Data processing method and device, and vehicle

Abstract

The present invention discloses a data processing method, device, and vehicle. The method comprises: receiving simulation data corresponding to a current push scenario, wherein the simulation data is used to simulate data output by a first device in a target vehicle; obtaining operating data of the target vehicle based on the simulation data and real-time data, wherein the real-time data is data output in real time by a second device other than the first device on the target vehicle; sending the operating data of the target vehicle to a server, and receiving push data corresponding to the current push scenario returned by the server. The present invention solves the technical problem in related technologies that intelligent recommendation real-vehicle testing consumes time, labor, vehicle wear, and other dimensions, resulting in reduced efficiency of real-vehicle testing.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a data processing method and device, and a vehicle. Background Art

[0002] The connected vehicle (IoV) industry is rapidly developing. To provide users with an exceptional IoV experience, automakers are diligently developing and deploying IoV features. The "Service Finder" feature of IoV primarily focuses on intelligent services. Based on user data and driving scenarios, the system dynamically recommends required features to users, a key path to intelligent service. As a key enabler of this "Service Finder" feature, intelligent recommendation technology has received significant attention within automakers. After the development of intelligent recommendation features, real-vehicle testing is subject to numerous constraints and limitations. For example, in the gas station recommendation scenario, the fuel level in the vehicle tank must be at a certain low level before the intelligent recommendation is triggered. Meeting this triggering condition requires significant wasted fuel, which in turn incurs significant time, labor, and vehicle wear and tear.

[0003] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0004] The embodiments of the present invention provide a data processing method, device, and vehicle to at least solve the technical problem in related technologies that intelligent recommendation real-vehicle testing consumes time costs, labor costs, vehicle losses, and other dimensions, resulting in reduced efficiency of real-vehicle testing.

[0005] According to one aspect of an embodiment of the present invention, a data processing method is provided, including: receiving simulation data corresponding to a current push scenario, wherein the simulation data is used to simulate data output by a first device in a target vehicle; obtaining operating data of the target vehicle based on the simulation data and real-time data, wherein the real-time data is data output in real time by a second device other than the first device on the target vehicle; sending the operating data of the target vehicle to a server, and receiving push data corresponding to the current push scenario returned by the server.

[0006] Optionally, based on the simulation data and real-time data, the operating data of the target vehicle is obtained, including: in response to receiving the simulation data, shielding the data output by the first device; obtaining the data output by the second device to obtain real-time data; and summarizing the simulation data and real-time data to obtain the operating data of the target vehicle.

[0007] Optionally, after receiving the push data corresponding to the current push scenario returned by the server, it also includes: outputting prompt information corresponding to the push data, wherein the prompt information is used to prompt whether to output the push information; in response to receiving a confirmation instruction corresponding to the prompt information, outputting the push data.

[0008] Optionally, receiving the simulation data corresponding to the current push scenario includes: injecting the simulation data into a bus of the target vehicle through a signal injection device.

[0009] Optionally, a simulated bus signal corresponding to the current push scenario is generated; the device identification of the first device is determined based on a preset mapping relationship, wherein the preset mapping relationship is used to characterize the correspondence between different bus signals and the device identifications of different devices in the target vehicle; and simulated data is generated based on the simulated bus signal and the device identification.

[0010] Optionally, generating the analog bus signal corresponding to the current push scenario includes: generating a push scenario set; randomly acquiring any one push scenario in the push scenario set to obtain the current push scenario; and generating the analog bus signal.

[0011] Optionally, after receiving the push data corresponding to the current push scenario returned by the server, it also includes: outputting the push data; receiving target feedback information corresponding to the push data; and obtaining a test result based on the degree of matching between the target feedback information and the preset feedback information corresponding to the push data.

[0012] Optionally, in response to the test result indicating that the degree of matching between the target feedback information and the preset feedback information is greater than a preset degree, simulation data corresponding to another test scenario in the push scenario set other than the current push scenario is generated.

[0013] According to another aspect of an embodiment of the present invention, a data processing device is also provided, including: a receiving module for receiving simulation data corresponding to a current push scenario, wherein the simulation data is used to simulate data output by a first device in a target vehicle; an obtaining module for obtaining operating data of the target vehicle based on the simulation data and real-time data, wherein the real-time data is data output in real time by a second device other than the first device on the target vehicle; an interaction module for sending the operating data of the target vehicle to a server, and receiving push data corresponding to the current push scenario returned by the server.

[0014] According to another aspect of an embodiment of the present invention, a vehicle is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the above-mentioned data processing method.

[0015] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned data processing method.

[0016] According to another aspect of an embodiment of the present invention, a processor is further provided. The processor is configured to run a program, wherein the above-mentioned data processing method is executed when the program is run.

[0017] In an embodiment of the present invention, a data processing method is adopted to comprehensively simulate the scenario data required for the intelligent recommendation test of the whole vehicle, dynamically generate the signals required by the vehicle according to the required recommendation scenario, and inject the various simulated signals into the vehicle so that the signals uploaded by the vehicle meet the requirements of the intelligent recommendation scenario, thereby achieving intelligent virtual simulation of the target signals corresponding to the target test scenarios in the actual vehicle testing process, avoiding the consumption of human and material resources caused by the generation of actual target signals, and achieving the purpose of intelligent recommendation actual vehicle testing by aggregating target test signals and non-target test signals, thereby achieving the technical effect of improving the efficiency of actual vehicle testing and reducing the consumption of resources and time costs, thereby solving the technical problem in related technologies that the intelligent recommendation actual vehicle testing has consumption in dimensions such as time cost, manpower cost, and vehicle loss, resulting in reduced efficiency of actual vehicle testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0019] Figure 1 is a flow chart of a data processing method according to an embodiment of the present invention;

[0020] Figure 2 is a schematic diagram of a device for improving the efficiency of real-vehicle intelligent recommendation testing according to an embodiment of the present invention;

[0021] Figure 3 is a schematic diagram of a data processing device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0023] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] Example 1

[0025] According to an embodiment of the present invention, an embodiment of a method for data processing is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0026] Figure 1 is a flow chart of a data processing method according to an embodiment of the present invention. Figure 1 As shown, the method includes the following steps:

[0027] Step S102, receiving simulation data corresponding to the current push scenario, wherein the simulation data is used to simulate data output by the first device in the target vehicle;

[0028] Wherein, step S102 includes:

[0029] The analog data is injected into the bus of the target vehicle through the signal injection device.

[0030] Also includes:

[0031] Generate analog bus signals corresponding to the current push scenario;

[0032] Determining a device identification of the first device based on a preset mapping relationship, wherein the preset mapping relationship is used to characterize a correspondence between different bus signals and device identifications of different devices in the target vehicle;

[0033] Generates simulated data based on simulated bus signals and device identification.

[0034] The process of generating an analog bus signal corresponding to the current push scenario also includes:

[0035] Generate push scene collection;

[0036] Randomly obtain any push scene in the push scene set to get the current push scene;

[0037] Generates an analog bus signal.

[0038] Specifically, the data processing method provided in this application can be used to improve the efficiency of real-vehicle intelligent recommendation testing. The data processing method provided in this application can be used to comprehensively simulate the scenario data required for the intelligent recommendation test of the entire vehicle, and dynamically generate the signals required by the vehicle according to the required recommendation scenario. Through signal injection equipment and control methods, the various simulated signals are injected into the actual bus of the entire vehicle, so that the signals uploaded by the vehicle meet the requirements of the intelligent recommendation scenario, thereby improving the efficiency of the actual vehicle test and reducing the consumption of resources and time costs.

[0039] In addition, the data processing method provided in this application needs to be carried out using a device that improves the efficiency of real-vehicle intelligent recommendation testing as a carrier for data processing, such as Figure 2 As shown, Figure 2 This is a device for improving the efficiency of real-vehicle intelligent recommendation testing. The device includes a PC host computer, a signal injector, a vehicle, and an intelligent recommendation cloud service module. Specifically:

[0040] The PC host computer is the main control center, including scenario management, device management, bypass mode management, test signal generation, and signal transmission modules. Its main task is to intelligently determine the signals required to support scenario testing based on pre-programmed simulation scenarios. Based on the settings of the entire vehicle network, it obtains the bus ID corresponding to the required signal. It then automatically finds the device ID corresponding to the electronic and electrical components in the vehicle corresponding to the signal. Through the device management module, it configures the corresponding electronic and electrical components inside the vehicle and activates the bypass inside the corresponding component to prepare for subsequent signal bypass. Subsequently, the test signal generation module in the PC host computer generates the signal required for the current scenario test and sends the signal to the signal injector through the signal transmission module.

[0041] The signal injector is a key component connecting the PC host computer and the vehicle's internal bus. It can inject data from the PC host computer into the vehicle's internal network. The vehicle's internal network includes, but is not limited to, CAN bus, FlexRay bus, and Ethernet. As these technologies are already well-established in the automotive field, they will not be discussed in detail here. Signal injectors include, but are not limited to, devices such as OBD and diagnostic tools that can access the vehicle's bus.

[0042] The vehicle is the primary vehicle for real-world testing of intelligent recommendations. Aside from signals that require injection and bypassing, all other signals originate from the vehicle's actual electronic and electrical components, ensuring the greatest possible reproduction of the vehicle's network signals. Vehicles contain numerous electronic and electrical components, and this method only describes a few that are particularly relevant to intelligent recommendations. The overall implementation of other components not mentioned is similar to that shown in the diagram. Each electronic and electrical component has a signal bypass and a normal signal path. When bypass mode is disabled, the component transmits and receives signals according to its pre-defined design. When the signal bypass is activated, it monitors the bus signal. When a signal requiring bypassing and a corresponding normal signal arrive, the normal signal is internally shielded, and the signal from the injector is processed and then sent back to the bus.

[0043] The intelligent recommendation cloud service module implements the scene recognition and intelligent recommendation functions. The data processing module receives signals processed by the bypass unit in the vehicle's onboard communication unit, performs preliminary processing, and sends them to the intelligent recommendation module. The onboard communication unit then transmits the recommendation results to the vehicle's main unit via a standard link. The main unit then displays the intelligent recommendation results on the vehicle's central control screen and, depending on the configuration of the intelligent recommendation module, displays the interface, announces them via voice, or launches the vehicle application.

[0044] More specifically, there are many scenarios for actual vehicle testing, including "recommending nearby gas stations and highway service areas to users" and "recommending nearby vehicle repair shops to users". In this application, the "recommending nearby gas stations to users" in the above-mentioned scenarios can be used as an example for explanation. The current push scenario can be interpreted as "when the vehicle is traveling on the highway, recommending the most suitable service areas and gas stations on the driving road". The simulation data corresponds to the current push scenario, which can be understood as supporting the current push scenario, that is, "when the vehicle is traveling on the highway, recommending the most suitable service areas and gas stations on the driving road", including the virtual GPS positioning data of the driving vehicle on the highway and the virtual fuel tank remaining oil data of the vehicle. Among them, the simulation data is used to simulate the data output by the first device in the target vehicle, and the first device can be understood as a vehicle-mounted device for outputting the above-mentioned GPS positioning data and the fuel tank remaining oil data of the vehicle in real time, corresponding to the vehicle-mounted communication unit and the oil displacement controller respectively. When the vehicle under test receives simulated data corresponding to the current push scenario, the simulated data can be injected into the bus of the target vehicle via a signal injection device. The signal injection device is the signal injector in the aforementioned apparatus for improving the efficiency of real-vehicle intelligent recommendation testing, and the target vehicle is the aforementioned vehicle under test. Furthermore, the vehicle bus is the underlying communication network in the vehicle network that interconnects vehicle devices or instrument clusters. Currently, widely used vehicle buses include the local area interconnect protocols LIN and CAN. Developing vehicle bus technologies include the high-speed, fault-tolerant network protocol FlexRay, MOST for automotive multimedia and navigation, and wireless network technologies such as Bluetooth and WLAN that are compatible with computer networks. Specifically, when receiving simulated data corresponding to the current push scenario, a simulated bus signal corresponding to the current push scenario can be generated. The simulated bus signal can be understood as the bus signal ID corresponding to the virtual GPS signal and the bus signal ID corresponding to the virtual fuel level signal used in the current push scenario. When generating the simulated bus signal, a push scenario set can be generated, and then any push scenario from the push scenario set can be randomly selected to obtain the current push scenario. The current push scenario is, for example, "When the vehicle is traveling on a highway, recommend the most suitable service areas and gas stations along the route." Then, an analog bus signal for this scenario is generated.

[0045] Furthermore, the device identification of the first device is determined based on a preset mapping relationship, wherein the preset mapping relationship is used to characterize the correspondence between different bus signals and the device identifications of different devices in the target vehicle, i.e., different bus signals correspond to different device identifications. Specifically, it can be understood that the on-board communication device corresponds to the bus signal ID corresponding to the aforementioned virtual GPS signal, and the oil displacement control device corresponds to the bus signal ID corresponding to the aforementioned virtual oil signal. The device identification can be interpreted as the virtual GPS signal ID corresponding to the on-board communication unit ID, and the virtual oil signal ID corresponding to the oil displacement controller ID. Furthermore, based on the simulated bus signal and the device identification, simulated data is generated.

[0046] In summary, if Figure 2 As shown, when the tester opens the PC host computer, he edits the intelligent recommendation scenario set to be tested in the scenario management module therein. Specifically, there is a script file in the PC host computer, and the description of the test scenario can be edited in the script file, that is, taking "when the vehicle is driving on the highway, recommend the most suitable service area and gas station on the driving road" as an example, a detailed description is given; the tester installs the injector on the actual vehicle to be tested, and confirms that the injector and the vehicle bus are correctly connected; the tester starts the intelligent recommendation real vehicle test on the PC host computer, and the test signal generation module on the PC host computer generates the bus signal set required for this scenario according to the first scenario to be tested, such as a continuous virtual vehicle GPS sequence and the current virtual oil level of the vehicle. The signal generation module searches for the bus signal ID corresponding to the virtual GPS signal and the virtual oil level signal through the built-in bus signal list; the device management module in the PC host computer searches for the bus device ID corresponding to the virtual signal according to the signal. For example, the virtual GPS signal ID corresponds to the ID of the on-board communication unit, and the virtual oil level signal ID corresponds to the oil level displacement controller ID; the bypass management module in the PC host computer registers and manages the on-board communication unit ID, the oil level displacement controller ID, the virtual GPS sequence, and the virtual oil level signal to generate the information required in this scenario, which is the simulation data. Since the types of on-board devices of each vehicle model are different, the bypass management module needs to know which devices are equipped on the tested vehicle and perform unified management, mainly including the discovery and deletion of on-board devices, the status of the devices, the corresponding signals in the devices, etc.

[0047] Furthermore, the scenario of "recommending the nearest vehicle repair shop to the user" can be used as an example for explanation. The current push scenario can be interpreted as "when the vehicle is traveling on the highway, recommending the nearest vehicle repair shop on the road". When the tester opens the PC host computer, he edits the intelligent recommendation scenario to be tested in the scenario management module, "when the vehicle is traveling on the highway, recommending the nearest vehicle repair shop on the road". The tester starts the intelligent recommendation real-vehicle test on the PC host computer. The test signal generation module on the PC host computer tests the scenario as needed and generates the corresponding required bus signal set, such as the virtual vehicle GPS sequence, the vehicle's current virtual tire pressure, the virtual chassis bottoming impact movement and other data that are suitable for the scenario. The signal generation module searches for the bus signal ID corresponding to the virtual vehicle GPS sequence, the vehicle's current virtual tire pressure, and the virtual chassis bottoming impact movement through the built-in bus signal list. Furthermore, the device management module on the PC host computer searches for the corresponding bus device ID according to the above-mentioned virtual signals, namely, the on-board communication unit ID corresponding to the virtual GPS serial signal, the tire pressure controller ID corresponding to the virtual tire pressure, the chassis controller ID corresponding to the virtual chassis bottom drag impact abnormality data, etc. Furthermore, the bypass management module on the PC host computer registers and manages the on-board communication unit ID, tire pressure controller ID, chassis controller ID, as well as the virtual GPS serial signal, virtual tire pressure, and virtual chassis bottom drag impact abnormality data one by one to generate the vehicle operation information under this scenario.

[0048] Furthermore, the signal sending module on the PC host computer sends the above vehicle operation information to the signal injector, so that after receiving the above information, the signal injector sends the on-board communication unit ID and the virtual GPS serial signal ID to the bypass module of the on-board communication unit through the on-board bus, so that after receiving the signal, the bypass module registers the virtual GPS serial signal ID; similarly, the signal injector sends the tire pressure controller ID and the virtual tire pressure ID to the bypass in the tire pressure controller, so that the virtual tire pressure ID is registered; similarly, the signal injector sends the chassis controller ID and the virtual chassis bottom drag impact abnormality data ID to the bypass in the chassis controller, so that the virtual chassis bottom drag impact abnormality data is registered. Furthermore, the on-board communication unit receives the virtual GPS sequence from the signal injector and simultaneously receives the normal GPS sequence sent from the GPS module via the bus. Using a bypass, the normal GPS sequence is shielded and processed, and then the virtual GPS sequence is sent to the bus and uploaded to the intelligent recommendation cloud service module. Similarly, the bypass in the tire pressure controller shields the normal tire pressure sent via the bus and uploads the virtual tire pressure to the intelligent recommendation cloud service module via the bus. Similarly, the bypass in the chassis controller shields the normal chassis data sent via the bus and uploads the virtual chassis bottoming impact anomaly data to the intelligent recommendation cloud service module via the bus. Finally, the intelligent recommendation cloud service module analyzes the received virtual GPS sequence, virtual tire pressure, and virtual chassis bottoming impact anomaly data, calculates the vehicle's mileage under the virtual tire pressure and virtual chassis bottoming impact anomaly data, and searches for vehicle repair shop data in the vehicle's travel direction based on the virtual GPS sequence to lock the location tag. Based on the tagged vehicle repair shop data, an intelligent recommendation result is generated. The vehicle-mounted communication unit is then used to forward the intelligent recommendation results to the vehicle-mounted host for subsequent logical processing, and selective voice and screen broadcast reminders are provided to the user to complete the intelligent recommendation test in this scenario.

[0049] Through the above steps, accurate virtual signal generation can be achieved for the signal required for the test scenario, ensuring that the virtual signal meets the technical effects required for the intelligent recommendation scenario.

[0050] Step S104 , obtaining the operating data of the target vehicle based on the simulation data and the real-time data, wherein the real-time data is the data output in real time by the second device other than the first device on the target vehicle.

[0051] Wherein, step S104 includes:

[0052] In response to receiving the analog data, masking the data output by the first device;

[0053] Acquire data output by the second device to obtain real-time data;

[0054] The simulation data and real-time data are aggregated to obtain the operating data of the target vehicle.

[0055] Specifically, after generating simulation data for scenario testing, it is also necessary to collect real-time data of the vehicle. Real-time data can be understood as other non-test signal data, including but not limited to engine control data, chassis control data, etc. Then, based on the simulation data and real-time data, the operating data of the target vehicle is obtained, wherein the real-time data is the data output in real time by the second device on the target vehicle other than the first device. The second device can be understood as the engine control module, chassis control module and other devices that output real-time data. In the process of obtaining the operating data of the target vehicle, the data output by the first device can be shielded in response to receiving the simulation data. In other words, the real-time GPS signal and the real-time oil level signal actually output by the first device are shielded with reference to the virtual GPS signal and the virtual oil level signal. By summarizing the simulation data and the real-time data, for example, the simulation data is defined as set A {virtual GPS signal, virtual oil level signal}, and the real-time data is defined as set B {real-time engine signal, real-time chassis signal, real-time battery signal...}. By taking the union of set A and set B, the set U {virtual GPS signal, virtual oil level signal, real-time engine signal, real-time chassis signal, real-time battery signal...} can be obtained, and the set U can be understood as the set of the said operating data.

[0056] In summary, after generating the information required in this scenario, such as Figure 2As shown, the signal transmission module in the PC host computer transmits the generated information to the signal injector. After receiving the information, the signal injector transmits it to the bypass module in the vehicle communication unit via the vehicle bus, based on the vehicle communication unit ID (bus device ID) and the virtual GPS signal ID (bus device ID). The bypass module receives the signal and registers the virtual GPS sequence signal ID. Simultaneously, the signal injector transmits the fuel displacement controller ID and the virtual fuel level ID to the bypass module in the fuel displacement controller. The bypass module receives the signal and registers the virtual fuel level ID. The vehicle communication unit receives the virtual GPS sequence from the injector and the normal GPS sequence sent from the GPS module via the bus. It then bypasses the sequence, masking the normal GPS sequence using the bypass module and sending the virtual GPS sequence to the bus, where it is uploaded to the cloud-based intelligent recommendation module. Simultaneously, the fuel displacement controller receives the virtual fuel level from the injector and the normal fuel level value sent from the fuel level sensor via the bus. It then bypasses the sequence, masking the normal fuel level value and sending the virtual fuel level to the bus. Then, after the on-board communication unit receives the virtual fuel level value, it uploads the virtual fuel level value to the cloud-based intelligent recommendation module; the data processing module in the cloud-based intelligent recommendation module receives the virtual GPS sequence and virtual fuel level, combines it with other normal signals of the vehicle, performs preliminary processing, and sends the processing results to the intelligent recommendation module.

[0057] Through the above steps, it is possible to achieve the bypass processing of the normal value of the test signal required for the test scenario, and merge the virtual data of the test signal and the normal data of the non-test signal to generate the vehicle's operating data, which is convenient for the subsequent actual vehicle test. Technical effect.

[0058] Step S106: Send the target vehicle's operating data to the server, and receive the push data corresponding to the current push scenario returned by the server.

[0059] After receiving the push data corresponding to the current push scenario returned by the server, the following is also included:

[0060] Outputting prompt information corresponding to the pushed data, wherein the prompt information is used to prompt whether to output the pushed information;

[0061] In response to receiving a confirmation instruction corresponding to the prompt information, the push data is output.

[0062] Specifically, after the vehicle's operating data is generated, it can be sent to a server. The server can be understood as the intelligent recommendation cloud service module in the above-mentioned device for improving the efficiency of real-vehicle intelligent recommendation testing. Specifically, the data processing module in the intelligent recommendation cloud service module receives the virtual GPS sequence and virtual fuel level, and then performs preliminary processing in combination with other normal signals of the vehicle. The preliminary processing mainly includes: filtering, sorting, padding, and counting the data uploaded by the terminal, to ensure that the vehicle's operating data reaches the optimal state. The processing results are then sent to the intelligent recommendation module. The intelligent recommendation module calculates the remaining mileage of the vehicle model based on the remaining fuel level, and searches for gas station and service area data in the vehicle's travel direction based on the virtual GPS sequence, calculates the most suitable gas station and service area locations, and generates intelligent recommendation results. The intelligent recommendation results are the push data corresponding to the current push scenario, that is, the gas station and service area locations that are most suitable for the vehicle.

[0063] Furthermore, after receiving the push data corresponding to the current push scenario returned by the server, the prompt information corresponding to the push data can be output, and then in response to receiving the confirmation instruction corresponding to the prompt information, the push data can be output. Specifically, the cloud-based intelligent recommendation module sends the generated intelligent recommendation results to the on-board communication unit, and the on-board communication unit forwards the results to the on-board host; the on-board host receives the intelligent recommendation results and performs subsequent logical processing according to the intelligent recommendation template. First, a dialog box pops up on the on-board central control screen, prompting "Recommend suitable gas stations and servers for you, do you want to go?" At the same time, the voice broadcast module is called to give a voice broadcast reminder to the user. If the user clicks yes, the navigation module is pulled up to automatically perform navigation operations for the user. The intelligent recommendation test in this scenario is completed. The push data is the test result under the target test scenario output by the device that utilizes the efficiency of the real vehicle intelligent recommendation test, after a series of data processing is performed on the vehicle to be tested.

[0064] Through the above steps, the test results can be evaluated, and the accuracy of the data processing method during the actual vehicle test of this application can be tested. By continuously optimizing the inaccurate processing process, the technical effect of improving the efficiency of the actual vehicle test can be achieved.

[0065] Optionally, after receiving the push data corresponding to the current push scenario returned by the server, the method further includes:

[0066] Output push data;

[0067] Receive target feedback information corresponding to the pushed data;

[0068] Obtaining a test result based on the degree of matching between the target feedback information and the preset feedback information corresponding to the pushed data;

[0069] In response to the test result indicating that the target feedback information matches the preset feedback information to a greater degree than a preset degree, simulation data corresponding to another test scenario in the push scenario set other than the current push scenario is generated.

[0070] Specifically, after receiving the push data corresponding to the current push scenario returned by the server, it can be output, that is, the location of the gas station and service area that is most suitable for the vehicle is sent to the on-board communication unit. The on-board communication unit forwards the result to the vehicle host. After receiving the intelligent recommendation result, the on-board host needs to perform subsequent logical processing according to the intelligent recommendation template. Specifically, the target feedback information corresponding to the pushed data can be received, wherein the target feedback information can be understood as the actual feedback data of the gas station and service area locations suitable for the vehicle output under the current test scenario. Correspondingly, the preset feedback information can be understood as the expected feedback data of the expected gas station and service area locations suitable for the vehicle under the preset current scenario. No specific setting is made here, and the evaluation should be based on the data in the actual test process. By testing whether the target feedback information meets the preset feedback information, if the test result is that the degree of match between the target feedback information and the preset feedback information is greater than the preset degree, it means that the target feedback information meets the preset feedback information, indicating that the data processing method of the present application can be used to improve the efficiency of smart recommendation real vehicle testing, and then, generate simulation data corresponding to another test scenario other than the current push scenario in the push scenario set, that is, generate simulation data for any remaining scenarios except the test scenario, and continue to perform simulation tests of different scenarios until all possible prediction scenarios are simulated.

[0071] After completing the intelligent recommendation test for any scenario through the above steps, the tester will operate on the PC host computer to confirm the test results and record the relevant data. After confirmation, the actual vehicle test of the next intelligent recommendation scenario will be started.

[0072] Example 2

[0073] According to an embodiment of the present invention, a data processing device is also provided, which can execute the data processing method provided in the above embodiment 1. The specific implementation method and preferred application scenario are the same as those of the above embodiment 1 and will not be repeated here.

[0074] Figure 3 FIG. 1 is a structural diagram of a data processing device according to an embodiment of the present invention. Figure 3 As shown, the device includes:

[0075] A receiving module 32 is configured to receive simulation data corresponding to a current push scenario, wherein the simulation data is used to simulate data output by a first device in a target vehicle;

[0076] An obtaining module 34 is configured to obtain operating data of the target vehicle based on the simulation data and the real-time data, wherein the real-time data is data output in real time by a second device other than the first device on the target vehicle;

[0077] The interaction module 36 is used to send the operating data of the target vehicle to the server and receive the push data corresponding to the current push scenario returned by the server.

[0078] Optionally, the obtaining module 34 includes: a shielding unit for shielding the data output by the first device in response to receiving the simulation data; an obtaining unit for obtaining the data output by the second device to obtain real-time data; and a summarizing unit for summarizing the simulation data and the real-time data to obtain the operating data of the target vehicle.

[0079] Optionally, the receiving module 32 includes: an output unit for outputting prompt information corresponding to the push data, wherein the prompt information is used to prompt whether to output the push information; an output unit for outputting the push data in response to receiving a confirmation instruction corresponding to the prompt information; an injection unit for injecting analog data into the bus of the target vehicle through a signal injection device; generating an analog bus signal corresponding to the current push scenario; a confirmation unit for determining the device identification of the first device based on a preset mapping relationship, wherein the preset mapping relationship is used to characterize the correspondence between different bus signals and the device identifications of different devices in the target vehicle; a generation unit for generating analog data based on the analog bus signal and the device identification.

[0080] Optionally, the injection unit includes: a generation unit for generating a push scene set; an acquisition unit for randomly acquiring any push scene in the push scene set to obtain a current push scene; and a generation unit for generating an analog bus signal.

[0081] Optionally, the exchange module 36 includes: an output unit for outputting push data; a receiving unit for receiving target feedback information corresponding to the push data; an obtaining unit for obtaining a test result based on the degree of matching between the target feedback information and the preset feedback information corresponding to the push data; and a generating unit for generating simulation data corresponding to another test scenario in the push scenario set other than the current push scenario in response to the test result being that the degree of matching between the target feedback information and the preset feedback information is greater than the preset degree.

[0082] Example 3

[0083] According to an embodiment of the present invention, a vehicle is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the above-mentioned data processing method.

[0084] Example 4

[0085] According to an embodiment of the present invention, a computer-readable storage medium is further provided. The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned data processing method.

[0086] Example 5

[0087] According to an embodiment of the present invention, a processor is further provided, and the processor is used to run a program, wherein the above-mentioned data processing method is executed when the program is run.

[0088] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0089] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0090] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0091] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0092] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0093] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc. Various media that can store program codes.

[0094] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A data processing method, characterized in that: include: Receive simulation data corresponding to the current push scenario, wherein the simulation data is used to simulate data output by the first device in the target vehicle; Obtaining operating data of the target vehicle based on the simulation data and the real-time data, wherein the real-time data is data output in real time by a second device other than the first device on the target vehicle; The operating data of the target vehicle is sent to the server, and the push data corresponding to the current push scenario returned by the server is received.

2. The method according to claim 1, characterized in that Based on the simulation data and the real-time data, the operating data of the target vehicle is obtained, including: In response to receiving the analog data, shielding data output by the first device; Acquire data output by the second device to obtain the real-time data; The simulation data and the real-time data are aggregated to obtain the operating data of the target vehicle.

3. The method according to claim 1, characterized in that After receiving the push data corresponding to the current push scenario returned by the server, the method further includes: Outputting prompt information corresponding to the pushed data, wherein the prompt information is used to prompt whether to output the pushed data; In response to receiving a confirmation instruction corresponding to the prompt information, the push data is output.

4. The method according to claim 1, wherein Receive simulation data corresponding to the current push scenario including: The simulation data is injected into the bus of the target vehicle through a signal injection device.

5. The method according to claim 4, characterized in that The method further comprises: Generate an analog bus signal corresponding to the current push scenario; Determining a device identification of the first device based on a preset mapping relationship, wherein the preset mapping relationship is used to represent a correspondence between different bus signals and device identifications of different devices in the target vehicle; The simulation data is generated based on the simulation bus signal and the device identification.

6. The method according to claim 5, characterized in that Generating the analog bus signal corresponding to the current push scenario includes: Generate push scene collection; Randomly obtain any one push scenario in the push scenario set to obtain the current push scenario; The analog bus signal is generated.

7. The method according to claim 1, characterized in that After receiving the push data corresponding to the current push scenario returned by the server, the method further includes: Outputting the pushed data; Receiving target feedback information corresponding to the pushed data; A test result is obtained based on the degree of matching between the target feedback information and the preset feedback information corresponding to the pushed data.

8. The method according to claim 7, characterized in that The method further comprises: In response to the test result being that the degree of matching between the target feedback information and the preset feedback information is greater than a preset degree, simulation data corresponding to another test scenario in the push scenario set other than the current push scenario is generated.

9. A data processing device, characterized in that: include: A receiving module, configured to receive simulation data corresponding to a current push scenario, wherein the simulation data is used to simulate data output by a first device in a target vehicle; an obtaining module, configured to obtain operating data of the target vehicle based on the simulation data and real-time data, wherein the real-time data is data output in real time by a second device other than the first device on the target vehicle; The interaction module is used to send the operating data of the target vehicle to the server and receive the push data corresponding to the current push scenario returned by the server.

10. A vehicle, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the data processing method according to any one of claims 1 to 4.

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