New energy automobile national standard data monitoring method, storage medium and equipment

Abstract

The invention relates to the technical field of vehicle national standard data processing, in particular to a new energy vehicle national standard data monitoring method, a storage medium and equipment, and the method comprises the steps: collecting vehicle end data, and transmitting the vehicle end data to an enterprise platform; the enterprise platform receives the vehicle end data and synchronizes the vehicle end data to a national vehicle data center, a local vehicle data center and an enterprise analysis platform used for data quality analysis; and the enterprise analysis platform generates a vehicle analysis report according to a preset data analysis rule and an analysis scene in combination with the vehicle end data. According to the method, the problem identification capability of a vehicle enterprise can be greatly improved, meanwhile, the situation that rectification is passively notified when the vehicle-related data of the enterprise is checked to be unqualified in the past is changed, the enthusiasm and initiative of the enterprise are brought into full play, and the problem of vehicle data quality can be found, analyzed and processed as early as possible.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile national standard data processing, and in particular to a method, storage medium and device for monitoring national standard data of new energy vehicles. Background Art

[0002] The national standard GB / T 32960 has clear requirements for the management and data of new energy vehicles. At the same time, the national and local new energy vehicle data centers have implemented supervision and monitoring requirements for the data of vehicle companies. Ensuring the quality, reliability and stability of vehicle data is a common problem faced by all car companies. To improve data quality, data is sorted out mainly from the following aspects:

[0003] 1. Consistency: Consistency refers to whether the data follows uniform specifications and whether the data set maintains a uniform format.

[0004] 2. Completeness: Completeness refers to whether there is any missing data information. The missing data may be the entire data record missing, or the missing record of a certain field in the data. The value of incomplete data will be greatly reduced, and it is also a more basic evaluation standard for data quality.

[0005] 3. Timeliness: Timeliness refers to the time interval from the generation of data to the time when it can be viewed, also known as the data delay. Timeliness is not a high requirement for data analysis itself, but if the data analysis cycle plus the time it takes to create data is too long, the conclusions drawn from the analysis may lose their reference value.

[0006] 4. Accuracy: Accuracy refers to whether the information recorded in the data is abnormal or wrong. Unlike consistency, data with accuracy problems is not just inconsistent in rules. More common data accuracy errors are garbled characters. Secondly, abnormally large or small data is also data that does not meet the conditions.

[0007] 5. Validity: The value and format of the data must comply with the requirements of the data definition or business definition, such as the format of location data and battery-related data.

[0008] 6. Uniqueness: There are no duplicate data values ​​for a certain data item or a certain group of data. The value must be unique, such as ID data.

[0009] At present, the review of enterprise vehicle-related data is conducted by the national and local new energy vehicle data centers. The national platform (i.e., the National New Energy Vehicle Data Center, referred to as the National Vehicle Data Center) and the local platform (i.e., the Local New Energy Vehicle Data Center, referred to as the Local Vehicle Data Center) review the enterprise vehicle-related data. When the review fails, the car company will be notified to make rectifications, which results in the car company having to passively wait for the notification to make rectifications, failing to give full play to the enthusiasm and initiative of the car company, and not conducive to the early discovery, early analysis and early treatment of vehicle data quality problems. In view of the above defects, the present invention has made improvements. Summary of the invention

[0010] In order to overcome the shortcomings of the background technology, the present invention provides a method, storage medium and equipment for monitoring national standard data of new energy vehicles. The method can greatly improve the problem identification ability of automobile companies, and at the same time change the previous situation of passively notifying companies to make rectifications when the vehicle-related data audit is unqualified, give full play to the enthusiasm and initiative of companies, and can discover, analyze and deal with vehicle data quality problems early.

[0011] To achieve the above object, the present invention provides a method for monitoring national standard data of new energy vehicles, the method comprising:

[0012] Collect vehicle-side data and send it to the enterprise platform;

[0013] The enterprise platform receives the vehicle-side data, and synchronizes the vehicle-side data to the national vehicle data center, the local vehicle data center, and the enterprise analysis platform for data quality analysis;

[0014] The enterprise analysis platform generates a vehicle analysis report based on preset data analysis rules and analysis scenarios and in combination with the vehicle-side data.

[0015] Further,

[0016] The analysis content of the vehicle analysis report includes data integrity problem analysis, data message invalidity problem analysis, data information front and back conflict problem analysis, data message value abnormality problem analysis, and data collection time and reporting time problem analysis.

[0017] Further,

[0018] The data integrity problem analysis includes identification and analysis of data integrity;

[0019] The analysis of invalid data message problems includes identification and analysis of field invalidation;

[0020] The analysis of the conflict between data and information includes the identification and analysis of field conflicts;

[0021] The data message value abnormality problem analysis includes identification and analysis of field 0 value;

[0022] The analysis of data collection time and reporting time issues includes identification and analysis of data transmission.

[0023] Further,

[0024] The preset data analysis rules and analysis scenarios are established based on national standard audit questions, landmark audit questions and the enterprise historical question library.

[0025] Further,

[0026] Collect vehicle-side data through the vehicle terminal and send it to the enterprise platform;

[0027] The vehicle-side data includes real-time data generated during vehicle use and national standard GB / T 32960 data.

[0028] Further,

[0029] The national vehicle data center analyzes the vehicle-side data and feeds back abnormal data to the enterprise analysis platform;

[0030] The local vehicle data center analyzes the vehicle-side data and feeds back abnormal data to the enterprise analysis platform;

[0031] The enterprise analysis platform notifies rectification after receiving abnormal data fed back by the national vehicle data center or the local vehicle data center.

[0032] Further,

[0033] The method further comprises:

[0034] The generated vehicle analysis report is sent to the enterprise regulatory department for analysis and rectification.

[0035] Further,

[0036] The enterprise supervision department includes the enterprise monitoring department and the enterprise analysis and processing department;

[0037] The enterprise monitoring department identifies abnormal data according to the vehicle analysis report and notifies the enterprise analysis and processing department of the abnormal data;

[0038] The enterprise analysis and processing department makes corrections based on the received abnormal data.

[0039] Based on the same inventive concept, the present invention also provides a computer-readable storage medium storing one or more programs, which, when executed, can implement the aforementioned method for monitoring national standard data of new energy vehicles.

[0040] Based on the same inventive concept, the present invention also provides an electronic device, including a processor, a communication interface, the aforementioned computer-readable storage medium and a communication bus; wherein the processor, the communication interface, and the computer-readable storage medium communicate with each other via the communication bus; and the processor is used to execute the program stored in the computer-readable storage medium.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] 1. By establishing an enterprise analysis platform for data quality analysis on the enterprise side and presetting data analysis rules and analysis scenarios to analyze and review the enterprise's vehicle-related data, the problem identification ability of the automobile enterprise can be greatly improved. At the same time, it changes the previous situation where the enterprise passively notifies rectification when the vehicle-related data review fails, fully exerts the enthusiasm and initiative of the enterprise, and can detect, analyze and handle vehicle data quality problems early;

[0043] 2. The analysis content of the vehicle analysis report includes analysis of data integrity issues, invalid data message issues, data information conflicts, abnormal data message values, and data collection and reporting time issues. The analysis content of the vehicle analysis report includes analysis of the above five aspects. The enterprise analysis platform can better meet the supervision and monitoring requirements of the national platform and landmark platform for vehicle data of automobile companies by analyzing and reviewing the above five aspects, which is conducive to early detection, early analysis and early treatment of vehicle data quality issues.

[0044] 3. Preset data analysis rules and analysis scenarios (i.e., establish historical rules) based on national standard audit issues, local standard audit issues, and the enterprise's historical problem database, and establish a national standard data analysis platform (i.e., enterprise analysis platform) based on historical rules. For the first time, enterprises have been able to independently identify and analyze abnormal data and make corrections. Other platforms in the existing technology have not established a national standard data analysis platform based on historical rules, but only passively accept feedback from the national data center.

[0045] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings.

[0046] The present invention will be further described below in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0048] Figure 1 A schematic diagram of a process for monitoring national standard data of new energy vehicles according to an embodiment of the present invention;

[0049] Figure 2 This is an overall system block diagram of national standard data monitoring for new energy vehicles according to an embodiment of the present invention;

[0050] Figure 3 A block diagram of an enterprise analysis platform according to an embodiment of the present invention;

[0051] Figure 4 The figure is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the implementation of the present invention clearer, the technical solution in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] like Figures 1 to 3 As shown, an embodiment of the present invention provides a method for monitoring national standard data of new energy vehicles, the method comprising:

[0054] Collect vehicle-side data and send it to the enterprise platform;

[0055] The enterprise platform receives the vehicle-side data, and synchronizes the vehicle-side data to the national vehicle data center, the local vehicle data center, and the enterprise analysis platform for data quality analysis;

[0056] The enterprise analysis platform generates a vehicle analysis report based on preset data analysis rules and analysis scenarios and in combination with the vehicle-side data.

[0057] In the above technical scheme, the vehicle-side data is collected in real time and sent to the enterprise platform. The enterprise platform receives the vehicle-side data collected in real time, and synchronizes the vehicle-side data with the data quality analysis platform (i.e., the enterprise analysis platform) established by the enterprise while synchronizing the vehicle-side data with the national vehicle data center and the local vehicle data center. The enterprise analysis platform generates a vehicle analysis report based on the preset data analysis rules and analysis scenarios, combined with the vehicle-side data; the vehicle analysis report better reflects the results of the vehicle-side data monitoring of new energy vehicles. This method can greatly improve the problem identification ability of automobile companies by establishing an enterprise analysis platform for data quality analysis on the enterprise side, and presetting data analysis rules and analysis scenarios to analyze and review the company's vehicle-related data. At the same time, it changes the previous situation of passively notifying rectification of unqualified vehicle-related data audits, giving full play to the enthusiasm and initiative of the company, and can detect, analyze and handle vehicle data quality problems early. In this embodiment, the national platform and the local platform are also abbreviated as national / local platforms.

[0058] As a preferred technical solution, the analysis content of the vehicle analysis report includes data integrity problem analysis, data message invalidity problem analysis, data information front and back conflict problem analysis, data message value abnormality problem analysis, and data collection time and reporting time problem analysis. In this embodiment, the analysis content of the vehicle analysis report includes the analysis of the above five aspects of the problem. The enterprise analysis platform can better meet the supervision and monitoring requirements of the national platform and landmark platform for vehicle data of automobile companies by analyzing and reviewing the above five aspects, which is conducive to early detection, early analysis and early processing of vehicle data quality problems.

[0059] Furthermore, the data integrity problem analysis includes the identification and analysis of data integrity; the data message invalidity problem analysis includes the identification and analysis of field invalidation; the data information front and back conflict problem analysis includes the identification and analysis of field conflicts; the data message value abnormality problem analysis includes the identification and analysis of field 0 value; the data collection time and reporting time problem analysis includes the identification and analysis of data sending.

[0060] As a preferred technical solution, the preset data analysis rules and analysis scenarios are established based on national standard audit issues, landmark audit issues, and the company's historical problem library. In this embodiment, for the preset data analysis rules and analysis scenarios, the national standard and landmark audit issues can make the preset data analysis rules and analysis scenarios better meet the supervision and monitoring requirements of the national platform and landmark platform for vehicle data of automobile companies. Establishing them based on the company's historical problem library can make the preset data analysis rules and analysis scenarios more targeted, which can be conducive to more effective analysis and review of company vehicle-related data. The preset data analysis rules and analysis scenarios in this embodiment are not only from national standards and landmark audit issues, but also established based on the company's historical problem library, etc., so that the company's vehicle-related data can be analyzed and reviewed more comprehensively. The embodiment of the present invention presets data analysis rules and analysis scenarios (i.e., establishes historical rules) according to national standard audit issues, local standard audit issues, and the enterprise's historical issue library, and establishes a national standard data analysis platform (i.e., an enterprise analysis platform) based on the historical rules. Other platforms in the prior art have not established a national standard data analysis platform based on historical rules, but only passively accept feedback from the national data center. It can be seen that the present invention is the first time that an enterprise has independently identified and analyzed abnormal data and made corrections, which is beneficial for the enterprise to discover, analyze and handle vehicle data quality problems early.

[0061] The following will explain the preset data analysis rules involved in the five aspects of problem analysis in the vehicle analysis report.

[0062] 1. Identification and analysis of data integrity

[0063] (1) In units of months, vehicles are filtered using the calculation logic "the calculation method of mileage completeness is: cumulative mileage of the month / (the last valid mileage of the month - the first valid mileage of the month)".

[0064] (2) On a monthly basis, vehicles are screened using the calculation logic "The calculation method for SOC completeness is: the cumulative increase in SOC for the month (increase range 0%-2%) / the total increase in SOC for the month (increase range 0%-100%)".

[0065] 2. Identification and analysis of field failures

[0066] In units of months, vehicles with expired fields are filtered out through field filtering.

[0067] 3. Identification and analysis of field conflicts

[0068] In months, filter by the following fields:

[0069] (1) Filter out vehicles with “non-negative current in charging state”.

[0070] (2) Filter out vehicles with "reporting time less than collection time (recorded for more than 2 seconds)".

[0071] (3) Filter out vehicles with “speed not zero and vehicle status being off”.

[0072] (4) Filter out vehicles with “speed not zero and gear position P”.

[0073] 4. Identification and analysis of field 0 values

[0074] Use the month as the unit to filter out vehicles with a field value of 0.

[0075] 5. Identification and analysis of data transmission

[0076] (1) By subtracting the time of accessing the static data of the vehicle on the national / local platform from the time of receiving the dynamic receipt data, vehicles whose static data access time on the national / local platform differs from the time of receiving the first dynamic data by more than 180 days are screened out.

[0077] (2) By subtracting the time of access to the static data of the vehicle on the national / local platform from the time of receiving the dynamic receipt data, the vehicles whose static data access time on the national / local platform and the time of receiving the first dynamic data are 30-180 days are screened out.

[0078] (3) By subtracting the time of accessing the static data of the vehicle on the national / local platform from the time of receiving the dynamic receipt data, the vehicles whose static data access time on the national / local platform and the time of receiving the first dynamic data are 1 to 30 days are screened out.

[0079] As a preferred technical solution, the vehicle-side data is collected through the vehicle-mounted terminal and sent to the enterprise platform; the vehicle-side data includes real-time data generated during the use of the vehicle and national standard GB / T 32960 data. In this embodiment, the vehicle-mounted terminal uses a vehicle-mounted communication device, preferably installed in the co-pilot position of the vehicle, so that the vehicle-side data can be collected in real time through the vehicle-mounted communication device and sent to the enterprise platform; the vehicle-side data in this embodiment includes real-time data generated during the use of the vehicle and national standard GB / T 32960 data (i.e., vehicle national standard data), which can facilitate the enterprise analysis platform to better conduct audit and analysis, so that problems with vehicle data quality can be discovered, analyzed and handled early. The real-time data of this embodiment mainly includes data such as steering wheel angle, and the national standard data mainly includes data such as battery power.

[0080] As a preferred technical solution, the national vehicle data center analyzes the vehicle-side data and feeds back the abnormal data to the enterprise analysis platform; the local vehicle data center analyzes the vehicle-side data and feeds back the abnormal data to the enterprise analysis platform; the enterprise analysis platform notifies for rectification after receiving the abnormal data fed back by the national vehicle data center or the local vehicle data center. When the national vehicle data center or the local vehicle data center analyzes abnormal data from the received vehicle-side data, it will feed back to the enterprise for rectification. In this embodiment, the abnormal data is fed back to the enterprise analysis platform, and then the enterprise analysis platform is used to notify for rectification. Specifically, the enterprise analysis platform can notify the enterprise monitoring department and the enterprise analysis and processing department to carry out corresponding rectification work.

[0081] As a preferred technical solution, the method further includes: sending the generated vehicle analysis report to the enterprise regulatory department for analysis and rectification. In this embodiment, the vehicle analysis report generated by the enterprise analysis platform will be sent to the enterprise regulatory department for further analysis and processing, so that the corresponding rectification work can be better carried out. In specific implementation, the enterprise analysis platform generates a vehicle analysis report and notifies the enterprise regulatory department, which can be notified by means including email. The content provided by the vehicle analysis report includes whether there are any abnormalities in the vehicle-side data.

[0082] As a preferred technical solution, the enterprise supervision department includes an enterprise monitoring department and an enterprise analysis and processing department; the enterprise monitoring department identifies abnormal data based on the vehicle analysis report and notifies the enterprise analysis and processing department of the abnormal data; the enterprise analysis and processing department makes rectifications based on the received abnormal data. In this embodiment, the enterprise monitoring department is mainly responsible for identifying abnormal data, and the enterprise analysis and processing department is mainly responsible for further analysis and rectification. Specifically, the enterprise monitoring department refers to a professional department within the enterprise that performs data monitoring, and the enterprise analysis and processing department refers to relevant professional departments within the enterprise, such as TBOX and other majors.

[0083] In some embodiments, the enterprise analysis and processing department is further subdivided into the enterprise analysis department and the enterprise processing department. After the enterprise monitoring department identifies abnormal data, it is transmitted to the enterprise analysis department for analysis and problem location. The problem located by the analysis is then transmitted to the enterprise processing department for problem rectification. After the problem rectification is completed, the rectification results can be reflected in the vehicle-side data.

[0084] In order to more intuitively demonstrate the method for monitoring national standard data of new energy vehicles of the present invention, this embodiment also provides an overall system block diagram of monitoring national standard data of new energy vehicles, such as Figure 2 shown.

[0085] exist Figure 2In the process, the vehicle-side data is uploaded to the enterprise platform. The enterprise platform forwards it to the national platform or local platform, and also forwards it to the enterprise analysis platform for analysis. Any abnormal data is fed back to the enterprise's monitoring, analysis, and processing departments for rectification. Finally, the vehicle-side data reflects the rectification results. Figure 2 In the figure, vehicle-side data is also written as vehicle data; the arrow between the enterprise processing department and the vehicle-mounted terminal indicates that the rectification results will be reflected by the vehicle-side after the rectification of the enterprise processing department is completed; the data transmitted from the enterprise platform to the enterprise analysis platform is the autonomous feedback within the enterprise, and the data transmitted from the national vehicle data center or the local vehicle data center to the enterprise analysis platform is the feedback from the national platform or the local platform.

[0086] The enterprise Internet of Vehicles platform of this embodiment includes an enterprise platform and an enterprise analysis platform. The enterprise Internet of Vehicles platform is preferably a Telematics platform. It is recommended to deploy a self-built computer room or Huawei Cloud, Alibaba Cloud, etc. The function of the enterprise Internet of Vehicles platform is mainly to receive, forward, and analyze data transmitted from the vehicle end.

[0087] The following will further explain the analysis of the enterprise analysis platform in combination with the preset data analysis rules and specific analysis scenarios. Among them, a specific analysis scenario corresponds to an actual working condition that causes data quality problems.

[0088] After the new energy vehicle purchased by the user is opened to the network through real-name authentication, the vehicle is connected to the enterprise's Internet of Vehicles platform. When the user is using the vehicle, the vehicle will actively report the real-time data and national standard GB / T 32960 data generated during the use of the vehicle. After the data is uploaded to the enterprise's Internet of Vehicles platform, the following logical program is used to actively identify and analyze problems.

[0089] The main functions of the enterprise analysis platform are as follows:

[0090] 1. Data integrity identification and analysis

[0091] Scenario 1: Low data integrity due to excessive re-sending of data

[0092] 1) In units of months, vehicles are screened out through the calculation logic "the calculation method of mileage completeness is: cumulative mileage of the month / (the last valid mileage of the month - the first valid mileage of the month)".

[0093] 2) Re-screen the vehicles filtered and counted above using the following logic:

[0094] a) Filter out vehicle data with reissued data;

[0095] b) Rescreen vehicles with reissued data. The logic is: when a vehicle has a fault alarm, data 30 seconds before and after the fault occurs will be reissued and uploaded to the enterprise platform. Such vehicles identified as normal will not be counted. If the reissued data is not generated by a fault alarm, it will be identified as abnormal data reissued and counted;

[0096] c) The identified vehicle is then combined with the vehicle GPS longitude and latitude data and the network environment data reported by the vehicle to filter out excessive re-sent data caused by unstable performance of the vehicle terminal in a no network or weak network environment.

[0097] Analysis conclusion:

[0098] The vehicles selected by the above logic are positioned as having low data integrity due to excessive re-sending of data in the network environment, and require further analysis and processing by related departments. The related departments in this embodiment refer to the enterprise monitoring department, enterprise analysis department, enterprise processing department and other internal professional departments of the enterprise, and the meaning of the related departments in the following text is the same. The analysis conclusion of this embodiment can be directly drawn by the enterprise analysis platform.

[0099] Scenario 2: Data irregularity and low integrity caused by data jumps

[0100] 1) In units of months, vehicles are screened out through the calculation logic "the calculation method of SOC completeness is: the cumulative increase in SOC (increase range 0%-2%) in the month / the total increase in SOC (increase range 0%-100%) in the month".

[0101] 2) Re-screen the vehicles filtered and counted above using the following logic:

[0102] a) Filter out vehicle data with a SOC jump greater than 2%;

[0103] b) Rescreen vehicles with SOC changes greater than 2%, and select the vehicle with a speed of 0 and a charging state signal of charging at the same time as the SOC jump;

[0104] c) Screening of vehicles with SOC variation greater than 2% and vehicle speed other than 0.

[0105] Analysis conclusion:

[0106] The vehicles screened out by the above logic are positioned as having low data integrity due to data jumps, which are divided into jumps in charging status and jumps in driving status, and require further analysis and processing by related departments.

[0107] In this embodiment, SOC is the abbreviation of State of Charge, which indicates the state of charge of the battery and the remaining power.

[0108] 2. Identification and analysis of field failures

[0109] Scenario: The vehicle reported data field is invalid

[0110] 1) In units of months, filter out vehicles with expired fields through field filtering.

[0111] 2) Re-screen the vehicles filtered and counted above using the following logic:

[0112] a) Filter out vehicle data with invalid fields when the vehicle is not powered on by vehicle power status model;

[0113] b) Filter out vehicle data with powered on and no vehicle speed failure field by vehicle power status model;

[0114] c) Filter out vehicle data with powered-on vehicles and vehicle speed and failure fields through the vehicle power status model.

[0115] Analysis conclusion:

[0116] Vehicles filtered by the above logic:

[0117] 1) For vehicles with failure fields when not powered on, the vehicle is abnormally awakened, the associated parts are in a non-working state, and the failure value is in a normal state;

[0118] 2) For vehicles that are powered on and have no speed failure field, the failure value issued by the on-board controller needs to be further analyzed and processed by the relevant departments;

[0119] 3) For vehicles that are powered on and have a speed failure field, the failure value issued by the on-board controller needs to be further analyzed and processed by the relevant departments.

[0120] 3. Identification and analysis of field conflicts

[0121] Scenario: Conflicts between data and status reported by vehicles

[0122] 1) Filter by field in months

[0123] a) Filter out vehicles with "non-negative current in charging state".

[0124] b) Filter out vehicles with "reporting time less than collection time (recorded for more than 2 seconds)".

[0125] c) Filter out vehicles with “speed not zero and vehicle status being off”.

[0126] d) Filter out vehicles with “speed not zero and gear position P”.

[0127] 2) Re-screen the vehicles filtered and counted above using the following logic:

[0128] a) Filter out vehicles that have a "non-negative current value in the charging state" state and are in the driving state;

[0129] b) Filter out vehicles that have a "non-negative current value in the charging state" state and are in a stationary state.

[0130] Analysis conclusion:

[0131] Vehicles filtered by the above logic:

[0132] 1) Vehicles with non-negative current in the charging state;

[0133] 2) Vehicles whose reporting time is less than the collection time (recorded for more than 2 seconds);

[0134] 3) The vehicle's speed is not zero and the vehicle status is off;

[0135] 4) The vehicle's speed is not zero and the gear position is P.

[0136] 4. Identification and analysis of field 0 values

[0137] Scenario: The value of the vehicle reported data field is 0

[0138] 1) Take the month as the unit and filter out vehicles with a field value of 0.

[0139] 2) Re-screen the vehicles filtered and counted above using the following logic:

[0140] First filter out the data with 0 value in the vehicle data;

[0141] a) re-screening the data with a value of 0, the logic is: screening out the vehicle data with a gear other than P and with a vehicle speed;

[0142] b) rescreening the data with a value of 0, and screening out the vehicle data in which the vehicle gear is in the P gear and has no vehicle speed;

[0143] c) The data with a value of 0 are rescreened, and the vehicle data with the vehicle gear in the P gear and the vehicle armed are screened out.

[0144] Analysis conclusion:

[0145] Vehicles filtered by the above logic:

[0146] 1) If the vehicle gear is not P gear and the speed is 0, an early warning is required and the relevant departments need to conduct further analysis and processing.

[0147] 2) For a vehicle with a value of 0 when the vehicle gear is in P gear and there is no speed, the synchronous related departments shall pay attention to whether the controller sends a value of 0 signal normally.

[0148] 3) For vehicles that are in P gear and armed, the relevant departments will simultaneously pay attention to whether the controller wakes up abnormally and whether the 0 value signal is sent normally.

[0149] The above-mentioned data screening and analysis process can be understood as: firstly, the vehicles are initially screened based on the first screening logic established based on the preset data analysis rules, and then the vehicles are re-screened based on the second screening logic established based on the preset analysis scenario, and then the analysis conclusion is drawn.

[0150] 5. Identification and analysis of data transmission

[0151] Scenario: National / local platform vehicle static data access and dynamic first data reception time

[0152] Through the ports of the national / local platform and the enterprise platform, the enterprise analysis platform receives the vehicle data with the difference between the static vehicle data access and the first dynamic data reception time that is regularly synchronized from the national / local platform;

[0153] 1) By subtracting the time of accessing the static data of vehicles on the national / local platform from the time of receiving the dynamic receipt data, filter out the vehicles whose static data access time on the national / local platform differs from the time of receiving the first dynamic data by more than 180 days;

[0154] 2) By subtracting the time of accessing the static data of vehicles on the national / local platform from the time of receiving the dynamic receipt data, the vehicles whose static data access time on the national / local platform differs from the time of receiving the first dynamic data by 30-180 days are selected;

[0155] 3) By subtracting the national / local platform vehicle static data access time from the dynamic receipt data reception time, filter out vehicles whose national / local platform static data access and dynamic first data reception time differ by 1-30 days.

[0156] Analysis conclusion:

[0157] Vehicles filtered by the above logic:

[0158] 1) If the difference is more than 180 days, notify the relevant departments to reissue the data to the national / local platform;

[0159] 2) If the difference is between 30-180 days, notify the relevant departments to reissue the data to the national / local platform;

[0160] 3) If the difference is 1-30 days, notify the relevant departments to organize and implement the forwarding of dynamic data.

[0161] Additional notes:

[0162] 1. Data screening can be flexibly configured by month, quarter, half year, etc.

[0163] 2. Scenario analysis includes but is not limited to the above scenarios. Scenario analysis can be based on national / local platform review issues or actual enterprise problems and optimization points, and can be flexibly added and processed. For example, for the first basis (country / region), for example: the vehicle online rate is lower than the national / local requirements; for the second basis (enterprise), for example: the battery feed rate is high, which is an actual visible problem of the enterprise.

[0164] In summary, the method provided by the embodiment of the present invention can play an important role in the whole link of vehicle-side data upload. Based on the preset data analysis rules and analysis scenarios, the enterprise can change from passively receiving feedback from the national data center to actively analyzing and rectifying, which greatly improves the enterprise's problem identification ability. The method for monitoring the national standard data of new energy vehicles provided by the embodiment of the present invention is actually a method for optimizing the management of national standard data of new energy vehicles. This method can better monitor and manage the national standard data of new energy vehicles, which is conducive to improving data quality.

[0165] The embodiment of the present invention also provides a block diagram of an enterprise analysis platform, such as Figure 3 As shown, the enterprise analysis platform block diagram includes an application layer, a service layer, a suite layer and a basic layer. The different levels in the figure reflect the entire chain of data collection, storage, synchronization, application, analysis, and rectification. The data analysis at the application layer corresponds to the analysis content of the vehicle analysis report in the embodiment of the present invention.

[0166] Based on the same inventive concept, the present invention also provides a computer-readable storage medium storing one or more programs, which, when executed, can implement the aforementioned method for monitoring national standard data of new energy vehicles.

[0167] Based on the same inventive concept, the present invention also provides an electronic device, such as Figure 4 As shown, it includes a processor, a communication interface, the aforementioned computer-readable storage medium and a communication bus; wherein the processor, the communication interface, and the computer-readable storage medium communicate with each other via the communication bus; and the processor is used to execute the program stored in the computer-readable storage medium.

[0168] It should be noted that, for the convenience of description, the aforementioned method embodiments are all described as a series of action combinations, but those skilled in the art should be aware that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0169] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0170] Parts not involved in the above embodiments are the same as the prior art or can be implemented by using the prior art, and will not be further described here.

[0171] Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent substitutions for some of the technical features therein; and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for monitoring national standard data of new energy vehicles, characterized in that: The method comprises: Collect vehicle-side data and send it to the enterprise platform; The enterprise platform receives the vehicle-side data, and synchronizes the vehicle-side data to the national vehicle data center, the local vehicle data center, and the enterprise analysis platform for data quality analysis; The enterprise analysis platform generates a vehicle analysis report based on preset data analysis rules and analysis scenarios and in combination with the vehicle-side data.

2. The method for monitoring national standard data of new energy vehicles according to claim 1, characterized in that: The analysis content of the vehicle analysis report includes data integrity problem analysis, data message invalidity problem analysis, data information front and back conflict problem analysis, data message value abnormality problem analysis, and data collection time and reporting time problem analysis.

3. The method for monitoring national standard data of new energy vehicles according to claim 2 is characterized in that: The data integrity problem analysis includes identification and analysis of data integrity; The analysis of invalid data message problems includes identification and analysis of field invalidation; The analysis of the data information front and back conflicts includes the identification and analysis of field conflicts; The data message value abnormality problem analysis includes identification and analysis of field 0 value; The analysis of data collection time and reporting time issues includes identification and analysis of data transmission.

4. The method for monitoring national standard data of new energy vehicles according to claim 1, characterized in that: The preset data analysis rules and analysis scenarios are established based on national standard audit questions, landmark audit questions and the enterprise historical question library.

5. The method for monitoring national standard data of new energy vehicles according to claim 1, characterized in that: Collect vehicle-side data through the vehicle terminal and send it to the enterprise platform; The vehicle-side data includes real-time data generated during vehicle use and national standard GB / T 32960 data.

6. The method for monitoring national standard data of new energy vehicles according to claim 1, characterized in that: The national vehicle data center analyzes the vehicle-side data and feeds back abnormal data to the enterprise analysis platform; The local vehicle data center analyzes the vehicle-side data and feeds back abnormal data to the enterprise analysis platform; The enterprise analysis platform notifies rectification after receiving abnormal data fed back by the national vehicle data center or the local vehicle data center.

7. A method for monitoring national standard data of new energy vehicles according to any one of claims 1 to 6, characterized in that: The method further comprises: The generated vehicle analysis report is sent to the enterprise regulatory department for analysis and rectification.

8. The method for monitoring national standard data of new energy vehicles according to claim 7, characterized in that: The enterprise supervision department includes the enterprise monitoring department and the enterprise analysis and processing department; The enterprise monitoring department identifies abnormal data according to the vehicle analysis report and notifies the enterprise analysis and processing department of the abnormal data; The enterprise analysis and processing department makes corrections based on the received abnormal data.

9. A computer-readable storage medium storing one or more programs, characterized in that: When the one or more programs are executed, the method for monitoring national standard data of new energy vehicles as described in any one of claims 1 to 8 can be implemented.

10. An electronic device comprising a processor, a communication interface, the computer-readable storage medium of claim 9, and a communication bus; wherein: The processor, the communication interface, and the computer-readable storage medium communicate with each other via a communication bus; It is characterized in that The processor is configured to execute a program stored in a computer-readable storage medium.

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