A vehicle control method, apparatus, device, and storage medium

Abstract

The application discloses a vehicle control method, device, equipment and storage medium, which are used for realizing accurate flood warning of a vehicle parking position and guaranteeing vehicle safety. In the application, position information and parking information uploaded by a target vehicle are acquired, flood risk assessment processing is performed on the target vehicle based on the position information and the parking information, a flood warning is generated if it is determined that a corresponding risk assessment level of the target vehicle is greater than a preset level, and the flood warning is sent to the target vehicle. In the embodiment of the application, after a vehicle parks, the position information and the parking information of the vehicle are sent to the cloud. The cloud can perform flood risk assessment on the position where the vehicle parks according to the position information and the parking information uploaded by the vehicle. When the corresponding risk assessment level of the vehicle is high, a flood warning is generated to inform the vehicle, and then the vehicle can change the parking position to avoid being damaged by the flood.

Description

Technical Field

[0001] This invention relates to the field of vehicle technology, and in particular to a vehicle control method, device, equipment, and storage medium. Background Technology

[0002] Flooding is very common in cities during heavy rainstorms. These floods can cause damage to vehicles or even threaten people's lives. Therefore, it is necessary to monitor and warn of potential flood risks in real time to mitigate or avoid property damage and casualties.

[0003] Flood monitoring and early warning systems in related technologies primarily rely on data from fixed monitoring points, leading to limitations in coverage and high setup and maintenance costs. Furthermore, these technologies cannot provide accurate flood warnings based on vehicle parking locations, thus compromising vehicle safety. Summary of the Invention

[0004] In view of this, this application provides a vehicle control method, device, equipment, and storage medium to facilitate accurate flood warning of vehicle parking location and ensure vehicle safety.

[0005] In a first aspect, embodiments of this application provide a vehicle control method applied in the cloud, the method comprising: Obtain the location and parking information uploaded by the target vehicle; Based on the location information and parking information, a flood risk assessment is performed on the target vehicle. If the risk assessment level of the target vehicle is determined to be greater than the preset level, a flood warning is generated. The flood warning is sent to the target vehicle.

[0006] In this embodiment, after the vehicle is parked, it sends its location information and parking information to the cloud. The cloud can then perform a flood risk assessment on the vehicle's parking location based on the uploaded location information and parking information. If the corresponding risk assessment level of the vehicle is high, a flood warning will be generated to notify the vehicle, so that the vehicle can change its parking location to avoid damage from flooding.

[0007] In some possible embodiments, the flood risk assessment of the target vehicle based on the location information and the parking information includes: Obtain meteorological information, and based on the location information and parking information, obtain map data and historical water accumulation data corresponding to the vehicle; Feature extraction is performed on meteorological information, map data, and historical water accumulation data to obtain flood feature values; The risk assessment level corresponding to the target vehicle is obtained based on the flood characteristics.

[0008] In some possible embodiments, obtaining the risk assessment level corresponding to the target vehicle based on the flood characteristic value includes: The target location is determined based on the location information; The target historical water accumulation data corresponding to the target location is obtained from the historical water accumulation data; The historical water accumulation data of the target location is processed using a time series model to obtain the first water accumulation risk value corresponding to the target location; Based on the flood characteristic values ​​and the geographic information system, the second water accumulation risk value corresponding to the target location is obtained; Based on the first water accumulation risk value, the second water accumulation risk value, and the flood characteristic value, a pre-trained risk prediction model is used to obtain the risk assessment level corresponding to the target vehicle.

[0009] In some possible embodiments, the step of obtaining the risk assessment level corresponding to the target vehicle based on the first flood risk value, the second flood risk value, and the flood characteristic value using a pre-trained risk prediction model includes: Based on the first water accumulation risk value, the second water accumulation risk value, and the flood characteristic value, a pre-trained risk prediction model is used to obtain the risk assessment level and water accumulation depth corresponding to the target vehicle. If it is determined that the risk assessment level corresponding to the target vehicle is greater than a preset level, a flood warning is generated, including: If it is determined that the risk assessment level corresponding to the target vehicle is greater than a preset level, and / or the water depth is greater than a preset water depth, then a flood warning is generated.

[0010] In some possible embodiments, the method further includes: The target location is determined based on the location information; The target water accumulation data corresponding to the target location is obtained from the historical water accumulation data; The historical maximum water depth is obtained based on the target water accumulation data; If the water depth corresponding to the target vehicle is greater than the historical maximum water depth, a risk map update prompt is generated and sent to the target vehicle.

[0011] In some possible embodiments, the method includes: Obtain the continuous image sequence uploaded by the target vehicle; Texture analysis is performed on the continuous image sequence to obtain texture features and water accumulation areas; Determine the area and location of the water accumulation corresponding to the water accumulation area; The water depth is obtained by using a water depth estimation algorithm model based on the water area and the texture features. The water depth and water location are sent to the target vehicle.

[0012] In some possible embodiments, after obtaining the water depth corresponding to the water accumulation location based on the texture features using a water depth estimation algorithm on the continuous image sequence, the method further includes: Obtain the dynamic data uploaded by the target vehicle; The impact of the water accumulation area on the target vehicle is determined based on the water depth and the dynamic data. The impact information is sent to the target vehicle.

[0013] Secondly, embodiments of this application provide a vehicle control device applied in the cloud, the device comprising: The acquisition module is used to acquire the location information and parking information uploaded by the target vehicle. An assessment module is used to perform a flood risk assessment on the target vehicle based on the location information and the parking information. The early warning module is used to generate a flood warning if it is determined that the risk assessment level of the target vehicle is greater than a preset level. A sending module is used to send the flood warning to the target vehicle.

[0014] In some possible embodiments, the evaluation module is specifically used for: Obtain meteorological information, and based on the location information and parking information, obtain map data and historical water accumulation data corresponding to the vehicle; Feature extraction is performed on meteorological information, map data, and historical water accumulation data to obtain flood feature values; The risk assessment level corresponding to the target vehicle is obtained based on the flood characteristics.

[0015] In some possible embodiments, the evaluation module is specifically used for: The target location is determined based on the location information; The target historical water accumulation data corresponding to the target location is obtained from the historical water accumulation data; The historical water accumulation data of the target location is processed using a time series model to obtain the first water accumulation risk value corresponding to the target location; Based on the flood characteristic values ​​and the geographic information system, the second water accumulation risk value corresponding to the target location is obtained; Based on the first water accumulation risk value, the second water accumulation risk value, and the flood characteristic value, a pre-trained risk prediction model is used to obtain the risk assessment level corresponding to the target vehicle.

[0016] In some possible embodiments, the evaluation module is specifically used for: Based on the first water accumulation risk value, the second water accumulation risk value, and the flood characteristic value, a pre-trained risk prediction model is used to obtain the risk assessment level and water accumulation depth corresponding to the target vehicle. If it is determined that the risk assessment level corresponding to the target vehicle is greater than a preset level, a flood warning is generated, including: If it is determined that the risk assessment level corresponding to the target vehicle is greater than a preset level, and / or the water depth is greater than a preset water depth, then a flood warning is generated.

[0017] In some possible embodiments, the warning module is also used for: The target location is determined based on the location information; The target water accumulation data corresponding to the target location is obtained from the historical water accumulation data; The historical maximum water depth is obtained based on the target water accumulation data; If the water depth corresponding to the target vehicle is greater than the historical maximum water depth, a risk map update prompt is generated and sent to the target vehicle.

[0018] In some possible embodiments, the acquisition module is also used for: Obtain the continuous image sequence uploaded by the target vehicle; Texture analysis is performed on the continuous image sequence to obtain texture features and water accumulation areas; Determine the area and location of the water accumulation corresponding to the water accumulation area; The water depth is obtained by using a water depth estimation algorithm model based on the water area and the texture features. The water depth and water location are sent to the target vehicle.

[0019] In some possible embodiments, the acquisition module is also used for: Obtain the dynamic data uploaded by the target vehicle; The impact of the water accumulation area on the target vehicle is determined based on the water depth and the dynamic data. The impact information is sent to the target vehicle.

[0020] Thirdly, another embodiment of this application also provides an electronic device, including at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform any of the methods provided in the first aspect embodiment of this application.

[0021] Fourthly, another embodiment of this application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for causing a computer to perform any of the methods provided in the first aspect of this application.

[0022] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic diagram illustrating an application scenario of a vehicle control method provided in an embodiment of this application; Figure 2 This is a schematic diagram of the overall process of a vehicle control method provided in an embodiment of this application; Figure 3 A schematic diagram illustrating the process of a vehicle control method for flood risk assessment of a target vehicle based on location information and parking information, provided in an embodiment of this application. Figure 4 A schematic diagram illustrating the process of obtaining the risk assessment level of a target vehicle based on flood feature values ​​in a vehicle control method provided in this application embodiment; Figure 5 A schematic diagram illustrating the process of determining whether a risk map update notification needs to be sent, as provided in an embodiment of this application, for a vehicle control method. Figure 6 This is a schematic diagram illustrating the process of a vehicle control method provided in this application to determine whether a moving vehicle can safely wade through water. Figure 7 A schematic diagram illustrating a safe wading process for a vehicle control method provided in this application embodiment; Figure 8 A schematic diagram of an apparatus for a vehicle control method provided in an embodiment of this application; Figure 9 This is a schematic diagram of an electronic device for a vehicle control method provided in an embodiment of this application. Detailed Implementation

[0025] To better understand the technical solution of this application, the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0026] It should be understood that the described embodiments are merely some, not all, of the embodiments in this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0027] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. The singular forms “a,” “the,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0028] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0029] The inventors' research revealed that urban flooding is very common during heavy rainstorms. These floods can cause damage to vehicles at best, and threaten people's lives at worst. Therefore, real-time monitoring and early warning of potential flood risks are necessary to mitigate or avoid property damage and casualties.

[0030] Flood monitoring and early warning systems in related technologies primarily rely on data from fixed monitoring points, leading to limitations in coverage and high setup and maintenance costs. Furthermore, these technologies cannot provide accurate flood warnings based on vehicle parking locations, thus compromising vehicle safety.

[0031] To address the aforementioned problems, embodiments of this application provide a vehicle control method, apparatus, device, and storage medium to solve these problems. The inventive concept of this application can be summarized as follows: acquiring location information and parking information uploaded by a target vehicle; performing a flood risk assessment on the target vehicle based on the location information and parking information; if it is determined that the corresponding risk assessment level of the target vehicle is greater than a preset level, generating a flood warning; and sending the flood warning to the target vehicle.

[0032] In this embodiment, after the vehicle is parked, it sends its location information and parking information to the cloud. The cloud can then perform a flood risk assessment on the vehicle's parking location based on the uploaded location information and parking information. If the corresponding risk assessment level of the vehicle is high, a flood warning will be generated to notify the vehicle, so that the vehicle can change its parking location to avoid damage from flooding.

[0033] For ease of understanding, the vehicle control method provided in this application embodiment will be described in detail below with reference to the accompanying drawings: like Figure 1 The diagram shown illustrates an application scenario of a vehicle control method according to an embodiment of this application. The diagram includes: a cloud platform 10 and a target vehicle 20; wherein: The cloud 10 obtains the location information and parking information uploaded by the target vehicle 20; performs a flood risk assessment on the target vehicle 20 based on the location information and parking information; if it is determined that the corresponding risk assessment level of the target vehicle 20 is greater than the preset level, a flood warning is generated; and the flood warning is sent to the target vehicle 20.

[0034] The description in this application focuses only on a single cloud platform 10 and target vehicle 20. However, those skilled in the art should understand that the illustrated cloud platform 10 and target vehicle 20 are intended to represent the operation of the cloud platform 10 and target vehicle 20 involved in the technical solution of this application, and do not imply any limitation on the number, type, or location of the cloud platform 10 and target vehicle 20. It should be noted that adding additional modules to or removing individual modules from the illustrated environment will not change the underlying concept of the exemplary embodiments of this application.

[0035] It should be noted that the vehicle control method proposed in this application is not only applicable to... Figure 1 The application scenarios shown are also applicable to any device that requires vehicle control.

[0036] like Figure 2 The diagram shown is a flowchart illustrating a vehicle control method provided in an embodiment of this application, wherein: In step 201: Obtain the location information and parking information uploaded by the target vehicle.

[0037] In this embodiment of the application, after the vehicle is parked, it will automatically send location information and parking information to the cloud. The location information can be the latitude and longitude information of the vehicle or the parking space number information of the parking lot where the vehicle is located. The parking information can be the distribution of parking spaces in the parking lot where the vehicle is located and the location information of the parking lot.

[0038] In step 202: Flood risk assessment is performed on the target vehicle based on location information and parking information.

[0039] In this embodiment of the application, after obtaining the location information and parking information of the target vehicle, the cloud can perform flood risk assessment on the location of the target vehicle, and then obtain the risk assessment level of the target vehicle.

[0040] In some possible embodiments, flood risk assessment of target vehicles is performed based on location and parking information, specifically as follows: Figure 3 The steps shown are as follows: In step 301: obtain meteorological information, and obtain map data and historical water accumulation data corresponding to the vehicle based on location information and parking information.

[0041] In this application embodiment, meteorological information includes, but is not limited to: current weather conditions, weather conditions within a future preset time period, rainfall within a future preset time period, rainfall intensity within a future preset time period, and duration of rainfall within a future preset time period.

[0042] In some possible embodiments, the cloud can determine the target location of the target vehicle based on location information and parking information, and then obtain map data associated with the target location. The map data includes, but is not limited to, terrain data, drainage system layout, and historical water accumulation records.

[0043] In this embodiment of the application, the target location determined based on the location information and parking information can be the block where the target vehicle is located or the residential area where the target vehicle is located.

[0044] In some possible embodiments, historical water accumulation data includes, but is not limited to: rainfall, water depth, and duration of each past rainfall event at the target location corresponding to the target vehicle.

[0045] In step 302: Feature extraction is performed on meteorological information, map data, and historical water accumulation data to obtain flood feature values.

[0046] In this embodiment of the application, in order to ensure the efficiency of data processing and the accuracy of the obtained risk assessment level, it is necessary to extract features from the meteorological information, map data and historical water accumulation data after obtaining them, and then use the obtained flood feature values ​​to continue to perform subsequent steps.

[0047] In some possible embodiments, feature extraction is performed on meteorological information, map data, and historical water accumulation data. Specifically, this can be implemented as follows: the future rainfall at the location of the target vehicle can be obtained based on the rainfall in the meteorological information within a preset future time period and the map data; the low-lying points at the target vehicle's location can be determined based on the water depth in the map data and historical water accumulation data; the drainage capacity at the target vehicle's location can be obtained based on the drainage system layout in the map data and historical water accumulation data, historical water accumulation records, and the rainfall in the preset future time period in the meteorological information; and the historical water accumulation frequency at the target vehicle's location can be obtained based on the historical water accumulation records in the map data and historical water accumulation data.

[0048] It should be noted that the flood characteristic values ​​in the embodiments of this application include, but are not limited to: future rainfall, low-lying areas, drainage capacity, and historical water accumulation frequency. In order to facilitate data processing, the flood characteristic values ​​are all numerical data.

[0049] For example, a value of 0-10 is used to represent drainage capacity, with a larger value corresponding to a stronger drainage capacity.

[0050] In step 303: the risk assessment level corresponding to the target vehicle is obtained based on the flood characteristic values.

[0051] In some possible embodiments, the risk assessment level corresponding to the target vehicle is obtained based on flood characteristic values, which can be specifically implemented as follows: Figure 4 The steps shown are as follows: In step 401: Determine the target location based on the location information.

[0052] In some possible embodiments, the cloud can determine the target location of the target vehicle based on location information and parking information, wherein the target location can be the block where the target vehicle is located or the residential area where the target vehicle is located.

[0053] In step 402: Obtain the target historical water accumulation data corresponding to the target location from the historical water accumulation data.

[0054] In this embodiment of the application, the historical water accumulation data includes the rainfall, water depth, and duration of each past rainfall event for each location. The target historical water accumulation data is the rainfall, water depth, and duration of each past rainfall event for the target location.

[0055] In step 403: The historical water accumulation data of the target is processed using a time series model to obtain the first water accumulation risk value corresponding to the target location.

[0056] In this application, the time series model can be an Autoregressive Integrated Moving Average (ARIMA) model or a Long Short-Term Memory (LSTM) network model. This application does not limit the specific implementation of the time series model and can set it according to the requirements.

[0057] The time series model used in this embodiment is a model trained on a sample set constructed based on water accumulation data and water accumulation risk values. Therefore, when the historical water accumulation data of the target is input into the time series model, the time series model will output the water accumulation risk value corresponding to the historical water accumulation data of the target, that is, the first water accumulation risk value corresponding to the target location.

[0058] In step 404: Based on flood characteristic values ​​and geographic information systems, the second water accumulation risk value corresponding to the target location is obtained.

[0059] In this embodiment of the application, the Geographic Information System (GIS) can predict the spatial distribution of water accumulation based on the daily precipitation and can predict the impact of topography on water accumulation.

[0060] In some possible embodiments, the following can be input into the geographic information system: future rainfall, low-lying areas, drainage capacity, and historical waterlogging frequency corresponding to the target location in the flood characteristic values. Then, the second waterlogging risk value corresponding to the target location can be obtained from the geographic information system output.

[0061] The geographic information system used in this application embodiment is trained based on the future rainfall, low-lying areas, drainage capacity, historical water accumulation frequency, and water accumulation risk value corresponding to historical rainfall events. The training process is the same as the model training process in related technologies, and will not be described in detail here.

[0062] In step 405: Based on the first water accumulation risk value, the second water accumulation risk value, and the flood characteristic value, a pre-trained risk prediction model is used to obtain the risk assessment level corresponding to the target vehicle.

[0063] In order to ensure that the risk assessment level obtained in this application is more accurate, after obtaining the first flood risk value and the second flood risk value, the flood characteristic value can be combined for fusion processing, so that the risk assessment level obtained can cover various factors that may lead to flooding, such as future weather conditions, the terrain of the target location, and drainage capacity.

[0064] In some possible embodiments, after obtaining the first water accumulation risk value and the second water accumulation risk value, the first water accumulation risk value, the second water accumulation risk value, and the flood characteristic value are used together as inputs to the risk prediction model, thereby obtaining the risk assessment level corresponding to the target vehicle and the water accumulation depth corresponding to the target location output by the risk prediction model.

[0065] In step 203: If the risk assessment level of the target vehicle is determined to be greater than the preset level, a flood warning is generated.

[0066] In this embodiment, to ensure the safety of the target vehicle, after determining the risk assessment level, it is necessary to compare the risk assessment level with a preset level. A flood warning is generated when the risk assessment level is greater than the preset level. Besides using the risk assessment level to determine whether a flood warning needs to be generated, it can also be determined based on the water depth. If the water depth is greater than a preset water depth, it indicates that the target vehicle may be at risk of being flooded, and therefore a flood warning also needs to be generated.

[0067] In step 204: The flood warning is sent to the target vehicle.

[0068] In this embodiment of the application, after generating a flood warning, the flood warning can be directly sent to the target vehicle and displayed on the central control screen of the target vehicle. Alternatively, the flood warning can be sent to the smart terminal (mobile phone, smartwatch, tablet, etc.) associated with the target vehicle via an application or SMS, so that the user can be informed of the situation in a timely manner and move the vehicle.

[0069] In some possible embodiments, after obtaining the map data and historical water accumulation data corresponding to the vehicle based on the location information and the parking information, in order to help users understand the water accumulation situation, the cloud can also obtain a water accumulation risk map based on the weather information, the map data and the historical water accumulation data, and send the risk map update prompt to the target vehicle so that the target vehicle can update its local water accumulation risk map according to the risk map update prompt.

[0070] In some possible embodiments, when the target vehicle has no risk of flooding, there is no need to download a flood risk map. Therefore, the cloud does not need to send risk map update notifications to vehicles without flood risk. Thus, before sending risk map update notifications to the target vehicle, it can be done through methods such as... Figure 5The steps shown indicate whether a risk map update notification needs to be sent, where: In step 501: Determine the target location based on the location information.

[0071] In this embodiment of the application, the vehicle will automatically send its location information to the cloud after parking. The location information may be the vehicle's latitude and longitude information or the parking space number information in the parking lot where the vehicle is located. In this embodiment of the application, the target location determined based on the location information may be the street where the target vehicle is located or the residential area where the target vehicle is located.

[0072] In step 502: Obtain the target water accumulation data corresponding to the target location from the historical water accumulation data.

[0073] In this embodiment, historical waterlogging data includes, but is not limited to, the rainfall, water depth, and duration of each past rainfall event at each location. To reduce computational load, not all historical waterlogging data needs to be included in subsequent calculations; only the target waterlogging data corresponding to the target location needs to be determined, namely, the rainfall, water depth, and duration of each past rainfall event at the target location.

[0074] In step 503: the historical maximum water depth is obtained based on the target water accumulation data.

[0075] In this embodiment of the application, the target water accumulation data includes water accumulation depths corresponding to multiple rainfall events, and the maximum value is selected as the historical maximum water accumulation depth.

[0076] In step 504: If the water depth corresponding to the target vehicle is greater than the historical maximum water depth, a risk map update prompt is generated and sent to the target vehicle.

[0077] When the water depth corresponding to the target vehicle is greater than the historical maximum water depth, the risk map needs to be updated. Therefore, a risk map update prompt needs to be generated and sent to the target vehicle so that the target vehicle can download the updated risk map from the cloud after receiving the risk map update prompt.

[0078] In some possible embodiments, the cloud may simultaneously receive location information and parking information sent by multiple target vehicles. After determining the risk assessment level of each target vehicle, cluster analysis can be performed on the vehicle market area based on the target location of each target vehicle. Target locations with the same risk assessment level can be classified into one category. Different early warning strategies can be set for each category, and the early warning strategies can be sent to the vehicles.

[0079] For example: the early warning strategy set for a target location with a risk assessment level of 1 is strategy 1, the early warning strategy set for a target location with a risk assessment level of 2 is strategy 2, and the early warning strategy set for a target location with a risk assessment level of 3 is strategy 3.

[0080] In some possible embodiments, after sending a flood warning to the target vehicle, the depth of water accumulation at the target location will also be monitored. If the water depth exceeds a preset value, a water accumulation notification will be sent to the smart terminal associated with the target vehicle via an application or SMS.

[0081] In some possible implementations, after sending a flood warning to the target vehicle, a risk assessment is performed on each parking space in the parking lot where the target vehicle is located based on meteorological and parking information. The best parking space is then recommended to the user, which could be the highest parking space in the parking lot. Simultaneously, the user is also provided with a route from the target location to the best parking space.

[0082] For autonomous vehicles, after sending a flood warning to the target vehicle, if the water depth at the target location is determined to be greater than a preset value, the cloud can send an evacuation command to the vehicle so that the vehicle can drive to a safe area according to the evacuation command. The safe area is an area with a risk assessment level that is lower than a preset level.

[0083] In some possible embodiments, a determination can also be made regarding whether a moving vehicle is safe to wade through water, specifically implemented as follows: Figure 6 The steps shown are as follows: In step 601: Obtain the continuous image sequence uploaded by the target vehicle.

[0084] In this embodiment of the application, the cloud implements the reception of a continuous image sequence uploaded by the target vehicle, and the continuous image sequence of the target vehicle is obtained based on the vehicle-mounted camera.

[0085] In step 602: texture analysis is performed on the continuous image sequence to obtain texture features and water accumulation areas.

[0086] In this embodiment of the application, in order to save computing power, it is necessary to perform texture analysis on the continuous image sequence. The subsequent steps are only continued when texture features and water accumulation areas are identified; otherwise, no subsequent steps are required, and the process returns to step 601.

[0087] For example: The cloud receives the first to tenth frames of images uploaded by the vehicle. If texture analysis is performed on the first to tenth frames and no texture features or water accumulation areas are identified, it is determined that there is no water accumulation. Then, the cloud receives the eleventh to twentieth frames of images uploaded by the vehicle. If texture analysis is performed on the eleventh to twentieth frames and it is determined that texture features and water accumulation areas are identified starting from the fifteenth frame, then the subsequent steps are executed.

[0088] In step 603: Determine the water accumulation area and location corresponding to the water accumulation area.

[0089] In this embodiment, after identifying the waterlogged area, it is necessary to determine the area and location of the waterlogged area to obtain the water depth. The location of the waterlogged area can be the latitude and longitude coordinates of the center point of the waterlogged area, or the distance between the center point of the waterlogged area and the target vehicle.

[0090] In step 604: the water depth corresponding to the water accumulation location is obtained by using a water depth estimation algorithm model based on the water accumulation area and texture features.

[0091] In this embodiment of the application, after obtaining the water accumulation area and texture features, the water accumulation area and texture features are used as inputs to the water depth estimation algorithm model, and the output of the water depth estimation algorithm is the water depth corresponding to the water accumulation location.

[0092] The water depth estimation algorithm model is obtained by training on training samples constructed based on historical water accumulation data. The training method of the water depth estimation algorithm model is the same as the model training method in related technologies, and will not be described in detail here.

[0093] In step 605: The water depth and location are sent to the target vehicle.

[0094] In this embodiment of the application, after obtaining the water depth, the water depth and water location need to be sent to the target vehicle so that the target vehicle can stop or change its route when the water depth is deep, thereby achieving safe wading of the vehicle.

[0095] To further ensure safe wading, after implementing step 604, the following can be implemented: Figure 7 The steps shown are as follows: In step 701: Obtain the dynamic data uploaded by the target vehicle.

[0096] In this embodiment, dynamic data includes, but is not limited to, vehicle speed, braking force, and the number of times the anti-lock braking system (ABS) is activated. By acquiring the dynamic data of the target vehicle, the current driving status and performance of the target vehicle can be determined.

[0097] In step 702: the impact of the water accumulation area on the target vehicle is determined based on the water depth and dynamic data.

[0098] In this embodiment of the application, it is considered that the deeper the water depth, the higher the performance requirements of the vehicle. Therefore, the impact of the water accumulation area on the target vehicle can be determined based on the water depth and dynamic data. Specifically, the dynamic data can be normalized and then weighted and summed to obtain the vehicle attribute value. The impact of the water accumulation area on the target vehicle can be obtained based on the ratio of the water depth to the vehicle attribute value. The higher the ratio, the more serious the impact of the water accumulation area on the target vehicle.

[0099] In step 703: The impact information is sent to the target vehicle.

[0100] In this embodiment of the application, after receiving the impact information, the target vehicle can determine whether it can safely wade through the water, and if it cannot safely wade through the water, it can change its route.

[0101] In some possible embodiments, in order to improve Figure 6 and Figure 7 The execution speed of the steps shown is therefore Figure 6 and Figure 7 The steps shown can also be performed by the target vehicle. That is, after the target vehicle acquires the continuous image sequence and dynamic data, it can determine the water depth and impact directly based on the continuous image sequence and dynamic data without uploading to the cloud.

[0102] Based on the same inventive concept, after introducing a vehicle control method provided by the embodiments of this application, as follows... Figure 8 As shown, a vehicle control device 800 provided in an embodiment of this application will be described below. The device includes: The acquisition module 8001 is used to acquire the location information and parking information uploaded by the target vehicle. Assessment module 8002 is used to perform flood risk assessment on the target vehicle based on the location information and the parking information; The early warning module 8003 is used to generate a flood warning if it is determined that the risk assessment level corresponding to the target vehicle is greater than a preset level. The sending module 8004 is used to send the flood warning to the target vehicle.

[0103] In some possible embodiments, the evaluation module 8002 is specifically used for: Obtain meteorological information, and based on the location information and parking information, obtain map data and historical water accumulation data corresponding to the vehicle; Feature extraction is performed on meteorological information, map data, and historical water accumulation data to obtain flood feature values; The risk assessment level corresponding to the target vehicle is obtained based on the flood characteristics.

[0104] In some possible embodiments, the evaluation module 8002 is specifically used for: The target location is determined based on the location information; The target historical water accumulation data corresponding to the target location is obtained from the historical water accumulation data; The historical water accumulation data of the target location is processed using a time series model to obtain the first water accumulation risk value corresponding to the target location; Based on the flood characteristic values ​​and the geographic information system, the second water accumulation risk value corresponding to the target location is obtained; Based on the first water accumulation risk value, the second water accumulation risk value, and the flood characteristic value, a pre-trained risk prediction model is used to obtain the risk assessment level corresponding to the target vehicle.

[0105] In some possible embodiments, the evaluation module 8002 is specifically used for: Based on the first water accumulation risk value, the second water accumulation risk value, and the flood characteristic value, a pre-trained risk prediction model is used to obtain the risk assessment level and water accumulation depth corresponding to the target vehicle. If it is determined that the risk assessment level corresponding to the target vehicle is greater than a preset level, a flood warning is generated, including: If it is determined that the risk assessment level corresponding to the target vehicle is greater than a preset level, and / or the water depth is greater than a preset water depth, then a flood warning is generated.

[0106] In some possible embodiments, the warning module 8003 is also used for: The target location is determined based on the location information; The target water accumulation data corresponding to the target location is obtained from the historical water accumulation data; The historical maximum water depth is obtained based on the target water accumulation data; If the water depth corresponding to the target vehicle is greater than the historical maximum water depth, a risk map update prompt is generated and sent to the target vehicle.

[0107] In some possible embodiments, the acquisition module 8001 is further configured to: Obtain the continuous image sequence uploaded by the target vehicle; Texture analysis is performed on the continuous image sequence to obtain texture features and water accumulation areas; Determine the area and location of the water accumulation corresponding to the water accumulation area; The water depth is obtained by using a water depth estimation algorithm model based on the water area and the texture features. The water depth and water location are sent to the target vehicle.

[0108] In some possible embodiments, the acquisition module 8001 is further configured to: Obtain the dynamic data uploaded by the target vehicle; The impact of the water accumulation area on the target vehicle is determined based on the water depth and the dynamic data. The impact information is sent to the target vehicle.

[0109] Corresponding to the above embodiments, this application also provides an electronic device. Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. The electronic device 900 may include a processor 901, a memory 902, and a communication unit 903. These components communicate through one or more buses. Those skilled in the art will understand that the structure of the electronic device shown in the figure does not constitute a limitation on the embodiment of the present invention. It may be a bus-shaped structure or a star-shaped structure, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0110] The communication unit 903 is used to establish a communication channel, enabling the electronic device to communicate with other devices. It receives user data from other devices or sends user data to other devices.

[0111] The processor 901 serves as the control center of the electronic device, connecting various parts of the device via interfaces and lines. It executes software programs and / or modules stored in the memory 902, and calls data stored in the memory to perform various functions and / or process data. The processor can be composed of integrated circuits (ICs), such as a single packaged IC or multiple packaged ICs with the same or different functions connected together. For example, the processor 901 may consist only of a central processing unit (CPU). In this embodiment, the CPU may have a single processing core or include multiple processing cores.

[0112] The memory 902 is used to store the execution instructions of the processor 901. The memory 902 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0113] When the execution instructions in memory 902 are executed by processor 901, the electronic device 900 is able to perform operations. Figure 7 Some or all of the steps in the illustrated embodiments.

[0114] In a specific implementation, the present invention also provides a computer storage medium, wherein the computer storage medium may store a program, which, when executed, may include some or all of the steps of the calling method provided by the present invention. The storage medium may be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0115] Those skilled in the art will clearly understand that the techniques in the embodiments of the present invention can be implemented using software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or certain parts of the embodiments of the present invention.

[0116] The same or similar parts between the various embodiments in this specification can be referred to mutually. In particular, the device embodiments and terminal embodiments are basically similar to the method embodiments, so the description is relatively simple, and the relevant parts can be referred to the description in the method embodiments.

Claims

1. A vehicle control method, characterized in that, Applied to the cloud, the method includes: Obtain the location and parking information uploaded by the target vehicle; Based on the location information and parking information, a flood risk assessment is performed on the target vehicle. If the risk assessment level of the target vehicle is determined to be greater than the preset level, a flood warning is generated. The flood warning is sent to the target vehicle.

2. The method according to claim 1, characterized in that, The process of performing a flood risk assessment on the target vehicle based on the location information and the parking information includes: Obtain meteorological information, and based on the location information and parking information, obtain map data and historical water accumulation data corresponding to the vehicle; Feature extraction is performed on meteorological information, map data, and historical water accumulation data to obtain flood feature values; The risk assessment level corresponding to the target vehicle is obtained based on the flood characteristics.

3. The method according to claim 2, characterized in that, The process of obtaining the risk assessment level corresponding to the target vehicle based on the flood characteristic value includes: The target location is determined based on the location information; The target historical water accumulation data corresponding to the target location is obtained from the historical water accumulation data; The historical water accumulation data of the target location is processed using a time series model to obtain the first water accumulation risk value corresponding to the target location; Based on the flood characteristic values ​​and the geographic information system, the second water accumulation risk value corresponding to the target location is obtained; Based on the first water accumulation risk value, the second water accumulation risk value, and the flood characteristic value, a pre-trained risk prediction model is used to obtain the risk assessment level corresponding to the target vehicle.

4. The method according to claim 3, characterized in that, The step of obtaining the risk assessment level corresponding to the target vehicle by using a pre-trained risk prediction model based on the first water accumulation risk value, the second water accumulation risk value, and the flood characteristic value includes: Based on the first water accumulation risk value, the second water accumulation risk value, and the flood characteristic value, a pre-trained risk prediction model is used to obtain the risk assessment level and water accumulation depth corresponding to the target vehicle. If it is determined that the risk assessment level corresponding to the target vehicle is greater than a preset level, a flood warning is generated, including: If it is determined that the risk assessment level corresponding to the target vehicle is greater than a preset level, and / or the water depth is greater than a preset water depth, then a flood warning is generated.

5. The method according to claim 1, characterized in that, The method further includes: The target location is determined based on the location information; Obtain the target water accumulation data corresponding to the target location from historical water accumulation data; The historical maximum water depth is obtained based on the target water accumulation data; If the water depth corresponding to the target vehicle is greater than the historical maximum water depth, a risk map update prompt is generated and sent to the target vehicle.

6. The method according to claim 1, characterized in that, The method includes: Obtain the continuous image sequence uploaded by the target vehicle; Texture analysis is performed on the continuous image sequence to obtain texture features and water accumulation areas; Determine the area and location of the water accumulation corresponding to the water accumulation area; The water depth is obtained by using a water depth estimation algorithm model based on the water area and the texture features. The water depth and water location are sent to the target vehicle.

7. The method according to claim 6, characterized in that, The method further includes: Obtain the dynamic data uploaded by the target vehicle; The impact of the water accumulation area on the target vehicle is determined based on the water depth and the dynamic data. The impact information is sent to the target vehicle.

8. A vehicle control device, characterized in that, The device, applied in the cloud, includes: The acquisition module is used to acquire the location information and parking information uploaded by the target vehicle. An assessment module is used to perform a flood risk assessment on the target vehicle based on the location information and the parking information. The early warning module is used to generate a flood warning if it is determined that the risk assessment level of the target vehicle is greater than a preset level. A sending module is used to send the flood warning to the target vehicle.

9. An electronic device, characterized in that, It includes a memory for storing computer program instructions and a processor for executing the program instructions, wherein when the computer program instructions are executed by the processor, the electronic device is triggered to perform the method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method according to any one of claims 1-7.

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