Weather type detection method, cloud server, storage medium and computer product

Abstract

The invention discloses a weather type detection method, a cloud server, a storage medium and a computer product, and relates to the technical field of vehicles, and the method specifically comprises the steps: receiving real-time weather data sent by a first vehicle, and determining first confidence data corresponding to the real-time weather data; determining a first validity result corresponding to the real-time weather data according to the first confidence data; when it is detected that the first validity result is data validity, querying a preset weather feature database according to the real-time weather data to determine a first weather type and second confidence data matched with the real-time weather data; and sending the first weather type and the second confidence data to the second vehicle, so that the second vehicle determines a target control strategy based on the first weather type and the second confidence data. By the adoption of the method and device, the situation that wrong weather types can be obtained when the sensor of the vehicle has a detection blind area can be avoided.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a weather type detection method, a cloud server, a storage medium, and a computer program product. Background Art

[0002] With the continuous development of the automobile industry, vehicles have become the preferred means of transportation for more and more users in their daily travel.

[0003] In related technologies, vehicles usually rely on their own sensors to detect the surrounding environment to determine the weather type corresponding to the area where they are located, and then determine the safety control strategy that can be adopted based on the weather type.

[0004] However, due to the limited perception range of sensors, when the vehicle is in extreme weather, the sensors are easily interfered with by the local environment, resulting in detection blind spots, causing the vehicle to identify the wrong weather type, resulting in a lag in safety control strategies and significantly increasing driving safety risks. Summary of the Invention

[0005] The main purpose of this application is to provide a weather type detection method, cloud server, storage medium and computer program product, aiming to solve the technical problem in related technologies that vehicles easily identify the wrong weather type.

[0006] To achieve the above objectives, the present application proposes a method for detecting weather types, which includes:

[0007] receiving real-time weather data sent by a first vehicle, and determining first confidence data corresponding to the real-time weather data;

[0008] Determining a first validity result corresponding to the real-time weather data according to the first confidence data;

[0009] When it is detected that the first validity result is that the data is valid, querying a preset weather feature database according to the real-time weather data to determine a first weather type and a second confidence data that the real-time weather data matches;

[0010] The first weather type and the second confidence data are sent to a second vehicle, so that the second vehicle determines a target control strategy based on the first weather type and the second confidence data.

[0011] In one embodiment, the step of determining a first validity result corresponding to the real-time weather data based on the first confidence data includes:

[0012] Obtaining a preset first confidence threshold;

[0013] When it is detected that the first confidence data reaches the first confidence threshold, determining that a first validity result corresponding to the real-time weather data is valid;

[0014] When it is detected that the first confidence data does not reach the first confidence threshold, it is determined that the first validity result corresponding to the real-time weather data is invalid data.

[0015] In one embodiment, after the step of determining a first validity result corresponding to the real-time weather data according to the first confidence data, the method further includes:

[0016] When it is detected that the first validity result is that the data is invalid, obtaining a preset second confidence threshold, wherein the second confidence threshold is less than the first confidence threshold;

[0017] In the case of detecting that the first confidence data reaches the second confidence threshold, determining a regional fence level included in the real-time weather data;

[0018] receiving a plurality of weather data to be filtered sent by a plurality of third vehicles, and filtering the plurality of weather data to be filtered according to the regional fence level to determine adjacent weather data that matches the real-time weather data;

[0019] The third confidence data and each second weather attribute data corresponding to the adjacent weather data are determined, and a second validity result of the adjacent weather data is determined based on the third confidence data and each second weather attribute data.

[0020] In one embodiment, the step of determining the second validity result of the adjacent weather data based on the third confidence data and each of the second weather attribute data includes:

[0021] Determining each first weather attribute data included in the real-time weather data;

[0022] Calculating the attribute data difference between each of the first weather attribute data and the corresponding second weather attribute data;

[0023] When it is detected that the attribute data differences are all smaller than the preset attribute difference threshold, and the third confidence data reaches the second confidence threshold, the second validity result is determined to be valid data.

[0024] In one embodiment, after the step of determining the second validity result of the adjacent weather data based on the third confidence data and each of the second weather attribute data, the method further includes:

[0025] generating target weather data based on the adjacent weather data and the real-time weather data when detecting that the second validity result is that the data is valid;

[0026] The weather characteristic database is queried according to the target weather data to determine a first weather type and second confidence data, and the first weather type and the second confidence data are sent to the second vehicle so that the second vehicle determines a target control strategy based on the first weather type and the second confidence data.

[0027] In one embodiment, after the step of querying a preset weather feature database based on the real-time weather data to determine the first weather type and the second confidence data that match the real-time weather data, the method further includes:

[0028] receiving vehicle chassis parameters sent by the second vehicle;

[0029] determining a driving state of the second vehicle according to the vehicle chassis parameters, wherein the driving state is a slipping state or a normal state;

[0030] When it is detected that the driving state is the slipping state, a target control strategy is determined according to the first weather type and the slipping state, and the target control strategy is sent to the second vehicle.

[0031] In one embodiment, the vehicle chassis parameters include a plurality of wheel end torque parameters, and the step of determining the driving state of the second vehicle based on the vehicle chassis parameters includes at least one of the following:

[0032] determining a first target torque parameter among the plurality of wheel-end torque parameters, and determining that the driving state of the second vehicle is the slipping state when it is detected that the first target torque parameter reaches a preset first torque threshold, wherein the first target torque parameter is the wheel-end torque parameter with the largest value;

[0033] dividing the plurality of wheel-end torque parameters into a plurality of torque parameter groups, and determining that the driving state is the slipping state when detecting that at least one torque parameter group reaches a preset second torque threshold, wherein the second torque threshold is greater than the first torque threshold;

[0034] When a second target torque parameter is detected among the plurality of wheel end torque parameters, the driving state is determined to be the slip state, wherein a torque direction of the second target torque parameter is opposite to a torque direction of the other wheel end torque parameters.

[0035] In one embodiment, the vehicle chassis parameters further include a plurality of wheel speed parameters, and the step of determining the driving state of the second vehicle based on the vehicle chassis parameters further includes:

[0036] Determining a target wheel speed parameter and calculating wheel speed differences corresponding to the target wheel speed parameter, wherein the target wheel speed parameter is the wheel speed parameter with the largest value;

[0037] Determining a target wheel speed difference and obtaining a preset first wheel speed difference threshold, wherein the target wheel speed difference is a wheel speed difference with a maximum value;

[0038] When it is detected that the target wheel speed difference reaches the first wheel speed difference threshold, the driving state is determined to be the slipping state.

[0039] In one embodiment, the step of determining the driving state of the second vehicle based on the vehicle chassis parameters further includes:

[0040] determining a wheel speed difference between the plurality of wheel speed parameters and determining a preset second wheel speed difference threshold, wherein the second wheel speed difference threshold is greater than the first wheel speed difference threshold;

[0041] When it is detected that at least one wheel speed difference reaches the second wheel speed difference threshold, the driving state is determined to be the slipping state.

[0042] In addition, to achieve the above-mentioned purpose, the present application also proposes a cloud server, which includes: a memory, a processor, and a computer program stored on the memory and runnable on the processor, and the computer program is configured to implement the steps of the weather type detection method as described above.

[0043] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium, and when the computer program is executed by the processor, the steps of the weather type detection method as described above are implemented.

[0044] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the weather type detection method as described above.

[0045] The weather type detection method provided in an embodiment of the present application receives real-time weather data sent by a first vehicle and determines first confidence data corresponding to the real-time weather data; determines a first validity result corresponding to the real-time weather data based on the first confidence data; when it is detected that the first validity result is valid data, queries a preset weather feature database based on the real-time weather data to determine a first weather type and second confidence data that matches the real-time weather data; and sends the first weather type and the second confidence data to a second vehicle, so that the second vehicle can determine a target control strategy based on the first weather type and the second confidence data.

[0046] In this embodiment, when a cloud server establishes a communication connection with multiple vehicles, it first receives real-time weather data transmitted by multiple first vehicles and reads first confidence data contained in the real-time weather data. The cloud server then verifies the reliability of the real-time weather data based on the first confidence data, thereby determining whether a first validity result of the real-time weather data is valid or invalid. Then, if the cloud server detects that the first validity result is valid, it queries a preset weather feature database based on the real-time weather data to determine a first weather type that matches the real-time weather data, and queries second confidence data that matches the first weather type. Finally, the cloud server sends the retrieved first weather type and second confidence data to a second vehicle, for the second vehicle to compare the second confidence data with fourth confidence data corresponding to the second weather type determined by the second vehicle. The cloud server then determines a target control strategy based on the first weather type if the fourth confidence data is detected to be less than the second confidence data, or determines a target control strategy based on the second weather type determined by the second vehicle if the fourth confidence data is detected to be greater than the second confidence data.

[0047] In this way, the present application solves the technical problem in the related art that vehicles easily identify the wrong weather type. That is, the present application obtains real-time weather data sent by the vehicle through the cloud server, and verifies the reliability of the real-time weather data based on the confidence data contained in the real-time weather data. When it is determined that the real-time weather data is reliable, the first weather type and the second confidence data matching the first weather type are queried in the preset weather feature database based on the real-time weather data. The vehicle receiving the first weather type and the second confidence data sent by the cloud server can determine the reliable weather type based on the second confidence data and the fourth confidence data corresponding to the second weather type collected by the vehicle itself, and further determine the target control strategy suitable for the current weather type, thereby avoiding the situation where the wrong weather type is obtained when the vehicle's own sensor has a detection blind spot. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] 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.

[0049] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0050] Figure 1 A flow chart of the first embodiment of the method for detecting weather type of the present application is provided;

[0051] Figure 2 This is a schematic diagram of a cloud server operating scenario involved in an embodiment of a method for detecting weather types of this application;

[0052] Figure 3 A brief flowchart of the weather type detection method for this application;

[0053] Figure 4 This is a schematic diagram of the module structure of the weather type detection device according to an embodiment of the present application;

[0054] Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the weather type detection method in the embodiment of the present application.

[0055] The purpose, features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0056] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0057] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0058] In this embodiment, for ease of description, the following description is based on a cloud server with a weather forecast subsystem internally configured, or a mobile terminal, data storage control terminal, PC or other terminal connected to an electronic control unit supporting the cloud server as the execution entity.

[0059] Based on the above-mentioned cloud server, the overall concept of the weather type detection method of this application is proposed here.

[0060] With the continuous development of the automotive industry, vehicles have become the preferred means of transportation for an increasing number of users. In related technologies, vehicles typically rely on their own sensors to detect the surrounding environment to determine the weather type corresponding to the area in which they are located, and then determine the safety control strategy that can be adopted based on the weather type. However, due to the limited perception range of sensors, when vehicles are in extreme weather, the sensors are easily interfered with by the local environment, resulting in detection blind spots, causing the vehicle to identify the wrong weather type, resulting in a lag in safety control strategies, and significantly increasing driving safety risks.

[0061] In response to the above phenomenon, the present application provides a method for detecting weather types, which includes: receiving real-time weather data sent by a first vehicle, and determining first confidence data corresponding to the real-time weather data; determining a first validity result corresponding to the real-time weather data based on the first confidence data; when it is detected that the first validity result is valid data, querying a preset weather feature database based on the real-time weather data to determine the first weather type and second confidence data matching the real-time weather data; sending the first weather type and the second confidence data to a second vehicle, so that the second vehicle can determine a target control strategy based on the first weather type and the second confidence data.

[0062] In this way, the present application solves the technical problem in the related art that vehicles easily identify the wrong weather type. That is, the present application obtains real-time weather data sent by the vehicle through the cloud server, and verifies the reliability of the real-time weather data based on the confidence data contained in the real-time weather data. When it is determined that the real-time weather data is reliable, the first weather type and the second confidence data matching the first weather type are queried in the preset weather feature database based on the real-time weather data. The vehicle receiving the first weather type and the second confidence data sent by the cloud server can determine the reliable weather type based on the second confidence data and the fourth confidence data corresponding to the second weather type collected by the vehicle itself, and further determine the target control strategy suitable for the current weather type, thereby avoiding the situation where the wrong weather type is obtained when the vehicle's own sensor has a detection blind spot.

[0063] Based on the overall concept of the weather type detection method of the present application, the embodiment of the present application provides a weather type detection method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the weather type detection method of the present application. In this embodiment, the weather type detection method includes steps S10 to S40:

[0064] Step S10: receiving real-time weather data sent by a first vehicle, and determining first confidence data corresponding to the real-time weather data;

[0065] Step S20: determining a first validity result corresponding to the real-time weather data according to the first confidence data;

[0066] It should be noted that the first confidence data is a reliability evaluation value obtained after the first vehicle evaluates the reliability of the real-time weather data when obtaining the real-time weather data. It is understandable that the first vehicle can make a comprehensive calculation through the accuracy of the attribute data contained in the weather data, the accuracy of the sensor, the regional coverage, etc. in the process of obtaining the real-time weather data. It is understandable that there are many ways to obtain the confidence data, and this application does not limit this. In addition, the first vehicle is a vehicle that can collect real-time weather data through its own configured sensors and upload the real-time weather data to the cloud server. In addition, the second vehicle is a vehicle that can collect real-time weather data through its own configured sensors and receive the first weather type issued by the cloud server. It is understandable that the first vehicle and the second vehicle are in an area with the same geo-fence level, wherein the regional fence level is the regional priority (such as street level, district level, city level) divided according to the regional fence.

[0067] In this embodiment, the cloud server first establishes communication connections with the first vehicle and the second vehicle respectively, and receives real-time weather data collected by sensors and sent by the first vehicle during driving. The cloud server inputs the acquired real-time weather data into its own configured weather forecast subsystem, and the weather forecast subsystem reads the first confidence data contained in the real-time weather data. Afterwards, the weather forecast subsystem evaluates the reliability of the real-time weather data based on the first confidence data, thereby determining the first validity result of the real-time weather data as valid data or invalid data.

[0068] Exemplarily, for example, the cloud server first establishes a communication connection with the first vehicle and the second vehicle. At this time, the first vehicle calls its own configured sensors and GNSS to obtain real-time weather data corresponding to the area where it is located during driving, and uploads the real-time weather data to the cloud server. The cloud server inputs the received real-time weather data into its own configured weather forecast subsystem, and the weather forecast subsystem reads the first confidence data contained in the real-time weather data. Afterwards, the weather forecast subsystem evaluates the validity of the real-time weather data based on the first confidence data, thereby determining that the first validity result of the real-time weather data is valid or invalid.

[0069] In addition, in this embodiment and another embodiment, the cloud server can also receive real-time weather data corresponding to the area in which the multiple first vehicles are located, obtained during driving, and upload the multiple real-time weather data to the cloud server. The cloud server inputs the received multiple real-time weather data into the weather forecast subsystem configured by itself. The weather forecast subsystem reads the first confidence data contained in each of the multiple real-time weather data, and evaluates the validity of the multiple real-time weather data based on each first confidence data, thereby determining whether the first validity result corresponding to each of the multiple real-time weather data is valid data or invalid data.

[0070] In this way, the cloud server can receive weather data uploaded by the first vehicle in the same geographic fence level area as the second vehicle for which the control strategy needs to be determined, thereby evaluating the reliability of the weather data to improve the ability to perceive extreme weather in the area where the first vehicle and the second vehicle are located.

[0071] In a feasible implementation manner, the above step S20 may specifically include steps S201 to S203:

[0072] Step S201: obtaining a preset first confidence threshold;

[0073] Step S202: when it is detected that the first confidence data reaches the first confidence threshold, determining that the first validity result corresponding to the real-time weather data is valid;

[0074] Step S203: When it is detected that the first confidence data does not reach the first confidence threshold, it is determined that the first validity result corresponding to the real-time weather data is invalid data.

[0075] In this embodiment, after reading the first confidence data contained in the real-time weather data, the weather forecast subsystem first reads the storage module configured in the cloud server to obtain a preset first confidence threshold. Afterwards, the weather forecast subsystem compares the first confidence data with the first confidence threshold, and thus, when it is detected that the first confidence data reaches the first confidence threshold, it determines that the first validity result of the real-time weather data is valid. Similarly, when the weather forecast subsystem detects that the first confidence data does not reach the first confidence threshold, it determines that the first validity result of the real-time weather data is invalid.

[0076] Exemplarily, for example, after reading the first confidence data contained in the real-time weather data, the weather forecast subsystem can also read the storage module configured in the cloud server to obtain a preset first confidence threshold, and compare the first confidence data with the first confidence threshold. Afterwards, if the weather forecast subsystem detects that the first confidence data reaches the first confidence threshold, it is determined that the first vehicle accurately evaluated the first weather attribute data in the real-time weather data when collecting the real-time weather data, thereby determining that the first validity result of the real-time weather data is valid. Similarly, if the weather forecast subsystem detects that the first confidence data does not reach the first confidence threshold A, it is determined that there is an abnormality in the evaluation of the first weather attribute data in the real-time weather data when the first vehicle collects the real-time weather data, thereby determining that the first validity result of the real-time weather data is invalid.

[0077] In this way, the cloud server can receive weather data uploaded by the first vehicle in the same geographic fence level area as the second vehicle for which the control strategy needs to be determined, thereby evaluating the reliability of the weather data to improve the ability to perceive extreme weather in the area where the first vehicle and the second vehicle are located.

[0078] Step S30: When it is detected that the first validity result is that the data is valid, querying a preset weather feature database according to the real-time weather data to determine a first weather type and a second confidence data that the real-time weather data matches;

[0079] Step S40: sending the first weather type and the second confidence data to a second vehicle, so that the second vehicle determines a target control strategy based on the first weather type and the second confidence data.

[0080] It should be noted that the second confidence data is a reliability evaluation value that characterizes the reliability of the results of the first weather type. It can be understood that each first weather type stored in the weather feature database has a preset second confidence data, that is, when the cloud server queries the weather feature database based on real-time weather data to obtain a matching first weather type, it can determine the second confidence data that matches the first weather type in the weather feature database.

[0081] In this embodiment, when the weather forecast subsystem determines that the first validity result of the acquired real-time weather data is valid, the weather forecast subsystem further queries a preset weather feature database based on the real-time weather data to determine a first weather type that matches the real-time weather data, and determines the second confidence data bound to the first weather type in the weather feature database. Finally, the weather forecast subsystem packages the first weather type and the second confidence data and sends them to the second vehicle so that the second vehicle can read the second confidence data corresponding to the first weather type, and compares the second confidence data with the fourth confidence data corresponding to the second weather type collected by the vehicle. When the second vehicle detects that the second confidence data is greater than or equal to the fourth confidence data, and the second confidence data reaches the above-mentioned first confidence threshold, the second vehicle selects a suitable target control strategy according to the first weather type detected by the cloud server, and then modifies the driving parameters according to the target control strategy.

[0082] Exemplarily, for example, when the weather forecast subsystem detects that the first validity result of the real-time weather data is valid, it further queries the preset weather feature database based on the real-time weather data, thereby screening out the target preset weather data that is consistent with the real-time weather data from the multiple preset weather data contained in the weather feature database, and then determining the preset weather type that matches the target preset weather data in the weather feature database as the first weather type that matches the real-time weather data, and determining the second confidence data bound to the first weather type in the weather feature database. Finally, the weather forecast subsystem packages the first weather type and the second confidence data and sends them to the second vehicle. At this time, the second vehicle reads the data collected by the sensors configured by itself. The cloud server receives the real-time environmental data, queries the weather feature database configured by itself to determine the second weather type and the fourth confidence data corresponding to the second weather type, and compares the second confidence data with the fourth confidence data. When it is determined that the second confidence data is higher than the fourth confidence data and the second confidence data reaches the above-mentioned first confidence threshold, the target control strategy to be executed is determined according to the first weather type sent by the cloud server, and then the driving parameters are modified according to the target control strategy. Similarly, when the second vehicle detects that the second confidence data is less than the fourth confidence data, and / or that the second confidence data is less than the above-mentioned first confidence threshold, it determines the target control strategy to be executed according to the second weather type determined by itself.

[0083] It should be noted that the fourth confidence data is the confidence data of the second weather type matched in the weather feature database after the second vehicle obtains real-time weather data and queries the weather feature database configured in the vehicle based on the real-time weather data to obtain the second weather type. It can be understood that the fourth confidence data is a reliability evaluation value that characterizes the reliability of the result of the second weather type.

[0084] In this embodiment, the cloud server first establishes communication connections with the first vehicle and the second vehicle respectively, and receives real-time weather data collected by sensors sent by the first vehicle during driving. The cloud server inputs the acquired real-time weather data into its own configured weather forecast subsystem, and the weather forecast subsystem reads the first confidence data contained in the real-time weather data. Afterwards, the weather forecast subsystem evaluates the reliability of the real-time weather data based on the first confidence data, thereby determining whether the first validity result of the real-time weather data is valid or invalid. Afterwards, the weather forecast subsystem further queries the preset weather feature data based on the real-time weather data when it determines that the first validity result of the acquired real-time weather data is valid. The cloud server detects the weather type and the second confidence data of the weather forecast subsystem, and then determines the weather forecast subsystem based on the first weather type detected by the cloud server. The cloud server detects the weather type and the second confidence data of the weather forecast subsystem, and determines the weather forecast subsystem based on the first weather type detected by the cloud server. The cloud server detects the weather type and the second confidence data of the weather forecast subsystem, and determines the weather forecast subsystem based on the first weather type detected by the cloud server. The cloud server detects the weather type and the second confidence data of the weather forecast subsystem, and determines the weather forecast subsystem based on the first weather type detected by the cloud server. The cloud server detects the weather type and the second confidence data of the weather forecast subsystem, and determines the weather forecast subsystem based on the first weather type detected by the cloud server. The cloud server detects the weather type and the second confidence data of the weather forecast subsystem based on the first weather type detected by the cloud server ...

[0085] In this way, the present application solves the technical problem in the related art that vehicles easily identify the wrong weather type. That is, the present application obtains real-time weather data sent by the vehicle through the cloud server, and verifies the reliability of the real-time weather data based on the confidence data contained in the real-time weather data. When it is determined that the real-time weather data is reliable, the first weather type and the second confidence data matching the first weather type are queried in the preset weather feature database based on the real-time weather data. The vehicle receiving the first weather type and the second confidence data sent by the cloud server can determine the reliable weather type based on the second confidence data and the fourth confidence data corresponding to the second weather type collected by the vehicle itself, and further determine the target control strategy suitable for the current weather type, thereby avoiding the situation where the wrong weather type is obtained when the vehicle's own sensor has a detection blind spot.

[0086] Based on the first embodiment of the present application, the second embodiment of the present application is proposed. In the second embodiment of the present application, the same or similar contents as those of the above embodiments can be referred to above and will not be described in detail. On this basis, after the above step S20, the weather type detection method of the present application can also include steps A10 to A40:

[0087] Step A10: When it is detected that the first validity result is invalid data, obtaining a preset second confidence threshold, wherein the second confidence threshold is smaller than the first confidence threshold;

[0088] Step A20: When it is detected that the first confidence data reaches the second confidence threshold, determining the regional fence level included in the real-time weather data;

[0089] Step A30: receiving a plurality of weather data to be filtered sent by a plurality of third vehicles, and filtering the plurality of weather data to be filtered according to the regional fence level to determine adjacent weather data that matches the real-time weather data;

[0090] Step A40: Determine the third confidence data and each second weather attribute data corresponding to the adjacent weather data, and determine the second validity result of the adjacent weather data based on the third confidence data and each second weather attribute data.

[0091] It should be noted that the adjacent weather data is weather data uploaded by a third vehicle within an adjacent geo-fenced area at the same regional fence level. Furthermore, the third confidence data is a reliability assessment value obtained by the third vehicle after evaluating the reliability of the real-time weather data when acquiring the real-time weather data. It is understood that the third vehicle can perform a comprehensive calculation based on the accuracy of the attribute data contained in the weather data, sensor accuracy, regional coverage, and other factors when acquiring the real-time weather data. It is understood that there are many ways to obtain this confidence data, and this application does not impose any restrictions thereon.

[0092] In this embodiment, when the weather forecast subsystem detects that the first validity result of the real-time weather data is invalid, it first reads the storage module to obtain a preset second confidence threshold that is less than the first confidence threshold. Then, if the weather forecast subsystem detects that the first confidence data reaches the second confidence threshold, it further determines the regional fence level corresponding to the real-time weather data. Thereafter, the weather forecast subsystem uploads the regional fence level to the cloud server. At this time, the cloud server receives the weather data to be filtered uploaded by multiple third vehicles connected to the cloud server, and filters each piece of weather data to be filtered based on the regional fence level to determine adjacent weather data that has the same regional fence level and matches the real-time weather data. Finally, the cloud server inputs the adjacent weather data into the weather forecast subsystem, which reads the third confidence data and second weather attribute data of the adjacent weather data and identifies the reliability of the adjacent weather data based on the third confidence data and the second weather attribute data to determine whether the second validity result corresponding to the second weather attribute data is valid or invalid.

[0093] For example, see Figure 2 , Figure 2 This is a schematic diagram of a cloud server operating scenario involved in an embodiment of the weather type detection method of this application, such as Figure 2 As shown, the cloud server can also establish communication connections with multiple third vehicles in other regions, and receive the weather data to be filtered uploaded by multiple third vehicles. At this time, when the weather forecast subsystem in the cloud server detects that the first validity result of the real-time weather data is invalid, it first reads the above-mentioned storage module to obtain a preset second confidence threshold that is less than the above-mentioned first confidence threshold. After that, the weather forecast subsystem compares the first confidence data with the second confidence threshold. When the weather forecast subsystem detects that the first confidence data is greater than or equal to the second confidence threshold, the weather forecast subsystem reads the local weather data corresponding to the real-time weather data. District fence level, and then, the weather forecast subsystem uploads the regional fence level to the cloud server, and the cloud server filters the multiple weather data to be filtered based on the regional fence level to determine the adjacent weather data uploaded by the third vehicle with the same regional fence level and located in the adjacent area of ​​the second vehicle, and inputs the adjacent weather data into the weather forecast subsystem. Finally, the weather forecast subsystem reads the third confidence data and the second weather attribute data contained in the adjacent weather data, and detects the reliability of the adjacent weather data according to the third confidence data and the second weather attribute data, so as to determine that the second validity result of the adjacent weather data is valid or invalid.

[0094] In this way, the cloud server can aggregate the weather data uploaded by vehicles in different regions, and thus, when it detects that the confidence level of the real-time weather data uploaded by the second vehicle is low, it can filter the adjacent weather data uploaded by the third vehicle in the adjacent area with the same geographic fence level as the second vehicle, and evaluate the reliability of the adjacent weather data. Furthermore, when it is assessed that the reliability of the adjacent weather data is high, the weather type of the area where the second vehicle is located is verified based on the weather data of the adjacent area, further avoiding the situation where the sensor in the second vehicle obtains the wrong weather type after a measurement blind spot occurs in extreme weather.

[0095] In a feasible implementation, the step of "determining the second validity result of the adjacent weather data based on the third confidence data and each of the second weather attribute data" in the above step A40 may specifically include steps A401 to A403:

[0096] Step A401: determining each first weather attribute data included in the real-time weather data;

[0097] Step A402: Calculate the attribute data difference between each of the first weather attribute data and the corresponding second weather attribute data;

[0098] Step A403: When it is detected that the attribute data differences are all smaller than the preset attribute difference threshold, and the third confidence data reaches the second confidence threshold, the second validity result is determined to be data validity.

[0099] In this embodiment, after determining the third confidence data and each second weather attribute data, the weather forecast subsystem first reads each first weather attribute data included in the real-time weather data. The weather forecast subsystem compares each first weather attribute data with its corresponding second weather attribute data to determine the attribute data difference corresponding to each first weather attribute data. Finally, the weather forecast subsystem obtains a preset attribute difference threshold and compares each attribute data difference with the attribute difference threshold. If the weather forecast subsystem detects that each attribute data difference is less than the attribute difference threshold and the third confidence data meets the second confidence threshold, the weather forecast subsystem determines that the second validity result of the adjacent weather data is valid. Similarly, if the weather forecast subsystem detects that at least one attribute data difference among the attribute data differences is greater than or equal to the attribute difference threshold and / or the third confidence data does not meet the second confidence threshold, the weather forecast subsystem determines that the second validity result of the adjacent weather data is invalid and eliminates the real-time weather data and the adjacent weather data, so that the second vehicle can determine the weather type corresponding to its own area based on its collected weather data.

[0100] Exemplarily, for example, after determining the second confidence data and each second weather attribute data, the weather forecast subsystem first reads a plurality of first weather attribute data such as the first temperature parameter and the first humidity parameter contained in the above-mentioned real-time weather data. Afterwards, the weather forecast subsystem compares the plurality of first weather attribute data with the respective corresponding second weather attribute data to determine the attribute data difference between each of the plurality of first weather attribute data and the corresponding second weather attribute data. Finally, the weather forecast subsystem reads the above-mentioned storage module to obtain a preset attribute difference threshold, and compares each attribute data difference with the attribute difference threshold respectively. The weather forecast subsystem then detects each attribute data. When the differences are all less than the attribute difference threshold and the third confidence data is greater than the second confidence threshold, the second validity result of the adjacent weather data is determined to be valid data; similarly, when the weather forecast subsystem detects that at least one attribute data difference among the attribute data differences is greater than the attribute difference threshold, and / or the third confidence data is less than or equal to the second confidence threshold, the second validity result of the adjacent weather data is determined to be invalid data. At this time, the weather forecast subsystem treats both the adjacent weather data and the above-mentioned real-time weather data as discarded data, and sends the detection result as empty to the second vehicle, so that the second vehicle can determine the weather type corresponding to its own area based on the weather data collected by itself.

[0101] In this way, the cloud server can aggregate the weather data uploaded by vehicles in different regions, and thus, when it detects that the confidence level of the real-time weather data uploaded by the second vehicle is low, it can filter the adjacent weather data uploaded by the third vehicle in the adjacent area with the same geographic fence level as the second vehicle, and evaluate the reliability of the adjacent weather data. Furthermore, when it is assessed that the reliability of the adjacent weather data is high, the weather type of the area where the second vehicle is located is verified based on the weather data of the adjacent area, further avoiding the situation where the sensor in the second vehicle obtains the wrong weather type after a measurement blind spot occurs in extreme weather.

[0102] Based on the first and / or second embodiments of the present application, a third embodiment of the present application is proposed. In the third embodiment of the present application, the same or similar contents as those of the above embodiments can be referred to above and will not be described in detail. On this basis, after the above step A40, the weather type detection method of the present application can also include steps B10 to B20:

[0103] Step B10: when it is detected that the second validity result is that the data is valid, generating target weather data based on the adjacent weather data and the real-time weather data;

[0104] Step B20: Query the weather characteristic database according to the target weather data to determine a first weather type and second confidence data, and send the first weather type and the second confidence data to the second vehicle so that the second vehicle can determine a target control strategy based on the first weather type and the second confidence data.

[0105] It should be noted that the target weather data weather forecast subsystem generates comprehensive weather data by merging real-time weather data and adjacent weather data, and includes optimized weather attribute data and confidence data.

[0106] In this embodiment, when the weather forecast subsystem detects that the second validity result of the above-mentioned adjacent weather data is valid, it fuses the above-mentioned first weather attribute data and the second attribute information to obtain the target weather attribute. At the same time, the weather forecast subsystem determines the adjusted target confidence data based on the first confidence data and the third confidence data, and integrates the target weather attribute and the target confidence data to obtain the target weather data. Finally, the weather forecast subsystem queries the preset weather feature database based on the target weather data to determine the first weather type that matches the target weather data, and determines whether the first weather type matches the target weather data in the weather feature database. Finally, the weather forecast subsystem packages the first weather type and the second confidence data and sends them to the second vehicle, so that the second vehicle can read the second confidence data corresponding to the first weather type, and compare the second confidence data with the fourth confidence data corresponding to the second weather type collected by the vehicle itself. When the second vehicle detects that the second confidence data is greater than or equal to the fourth confidence data and that the second confidence data reaches the above-mentioned first confidence threshold, it selects a suitable target control strategy according to the first weather type detected by the cloud server, and then modifies the driving parameters according to the target control strategy.

[0107] For example, when the weather forecast subsystem detects that the second validity result of the adjacent weather data is valid, it fuses the first weather attribute data with the corresponding second weather attribute data to obtain multiple target weather data. At the same time, the weather forecast subsystem performs weighting processing on the first confidence data and the third confidence data to obtain the target confidence R 合并 :

[0108] R 合并 =R α / (R β / R α ), where R α >R β , when R α When it is the first confidence data, R β is the third confidence data. Similarly, when Rα When it is the second confidence data, R β is the first confidence data;

[0109] Finally, the weather forecast subsystem converts the fused target confidence data R 合并 The target weather attribute data is integrated with the target weather attribute data to obtain the target weather data. The target weather data is then queried in a preset weather feature database to determine a first weather type that matches the target weather data and second confidence data associated with the first weather type in the weather feature database. Finally, the weather forecast subsystem packages the first weather type and the second confidence data and sends them to the second vehicle. The second vehicle then reads the real-time environmental data collected by its own sensors, queries its own weather feature database to determine the second weather type and fourth confidence data corresponding to the second weather type, and compares the second confidence data with the fourth confidence data. If the second confidence data is determined to be higher than the fourth confidence data and the second confidence data reaches the first confidence threshold, the second vehicle determines a target control strategy to be executed based on the first weather type sent by the cloud server and then modifies the driving parameters according to the target control strategy. Similarly, if the second vehicle detects that the second confidence data is lower than the fourth confidence data and / or that the second confidence data is lower than the first confidence threshold, the second vehicle determines a target control strategy to be executed based on the second weather type determined by the second vehicle.

[0110] In this way, the cloud server can perform weighted fusion of the real-time weather data uploaded by the second vehicle and the adjacent weather data uploaded by the third vehicle to obtain the target weather data after determining that the real-time weather data uploaded by the second vehicle and the adjacent weather data uploaded by the third vehicle are valid data and have relatively close reliability, thereby improving the confidence of the weather data, and verifying the weather type of the area where the second vehicle is located through the weather data of the adjacent area, so as to reduce the detection blind spot of the second vehicle and further increase the global perception capability of the cloud server in extreme weather environments.

[0111] Based on the various embodiments of the present application, a fourth embodiment of the present application is proposed. In the fourth embodiment of the present application, the same or similar contents as those of the above embodiments can be referred to above and will not be described in detail. On this basis, after the above step S30, the weather type detection method of the present application can also include steps C10 to C30:

[0112] Step C10: receiving vehicle chassis parameters sent by the second vehicle;

[0113] Step C20: determining the driving state of the second vehicle according to the vehicle chassis parameters, wherein the driving state is a slipping state or a normal state;

[0114] Step C30: When it is detected that the driving state is the slipping state, a target control strategy is determined according to the first weather type and the slipping state, and the target control strategy is sent to the second vehicle.

[0115] It should be noted that the vehicle chassis parameters are chassis-related data uploaded by the second vehicle, including wheel-end torque parameters and wheel speed parameters, which are used to characterize the contact characteristics between the tires and the road during driving. Furthermore, the slipping state occurs when insufficient friction between the vehicle's tires and the road surface causes a significant difference between the wheel speed and the actual vehicle speed. Understandably, in a slipping state, there is a high risk of vehicle loss of control.

[0116] In this embodiment, after filtering out the first weather type, the weather forecast subsystem may also first receive the vehicle chassis parameters uploaded by the second vehicle. Afterwards, the weather forecast subsystem reads the multiple wheel-end torque parameters and multiple wheel speed parameters contained in the vehicle chassis parameters, and determines whether the corresponding driving state of the second vehicle is a slipping state or a normal state based on the multiple wheel-end torque parameters and / or the multiple wheel speed parameters. Finally, when the weather forecast subsystem detects that the driving state of the second vehicle is a slipping state, it determines the target control strategy based on the filtered first weather type and the slipping state, and sends the target control strategy to the second vehicle so that the second vehicle can adjust the driving parameters according to the target control strategy.

[0117] Exemplarily, for example, after filtering out the first weather type, the weather forecast subsystem may first receive the vehicle chassis parameters uploaded by the second vehicle. Afterwards, the weather forecast subsystem reads the multiple wheel-end torque parameters and multiple wheel speed parameters contained in the vehicle chassis parameters, and judges the contact state between the vehicle's tires and the road surface based on the wheel-end torque parameters and / or wheel speed parameters, and then determines whether the vehicle's driving state is a slipping state or a normal state. Finally, when the weather forecast subsystem detects that the vehicle's driving state is a slipping state, it determines, based on the determined first weather type and slipping state, under multiple preset control strategies, a target control strategy that can enable the vehicle to adjust the chassis to a four-wheel drive mode to enhance the vehicle's grip and stability, and cope with severe weather environments such as heavy fog / dust, and then sends the target control strategy to the vehicle so that the vehicle can control the vehicle according to the target control strategy.

[0118] In this way, the cloud server can identify the weather type and detect in real time whether the vehicle is in a slipping state through the chassis parameters uploaded by the second vehicle, thereby quickly screening the control strategy in combination with the weather type to reduce the incidence of vehicle slipping accidents in extreme weather.

[0119] In a feasible implementation manner, the vehicle chassis parameters include a plurality of wheel end torque parameters, and the above step C20 includes at least one of steps C201 to C203:

[0120] Step C201: determining a first target torque parameter among the plurality of wheel-end torque parameters, and determining that the driving state of the second vehicle is the slipping state when it is detected that the first target torque parameter reaches a preset first torque threshold, wherein the first target torque parameter is the wheel-end torque parameter with the largest value;

[0121] Step C202: Dividing the plurality of wheel-end torque parameters into a plurality of torque parameter groups, and determining that the driving state is the slipping state when detecting that at least one torque parameter group reaches a preset second torque threshold, wherein the second torque threshold is greater than the first torque threshold;

[0122] Step C203: When a second target torque parameter is detected among the plurality of wheel end torque parameters, determining that the driving state is the slipping state, wherein the torque direction of the second target torque parameter is opposite to the torque direction of the other wheel end torque parameters.

[0123] In this embodiment, after obtaining the vehicle chassis parameters uploaded by the second vehicle, the weather forecast subsystem may first read multiple wheel-end torque parameters included in the vehicle chassis parameters and determine a first target torque parameter with the largest value among the multiple wheel-end torque parameters. Thereafter, when the weather forecast subsystem detects that the first target torque parameter reaches a preset first torque threshold, it determines that the driving state of the second vehicle is a slipping state.

[0124] or,

[0125] The weather forecast subsystem may also read multiple wheel-end torque parameters included in the vehicle chassis parameters and divide the multiple wheel-end torque parameters according to the wheel position information, so as to divide the wheel-end torque parameters corresponding to the wheels on the same side / level into a torque parameter group. Thereafter, the weather forecast subsystem obtains a second torque threshold value that is greater than the first torque threshold value, and compares the multiple torque parameter groups with the second torque threshold value respectively. When it is detected that at least one torque parameter group reaches the second torque threshold value, it is determined that the driving state of the second vehicle is a slipping state.

[0126] or,

[0127] The weather forecast subsystem can also read multiple wheel-end torque parameters included in the vehicle chassis parameters, and determine that the driving state of the second vehicle is a slipping state when it detects that there is a second target torque parameter among the multiple wheel-end torque parameters, the torque direction of which is opposite to the torque direction of other wheel-end torque parameters.

[0128] For example, after receiving the vehicle chassis parameters uploaded by the second vehicle, the weather forecast subsystem may first read the four wheel-end torque parameters included in the vehicle chassis parameters and determine a first target torque parameter having the largest value among the four wheel-end torque parameters. Thereafter, the weather forecast subsystem reads the above-mentioned storage module to obtain a preset first torque threshold and compares the first target torque parameter with the first torque threshold. If the weather forecast subsystem detects that the first target torque parameter is greater than or equal to the first torque threshold, the weather forecast subsystem determines a duration of the first target torque parameter and, if the weather forecast subsystem detects that the duration reaches a preset time threshold, determines that the driving state of the second vehicle is a slipping state.

[0129] Similarly,

[0130] After reading the four wheel-end torque parameters contained in the vehicle chassis parameters, the weather forecast subsystem may further divide the four wheel-end torque parameters according to the wheel position information, so as to divide the wheel-end torque parameters corresponding to the wheels on the same side into a torque parameter group. Then, the weather forecast subsystem reads the storage module to obtain a preset second torque threshold value that is greater than the first torque threshold value. The weather forecast subsystem then compares the two torque parameter groups with the second torque threshold value, respectively. Thus, when it is detected that the two wheel-end torque parameters contained in at least one torque parameter group are both higher than the second torque threshold value D, it is determined that the driving state of the second vehicle is a slipping state. Alternatively, the weather forecast subsystem may further divide the wheel-end torque parameters corresponding to the wheels in a horizontal position into a torque parameter group. Then, the weather forecast subsystem compares the two torque parameter groups with the second torque threshold value, respectively. Thus, when it is detected that the two wheel-end torque parameters contained in at least one wheel-end torque parameter group are both higher than the second torque threshold value, it is determined that the driving state of the second vehicle is a slipping state.

[0131] Similarly,

[0132] After reading the four wheel-end torque parameters contained in the vehicle chassis parameters, the weather forecast subsystem can also first detect the torque direction corresponding to each of the four wheel-end torque parameters, so as to determine that the driving state of the second vehicle is a slipping state when it is detected that there is at least one second target torque parameter with an opposite direction among the four wheel-end torque parameters.

[0133] In this way, the cloud server can identify the weather type and detect in real time whether the vehicle is in a slipping state through the chassis parameters uploaded by the second vehicle, thereby quickly screening the control strategy in combination with the weather type to reduce the incidence of vehicle slipping accidents in extreme weather.

[0134] In a feasible implementation manner, the vehicle chassis parameters further include a plurality of wheel speed parameters, and the above step C20 may further include steps C204 to C205:

[0135] Step C204: determining a target wheel speed parameter, and calculating wheel speed differences corresponding to the target wheel speed parameter, wherein the target wheel speed parameter is the wheel speed parameter with the largest value;

[0136] Step C205: determining a target wheel speed difference and obtaining a preset first wheel speed difference threshold, wherein the target wheel speed difference is the wheel speed difference with the largest value;

[0137] Step C206: When it is detected that the target wheel speed difference reaches the first wheel speed difference threshold, determining that the driving state is the slipping state.

[0138] In this embodiment, after obtaining the vehicle chassis parameters uploaded by the second vehicle, the weather forecast subsystem can first read the multiple wheel speed parameters contained in the vehicle chassis parameters, and determine the target wheel speed parameter with the largest value among the multiple wheel speed parameters. The weather forecast subsystem then calculates the wheel speed differences generated between the target wheel speed parameter and other wheel speed parameters. Afterwards, the weather forecast subsystem determines the target wheel speed difference with the largest value among the wheel speed differences. At the same time, the weather forecast subsystem reads the storage module to obtain a preset first wheel speed difference threshold. Finally, when the weather forecast subsystem detects that the target wheel speed difference reaches the first wheel speed difference threshold, it determines that the driving state of the second vehicle is a slipping state.

[0139] For example, after obtaining the vehicle chassis parameters uploaded by the second vehicle, the weather forecast subsystem may first read the wheel speed parameters corresponding to each of the four tires included in the vehicle chassis parameters, and select the target wheel speed parameter with the largest value from among the four wheel speed parameters. The weather forecast subsystem then calculates the wheel speed difference between the target wheel speed parameter and the other wheel speed parameters. The weather forecast subsystem then determines the target wheel speed parameter with the largest value from among the wheel speed differences, and reads the aforementioned storage module to obtain a preset first wheel speed difference threshold. Finally, the weather forecast subsystem compares the target wheel speed difference with the first wheel speed difference threshold. If the target wheel speed difference is detected to be greater than the first wheel speed difference threshold, the weather forecast subsystem determines a duration corresponding to the target wheel speed difference. If the weather forecast subsystem determines that the duration reaches a preset time threshold, the weather forecast subsystem determines that the driving state of the second vehicle is a slipping state.

[0140] In this way, the cloud server can identify the weather type and detect in real time whether the vehicle is in a slipping state through the chassis parameters uploaded by the second vehicle, thereby quickly screening the control strategy in combination with the weather type to reduce the incidence of vehicle slipping accidents in extreme weather.

[0141] In a feasible implementation manner, the above step C20 may further include steps C207 to C208:

[0142] Step C207: determining wheel speed differences between the plurality of wheel speed parameters, and determining a preset second wheel speed difference threshold, wherein the second wheel speed difference threshold is greater than the first wheel speed difference threshold;

[0143] Step C208: When it is detected that at least one wheel speed difference reaches the second wheel speed difference threshold, determining that the driving state is the slipping state.

[0144] In this embodiment, after obtaining the vehicle chassis parameters uploaded by the second vehicle, the weather forecast subsystem can first read the multiple wheel speed parameters contained in the vehicle chassis parameters, and calculate the wheel speed difference generated between the multiple wheel speed parameters. At the same time, the weather forecast subsystem obtains a second wheel speed difference threshold that is preset to be greater than the above-mentioned first wheel speed difference threshold, and compares each wheel speed difference with the second wheel speed difference threshold respectively. Afterwards, when the weather forecast subsystem detects that at least one wheel speed difference among the wheel speed differences reaches the second wheel speed difference threshold, it determines that the driving state of the second vehicle at this time is a slipping state.

[0145] Exemplarily, for example, after obtaining the vehicle chassis parameters uploaded by the second vehicle, the weather forecast subsystem can first read the wheel speed parameters corresponding to each of the four tires contained in the vehicle chassis parameters, and calculate the four wheel speed parameters to obtain the wheel speed difference between the tires on the same side / at the same level. At the same time, the weather forecast subsystem reads the above-mentioned storage module to obtain a second wheel speed difference threshold that is greater than the first wheel speed difference threshold. Afterwards, the weather forecast subsystem compares each wheel speed difference with the second wheel speed difference threshold respectively, and thus determines that the driving state of the second vehicle is a slipping state when it is detected that at least one wheel speed difference reaches the second wheel speed difference threshold.

[0146] In this way, the cloud server can identify the weather type and detect in real time whether the vehicle is in a slipping state through the chassis parameters uploaded by the second vehicle, thereby quickly screening the control strategy in combination with the weather type to reduce the incidence of vehicle slipping accidents in extreme weather.

[0147] For example, to help understand the implementation process of the weather type detection method obtained by combining this embodiment with the above embodiments, please refer to Figure 3 , Figure 3 This is a brief flowchart of the weather type detection method of this application, specifically:

[0148] In this embodiment, the cloud server first establishes communication connections with the first vehicle and the second vehicle, respectively. While driving, the first vehicle collects weather attribute data corresponding to the region in which it is located through its own sensors and assigns first confidence data to the weather attribute data. Simultaneously, the first vehicle determines its own location information and the regional fence level corresponding to the location information through its own positioning system. The first vehicle then integrates the weather attribute data, the first confidence data, the regional fence level, and the location information to obtain real-time weather data, and uploads the real-time weather data to the cloud server.

[0149] The cloud server receives the real-time weather data uploaded by the first vehicle and reads the first confidence data and each first weather attribute data contained in the real-time weather data. The cloud server then compares the first confidence data with a preset first confidence threshold. When detecting that the first confidence data reaches the first confidence threshold, the cloud server directly queries a preset weather feature database based on the real-time weather data to determine a first weather type that matches the real-time weather data, and determines second confidence data that matches the first weather type in the weather feature database. The cloud server packages the first weather type and the second confidence data and sends the packaged first weather type and the second confidence data to the second vehicle, so that the second vehicle compares the second confidence data with fourth confidence data corresponding to the second weather type determined by the second vehicle. When the second vehicle detects that the second confidence data is greater than the fourth confidence data and that the second confidence data reaches the preset first confidence threshold, the cloud server determines a matching target control strategy based on the first weather type.

[0150] Similarly, when the cloud server detects that the first confidence data does not reach the first confidence threshold, it further obtains a second confidence threshold that is less than the first confidence threshold, and when it detects that the first confidence data reaches the second confidence threshold, based on the regional fence level and location information in the real-time weather data, it filters out adjacent weather data uploaded by third vehicles in adjacent areas at the same geographic fence level from multiple weather data to be filtered uploaded by multiple third vehicles, and reads the second weather attribute data and the third confidence data contained in the adjacent weather data. When it detects that the third confidence data reaches the second confidence threshold, the cloud server fuses the real-time weather data and the adjacent weather data to obtain target weather data, and queries a preset weather feature database based on the target weather data to determine a first weather type that matches the real-time weather data.

[0151] At this time, the cloud server receives the vehicle chassis parameters uploaded by the second vehicle, and reads multiple wheel-end torque parameters / wheel speed parameters contained in the vehicle chassis parameters. The cloud server then detects whether the driving state of the second vehicle is in a slipping state or a normal state based on the multiple wheel-end torque parameters / wheel speed parameters. Finally, when the cloud server detects that the driving state of the second vehicle is in a slipping state, it directly determines the target control strategy based on the filtered first weather type and slipping state, and sends the target control strategy to the second vehicle so that the vehicle can adjust the driving parameters according to the target control strategy.

[0152] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the weather type detection method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0153] This application also provides a weather type detection device, please refer to Figure 4 , the device comprises:

[0154] The data receiving module 10 is configured to receive real-time weather data sent by a first vehicle and determine first confidence data corresponding to the real-time weather data;

[0155] a reliability verification module 20, configured to determine a first validity result corresponding to the real-time weather data based on the first confidence data;

[0156] a type identification module 30 for, when detecting that the first validity result is that the data is valid, querying a preset weather feature database according to the real-time weather data to determine a first weather type and second confidence data that the real-time weather data matches;

[0157] The data sending module 40 is configured to send the first weather type and the second confidence data to a second vehicle, so that the second vehicle can determine a target control strategy based on the first weather type and the second confidence data.

[0158] In a feasible implementation manner, the reliability verification module 20 is further configured to:

[0159] Obtaining a preset first confidence threshold;

[0160] When it is detected that the first confidence data reaches the first confidence threshold, determining that a first validity result corresponding to the real-time weather data is valid;

[0161] When it is detected that the first confidence data does not reach the first confidence threshold, it is determined that the first validity result corresponding to the real-time weather data is invalid data.

[0162] In a feasible implementation manner, the reliability verification module 20 is further configured to:

[0163] When it is detected that the first validity result is that the data is invalid, obtaining a preset second confidence threshold, wherein the second confidence threshold is less than the first confidence threshold;

[0164] In the case of detecting that the first confidence data reaches the second confidence threshold, determining a regional fence level included in the real-time weather data;

[0165] receiving a plurality of weather data to be filtered sent by a plurality of third vehicles, and filtering the plurality of weather data to be filtered according to the regional fence level to determine adjacent weather data that matches the real-time weather data;

[0166] The third confidence data and each second weather attribute data corresponding to the adjacent weather data are determined, and a second validity result of the adjacent weather data is determined based on the third confidence data and each second weather attribute data.

[0167] In a feasible implementation manner, the reliability verification module 20 is further configured to:

[0168] Determining each first weather attribute data included in the real-time weather data;

[0169] Calculating the attribute data difference between each of the first weather attribute data and the corresponding second weather attribute data;

[0170] When it is detected that the attribute data differences are all smaller than the preset attribute difference threshold, and the third confidence data reaches the second confidence threshold, the second validity result is determined to be valid data.

[0171] In a feasible implementation manner, the type identification module 30 is further configured to:

[0172] generating target weather data based on the adjacent weather data and the real-time weather data when detecting that the second validity result is that the data is valid;

[0173] The weather characteristic database is queried according to the target weather data to determine a first weather type and second confidence data, and the first weather type and the second confidence data are sent to the second vehicle so that the second vehicle determines a target control strategy based on the first weather type and the second confidence data.

[0174] In a feasible implementation manner, the type identification module 30 is further configured to:

[0175] receiving vehicle chassis parameters sent by the second vehicle;

[0176] determining a driving state of the second vehicle according to the vehicle chassis parameters, wherein the driving state is a slipping state or a normal state;

[0177] When it is detected that the driving state is the slipping state, a target control strategy is determined according to the first weather type and the slipping state, and the target control strategy is sent to the second vehicle.

[0178] In a feasible implementation manner, the vehicle chassis parameters include multiple wheel end torque parameters, and the type identification module 30 is further configured to:

[0179] determining a first target torque parameter among the plurality of wheel-end torque parameters, and determining that the driving state of the second vehicle is the slipping state when it is detected that the first target torque parameter reaches a preset first torque threshold, wherein the first target torque parameter is the wheel-end torque parameter with the largest value;

[0180] dividing the plurality of wheel-end torque parameters into a plurality of torque parameter groups, and determining that the driving state is the slipping state when detecting that at least one torque parameter group reaches a preset second torque threshold, wherein the second torque threshold is greater than the first torque threshold;

[0181] When a second target torque parameter is detected among the plurality of wheel end torque parameters, the driving state is determined to be the slip state, wherein a torque direction of the second target torque parameter is opposite to a torque direction of the other wheel end torque parameters.

[0182] In a feasible implementation manner, the vehicle chassis parameters further include a plurality of wheel speed parameters, and the type identification module 30 is further configured to:

[0183] Determining a target wheel speed parameter and calculating wheel speed differences corresponding to the target wheel speed parameter, wherein the target wheel speed parameter is the wheel speed parameter with the largest value;

[0184] Determining a target wheel speed difference and obtaining a preset first wheel speed difference threshold, wherein the target wheel speed difference is a wheel speed difference with a maximum value;

[0185] When it is detected that the target wheel speed difference reaches the first wheel speed difference threshold, the driving state is determined to be the slipping state.

[0186] In a feasible implementation manner, the type identification module 30 is further configured to:

[0187] determining a wheel speed difference between the plurality of wheel speed parameters and determining a preset second wheel speed difference threshold, wherein the second wheel speed difference threshold is greater than the first wheel speed difference threshold;

[0188] When it is detected that at least one wheel speed difference reaches the second wheel speed difference threshold, the driving state is determined to be the slipping state.

[0189] The weather type detection device provided in this application, which utilizes the weather type detection method described in the aforementioned embodiments, can resolve the technical issue in related art where vehicles are prone to incorrectly identifying the weather type. Compared to the prior art, the beneficial effects of the weather type detection device provided in this application are the same as those of the weather type detection method described in the aforementioned embodiments. Other technical features of the weather type detection device are the same as those disclosed in the aforementioned embodiments and are not further elaborated here.

[0190] The present application provides a cloud server, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the weather type detection method in the above-mentioned embodiment one.

[0191] Reference below Figure 5 , which shows a schematic diagram of the structure of a cloud server suitable for implementing the embodiments of the present application. The cloud server in the embodiments of the present application may include, but is not limited to, a cloud server with an internal weather forecast subsystem, or a mobile terminal, data storage control terminal, PC, or other terminal connected to an electronic control unit supporting the cloud server. Figure 5 The cloud server shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0192] like Figure 5As shown, the cloud server may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory 1002 or programs loaded from a storage device 1003 into a random access memory 1004. Random access memory 1004 also stores various programs and data required for the operation of the cloud server. The processing device 1001, the read-only memory 1002, and the random access memory 1004 are connected to each other via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems may be connected to the input / output interface 1006: an input device 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, hard disk, etc.; and a communication device 1009. The communication device 1009 may allow the cloud server to communicate with other devices wirelessly or wired to exchange data. Although the figures show a cloud server with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have instead.

[0193] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a read-only memory 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are performed.

[0194] The cloud server provided by this application, employing the weather type detection method described in the aforementioned embodiment, can resolve the technical issue in related art where vehicles are prone to incorrectly identifying the weather type. Compared to the prior art, the cloud server provided by this application achieves the same beneficial effects as the weather type detection method described in the aforementioned embodiment. Other technical features of this cloud server are the same as those disclosed in the aforementioned embodiment and are not further elaborated here.

[0195] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0196] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0197] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, a computer program) stored thereon, and the computer-readable program instructions are used to execute the weather type detection method in the above embodiment.

[0198] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0199] The computer-readable storage medium may be included in the cloud server, or may exist independently without being installed in the cloud server.

[0200] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the cloud server, the cloud server: receives real-time weather data sent by the first vehicle, and determines first confidence data corresponding to the real-time weather data; determines a first validity result corresponding to the real-time weather data based on the first confidence data; when it is detected that the first validity result is valid data, queries a preset weather feature database based on the real-time weather data to determine the first weather type and second confidence data matching the real-time weather data; sends the first weather type and the second confidence data to the second vehicle, so that the second vehicle can determine a target control strategy based on the first weather type and the second confidence data.

[0201] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0202] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0203] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0204] The computer-readable storage medium provided in this application stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned weather type detection method. This computer-readable storage medium can address the technical issue in related art where vehicles are prone to incorrectly identifying weather types. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the weather type detection method provided in the aforementioned embodiment, and are not further elaborated here.

[0205] The present application also provides a computer program product, comprising a computer program, which implements the steps of the above-mentioned weather type detection method when executed by a processor.

[0206] The computer program product provided in this application can resolve the technical problem in related art where vehicles are prone to incorrectly identifying weather types. Compared to the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the weather type detection method provided in the above-mentioned embodiment, and will not be further elaborated here.

[0207] The above description is only part of the embodiments of the present application and does not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for detecting weather type, characterized in that: The method for detecting the weather type includes: receiving real-time weather data sent by a first vehicle, and determining first confidence data corresponding to the real-time weather data; Determining a first validity result corresponding to the real-time weather data according to the first confidence data; When it is detected that the first validity result is that the data is valid, querying a preset weather feature database according to the real-time weather data to determine a first weather type and a second confidence data that the real-time weather data matches; The first weather type and the second confidence data are sent to a second vehicle, so that the second vehicle determines a target control strategy based on the first weather type and the second confidence data.

2. The method for detecting weather type according to claim 1, wherein: The step of determining a first validity result corresponding to the real-time weather data according to the first confidence data includes: Obtaining a preset first confidence threshold; When it is detected that the first confidence data reaches the first confidence threshold, determining that a first validity result corresponding to the real-time weather data is valid; When it is detected that the first confidence data does not reach the first confidence threshold, it is determined that the first validity result corresponding to the real-time weather data is invalid data.

3. The method for detecting weather type according to claim 1, wherein: After the step of determining a first validity result corresponding to the real-time weather data according to the first confidence data, the method further includes: When it is detected that the first validity result is that the data is invalid, obtaining a preset second confidence threshold, wherein the second confidence threshold is less than the first confidence threshold; In the case of detecting that the first confidence data reaches the second confidence threshold, determining a regional fence level included in the real-time weather data; receiving a plurality of weather data to be filtered sent by a plurality of third vehicles, and filtering the plurality of weather data to be filtered according to the regional fence level to determine adjacent weather data that matches the real-time weather data; The third confidence data and each second weather attribute data corresponding to the adjacent weather data are determined, and a second validity result of the adjacent weather data is determined based on the third confidence data and each second weather attribute data.

4. The method for detecting weather type according to claim 3, wherein: The step of determining the second validity result of the adjacent weather data based on the third confidence data and each of the second weather attribute data includes: Determining each first weather attribute data included in the real-time weather data; Calculating the attribute data difference between each of the first weather attribute data and the corresponding second weather attribute data; When it is detected that the attribute data differences are all smaller than the preset attribute difference threshold, and the third confidence data reaches the second confidence threshold, the second validity result is determined to be valid data.

5. The method for detecting weather type according to claim 3, wherein: After the step of determining the second validity result of the adjacent weather data based on the third confidence data and each of the second weather attribute data, the method further includes: generating target weather data based on the adjacent weather data and the real-time weather data when detecting that the second validity result is that the data is valid; The weather characteristic database is queried according to the target weather data to determine a first weather type and second confidence data, and the first weather type and the second confidence data are sent to the second vehicle so that the second vehicle determines a target control strategy based on the first weather type and the second confidence data.

6. The method for detecting weather type according to claim 1, wherein: After the step of querying a preset weather feature database according to the real-time weather data to determine the first weather type and the second confidence data that match the real-time weather data, the method further includes: receiving vehicle chassis parameters sent by the second vehicle; determining a driving state of the second vehicle according to the vehicle chassis parameters, wherein the driving state is a slipping state or a normal state; When it is detected that the driving state is the slipping state, a target control strategy is determined according to the first weather type and the slipping state, and the target control strategy is sent to the second vehicle.

7. The method for detecting weather type according to claim 6, wherein: The vehicle chassis parameters include a plurality of wheel end torque parameters, and the step of determining the driving state of the second vehicle based on the vehicle chassis parameters includes at least one of the following: determining a first target torque parameter among the plurality of wheel-end torque parameters, and determining that the driving state of the second vehicle is the slipping state when it is detected that the first target torque parameter reaches a preset first torque threshold, wherein the first target torque parameter is the wheel-end torque parameter with the largest value; dividing the plurality of wheel-end torque parameters into a plurality of torque parameter groups, and determining that the driving state is the slipping state when detecting that at least one torque parameter group reaches a preset second torque threshold, wherein the second torque threshold is greater than the first torque threshold; When a second target torque parameter is detected among the plurality of wheel end torque parameters, the driving state is determined to be the slip state, wherein a torque direction of the second target torque parameter is opposite to a torque direction of the other wheel end torque parameters.

8. The method for detecting weather type according to claim 6, wherein: The vehicle chassis parameters further include a plurality of wheel speed parameters, and the step of determining the driving state of the second vehicle based on the vehicle chassis parameters further includes: Determining a target wheel speed parameter and calculating wheel speed differences corresponding to the target wheel speed parameter, wherein the target wheel speed parameter is the wheel speed parameter with the largest value; Determining a target wheel speed difference and obtaining a preset first wheel speed difference threshold, wherein the target wheel speed difference is a wheel speed difference with a maximum value; When it is detected that the target wheel speed difference reaches the first wheel speed difference threshold, the driving state is determined to be the slipping state.

9. The weather type detection method according to claim 8, wherein the step of determining the driving state of the second vehicle based on the vehicle chassis parameters further comprises: determining a wheel speed difference between the plurality of wheel speed parameters and determining a preset second wheel speed difference threshold, wherein the second wheel speed difference threshold is greater than the first wheel speed difference threshold; When it is detected that at least one wheel speed difference reaches the second wheel speed difference threshold, the driving state is determined to be the slipping state.

10. A cloud server, characterized in that: The cloud server includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the weather type detection method according to any one of claims 1 to 9.

11. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the weather type detection method according to any one of claims 1 to 9 are implemented.

12. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the weather type detection method according to any one of claims 1 to 9 are implemented.

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