A method for monitoring new energy vehicle national standard data, a storage medium and an equipment

Abstract

The present application relates to the technical field of automobile national standard data processing, and particularly relates to a new energy automobile national standard data monitoring method, a storage medium and equipment, the method comprising: collecting vehicle terminal data and sending the data to an enterprise platform; the enterprise platform receives the vehicle terminal data, synchronizes the vehicle terminal data to a national vehicle data center, a local vehicle data center and an enterprise analysis platform for data quality analysis; the enterprise analysis platform generates a vehicle analysis report according to preset data analysis rules and analysis scenarios and in combination with the vehicle terminal data. The present application can greatly improve the problem identification capability of vehicle enterprises, change the situation of passive notification and rectification of unqualified enterprise vehicle data audit in the past, fully exert the enthusiasm and initiative of enterprises, and early discover, analyze and process vehicle data quality problems.

Description

Technical Field

[0001] This invention relates to the field of national standard data processing technology for automobiles, and in particular to a method, storage medium, and device for monitoring national standard data of new energy vehicles. Background Technology

[0002] The national standard GB / T 32960 has clear requirements for the management and data of new energy vehicles. Meanwhile, national and local new energy vehicle data centers have implemented supervision and monitoring requirements for vehicle data from automakers. Ensuring the quality, reliability, and stability of vehicle data is a common challenge faced by all automakers. Improving data quality mainly involves streamlining the data from the following aspects:

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

[0004] 2. Completeness: Completeness refers to whether there are any missing data entries. Missing data may mean the entire record is missing, or only a specific field is missing. Incomplete data has significantly reduced value and is a fundamental criterion for evaluating data quality.

[0005] 3. Timeliness: Timeliness refers to the time interval between data generation and its availability for viewing, also known as data latency. While timeliness isn't a high requirement for data analysis itself, if the data analysis cycle plus the time spent creating the data is too long, the conclusions drawn may lose their relevance.

[0006] 4. Accuracy: Accuracy refers to whether the information recorded in the data is abnormal or erroneous. Unlike consistency, data with accuracy problems is not merely inconsistent with rules. More common data accuracy errors include garbled text. Furthermore, abnormally large or small data points also constitute invalid data.

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

[0008] 6. Uniqueness: For a given data item or set of data, there are no duplicate data values. Values ​​must be unique, such as ID-type data.

[0009] Currently, the review of vehicle-related data for enterprises is conducted by national and local new energy vehicle data centers. These centers review enterprise vehicle data through a national platform (i.e., the National New Energy Vehicle Data Center, hereinafter referred to as the National Vehicle Data Center) and local platforms (i.e., local New Energy Vehicle Data Centers, hereinafter referred to as Local Vehicle Data Centers). When data fails the review, the vehicle manufacturer is notified to rectify the issues. This forces vehicle manufacturers to passively wait for notifications, failing to fully leverage their initiative and proactiveness, and hindering the early detection, analysis, and handling of vehicle data quality problems. To address these shortcomings, this invention provides an improvement. Summary of the Invention

[0010] To overcome the shortcomings of the prior art, this invention provides a method, storage medium, and device for monitoring national standard data of new energy vehicles. This method can significantly improve the problem identification capabilities of car manufacturers, and change the previous situation where companies were passively notified to rectify unqualified vehicle-related data. It fully leverages the initiative and proactiveness of enterprises, enabling early detection, analysis, and handling of vehicle data quality issues.

[0011] To achieve the above objectives, 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 with the national vehicle data center, local vehicle data centers, and the enterprise analysis platform used for data quality analysis.

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

[0015] Furthermore,

[0016] The analysis content of the vehicle analysis report includes data integrity issues, invalid data messages, data information conflicts, abnormal data message values, and data collection and reporting time issues.

[0017] Furthermore,

[0018] The data integrity issue analysis includes data integrity identification and analysis;

[0019] The analysis of invalid data packets includes the identification and analysis of invalid fields;

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

[0021] The analysis of abnormal data message values ​​includes the identification and analysis of field 0 values;

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

[0023] Furthermore,

[0024] The preset data analysis rules and analysis scenarios are established based on national standard review issues, local standard review issues, and a database of historical issues of enterprises.

[0025] Furthermore,

[0026] The vehicle-mounted terminal collects vehicle-side data and sends it to the enterprise platform.

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

[0028] Furthermore,

[0029] The National Vehicle Data Center analyzes the vehicle-side data and feeds back any abnormal data to the enterprise analysis platform.

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

[0031] The enterprise analytics platform receives abnormal data from the national or local vehicle data center and then issues a notification for rectification.

[0032] Furthermore,

[0033] The method further includes:

[0034] The generated vehicle analysis report will be sent to the enterprise's regulatory department for analysis and rectification.

[0035] Furthermore,

[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 based on the vehicle analysis report and notifies the enterprise analysis and processing department of the abnormal data.

[0038] The company's analysis and processing department will rectify the abnormal data received.

[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 realize 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 through the communication bus; the processor is used to execute a program stored in the computer-readable storage medium.

[0041] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0042] 1. By establishing an enterprise analysis platform for data quality analysis on the enterprise side, and pre-setting data analysis rules and scenarios to analyze and audit vehicle-related data, the problem identification capability of car companies can be greatly improved. At the same time, it changes the previous situation where enterprises were passively notified to rectify when their vehicle-related data failed the audit. It gives full play to the initiative and proactiveness of enterprises, and can detect, analyze and deal with vehicle data quality problems early.

[0043] 2. The analysis content of the vehicle analysis report includes data integrity issues, invalid data messages, conflicting data information, abnormal data message values, and issues related to data collection and reporting time. The analysis content of the vehicle analysis report covers these five aspects. By analyzing and reviewing these five aspects, the enterprise analysis platform can better meet the national and local platform requirements for supervising and monitoring vehicle data from automakers, facilitating the early detection, analysis, and handling of vehicle data quality issues.

[0044] 3. Based on the national standard review issues, local standard review issues, and the enterprise's historical issue database, pre-set data analysis rules and analysis scenarios (i.e., establish historical rules), and establish a national standard data analysis platform (i.e., enterprise analysis platform) based on historical rules. For the first time, it has enabled enterprises to independently identify, analyze, and rectify abnormal data. Other existing platforms have not established a national standard data analysis platform based on historical rules, but have only passively accepted feedback from the national data center.

[0045] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings.

[0046] The invention will now be further described with reference to the accompanying drawings. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a flowchart illustrating a method 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 a national standard data monitoring system for new energy vehicles according to an embodiment of the present invention;

[0050] Figure 3 This is a block diagram of an enterprise analytics platform according to an embodiment of the present invention;

[0051] Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0053] like Figures 1 to 3 As shown in the figure, this embodiment of the 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 with the national vehicle data center, local vehicle data centers, and the enterprise analysis platform used for data quality analysis.

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

[0057] In the above technical solution, vehicle-side data is collected in real time and sent to the enterprise platform. The enterprise platform receives the real-time collected vehicle-side data and synchronizes it with the national and local vehicle data centers, while simultaneously synchronizing the vehicle-side data with the enterprise's established data quality analysis platform (i.e., the enterprise analysis platform). The enterprise analysis platform generates a vehicle analysis report based on preset data analysis rules and scenarios, combined with the vehicle-side data. The vehicle analysis report effectively reflects the results of monitoring new energy vehicle-side data. This method, by establishing an enterprise analysis platform for data quality analysis on the enterprise side and pre-setting data analysis rules and scenarios to analyze and review the enterprise's vehicle-related data, can significantly improve the vehicle manufacturers' ability to identify problems. It also changes the previous situation where enterprises passively notified rectification of unqualified vehicle-related data, fully leveraging the enterprise's initiative and proactiveness, enabling early detection, analysis, and handling of vehicle data quality issues. In this embodiment, the national platform and local platform are also abbreviated as national / local platform.

[0058] As a preferred technical solution, the vehicle analysis report includes analyses of data integrity issues, invalid data packets, conflicting data information, abnormal data packet values, and issues related to data collection and reporting times. In this embodiment, the vehicle analysis report includes analyses of these five aspects. By analyzing and reviewing these five aspects, the enterprise analysis platform can better meet the regulatory and monitoring requirements of national and local platforms for vehicle data, facilitating the early detection, analysis, and handling of vehicle data quality issues.

[0059] Furthermore, the data integrity problem analysis includes data integrity identification and analysis; the invalid data message problem analysis includes field invalidation identification and analysis; the data information conflict problem analysis includes field conflict identification and analysis; the abnormal data message value problem analysis includes field 0 value identification and analysis; and the data acquisition time and reporting time problem analysis includes data transmission identification and analysis.

[0060] As a preferred technical solution, the preset data analysis rules and scenarios are established based on national standard review issues, local standard review issues, and a database of historical issues for enterprises. In this embodiment, the preset data analysis rules and scenarios derived from national standard and local standard review issues enable them to better meet the regulatory and monitoring requirements of national and local standard platforms for vehicle data from automakers. Establishing them based on a database of historical issues for enterprises makes the preset data analysis rules and scenarios more targeted, facilitating more effective analysis and review of vehicle-related data. This embodiment's preset data analysis rules and scenarios not only originate from national standard and local standard review issues but also from a database of historical issues for enterprises, thus enabling a more comprehensive analysis and review of vehicle-related data. This invention, in its embodiments, pre-sets data analysis rules and scenarios (i.e., establishes historical rules) based on national standard review issues, local standard review issues, and a database of historical issues for enterprises. It then establishes a national standard data analysis platform (i.e., an enterprise analysis platform) based on these historical rules. In contrast, other existing platforms do not establish national standard data analysis platforms based on historical rules; they merely passively accept feedback from the national data center. This invention is the first to enable enterprises to independently identify, analyze, and rectify abnormal data, which is beneficial for enterprises to discover, analyze, and handle vehicle data quality issues early.

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

[0062] 1. Data integrity identification and analysis

[0063] (1) Using a monthly unit, vehicles are filtered by the calculation logic "The calculation method for mileage completeness is: the 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 filtered by the calculation logic "SOC completeness is calculated as: the cumulative increase in SOC in the month (0%-2%) / the total increase in SOC in the month (0%-100%)".

[0065] 2. Identification and analysis of field failures

[0066] On a monthly basis, vehicles with invalid fields are filtered out.

[0067] 3. Identification and analysis of field conflicts

[0068] Filter by month using the following fields:

[0069] (1) Filter out vehicles that have a non-negative current value when charging.

[0070] (2) Filter out vehicles that have a “reporting time less than the collection time (recording more than 2 seconds)”.

[0071] (3) Filter out vehicles with "speed not zero and vehicle status is off".

[0072] (4) Filter out vehicles that have "speed not zero and gear in P".

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

[0074] Filter vehicles by month, selecting those with a value of 0 in a given field.

[0075] 5. Identification and analysis of data transmissions

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

[0077] (2) By subtracting the time of access to the static data of the vehicle from the time of dynamic receipt data reception, vehicles whose time of access to the static data of the vehicle from the time of dynamic first data reception are 30-180 days apart are selected.

[0078] (3) By subtracting the time of access to the static data of the vehicle from the time of dynamic receipt data reception from the time of access to the vehicle static data of the national / local platform, vehicles whose time of access to the vehicle static data of the national / local platform differs from the time of reception of the first dynamic data are within 1-30 days.

[0079] As a preferred technical solution, vehicle-side data is collected via an in-vehicle terminal and sent to the enterprise platform. This in-vehicle data includes real-time data generated during vehicle use and GB / T 32960 standard data. In this embodiment, the in-vehicle terminal uses an in-vehicle communication device, preferably installed in the passenger seat. This allows for real-time data collection and transmission to the enterprise platform. The vehicle-side data in this embodiment includes both real-time data generated during vehicle use and GB / T 32960 standard data (i.e., vehicle standard data), facilitating better review and analysis by the enterprise analysis platform. This enables early detection, analysis, and handling of vehicle data quality issues. The real-time data in this embodiment mainly includes data such as steering wheel angle, while the standard data mainly includes data such as battery level.

[0080] As a preferred technical solution, 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, upon receiving the abnormal data from the national or local vehicle data center, issues a notification for rectification. When the national or local vehicle data center detects abnormal data in the received vehicle-side data, it feeds it 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 issues the notification for rectification. Specifically, the enterprise analysis platform can notify the enterprise's monitoring department and 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's regulatory department for analysis and rectification. In this embodiment, the vehicle analysis report generated by the enterprise analysis platform is sent to the enterprise's regulatory department for further analysis and processing, enabling better rectification work. In specific implementation, the enterprise analysis platform notifies the enterprise's regulatory department when it generates the vehicle analysis report, which can be done via methods including email. The vehicle analysis report includes information such as whether there are any anomalies in the vehicle-side data.

[0082] As a preferred technical solution, the enterprise monitoring 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 then rectifys the abnormal data received. In this embodiment, the enterprise monitoring department is primarily responsible for identifying abnormal data, while the enterprise analysis and processing department is primarily 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 a relevant professional department within the enterprise, such as TBOX.

[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 transmits it to the enterprise analysis department for analysis and problem location. Then, the analyzed and located problem is transmitted to the enterprise processing department for problem rectification. After the problem rectification is completed, the rectification results can be reflected by the vehicle-side data.

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

[0085] exist Figure 2In the process, vehicle-side data is uploaded to the enterprise platform, which then forwards it to the national or local platform, as well as 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 rectification results are reflected in the vehicle-side data. Figure 2 In this context, vehicle-side data is also referred to as vehicle data; the arrow between the enterprise processing department and the vehicle terminal indicates that the rectification results are reflected on the vehicle terminal after the enterprise processing department completes the rectification; data transmitted from the enterprise platform to the enterprise analysis platform is autonomous feedback within the enterprise, while data transmitted from the national vehicle data center or local vehicle data center to the enterprise analysis platform is feedback from the national or local platform.

[0086] The enterprise vehicle networking platform in this embodiment includes an enterprise platform and an enterprise analytics platform. The enterprise vehicle networking platform is preferably a Telematics platform, and it is recommended to deploy in a self-built data center or on Huawei Cloud, Alibaba Cloud, etc. The main function of the enterprise vehicle networking platform is to receive, forward, and parse the data transmitted from the vehicle.

[0087] The following section will further explain the analysis provided by the enterprise analytics platform, combining pre-defined data analysis rules with specific analysis scenarios. Each specific analysis scenario corresponds to an actual working condition that causes data quality issues.

[0088] After a user's purchased new energy vehicle is registered and connected to the network through real-name authentication, the vehicle connects to the company's vehicle network platform. When the user is using the vehicle, the vehicle will proactively report real-time data generated during the usage process and data from the national standard GB / T 32960, etc. After the data is uploaded to the company's vehicle network platform, the following logic program will be used to proactively identify and analyze problems.

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

[0090] 1. Data integrity identification and analysis

[0091] Scenario 1: Low data integrity due to excessive resending of data

[0092] 1) Using a monthly basis, vehicles are filtered out by the calculation logic "The calculation method for mileage completeness is: the cumulative mileage of the month / (the last valid mileage of the month - the first valid mileage of the month)".

[0093] 2) The vehicles selected above are further filtered using the following logic:

[0094] a) Filter out vehicle data that has replacement data;

[0095] b) Re-filter vehicles with re-uploaded data. The logic is as follows: when a vehicle alarms for a fault, data within 30 seconds before and after the time of the fault will be re-uploaded to the enterprise platform. These are identified as normal vehicles and are not counted. Re-uploaded data that is not generated by a fault alarm is identified as abnormal data and re-uploaded for statistics.

[0096] c) The identified vehicles are then combined with the vehicle's GPS latitude and longitude data and the network environment data reported by the vehicle to filter out those that have sent too much resend data due to unstable performance of the vehicle terminal in environments with no network or weak network.

[0097] Analysis conclusion:

[0098] Vehicles selected through the above logic are identified as having low data integrity due to excessive data retransmission caused by the network environment, requiring further analysis and processing by relevant departments. In this embodiment, "relevant departments" refers to internal professional departments such as the enterprise monitoring department, enterprise analysis department, and enterprise processing department; the meaning of "relevant departments" in the following text is the same. The analysis conclusions of this embodiment can be directly derived from the enterprise analysis platform.

[0099] Scenario 2: Data irregularities and low completeness caused by data jumps

[0100] 1) On a monthly basis, vehicles are filtered out by the calculation logic "SOC completeness is calculated as: cumulative increase in SOC (0%-2%) in the month / total increase in SOC (0%-100%) in the month".

[0101] 2) The vehicles selected above are further filtered using the following logic:

[0102] a) Filter out vehicle data with a SOC change greater than 2% before and after;

[0103] b) Vehicles with a SOC change greater than 2% are further screened, and those with a SOC change at the same time as the vehicle's speed is 0 and the charging status signal is charging are statistically screened into one category.

[0104] c) Select vehicles with a SOC change greater than 2% and a vehicle dynamic speed that is not 0.

[0105] Analysis conclusion:

[0106] The vehicles identified through the above logic were found to have low data integrity due to data jumps, which were categorized into jumps during charging and jumps during driving. Further analysis and processing by relevant departments are required.

[0107] In this embodiment, SOC is short for State of Charge, which represents the battery's state of charge and remaining capacity.

[0108] 2. Identification and analysis of field invalidity

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

[0110] 1) Filter out vehicles with invalid fields by month using field filtering.

[0111] 2) The vehicles selected above are further filtered using the following logic:

[0112] a) Filter vehicle data that is not powered on and has a failure field by vehicle power status model;

[0113] b) Filter vehicle data by vehicle power status model to identify vehicles that are powered on and do not have a failed vehicle speed field;

[0114] c) Filter vehicle data by vehicle power status model to find vehicles that are powered on and have a vehicle speed field that is invalid.

[0115] Analysis conclusion:

[0116] Vehicles selected using the above logic:

[0117] 1) For vehicles with a failure field when not powered on, the vehicle is abnormally woken up, and the associated component is in a non-working state with the failure value being normal.

[0118] 2) For vehicles that are powered on and do not have a speed failure field, the failure value issued by the vehicle controller needs to be further analyzed and processed by the relevant department.

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

[0120] 3. Identification and analysis of field conflicts

[0121] Scenario: Conflicts occur between vehicle-reported data and status.

[0122] 1) Filter by month using specific fields.

[0123] a) Filter out vehicles that have a "non-negative current value during charging".

[0124] b) Filter out vehicles that have a "reporting time less than the collection time (recording more than 2 seconds)".

[0125] c) Filter out vehicles that have "speed not zero and vehicle status is off".

[0126] d) Filter out vehicles that have "non-zero speed and are in P gear".

[0127] 2) The vehicles selected above are further filtered using the following logic:

[0128] a) Filter out vehicles that are in a driving state and have a non-negative current during charging.

[0129] b) Filter out vehicles that are stationary and have a non-negative current during charging.

[0130] Analysis conclusion:

[0131] Vehicles selected using the above logic:

[0132] 1) Vehicles with non-negative current during charging;

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

[0134] 3) Vehicles with a non-zero speed and an engine-off status;

[0135] 4) Vehicles with a non-zero speed and in P gear.

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

[0137] Scenario: A field in the vehicle's reported data contains a value of 0.

[0138] 1) Filter by month to find vehicles with a field value of 0.

[0139] 2) The vehicles selected above are further filtered using the following logic:

[0140] First, filter out vehicle data containing a value of 0;

[0141] a) Further filter the data with a value of 0. The logic is to filter out the vehicle data where the vehicle is not in P gear and has a speed.

[0142] b) Further filter the data with a value of 0 to filter out the vehicle data where the vehicle is in P gear and has no speed.

[0143] c) Further filter the data with a value of 0 to identify vehicles that are in P gear and armed.

[0144] Analysis conclusion:

[0145] Vehicles selected using the above logic:

[0146] 1) For vehicles that are not in P gear and have a speed but show a value of 0, an alert should be issued and relevant departments should be contacted for further analysis and processing.

[0147] 2) For vehicles that show a 0 value when the gear is in P and there is no speed, the relevant departments should check whether the controller is sending a 0 value signal normally.

[0148] 3) For vehicles in P gear and armed, the relevant departments should simultaneously monitor whether the controller wakes up abnormally and whether the O value signal is sent normally.

[0149] The above data filtering and analysis process can be understood as follows: first, a first filtering logic based on preset data analysis rules is used to initially filter vehicles; then, a second filtering logic based on preset analysis scenarios is used to further filter vehicles; and finally, an analysis conclusion is drawn.

[0150] 5. Identification and analysis of data transmissions

[0151] Scenario: Time between static vehicle data access and the first dynamic data reception on national / local platforms

[0152] Through the ports of the national / local platform and the enterprise platform, the enterprise analysis platform receives vehicle data with differences in the time of receiving the first dynamic data from the national / local platform, which are regularly synchronized.

[0153] 1) By subtracting the time of access to the static vehicle data from the national / local platform from the time of dynamic receipt data reception, we can filter out vehicles whose static vehicle data access time and the time of dynamic first receipt data reception differ from the time of dynamic receipt data reception by more than 180 days.

[0154] 2) By subtracting the time of accessing the static vehicle data from the national / local platform from the time of receiving the dynamic receipt data, we can filter out vehicles whose static vehicle data access time and the time of receiving the first dynamic receipt data differ from the time of dynamic receipt data by 30-180 days.

[0155] 3) By subtracting the time for accessing the static vehicle data from the national / local platform from the time for receiving the dynamic receipt data, we can filter out vehicles whose static vehicle data access time and the time for receiving the first dynamic receipt data differ from each other by 1 to 30 days.

[0156] Analysis conclusion:

[0157] Vehicles selected using the above logic:

[0158] 1) For discrepancies exceeding 180 days, the relevant departments or organizations will be notified to reissue the data to the national / local platform;

[0159] 2) For discrepancies within 30-180 days, the relevant departments or organizations will be notified to reissue the data to the national / local platform.

[0160] 3) For differences within 1-30 days, the relevant departments will be notified to organize and implement the forwarding of dynamic data.

[0161] Additional notes:

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

[0163] 2. Scenario analysis includes, but is not limited to, the scenarios mentioned above. Scenario analysis can be based on national / local platform review issues, or on the company's actual problems and optimization points, and can be flexibly added and processed. For example, for the first type of basis (national / local), such as: vehicle online rate is lower than national / local requirements; for the second type of basis (company), such as: high battery depletion, which is a problem that is actually visible to the company.

[0164] In summary, the method provided in this invention plays a crucial role in the entire data upload process from the vehicle end. Based on preset data analysis rules and scenarios, it transforms enterprises from passively receiving feedback from the national data center to proactively analyzing and rectifying issues, significantly improving their problem identification capabilities. The method for monitoring national standard data of new energy vehicles provided in this invention is essentially a method for optimizing and governing national standard data of new energy vehicles. This method enables better monitoring and governance of national standard data of new energy vehicles, thereby improving data quality.

[0165] This invention also provides a block diagram of an enterprise analytics platform, such as... Figure 3 As shown in the diagram, the enterprise analysis platform includes an application layer, a service layer, a suite layer, and a foundation layer. Each layer in the diagram represents the entire data collection, storage, synchronization, application, analysis, and rectification process. The data analysis in the application layer corresponds to the analysis content of the vehicle analysis report in this embodiment of the 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 realize 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; the processor is used to execute a program stored in the computer-readable storage medium.

[0168] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0169] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0170] The parts not mentioned in the above embodiments are the same as or can be implemented using existing technologies, and will not be further described here.

[0171] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to 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 includes: 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 with the national vehicle data center, local vehicle data centers, and the enterprise analysis platform used for data quality analysis. The enterprise analytics platform generates vehicle analysis reports based on preset data analysis rules and scenarios, combined with the vehicle-side data. The generated vehicle analysis report will be sent to the enterprise's regulatory department for analysis and rectification. The preset data analysis rules and analysis scenarios are established based on national standard review issues, local standard review issues, and a database of historical issues of enterprises. The analysis content of the vehicle analysis report includes data integrity problem analysis, invalid data message problem analysis, conflicting data information problem analysis, abnormal data message value problem analysis, and data collection time and reporting time problem analysis. The data integrity problem analysis includes data integrity identification and analysis; The analysis of invalid data packets includes the identification and analysis of invalid fields; The analysis of data information conflicts includes the identification and analysis of field conflicts; The analysis of abnormal data message values ​​includes the identification and analysis of field 0 values; The analysis of data acquisition and reporting time issues includes the identification and analysis of data transmission. The generation of the vehicle analysis report includes: performing logical conflict detection on the vehicle-side data. The logical conflict detection includes: detecting whether the total current value is non-negative during charging, and / or detecting whether the vehicle is in a turned-off state or in P gear when the speed is not zero.

2. The method for monitoring national standard data of new energy vehicles according to claim 1, characterized in that, The vehicle-mounted terminal collects vehicle-side data and sends it to the enterprise platform. The vehicle-side data includes real-time data generated during vehicle use and data from the national standard GB / T 32960.

3. 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 any abnormal data to the enterprise analysis platform. The local vehicle data center analyzes the vehicle-side data and feeds back any abnormal data to the enterprise analysis platform. The enterprise analytics platform receives abnormal data from the national or local vehicle data center and then issues a notification for rectification.

4. The method for monitoring national standard data of new energy vehicles according to claim 1, 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 based on the vehicle analysis report and notifies the enterprise analysis and processing department of the abnormal data. The company's analysis and processing department will rectify the abnormal data received.

5. A computer-readable storage medium storing one or more programs, characterized in that, When one or more of these programs are executed, the method for monitoring national standard data of new energy vehicles as described in any one of claims 1-4 can be implemented.

6. An electronic device, comprising a processor, a communication interface, a computer-readable storage medium as described in claim 5, and a communication bus; wherein, The processor, communication interface, and computer-readable storage medium communicate with each other via a communication bus; characterized in that the processor is used to execute a program stored in the computer-readable storage medium.

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