Vehicle signal data processing method, device, equipment and medium

Abstract

The invention provides a vehicle signal data processing method and device, equipment and a medium. The method comprises the steps of obtaining a to-be-processed signal of a target vehicle through a cloud, determining a first signal popularity value of the to-be-processed signal in the target vehicle according to a first signal popularity model, and determining a second signal popularity value of the to-be-processed signal in a target vehicle type according to a second signal popularity model, the signal popularity of the to-be-processed signal is determined by combining the first signal popularity value and the second signal popularity value, the signal popularity of the to-be-processed signal in the target vehicle and the signal popularity of the to-be-processed signal in the target vehicle type are comprehensively considered, the accuracy of the signal popularity is improved, the signal data can be better classified, and the accuracy of the signal popularity is improved. And finally, the target vehicle performs classification processing on the signal data of the to-be-processed signal according to the signal popularity, so that the signal data processing efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a vehicle signal data processing method, device, equipment and medium. Background Art

[0002] With the rapid development of the new energy vehicle industry, the amount of data generated by vehicles has exploded. Each new energy vehicle now generates over 5,000 signals, and these signals are typically collected every second and uploaded to data centers at a sub-second frequency, which has become a consensus in the industry.

[0003] How to process this data efficiently has become a technical problem that needs to be solved urgently. Summary of the Invention

[0004] The present application provides a vehicle signal data processing method, apparatus, device and medium to solve the technical problem of how to efficiently process such data in the prior art.

[0005] In a first aspect, the present application provides a vehicle signal data processing method, comprising:

[0006] Obtaining a signal to be processed from a target vehicle;

[0007] Predicting the signal heat of the signal to be processed in the target vehicle according to a preset first signal heat model to obtain a first signal heat value;

[0008] Predicting the signal heat of the signal to be processed in the target vehicle type according to a preset second signal heat model to obtain a second signal heat value, where the target vehicle type is the vehicle type of the target vehicle;

[0009] Determining a signal heat of the signal to be processed according to the first signal heat value and the second signal heat value;

[0010] Send the signal heat to the target vehicle.

[0011] In this application, the signal heat of the signal to be processed in the target vehicle is predicted according to the preset first signal heat model to obtain the first signal heat value, including:

[0012] Determining a first access count and a first fault association count of a signal to be processed in a preset time period;

[0013] The first access count and the first fault association count are input into a preset first signal heat model for prediction processing to obtain a first signal heat value of the signal to be processed.

[0014] In this application, the signal heat of the signal to be processed in the target vehicle type is predicted according to the preset second signal heat model to obtain the second signal heat value, including:

[0015] According to the target vehicle type of the target vehicle, obtaining vehicles of the target vehicle type that are in use in a preset time period, and a second access count and a second fault association count of the signal to be processed related to all vehicles in use in the preset time period;

[0016] The number of vehicles in use, the second number of accesses, and the second number of fault associations are input into a preset second signal heat model for prediction processing to obtain a second signal heat value of the signal to be processed.

[0017] In the present application, determining the signal heat of the signal to be processed according to the first signal heat value and the second signal heat value includes:

[0018] Obtain a first weight of the first signal heat value and a second weight of the second signal heat value;

[0019] The signal heat of the signal to be processed is obtained according to the first weight, the second weight, the first signal heat value, and the second signal heat value.

[0020] In the present application, before obtaining the first weight of the first signal heat value and the second weight of the second signal heat value, the method further includes:

[0021] Obtaining the failure rate of the signal to be processed within a preset update time period;

[0022] According to the failure rate, determine the failure level corresponding to the failure rate;

[0023] According to the fault classification, a first weight and a second weight corresponding to the fault classification are determined.

[0024] In this application, the method further comprises:

[0025] Receive the signal data uploaded by the target vehicle, which is obtained after sending and processing according to the signal heat;

[0026] Store and process signal data.

[0027] In this application, when the signal heat is less than the first threshold, the signal data is stored and processed, including:

[0028] Receive data files sent by target vehicles using offline tasks during off-peak hours. The data files are generated by the target vehicles using signal data according to preset data requirements. The data requirements are used to indicate the collection time period and file size of the data files.

[0029] During non-busy hours, the signal data is decrypted using an offline task to obtain decrypted signal data;

[0030] The decrypted signal data is stored in a preset storage format.

[0031] In this application, when the signal heat is greater than the second threshold, and the second threshold is greater than or equal to the first threshold, the signal data is stored and processed, including:

[0032] Receive signal data uploaded by the target vehicle in real time;

[0033] Decode the signal data and write it to the data lake.

[0034] In a second aspect, the present application provides a vehicle signal data processing method, comprising:

[0035] Obtain the signal heat of the signal to be processed from the cloud;

[0036] The signal data of the signal to be processed is processed according to the signal heat.

[0037] In this application, signal data of a signal to be processed is processed according to the signal heat, including:

[0038] When the signal heat is less than the first threshold, the signal data is used to generate a data file according to the preset data requirements, where the data requirements are used to indicate the collection time period and file size of the data file;

[0039] Use offline tasks to send data files to the cloud during off-peak hours.

[0040] In this application, the method further comprises:

[0041] If the data file fails to upload, the data file will be buffered locally and retried asynchronously until the upload is successful.

[0042] In this application, signal data of a signal to be processed is processed according to the signal heat, including:

[0043] When the signal heat is greater than the second threshold, and the second threshold is greater than or equal to the first threshold, the signal data is sent to the cloud in real time.

[0044] In a third aspect, the present application provides a vehicle signal data processing device, comprising:

[0045] A first acquisition module is used to acquire a signal to be processed of a target vehicle;

[0046] A first prediction module is configured to predict the signal heat of the signal to be processed in the target vehicle according to a preset first signal heat model to obtain a first signal heat value;

[0047] a second prediction module, configured to predict the signal heat of the signal to be processed in the target vehicle type according to a preset second signal heat model to obtain a second signal heat value, where the target vehicle type is the vehicle type of the target vehicle;

[0048] a signal heat module, configured to determine a signal heat of a signal to be processed based on a first signal heat value and a second signal heat value;

[0049] The sending module is used to send the signal heat to the target vehicle.

[0050] In a fourth aspect, the present application provides a vehicle signal data processing device, comprising:

[0051] The second acquisition module is used to obtain the signal heat of the signal to be processed from the cloud;

[0052] The data processing module is used to process the signal data of the signal to be processed according to the signal heat.

[0053] In a fifth aspect, the present application provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor;

[0054] Memory stores computer-executable instructions;

[0055] The processor executes the computer-executable instructions stored in the memory to implement the method provided in this application.

[0056] In a sixth aspect, the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the method provided by the present application.

[0057] The vehicle signal data processing method, device, equipment and medium provided in the present application obtain the signal to be processed of the target vehicle through the cloud, and determine the first signal heat value of the signal to be processed in the target vehicle according to the first signal heat model, and determine the second signal heat value of the signal to be processed in the target vehicle type according to the second signal heat model, and then determine the signal heat of the signal to be processed in combination with the first signal heat value and the second signal heat value. The signal heat of the signal to be processed in the target vehicle and the signal heat in the target vehicle type are comprehensively considered, and the accuracy of the signal heat is improved, so that the signal data can be better classified. Finally, the target vehicle classifies and processes the signal data of the signal to be processed according to the signal heat, thereby improving the efficiency of signal data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0059] Figure 1 A schematic diagram of a vehicle signal data processing scenario provided in an embodiment of the present application;

[0060] Figure 2 A flow chart of a vehicle signal data processing method provided in an embodiment of the present application;

[0061] Figure 3 A flowchart of another vehicle signal data processing method provided in an embodiment of the present application;

[0062] Figure 4 A schematic structural diagram of a vehicle signal data processing device provided in an embodiment of the present application;

[0063] Figure 5 A schematic structural diagram of another vehicle signal data processing device provided in an embodiment of the present application;

[0064] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0065] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0066] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0067] In order to clearly understand the technical solution of the present application, the solution of the prior art is first introduced in detail.

[0068] With the rapid development of the new energy vehicle industry, the amount of data generated by vehicles has exploded. Each new energy vehicle now generates over 5,000 signals, and these signals are typically collected every second and uploaded to data centers at a sub-second frequency. This has become an industry consensus. Efficiently processing this data has become a pressing technical challenge.

[0069] In addition, existing automotive big data processing systems face the following technical problems:

[0070] 1. Unclear data classification: The amount of data generated by cars is huge and diverse. From a technical perspective, how to effectively classify the data for targeted storage and processing is a major challenge.

[0071] 2. High storage cost: Due to the huge amount of automobile data and the different life cycles of the data, if all data is not differentiated and processed according to the same storage strategy, the storage cost will be too high.

[0072] 3. Low data processing efficiency: Traditional data processing methods are often unable to efficiently process automotive big data, especially due to bottlenecks in data collection, cleaning, and analysis.

[0073] 4. Lack of flexibility: Existing data processing systems often lack sufficient flexibility to adapt to changing data requirements and application scenarios.

[0074] In response to at least one technical problem in the above-mentioned prior art, the inventors discovered in their research that it is possible to obtain the signal to be processed of the target vehicle, determine the signal heat of the signal to be processed based on the number of accesses and the number of fault associations of the signal to be processed, and provide a basis for signal data processing of the signal to be processed based on the signal heat.

[0075] The following introduces the application scenarios of the vehicle signal data processing method provided in the embodiments of the present application.

[0076] Figure 1 A schematic diagram of a vehicle signal data processing scenario provided in an embodiment of the present application is shown as follows: Figure 1 As shown, the scenario includes a target vehicle and a cloud, wherein the target vehicle includes multiple signals. The cloud predicts the first signal heat value of each signal in the target vehicle based on the first signal heat model, and then predicts the second signal heat value of each signal in the target vehicle type based on the second signal heat model. Then, the signal heat of each signal can be obtained based on the first signal heat value and the second signal heat value, and the signal data of each signal is processed according to the signal heat of each signal.

[0077] Figure 2 A flow chart of a vehicle signal data processing method provided in an embodiment of the present application is shown as follows: Figure 2 As shown, the method is applied to the target vehicle and the cloud, and includes the following steps:

[0078] S201. The cloud obtains a signal to be processed from a target vehicle.

[0079] In this embodiment, the cloud can refer to a server or electronic device that provides cloud computing services for the target vehicle. The signal to be processed can be any signal from the target vehicle, such as powertrain signals, vehicle status signals, environmental signals, safety signals, navigation and location signals, diagnostic signals, communication signals, or in-vehicle comfort signals. The cloud can communicate with the target vehicle and, based on the target vehicle's status or configuration information, obtain the signal to be processed as the signal to be processed.

[0080] S202: The cloud predicts the signal heat of the signal to be processed in the target vehicle according to a preset first signal heat model to obtain a first signal heat value.

[0081] Among them, the first signal heat model is used to predict the signal heat of the signal to be processed in the target vehicle. It can be obtained by obtaining a first training sample set of multiple signals of the target vehicle in the first training time period. The first training sample set includes multiple groups of first training samples corresponding to multiple signals. Each group of first training samples includes the first access count sample and the first fault-related count sample of the signal related to the target vehicle in the first training time period, as well as the first signal heat label. Then, based on the first training sample set, the pre-acquired first to-be-trained model is trained to obtain the first signal heat model. The first signal heat label can be obtained by manually judging based on the first access count sample and the first fault-related count sample.

[0082] In one possible implementation, predicting the signal heat of the signal to be processed in the target vehicle based on a preset first signal heat model to obtain the first signal heat value may include:

[0083] Determining a first access count and a first fault association count of a signal to be processed in a preset time period;

[0084] The first access count and the first fault association count are input into a preset first signal heat model for prediction processing to obtain a first signal heat value of the signal to be processed.

[0085] In this embodiment, data processing of the signal to be processed often requires a specific time period, such as processing signal data within a certain time period. Therefore, the time period for signal data processing can be determined first, which can be referred to as a preset time period. The number of visits and fault associations between the signal to be processed and the target vehicle during the preset time period is then counted, referred to herein as the first number of visits and the first number of fault associations. The first number of visits and the first number of fault associations can then be input into a preset first signal heat model for prediction, yielding a first signal heat value.

[0086] Specifically, the cloud can divide the signal to be processed into fields, then count the number of accesses and fault associations for each field, and then add up the number of accesses and fault associations for each field, removing the repeated times of the same access or fault corresponding to multiple fields, to obtain the number of accesses and fault associations for the signal to be processed.

[0087] S203 . Predict the signal heat of the signal to be processed in the target vehicle type according to a preset second signal heat model to obtain a second signal heat value, where the target vehicle type is the vehicle type of the target vehicle.

[0088] Among them, the second signal heat model is used to predict the signal heat of the signal to be processed in all vehicles in use. It can be achieved by obtaining a second training sample set of multiple signals of the target vehicle type in the second training time period. The second training sample set includes multiple groups of second training samples corresponding to multiple signals. Each group of second training samples includes a number sample of vehicle samples of the target vehicle type in use in the second training time period, a second access count sample and a second fault association count sample related to all vehicle samples in use in the second training time period, and a second signal heat label; according to the second training sample set, the pre-acquired second model to be trained is trained to obtain a second signal heat model.

[0089] In one possible implementation, predicting the signal heat of the signal to be processed in the target vehicle type according to a preset second signal heat model to obtain the second signal heat value may include:

[0090] According to the target vehicle type of the target vehicle, obtaining vehicles of the target vehicle type that are in use in a preset time period, and a second access count and a second fault association count of the signal to be processed related to all vehicles in use in the preset time period;

[0091] The number of vehicles in use, the second number of accesses, and the second number of fault associations are input into a preset second signal heat model for prediction processing to obtain a second signal heat value of the signal to be processed.

[0092] In this embodiment, the signal heat value of the signal to be processed in vehicles of the same type can be obtained based on the number of accesses and the number of fault associations of the signal to be processed in vehicles of the same type. The signal heat value of the signal to be processed can then be comprehensively determined based on the two signal heat values, thereby improving the accuracy of the signal heat determination. Furthermore, since the signal types in vehicles of the same type are the same, it is easier to determine the status of each signal of the target vehicle. Therefore, based on the target vehicle type of the target vehicle, the number of vehicles in service during a preset time period can be determined. A vehicle in service can refer to a vehicle that was used within the preset time period. For example, if the preset time period is one month, a vehicle in service can be a vehicle that was on the road or powered on during this one-month period. The second number of accesses and the second number of fault associations of the signal to be processed in relation to all vehicles in service during the preset time period are then counted. The number of vehicles in service, the second number of accesses, and the second number of fault associations are then input into a preset second signal heat model for prediction processing to obtain the second signal heat value of the signal to be processed.

[0093] The first model to be trained and the second model to be trained may respectively include an AI large model, a Convolutional Neural Network (CNN) model, a Recurrent Neural Network (RNN) model, or a Generative Adversarial Network (GAN) model. When both the first model to be trained and the second model to be trained use the AI ​​large model, compared to other models, it can handle more complex data relationships and patterns, thereby improving the accuracy and robustness of predictions.

[0094] Lora can also be used to regularly fine-tune the first and second models to be trained based on the recent number of accesses and fault associations of each signal to adapt to the changing trend of signal heat and provide more accurate prediction results.

[0095] S204: Determine the signal heat of the signal to be processed according to the first signal heat value and the second signal heat value.

[0096] In the present application, after obtaining the first signal heat value and the second signal heat value, the two signal heat values ​​can be combined to obtain the signal heat of the signal to be processed.

[0097] Specifically, determining the signal heat of the signal to be processed according to the first signal heat value and the second signal heat value may include:

[0098] Obtain a first weight of the first signal heat value and a second weight of the second signal heat value;

[0099] The signal heat of the signal to be processed is obtained according to the first weight, the second weight, the first signal heat value, and the second signal heat value.

[0100] In this embodiment, the sum of the first weight and the second weight may be 1, so after the first weight is determined, the second weight may be obtained.

[0101] Furthermore, before obtaining the first weight of the first signal heat value and the second weight of the second signal heat value, the method may further include:

[0102] Obtaining the failure rate of the signal to be processed in a preset update time period;

[0103] According to the failure rate, determine the failure level corresponding to the failure rate;

[0104] According to the fault classification, a first weight and a second weight corresponding to the fault classification are determined.

[0105] In the present application, the first weight and the second weight can be updated regularly according to the failure rate so that the signal heat of the signal to be processed can be dynamically adjusted according to the dynamic failure rate of the target vehicle type, so as to better process the signal data of the signal to be processed according to the failure condition of the signal to be processed in the target vehicle type.

[0106] The preset update period is, for example, 1 month.

[0107] The failure rate may refer to the failure rate of the signal to be processed in all vehicles corresponding to the target vehicle type.

[0108] Fault classification can refer to the level divided according to the failure rate, which is used to classify the failure rate. For example, a failure rate of 0-0.5% is a first-level fault classification, a failure rate of 0.5%-0.8% is a second-level fault classification, and a failure rate greater than 0.8% is a third-level fault classification.

[0109] Each fault level corresponds to a different weight value. For example, the weight value corresponding to the first level fault level is 50%, the weight value corresponding to the second level fault level is 60%, and the weight value corresponding to the third level fault level is 70%.

[0110] The method for determining the first weight and the second weight based on the fault grade may include: since the failure rate reflects the failure condition of the target vehicle type, the second weight can be adjusted directly according to the failure rate, and the weight value corresponding to the fault grade is used as the second weight, and the difference between the second weight and 100% is used as the first weight.

[0111] When there are multiple fault types corresponding to the fault signal to be processed, the value with the largest fault rate among the fault types is selected.

[0112] S205: The cloud sends the signal heat to the target vehicle.

[0113] In this application, after the cloud calculates the signal heat of the signal to be processed, it needs to send the signal heat to the target vehicle so that the target vehicle can perform corresponding processing on the signal data of the signal to be processed according to the signal heat of the signal to be processed.

[0114] S206: The target vehicle processes the signal data of the signal to be processed according to the signal heat.

[0115] In this embodiment, after calculating the signal heat of the signal to be processed, the cloud can construct a configuration file in JSON format. When the target vehicle is powered on, the target vehicle can pull the configuration file from the cloud and process the signal data of the signal to be processed based on the signal heat of the signal to be processed. Furthermore, signal data can be automatically classified according to signal heat, which helps optimize data storage strategies, improve storage efficiency, and reduce costs.

[0116] A vehicle signal data processing method provided in an embodiment of the present application obtains a signal to be processed of a target vehicle through the cloud, and determines a first signal heat value of the signal to be processed in the target vehicle according to a first signal heat model, and determines a second signal heat value of the signal to be processed in the target vehicle type according to a second signal heat model, and then determines the signal heat of the signal to be processed in combination with the first signal heat value and the second signal heat value. This method comprehensively considers the signal heat of the signal to be processed in the target vehicle and the signal heat in the target vehicle type, improves the accuracy of the signal heat, and thus can better classify the signal data. Finally, the target vehicle classifies and processes the signal data of the signal to be processed according to the signal heat, thereby improving the efficiency of signal data processing.

[0117] Figure 3 A flow chart of another vehicle signal data processing method provided in an embodiment of the present application is shown as follows: Figure 3 As shown, the method is applied to the cloud and the target vehicle, and the method includes:

[0118] S301. The cloud obtains the signal to be processed of the target vehicle.

[0119] S302: Predict the signal heat of the signal to be processed in the target vehicle according to a preset first signal heat model to obtain a first signal heat value.

[0120] S303 : Predict the signal heat of the signal to be processed in the target vehicle type according to a preset second signal heat model to obtain a second signal heat value, where the target vehicle type is the vehicle type of the target vehicle.

[0121] S304: Determine the signal heat of the signal to be processed according to the first signal heat value and the second signal heat value.

[0122] S305: The cloud sends the signal heat to the target vehicle.

[0123] S306: The target vehicle uploads the signal data of the signal to be processed according to the signal heat.

[0124] In this embodiment, after the target vehicle obtains the signal heat of the signal to be processed, it determines the signal data uploading method of the signal to be processed according to the signal heat, so that the signal data with lower signal heat is uploaded offline, and the signal data with higher signal heat is uploaded in real time, so as to avoid the signal data with lower signal heat occupying the real-time upload resources and improve the upload processing efficiency of the signal data.

[0125] In one embodiment, the target vehicle uploads signal data of the signal to be processed based on the signal heat, which may include:

[0126] When the signal heat is less than the first threshold, the signal data is used to generate a data file according to the preset data requirements, where the data requirements are used to indicate the collection time period and file size of the data file;

[0127] Use offline tasks to send data files to the cloud during off-peak hours.

[0128] In this embodiment, the first threshold value may refer to a parameter used to distinguish different types of signal heat. When the signal heat is less than the first threshold value, it indicates that the signal heat of the signal to be processed is low. The signal data can be assembled on the target vehicle side according to the preset data requirements to obtain multiple data files. For example, the signal data in the target vehicle is assembled into data files at most every 30 minutes, and the size of each data file is fixed at 4MB. When the target vehicle is not busy, the data file is sent to the cloud through an offline task. If the data file upload fails, the data file is buffered locally and asynchronously retried until the upload is successful.

[0129] In another embodiment, the target vehicle uploads the signal data of the signal to be processed according to the signal heat, and the process may further include:

[0130] When the signal heat is greater than the second threshold, and the second threshold is greater than or equal to the first threshold, the signal data is sent to the cloud in real time.

[0131] In this embodiment, when the signal heat is greater than the second threshold, and the second threshold is greater than or equal to the first threshold, it indicates that the signal heat of the signal to be processed is high, and the signal data needs to be sent to the cloud in real time so that the cloud can obtain the signal data in a timely manner for subsequent data analysis. For example, the target vehicle can transmit the signal data to the cloud via MQTT (Message Queuing Telemetry Transport, a lightweight publish / subscribe messaging protocol).

[0132] S307: The cloud receives the signal data uploaded by the target vehicle.

[0133] S308: The cloud stores and processes the signal data.

[0134] In this embodiment, when the cloud receives the signal data uploaded by the target vehicle, it uses different storage methods to store the data according to the uploading method of the target vehicle, thereby improving the storage efficiency of the data.

[0135] Specifically, when the signal heat is less than the first threshold, the signal data is stored and processed, including:

[0136] Receive data files sent by target vehicles using offline tasks during off-peak hours. The data files are generated by the target vehicles using signal data according to preset data requirements. The data requirements are used to indicate the collection time period and file size of the data files.

[0137] During non-busy hours, the signal data is decrypted using an offline task to obtain decrypted signal data;

[0138] The decrypted signal data is stored in a preset storage format.

[0139] In this embodiment, to prevent data leakage, the target vehicle encrypts the signal data when uploading it. After the cloud receives the signal data uploaded by the target vehicle, it decrypts the signal data using the OSS (Object Storage Service) file and stores it in the OSS storage cluster using the Parquet file format (a columnar storage file format). This not only saves storage space but also supports efficient query operations, facilitating subsequent data analysis and business processing.

[0140] Specifically, when the signal heat is greater than the second threshold, and the second threshold is greater than or equal to the first threshold, the signal data is stored and processed, including:

[0141] Receive signal data uploaded by the target vehicle in real time;

[0142] Decode the signal data and write it to the data lake.

[0143] In this embodiment, the Flink MQTT Connector can be used to decode signal data, obtain decoded signal data, and then write the decoded signal data to a data lake, which can be a Paimon data lake, to achieve fast response and efficient business processing. The Flink MQTT Connector is used to integrate Flink (an open source stream processing framework) with the MQTT protocol, allowing MQTT to be used as a data source or data sink in stream processing applications.

[0144] Another vehicle signal data processing method provided by an embodiment of the present application obtains a target vehicle's signal to be processed through the cloud. The cloud determines the signal heat of the signal to be processed based on a first signal heat model and a second signal heat model. The target vehicle uploads the signal data of the signal to be processed for processing based on the signal heat. After the cloud receives the signal data uploaded by the target vehicle, it stores and processes the signal data, thereby improving the upload efficiency and storage efficiency of the vehicle signal data.

[0145] Figure 4 A schematic diagram of the structure of a vehicle signal data processing device provided in an embodiment of the present application is shown in FIG. Figure 4 As shown, the device 40 includes:

[0146] A first acquisition module 401 is used to acquire a signal to be processed of a target vehicle;

[0147] A first prediction module 402 is configured to predict the signal heat of the signal to be processed in the target vehicle according to a preset first signal heat model to obtain a first signal heat value;

[0148] A second prediction module 403 is configured to predict the signal heat of the signal to be processed in the target vehicle type according to a preset second signal heat model to obtain a second signal heat value, where the target vehicle type is the vehicle type of the target vehicle;

[0149] A signal heat module 404 is configured to determine the signal heat of the signal to be processed based on the first signal heat value and the second signal heat value;

[0150] The sending module 405 is used to send the signal heat to the target vehicle.

[0151] In some implementations, the first prediction module 402 is further configured to:

[0152] Determining a first access count and a first fault association count of a signal to be processed in a preset time period;

[0153] The first access count and the first fault association count are input into a preset first signal heat model for prediction processing to obtain a first signal heat value of the signal to be processed.

[0154] In some implementations, the second prediction module 403 is further configured to:

[0155] According to the target vehicle type of the target vehicle, obtaining vehicles of the target vehicle type that are in use in a preset time period, and a second access count and a second fault association count of the signal to be processed related to all vehicles in use in the preset time period;

[0156] The number of vehicles in use, the second number of accesses, and the second number of fault associations are input into a preset second signal heat model for prediction processing to obtain a second signal heat value of the signal to be processed.

[0157] In some implementations, the signal heat module 404 is further configured to:

[0158] Obtain a first weight of the first signal heat value and a second weight of the second signal heat value;

[0159] The signal heat of the signal to be processed is obtained according to the first weight, the second weight, the first signal heat value, and the second signal heat value.

[0160] Figure 5 A structural diagram of another vehicle signal data processing device provided in an embodiment of the present application is shown in FIG. Figure 5 As shown, the device 50 includes:

[0161] The second acquisition module 501 is used to obtain the signal heat of the signal to be processed from the cloud;

[0162] The data processing module 502 is used to process the signal data of the signal to be processed according to the signal heat.

[0163] In some embodiments, the data processing module 502 is further configured to:

[0164] When the signal heat is less than the first threshold, the signal data is used to generate a data file according to the preset data requirements, where the data requirements are used to indicate the collection time period and file size of the data file;

[0165] Use offline tasks to send data files to the cloud during off-peak hours.

[0166] In some embodiments, the data processing module 502 is further configured to:

[0167] When the signal heat is greater than the second threshold, and the second threshold is greater than or equal to the first threshold, the signal data is sent to the cloud in real time.

[0168] Figure 6 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present application. Figure 6 As shown, the electronic device 60 includes:

[0169] The electronic device 60 may include one or more processors 601 , one or more computer-readable storage media memories 602 , a communication component 603 , and other components. The processor 601 , the memory 602 , and the communication component 603 are connected via a bus 604 .

[0170] In a specific implementation process, at least one processor 601 executes the computer-executable instructions stored in the memory 602 , so that the at least one processor 601 performs the above vehicle signal data processing method.

[0171] The specific implementation process of the processor 601 can be found in the above method embodiment. Its implementation principle and technical effects are similar and will not be repeated here in this embodiment.

[0172] In the above Figure 6 In the illustrated embodiment, it should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASICs), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly executed by a hardware processor or by a combination of hardware and software modules within the processor.

[0173] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (NVM), such as at least one disk memory.

[0174] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be classified into address buses, data buses, and control buses. For ease of illustration, the buses in the drawings of this application are not limited to just one bus or just one type of bus.

[0175] In some embodiments, a computer program product is further provided, comprising a computer program or instructions, which implement the steps in any one of the above-mentioned vehicle signal data processing methods when executed by a processor.

[0176] The specific implementation of the above operations can be found in the previous embodiments and will not be repeated here.

[0177] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be accomplished by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0178] To this end, an embodiment of the present application provides a computer-readable storage medium, which stores multiple instructions. The instructions can be loaded by a processor to execute the steps in any vehicle signal data processing method provided in the embodiment of the present application.

[0179] The storage medium may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0180] According to one aspect of the present application, a computer program product or a computer program is provided. The computer program product or the computer program comprises computer instructions stored in a computer-readable storage medium.

[0181] Since the instructions stored in the storage medium can execute the steps in any vehicle signal data processing method provided in the embodiments of the present application, the beneficial effects that can be achieved by any vehicle signal data processing method provided in the embodiments of the present application can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0182] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of the present application and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0183] It should be understood that the present application is not limited to the exact structure described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A vehicle signal data processing method, characterized in that: include: Obtaining a signal to be processed of a target vehicle; Predicting the signal heat of the signal to be processed in the target vehicle according to a preset first signal heat model to obtain a first signal heat value; According to a preset second signal heat model, predicting the signal heat of the signal to be processed in a target vehicle type to obtain a second signal heat value, wherein the target vehicle type is the vehicle type of the target vehicle; Determining the signal heat of the signal to be processed according to the first signal heat value and the second signal heat value; The signal heat is sent to the target vehicle.

2. The method according to claim 1, characterized in that The predicting the signal heat of the signal to be processed in the target vehicle according to the preset first signal heat model to obtain the first signal heat value includes: Determine a first access count and a first fault association count of the signal to be processed in a preset time period; The first access count and the first fault association count are input into a preset first signal heat model for prediction processing to obtain a first signal heat value of the signal to be processed.

3. The method according to claim 2, characterized in that The predicting the signal heat of the signal to be processed in the target vehicle type according to the preset second signal heat model to obtain the second signal heat value includes: According to the target vehicle type of the target vehicle, obtaining vehicles of the target vehicle type put into use in the preset time period, and a second access count and a second fault association count of the signal to be processed related to all vehicles put into use in the preset time period; The number of vehicles put into use, the second number of accesses and the second number of fault associations are input into a preset second signal heat model for prediction processing to obtain a second signal heat value of the signal to be processed.

4. The method according to claim 3, characterized in that The determining the signal heat of the signal to be processed according to the first signal heat value and the second signal heat value includes: Obtaining a first weight of the first signal heat value and a second weight of the second signal heat value; The signal heat of the signal to be processed is obtained according to the first weight, the second weight, the first signal heat value, and the second signal heat value.

5. The method according to claim 4, characterized in that Before obtaining the first weight of the first signal heat value and the second weight of the second signal heat value, the method further includes: Obtaining the failure rate of the signal to be processed in a preset update time period; According to the failure rate, determining a failure level corresponding to the failure rate; According to the fault classification, a first weight and a second weight corresponding to the fault classification are determined.

6. The method according to any one of claims 1 to 5, characterized in that: The method further comprises: Receiving signal data uploaded by the target vehicle, the signal data being obtained after sending and processing according to the signal heat; The signal data is stored and processed.

7. The method according to claim 6, characterized in that When the signal heat is less than a first threshold, storing and processing the signal data includes: Receiving a data file sent by the target vehicle using an offline task during a non-busy period, wherein the data file is generated by the target vehicle using the signal data according to a preset data requirement, wherein the data requirement is used to indicate a collection time period and a file size of the data file; Decrypting the signal data using an offline task during a non-busy period to obtain decrypted signal data; The decrypted signal data is stored in a preset storage format.

8. The method according to claim 6, characterized in that When the signal heat is greater than a second threshold, and the second threshold is greater than or equal to the first threshold, storing and processing the signal data includes: Receiving signal data uploaded in real time by the target vehicle; The signal data is decoded and written into the data lake.

9. A vehicle signal data processing method, characterized in that: include: Obtain the signal heat of the signal to be processed from the cloud; The signal data of the signal to be processed is processed according to the signal heat.

10. The method according to claim 9, characterized in that The processing of the signal data of the signal to be processed according to the signal heat includes: When the signal heat is less than a first threshold, generating a data file from the signal data according to a preset data requirement, wherein the data requirement is used to indicate a collection time period and a file size of the data file; The data file is sent to the cloud using an offline task during non-busy hours.

11. The method according to claim 10, characterized in that The method further comprises: If the data file fails to be uploaded, the data file is buffered locally and asynchronously retried until the upload is successful.

12. The method according to claim 9, characterized in that The processing of the signal data of the signal to be processed according to the signal heat includes: When the signal heat is greater than a second threshold, and the second threshold is greater than or equal to the first threshold, the signal data is sent to the cloud in real time.

13. A vehicle signal data processing device, characterized in that: include: A first acquisition module, used to acquire a signal to be processed of a target vehicle; A first prediction module is used to predict the signal heat of the signal to be processed in the target vehicle according to a preset first signal heat model to obtain a first signal heat value; a second prediction module, configured to predict the signal heat of the signal to be processed in a target vehicle type according to a preset second signal heat model to obtain a second signal heat value, wherein the target vehicle type is a vehicle type of the target vehicle; A signal heat module, used to determine the signal heat of the signal to be processed according to the first signal heat value and the second signal heat value; A sending module is used to send the signal heat to the target vehicle.

14. A vehicle signal data processing device, characterized in that: include: The second acquisition module is used to obtain the signal heat of the signal to be processed from the cloud; The data processing module is used to process the signal data of the signal to be processed according to the signal heat.

15. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 12.

16. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 12 when executed by a processor.

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