Parking lot flood risk identification method, device, medium and equipment

Abstract

The disclosure provides a parking lot flood risk identification method, device, medium and equipment, the method comprises the following steps: according to the current position of the target vehicle, determining the candidate parking lot located in the first predetermined range of the current position and having available parking spaces; using a pre-constructed and trained flood risk prediction model, predicting the flood risk of each candidate parking lot to obtain the corresponding flood risk index of each candidate parking lot; according to the corresponding flood risk index of each candidate parking lot, the number of available parking spaces and the distance between the current position of the target vehicle, determining at least one target recommended parking lot from the candidate parking lot; according to the at least one target recommended parking lot, generating and pushing corresponding recommendation information. The technical scheme of the embodiment of the application can accurately identify the parking lot with flood risk, and ensure the safety of the recommended parking lot.

Description

TECHNICAL FIELD

[0001] The present disclosure belongs to the technical field of intelligent transportation, and in particular relates to a parking lot flood risk identification method, device, medium and equipment. BACKGROUND

[0002] With the acceleration of urbanization and the influence of climate change, urban flood has become one of the increasingly serious urban disasters. Flood not only brings serious impact to the urban transportation system, but also poses new challenges to parking lot management and urban lifeline engineering construction. In the current technical solution, the parking recommendation system is mostly based on the idle condition of parking spaces and the distance between vehicles and parking lots to recommend parking lots for drivers. However, when there is severe weather (such as heavy rain, etc.), it is impossible to determine the parking lot with flood risk in time and accurately, thereby causing corresponding losses. SUMMARY

[0003] Embodiments of the present application provide a parking lot flood risk identification method, device, medium and equipment, thereby at least to some extent, the parking lot with flood risk can be accurately identified, and the safety of the recommended parking lot is ensured.

[0004] Other characteristics and advantages of the present application will become apparent from the following detailed description, or will be learned by practice of the present application.

[0005] According to one aspect of the present application, a parking lot flood risk identification method is provided, comprising: determining a candidate parking lot with available parking spaces within a first predetermined range of a current position of a target vehicle according to the current position; using a pre-constructed and trained flood risk prediction model to predict the flood risk of each candidate parking lot, to obtain a corresponding flood risk index of each candidate parking lot, the flood risk prediction model being trained according to corresponding historical hydrological data, historical meteorological data, parking lot attribute information, parking lot flood data and parking lot flood protection information; determining at least one target recommended parking lot from the candidate parking lots according to the corresponding flood risk index of each candidate parking lot, the number of available parking spaces and the distance between the current position of the target vehicle; and generating and pushing corresponding recommendation information according to the at least one target recommended parking lot.

[0006] According to one embodiment of the present application, the at least one target recommended parking lot is determined from the candidate parking lots according to the flood risk indexes, the available parking space numbers and the distances between the candidate parking lots and the current position of the target vehicle, including: determining a recommended score corresponding to each of the candidate parking lots according to the flood risk index, the available parking space number and the distance between the candidate parking lot and the current position of the target vehicle; and determining a candidate parking lot whose recommended score meets a predetermined rule as the target recommended parking lot.

[0007] According to one embodiment of the present application, the corresponding recommended information is generated and pushed according to the at least one target recommended parking lot, including: obtaining a currently confirmed disaster-affected parking lot and a disaster-affected road section; generating and pushing corresponding recommended information for other parking lots in the target recommended parking lot except the disaster-affected parking lot, wherein the recommended information includes a navigation route that avoids the disaster-affected road section.

[0008] According to one embodiment of the present application, the method further includes: performing flood risk prediction on a monitored parking lot in a second predetermined range based on the flood risk prediction model to obtain a corresponding flood risk index; and selecting and executing a corresponding vehicle evacuation strategy according to the flood risk index and the number of parked vehicles of each of the monitored parking lots.

[0009] According to one embodiment of the present application, the corresponding vehicle evacuation strategy is selected and executed according to the flood risk index and the number of parked vehicles of each of the monitored parking lots, including: when the flood risk index of the monitored parking lot is in a first numerical interval, no vehicle evacuation processing is performed; when the flood risk index of the monitored parking lot is in a second numerical interval, determining a maximum number of currently available parking spaces of the monitored parking lot according to a parking space proportion threshold corresponding to the second numerical interval; when the maximum number of available parking spaces is less than the number of parked vehicles of the monitored parking lot, determining a vehicle to be transferred in the monitored parking lot and sending a vehicle transfer prompt information to the corresponding vehicle owner; and when the flood risk index of the monitored parking lot is greater than a numerical value in the second numerical interval, sending a vehicle transfer prompt information to the vehicle owner of the parked vehicle in the monitored parking lot.

[0010] According to one embodiment of the present application, the vehicle to be transferred in the monitored parking lot is determined, including: determining a minimum threshold of the number of vehicles to be transferred according to the maximum number of currently available parking spaces and the number of parked vehicles of the monitored parking lot; and determining the vehicle to be transferred from the parked vehicles in the monitored parking lot according to the minimum threshold of the number of vehicles to be transferred, the entry time, the vehicle type and the parking position of the parked vehicles in the monitored parking lot.

[0011] According to one embodiment of the present application, the flood risk prediction model includes several decision trees, and a corresponding flood risk index is generated by integrating the flood risk prediction results of several decision trees.

[0012] According to another aspect of the present application, a flood risk identification device for a parking lot is provided, comprising: a determination module for determining, based on the current position of a target vehicle, a candidate parking lot that is located within a first predetermined range of the current position and has available parking spaces; a prediction module for using a pre-constructed and trained flood risk prediction model to perform flood risk prediction on each of the candidate parking lots, and obtain a flood risk index corresponding to each of the candidate parking lots, wherein the flood risk prediction model is trained based on corresponding historical hydrological data, historical meteorological data, parking lot attribute information, parking lot flood data, and parking lot flood protection information; a selection module for determining at least one target recommended parking lot from the candidate parking lots based on the flood risk index corresponding to each of the candidate parking lots, the number of available parking spaces, and the distance from the current position of the target vehicle; and a processing module for generating and pushing corresponding recommendation information based on the at least one target recommended parking lot.

[0013] According to another aspect of the present application, a computer-readable medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method for identifying flood risks in a parking lot as described in any one of the above items is implemented.

[0014] According to another aspect of the present application, an electronic device is provided, characterized in that it includes: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the flood risk identification method for parking lots as described in any one of the above.

[0015] In the technical solutions provided in some embodiments of the present application, based on the current location of a target vehicle, candidate parking lots with available parking spaces within a first predetermined range of the current location are determined. A pre-built and trained flood risk prediction model is used to perform a flood risk prediction on each candidate parking lot, obtaining a corresponding flood risk index for each candidate parking lot. The flood risk prediction model is trained based on corresponding historical hydrological data, historical meteorological data, parking lot attribute information, parking lot flood data, and parking lot flood protection information. Subsequently, based on the flood risk index, the number of available parking spaces, and the distance from the current location of each candidate parking lot, at least one target recommended parking lot is determined from the candidate parking lots. Recommendation information is then generated and pushed based on the at least one target recommended parking lot. Thus, before making a parking lot recommendation, a flood risk prediction is performed on each candidate parking lot using the pre-trained flood risk prediction model to obtain a corresponding flood risk index. The target recommended parking lot is then determined based on the flood risk index and other information, thereby accurately identifying the flood risk of each candidate parking lot and ensuring the safety of the recommended parking lots.

[0016] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0018] Figure 1 A schematic flow chart of a method for identifying flood risks in a parking lot according to an embodiment of the present application is shown.

[0019] Figure 2 A block diagram of a parking lot flood risk identification device according to an embodiment of the present application is shown.

[0020] Figure 3 A schematic diagram of the structure of a computer system suitable for implementing an electronic device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0021] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0022] Moreover, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the application. One skilled in the relevant art will recognize, however, that the application can be practiced without one or more of the specific details, or with other methods, components, materials, and so forth. In other instances, well-known structures, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the application.

[0023] The block diagrams in the drawings show only the functionality of the features and do not necessarily imply a particular sequence of operations, or a necessity of each feature to be performed in the specific sequence shown. In some embodiments, the functions can be performed in different sequences. In some embodiments, various operations can be combined or partially combined. In some embodiments, the functions can be performed by different components or in different components.

[0024] The flow diagrams shown in the drawings are examples only and are not necessarily to scale. Also, the flow diagrams can not include all of the steps or options discussed. Nor need the steps or options include every example of possible implementations. For instance, acts can be shown out of order from those described or other orders can be

[0025] Figure 1 A flow diagram of a method for identifying flood risk of a parking lot is shown according to an embodiment of the application.

[0026] The method can be applied in a terminal device or a server, wherein the terminal device can include, but is not limited to, one or more of a smartphone, a tablet computer, a laptop computer, and a desktop computer; and the server can be a physical server or a cloud server.

[0027] Referring to Figure 1 The method for identifying flood risk of a parking lot includes steps S110-S140, which are described in detail as follows (the method is taken as an example applied in a server):

[0028] In step S110, a candidate parking lot having an idle parking space within a predetermined range of a current location of a target vehicle is determined according to the current location of the target vehicle.

[0029] The target vehicle can be a vehicle currently requesting a recommended parking lot.

[0030] In an embodiment, the driver of the target vehicle can communicate with the server through a terminal on the vehicle and send information requesting a recommended parking lot to the server. After receiving the request information, the server can obtain the current position of the target vehicle, which can be obtained by a positioning system of the target vehicle itself.

[0031] Next, the server can determine parking lots located within a first predetermined range of the current position of the target vehicle according to the obtained current position of the target vehicle, where the first predetermined range can be pre-set by a person skilled in the art according to prior experience, for example, a developer can determine a maximum parking distance acceptable by most drivers according to statistical results as the first predetermined range. In other examples, the first predetermined range can also be specified by the driver of the target vehicle, for example, when the current weather is relatively severe, the driver can select a shorter distance as the first predetermined range, etc.

[0032] It should be noted that a person skilled in the art can select a corresponding determination method of the first predetermined range according to actual implementation needs, which is not specially limited.

[0033] When the parking lots located within the first predetermined range are determined, the server can obtain relevant information of each parking lot within the range to determine whether the parking lot has available parking spaces. In an example, the server can access the background of each parking lot to obtain relevant information such as the number of parking spaces of the parking lot, the number of parked vehicles, etc., and further determine whether the parking lot has available parking spaces.

[0034] When it is determined that a parking lot is located within the first predetermined range of the current position of the target vehicle and has available parking spaces, the parking lot can be determined as a candidate parking lot. It should be understood that the number of candidate parking lots can be one, two, or any number of more than two, which is not specially limited.

[0035] In step S120, a pre-constructed and trained flood risk prediction model is used to predict the flood risk of each candidate parking lot to obtain a corresponding flood risk index of each candidate parking lot. The flood risk prediction model is trained according to corresponding historical hydrological data, historical meteorological data, parking lot attribute information, parking lot flood data, and parking lot flood protection information.

[0036] In this embodiment, a person skilled in the art can pre-construct and train a flood risk prediction model, which can be used to predict the flood risk of each parking lot according to relevant information and output a corresponding flood risk index.

[0037] When the candidate parking lot is determined, the server can call the trained flood risk prediction model to predict the flood risk of each candidate parking lot, and obtain the corresponding flood risk index of each candidate parking lot. It should be understood that the flood risk index is used to represent the possibility of the candidate parking lot suffering from flood disaster in the future period of time. If the flood risk index of a parking lot is larger, it means that the possibility of the candidate parking lot suffering from flood disaster is larger, and vice versa. If the flood risk index is smaller, the possibility of the parking lot suffering from flood disaster is smaller.

[0038] In an embodiment, the corresponding historical hydrological data, historical meteorological data, parking lot attribute information, parking lot flood data and parking lot flood protection information can be obtained in advance by those skilled in the art when training the flood risk prediction model, so as to train the flood risk prediction model.

[0039] Specifically, the historical hydrological data can include but is not limited to the water level height, duration and other information of historical flood; the historical meteorological data can include but is not limited to rainfall, rainfall intensity and other meteorological data; the parking lot attribute information can include but is not limited to the geographic information of the parking lot (such as altitude, slope, etc.), the structure information of the parking lot itself (such as underground parking lot, open-air parking lot, structure sealing degree of parking lot building, etc.), and the surrounding environment information of the parking lot (such as the straight-line distance to the nearest river or lake, the distribution density of the surrounding water system, and the historical flood data of the surrounding area); the parking lot flood data can include but is not limited to whether the historical parking lot is flooded, the water depth when flooded, and the duration when flooded; the parking lot flood protection information can include the existence of flood control facilities (such as flood control cofferdam, water retaining facilities, etc.), drainage system (such as the number of drainage pipes, drainage efficiency, etc.) of the parking lot, and the response history record of the parking lot management personnel to the flood warning information, etc.

[0040] Therefore, training the flood risk prediction model based on the above historical data can make the flood risk prediction model consider various factors when predicting the flood risk, thereby ensuring the accuracy of the output flood risk index.

[0041] In an example, after obtaining the above historical data, data preprocessing can be performed first, which can include data cleaning and data filling. Thus, the quality of the training data is improved, and the subsequent training effect is ensured.

[0042] In one embodiment, the flood risk prediction model may include several decision trees, and the corresponding flood risk index is generated by aggregating the flood risk prediction results of the decision trees. Specifically, assuming that the number of decision trees in the flood risk prediction model is N, during the training process, for a given sample data X containing characteristics such as the water level of historical floods, the flooded parking lot and its water depth, the duration of flooding, rainfall, and rainfall intensity, the flood risk assessment calculation formula of the flood risk prediction model is as follows:

[0043] .

[0044] in, RiskScore is the flood risk index, N is the number of decision trees, For the i A decision tree for sample data X The prediction function, are model parameters.

[0045] Introducing specific factors into random forests and gradient boosting trees, including parking lot geographic information factors F 1 , Environmental factors surrounding the parking lot F 2 , Parking lot structural factors F 3 , artificial intervention factors F 4 Among them, the geographic information factor is obtained from the geographic information of the parking lot contained in the parking lot attribute information, the parking lot surrounding environment factor is obtained from the parking lot surrounding environment information; the parking lot structure factor is obtained from the structural information of the parking lot itself; and the human intervention factor is obtained from the parking lot flood protection information.

[0046] The server can encode the relevant information according to predetermined rules to obtain a vector representation of each factor. For example, a binary variable can be used to represent the presence or absence of flood control facilities, such as 1 for the presence of flood control facilities and 0 for the absence of flood control facilities. The efficiency of the drainage system can be represented by a drainage system efficiency score, which can range from 0 to 1 to indicate the relative efficiency of the drainage system in flood conditions.

[0047] The response of parking lot managers to flood warning information can be expressed by the response speed and response measures scores. For response speed, it can be quantified based on how long after the flood warning is issued, the managers take action. For example, the score for responding within 1 hour after the warning is issued is 1, and the score for not responding for more than 4 hours is 0. The scores at other time points are calculated according to linear or nonlinear functions. For response measures, the scores are based on the effectiveness of the measures taken. For example, the score for installing and effectively installing temporary flood control facilities is 1, and the score for not taking any response measures is 0.

[0048] Based on the specific factors introduced, the decision tree construction and training formula is as follows:

[0049] ,

[0050] Right now .

[0051] This formula describes how a single decision tree makes predictions in a random forest, where For the i The prediction function of a decision tree. M is the number of leaf nodes in the decision tree. It is j The weight coefficient of the leaf node, It is j The predicted value of a leaf node, θ i,j For the i In the decision tree j Model parameters related to each feature need to be explained. θ i,j The value of can be determined through the training process, which is used to represent the split point, feature selection, leaf node output value and weight coefficient after introducing specific factors in the decision tree, thereby optimizing the prediction ability of the model; F is a specific factor vector, is the weight coefficient of a specific factor, 、 、 as well as Parking lot geographic information factors F 1 , Environmental factors surrounding the parking lot F 2 , Parking lot structural factors F 3 , artificial intervention factors F 4 Corresponding weight coefficients. In one example, the weight coefficients of specific factors can be optimized through methods such as cross-validation and grid search to ensure the accuracy of the prediction results of the decision tree.

[0052] Therefore, the flood risk prediction model provided by the embodiments of this application introduces specific factors into the traditional decision tree model for flood risk prediction, thereby enhancing the decision tree's predictive capabilities. The resulting flood risk prediction model can more comprehensively and accurately predict parking lot flooding risks during severe weather.

[0053] Please continue to refer to Figure 1 In step S130, at least one target recommended parking lot is determined from the candidate parking lots based on the flood risk index, the number of available parking spaces, and the distance from the current location of the target vehicle corresponding to each of the candidate parking lots.

[0054] In one embodiment, step S130 includes: determining a recommendation score corresponding to each of the candidate parking lots based on the flood risk index, the number of available parking spaces, and the distance from the current location of the target vehicle corresponding to each of the candidate parking lots; and determining the candidate parking lot whose recommendation score meets predetermined rules as the target recommended parking lot.

[0055] In this embodiment, after determining the flood risk index of each candidate parking lot, the server can score each candidate parking lot based on the flood risk index of each candidate parking lot, the number of available parking spaces, and the distance between the candidate parking lot and the current location of the target vehicle to obtain a corresponding recommendation score.

[0056] In one example, assume there are n parking lots to be selected, numbered 1, 2, ..., n. The weighted sum of the distance score, the available parking space score, and the flood risk score is used to obtain the recommendation score for each parking lot. The calculation formula is as follows: ,in, w d 、 w a and w f These are the weights of the distance-weighted score, the available parking space-weighted score, and the flood risk-weighted score.

[0057] For distance-weighted scoring, the server can calculate the distance between itself and the target vehicle's current location and convert it into a distance-weighted score. D i , , d i Indicates the current location of the target vehicle and the parking lot i The closer the distance, the higher the distance weighted score.

[0058] For the available parking space quantity weighted score, the server can calculate the proportion of the available parking space quantity to the total parking space quantity and convert it into an available parking space quantity weighted score A i , A i = a i / A , a i the available parking space quantity of the parking lot i , A the total parking space quantity of the parking lot i , the more available parking spaces, the higher the available parking space quantity weighted score.

[0059] For the flood risk weighted score, the corresponding flood risk grade can be determined according to the numerical value of the flood risk index, and different weights can be given according to different flood risk grades to convert it into a flood risk weighted score F i , , wherein Risk i the flood risk grade of the parking lot i , Weight ( Risk i ) is the weight corresponding to the flood risk grade. The lower the flood risk grade, the higher the flood risk weighted score.

[0060] Thus, the server can determine the recommendation score corresponding to each candidate parking lot based on the calculation rules of the above recommendation score. In an example, the server can select a predetermined number of candidate parking lots with the highest recommendation score as the target recommended parking lot. In another example, the server can also determine the candidate parking lot with the highest recommendation score as the best parking lot and the candidate parking lot with the second highest recommendation score as the alternative parking lot, so as to recommend the best parking lot and the alternative parking lot as the target recommended parking lot.

[0061] Please continue to refer to Figure 1 , in step S140, according to the at least one target recommended parking lot, the corresponding recommendation information is generated and pushed.

[0062] In this embodiment, the server can calculate the distance between the target vehicle and each target recommended parking lot according to the current position of the target vehicle according to the determined target recommended parking lot, and generate the corresponding navigation route, and generate the corresponding recommendation information and push based on the two.

[0063] In an example, the server can push the recommendation information to the on-board terminal of the target vehicle for display, for the driver of the target vehicle to make a selection. In another example, the server can also push the recommendation information to the mobile terminal carried by the driver of the target vehicle for display.

[0064] It is worth noting that when the server is in communication with the mobile terminal carried by the driver of the target vehicle, the aforementioned "current location of the target vehicle" can be obtained by the positioning device of the mobile terminal itself, i.e. the current location of the mobile terminal is taken as the current location of the target vehicle for subsequent processing.

[0065] In an embodiment, according to the at least one target recommended parking lot, corresponding recommendation information is generated and pushed, including: obtaining the currently confirmed disaster-affected parking lot and disaster-affected road section; for other parking lots in the target recommended parking lot except the disaster-affected parking lot, corresponding recommendation information is generated and pushed, and the recommendation information includes a navigation route that avoids the disaster-affected road section.

[0066] In an embodiment, the server can obtain real-time data of the city traffic management department, which usually contains information of the disaster-affected road section, and the server can also obtain real-time data of the city management system to determine which parking lots or roads have been marked as flood disaster affected areas.

[0067] In another embodiment, the server can also extract and analyze flood-related information by real-time monitoring of social media and news websites, in addition, social media users often post photos and descriptions of disaster situations, which can also be used as a supplement to real-time data.

[0068] In still another embodiment, the server can also use the Internet open flood disaster data interface (such as the API provided by the meteorological bureau and the disaster warning center) to obtain the latest flood disaster information.

[0069] The server can fuse and filter the real-time data obtained in the above manner, i.e. the sensor data, traffic management department data, city management system data, and Internet real-time data can be fused and filtered to determine which parking lots and road sections are currently affected by flood disasters, and then determine the disaster-affected parking lot and disaster-affected road section.

[0070] After identifying the affected parking lots and roads, the server can filter and remove any affected parking lots from the target recommended parking lots. It then generates and pushes recommendations based on the remaining target recommended parking lots. This recommendation can include navigation routes to the remaining target recommended parking lots, avoiding the affected roads. This ensures that the target vehicle is not affected by the flood on its way to the parking lot, ensuring its safety.

[0071] Therefore, based on Figure 1 In the illustrated embodiment, based on the current location of the target vehicle, candidate parking lots with available parking spaces within a first predetermined range of the current location are identified. A pre-built and trained flood risk prediction model is used to perform a flood risk prediction on each candidate parking lot, obtaining a corresponding flood risk index for each candidate parking lot. The model is trained based on corresponding historical hydrological data, historical meteorological data, parking lot attribute information, parking lot flood data, and parking lot flood protection information. Next, based on each candidate parking lot's corresponding flood risk index, the number of available parking spaces, and the distance from the current location of the target vehicle, at least one target recommended parking lot is determined from the candidate parking lots. Recommendation information is then generated and pushed based on this at least one target recommended parking lot. Thus, before making parking lot recommendations, a flood risk prediction is performed on each candidate parking lot using the pre-trained flood risk prediction model to obtain a corresponding flood risk index. The target recommended parking lot is then determined based on this flood risk index and other information, thereby accurately identifying the flood risk of each candidate parking lot and ensuring the safety of the recommended parking lots.

[0072] In one embodiment of the present application, the method for identifying flood risks in the parking lot also includes: based on the flood risk prediction model, identifying flood risks for the monitored parking lots within the second predetermined range to obtain corresponding flood risk indexes; and selecting and executing corresponding vehicle evacuation strategies based on the flood risk index corresponding to each of the monitored parking lots and the number of parked vehicles.

[0073] In this embodiment, in addition to responding to user requests to perform flood risk prediction, the server can also actively perform flood risk prediction for the monitored parking lots within the second predetermined range according to a predetermined period to obtain a flood risk index corresponding to each monitored parking lot.

[0074] The second predetermined range may be a certain district / county, a certain city, etc. Those skilled in the art may determine the coverage of the second predetermined range according to actual implementation needs, and no special limitation is imposed on this.

[0075] The monitored parking lot can be a parking lot that is subject to flood risk monitoring at the request of its management personnel. In other examples, the server can also proactively include parking lots that are within the second predetermined range and meet the corresponding flood risk prediction requirements (for example, meet relevant data acquisition requirements or permissions, etc.) as monitored parking lots.

[0076] After determining the flood risk index for each monitored parking lot, the server can select and execute a corresponding vehicle evacuation strategy based on the corresponding flood risk index and the number of vehicles parked in the monitored parking lot. This vehicle evacuation strategy can be a protective measure pre-defined by those skilled in the art to ensure the safety of vehicles in the parking lot.

[0077] In one embodiment, a corresponding vehicle evacuation strategy is selected and executed based on the flood risk index and the number of parked vehicles corresponding to each monitored parking lot, including: when the flood risk index of the monitored parking lot is in a first numerical interval, no vehicle evacuation is performed; when the flood risk index of the monitored parking lot is in a second numerical interval, the current maximum number of parked vehicles in the monitored parking lot is determined based on the available parking space ratio threshold corresponding to the second numerical interval; when the maximum number of parked vehicles is less than the number of parked vehicles in the monitored parking lot, the number of vehicles to be transferred in the monitored parking lot is determined and a vehicle transfer prompt message is sent to the corresponding owner; when the flood risk index of the monitored parking lot is greater than the value in the second numerical interval, a vehicle transfer prompt message is sent to the owner of the parked vehicle in the monitored parking lot.

[0078] In this embodiment, when the flood risk index corresponding to the monitored parking lot is in the first numerical range, it indicates that the monitored parking lot is less likely to suffer from flood disasters. At this time, no vehicle evacuation is required regardless of the number of vehicles parked in the monitored parking lot.

[0079] When the flood risk index for a monitored parking lot is within the second numerical range, where the values ​​within the second numerical range are greater than those within the first numerical range, this indicates that the monitored parking lot is at some risk of flooding. Therefore, the server can implement parking restrictions for the monitored parking lot, temporarily closing the parking lot or limiting the number of available parking spaces. The server can also update the parking lot status on the system to mitigate the risk of flooding, prevent new vehicles from entering the affected area, and reduce losses.

[0080] When limiting the number of parking spaces available, the server may determine the current maximum number of parking spaces available in the monitored parking lot based on the available parking space ratio threshold corresponding to the second numerical interval. For example, assuming the monitored parking lot has 200 parking spaces and the available parking space ratio threshold is currently 0.5, the current maximum number of parking spaces available in the monitored parking lot is 200×0.5=100. If the available parking space ratio threshold is 0.3, the corresponding maximum number of parking spaces available is 200×0.3=60, and so on. It should be understood that the maximum number of parking spaces available refers to the number of vehicles already parked in the monitored parking lot, and does not refer to the number of vehicles that can still park in the monitored parking lot.

[0081] In one example, after determining the maximum number of available parking spaces, the server can update the maximum number of available spaces in the parking lot management system. Based on the number of vehicles parked in the monitored parking lot, the system can update the number of remaining available spaces and notify parking lot managers and users. Parking lot managers can control vehicle access to the monitored parking lot based on system prompts. The server can also monitor parking space usage in the monitored parking lot in real time using sensors and / or cameras to dynamically adjust the number of remaining available spaces based on actual conditions.

[0082] The server can compare the maximum number of available parking spaces with the number of vehicles already parked in the monitored parking lot. If the number of vehicles already parked is less than or equal to the maximum number of available parking spaces, no action can be taken or further vehicle admission can be restricted. If the number of vehicles already parked exceeds the maximum number of available parking spaces, the monitored parking lot requires evacuation. The server can send a vehicle transfer prompt to vehicle owners, suggesting that some vehicles be moved to other safe parking lots (the method for determining safe parking lots can be determined using the method provided in the previous embodiment and will not be further described here).

[0083] In one example, the server may send a vehicle transfer prompt message to all owners of vehicles in the monitored parking lot, so that each owner can choose whether to transfer the vehicle until the number of parked vehicles is less than or equal to the maximum number of available parking spaces.

[0084] In another example, the server can subtract the number of parked vehicles from the maximum number of available parking spaces in the monitored parking lot to obtain the corresponding minimum threshold number of vehicles that need to be transferred. It should be understood that the minimum threshold number of vehicles that need to be transferred represents the minimum number of vehicles that should be transferred in the monitored parking lot.

[0085] The server can then determine the vehicles to be transferred from the parked vehicles in the monitored parking lot based on the minimum threshold for the number of vehicles to be transferred, as well as the entry time, vehicle type, and parking location of the parked vehicles in the monitored parking lot. It should be understood that the number of vehicles to be transferred can be equal to the minimum threshold for the number of vehicles to be transferred, or it can be greater than the number of vehicles to be transferred to prevent some vehicle owners from being unwilling or unable to transfer their vehicles in a timely manner.

[0086] Specifically, based on the entry time, the server can determine the parking time of each vehicle. When identifying vehicles to be relocated, it prioritizes vehicles with shorter parking times to move to other parking lots. This ensures that owners can find parking spaces more easily and reduces disruption to long-term parked vehicles. Furthermore, vehicles with shorter parking times are more flexible, and owners are more willing to cooperate with temporary adjustments.

[0087] Regarding vehicle types, priority can be given to small cars that are easy to move or vehicles without special requirements (such as electric vehicles requiring charging stations), so that large vehicles and vehicles with special needs should not be adjusted as much as possible to avoid inconvenience.

[0088] As for parking locations, the server can prioritize moving vehicles located in areas prone to flooding (such as low-lying areas or areas with poor drainage) based on the geographical location of the parking lot and the parking location of the vehicle.

[0089] For the above three factors, the server can give priority to a comprehensive consideration method, which will help improve the rationality of determining the vehicles to be transferred.

[0090] In addition, the server can also provide rewards (such as parking fee coupons, etc.) to the car owner when sending the vehicle transfer reminder to encourage the car owner to cooperate in moving the car. The server can send the vehicle transfer reminder to the car owner via SMS, WeChat official account, etc.

[0091] If the flood risk index for a monitored parking lot exceeds the second range, indicating a high likelihood of flooding, the server can send a vehicle relocation message to owners of vehicles parked in the monitored parking lot, prompting them to relocate their vehicles as soon as possible to avoid or minimize property damage. Similarly, the server can include a recommended safe parking lot and route in the relocation message, allowing owners to quickly determine their relocation destination.

[0092] In one embodiment of the present application, after the flood risk prediction model is launched, the server can monitor and adjust it in real time to promptly respond to changing environmental factors and data changes, thereby ensuring the accuracy of the model's prediction results.

[0093] The server can also collect data after actual flood events occur, and / or collect feedback information from drivers (including the actual situation of the parking lot, the accuracy of the parking lot recommendation, etc.) to provide feedback and improve the flood risk prediction model and improve the model's prediction performance.

[0094] Based on the above factors, the server can prioritize a comprehensive approach, integrating feedback and adjustments from all factors. This helps to comprehensively improve the performance and reliability of the system. Of course, considering each factor separately can help to further optimize specific issues. In practical applications, those skilled in the art can choose the appropriate method based on the specific situation, or combine the advantages of both methods to perform comprehensive optimization. This application does not impose any special restrictions on this.

[0095] In one embodiment, the server may optimize the flood risk prediction model in the following manner.

[0096] (1) Real-time monitoring and dynamic adjustment: The flood risk prediction model is dynamically adjusted based on real-time data to ensure that the model can reflect current environmental changes in a timely manner.

[0097] (2) Feedback of actual events for model correction: Feedback the data of actual events into the model for error analysis and correction to improve the model’s prediction accuracy.

[0098] (3) Algorithm optimization based on driver feedback: Improve the recommendation algorithm based on driver feedback, such as adjusting the scoring weight and optimizing the recommendation logic.

[0099] Specifically, in the above (1), it may include the following steps.

[0100] 1. Data Preprocessing

[0101] a. Data cleaning

[0102] b. Data fusion

[0103] Time synchronization: synchronize the data from different data sources to ensure that the timestamps of each data source are consistent;

[0104] Spatial fusion: spatially fuse data from different locations to establish a unified urban flood monitoring dataset.

[0105] 2. Real-time model calculation

[0106] a. Data Input

[0107] Inputting real-time collected and pre-processed data into flood risk prediction models, including hydrological data, meteorological data, traffic data, and parking lot status data;

[0108] b. Model update

[0109] Dynamically adjust model parameters: Based on real-time data, dynamically adjust the parameters of the flood risk prediction model to ensure that the model can accurately reflect current environmental changes;

[0110] Real-time prediction and calculation: Use real-time data to predict flood risks and calculate the flood risk index for each parking lot.

[0111] 3. Real-time monitoring and alarm

[0112] a. Risk Monitoring

[0113] Real-time monitoring system: Establish a real-time monitoring system to monitor changes in the flood risk index of each parking lot;

[0114] Threshold setting: Set the alarm threshold of the flood risk index. When the flood risk index of a parking lot exceeds the alarm threshold, the alarm mechanism is triggered;

[0115] b. Early Warning Notice.

[0116] 4. Real-time adjustment and optimization

[0117] a. Data Feedback

[0118] Feedback on actual flood events: Collect data on actual flood events, including information on parking lot impacts, time, and water levels;

[0119] User feedback: Collect user feedback on flood warning information, including the accuracy of warnings and responses;

[0120] b. Model Adjustment

[0121] Error analysis: Analyze the error between the model prediction and the actual situation, and determine the factors that affect the accuracy of the prediction;

[0122] Parameter optimization: Based on the error analysis results, optimize and adjust the model parameters to improve the model's prediction accuracy;

[0123] Model training: Retrain the model using new datasets to ensure that the model can adapt to the latest environmental changes and data characteristics.

[0124] Therefore, by continuously optimizing the flood risk prediction model in the above manner, the accuracy of the prediction results of the flood risk prediction model can be ensured.

[0125] In the above (2), the actual event data used may include the following.

[0126] 1. Hydrological data

[0127] Water level: water level changes during floods, including peak water levels and duration;

[0128] Flow rate: the speed of water flow during floods;

[0129] Water depth: the depth of water in each affected area;

[0130] Flood spread: The geographical range and spread speed of the impact of a flood event.

[0131] 2. Weather data

[0132] Rainfall: total rainfall and rainfall distribution during the flood period;

[0133] Rainfall intensity: the intensity and duration of rainfall;

[0134] Temporal distribution of rainfall: the specific time periods and frequency of rainfall occurrence.

[0135] 3. Parking lot data

[0136] Parking lot information affected: which parking lots were affected and the extent of the impact (e.g., completely flooded, partially flooded);

[0137] Parking lot location: The geographical location of the affected parking lot;

[0138] Parking space status: parking space usage in the affected parking lot during the flood period, including the number of vehicles in the parking lot and damage;

[0139] Parking lot structure: Structural characteristics of the affected parking lot (e.g. underground parking lot, open-air parking lot, etc.).

[0140] 4. Traffic data

[0141] Damage to road sections: which roads were affected by flooding, the extent and scope of the damage;

[0142] Traffic flow changes: changes in traffic flow during and after floods;

[0143] Traffic conditions: which roads are completely blocked and which roads are partially restricted.

[0144] 5. Flood control facility data

[0145] Flood control facility status: The operational status of flood control facilities during floods, such as the working status of drainage systems and the effectiveness of flood walls;

[0146] Facility failure situation: whether there is any failure in flood control facilities, the specific situation and impact of the failure.

[0147] 6. Socioeconomic data

[0148] Economic Loss: Direct and indirect economic losses caused by the flooding event, including vehicle loss, parking facility loss, etc.

[0149] Personnel Affected: Impact on vehicle owners and parking facility managers, such as evacuation, property loss, etc.

[0150] 7. User Feedback Data

[0151] Early Warning Feedback: Feedback from vehicle owners and parking facility managers on the flooding early warning information, including timeliness and accuracy of the warning;

[0152] Dispatch Feedback: Feedback on vehicle dispatch recommendations, including effectiveness and execution of the dispatch.

[0153] 8. Environmental Data

[0154] Topographic Changes: Changes in the terrain after the flooding, such as ground subsidence, geological disasters, etc.

[0155] Therefore, by obtaining one or more of the above actual event data, the flooding risk prediction model can be optimized, which can improve the accuracy of the prediction results of the flooding risk prediction model.

[0156] The following describes an apparatus embodiment of the present application, which can be used to perform the parking lot flooding risk identification method in the above embodiments of the present application. For details not disclosed in the apparatus embodiment of the present application, please refer to the above embodiments of the parking lot flooding risk identification method of the present application.

[0157] Figure 2 A block diagram of a parking lot flooding risk identification apparatus according to an embodiment of the present application is shown.

[0158] Referring to Figure 2 According to an embodiment of the present application, the parking lot flooding risk identification apparatus includes: a determination module configured to determine candidate parking lots having available parking spaces within a first predetermined range of a current location of a target vehicle according to the current location of the target vehicle; a prediction module configured to perform flooding risk prediction on each of the candidate parking lots using a pre-constructed and trained flooding risk prediction model, to obtain a flooding risk index corresponding to each of the candidate parking lots, the flooding risk prediction model being trained according to corresponding historical hydrological data, historical meteorological data, parking lot attribute information, parking lot flooding data, and parking lot flooding protection information; a selection module configured to determine at least one target recommended parking lot from the candidate parking lots according to the flooding risk index corresponding to each of the candidate parking lots, the number of available parking spaces, and the distance between the target recommended parking lot and the current location of the target vehicle; and a processing module configured to generate and push corresponding recommended information according to the at least one target recommended parking lot.

[0159] In one embodiment of the present application, at least one target recommended parking lot is determined from the candidate parking lots based on the flood risk index, the number of available parking spaces, and the distance from the current position of the target vehicle corresponding to each of the candidate parking lots, including: determining a recommendation score corresponding to each of the candidate parking lots based on the flood risk index, the number of available parking spaces, and the distance from the current position of the target vehicle corresponding to each of the candidate parking lots; and determining the candidate parking lot whose recommendation score meets predetermined rules as the target recommended parking lot.

[0160] In one embodiment of the present application, corresponding recommendation information is generated and pushed based on the at least one target recommended parking lot, including: obtaining the currently confirmed affected parking lots and affected road sections; for other parking lots in the target recommended parking lots except the affected parking lot, corresponding recommendation information is generated and pushed, and the recommendation information includes a navigation route that has avoided the affected road section.

[0161] In one embodiment of the present application, the prediction module is also used to: based on the flood risk prediction model, predict flood risks for the monitored parking lots within the second predetermined range to obtain corresponding flood risk indexes; and select and execute corresponding vehicle evacuation strategies based on the flood risk index corresponding to each of the monitored parking lots and the number of parked vehicles.

[0162] In one embodiment of the present application, a corresponding vehicle evacuation strategy is selected and executed based on the flood risk index and the number of parked vehicles corresponding to each monitored parking lot, including: when the flood risk index of the monitored parking lot is in a first numerical interval, no vehicle evacuation is performed; when the flood risk index of the monitored parking lot is in a second numerical interval, the current maximum number of parked vehicles in the monitored parking lot is determined based on the available parking space ratio threshold corresponding to the second numerical interval; when the maximum number of parked vehicles is less than the number of parked vehicles in the monitored parking lot, the number of vehicles to be transferred in the monitored parking lot is determined and a vehicle transfer prompt message is sent to the corresponding owner; when the flood risk index of the monitored parking lot is greater than the value in the second numerical interval, a vehicle transfer prompt message is sent to the owner of the parked vehicle in the monitored parking lot.

[0163] In one embodiment of the present application, determining the vehicles to be transferred in the monitored parking lot includes: determining a minimum threshold value for the number of vehicles to be transferred based on the current maximum number of available parking spaces and the number of vehicles already parked in the monitored parking lot; and determining the vehicles to be transferred from the parked vehicles in the monitored parking lot based on the minimum threshold value for the number of vehicles to be transferred and the entry time, vehicle type, and parking location of the parked vehicles in the monitored parking lot.

[0164] Figure 3A structural diagram of a computer system of an electronic device suitable for implementing the embodiments of the present application is shown.

[0165] It should be noted that, Figure 3 The computer system of the electronic device shown is only an example and should not bring any limitation to the functions and usage range of the embodiments of the present application.

[0166] As Figure 3 shown, the computer system includes a central processing unit (CPU) 301 which can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 302 or loaded from a storage section 308 into a random access memory (RAM) 303, such as performing the methods described in the above embodiments. Various programs and data required for system operation are also stored in the RAM 303. The CPU 301, the ROM 302, and the RAM 303 are connected to each other through a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0167] The following components are connected to the I / O interface 305: an input section 306 including a keyboard, a mouse, etc.; an output section 307 including a display such as a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; the storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as necessary. A removable recording medium 311 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is attached to the drive 310 as necessary, so that a computer program read therefrom is installed into the storage section 308 as necessary.

[0168] In particular, according to embodiments of the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for executing the methods illustrated in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via the communication section 309 and / or installed from removable media 311. When executed by the central processing unit (CPU) 301, the computer program performs the various functions defined in the system of the present application.

[0169] It should be noted that the computer-readable medium described in the embodiments of the present application may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. Examples of computer-readable storage media include, but are not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or component. In the present application, a computer-readable signal medium may include a data signal transmitted in baseband or as part of a carrier wave, which carries a computer-readable computer program. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. A computer program embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0170] The flowcharts and block diagrams in the drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowcharts or block diagrams can represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks can sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flowchart illustrations, and combinations thereof, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or combinations of hardware and software.

[0171] The units described in the embodiments of the present application can be implemented by software, or by hardware, or by a combination of software and hardware. The units described may

[0172] As another aspect, the present application also provides a computer readable medium, which can be included in the electronic device described in the above embodiments, or can exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to implement the method described in the above embodiments.

[0173] It should be noted that although several modules or units of the device for action execution are mentioned in the above detailed description, the division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into a plurality of modules or units.

[0174] From the above description of the embodiments, those skilled in the art will readily appreciate that the example embodiments described herein can be implemented by software and / or by hardware coupled with software. Accordingly, the technical solutions according to the embodiments of the present application can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, or the like) or on a network, and includes a number of instructions for causing a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to perform the methods according to the embodiments of the present application.

[0175] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of this application and include common knowledge or customary techniques in the art that are not disclosed herein.

[0176] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for identifying flood risk in a parking lot, characterized in that: include: Determining, based on the current location of the target vehicle, a parking lot to be selected that is within a first predetermined range of the current location and has available parking spaces; A pre-built and trained flood risk prediction model is used to predict flood risk for each of the candidate parking lots, and a flood risk index corresponding to each of the candidate parking lots is obtained. The flood risk prediction model is trained based on corresponding historical hydrological data, historical meteorological data, parking lot attribute information, parking lot flood data, and parking lot flood protection information. The flood risk prediction model includes several decision trees, and the corresponding flood risk index is generated by integrating the flood risk prediction results of the several decision trees. Determining at least one target recommended parking lot from the candidate parking lots based on the flood risk index, the number of available parking spaces, and the distance from the current location of the target vehicle corresponding to each of the candidate parking lots; According to the at least one target recommended parking lot, corresponding recommendation information is generated and pushed.

2. The method according to claim 1, characterized in that Determining at least one target recommended parking lot from the candidate parking lots based on the flood risk index, the number of available parking spaces, and the distance from the current location of the target vehicle corresponding to each candidate parking lot includes: Determining a recommendation score for each of the candidate parking lots based on the flood risk index, the number of available parking spaces, and the distance from the current location of the target vehicle to each of the candidate parking lots; The candidate parking lot whose recommendation score meets the predetermined rules is determined as the target recommended parking lot.

3. The method according to claim 2, characterized in that Generating and pushing corresponding recommendation information based on the at least one target parking lot recommendation includes: Obtain the currently confirmed affected parking lots and affected road sections; For other parking lots in the target recommended parking lots except the parking lot that has been affected by the disaster, corresponding recommendation information is generated and pushed, wherein the recommendation information includes a navigation route that has avoided the road section that has been affected by the disaster.

4. The method according to claim 1, wherein The method further comprises: Based on the flood risk prediction model, performing flood risk prediction on the monitored parking lots within the second predetermined range to obtain a corresponding flood risk index; According to the flood risk index corresponding to each monitored parking lot and the number of vehicles parked, a corresponding vehicle evacuation strategy is selected and executed.

5. The method according to claim 4, characterized in that Based on the flood risk index and the number of vehicles parked in each monitored parking lot, a corresponding vehicle evacuation strategy is selected and executed, including: When the flood risk index of the monitored parking lot is within the first value range, no vehicle evacuation is performed; When the flood risk index of the monitored parking lot is within a second numerical range, determining the current maximum number of available parking spaces in the monitored parking lot based on a threshold value for the proportion of available parking spaces corresponding to the second numerical range; when the maximum number of available parking spaces is less than the number of vehicles already parked in the monitored parking lot, determining the number of vehicles to be relocated from the monitored parking lot and sending a vehicle relocation reminder message to the corresponding vehicle owners; When the flood risk index of the monitored parking lot is greater than the upper limit value in the second numerical range, a vehicle transfer prompt message is sent to the owners of the vehicles parked in the monitored parking lot.

6. The method according to claim 5, characterized in that Determining the vehicles to be transferred from the monitored parking lot includes: Determine the minimum threshold for the number of vehicles to be transferred based on the maximum number of available parking spaces and the number of vehicles already parked in the monitored parking lot; The vehicles to be transferred are determined from the parked vehicles in the monitored parking lot according to the minimum threshold of the number of vehicles to be transferred and the entry time, vehicle type and parking position of the parked vehicles in the monitored parking lot.

7. A parking lot flood risk identification device, characterized in that: include: A determination module is configured to determine, based on a current position of the target vehicle, a parking lot to be selected that is within a first predetermined range of the current position and has available parking spaces; A prediction module is configured to use a pre-built and trained flood risk prediction model to predict flood risk for each of the candidate parking lots, thereby obtaining a flood risk index corresponding to each of the candidate parking lots. The flood risk prediction model is trained based on corresponding historical hydrological data, historical meteorological data, parking lot attribute information, parking lot flood data, and parking lot flood protection information. The flood risk prediction model includes a plurality of decision trees, and the corresponding flood risk index is generated by integrating the flood risk prediction results of the plurality of decision trees. A selection module is configured to determine at least one target recommended parking lot from the candidate parking lots based on the flood risk index, the number of available parking spaces, and the distance from the current location of the target vehicle corresponding to each of the candidate parking lots; The processing module is used to generate and push corresponding recommendation information based on the at least one target recommended parking lot.

8. A computer-readable medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for identifying flood risks in a parking lot according to any one of claims 1 to 6 is implemented.

9. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to implement the flood risk identification method for a parking lot as described in any one of claims 1 to 6.

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