that the vehicle is offline, and the last engine data is used as the vehicle log-out time, and the latest data after this data is the next vehicle login time. For the case where the vehicle logs in but not out, the vehicle login time can be used as the start time, and the last real-time data before the next login time can be used as the vehicle log-out time to calculate the online time. For the case where the vehicle does not log in but logs out, the latest real-time data after the last log-out time can be used as the vehicle login time, and the vehicle log-out time can be used as the end time to calculate the online time. For the case where the vehicle does not log in and out, if there are multiple offlines in the interval based on the vehicle offline rule, that is, there are multiple intervals, that is, the manually determined offline time is used as the log-out time, and the next frame of the latest data is used as the login time of the next trip.

[0092] Because the daily update is based on T+1, if the vehicle logs in and out across days, 00:00:00 is used as the vehicle log-in time and 23:59:59 is used as the vehicle log-out time. The average collection cycle of statistical data is used to determine the evaluation threshold. Two statistical ideas are provided based on the average value of the collection cycle and the continuous value. The specific statistical rules are as follows (statistics can be performed based on the time period of year / month / week / day, and the weekly dimension is used as an example):

[0093] Statistics are performed based on the average value of the collection cycle. The average data collection cycle within a week can be calculated based on the definition of the data collection cycle; the range of the average data collection cycle is divided according to a certain interval, such as: (5, 10], (10, 20], (20, 30], (30, 40], (40, 50], >50; the number of vehicles in each interval is counted, and the vehicle proportion is calculated, where the proportion = the number of vehicles in the interval / the number of vehicles online. According to the proportion in different intervals, the data average collection cycle evaluation threshold, that is, the data average collection cycle evaluation index, is set.

[0094] Statistics can also be performed according to continuous values ​​of the collection cycle. The average data collection cycle of the day can be calculated according to the data collection cycle definition; the number of problem vehicles whose average data collection cycle exceeds 1Hz for a certain number of consecutive days (such as 2 days, 3 days, 4 days, 5 days, 6 days, and 7 days); the proportion of problem vehicles on each day is calculated, and the proportion = the number of problem vehicles on each day / the number of online vehicles. According to the proportion on different days, the data average collection cycle evaluation threshold, that is, the data average collection cycle evaluation index, is set.

[0095] In this way, the collected data can be guaranteed to be representative and data deviation caused by unreasonable collection cycle can be reduced. A reasonable collection cycle can balance the data volume and system burden, avoid data redundancy or loss, and optimize the overall performance of the system.

[0096] The data packet loss rate evaluation index can be generated in the following ways. According to the standard requirements of HJ 1239.2-2021 "Heavy-duty vehicle emission remote monitoring technical specifications part 2 enterprise platform": the data packet loss rate is defined as the proportion of data packets lost during data transmission to the total number of data packets sent, and the packet loss rate requirement is that the packet loss rate is no more than 1%. The data packet loss rate problem vehicle situation is counted and the evaluation threshold is determined. Two statistical ideas are provided based on the average value and continuous value of the packet loss rate. The specific statistical rules are as follows (statistics can be performed by year / month / week / day as the time period, and the weekly dimension is used as an example): Calculate the packet loss rate: parse the data packet and count the number of data frames in a single data packet (usually 10 frames or 1 frame); parse and remove duplicates from all data within a week, and sort them by time; calculate the number of data frames to be sent based on the login and logout time, and calculate the total number of data packets that the enterprise platform should send within a week (the total number of data frames to be sent / the number of frames of a single data packet); count the number of data packets actually received by the monitoring platform within a week as the total number of data packets actually sent; Data packet loss rate = (total number of data packets to be sent - total number of data packets actually sent) / total number of data packets to be sent * 100%.

[0097] The average packet loss rate can be used for statistics, and the data packet loss rate range can be divided according to a certain interval, such as: (5%, 10%], (10%, 20%], (20%, 30%], (30%, 40%], (40%, 50%], >50%; count the number of vehicles in each interval, and calculate the vehicle proportion, proportion = number of vehicles in the interval / number of online vehicles. According to the proportion in different intervals, the data packet loss rate evaluation threshold, that is, the data packet loss rate evaluation index, is set.

[0098] It can also be counted according to continuous values, and the number of problem vehicles whose packet loss rate exceeds 1% for a certain number of consecutive days (such as 2 consecutive days, 3 days, 4 days, 5 days, 6 days, and 7 days) can be counted; the proportion of problem vehicles on each day is calculated, and the proportion = the number of problem vehicles on each day / the number of online vehicles. According to the proportion on different days, the data packet loss rate evaluation threshold, that is, the data packet loss rate evaluation index, is set.

[0099] The data packet loss rate evaluation index helps to identify and solve problems in data transmission, thereby improving the reliability and stability of data transmission; data packet loss may lead to the loss of key information. The generation of data packet loss rate evaluation indicators helps to ensure the integrity and security of data and prevent security issues caused by data loss.

[0100] In some optional implementations, the networking status data evaluation index can be generated in the following way: the phenomenon that a vehicle is offline for a certain number of consecutive days can be defined as long-term offline. Statistics of long-term offline vehicles are collected to determine the evaluation threshold. The specific statistical rules are as follows (statistics can be performed based on the time period of year / month / week / day, and an example is given based on the weekly dimension): the statistical period is one week, and the number of consecutive offline days of a single vehicle is counted (the number of days from the last day of the statistical period to the last day of the data uploaded in the last frame); vehicles that have been offline for a certain number of consecutive days are recorded as long-term offline vehicles, and the range within the long-term offline interval is set, such as (30, 60], (60, 90], (90, 120], (120, 150], (150, 180], >180, and the number of long-term offline vehicles is counted; the cumulative number of connected vehicles is counted; the long-term offline rate is calculated = the number of long-term offline vehicles / the cumulative number of connected vehicles; according to the long-term offline rate within the interval range, the long-term offline evaluation threshold is set, that is, the networking status data evaluation index. In this way, the regulatory authorities can grasp the vehicle situation in real time and improve regulatory efficiency.

[0101] In some optional implementations, after the terminal collects and evaluates, the evaluation is performed based on the actual operating status of the vehicle, and the data abnormalities and data jumps are recorded respectively. The data abnormality problem items are as follows:

[0102] Table 7 Abnormal data table generated by vehicle operation

[0103]

[0104]

[0105] The abnormal data evaluation index can be generated in the following way: the problem vehicles of each data abnormality problem type can be counted separately to determine the evaluation threshold. The specific statistical rules are as follows (the statistics can be performed by year / month / week / day as the time period, and the weekly dimension is used as an example): Count the consecutive frames of abnormal data for each problem item within a week; if the number of consecutive failed frames exceeds a certain threshold, such as 60s, 120s, 180s, 240s, 300s, 600s or 1200s, this vehicle is recorded as a problem vehicle and the number of problem vehicles is counted; count the number of vehicles online within a week; calculate the proportion of vehicles with data abnormality, proportion = number of problem vehicles / number of vehicles online, and set the data abnormality evaluation threshold according to the proportion under different thresholds.

[0106] In this way, by evaluating the abnormal data generated by the operating status of heavy vehicles, potential safety hazards of vehicles can be discovered in a timely manner, accidents can be prevented, and the safety of drivers, passengers and other traffic participants can be guaranteed.

[0107] In some optional implementations, the jump data table generated by vehicle operation is as follows:

[0108] Table 8 Jump data table generated by vehicle operation

[0109] Serial number Abnormal data items definition 1 Speed ​​jump Continuous jumps of two seconds before and after ≥36km / h 2 Atmospheric pressure Continuous jumps of ≥10kpa within two seconds 3 Speed Continuous two-second jump ≥1500rpm 4 Torque Continuous jumps between the previous and next two seconds ≥ 50% 5 Reagent balance Continuous jumps between the previous and next two seconds ≥ 20% 6 SCR inlet temperature Continuous jumps within two seconds ≥10℃ 7 DPF pressure difference Continuous jumps between the first and second intervals ≥20Kpa 8 Fuel tank level Continuous jumps between the previous and next two seconds ≥ 20% 9 Engine coolant temperature Continuous jumps within two seconds ≥10℃ 10 longitude Continuous jumps between the first and second intervals ≥ 0.001 11 latitude Continuous jumps between the first and second intervals ≥ 0.001 12 mileage Continuous jumps of two seconds before and after ≥ 1km

[0110] The data jump evaluation index can be generated in the following way. The problem vehicles of each data jump problem type can be counted separately to determine the data jump evaluation threshold. The specific statistical rules are as follows (the statistics can be performed according to the time period of year / month / week / day, and the weekly dimension is used as an example): Count the continuous frames of data jumps of each problem item within a week; if the number of continuous failed frames exceeds a certain threshold, such as 60s, 120s, 180s, 240s, 300s, 600s or 1200s, this vehicle is recorded as a problem vehicle and the number of problem vehicles is counted; count the number of vehicles online within a week; calculate the proportion of data jump vehicles, proportion = number of problem vehicles / number of online vehicles, and set the data jump evaluation threshold, that is, the data jump evaluation index, according to the proportion under different thresholds.

[0111] In this way, by analyzing the jump data generated by the operation of heavy vehicles, the performance problems of the vehicles can be discovered in a timely manner, providing a basis for optimizing vehicle performance and improving overall driving safety.

[0112] In an optional embodiment, the weight of each data evaluation index in the comprehensive evaluation process of heavy vehicles is determined, including: normalizing each index value in the target data evaluation index to obtain a normalized target data evaluation index value; determining a first weight of the target data evaluation index based on a ratio of the normalized target data evaluation index value to the first index value, the first index value being the sum of the normalized target data evaluation index values; determining an entropy value of the target data evaluation index based on the first weight; determining an information entropy redundancy of the target data evaluation index based on the entropy value of the target data evaluation index; determining a weight corresponding to the target data evaluation index in the comprehensive evaluation process based on a ratio of the information entropy redundancy of the target data evaluation index to the first information entropy redundancy, the first information entropy redundancy being the sum of the information entropy redundancy of each data evaluation index.

[0113] In this embodiment, the problem vehicle proportion A of each data evaluation index is counted. ij , calculate the score x of each indicator ij , that is: x ij =(1-A ij )*100, the entropy weight method can be used to calculate the weight of data quality evaluation indicators. The entropy weight method judges the discrete degree of each indicator based on the entropy value. The specific calculation method is as follows: for n samples and m indicators, then x ij is the value of the jth index of the i-th sample (i = 1, 2..., n; j = 1, 2,..., m); the normalized data evaluation index xi ′ j The calculation method of is as follows,

[0114]

[0115] The proportion of the i-th sample value under the j-th indicator to the indicator P ij The calculation method of is as follows:

[0116]

[0117] Among them, i=1,…,n, j=1,…,m.

[0118] The entropy value e of the jth indicator j The calculation method of is as follows:

[0119]

[0120] Among them, K=1 / ln(n)>0, satisfying e j ≥0;

[0121] Information entropy redundancy (difference) d j The calculation method of is as follows:

[0122] d j =1-e j

[0123] The weight w of each data evaluation index j The calculation method of is as follows:

[0124]

[0125] Comprehensive evaluation results of each sample j The calculation method of is as follows:

[0126]

[0127] In this way, comprehensive consideration of multiple data evaluation indicators can fully reflect all aspects of remote monitoring data, avoid the one-sidedness of a single indicator, and the weight allocation is based on the actual importance and impact, which can make the evaluation results more objective and accurate.

[0128] In this embodiment, a device for evaluating remote monitoring data of heavy vehicles is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0129] Figure 2 The structure diagram of the device for evaluating the remote monitoring data of heavy vehicles provided by the embodiment of the present invention is shown. This embodiment provides a device for evaluating the remote monitoring data of heavy vehicles, such as Figure 2 As shown, including:

[0130] The data acquisition module 201 is used to acquire the remote monitoring data of the heavy vehicle, and the remote monitoring data includes: on-board diagnostic system data and engine data.

[0131] The data evaluation index acquisition module 202 is used to generate data evaluation indicators based on remote monitoring data, and the data evaluation indicators include at least one of the following: a terminal activation quality evaluation index for evaluating data generated by activation of an on-board terminal set in a heavy-duty vehicle, a data verification quality evaluation index for evaluating data generated by data verification of the heavy-duty vehicle, a data transmission quality evaluation index for evaluating data generated by data transmission of the heavy-duty vehicle, a data collection quality evaluation index for evaluating data generated by data collection of the heavy-duty vehicle, a networking status data evaluation index for evaluating data generated by networking of the heavy-duty vehicle, an abnormal data evaluation index for evaluating abnormal data generated during the operation of the heavy-duty vehicle, and a data jump evaluation index for evaluating jump data generated during the operation.

[0132] The weight determination module 203 is used to determine the weight of each data evaluation index in the comprehensive evaluation process of heavy vehicles.

[0133] The comprehensive evaluation acquisition module 204 is used to generate a comprehensive evaluation result of the remote monitoring data based on the data evaluation indicators and the weights corresponding to the data evaluation indicators.

[0134] In some optional implementations, the data evaluation index acquisition module includes:

[0135] A data evaluation index generating unit is used to obtain the number of consecutive frames with abnormal data items in the data generation of heavy vehicles within a target preset period; determine the number of heavy vehicles with abnormal data based on the number of consecutive frames with abnormal data items in the data generation of heavy vehicles; obtain the initial number of heavy vehicles that perform data generation operations within the target preset period; obtain data evaluation indicators based on the ratio of the number of heavy vehicles with abnormal data to the initial number of heavy vehicles; wherein the data generation process includes at least one of the following: a process of activating the vehicle terminal, a process of data verification, a process of data transmission, a process of data collection, a process of networking, and a process of operation.

[0136] In some optional implementations, the data evaluation index generating unit includes:

[0137] The data evaluation index generation subunit is used to generate data collection quality evaluation indicators, which include at least one of the following: a data cross-border rate evaluation index for heavy vehicles, a data inefficiency evaluation index for heavy vehicles, an average data collection cycle evaluation index for heavy vehicles, and a data packet loss rate evaluation index for heavy vehicles.

[0138] In some optional embodiments, the data evaluation index generation subunit is also used to generate a data cross-border rate evaluation index in the following manner: obtain the cumulative cross-border frame number of each data item of the first heavy vehicle within the fourth preset period, the cross-border frame number is the number of data frames whose numerical range of the data item is outside the standard numerical range; obtain the total transmission frame number of each data item of the first heavy vehicle within the fourth preset period; determine the data cross-border rate of the first heavy vehicle based on the ratio of the cumulative cross-border frame number to the total transmission frame number; divide the data cross-border range based on preset intervals to generate multiple data cross-border intervals; obtain the seventh number of heavy vehicles in each data cross-border interval, and obtain the eighth number of heavy vehicles that perform data acquisition operations within the fourth preset period; determine the data cross-border rate evaluation index corresponding to each data item based on the ratio of the seventh number to the eighth number.

[0139] In some optional embodiments, the data evaluation index generation subunit is also used to generate a data cross-border rate evaluation index in the following manner: obtaining the ninth number of cross-border heavy vehicles corresponding to each data item within the fifth preset period, and the cross-border heavy vehicles include heavy vehicles whose consecutive cross-border frames exceed the first preset threshold; obtaining the tenth number of heavy vehicles performing data collection operations within the fifth preset period; based on the ratio of the ninth number to the tenth number, determining the data cross-border rate evaluation index corresponding to each data item.

[0140] In some optional implementations, the specific gravity determination module includes:

[0141] The weight determination unit is used to normalize each indicator value in the target data evaluation indicator to obtain the normalized target data evaluation indicator value; based on the ratio of the normalized target data evaluation indicator value to the first indicator value, determine the first weight of the target data evaluation indicator, the first indicator value is the sum of the normalized target data evaluation indicator values; based on the first weight, determine the entropy value of the target data evaluation indicator; based on the entropy value of the target data evaluation indicator, determine the information entropy redundancy of the target data evaluation indicator; based on the ratio of the information entropy redundancy of the target data evaluation indicator to the first information entropy redundancy, determine the corresponding weight of the target data evaluation indicator in the comprehensive evaluation process, the first information entropy redundancy is the sum of the information entropy redundancy of each data evaluation indicator.

[0142] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0143] The device for evaluating the remote monitoring data of heavy vehicles provided by the embodiment of the present invention can evaluate the remote monitoring data of heavy vehicles from the dimensions of data evaluation indicators such as terminal activation quality, data verification quality, data transmission quality, data acquisition quality, heavy vehicle networking quality, abnormal data identification quality and jump data identification quality based on national standards and data logical relationships. A comprehensive evaluation result is generated by weighting on the basis of each data evaluation indicator. The data has a wide coverage and high utilization rate. The evaluation method is scientific and accurate. The data quality problems of the remote monitoring platform of heavy vehicles can be checked, and a reasonable and accurate evaluation of data quality can be completed, which helps enterprises to complete data governance, helps the competent departments to improve data quality, gives full play to the role of the remote monitoring platform of heavy vehicles, realizes effective dynamic monitoring of heavy vehicles, and lays the foundation for big data analysis and mining and establishment of application models.

[0144] The device for evaluating the remote monitoring data of heavy vehicles in this embodiment is presented in the form of a functional unit, where the unit refers to an application specific integrated circuit (ASIC) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0145] The embodiment of the present invention also provides a computer device having the above Figure 2 The device shown is used for evaluating remote monitoring data of heavy vehicles.

[0146] See also Figure 3 , Figure 3 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 3 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in or on the memory to display graphical information of a graphical user interface on an external input / output device (such as a display device coupled to an interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 3 A processor 10 is taken as an example.

[0147] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0148] The aforementioned memory 20 stores instructions executable by at least one processor 10, so that the aforementioned at least one processor 10 executes the method shown in the above embodiment.

[0149] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0150] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0151] The computer device also includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 3 The example of connecting through bus is taken in the following.

[0152] The input device 30 can receive input digital or character information, and generate key signal input related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator rod, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (such as a light emitting diode) and a tactile feedback device (such as a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0153] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0154] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0155] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for evaluating remote monitoring data of heavy vehicles, characterized in that: The method comprises: Acquiring remote monitoring data of a heavy vehicle, the remote monitoring data comprising: on-board diagnostic system data and engine data; Generate data evaluation indicators based on the remote monitoring data, the data evaluation indicators including at least one of the following: a terminal activation quality evaluation indicator for evaluating data generated by activation of an on-board terminal disposed on the heavy vehicle, a data verification quality evaluation indicator for evaluating data generated by data verification of the heavy vehicle, a data transmission quality evaluation indicator for evaluating data generated by data transmission of the heavy vehicle, a data collection quality evaluation indicator for evaluating data generated by data collection of the heavy vehicle, a networking status data evaluation indicator for evaluating data generated by networking of the heavy vehicle, an abnormal data evaluation indicator for evaluating abnormal data generated during operation of the heavy vehicle, and a data jump evaluation indicator for evaluating jump data generated during operation; Determining the weight of each of the data evaluation indicators in the comprehensive evaluation process of the heavy vehicle; Based on the data evaluation indicators and the weights corresponding to the data evaluation indicators, a comprehensive evaluation result of the remote monitoring data is generated.

2. The method according to claim 1, characterized in that The data evaluation index is generated in the following way: Acquire the number of consecutive frames of data generated by the heavy vehicle in which abnormal data items exist within a target preset period; Generating a number of consecutive frames with abnormal data items based on the data of the heavy-duty vehicles, and determining the number of heavy-duty vehicles with abnormal data; Obtaining an initial number of heavy vehicles that perform data generation operations within the target preset period; Obtaining the data evaluation index based on the ratio of the number of heavy vehicles with abnormal data to the initial number of heavy vehicles; Among them, the data generation process includes at least one of the following: the process of activating the vehicle terminal, the process of data verification, the process of data transmission, the process of data collection, the process of networking and the operation process.

3. The method according to claim 1 or 2, characterized in that: The data collection quality evaluation index includes at least one of the following: a data cross-border rate evaluation index of the heavy vehicle, a data inefficiency evaluation index of the heavy vehicle, an average data collection cycle evaluation index of the heavy vehicle, and a data packet loss rate evaluation index of the heavy vehicle.

4. The method according to claim 3, characterized in that The data crossing rate evaluation index is generated in the following way: Obtaining the cumulative number of out-of-bounds frames of each data item of the first heavy vehicle within a fourth preset period, the number of out-of-bounds frames being the number of data frames whose value range of the data item is outside the standard value range; Obtaining the total number of transmission frames of each of the data items of the first heavy vehicle within the fourth preset period; Determining a data crossing-boundary rate of the first heavy vehicle based on a ratio of the accumulated crossing-boundary frame number to the total transmission frame number; Divide the data out-of-bounds range based on preset intervals to generate multiple data out-of-bounds intervals; Acquire a seventh number of heavy vehicles within each of the data crossing intervals, and acquire an eighth number of heavy vehicles performing data collection operations within the fourth preset period; Based on the ratio of the seventh number to the eighth number, the data cross-border rate evaluation index corresponding to each of the data items is determined.

5. The method according to claim 3, characterized in that: The data crossing rate evaluation index is also generated in the following way: Obtaining a ninth number of heavy vehicles crossing the border corresponding to each of the data items within a fifth preset period, wherein the heavy vehicles crossing the border include heavy vehicles whose consecutive crossing-border frames exceed a first preset threshold; Acquiring a tenth number of heavy vehicles performing data collection operations within the fifth preset period; Based on the ratio of the ninth number to the tenth number, the data cross-border rate evaluation index corresponding to each of the data items is determined.

6. The method according to claim 1, characterized in that The step of determining the weight of each of the data evaluation indicators in the comprehensive evaluation process of the heavy vehicle includes: Normalizing each indicator value in the target data evaluation indicator to obtain a normalized target data evaluation indicator value; Determining a first weight of the target data evaluation index based on a ratio of the normalized target data evaluation index value to a first index value, wherein the first index value is the sum of the normalized target data evaluation index values; Based on the first weight, determining an entropy value of the target data evaluation index; Determining the information entropy redundancy of the target data evaluation indicator based on the entropy value of the target data evaluation indicator; Based on the ratio of the information entropy redundancy of the target data evaluation indicator to the first information entropy redundancy, the weight corresponding to the target data evaluation indicator in the comprehensive evaluation process is determined, and the first information entropy redundancy is the sum of the information entropy redundancy of each of the data evaluation indicators.

7. A device for evaluating remote monitoring data of heavy vehicles, characterized in that: The device comprises: A data acquisition module, used to acquire remote monitoring data of a heavy vehicle, wherein the remote monitoring data includes: on-board diagnostic system data and engine data; A data evaluation index acquisition module, used to generate data evaluation indicators based on the remote monitoring data, wherein the data evaluation indicators include at least one of the following: a terminal activation quality evaluation index for evaluating data generated by activation of an on-board terminal set on the heavy vehicle, a data verification quality evaluation index for evaluating data generated by data verification of the heavy vehicle, a data transmission quality evaluation index for evaluating data generated by data transmission of the heavy vehicle, a data collection quality evaluation index for evaluating data generated by data collection of the heavy vehicle, a networking status data evaluation index for evaluating data generated by networking of the heavy vehicle, an abnormal data evaluation index for evaluating abnormal data generated during the operation of the heavy vehicle, and a data jump evaluation index for evaluating jump data generated during the operation; A weight determination module, used to determine the weight of each of the data evaluation indicators in the comprehensive evaluation process of the heavy vehicle; A comprehensive evaluation acquisition module is used to generate a comprehensive evaluation result of the remote monitoring data based on the data evaluation indicators and the weights corresponding to the data evaluation indicators.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for evaluating remote monitoring data of heavy vehicles according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method for evaluating heavy vehicle remote monitoring data according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the method for evaluating remote monitoring data of a heavy vehicle according to any one of claims 1 to 6.

Images