Park electric vehicle management method and device and storage medium

Abstract

The invention relates to the technical field of park management, and provides a park electric vehicle management method and device and a storage medium. The park electric vehicle management method comprises the steps that under the condition that the number of electric vehicles parked in a target parking shed is larger than a preset first number threshold value, the number of electric vehicles being charged in the target parking shed is determined; under the condition that the number of the electric vehicles being charged is larger than a preset second number threshold value, risk evaluation parameters corresponding to the target parking shed are determined based on the number of the parked electric vehicles and the number of the electric vehicles being charged; and generating risk prompt information corresponding to the target parking shed based on the risk evaluation parameter. Through adoption of the technical scheme, the risk evaluation parameter corresponding to the target parking shed can be determined by combining the number of the parked electric vehicles and the number of the electric vehicles being charged under the condition that the safety prompt for the parking shed is determined, so that the corresponding risk prompt information is generated; and therefore, the safety of centralized and unified management of the electric vehicles can be improved.

Description

Technical Field

[0001] The present application relates to the field of park management technology, and in particular to a park electric vehicle management method, equipment and storage medium. Background Art

[0002] Vehicle management is an important part of park management, which usually includes vehicle parking management, vehicle charging management, etc. With the application of information technology, wireless sensing equipment, cameras and other information equipment are gradually put into the park, which brings convenience to the park's vehicle management.

[0003] In the related art, a parking shed is set up and charging facilities dedicated to electric vehicles are set up in the parking shed to provide electric vehicle parking and charging services for users in the park, thereby facilitating centralized and unified management of electric vehicles in the park.

[0004] However, electric vehicles themselves are somewhat dangerous, especially during the charging process, there is a risk of fire, and in the process of centralized and unified management of electric vehicles, the probability of danger occurring in the parking shed is greatly increased, which puts higher demands on the management of electric vehicles in the park. Summary of the invention

[0005] In order to facilitate the safe management of electric vehicles in a park, the present application provides a park electric vehicle management method, device and storage medium.

[0006] In the first aspect, the present application provides a method for managing electric vehicles in a park, which adopts the following technical solution: A method for managing electric vehicles in a park, the method comprising: Monitor the number of electric vehicles parked in the target parking shed; When the number of parked electric vehicles is greater than a preset first number threshold, determining the number of electric vehicles being charged in the target parking shed; In a case where the number of the electric vehicles being charged is greater than a preset second number threshold, determining a risk assessment parameter corresponding to the target parking shed based on the number of the parked electric vehicles and the number of the electric vehicles being charged; Risk warning information corresponding to the target parking shed is generated based on the risk assessment parameters.

[0007] By adopting the above technical solution, when determining the safety warning for the parking shed, the risk assessment parameters corresponding to the target parking shed can be determined based on the number of parked electric vehicles and the number of electric vehicles being charged to generate corresponding risk warning information, which can help improve the safety of centralized and unified management of electric vehicles.

[0008] Optionally, the monitoring of the number of electric vehicles parked in the target parking shed includes: Acquire first image information of a parking area corresponding to the target parking shed; Preprocessing the first image information to obtain a preprocessed image; Inputting the preprocessed image into a pre-trained object recognition model to obtain an object type corresponding to each object in the first image information; The number of objects whose object type is electric vehicles in the first image information is determined as the number of parked electric vehicles.

[0009] By adopting the above technical solution, the number of electric vehicles actually parked in the parking shed can be counted by image recognition, which can help improve the accuracy of monitoring the number of parked electric vehicles.

[0010] Optionally, the object recognition model is further used to output the confidence of the object type corresponding to each object; before determining the number of objects whose object type is electric vehicles in the first image information as the number of parked electric vehicles, it also includes: For a target object whose object type in the first image information is an electric vehicle, determining whether the confidence corresponding to the object type is greater than a preset confidence threshold; When the confidence level corresponding to the object type is less than or equal to the confidence level threshold, intercepting a target image corresponding to the target object; Inputting the target image into a pre-trained object classification model to obtain a classification result; The object type corresponding to the target object is updated based on the classification result.

[0011] By adopting the above technical solution, the object classification network can be used to further judge the type of the target object whose object type is determined to be a bicycle by the object recognition model but with low confidence, thereby helping to improve the accuracy of electric vehicle recognition and further help to achieve precise management of electric vehicles.

[0012] Optionally, before determining the risk assessment parameter based on the number of parked electric vehicles and the number of electric vehicles being charged, the method further includes: The number of electric vehicles of tenants is obtained based on the number of electronic tags detected by the electronic tag detection component set in the target parking shed, wherein the electronic tags are pre-set in the electric vehicles of the tenants in the park; Determining whether the number of the tenant electric vehicles is the same as the number of the parked electric vehicles; In the case where the number of the tenant electric vehicles is less than the number of the parked electric vehicles, determining that there are external electric vehicles parked in the parking shed; When the number of the tenant electric vehicles is equal to the number of the parked electric vehicles, determining that no external electric vehicles are parked in the parking shed; The determining of the risk assessment parameter based on the number of parked electric vehicles and the number of electric vehicles being charged includes: The risk assessment parameters are determined based on the number of parked electric vehicles, the number of electric vehicles being charged, and whether there are any foreign electric vehicles parked in the parking shed.

[0013] By adopting the above technical solution, it is possible to determine whether there are external electric vehicles parked in the parking shed based on the relationship between the number of tenants' electric vehicles and the number of parked electric vehicles, and take the impact of the number of external electric vehicles into account in the process of calculating risk assessment parameters, which can help improve the accuracy of the calculated risk assessment parameters.

[0014] Optionally, after determining whether the number of the tenant electric vehicles is the same as the number of the parked electric vehicles, the method further includes: In the case where the number of the tenant electric vehicles is greater than the number of the parked electric vehicles, determining that there is an abnormality in the number of the parked electric vehicles; Acquire second image information of a monitoring area corresponding to the target parking shed, wherein the monitoring area includes a parking area corresponding to the target parking shed; determining the number of electric vehicles in the monitoring area based on the second image information; When the number of electric vehicles in the monitoring area is greater than or equal to the number of the tenant electric vehicles, determining vehicle image information of electric vehicles located outside the parking area; Generate illegal parking alarm information based on the vehicle image information.

[0015] By adopting the above-mentioned technical solution, when the number of tenants' electric vehicles is greater than the number of parked electric vehicles, it can be determined whether there are illegally parked vehicles based on the image information in the monitoring area. When illegally parked vehicles are determined, illegal parking alarm information can be generated based on the vehicle image information of electric vehicles located outside the parking area, which can help manage target electric vehicles near the parking shed.

[0016] Optionally, the method further includes: For the power socket in the target parking shed, when the power socket is in a working state, monitoring the output current of the power socket; When it is detected that the output current is greater than a preset output current threshold, the risk assessment parameter is adjusted to increase the risk level corresponding to the target parking shed; In the case where it is monitored that the output current is less than or equal to the output current threshold, determining whether the sum of the output currents of the power sockets in working state is greater than a preset maximum output current; When the sum of the output currents is greater than the maximum output current, the risk assessment parameter is adjusted to increase the risk level corresponding to the target parking shed.

[0017] By adopting the above technical solution, the output current of the power socket in a working state can be monitored, and the risk assessment parameters can be adjusted in combination with the output current of the power socket, which can help improve the accuracy of the risk assessment parameters and further help to accurately guide the safety management of electric vehicles.

[0018] Optionally, when it is monitored that the output current is greater than a preset output current threshold, the method further includes: Determine a charging area corresponding to the power socket; Acquiring infrared image information corresponding to the charging area; Determine whether there is an abnormal location in the charging area with a temperature greater than a preset temperature threshold based on the infrared image information; In the case where an abnormal position exists within the fully charged area, the risk assessment parameter is adjusted to further increase the risk level corresponding to the target parking shed.

[0019] By adopting the above technical solution, when it is monitored that the output current of the power socket is greater than the preset output current threshold, the risk assessment parameters can be adjusted in combination with the temperature of the charging area, which can help improve the accuracy of the risk assessment parameters and further help to accurately guide the safety management of electric vehicles.

[0020] Optionally, the method further includes: Acquire image information of a prohibited parking area, wherein the prohibited parking area does not include the parking shed; Determining whether a target electric vehicle exists in the prohibited parking area based on the image information of the prohibited parking area; In the case where the target electric vehicle exists in the prohibited parking area, determining whether the residence time of the target electric vehicle in the prohibited parking area exceeds a preset residence time threshold; In the case where the stay time exceeds the stay time threshold, determining whether a vehicle identification corresponding to the target electric vehicle is identified, the vehicle identification being pre-set on the electric vehicle of the park tenant; When the vehicle identification is identified, the target tenant corresponding to the target electric vehicle is queried based on the vehicle identification, and illegal parking reminder information is sent to the target tenant to prompt the target tenant to handle the target electric vehicle; In the case where the vehicle identification is not recognized, illegal parking processing prompt information is sent to the security personnel closest to the illegally parked vehicle to prompt the security personnel to handle the target electric vehicle.

[0021] By adopting the above technical solution, illegally parked electric vehicles can be automatically identified based on image information in prohibited parking areas. When illegally parked electric vehicles are identified, the corresponding handling method can be dynamically determined based on whether the corresponding identification of the electric vehicle is queried, which can help manage the target electric vehicles.

[0022] In a second aspect, the present application provides an electronic device, which adopts the following technical solution: An electronic device, comprising: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute any one of the park electric vehicle management methods provided in the first aspect.

[0023] In a third aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium stores a computer program, which, when executed in a computer, causes the computer to execute any one of the park electric vehicle management methods provided in the first aspect.

[0024] In summary, the present application includes at least one of the following beneficial technical effects: 1. When it is determined that a safety warning is required for a parking shed, the risk assessment parameters corresponding to the target parking shed can be determined in combination with the number of parked electric vehicles and the number of electric vehicles being charged, so as to generate corresponding risk warning information. In this way, when there is a risk in the parking shed, a risk warning can be given in combination with the actual risk situation of the parking shed, which can help improve the safety of centralized and unified management of electric vehicles; 2. Whether to issue a risk warning for the parking shed can be determined step by step based on the number of parked electric vehicles and the number of electric vehicles being charged. When it is determined that a safety warning should be issued for the parking shed, the risk assessment parameters can be determined based on the number of parked electric vehicles and the number of electric vehicles being charged, and risk warning information can be accurately generated. This can help improve the necessity and accuracy of risk warning information. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a flow chart of a method for managing electric vehicles in a park provided in an embodiment of the present application; Figure 2It is a flow chart of a method for determining the number of parked electric vehicles provided in an embodiment of the present application; Figure 3 It is a flow chart of an electric vehicle identification method provided in an embodiment of the present application; Figure 4 It is a flow chart of a method for determining risk assessment parameters provided in an embodiment of the present application; Figure 5 It is a flowchart of a parking violation alarm method provided in an embodiment of the present application; Figure 6 It is a flow chart of a risk assessment parameter correction method provided in an embodiment of the present application; Figure 7 It is a flow chart of another risk assessment parameter correction method provided in an embodiment of the present application; Figure 8 It is a flowchart of a method for prompting illegal parking provided in an embodiment of the present application; Fig. 9 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solutions and advantages of this application more clear, the following Figures 1 to 9 It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0027] The present application embodiment discloses a method for managing electric vehicles in a park. Figure 1 , the park electric vehicle management method includes the following steps: Step 101, monitoring the number of electric vehicles parked in the target parking shed.

[0028] The target parking shed is located in the park. Specifically, the target parking shed may be any parking shed in the park that provides an electric vehicle charging function, or may be a parking shed designated by an administrator. This embodiment does not limit the method for determining the target parking shed.

[0029] In one example, monitoring the number of electric vehicles parked in a target parking shed includes: obtaining first image information of a parking area corresponding to the target parking shed; and determining the number of electric vehicles parked in the target parking shed based on the first image information. For example, determining the number of occupied parking spaces in the parking area based on the first image information, and determining the number of occupied parking spaces as the number of electric vehicles in the parking area.

[0030] In another example, an electronic tag (such as an RFID tag) is set on the electric vehicle entering the park, and a tag reader corresponding to the electronic tag (such as an RFID reader) is set in the target parking shed, which can read the signal corresponding to the electronic tag. At this time, the number of electric vehicles parked in the target parking shed is monitored, including: the number of electronic tags read by the tag reader set in the parking shed is determined as the number of electric vehicles parked in the parking shed. In actual implementation, the electronic tags can be pre-set in the electric vehicles of the owners in the park. Furthermore, for the electric vehicles of temporary visitors entering the park, temporary electronic tags can also be given upon entry to facilitate the management of the visitors' electric vehicles.

[0031] Step 102: When the number of parked electric vehicles is greater than a preset first number threshold, determine the number of electric vehicles being charged in the target parking shed.

[0032] The preset first quantity threshold may be set in combination with the actual situation of the target parking shed, for example, in combination with the size and / or number of parking spaces of the target parking shed, so that the parking status of the vehicles in the target parking shed can be accurately determined.

[0033] In one example, the number of electric vehicles being charged in the target parking shed is determined based on the number of working power sockets in the target parking shed. Generally speaking, one socket can charge one electric vehicle. In one example, the charging socket is a smart charging socket, which is connected to the charging management platform through the network, so that the working status of the socket can be obtained through the charging management platform.

[0034] In actual implementation, image analysis can also be used to determine whether each charging socket in the parking shed has been plugged in, that is, the charging socket connected to the plug is in a working state, and the socket not connected to the plug is not in a working state, for example: in a standby state.

[0035] Step 103: When the number of electric vehicles being charged is greater than a preset second number threshold, a risk assessment parameter corresponding to the target parking shed is determined based on the number of parked electric vehicles and the number of electric vehicles being charged.

[0036] The second quantity threshold may be set in combination with the actual situation of the target parking shed. For example, the second quantity threshold may be determined in combination with the number of charging plugs provided by the target parking shed, so that the charging status of the vehicles in the target parking shed can be accurately determined.

[0037] Optionally, a risk assessment parameter corresponding to the target parking shed is determined based on the number of parked electric vehicles and the number of electric vehicles being charged, including: determining a weighted sum of the number of parked electric vehicles and the number of electric vehicles being charged as the risk assessment parameter.

[0038] The weight corresponding to the number of parked electric vehicles and the weight corresponding to the number of electric vehicles being charged are preset, and the weight of the number of electric vehicles being charged is greater than the weight corresponding to the number of parked electric vehicles.

[0039] Step 104: Generate risk warning information corresponding to the target parking shed based on the risk assessment parameters.

[0040] Optionally, risk warning information corresponding to the target parking shed is generated based on the risk assessment parameters, including: determining a parameter interval to which the risk assessment parameters belong; and generating risk warning information corresponding to the target parking shed based on a risk type corresponding to the parameter interval.

[0041] The parameter interval is obtained by dividing the value range of the risk assessment parameter, and the risk types corresponding to different parameter intervals are preset. In actual implementation, the risk type can be represented by the risk level.

[0042] Accordingly, the risk warning information includes the risk type.

[0043] Furthermore, different risk warning information output methods can be set for different risk types. For example, for low-risk risk types, the corresponding risk warning information can be directly displayed on the background park management page; for medium-risk risk types, the corresponding risk warning information can be displayed on the background risk warning page, and the administrator can be reminded; for high-risk risk types, an alarm prompt message can be directly sent to the corresponding administrator of the parking shed to prompt the administrator to manage in time. This can help assist managers in making decisions through intelligent analysis and judgment, and then help manage electric vehicles in the park and improve the safety of the park.

[0044] The implementation principle of the park electric vehicle management method provided by the embodiment of the present application is: monitor the number of electric vehicles parked in the target parking shed; when the number of parked electric vehicles is greater than the preset first number threshold, determine the number of electric vehicles being charged in the target parking shed; when the number of electric vehicles being charged is greater than the preset second number threshold, determine the risk assessment parameters corresponding to the target parking shed based on the number of parked electric vehicles and the number of electric vehicles being charged; generate risk warning information corresponding to the target parking shed based on the risk assessment parameters. By adopting the above technical solution, the number of electric vehicles parked in the parking shed can be monitored in real time, and when the number of parked electric vehicles is large, it can be further combined with the number of electric vehicles being charged to determine whether to conduct a risk assessment, and when the number of electric vehicles being charged is large, the risk assessment parameters corresponding to the target parking shed can be determined in combination with the number of parked electric vehicles and the number of electric vehicles being charged to generate corresponding risk warning information, so that when there is a risk in the parking shed, a risk warning can be given in combination with the actual risk situation of the parking shed, which can help improve the safety of centralized and unified management of electric vehicles.

[0045] In addition, since it is possible to determine in steps whether to issue a risk warning for the parking shed based on the number of parked electric vehicles and the number of electric vehicles being charged, when it is determined that a safety warning should be issued for the parking shed, the risk assessment parameters can be determined based on the number of parked electric vehicles and the number of electric vehicles being charged, and risk warning information can be accurately generated. This can help improve the necessity and accuracy of risk warning information.

[0046] In some embodiments, reference Figure 2 The above step 101, monitoring the number of electric vehicles parked in the target parking shed, specifically includes the following steps: Step 201, obtaining first image information of a parking area corresponding to a target parking shed.

[0047] In one example, obtaining first image information of a parking area corresponding to a parking shed in a target area includes: obtaining original image information acquired by an image acquisition component corresponding to the target parking shed; and intercepting an image corresponding to the parking area in the original image information to obtain first image information. In this way, the influence of images of other areas outside the parking area on the recognition result can be avoided.

[0048] Step 202: preprocess the first image information to obtain a preprocessed image.

[0049] Optionally, preprocessing the first image to obtain a preprocessed image includes: removing the background of the first image based on a preset background image to obtain the preprocessed image. The background image is collected when no other objects are placed in the parking area.

[0050] Furthermore, in the process of background removal, an edge detection algorithm (such as Sobel, Canny) can be used to extract the contours of objects in the image, and then the object can be separated from the background based on the contours of the object, which can help improve the accuracy of background removal.

[0051] In actual implementation, the first image information may also be preprocessed by noise reduction, grayscale processing, etc. This embodiment does not limit the preprocessing method of the first image.

[0052] Step 203: input the preprocessed image into a pre-trained object recognition model to obtain the object type corresponding to each object in the first image information.

[0053] The object recognition model is obtained by training the initial network model using sample data. The object type includes at least electric vehicles. In actual implementation, the object type may also include the types of objects that may appear in the parking shed, such as bicycles, scooters, small animals, etc., which can be flexibly set during the training process of the object recognition model.

[0054] In one example, the sample data may be obtained by annotating the types of objects in the image information of the parking area collected during the actual use of the parking shed. Furthermore, in order to expand the number of samples, the original collected data may be processed (for example, rotated, scaled, cropped, and color changed) to obtain new sample data.

[0055] Step 204: Determine the number of objects whose object type is electric vehicles in the first image information as the number of parked electric vehicles.

[0056] Specifically, after the first image information is output to the object recognition model, the object recognition model can output the position and object type of the recognized object in the first image information, so that the number of objects of the electric vehicle type in the first image information can be determined based on the output result of the object recognition model.

[0057] In the above implementation, since the first image information of the parking area corresponding to the parking shed can be preprocessed and the preprocessed image can be input into the object recognition model to obtain the objects and object types in the first image information, the number of electric vehicles in the parking shed can be determined. In this way, the actual number of electric vehicles parked in the parking shed can be counted, which can help improve the accuracy of monitoring the number of parked electric vehicles.

[0058] Furthermore, the object recognition model is also used to output the confidence of the object type corresponding to each object. Specifically, the confidence is used to reflect the accuracy of the object type, and generally speaking, the confidence is a value between 0 and 1.

[0059] Accordingly, reference Figure 3 In the above step 204, before determining the number of objects of electric vehicles in the first image information as the number of parked electric vehicles, the method further includes: Step 301: for a target object whose object type in the first image information is an electric vehicle, determine whether the confidence corresponding to the object type is greater than a preset confidence threshold.

[0060] Specifically, the confidence threshold can be set according to actual experience, for example, the confidence threshold is 70%.

[0061] Step 302: When the confidence level corresponding to the object type is less than or equal to the confidence level threshold, capture a target image corresponding to the target object.

[0062] Specifically, the output result of the object recognition model includes the position of the object in the first image information, so that the object position output by the object recognition model intercepts the target image corresponding to the target object. In one example, the position of the object in the first image information is represented by a detection frame in the first image information, so that the image within the detection frame can be directly determined as the target image corresponding to the target object.

[0063] Step 303: input the target image into a pre-trained object classification model to obtain a classification result.

[0064] Among them, the classification results of the object classification model at least include electric vehicles. In actual implementation, since electric vehicles and bicycles have certain similarities in shape, and unlike electric vehicles, bicycles do not have obvious safety hazards, it is very necessary to distinguish between electric vehicles and bicycles, so as to reduce the probability of judging bicycles as electric vehicles, and then accurately guide the management of electric vehicles.

[0065] Specifically, unlike the above-mentioned object detection model, the object classification model only needs to classify the objects in the input image without detecting the position of the objects in the image. In this way, during the training process, the object classification model can better learn the characteristics of different types of objects, which can help improve the accuracy of the classification results.

[0066] In one example, the object classification model is trained using a two-wheeled vehicle sample data set, which includes electric vehicle images and bicycle images. Accordingly, the classification results of the object classification model include bicycles and electric vehicles. This allows the classification model to learn the characteristics of electric vehicles and bicycles well, and then accurately distinguish between electric vehicles and bicycles.

[0067] Furthermore, the object classification model is a binary classification model of electric vehicles and bicycles, which can help improve the pertinence of model classification and further improve the accuracy of electric bicycle judgment.

[0068] Step 304: Update the object type corresponding to the target object based on the classification result.

[0069] In the above implementation, before counting the number of target objects whose object type is electric vehicles, the object classification model can be used to classify the target images corresponding to the target objects whose object type is electric vehicles and whose confidence level is less than the confidence level threshold, and the object type corresponding to the target object can be updated based on the classification result. In this way, the object classification network can be used to further judge the type of the target object whose object type is determined to be a bicycle by the object recognition model but whose confidence level is lower, thereby helping to improve the accuracy of electric vehicle recognition and further helping to achieve precise management of electric vehicles.

[0070] In some embodiments, reference Figure 4 In the above step 103, before determining the risk assessment parameter corresponding to the target parking shed based on the number of parked electric vehicles and the number of electric vehicles being charged, the following steps are also included: Step 401, based on the number of electronic tags detected by the electronic tag detection component installed in the target parking shed, the number of tenant electric vehicles is obtained.

[0071] Among them, electronic tags are pre-installed in the electric vehicles of park tenants.

[0072] In one example, the electronic tag is an RFID tag, and accordingly, the electronic tag detection component is an RFID reader.

[0073] In one example, different electronic tags correspond to different tag information. In this case, multiple electronic tag detection components can be set at different locations in the parking shed to detect the electronic tags, and the electronic tags detected by different electronic tag detection components can be deduplicated to obtain the final number of electronic tags. This can help improve the accuracy of the determined number of electronic tags.

[0074] Step 402, determining whether the number of tenant electric vehicles is the same as the number of parked electric vehicles.

[0075] The number of parked electric vehicles is obtained based on the first image information of the parking area corresponding to the target parking shed.

[0076] Specifically, since the number of tenant electric vehicles is counted based on the number of electronic tags, and there are no electronic tags on external electric vehicles, it is possible to determine whether there are external electric vehicles parked in the parking shed based on the number of tenant electric vehicles and the number of parked electric vehicles. This helps to judge the risk of the parking shed based on the situation of external electric vehicles.

[0077] Step 403: When the number of tenant electric vehicles is less than the number of parked electric vehicles, it is determined that there are external electric vehicles parked in the parking shed.

[0078] Step 404: When the number of tenant electric vehicles is equal to the number of parked electric vehicles, it is determined that no external electric vehicles are parked in the parking shed.

[0079] Accordingly, in step 103, risk assessment parameters are determined based on the number of parked electric vehicles and the number of electric vehicles being charged, including: Step 405 , determining risk assessment parameters based on the number of parked electric vehicles, the number of electric vehicles being charged, and whether there are any external electric vehicles parked in the parking shed.

[0080] Optionally, risk assessment parameters are determined based on the number of parked electric vehicles, the number of electric vehicles being charged, and whether there are any external electric vehicles parked in the parking shed, including: determining the weighted sum of the number of parked electric vehicles and the number of electric vehicles being charged as the initial evaluation parameter; and determining the product of the initial evaluation parameter and the external risk assessment factor as the risk assessment parameter.

[0081] Among them, the external risk assessment factor is determined based on whether the external electric vehicle is parked, for example: when the external electric vehicle is not parked, the external risk assessment factor is 1; when the external electric vehicle is parked, the external risk assessment factor is a value greater than 1, for example: the external risk assessment factor is 2.

[0082] Furthermore, when it is determined that there are external electric vehicles parked, the external risk assessment factor can be dynamically determined in combination with the relationship between the number of tenant electric vehicles and the number of parked electric vehicles. For example, the ratio of the number of parked electric vehicles to the number of tenant electric vehicles is determined as the number of external electric vehicles. In this way, the impact of the number of external electric vehicles can be fully considered in the process of calculating the risk assessment parameters, which can help improve the accuracy of the calculated risk assessment parameters.

[0083] In the above implementation, since the number of tenant electric vehicles in the parking shed can be determined based on the number of identified electronic tags, and then the relationship between the number of tenant electric vehicles and the number of parked electric vehicles can be used to determine whether there are any foreign electric vehicles parked in the parking shed, and the risk assessment parameters corresponding to the target parking shed can be determined based on whether there are any foreign electric vehicles parked in the parking shed, the impact of the number of foreign electric vehicles can be taken into account in the process of calculating the risk assessment parameters, which can help improve the accuracy of the calculated risk assessment parameters.

[0084] For further reference, Figure 5 After the above step 402 determines whether the number of tenant electric vehicles is the same as the number of parked electric vehicles, the following steps are also included: Step 501: When the number of tenant electric vehicles is greater than the number of parked electric vehicles, it is determined that there is an abnormality in the number of parked electric vehicles.

[0085] Specifically, since the number of parked electric vehicles only counts the number of electric vehicles parked in the parking shed, and due to the working characteristics of the electronic tag detection component, it may detect electric vehicles near the parking shed. Therefore, the number of tenant electric vehicles finally determined may be greater than the number of parked electric vehicles. Parking electric vehicles outside the parking shed is not conducive to the management of electric vehicles and may also affect the traffic within the park. Therefore, it is necessary to identify the electric vehicles parked near the parking shed.

[0086] Step 502: Acquire second image information of the monitoring area corresponding to the target parking shed.

[0087] Among them, the monitoring area includes the parking area corresponding to the target parking shed. In actual implementation, the monitoring area includes not only the parking area, but also the area near the parking area, which can help to confirm whether there are illegally parked vehicles near the parking area.

[0088] In one example, the second image information is collected by a video acquisition component set outside the parking shed. In this case, the video acquisition component can be fixed for collecting information corresponding to the monitoring area, or the collection direction can be adjustable. In this case, when it is determined that there is an abnormality in the number of parked electric vehicles, it is necessary to control the video acquisition component to adjust the collection direction to obtain the second image information corresponding to the monitoring area.

[0089] It should be noted that, in the present embodiment, the second image information is acquired only when it is determined that there is an abnormality in the number of parked electric vehicles, and corresponding judgment is made in combination with the second image information. This can help reduce the amount of calculation in the monitoring process while ensuring the monitoring effect.

[0090] Step 503: Determine the number of electric vehicles in the monitoring area based on the second image information.

[0091] Specifically, the method of determining the number of electric vehicles in the monitoring area based on the second image information is the same as the method of determining the number of parked electric vehicles based on the first image in the above steps 202 to 204, and this embodiment will not be repeated here.

[0092] Since the monitoring area includes the parking area corresponding to the target parking shed, generally speaking, the number of electric vehicles determined in the monitoring area is not less than the number of electric vehicles parked in the parking area.

[0093] In the actual implementation process, if the number of electric vehicles determined in the monitoring area is less than the number of electric vehicles parked in the parking area, it means that there is an identification error. At this time, an error alarm can be generated to prompt the park management personnel to go to the site for verification.

[0094] Step 504: When the number of electric vehicles in the monitoring area is greater than or equal to the number of tenant electric vehicles, determine the vehicle image information of the electric vehicles located outside the parking area.

[0095] Specifically, when the number of electric vehicles in the monitoring area is greater than or equal to, it means that there is a target electric vehicle parked outside the parking area. At this time, the target electric vehicle needs to be identified and further processed.

[0096] In one example, the vehicle image information includes information about the vehicle and the environment around the vehicle, which can help determine the location of the vehicle based on the vehicle information.

[0097] Step 505: Generate illegal parking alarm information based on the vehicle image information.

[0098] In one example, after the illegal parking alarm prompt information is generated, it is sent to the administrator terminal corresponding to the park administrator to prompt the park administrator to handle the illegal parking.

[0099] In the above embodiment, since the number of tenants' electric vehicles is greater than the number of parked electric vehicles, it indicates that there may be illegally parked vehicles near the parking shed. At this time, the second image information of the monitoring area corresponding to the target parking shed is collected to determine the number of electric vehicles in the monitoring area, and when the number of electric vehicles in the monitoring area is greater than or equal to the number of tenants' electric vehicles, illegal parking alarm information is generated based on the vehicle image information of electric vehicles located outside the parking area, which can help to manage the target electric vehicles near the parking shed.

[0100] In some embodiments, reference Figure 6 The park electric vehicle management method provided in the embodiment of the present application also includes the following steps: Step 601, for the power socket in the target parking shed, when the power socket is in working state, the output current of the power socket is monitored.

[0101] Specifically, the method for determining the working status of the power socket refers to the specific implementation method corresponding to the above step 102, which will not be repeated in this embodiment.

[0102] Step 602: When it is detected that the output current is greater than a preset output current threshold, the risk assessment parameter is adjusted to increase the risk level corresponding to the target parking shed.

[0103] The output current threshold is set based on the maximum current of normal charging of the electric vehicle.

[0104] In one example, adjusting the risk assessment parameter includes: determining the product of the risk assessment parameter and a preset first adjustment coefficient as the adjusted risk assessment coefficient.

[0105] In another example, adjusting the risk assessment parameter includes: increasing the risk assessment parameter by a preset first adjustment range to obtain an adjusted risk assessment coefficient, wherein the first adjustment range is not less than a parameter difference corresponding to a risk level, so that the risk level corresponding to the risk assessment parameter can be increased.

[0106] Step 603: When it is detected that the output current is less than or equal to the output current threshold, determine whether the sum of the output currents of the power sockets in working state is greater than a preset maximum output current.

[0107] The maximum operating current is set based on the configuration of the power supply system corresponding to the parking shed. Specifically, each power socket provided on the parking shed is powered by a dedicated power supply system, so the maximum operating current is set based on the configuration of the power supply system of the parking shed.

[0108] Since each power socket needs to be powered by the power supply system, and the maximum output power of the power supply system is usually limited, it is necessary to monitor the maximum output power of the power supply system through the sum of the output currents of each power socket to ensure the safety of power supply of the power supply system.

[0109] Step 604: When the sum of the output currents is greater than the maximum output current, the risk assessment parameters are adjusted to increase the risk level corresponding to the target parking shed.

[0110] Generally speaking, when the sum of the output currents of all working power sockets is greater than the maximum working current, it means that the power supply system of the parking shed is overloaded. At this time, there is a major safety hazard, so it is necessary to increase the risk level of the target parking shed.

[0111] In one example, adjusting the risk assessment parameter includes: determining the product of the risk assessment parameter and a preset second adjustment coefficient as the adjusted risk assessment coefficient, wherein the second adjustment coefficient is greater than 1.

[0112] In another example, adjusting the risk assessment parameter includes: increasing the risk assessment parameter by a preset second adjustment range to obtain an adjusted risk assessment coefficient, wherein the second adjustment range is not less than a parameter difference corresponding to a risk level, so that the risk level corresponding to the risk assessment parameter can be increased.

[0113] In the above embodiment, the output current of the power socket in a working state can be monitored, and when it is monitored that the output current is greater than the output current threshold power socket or the sum of the output currents of the working socket is greater than the preset maximum output current, the risk assessment parameters can be adjusted. In this way, the risk assessment parameters can be adjusted in combination with the output current of the power socket, which can help improve the accuracy of the risk assessment parameters, and further help to accurately guide the safety management of electric vehicles.

[0114] For further reference, Figure 7 In step 602, when the output current is detected to be greater than the preset output current threshold, the following steps are also included: Step 701, determining a charging area corresponding to a power socket.

[0115] The charging area is obtained by dividing the parking area corresponding to the parking shed. In one example, the parking area can be divided into several parking spaces, and one power socket corresponds to one or several parking spaces.

[0116] Step 702: Acquire infrared image information corresponding to the charging area.

[0117] In one example, the infrared image information is collected using a camera with an infrared sensing function.

[0118] In actual implementation, the infrared image information corresponding to the parking area can be directly acquired, and the infrared image information can be intercepted based on the position of the charging area in the parking area to obtain the infrared image information corresponding to the charging area.

[0119] Step 703 : determining whether there is an abnormal location in the charging area with a temperature greater than a preset temperature threshold based on the infrared image information.

[0120] Specifically, the method of determining temperature based on infrared images is based on parameter settings of a device for collecting infrared images, and can be achieved by those skilled in the art through conventional means, which will not be introduced here.

[0121] During the charging process, the battery temperature may rise due to aging of the electric vehicle's battery, poor line contact, etc. If not handled in time, it may even cause the electric vehicle to catch fire. Therefore, it is necessary to monitor the temperature during the charging process.

[0122] In this embodiment, taking into account the high cost of temperature monitoring and the fact that the increase in battery temperature is often caused by an increase in current, in this embodiment, the temperature of the charging area corresponding to the charging socket is monitored only when it is monitored that the output current of the charging socket is greater than a preset output current threshold. This can help to accurately determine the range of temperature monitoring required, and thus can reasonably control the cost of temperature monitoring while achieving temperature monitoring.

[0123] Step 704, when there is an abnormal position in the fully charged area, the risk assessment parameters are adjusted to further increase the risk level corresponding to the target parking shed.

[0124] Among them, further increasing the risk level corresponding to the target parking shed means: when it is monitored that the output current is greater than a preset output current threshold, after adjusting the risk assessment parameters, the risk assessment parameters are adjusted again, thereby further increasing the risk level corresponding to the target parking shed.

[0125] In the above implementation, since it is possible to further determine whether there is an abnormal location in the charging area with a temperature greater than a preset temperature threshold based on the infrared image information of the charging area corresponding to the power socket when the output current of the power socket is greater than the preset output current threshold, and if there is an abnormal location in the charging area, the risk level corresponding to the target parking shed is further increased. In this way, the risk assessment parameters can be adjusted in combination with the temperature of the charging area, which can help improve the accuracy of the risk assessment parameters, and further help to accurately guide the safety management of electric vehicles.

[0126] In some embodiments, reference Figure 8 The park electric vehicle management method provided in the embodiment of the present application also includes the following steps: Step 801, obtaining image information of a prohibited parking area.

[0127] Specifically, the no-parking area may be all areas except the parking shed, or may be areas designated by an administrator, such as key sections of a road, etc. This embodiment does not limit the method for determining the no-parking area.

[0128] Step 802: Determine whether there is a target electric vehicle in the prohibited parking area based on the image information of the prohibited parking area.

[0129] Specifically, the method of determining whether there is a target electric vehicle in the prohibited parking area based on the image information of the prohibited parking area is the same as the method of determining the number of parked electric vehicles based on the first image in the above steps 202 to 204, and this embodiment will not be repeated here.

[0130] In one example, the target electric vehicles are all electric vehicles in a prohibited parking area.

[0131] In another example, the target electric vehicle is an electric vehicle that is stationary in a prohibited parking area, which can help avoid the influence of electric vehicles passing through the prohibited parking area on the detection results, thereby reducing the number of target electric vehicles. Specifically, the judgment standard for a stationary electric vehicle can be an electric vehicle whose position has not changed in a number of consecutive image frames, or an electric vehicle with no one on board. This embodiment does not limit the judgment method for a stationary electric vehicle.

[0132] Step 803: When there is a target electric vehicle in the prohibited parking area, determine whether the residence time of the target electric vehicle in the prohibited parking area exceeds a preset residence time threshold.

[0133] The stay time threshold is set according to actual needs. In actual implementation, the stay time thresholds corresponding to different no-parking areas can be set separately.

[0134] Step 804: When the stay time exceeds the stay time threshold, determine whether the vehicle identification corresponding to the target electric vehicle is recognized.

[0135] The vehicle identification is pre-set on the electric vehicles of the park tenants, and different electric vehicles correspond to different vehicle identifications. In one example, the vehicle identification is a license plate. In actual implementation, the vehicle identification can also be an identification pattern pasted on the vehicle, as long as the vehicle can be identified through the vehicle identification. This embodiment does not limit the setting method of the vehicle identification.

[0136] In this embodiment, a tenant vehicle information database is pre-set, and the correspondence between vehicle identifications and tenants is stored in the tenant vehicle information database, so that the corresponding tenant can be easily identified based on the vehicle identification.

[0137] Step 805, when the vehicle identification is identified, the target tenant corresponding to the target electric vehicle is queried based on the vehicle identification, and illegal parking reminder information is sent to the target tenant to prompt the target tenant to handle the target electric vehicle.

[0138] Specifically, querying the target tenant corresponding to the target electric vehicle based on the vehicle identification information includes: querying the target tenant corresponding to the vehicle identification information from a tenant vehicle information database based on the vehicle identification information.

[0139] Step 806, when the vehicle identification is not recognized, a parking violation processing prompt message is sent to the security personnel closest to the illegally parked vehicle to prompt the security personnel to handle the target electric vehicle.

[0140] In the above technical solution, the target electric vehicle in the prohibited parking area can be monitored through the image information in the prohibited parking area, and when it is monitored that the stay time of the target electric vehicle exceeds the stay time threshold, the identification information of the target electric vehicle is identified, and the corresponding prompt method is determined based on whether the identification information corresponding to the target electric vehicle is identified, so as to prompt the tenant or security personnel to deal with the target electric vehicle. This can help to realize the automatic judgment of illegally parked electric vehicles and the dynamic determination of the handling method, which can further help to manage the target electric vehicles.

[0141] The present application also provides an electronic device. Fig. 9 As shown, Fig. 9 The electronic device 900 shown includes: a processor 901 and a memory 903. The processor 901 and the memory 903 are connected, such as through a bus 902. Optionally, the electronic device 900 may also include a transceiver 904. It should be noted that in actual applications, the transceiver 904 is not limited to one, and the structure of the electronic device 900 does not constitute a limitation on the embodiments of the present application.

[0142] The processor 901 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor 901 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0143] The bus 902 may include a path to transmit information between the above components. The bus 902 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 902 may be divided into an address bus, a data bus, etc. For ease of representation, Fig. 9 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0144] The memory 903 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program codes in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0145] The memory 903 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 901. The processor 901 is used to execute the application code stored in the memory 903 to implement the contents shown in the above method embodiment.

[0146] The electronic devices include, but are not limited to, mobile terminals such as mobile phones, laptop computers, PDAs (personal digital assistants), PADs (tablet computers), etc., and fixed terminals such as digital TVs, desktop computers, etc. It can also be a server terminal, etc. Fig. 9 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0147] An embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed in a computer, the computer is caused to execute the park electric vehicle management method provided in the above embodiment.

[0148] It should be understood that although the steps in the flowchart of the accompanying drawings are shown in sequence according to the instructions of the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise clearly stated in this document, the execution of these steps is not strictly limited in order and can be performed in other orders.

[0149] The above are only some implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for managing electric vehicles in a park, characterized in that: The method comprises: Monitor the number of electric vehicles parked in the target parking shed; When the number of parked electric vehicles is greater than a preset first number threshold, determining the number of electric vehicles being charged in the target parking shed; In a case where the number of the electric vehicles being charged is greater than a preset second number threshold, determining a risk assessment parameter corresponding to the target parking shed based on the number of the parked electric vehicles and the number of the electric vehicles being charged; Risk warning information corresponding to the target parking shed is generated based on the risk assessment parameters.

2. The method according to claim 1, characterized in that The monitoring of the number of electric vehicles parked in the target parking shed includes: Acquire first image information of a parking area corresponding to the target parking shed; Preprocessing the first image information to obtain a preprocessed image; Inputting the preprocessed image into a pre-trained object recognition model to obtain an object type corresponding to each object in the first image information; The number of objects whose object type is electric vehicles in the first image information is determined as the number of parked electric vehicles.

3. The method according to claim 2, characterized in that The object recognition model is also used to output the confidence of the object type corresponding to each object; before determining the number of objects whose object type is electric vehicles in the first image information as the number of parked electric vehicles, it also includes: For a target object whose object type in the first image information is an electric vehicle, determining whether the confidence corresponding to the object type is greater than a preset confidence threshold; When the confidence level corresponding to the object type is less than or equal to the confidence level threshold, intercepting a target image corresponding to the target object; Inputting the target image into a pre-trained object classification model to obtain a classification result; The object type corresponding to the target object is updated based on the classification result.

4. The method according to claim 2, characterized in that: Before determining the risk assessment parameter based on the number of parked electric vehicles and the number of electric vehicles being charged, the method further includes: The number of electric vehicles of tenants is obtained based on the number of electronic tags detected by the electronic tag detection component set in the target parking shed, wherein the electronic tags are pre-set in the electric vehicles of the tenants in the park; Determining whether the number of the tenant electric vehicles is the same as the number of the parked electric vehicles; In the case where the number of the tenant electric vehicles is less than the number of the parked electric vehicles, determining that there are external electric vehicles parked in the parking shed; When the number of the tenant electric vehicles is equal to the number of the parked electric vehicles, determining that no external electric vehicles are parked in the parking shed; The determining of the risk assessment parameter based on the number of parked electric vehicles and the number of electric vehicles being charged includes: The risk assessment parameters are determined based on the number of parked electric vehicles, the number of electric vehicles being charged, and whether there are any foreign electric vehicles parked in the parking shed.

5. The method according to claim 4, characterized in that After determining whether the number of the tenant electric vehicles is the same as the number of the parked electric vehicles, the method further includes: In the case where the number of the tenant electric vehicles is greater than the number of the parked electric vehicles, determining that there is an abnormality in the number of the parked electric vehicles; Acquire second image information of a monitoring area corresponding to the target parking shed, wherein the monitoring area includes a parking area corresponding to the target parking shed; determining the number of electric vehicles in the monitoring area based on the second image information; When the number of electric vehicles in the monitoring area is greater than or equal to the number of the tenant electric vehicles, determining vehicle image information of electric vehicles located outside the parking area; Generate illegal parking alarm information based on the vehicle image information.

6. The method according to claim 1, characterized in that The method further comprises: For the power socket in the target parking shed, when the power socket is in working state, monitoring the output current of the power socket; When it is detected that the output current is greater than a preset output current threshold, the risk assessment parameter is adjusted to increase the risk level corresponding to the target parking shed; In the case where it is monitored that the output current is less than or equal to the output current threshold, determining whether the sum of the output currents of the power sockets in working state is greater than a preset maximum output current; When the sum of the output currents is greater than the maximum output current, the risk assessment parameter is adjusted to increase the risk level corresponding to the target parking shed.

7. The method according to claim 6, characterized in that In the case where it is monitored that the output current is greater than a preset output current threshold, the method further includes: Determine a charging area corresponding to the power socket; Acquiring infrared image information corresponding to the charging area; Determine whether there is an abnormal location in the charging area with a temperature greater than a preset temperature threshold based on the infrared image information; In the case where an abnormal position exists within the fully charged area, the risk assessment parameter is adjusted to further increase the risk level corresponding to the target parking shed.

8. The method according to claim 1, characterized in that The method further comprises: Acquire image information of a prohibited parking area, wherein the prohibited parking area does not include the parking shed; Determining whether a target electric vehicle exists in the prohibited parking area based on the image information of the prohibited parking area; In the case where the target electric vehicle exists in the prohibited parking area, determining whether the residence time of the target electric vehicle in the prohibited parking area exceeds a preset residence time threshold; In the case where the stay time exceeds the stay time threshold, determining whether a vehicle identification corresponding to the target electric vehicle is identified, the vehicle identification being pre-set on the electric vehicle of the park tenant; When the vehicle identification is identified, the target tenant corresponding to the target electric vehicle is queried based on the vehicle identification, and illegal parking reminder information is sent to the target tenant to prompt the target tenant to handle the target electric vehicle; In the case where the vehicle identification is not recognized, illegal parking processing prompt information is sent to the security personnel closest to the illegally parked vehicle to prompt the security personnel to handle the target electric vehicle.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the campus electric vehicle management method as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed in a computer, the computer is caused to execute the park electric vehicle management method according to any one of claims 1 to 8.

Images