Wetland mode recommendation method, electronic equipment, storage medium and system

Abstract

The invention relates to the technical field of intelligent driving, particularly provides a wetland mode recommendation method, electronic equipment, a storage medium and a system, and aims to solve the technical problems that the reaching rate of vehicle wetland mode switching is low, and user experience and driving safety are affected. In order to achieve the purpose, the method comprises the steps that basic weather information, weather flow data and short temporary rainfall flow data of each area are acquired; acquiring comprehensive weather information of each region based on the weather flow data and the short temporary rainfall flow data; based on the basic weather information and the comprehensive weather information, the water accumulation amount of each area is obtained; and determining a wetland mode recommendation state of the driving equipment in the region based on the water accumulation amount of each region. Through the above implementation mode, multi-source data can be fused to calculate the water accumulation amount of each area, so that the wetland mode recommendation state better fits an actual driving scene, improvement of the reaching rate of wetland mode switching is facilitated, driving of a user on a wet and slippery road surface in rainy and snowy days is guaranteed, and user experience and driving safety are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent driving, in particular to a wet mode recommendation method, an electronic device, a storage medium and a system. BACKGROUND

[0002] With the development of intelligent driving, the driving safety of vehicles in complex weather conditions is increasingly valued. To deal with wet and slippery road surface scenarios such as rainy and snowy weather, vehicles are usually equipped with a wet mode, which adjusts parameters such as power output and braking system to reduce the risk of slipping and improve driving stability.

[0003] However, the current wet mode switching of vehicles has a low reach rate. When driving in rainy and snowy weather, it is difficult to switch to the wet mode in time to improve driving safety, which increases the probability of slipping during driving and affects user experience and driving safety.

[0004] Correspondingly, there is a need in the art for a new technical solution to solve the above problems. SUMMARY

[0005] In order to overcome the above defects, the present application is proposed to provide a wet mode recommendation method, an electronic device, a storage medium and a system to solve or at least partially solve the technical problem of low reach rate of vehicle wet mode switching, affecting user experience and driving safety.

[0006] In a first aspect, the present application provides a wet mode recommendation method, comprising:

[0007] obtaining basic weather information, weather flow data and short-term precipitation flow data of each region;

[0008] obtaining comprehensive weather information of each region based on the weather flow data and the short-term precipitation flow data;

[0009] obtaining the amount of water accumulation in each region based on the basic weather information and the comprehensive weather information;

[0010] determining the wet mode recommendation state of the driving device in the region based on the amount of water accumulation in each region

[0011] In one technical solution of the above wet mode recommendation method, the basic weather information includes a basic water amount level; and the obtaining of the basic weather information of each region comprises:

[0012] obtaining wiper data and positioning data of each driving device based on a preset time window;

[0013] processing the wiper data to obtain the number of wiper times of each driving device in the preset time window;

[0014] obtain a high-speed wiper request number and a total wiper number in each region based on the preset grid division information, the wiper data and the positioning data;

[0015] obtain a basic water amount level of each region based on the high-speed wiper request number and the total wiper number.

[0016] In one of the technical solutions of the wetland mode recommendation method, the obtaining of the basic water amount level of each region based on the high-speed wiper request number and the total wiper number comprises:

[0017] determining a wiper scene type based on the high-speed wiper request number; the wiper scene type comprises a low-speed wiper dominant scene or a high-speed wiper dominant scene;

[0018] dividing the basic water amount level based on the wiper scene type and the total wiper number.

[0019] In one of the technical solutions of the wetland mode recommendation method, the determining of the wiper scene type based on the high-speed wiper request number comprises:

[0020] determining whether the high-speed wiper request number is less than a preset request threshold;

[0021] if yes, determining that the wiper scene type is the low-speed wiper dominant scene; otherwise, determining that the wiper scene type is the high-speed wiper dominant scene.

[0022] In one of the technical solutions of the wetland mode recommendation method, the processing of the wiper data comprises:

[0023] performing filtering processing on the wiper data to screen out wiper data meeting an abnormal weather scene;

[0024] obtaining driving data and a corresponding precipitation speed of each driving device; the driving data at least comprises a vehicle speed;

[0025] performing normalization processing on the screened wiper data based on the driving data and the precipitation speed.

[0026] In one of the technical solutions of the wetland mode recommendation method, the obtaining of the weather flow data of each region comprises:

[0027] obtaining weather data;

[0028] determining whether the basic weather information exists in each region based on the weather data and a region ID, and setting a first time threshold according to a determination result; determining whether the weather data of each region is fresh based on the first time threshold;

[0029] If yes, weather flow data of the region is obtained based on the weather data; otherwise, real-time weather data is requested, and the weather data and the real-time weather data are processed to obtain the weather flow data of the region.

[0030] In one of the technical solutions of the wetland mode recommendation method, the obtaining of the short-time precipitation flow data of each region comprises:

[0031] Obtaining short-time precipitation data;

[0032] Based on the short-time precipitation data and the region ID, it is determined whether the basic weather information exists in each region, and a second time threshold is set according to the determination result;

[0033] If yes, short-time precipitation flow data of the region is obtained based on the short-time precipitation data; otherwise, real-time short-time precipitation data is requested, and short-time precipitation flow data of the region is obtained based on the short-time precipitation data and the real-time short-time precipitation data.

[0034] In one of the technical solutions of the wetland mode recommendation method, the obtaining of the short-time precipitation flow data of each region comprises:

[0035] Obtaining a first timestamp of the weather flow data and a second timestamp of the short-time precipitation flow data of each region;

[0036] Matching the weather flow data and the short-time precipitation flow data based on the first timestamp and the second timestamp;

[0037] If the first timestamp and the second timestamp are the same, precipitation intensity is obtained based on the short-time precipitation flow data, and comprehensive weather information of each region is calculated according to the precipitation intensity.

[0038] In one of the technical solutions of the wetland mode recommendation method, the obtaining of the short-time precipitation flow data of each region comprises:

[0039] Obtaining a timestamp difference value of the basic weather information and the comprehensive weather information of each region;

[0040] Determining whether the timestamp difference value is within a preset threshold interval;

[0041] If yes, the water accumulation of each region is calculated based on the basic weather information and the comprehensive weather information.

[0042] In one of the technical solutions of the wetland mode recommendation method, the obtaining of the short-time precipitation flow data of each region comprises:

[0043] acquire a weight parameter based on the number of driving devices in each region;

[0044] calculate a total water amount of each region based on the basic weather information, the comprehensive weather information and the weight parameter;

[0045] perform time decay filtering processing on the total water amount to obtain a waterlogging amount of each region.

[0046] In one of the technical solutions of the wetland mode recommendation method, the time decay filtering processing on the total water amount to obtain a waterlogging amount of each region comprises:

[0047] acquire a last waterlogging level and a record time based on historical data;

[0048] determine whether the last waterlogging level is within a valid time range and environmental parameters are complete; the environmental parameters comprise at least one of temperature, humidity and weather;

[0049] if yes, acquire a time difference value between a current time and the record time, and determine a time decay coefficient based on the environmental parameters; the time decay coefficient comprises a waterlogging rising time constant or a waterlogging falling time constant;

[0050] calculate the waterlogging amount of each region based on a size relationship between the total water amount and the last waterlogging level, and the time difference value, the time decay coefficient.

[0051] In one of the technical solutions of the wetland mode recommendation method, the waterlogging amount comprises a waterlogging level; and the determination of the wetland mode recommendation state of the driving device in the region based on the waterlogging amount of each region comprises:

[0052] when the waterlogging level is less than or equal to a first threshold value, determining that the wetland mode recommendation state of the driving device in the region is a closed wetland mode;

[0053] when the waterlogging level is greater than or equal to a second threshold value, determining that the wetland mode recommendation state of the driving device in the region is an open wetland mode;

[0054] when the waterlogging level is greater than the first threshold value and less than the second threshold value, determining that the wetland mode recommendation state of the driving device in the region is to maintain a current driving mode;

[0055] wherein the first threshold value is less than the second threshold value.

[0056] In one of the technical solutions of the wetland mode recommendation method, the method further comprises:

[0057] When the water accumulation level is greater than or equal to the second threshold value and the driving mode of the driving device in the region is not the wetland mode, a driving mode switching reminder is sent to the driving device, or the driving device is controlled to switch the driving mode to the wetland mode.

[0058] In one of the technical solutions of the wetland mode recommendation method described above, the method further comprises:

[0059] establishing a wetland map based on the water accumulation amount to display the water accumulation amount of each region;

[0060] The wetland map at least includes a weather layer and a water accumulation layer.

[0061] In a second aspect, the present application provides an electronic device, which comprises a processor and a memory, the memory is adapted to store a plurality of program codes, the program codes are adapted to be loaded and run by the processor to execute the wetland mode recommendation method in any one of the technical solutions of the wetland mode recommendation method described above.

[0062] In a third aspect, the present application provides a computer readable storage medium, which stores a plurality of program codes, the program codes are adapted to be loaded and run by a processor to execute the wetland mode recommendation method in any one of the technical solutions of the wetland mode recommendation method described above.

[0063] In a fourth aspect, the present application provides a wetland mode recommendation system, which comprises a cloud server and a plurality of driving devices in communication connection with the cloud server; wherein,

[0064] The cloud server comprises the electronic device in the technical solution of the electronic device described above;

[0065] The plurality of driving devices are configured to send wiper data and positioning data to the cloud server based on a preset time window.

[0066] Scheme 1. A wetland mode recommendation method, characterized in that the method comprises:

[0067] obtaining basic weather information, weather flow data and short-impromptu precipitation flow data of each region;

[0068] obtaining comprehensive weather information of each region based on the weather flow data and the short-impromptu precipitation flow data;

[0069] obtaining the water accumulation amount of each region based on the basic weather information and the comprehensive weather information;

[0070] determining the wetland mode recommendation state of the driving device in the region based on the water accumulation amount of each region.

[0071] The method of wetland mode recommendation according to the scheme 1, characterized in that the basic weather information comprises a basic water level; and the obtaining of the basic weather information of each region comprises:

[0072] Based on a preset time window, obtain the wiper data and positioning data of each driving device;

[0073] Process the wiper data to obtain the number of wiper times of each driving device within the preset time window;

[0074] Based on preset grid division information, the wiper data and the positioning data, obtain the number of high-speed wiper requests and the total number of wiper times in each region;

[0075] Based on the number of high-speed wiper requests and the total number of wiper times, obtain the basic water level of each region.

[0076] The method of wetland mode recommendation according to the scheme 2, characterized in that the obtaining of the basic water level of each region based on the number of high-speed wiper requests and the total number of wiper times comprises:

[0077] Determine the wiper scene type based on the number of high-speed wiper requests; the wiper scene type comprises a low-speed wiper dominant scene or a high-speed wiper dominant scene;

[0078] Divide the basic water level based on the wiper scene type and the total number of wiper times.

[0079] The method of wetland mode recommendation according to the scheme 3, characterized in that the determination of the wiper scene type based on the number of high-speed wiper requests comprises:

[0080] Determine whether the number of high-speed wiper requests is less than a preset request threshold;

[0081] If yes, determine that the wiper scene type is the low-speed wiper dominant scene; otherwise, determine that the wiper scene type is the high-speed wiper dominant scene.

[0082] The method of wetland mode recommendation according to the scheme 2, characterized in that the processing of the wiper data comprises:

[0083] Filter the wiper data to screen out wiper data meeting an abnormal weather scene;

[0084] Obtain the driving data and corresponding precipitation speed of each driving device; the driving data at least comprises a vehicle speed;

[0085] Based on the driving data and the precipitation speed, perform normalization processing on the screened wiper data.

[0086] Scheme 6. The wetland mode recommendation method according to scheme 2, characterized in that the obtaining of the weather flow data of each region comprises:

[0087] obtaining weather data;

[0088] judging whether the basic weather information exists for each region based on the weather data and the region ID, and setting a first time threshold according to the judgment result;

[0089] determining whether the weather data of each region is fresh based on the first time threshold;

[0090] if yes, obtaining the weather flow data of the region based on the weather data; otherwise, requesting real-time weather data, and performing merging processing on the weather data and the real-time weather data to obtain the weather flow data of the region.

[0091] Scheme 7. The wetland mode recommendation method according to scheme 2, characterized in that the obtaining of the short-term precipitation flow data of each region comprises:

[0092] obtaining short-term precipitation data;

[0093] judging whether the basic weather information exists for each region based on the short-term precipitation data and the region ID, and setting a second time threshold according to the judgment result;

[0094] determining whether the short-term precipitation data of each region is fresh based on the second time threshold;

[0095] if yes, obtaining the short-term precipitation flow data of the region based on the short-term precipitation data; otherwise, requesting real-time short-term precipitation data, and obtaining the short-term precipitation flow data of the region based on the short-term precipitation data and the real-time short-term precipitation data.

[0096] Scheme 8. The wetland mode recommendation method according to scheme 1, characterized in that the obtaining of the comprehensive weather information of each region based on the weather flow data and the short-term precipitation flow data comprises:

[0097] obtaining a first time stamp of the weather flow data and a second time stamp of the short-term precipitation flow data of each region;

[0098] matching the weather flow data and the short-term precipitation flow data based on the first time stamp and the second time stamp;

[0099] if the first time stamp and the second time stamp are the same, obtaining precipitation intensity based on the short-term precipitation flow data, and calculating the comprehensive weather information of each region according to the precipitation intensity.

[0100] Scheme 9. The wetland mode recommendation method according to scheme 1, wherein the obtaining of the water accumulation in each region based on the basic weather information and the comprehensive weather information comprises:

[0101] obtaining a timestamp difference value of the basic weather information and the comprehensive weather information of each region;

[0102] determining whether the timestamp difference value is within a preset threshold interval;

[0103] if yes, calculating the water accumulation in each region based on the basic weather information and the comprehensive weather information.

[0104] Scheme 10. The wetland mode recommendation method according to scheme 9, wherein the calculating of the water accumulation in each region based on the basic weather information and the comprehensive weather information comprises:

[0105] obtaining a weight parameter based on the number of driving devices in each region;

[0106] calculating a total water amount in each region based on the basic weather information, the comprehensive weather information and the weight parameter;

[0107] performing time decay filtering processing on the total water amount to obtain the water accumulation in each region.

[0108] Scheme 11. The wetland mode recommendation method according to scheme 10, wherein the performing of the time decay filtering processing on the total water amount to obtain the water accumulation in each region comprises:

[0109] obtaining a last water accumulation level and a record time based on historical data;

[0110] determining whether the last water accumulation level is within a valid time range and environmental parameters are complete; the environmental parameters comprise at least one of temperature, humidity and weather;

[0111] if yes, obtaining a time difference value between a current time and the record time, and determining a time decay coefficient based on the environmental parameters; the time decay coefficient comprises a water accumulation rising time constant or a water accumulation falling time constant;

[0112] calculating the water accumulation in each region based on a size relationship between the total water amount and the last water accumulation level, and the time difference value, the time decay coefficient.

[0113] Scheme 12. The wetland mode recommendation method according to scheme 1, wherein the water accumulation comprises a water accumulation level; and the determining of a wetland mode recommendation state of a driving device in the region based on the water accumulation in each region comprises:

[0114] determining that the wetland mode recommended state of the driving device in the region is to be closed wetland mode when the water accumulation level is less than or equal to a first threshold value;

[0115] determining that the wetland mode recommended state of the driving device in the region is to be opened wetland mode when the water accumulation level is greater than or equal to a second threshold value;

[0116] determining that the wetland mode recommended state of the driving device in the region is to keep the current driving mode when the water accumulation level is greater than the first threshold value and less than the second threshold value;

[0117] wherein the first threshold value is less than the second threshold value.

[0118] Scheme 13. The wetland mode recommendation method according to Scheme 12, characterized in that the method further comprises:

[0119] when the water accumulation level is greater than or equal to the second threshold value, and the driving mode of the driving device in the region is not the wetland mode, issuing a driving mode switching reminder to the driving device, or controlling the driving device to switch the driving mode to the wetland mode.

[0120] Scheme 14. The wetland mode recommendation method according to any one of Schemes 1 to 13, characterized in that the method further comprises:

[0121] establishing a wetland map based on the water accumulation amount to display the water accumulation amount of each region;

[0122] wherein the wetland map comprises at least a weather layer and a water accumulation layer.

[0123] Scheme 15. An electronic device comprising a processor and a memory, the memory being adapted to store a plurality of program codes, characterized in that the program codes are adapted to be loaded and run by the processor to execute the wetland mode recommendation method according to any one of Schemes 1 to 14.

[0124] Scheme 16. A computer-readable storage medium having a plurality of program codes stored therein, characterized in that the program codes are adapted to be loaded and run by a processor to execute the wetland mode recommendation method according to any one of Schemes 1 to 14.

[0125] Scheme 17. A wetland mode recommendation system, characterized in that the system comprises a cloud server, and a plurality of driving devices in communication connection with the cloud server; wherein,

[0126] the cloud server comprises the electronic device according to Scheme 15;

[0127] The plurality of driving devices are configured to send wiper data and positioning data to the cloud server based on a preset time window.

[0128] The above one or more technical solutions of this application have at least one or more of the following beneficial effects:

[0129] In implementing the technical solution of the present application, the basic weather information, weather flow data, and short-term precipitation flow data of each area are first obtained. Then, based on the weather flow data and short-term precipitation flow data, the comprehensive weather information of each area is obtained. The amount of water accumulated in each area is obtained based on the basic weather information and the comprehensive weather information. Finally, based on the amount of water accumulated in each area, the recommended wetland mode status of the driving equipment in the area is determined. Through the above-mentioned implementation method, the comprehensive weather and water accumulation of each area are calculated by fusing multi-source data, and the recommended wetland mode status of the driving equipment in the area is determined based on the amount of water accumulated. This can achieve accurate recommendations for wetland mode switching, make the wetland mode recommendation status more in line with actual driving scenarios, help improve the reach rate of wetland mode switching, and escort users on slippery roads in severe weather such as rain, snow, and ice, thereby improving user experience and driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0130] The disclosure of this application will become more easily understood with reference to the accompanying drawings. Those skilled in the art will readily appreciate that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of this application. Among them:

[0131] Figure 1 This is a flow chart of the main steps of a wetland model recommendation method according to an embodiment of the present application;

[0132] Figure 2 This is a flowchart of the main steps for obtaining basic weather information for each area according to an embodiment of the present application;

[0133] Figure 3 This is a flowchart of the main steps for calculating the amount of accumulated water in each area based on basic weather information and comprehensive weather information according to an embodiment of the present application;

[0134] Figure 4 is a real-time data processing flow chart of a wetland model recommendation method according to an embodiment of the present application;

[0135] Figure 5 is a schematic diagram of the main process of a wetland model recommendation method according to an embodiment of the present application;

[0136] Figure 6 It is a schematic diagram of the main structure of an electronic device according to an embodiment of the present application.

[0137] List of reference numerals:

[0138] 61: processor; 62: memory. DETAILED DESCRIPTION

[0139] Some embodiments of the present application will be described below with reference to the accompanying drawings. Those skilled in the art will understand that the embodiments are only used to explain the technical principles of the present application, and are not intended to limit the protection scope of the present application.

[0140] In the description of the present application, "module" and "processor" can include hardware, software or a combination of both. A module can include hardware circuitry, various suitable sensors, communication ports, memory, and can also include a software part such as program code, and can be a combination of software and hardware. The processor can be a central processor, a microprocessor, an image processor, a digital signal processor or any other suitable processor. The processor has data and / or signal processing functions. The processor can be implemented in software, hardware or a combination of both. The non-transitory computer readable storage medium includes any suitable medium that can store program code, such as a magnetic disk, a hard disk, an optical disk, a flash memory, a read-only memory, a random access memory, etc.

[0141] The term "A and / or B" means all possible combinations of A and B, such as only A, only B, or A and B. The term "at least one of A or B" or "at least one of A and B" has a similar meaning to "A and / or B" and can include only A, only B, or A and B. The singular form of the term "one", "this" can also include the plural form.

[0142] As described in the background, the current vehicle wetland mode switching has a low reach rate. In rainy and snowy weather, it is difficult to switch to the wetland mode in time to improve driving safety, resulting in an increased probability of slipping during driving, affecting user experience and driving safety.

[0143] To solve the above problems, the present application provides a wetland mode recommendation method, an electronic device, a storage medium and a system.

[0144] Referring to the accompanying drawings Figure 1 , Figure 1 is the main step flowchart of the wetland mode recommendation method according to an embodiment of the present application. As shown in Figure 1 , the wetland mode recommendation method in the embodiment of the present application mainly includes the following steps S101 to S104.

[0145] Step S101: obtaining the basic weather information, weather flow data and short-acting precipitation flow data of each region;

[0146] The basic weather information can be basic rainfall information, basic snowfall information, basic icing information, etc.

[0147] Step S102: Obtain comprehensive weather information of each region based on the weather flow data and the short-term precipitation flow data;

[0148] The comprehensive weather information can include weather rainfall information, weather snowfall information, icing information, etc.

[0149] Step S103: Obtain the water accumulation of each region based on the basic weather information and the comprehensive weather information;

[0150] Step S104: Determine the wetland mode recommendation state of the driving device in the region based on the water accumulation of each region.

[0151] Based on the method described in steps S101 to S104, the comprehensive weather and water accumulation of each region are calculated by fusing multi-source data, and the wetland mode recommendation state of the driving device in the region is determined according to the water accumulation, which can realize accurate recommendation of wetland mode switching, make the wetland mode recommendation state more suitable for actual driving scenarios, help to improve the reach rate of wetland mode switching, provide protection for users driving on slippery road surfaces in adverse weather such as rain, snow and icing, and improve user experience and driving safety.

[0152] The steps 101 to S104 described above are further described below.

[0153] In some embodiments of step S101, the basic weather information of each region can be obtained first, such as basic rainfall information, basic snowfall information, basic icing information, etc. The basic weather information can include a basic water level. Taking the basic weather information as an example of basic rainfall information, the basic water level can include 0 level (no rain), 1 level (light rain), 3 level (moderate rain), 5 level (heavy rain), 7 level (heavy rain), 9 level (heavy rain), 11 level (heavy rain), 13 level (heavy rain), 15 level (extreme rain) and the like. The basic water level corresponding to the basic snowfall information and the basic icing information and other basic weather information is the same.

[0154] Referring to FIG. 2, Figure 2 , Figure 2 is a main step flow diagram for obtaining the basic weather information of each region according to an embodiment of the present application. As shown in Figure 2 , it mainly includes the following steps S201 to S204.

[0155] Step S201: Obtain the wiper data and positioning data of each driving device based on a preset time window;

[0156] Specifically, a preset time window of 30s can be set as a basic unit of data collection and processing to ensure periodic and regular acquisition of relevant data of the driving device. The cloud server receives the data reported by each driving device in real time through a data stream every 30 seconds, which can specifically include wiper data and positioning data of the driving device.

[0157] The wiper data can include the number of wiper operations, gear distribution information, etc., and can be collected through a rain light sensor (RLS) of the driving device. The positioning data can be collected by a positioning module (such as GPS, Beidou, etc.) carried by the driving device, and is used to determine the geographical position of the driving device to provide spatial coordinate basis for subsequent regional division.

[0158] Step S202: processing the wiper data to obtain the number of wiper operations of each driving device in the preset time window;

[0159] In some embodiments, step S202 can include steps S2021 to S2023.

[0160] Step S2021: filtering and processing the wiper data to screen out wiper data meeting the abnormal weather scenario;

[0161] The abnormal weather scenario can include a rain scenario, a snow scenario, an icing scenario, a heavy fog scenario, and a hail scenario, etc.

[0162] Specifically, the wiper operation under non-abnormal weather conditions, including glass cleaning, wiper replacement, and vehicle not in driving state, etc., can be filtered to obtain wiper data meeting the abnormal weather scenario.

[0163] Step S2022: obtaining driving data of each driving device and corresponding precipitation speed;

[0164] The driving data can include vehicle speed and other vehicle driving state information, which is collected and uploaded by the vehicle control system of the driving device. The precipitation speed includes rain speed, snow speed, and hail speed, etc., which can be obtained according to the timestamp of the driving data through a precipitation speed sensor or a related meteorological platform.

[0165] Step S2023: normalizing the screened wiper data based on the driving data and the precipitation speed.

[0166] In some embodiments, the screened wiper data can be normalized by the following formula (1):

[0167] Normalized value = (wiper number 2) / [1 + (average speed 2 / precipitation speed 2)] (1)

[0168] Wherein, the normalized value is the wiper count obtained by normalization, i.e., the wiper count of the driving device in the preset time window; and the average vehicle speed is the average speed of the driving device in the preset time window.

[0169] The above is a description of step S202.

[0170] Step S203: Based on the preset grid division information, the wiper data and the positioning data, the high-speed wiper request count and the wiper count sum in each region are obtained.

[0171] Specifically, according to the preset basic grid division information, the positioning data of each driving device is combined to determine the region to which each device belongs, and then the high-speed wiper request count and the wiper count sum in each region are obtained by counting all the wiper data in the region. Wherein, the high-speed wiper request count is the total request count of the wiper system in high gear in the region reported by all driving devices.

[0172] Step S204: Based on the high-speed wiper request count and the wiper count sum, the basic water amount grade of each region is obtained.

[0173] In some embodiments, step S204 can include steps S2041 to S2042.

[0174] Step S2041: Determine the wiper scene type based on the high-speed wiper request count.

[0175] Wherein, the wiper scene type includes a low-speed wiper dominant scene or a high-speed wiper dominant scene.

[0176] Specifically, it can be determined whether the high-speed wiper request count is less than a preset request threshold (such as 9); further, if the high-speed wiper request count is less than the preset request threshold, the wiper scene type is determined to be the low-speed wiper dominant scene; if the high-speed wiper request count is greater than or equal to the preset request threshold, the wiper scene type is determined to be the high-speed wiper dominant scene.

[0177] Step S2042: Divide the basic water amount grade based on the wiper scene type and the wiper count sum.

[0178] Specifically, for the low-speed wiper dominant scene (such as high-speed wiper request count < 9), the basic water amount grade can be divided according to the normalized wiper count sum (stateCount), and the high-speed wiper request count and the low-speed wiper proportion can be fine-tuned when stateCount is high.

[0179] Taking the basic weather information as an example, the basic water amount level can be determined as 0 (no rain) when stateCount<1, as 1 (light rain) when 1<=stateCount<5, as 3 (moderate rain) when 5<=stateCount<10, as 5 (heavy rain) when 10<=stateCount<29, and as 9 (heavy rain) when stateCount>=29. The fine-tuning logic can specifically include: if the high-speed wiper request times account for more than 0.66 (i.e., 66%), the basic water amount level is determined as 9 (heavy rain); if the low-speed wiper account for more than 0.33 (33%), the basic water amount level is determined as 7 (heavy rain on the strong side); otherwise, the basic water amount level is determined as 5 (heavy rain).

[0180] For a low-speed wiper dominant scene (e.g., high-speed wiper request times>=9), the basic water amount level can be overall increased by one level, and divided according to stateCount.

[0181] Taking the basic weather information as an example, the basic water amount level can be determined as 7 (heavy rain on the strong side) when stateCount<3, as 9 (heavy rain) when 3<=stateCount<10, as 11 (heavy rainstorm) when 10<=stateCount<19, as 13 (extraordinary rainstorm) when 19<=stateCount<29, and as 15 (extreme rainstorm) when stateCount>=29.

[0182] It should be noted that the above examples of dividing the basic water amount level are only illustrative, and in actual applications, those skilled in the art can divide the basic water amount level according to specific scenes, which is not limited herein.

[0183] The above is a description of obtaining the basic weather information of each region.

[0184] Further, in some embodiments of step S101, weather data can be obtained, and based on the weather data and the region ID, it is determined whether there is basic weather information for each region, and a first time threshold is set according to the determination result; it is determined whether the weather data of each region is fresh based on the first time threshold; if yes, weather flow data of the region is obtained based on the weather data; otherwise, real-time weather data is requested, and the weather data and the real-time weather data are processed to obtain the weather flow data of the region.

[0185] Specifically, the cloud server can asynchronously read the hourly / minute weather data of each region stored in the database (such as the open-source in-memory data storage system redis), determine whether there is basic weather information for each region according to the region ID, and set a first time threshold according to the determination result, that is, set different time thresholds according to whether there is basic rainfall in the region. For example, the first time threshold is set to 1 hour when there is basic weather information, and the first time threshold is set to 2 hours when there is no basic weather information. Then, based on the first time threshold, it is determined whether the weather data of each region is fresh, that is, whether the timestamp exceeds the first time threshold.

[0186] Further, different labels can be assigned to region IDs according to the determination result, for example, when the update time is fresh, a sufficiently fresh (storedOutputTag) label is assigned, and when the update time is not fresh, a requested (requestedOutputTag) label is assigned.

[0187] For regions that are fresh enough, the weather data stored in redis can be passed to the downstream module as part of the weather stream data for processing; for regions that are not fresh enough, real-time weather data needs to be asynchronously requested from an external meteorological service platform or data source that provides real-time weather data through the HyperText Transfer Protocol (HTTP), wherein the real-time weather data requested asynchronously contains latitude and longitude information, and the real-time weather data can be matched to the corresponding region in the basic grid through the latitude and longitude information. Further, the weather data stored in redis can be merged and processed with the real-time weather data requested asynchronously, and passed to the downstream module as part of the weather stream data for processing.

[0188] The above is a description of obtaining weather stream data for each region.

[0189] Further, in some embodiments of step S101, short-term precipitation data can be obtained, and based on the short-term precipitation data and the region ID, it is determined whether there is basic weather information for each region, and a second time threshold is set according to the determination result; based on the second time threshold, it is determined whether the short-term precipitation data of each region is fresh; if so, the short-term precipitation stream data of the region is obtained based on the short-term precipitation data; otherwise, real-time short-term precipitation data is requested, and the short-term precipitation stream data of the region is obtained based on the short-term precipitation data and the real-time short-term precipitation data.

[0190] Specifically, the cloud server can asynchronously read the minute / hourly short-term precipitation data of each region stored in redis, determine whether there is basic weather information for each region according to the region ID, and set a second time threshold according to the determination result, that is, set different time thresholds according to whether there is basic weather information for the region. For example, set the second time threshold to 24 minutes when there is basic weather information, and set the second time threshold to 114 minutes when there is no basic weather information. Then determine whether the short-term precipitation data of each region is fresh based on the second time threshold, that is, whether the timestamp exceeds the second time threshold.

[0191] Further, different labels can be assigned to region IDs according to the determination result, for example, when the update time is fresh, assign a sufficiently fresh (storedOutputTag) label, and when the update time is not fresh, assign a requestedOutputTag) label.

[0192] For regions that are fresh enough, the short-term precipitation data stored in redis can be passed to downstream modules as part of the short-term precipitation stream data for processing; for regions that are not fresh enough, real-time short-term precipitation data needs to be asynchronously requested from external meteorological service platforms or data sources that provide real-time short-term precipitation data through HTTP. The real-time short-term precipitation data asynchronously requested also contains latitude and longitude information, which can be used to match real-time weather data to the corresponding region in the basic grid. Further, the short-term precipitation data stored in redis and the real-time short-term precipitation data asynchronously requested can be passed to downstream modules as part of the weather stream data for processing.

[0193] The above is a further description of step S101, and the following will continue to further describe step S102.

[0194] In some embodiments of the above step S102, a weather state backend (weatherState) and a short-term state backend (shortTermState) can be created on the cloud server, and weather stream data can be saved through weatherState and short-term precipitation stream data can be saved through shortTermState. When any of the weather stream data and short-term precipitation stream data is input, the data of the corresponding region can be read from the other state backend.

[0195] Furthermore, the first timestamp of the weather stream data and the second timestamp of the short-term precipitation stream data for each region can be obtained, and the weather stream data and the short-term precipitation stream data can be matched based on the first and second timestamps. If the first and second timestamps are the same, the precipitation intensity is obtained based on the short-term precipitation stream data, and the comprehensive weather information for each region is calculated based on the precipitation intensity. The comprehensive weather information may include weather rainfall information, weather snow information, ice information, etc.

[0196] Specifically, if the first timestamp recording the weather data update time and the second timestamp recording the short-term precipitation data update time are the same, it means that the two types of data belong to the observation results of the same time node. At this time, the precipitation intensity (intensity) parameter can be extracted from the field carried by the short-term precipitation flow data. If the intensity is not extracted from the short-term precipitation flow data, the specific precipitation intensity value can also be obtained through the mapping relationship between the precipitation level and the preset precipitation intensity range (for example: light rain corresponds to 0.1-10 mm / hour, moderate rain corresponds to 10-25 mm / hour, etc.), which is finally used for the calculation of comprehensive weather.

[0197] Taking the comprehensive weather information as weather and rainfall information as an example, in some embodiments, the weather and rainfall information of each area can be calculated by the following formula (2):

[0198] Rainfall = 54.713 × intensity 4 -100.91×intensity 3 +49.806×intensity 2 +11.492×intensity+0.1022(2)

[0199] Furthermore, the calculated comprehensive weather information for each area can be output to downstream modules for processing.

[0200] The above is a further description of step S102 , and the following further describes step S103 .

[0201] In some implementations of the above step S103, a grid comprehensive weather state backend (gridWeatherState) and a water accumulation state backend (pongdingState) can be created on the cloud server, and the comprehensive weather information of each area can be saved through gridWeatherState, and the water accumulation of each area can be saved through pongdingState.

[0202] Specifically, the comprehensive weather information input by the upstream module can be stored in gridWeatherState, so that when the basic weather information is received, the corresponding comprehensive weather information of the area can be obtained from gridWeatherState.

[0203] Furthermore, the timestamp difference between the basic weather information and the comprehensive weather information of each area can be obtained, and it can be determined whether the timestamp difference is within a preset threshold range; if so, the amount of accumulated water in each area is calculated based on the basic weather information and the comprehensive weather information.

[0204] Specifically, if the timestamp difference between the basic weather information and the comprehensive weather information for the same area is within a preset threshold, it means that the two pieces of information belong to the same time period. In this case, the amount of accumulated water in each area can be calculated based on the basic weather information and the comprehensive weather information.

[0205] In some embodiments, see Appendix Figure 3 , Figure 3 This is a flow chart of the main steps for calculating the amount of water accumulation in each area based on basic weather information and comprehensive weather information according to an embodiment of the present application. Figure 3 As shown, it mainly includes the following steps S301 to S303.

[0206] Step S301: Obtaining a weight parameter based on the number of driving devices in each area;

[0207] Specifically, the weight parameter (weight) can be obtained by the following formula (3):

[0208] weight=carNumber / processVehicleCountWeight (3)

[0209] Among them, carNumber is the total number of driving devices participating in wiper data statistics in the current area; processVehicleCountWeight is a configurable constant that can be configured based on scenarios such as area size and driving device density. It is mainly used to limit the range of the weight parameter to avoid the weight parameter being too large when there are too many driving devices or too small when there are too few driving devices.

[0210] Step S302: Calculate the total water volume of each area based on basic weather information, comprehensive weather information and weight parameters;

[0211] Taking the rainfall scenario as an example, in some embodiments, the total amount of water (rainfall) in each area can be calculated using the following formula (4):

[0212] rainfall = baseRainfall * weight + weatherRainfall * (1 - weight) (4)

[0213] wherein, baseRainfall is the base rainfall information (base weather information); weatherRainfall is the weather rainfall information (comprehensive weather information); and weight is a weight parameter.

[0214] Step S303: performing time decay filtering processing on the total water amount to obtain the waterlogging amount of each region.

[0215] In some embodiments, step S303 can include steps S3031 to S3034.

[0216] Step S3031: obtaining the last waterlogging level and the record time based on historical data;

[0217] Specifically, the last waterlogging level (pondingLastTime.f0) and the record time (pondingLastTime) can be obtained from the data stored in the waterlogging state backend (pongdingState).

[0218] Step S3032: determining whether the last waterlogging level is within the valid time range and the environmental parameters are complete.

[0219] wherein, the environmental parameters include at least one of temperature, humidity and weather.

[0220] That is, it is determined that the last waterlogging level exists and the record time has not expired; in addition, the environmental parameters such as temperature, humidity, weather code, etc. are complete (all are not null).

[0221] Further, if the last waterlogging level is within the valid time range and the environmental parameters are complete, step S3033 is performed for dynamic calculation, otherwise step S3031 is returned.

[0222] Step S3033: obtaining the time difference value between the current time and the record time, and determining the time decay coefficient based on the environmental parameters.

[0223] wherein, the time decay coefficient includes a waterlogging rising time constant or a waterlogging falling time constant.

[0224] Specifically, the time difference value (minute) timeDiff = (currentProcessingTime-pondingLastTime.f1) / 60000; wherein, currentProcessingTime is the current time.

[0225] Further, according to the temperature and humidity, the parameter k1 can be found through the K1 Cache table (see Table 1 below for details) or calculated by interpolation, and k1 is a coefficient reflecting the influence of temperature and humidity on the speed of water recession.

[0226] Table 1

[0227] Through the above Table 1, the corresponding value can be found according to the current humidity (row) and temperature (column), and if the temperature / humidity is not in the enumerated values of the table, it can be calculated by interpolation method (such as linear interpolation) to obtain K1.

[0228] Further, according to the weather code, the road drying speed coefficient k2 and the water rising time constant tcUp (i.e. road water speed coefficient, unit: minute) in the time decay coefficient can be found through the K2 Cache table (see Table 2 below for details).

[0229] Table 2

[0230] Through the above Table 2, the corresponding road drying speed coefficient k2 and the water rising time constant tcUp in the time decay coefficient can be found according to the weather code.

[0231] Step S3034: Based on the size relationship between the total water amount and the last water level, and the time difference value, the time decay coefficient, the water amount in each area is calculated.

[0232] Specifically, the current total water amount and the last water level can be compared first.

[0233] If the current total water amount is greater than or equal to the last water level (i.e. the water amount is increasing), the water amount (ponding) in the area can be calculated by the following formula (5):

[0234] ponding = rainfall + (pondingLastTime.f0 - rainfall) * exp(-timeDiff / tcUp) (5)

[0235] Wherein, rainfall is the total water amount, pondingLastTime.f0 is the last water level, exp is the exponential function, timeDiff is the time difference value between the current time and the time of the last water level record, and tcUp is the water rising time constant.

[0236] If the current total water amount is less than the last time ponding level (i.e. the water amount is decreasing), the ponding drop time constant tcDown in the time decay coefficient can be calculated by the following formula (6) first:

[0237] tcDown = k1 * k2 (6)

[0238] wherein k1 is a coefficient reflecting the influence of temperature and humidity on the ponding recession speed, and k2 is a road surface drying speed coefficient.

[0239] Further, the ponding of the region can be calculated by the following formula (7):

[0240] ponding = rainfall + (pondingLastTime.f0 - rainfall) * exp(-timeDiff / tcDown) (7)

[0241] wherein rainfall is the total water amount, pondingLastTime.f0 is the last time ponding level, exp is an exponential function, timeDiff is the time difference between the current time and the time of recording the last time ponding level, and tcDown is the ponding drop time constant.

[0242] The ponding calculated by the above formula (5) or formula (7) is the ponding level of each region, which can include, for example, 0 level (no rain), 1 level (light rain), 3 level (moderate rain), 5 level (heavy rain), 7 level (heavy rain on the strong side), 9 level (heavy rain), 11 level (heavy rain), 13 level (extra heavy rain), 15 level (extreme heavy rain), etc. in the rain scenario.

[0243] The above steps S3031 to S3034 dynamically calculate the ponding level in combination with the time decay formula, which can make the result more consistent with the actual physical process, such as fast ponding recession in high temperature and low humidity, and fast ponding accumulation in precipitation, etc., thereby providing accurate ponding data support for the wetland mode recommendation.

[0244] The above is a further description of step S103, and the following continues to further describe step S104.

[0245] In some embodiments of the above step S104, based on the calculated ponding level of each region, it can be determined whether the driving device in the region should enter the wetland mode, exit the wetland mode, or remain the status quo.

[0246] Specifically, when the water accumulation level is less than or equal to the first threshold, it can be determined that the wetland mode recommended state of the driving device in the region is to exit the wetland mode; when the water accumulation level is greater than or equal to the second threshold, it can be determined that the wetland mode recommended state of the driving device in the region is to enter the wetland mode; when the water accumulation level is greater than the first threshold and less than the second threshold, it can be determined that the wetland mode recommended state of the driving device in the region is to maintain the current driving mode; wherein the first threshold is less than the second threshold.

[0247] For example, when the first threshold is 1 and the second threshold is 3, when the water accumulation level of the current road surface of a certain region is less than or equal to 1, it is recommended that the driving device in the region turn off the wetland mode; when the water accumulation level is greater than or equal to 3, it is recommended that the driving device in the region turn on the wetland mode; in other cases (i.e. 1 < water accumulation level < 3), the current driving mode is maintained.

[0248] Further, in some embodiments, when the water accumulation level is greater than or equal to the second threshold, and the driving mode of the driving device in the region is not the wetland mode, a driving mode switching reminder can be sent to the driving device, or the driving device can be controlled to switch the driving mode to the wetland mode.

[0249] Specifically, when it is determined that the wetland mode needs to be turned on at the location of the driving device and the wetland mode is not turned on, the driver can be reminded to switch the wetland mode through a pop-up window of the driving device display screen, or the driver can be helped to better access the wetland mode switching in an active switching manner, so as to improve the user experience and improve the safety of driving.

[0250] Further, in some embodiments, after obtaining the water accumulation of each region, a wetland map can also be established based on the water accumulation to display the water accumulation of each region; wherein the wetland map at least includes a weather layer and a water accumulation layer.

[0251] Specifically, an S2-based wetland map (S2 is a spatial indexing technology that can divide the earth's surface into multiple levels of grids, similar to a refined division of latitude and longitude grids, each grid corresponds to a unique geographical area, and can efficiently realize geographical spatial positioning, indexing and regional division) can be established in the cloud server, that is, in the cloud server, the geographical area is divided into a plurality of small units based on the S2 grid, each unit is associated with the weather, water accumulation and other data of the region, forming a dynamically updated wetland map. The map can include a weather layer (such as a water amount layer, a snow amount layer, an icing layer, etc.) and a water accumulation layer, which can directly show the weather state and water accumulation state of different regions.

[0252] The above is a further description of steps S101 to S104.

[0253] The following will be described in conjunction with the accompanying drawings Figure 4 and the accompanying drawings Figure 5The wetland mode recommendation method provided in the application is introduced.

[0254] Referring to the accompanying Figure 4 , Figure 4 is a real-time data processing flowchart of the wetland mode recommendation method according to an embodiment of the application. As shown in Figure 4 , the following steps S401 to S4015 are mainly included.

[0255] Step S401: Start Flink environment and set RocksDB state backend;

[0256] Flink is a distributed stream processing framework, which provides a running basis for real-time data processing; RocksDB is used as a state backend for reliably storing and managing data states in Flink jobs, ensuring the consistency and recoverability of data processing.

[0257] Step S402: Kafka Source reads basic weather information of each region;

[0258] Kafka is a distributed message queue, and Kafka Source, as a data input interface, can read the basic weather information of each region from the Kafka cluster to provide data sources for subsequent processing.

[0259] Step S403: Grouping by region ID and using a verification function to verify regional data;

[0260] Specifically, the data can be grouped according to the region ID, so that the data of the same region enters the same processing unit, and the verification function is used to verify the legality and integrity of the regional data, ensuring the quality of the subsequent processing data.

[0261] Step S404: Asynchronously read weather data from redis;

[0262] Further, if the weather data stored in redis is not timely, step S406 is executed;

[0263] Step S405: Asynchronously read short-term precipitation data from redis;

[0264] Further, if the short-term precipitation data stored in redis is not timely, step S407 is executed;

[0265] Step S406: Asynchronously request real-time weather data through an HTTP interface; then execute step S408;

[0266] Asynchronously obtain real-time weather data through an HTTP interface to supplement and update the data, ensuring real-time and accuracy.

[0267] Step S407: Asynchronously request real-time short-term precipitation data through the HTTP interface; then execute step S409;

[0268] Asynchronously acquire real-time short-term precipitation data through the HTTP interface, which can supplement and update data to ensure real-time and accuracy.

[0269] Step S408: Write real-time weather data back to redis; then execute step S4010;

[0270] Step S409: Write real-time short-term precipitation data back to redis; then execute step S4010;

[0271] Step S4010: Merge weather stream data and short-term precipitation stream data to obtain comprehensive weather information;

[0272] Step S4011: Obtain comprehensive weather information of each region according to the region ID;

[0273] Step S4012: Obtain the water accumulation of each region based on the basic weather information and the comprehensive weather information;

[0274] Step S4013: Perform wetland detection based on the water accumulation of each region to determine the wetland mode recommendation state of the driving device in the region;

[0275] Step S4014: Write the detection result into the Kafka cluster;

[0276] Step S4015: Data stream processing is completed, and the next batch of data is waited for;

[0277] Specifically, after completing the processing of the current batch of data, the next batch of data is received, and real-time data monitoring and wetland mode recommendation are continuously performed.

[0278] The above is a further description of Figure 4 .

[0279] Refer to the accompanying Figure 5 , Figure 5 is the main flow diagram of the wetland mode recommendation method according to an embodiment of the present application. As shown in Figure 5 , it mainly includes a driving device data link, a weather data link, and a data fusion and wetland mode recommendation link.

[0280] The driving device data link mainly includes: transmitting driving device related data from the "vehicle gateway" on the driving device side to the "Kafka message queue of the driving device" on the cloud server through the "remote service platform" to obtain the "positioning data", "wiper data" and "current driving mode" of the driving device. The "wiper data" is subjected to group data statistics and normalization processing by the "Flink wiper processing task" to generate "wiper data of each area" to provide data support for the calculation of basic weather information.

[0281] The weather data link mainly includes: taking the "live weather service" as the source of meteorological data by the cloud server, collecting real-time weather data of each area through the "real-time weather acquisition task" to form "weather flow data and short-term precipitation flow data of each area" for subsequent fusion calculation.

[0282] The data fusion and wetland detection link mainly includes: associating and fusing the wiper data of each area, the weather flow data and the short-term precipitation flow data of each area according to the area ID by the "Flink area confluence processing" of the cloud server to generate a "Flink area data stream" containing multi-dimensional information, inputting the data stream into the "wetland mode recommendation module", combining the "group wetland model" (a wetland model trained based on historical data) to calculate the water accumulation and wetland mode recommendation state of each area, and writing the calculation result into the "Kafka message queue of the driving device" and transmitting it to the "group wiper decision App" on the driving device side and the "vehicle-mounted interaction system" (such as the vehicle-mounted intelligent assistant NOMI, UX, etc.) of the driving device for interaction to realize the display and application of the wetland mode recommendation result.

[0283] The above wetland mode recommendation method realizes group intelligence by fusing a large amount of information of wipers and other vehicle body sensors of driving devices, and realizes real-time weather rainfall and water accumulation analysis by combining real-time weather flow data and short-term precipitation flow data, thereby providing data support for the recommendation of the wetland mode and providing protection for the user driving on the wet and slippery road surface in rainy and snowy weather.

[0284] The above is a further description of the wetland mode recommendation method provided by the present application.

[0285] It should be noted that although the above embodiments describe the steps in a specific order, those skilled in the art can understand that, in order to achieve the effect of the present application, the different steps do not necessarily have to be executed in this order, they can be executed simultaneously (in parallel) or in other order, and these changes are within the protection scope of the present application.

[0286] It will be understood by those skilled in the art that all or part of the processes in the method for implementing the above embodiment of the present application can also be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above-mentioned various method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device, medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal and software distribution medium, etc. that can carry the computer program code.

[0287] Furthermore, the present application also provides an electronic device. Figure 6 , Figure 6 FIG. 1 is a schematic diagram of the main structure of an electronic device according to an embodiment of the present application. Figure 6 As shown, the electronic device in the embodiment of the present application primarily includes a processor 61 and a memory 62. Memory 62 can be configured to store a program for executing the wetland mode recommendation method of the above-described method embodiment, and processor 61 can be configured to execute the program in memory 62, including but not limited to a program for executing the wetland mode recommendation method of the above-described method embodiment. For ease of illustration, only the portions relevant to the embodiment of the present application are shown. For specific technical details not disclosed, please refer to the method section of the embodiment of the present application.

[0288] In some possible implementations of the present application, the electronic device may include multiple processors 61 and multiple memories 62. The program for executing the wetland mode recommendation method of the above-mentioned method embodiment can be divided into multiple sub-programs, and each sub-program can be loaded and run by the processor 61 to execute different steps of the wetland mode recommendation method of the above-mentioned method embodiment. Specifically, each sub-program can be stored in different memories 62 respectively, and each processor 61 can be configured to execute the programs in one or more memories 62 to jointly implement the wetland mode recommendation method of the above-mentioned method embodiment, that is, each processor 61 executes different steps of the wetland mode recommendation method of the above-mentioned method embodiment respectively to jointly implement the wetland mode recommendation method of the above-mentioned method embodiment.

[0289] The plurality of processors 61 can be processors deployed on the same device. For example, the electronic device can be a high-performance device composed of a plurality of processors, and the plurality of processors 61 can be processors configured on the high-performance device. In addition, the plurality of processors 61 can also be processors deployed on different devices. For example, the electronic device can be a server cluster, the plurality of processors 61 can be processors on different servers in the server cluster, or the computer device can be a driving device cluster, and the plurality of processors 61 can be processors on different driving devices in the driving device cluster.

[0290] Further, the present application also provides a computer readable storage medium. In an embodiment of the computer readable storage medium according to the present application, the computer readable storage medium can be configured to store a program for executing the wetland mode recommendation method described above. The program can be loaded and run by the processor to implement the wetland mode recommendation method described above. For the convenience of description, only the parts related to the embodiments of the present application are shown, and the specific technical details not disclosed are referred to the method part of the embodiments of the present application. The computer readable storage medium can be a memory device formed by various electronic devices. Optionally, the computer readable storage medium in the embodiments of the present application is a non-transitory computer readable storage medium.

[0291] Further, the present application also provides a wetland mode recommendation system. In an embodiment of the wetland mode recommendation system according to the present application, the system can include a cloud server and a plurality of driving devices in communication connection with the cloud server.

[0292] The cloud server includes the electronic device described in the electronic device embodiment described above.

[0293] The plurality of driving devices are configured to send the wiper data and the positioning data to the cloud server based on a preset time window.

[0294] The wetland mode recommendation system described above is configured to execute the wetland mode recommendation method described above. Figure 1 The technical principles, technical problems solved and technical effects of the wetland mode recommendation method embodiment shown above are similar. Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process and related description of the wetland mode recommendation system can refer to the description of the wetland mode recommendation method embodiment, which will not be repeated here.

[0295] It should be noted that the related user personal information involved in the embodiments of the present application is strictly in accordance with the requirements of laws and regulations, and follows the principles of legality, legitimacy and necessity. Based on the reasonable purpose of the business scene, the personal information provided by the user in the process of using the product / service or generated due to the use of the product / service, and the personal information obtained by the user's authorization.

[0296] The user personal information handled by the present application may vary according to specific product / service scenarios, and shall be subject to the specific scenarios in which the user uses the product / service, and may involve the user's account information, device information, driving information, sensor information or other related information. The present application will treat the user's personal information and its processing with a high degree of diligence.

[0297] The present application attaches great importance to the security of user personal information, and has taken security protection measures in accordance with industry standards, which are reasonable and feasible to protect the user's information, to prevent unauthorized access, public disclosure, use, modification, damage or loss of personal information.

[0298] So far, the technical solution of the present application has been described in conjunction with one embodiment shown in the drawings, but those skilled in the art can easily understand that the protection scope of the present application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to the related technical features without departing from the principles of the present application, and the technical solutions after these changes or replacements will fall within the protection scope of the present application.

Claims

1. A wetland pattern recommendation method, characterized in that: The method comprises: Obtain basic weather information, weather flow data and short-term precipitation flow data for each area; Acquire comprehensive weather information of each area based on the weather flow data and the short-term precipitation flow data; Obtaining the amount of accumulated water in each area based on the basic weather information and the comprehensive weather information; Based on the amount of accumulated water in each area, a recommended wet mode state of the driving device in the area is determined.

2. The wetland mode recommendation method according to claim 1, characterized in that: The basic weather information includes the basic water level; obtaining the basic weather information of each area includes: Based on a preset time window, obtain the wiper data and positioning data of each driving device; Processing the wiper data to obtain the number of wipers for each driving device within the preset time window; Based on the preset grid division information, the wiper data and the positioning data, obtaining the number of high-speed wiper requests and the total number of wiper times in each area; Based on the number of high-speed wiper requests and the total number of wiper times, a basic water level of each area is obtained.

3. The wetland mode recommendation method according to claim 2, characterized in that: The obtaining of the basic water level of each area based on the number of high-speed wiper requests and the total number of wiper times includes: Determining a wiper scene type based on the number of high-speed wiper requests; the wiper scene type includes a low-speed wiper dominant scene or a high-speed wiper dominant scene; The basic water level is divided based on the wiper scene type and the total number of wiper times.

4. The wetland mode recommendation method according to claim 3, characterized in that: Determining the wiper scene type based on the number of high-speed wiper requests includes: Determining whether the number of high-speed wiper requests is less than a preset request threshold; If so, it is determined that the wiper scene type is the low-speed wiper dominant scene; otherwise, it is determined that the wiper scene type is the high-speed wiper dominant scene.

5. The wetland mode recommendation method according to claim 2, characterized in that: The processing of the wiper data comprises: Filtering the wiper data to select wiper data that meets abnormal weather scenarios; Acquire driving data and corresponding precipitation speed of each driving device; the driving data at least includes vehicle speed; The filtered wiper data is normalized based on the driving data and the precipitation speed.

6. The wetland mode recommendation method according to claim 2, characterized in that: Acquiring weather flow data for each area includes: Get weather data; Based on the weather data and the area ID, determining whether the basic weather information exists in each area, and setting a first time threshold according to the determination result; determining whether the weather data for each area is fresh based on the first time threshold; If so, obtain the weather streaming data of the area based on the weather data; otherwise, request real-time weather data, and merge the weather data and the real-time weather data to obtain the weather streaming data of the area.

7. The wetland mode recommendation method according to claim 2, characterized in that: The acquisition of short-term precipitation flow data for each region includes: Obtain short-term precipitation data; Based on the short-term precipitation data and the area ID, determining whether the basic weather information exists in each area, and setting a second time threshold according to the determination result; determining whether the short-term precipitation data of each region is fresh based on the second time threshold; If so, the short-term precipitation flow data of the area is obtained based on the short-term precipitation data; otherwise, real-time short-term precipitation data is requested, and the short-term precipitation flow data of the area is obtained based on the short-term precipitation data and the real-time short-term precipitation data.

8. The wetland mode recommendation method according to claim 1, characterized in that: The obtaining of comprehensive weather information of each area based on the weather flow data and the short-term precipitation flow data includes: Obtaining a first timestamp of the weather flow data and a second timestamp of the short-term precipitation flow data for each region; matching the weather flow data and the short-term precipitation flow data based on the first timestamp and the second timestamp; If the first timestamp and the second time are the same, precipitation intensity is obtained based on the short-term precipitation flow data, and comprehensive weather information of each area is calculated according to the precipitation intensity.

9. The wetland mode recommendation method according to claim 1, characterized in that: Obtaining the amount of accumulated water in each area based on the basic weather information and the comprehensive weather information includes: Obtaining the timestamp difference between the basic weather information and the comprehensive weather information for each area; Determining whether the timestamp difference is within a preset threshold range; If so, the amount of accumulated water in each area is calculated based on the basic weather information and the comprehensive weather information.

10. The wetland mode recommendation method according to claim 9, characterized in that: Calculating the amount of accumulated water in each area based on the basic weather information and the comprehensive weather information includes: Obtaining weight parameters based on the number of driving devices in each area; Calculate the total water volume of each area based on the basic weather information, the comprehensive weather information and the weight parameter; The total water volume is subjected to time-attenuated filtering to obtain the accumulated water volume in each area.

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