Parking space occupation prevention method and system

Abstract

The invention is suitable for the technical field of charging parking space management, and provides a parking space occupation prevention method and system, and the method comprises the steps: obtaining an activity video record of a designated place where a charging region belongs when the remaining charging time of a new energy vehicle of a target user in the charging region is lower than a first preset threshold value; and analyzing the activity video record, determining the current activity area of the target user, and screening out a plurality of historical local videos of the target user returning from the current activity area to the charging area. The load change of the target user and the remaining charging time of the new energy vehicle are monitored in real time, the historical activity video data and the preset load reference model are combined, accurate prediction of the return time of the user is achieved, then the vehicle moving instruction is sent to the user before charging is completed, and the charging parking space is prevented from being occupied.

Description

Technical Field

[0001] The present invention belongs to the technical field of charging parking space management, and in particular relates to a parking space anti-occupancy method and system. Background Art

[0002] With the rapid popularization of new energy vehicles, the construction and management of charging infrastructure have become key issues that need to be urgently addressed. Especially in the management of charging parking spaces, how to efficiently utilize charging pile resources and avoid parking spaces being occupied has become a major challenge facing charging station operators. Existing charging parking space management technologies mainly rely on manual user operations and simple parking space occupancy detection, such as judging whether charging is complete by the connection status between the charging pile and the vehicle, or monitoring parking space occupancy through traditional sensors. Although these methods can detect parking space occupancy to a certain extent, they often have problems such as low accuracy, slow response, and untimely information updates, resulting in a waste of charging parking space resources and affecting the efficiency of charging piles.

[0003] With the rapid development of the Internet of Things, video surveillance and image recognition technologies, although some technologies have begun to be applied to parking space occupancy monitoring, existing technologies still have many limitations. For example, solutions based on video surveillance and image recognition technologies usually only focus on the physical occupancy status of parking spaces, while ignoring the actual load and return time of users. In addition, existing technologies cannot determine in real time whether users can return to charging parking spaces in time, which leads to the situation that in some cases, when users leave the parking spaces, the charging parking spaces are occupied by other users or cannot be used efficiently, which seriously affects the rational allocation and utilization of charging resources.

[0004] Therefore, the existing technology has great deficiencies in the precise management and dynamic adjustment of charging parking spaces to prevent occupation. In particular, there is still no effective solution on how to predict the return time of the target user based on the load and charging time of the target user, and then send a moving instruction to the user before charging is completed to avoid occupation and improve the efficiency of parking space use. Therefore, in response to this problem, there is an urgent need for a comprehensive prediction method based on load changes and charging time to achieve accurate charging parking space management, thereby improving the utilization efficiency and overall management level of charging resources. Summary of the invention

[0005] The purpose of the present invention is to provide a parking space anti-occupancy method and system, aiming to solve the problems raised in the background technology.

[0006] The present invention is implemented as follows: a parking space anti-occupancy method, the method comprising:

[0007] When the remaining charging time of the target user's new energy vehicle in the charging area is lower than a first preset threshold, obtaining an activity video record of a designated site to which the charging area belongs;

[0008] Analyze the activity video records, determine the current activity area of ​​the target user, and filter out several historical partial videos of the target user returning from the current activity area to the charging area;

[0009] Intelligently analyze each historical partial video, calculate the return time of the target user in each historical partial video, evaluate the load situation of the target user in each historical partial video based on the preset load reference model, and associate the return time with the load situation to generate a reference line chart;

[0010] The target user's load changes and the remaining charging time of the new energy vehicle are monitored in real time. When the target user's load and the remaining charging time are detected to match a point in the reference line graph, a moving instruction is issued to the target user.

[0011] As a further limitation of the technical solution of the embodiment of the present invention, the steps of parsing the activity video record, determining the current activity area of ​​the target user, and filtering out a plurality of historical partial videos of the target user returning from the current activity area to the charging area include:

[0012] Analyze the activity video records of the designated venue and identify the area where the target user is currently located;

[0013] Determine whether the activity time of the target user in the current area exceeds a second preset threshold, and if so, determine that the area is the current activity area of ​​the target user;

[0014] Several video segments related to the current activity area of ​​the target user are screened out from the activity video records, and these video segments are screened again to find several historical partial videos of the target user returning from the current activity area to the charging area.

[0015] As a further limitation of the technical solution of the embodiment of the present invention, the preset weight reference model refers to a standard model used to set weight values ​​for different items according to the type, weight, and volume of the items. The model is specifically used to evaluate the weight situation of users carrying items.

[0016] As a further limitation of the technical solution of the embodiment of the present invention, the steps of intelligently analyzing each historical partial video, calculating the return time of the target user in each historical partial video, evaluating the load condition of the target user in each historical partial video based on a preset load reference model, and associating the return time with the load condition, and generating a reference line graph include:

[0017] Intelligently analyze each historical local video to extract the target user's movement trajectory and time information, and calculate the return time required for the target user to return from the current activity area to the charging area based on the timestamp and the user's movement trajectory;

[0018] Capturing images of the target user carrying items in the current activity area from the historical local video, and inputting the images into a preset weight reference model to evaluate the weight value of the target user;

[0019] The return duration and load value of all historical local videos are associated, and the data points are plotted to generate a reference line graph.

[0020] As a further limitation of the technical solution of the embodiment of the present invention, the step of monitoring the load change of the target user and the remaining charging time of the new energy vehicle in real time, and when the load condition and the remaining charging time of the target user are matched with a certain point in the reference line graph, issuing a vehicle moving instruction to the target user includes:

[0021] Monitor the weight changes of the target user in the current activity area in real time, and continuously obtain the weight value of the target user;

[0022] While monitoring the target user's load changes, it also tracks the remaining charging time of new energy vehicles in real time;

[0023] When the real-time load value and the real-time remaining charging time of the target user are monitored to match the load value and return time corresponding to a point in the reference line graph, it is determined that the remaining charging time at this time is consistent with the return time required by the target user based on the current load situation, and a moving instruction is issued to the target user.

[0024] As a further limitation of the technical solution of the embodiment of the present invention, when monitoring whether the target user's weight condition and the remaining charging time match a point in the reference line graph, the point can be the interpolation result of the line connecting two data points in the reference line graph, and is not limited to the actual data points.

[0025] A parking space anti-occupancy system, the system comprising: a data acquisition module, a video screening module, a line graph generation module and an instruction generation module, wherein:

[0026] A data acquisition module, configured to acquire an activity video record of a designated site to which the charging area belongs when the remaining charging time of the target user's new energy vehicle in the charging area is lower than a first preset threshold;

[0027] A video screening module is used to parse the activity video records, determine the current activity area of ​​the target user, and screen out several historical partial videos of the target user returning from the current activity area to the charging area;

[0028] A line graph generation module is used to intelligently analyze each historical partial video, calculate the return time of the target user in each historical partial video, evaluate the load situation of the target user in each historical partial video based on a preset load reference model, and associate the return time with the load situation to generate a reference line graph;

[0029] The preset load reference model refers to a standard model used to set load values ​​for different items according to the type, weight, and volume of the items. The model is specifically used to evaluate the load of the items carried by the user.

[0030] The instruction generation module is used to monitor the changes in the target user's load and the remaining charging time of the new energy vehicle in real time. When the target user's load and remaining charging time are detected to match a point in the reference line graph, a vehicle moving instruction is issued to the target user.

[0031] As a further limitation of the technical solution of the embodiment of the present invention, the video screening module specifically includes:

[0032] A region identification unit is used to analyze the activity video records of a specified venue and identify the region where the target user is currently located;

[0033] an activity area determination unit, used to determine whether the activity time of the target user in the current area exceeds a second preset threshold, and if so, determine the area as the current activity area of ​​the target user;

[0034] The video screening unit is used to screen out several video segments related to the current activity area of ​​the target user from the activity video record, and perform secondary screening on these video segments to find out several historical local videos of the target user returning from the current activity area to the charging area.

[0035] As a further limitation of the technical solution of the embodiment of the present invention, the line graph generation module specifically includes:

[0036] The video analysis unit is used to intelligently analyze each historical local video, extract the target user's movement trajectory and time information, and calculate the return time required for the target user to return from the current activity area to the charging area based on the timestamp and the user's movement trajectory;

[0037] A load value evaluation unit is used to capture images of the target user carrying items in the current activity area from the historical local video, and input the images into a preset load reference model to evaluate the load value of the target user;

[0038] The reference line graph generating unit is used to associate the return duration and load value of all historical local videos, and generate a reference line graph by drawing data points.

[0039] As a further limitation of the technical solution of the embodiment of the present invention, the instruction generation module specifically includes:

[0040] A load value real-time acquisition unit is used to monitor the load changes of the target user in the current activity area in real time and continuously acquire the load value of the target user;

[0041] A real-time tracking unit for remaining charging time is used to track the remaining charging time of new energy vehicles in real time while monitoring the load changes of the target user;

[0042] The vehicle moving instruction issuing unit is used to determine that the remaining charging time at this time is consistent with the return time required by the target user according to the current load situation when the real-time load level value and the real-time remaining charging time of the target user match the load level value and the return time corresponding to a certain point in the reference line graph, and issue a vehicle moving instruction to the target user;

[0043] When monitoring whether the target user's load condition and remaining charging time match a certain point in the reference line graph, the point may be an interpolation result of a line connecting two data points in the reference line graph, and is not limited to the actual data points.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The present invention realizes accurate prediction of the user's return time by real-time monitoring of the target user's weight changes and the remaining charging time of the new energy vehicle, combining historical activity video data and a preset weight reference model, and then issues a move instruction to the user before charging is completed to prevent the charging parking space from being occupied. By analyzing the image of the target user's items, dynamically evaluating their weight level, and combining the charging time and weight data to generate a reference line graph, the system can accurately determine the time required for the user to return to the charging area. This process does not require additional sensors or equipment, and relies on existing video surveillance and object recognition technology to efficiently obtain weight information, avoiding the limitations of other monitoring methods.

[0046] This technical solution can adjust the timing of sending the moving command in real time according to the load and charging time, ensuring that the target user can return in time before charging is completed, avoiding low charging efficiency or waste of resources due to space occupation. Even in the case of no load, the system still calculates through the data in the line chart to ensure accurate matching and command sending under all conditions. This technology has significant application value in the field of new energy vehicle charging management, improves the utilization rate and management efficiency of charging resources, and has broad market prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1A flowchart of a method provided by an embodiment of the present invention;

[0048] Figure 2 A flow chart of parsing and screening activity video records in the method provided in an embodiment of the present invention;

[0049] Figure 3 A flow chart of generating a reference line graph based on a plurality of historical local videos in the method provided in an embodiment of the present invention;

[0050] Figure 4 A flow chart of generating and issuing a vehicle moving instruction to a target user in the method provided in an embodiment of the present invention;

[0051] Figure 5 An application architecture diagram of a system provided by an embodiment of the present invention;

[0052] Figure 6 A structural block diagram of a video screening module in a system provided by an embodiment of the present invention;

[0053] Figure 7 A structural block diagram of a line graph generation module in a system provided by an embodiment of the present invention;

[0054] Figure 8 This is a structural block diagram of an instruction generation module in a system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0056] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0057] Specifically, a parking space anti-occupancy method comprises the following steps:

[0058] Step S100, when the remaining charging time of the target user's new energy vehicle in the charging area is lower than a first preset threshold, an activity video record of a designated site to which the charging area belongs is obtained.

[0059] In an embodiment of the present invention, the remaining charging time can be obtained through real-time data transmission between the charging device and the battery management system. The charging device continuously monitors the battery charging status of the target new energy vehicle and calculates the remaining charging time based on the battery power and charging power. This data is transmitted to the system through the communication interface to ensure that the current charging progress of the target new energy vehicle is reflected in real time.

[0060] Designated sites usually refer to parking areas, charging stations, parking buildings or other places where charging piles may be installed. These sites need to be equipped with appropriate monitoring facilities to obtain video records of relevant activities. The best application sites are usually charging stations specially set up for electric vehicle charging or shopping malls equipped with charging stations, because these sites not only have real-time monitoring equipment, but are also specially designed for the charging needs of electric vehicles, and can effectively capture the charging process and activities of target users.

[0061] The video records of activities at a designated venue are from surveillance cameras installed in the venue. The cameras record the activities in the venue in real time and transmit the video data to the video storage system. By managing and accessing the video data, the system can retrieve the video records of activities related to the target user when needed, and then conduct subsequent analysis.

[0062] The first preset threshold refers to the critical value of the remaining charging time. When the remaining charging time of the target user is lower than this threshold, the system considers that the charging is about to be completed and starts the subsequent operation. The basis for setting this threshold is usually based on a comprehensive consideration of the battery type, charging power and the user's charging habits. The purpose is to promptly initiate anti-occupancy measures when charging is about to be completed, to avoid the vehicle occupying the charging area for a long time after charging is completed, thereby improving the utilization and efficiency of the charging station.

[0063] Furthermore, the parking space anti-occupancy method further comprises the following steps:

[0064] Step S200 , parsing the activity video record, determining the current activity area of ​​the target user, and filtering out a number of historical partial videos of the target user returning from the current activity area to the charging area.

[0065] Specifically, Figure 2 A flow chart for parsing and filtering active video records is shown.

[0066] The process of parsing the activity video records, determining the current activity area of ​​the target user, and selecting a number of historical partial videos of the target user returning from the current activity area to the charging area specifically includes the following steps:

[0067] Step S201, parsing the activity video record of the designated venue to identify the area where the target user is currently located;

[0068] Step S202, determining whether the activity time of the target user in the current area exceeds a second preset threshold, and if so, determining the area as the current activity area of ​​the target user;

[0069] Step S203, filtering out several video segments related to the current activity area of ​​the target user from the activity video records, and performing secondary screening on these video segments to find out several historical partial videos of the target user returning from the current activity area to the charging area.

[0070] In the embodiment of the present invention, to analyze the activity video record of the designated venue and identify the current area of ​​the target user, it is first necessary to analyze the video data of the venue through video analysis technology. These video data can come from surveillance cameras in the charging area or its surroundings. Video analysis technology can identify and locate moving objects in the video, thereby determining the dynamic trajectory and stop position of the target user. By analyzing the behavior pattern of the target user through an algorithm, the current activity location of the target user can be identified, which is usually near the charging area or within the designated activity area.

[0071] The basis for setting the second preset threshold depends mainly on the nature of the target user's activities and the time limit related to charging. The setting of this threshold needs to take into account whether the target user stays in a specific area long enough to determine whether he has entered a specific activity state. If the target user's activity time in a certain area exceeds the second preset threshold, it means that the area becomes the current activity area of ​​the target user. The setting of the threshold can be adjusted according to the actual site usage, user behavior patterns, and the time required for charging to complete. If it is found in the video recording that the target user's activity time does not exceed the threshold, it means that the area is not the target user's activity area for the time being. If the user's activity time does not reach the threshold, you can consider not proceeding to the next step of screening, or choose to extend the time before making a judgment.

[0072] Several video segments related to the target user's current activity area are screened out from the activity video records, and secondary screening is performed to find several historical local videos of the target user returning from the current activity area to the charging area. This process first involves identifying the target user's current area, and then extracting video segments related to activities in the area from historical videos through video parsing technology. The purpose of the secondary screening is to ensure that these video segments contain the target user without additional stops or activities during the process of returning to the charging area. The screening criteria should ensure that the target user's trajectory and behavior meet the conditions of "returning from the current activity area to the charging area", and that there is no deviation or other activities during the return process.

[0073] During the implementation process, for the condition of "returning from the current activity area to the charging area", a special algorithm can be set to exclude the possible deviation of the user's activity trajectory during the journey. For example, if the target user enters other areas or stays for too long on the way back, the video segment will be excluded to ensure that the screened video segment accurately reflects the user's trajectory of returning directly from the current activity area to the charging area.

[0074] Furthermore, the parking space anti-occupancy method further comprises the following steps:

[0075] Step S300, intelligently analyze each historical local video, calculate the return time of the target user in each historical local video, evaluate the load situation of the target user in each historical local video based on a preset load reference model, associate the return time with the load situation, and generate a reference line graph.

[0076] The preset weight reference model refers to a standard model used to set weight values ​​for different items based on the type, weight, and volume of the items. The model is specifically used to evaluate the weight of items carried by users.

[0077] Specifically, Figure 3 A flow chart for generating a reference line graph based on several historical local videos is shown.

[0078] Among them, intelligently analyzing each historical partial video, calculating the return time of the target user in each historical partial video, evaluating the load situation of the target user in each historical partial video based on a preset load reference model, and associating the return time with the load situation, and generating a reference line graph specifically includes the following steps:

[0079] Step S301, intelligently analyze each historical local video, extract the target user's movement trajectory and time information, and calculate the return time required for the target user to return from the current activity area to the charging area based on the timestamp and the user's movement trajectory;

[0080] Step S302, capturing an image of the target user carrying items in the current activity area from the historical local video, and inputting the image into a preset load reference model to evaluate the load value of the target user;

[0081] Step S303, associating the return duration and load value of all historical local videos, and generating a reference line graph by drawing data points.

[0082] In an embodiment of the present invention, the design of the preset load reference model can make full use of existing technologies, and establish a corresponding database or data set based on the multi-dimensional characteristics of the item, such as the type, weight, and volume. On this basis, the load value of the item can be obtained by manual marking or by algorithm calculation. This model is usually combined with an item classification system, and uses various parameters of the item to calculate and evaluate the load, thereby providing accurate load assessment for different items. The core of this method is to achieve quantitative assessment of the load through the physical properties of the item.

[0083] When processing historical local videos, we first apply computer vision technology to analyze the video frames and extract the spatial movement information of the target user by tracking his / her movement trajectory. As each frame of the image is processed step by step, combined with the timestamp data, the user's position change can be accurately recorded. This data allows us to calculate the time required for the target user to return to the charging area from the current activity area, and further accurately measure the return time.

[0084] At the same time, in the case of users carrying items, image recognition technology can be used to extract images of the items carried by the target user from the video. In this process, the object recognition algorithm will analyze the characteristics of the items in the video, and match the items with the corresponding weight data according to the preset object classification system, and then evaluate the user's weight. This step requires the support of image processing technology to ensure that clear images of objects are extracted from the video of the activity area, and the weight is evaluated by combining it with the weight reference model.

[0085] Finally, after associating the return time of all historical partial videos with the load value, a reference line graph is generated. This graph uses a standard XY axis structure, where the X axis represents the return time of the target user (usually in seconds or minutes), and the Y axis represents the corresponding load value. By plotting the return time and load value of each historical partial video in the graph, and connecting these data points to form a line graph, the relationship between the user's load value and return time can be intuitively displayed.

[0086] Furthermore, the parking space anti-occupancy method further comprises the following steps:

[0087] Step S400, real-time monitoring of the target user's load changes and the remaining charging time of the new energy vehicle, when the target user's load and the remaining charging time are detected to match a point in the reference line graph, a vehicle moving instruction is issued to the target user.

[0088] Specifically, Figure 4 A flow chart of generating and issuing a vehicle moving instruction to a target user is shown.

[0089] The real-time monitoring of the target user's load change and the remaining charging time of the new energy vehicle, when the target user's load and the remaining charging time are matched with a certain point in the reference line graph, the specific steps of issuing a vehicle moving instruction to the target user include:

[0090] Step S401, monitoring the weight change of the target user in the current activity area in real time, and continuously obtaining the weight degree value of the target user;

[0091] Step S402, while monitoring the load change of the target user, tracking the remaining charging time of the new energy vehicle in real time;

[0092] Step S403, when the real-time load level value and the real-time remaining charging time of the target user are monitored to match the load level value and return time corresponding to a certain point in the reference line graph, it is determined that the remaining charging time at this time is consistent with the return time required by the target user based on the current load situation, and a moving instruction is issued to the target user.

[0093] When monitoring whether the target user's load condition and remaining charging time match a certain point in the reference line graph, the point may be an interpolation result of a line connecting two data points in the reference line graph, and is not limited to the actual data points.

[0094] In an embodiment of the present invention, during the real-time monitoring process, the target user's weight change is evaluated by analyzing the image of the items he carries through video monitoring, and combined with a preset weight reference model. The monitoring system first identifies the items carried by the user through image processing technology, and then calculates the user's weight value based on the type, weight, and volume of the items. These data will be transmitted to the processing system in real time to be synchronized with the remaining charging time data of the new energy vehicle.

[0095] The remaining charging time of new energy vehicles is obtained by connecting to the real-time information system of the charging station and obtaining charging progress data from the charging pile or vehicle communication interface. The system will continuously track and record the charging progress to ensure that the remaining charging time is always accurate.

[0096] Once the system obtains the target user's load value and remaining charging time data, this information will be input into the model and compared with the data in the reference line graph. The reference line graph shows the relationship between different load values ​​and corresponding return times, helping the system determine whether the user's load and charging time at the current moment match the data at a certain point. If the real-time monitoring shows that the user's load value and charging time match a certain data point, the system will calculate the return time required by the user.

[0097] For example, when the reference line chart shows that the load value is 20, the user needs 10 minutes to return to the charging area, and the system monitors the target user's load value in real time as 20, and the remaining charging time is 10 minutes, then the system can accurately calculate that the target user can reach the charging area 10 minutes before charging is completed. At this time, the system will issue a move command to ensure that the user can move the car in time to avoid occupying the charging parking space.

[0098] In the absence of a load, the system will refer to the return time when the load is 0 in the line graph. By monitoring the target user's load changes in real time, when the system recognizes that the user is not carrying anything, it will compare this state with the return time when the load is 0 in the line graph. If the remaining charging time at this time is consistent with the return time when the load is 0, the system can accurately determine the user's return time and issue a move instruction to the user.

[0099] Furthermore, Figure 5 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0100] Among them, in another preferred embodiment provided by the present invention, a parking space anti-occupancy system includes:

[0101] The data acquisition module 100 is used to acquire the activity video record of the designated site to which the charging area belongs when the remaining charging time of the new energy vehicle of the target user in the charging area is lower than a first preset threshold.

[0102] In an embodiment of the present invention, the remaining charging time can be obtained through real-time data transmission between the charging device and the battery management system. The charging device continuously monitors the battery charging status of the target new energy vehicle and calculates the remaining charging time based on the battery power and charging power. This data is transmitted to the system through the communication interface to ensure that the current charging progress of the target new energy vehicle is reflected in real time.

[0103] Designated sites usually refer to parking areas, charging stations, parking buildings or other places where charging piles may be installed. These sites need to be equipped with appropriate monitoring facilities to obtain video records of relevant activities. The best application sites are usually charging stations specially set up for electric vehicle charging or shopping malls equipped with charging stations, because these sites not only have real-time monitoring equipment, but are also specially designed for the charging needs of electric vehicles, and can effectively capture the charging process and activities of target users.

[0104] The video records of activities at a designated venue are from surveillance cameras installed in the venue. The cameras record the activities in the venue in real time and transmit the video data to the video storage system. By managing and accessing the video data, the system can retrieve the video records of activities related to the target user when needed, and then conduct subsequent analysis.

[0105] The first preset threshold refers to the critical value of the remaining charging time. When the remaining charging time of the target user is lower than this threshold, the system considers that the charging is about to be completed and starts the subsequent operation. The basis for setting this threshold is usually based on a comprehensive consideration of the battery type, charging power and the user's charging habits. The purpose is to promptly initiate anti-occupancy measures when charging is about to be completed, to avoid the vehicle occupying the charging area for a long time after charging is completed, thereby improving the utilization and efficiency of the charging station.

[0106] Furthermore, the parking space anti-occupancy system further includes:

[0107] The video screening module 200 is used to parse the activity video record, determine the current activity area of ​​the target user, and screen out a number of historical partial videos of the target user returning from the current activity area to the charging area.

[0108] Specifically, Figure 6 The structure block diagram of the video screening module 200 in the system provided by the embodiment of the present invention is shown.

[0109] Among them, in the preferred implementation manner provided by the present invention, the video screening module 200 specifically includes:

[0110] The area identification unit 201 is used to analyze the activity video record of the designated venue and identify the area where the target user is currently located;

[0111] The activity area determination unit 202 is used to determine whether the activity time of the target user in the current area exceeds a second preset threshold, and if so, determine the area as the current activity area of ​​the target user;

[0112] The video screening unit 203 is used to screen out several video segments related to the current activity area of ​​the target user from the activity video record, and perform secondary screening on these video segments to find out several historical local videos of the target user returning from the current activity area to the charging area.

[0113] In the embodiment of the present invention, to analyze the activity video record of the designated venue and identify the current area of ​​the target user, it is first necessary to analyze the video data of the venue through video analysis technology. These video data can come from surveillance cameras in the charging area or its surroundings. Video analysis technology can identify and locate moving objects in the video, thereby determining the dynamic trajectory and stop position of the target user. By analyzing the behavior pattern of the target user through an algorithm, the current activity location of the target user can be identified, which is usually near the charging area or within the designated activity area.

[0114] The basis for setting the second preset threshold depends mainly on the nature of the target user's activities and the time limit related to charging. The setting of this threshold needs to take into account whether the target user stays in a specific area long enough to determine whether he has entered a specific activity state. If the target user's activity time in a certain area exceeds the second preset threshold, it means that the area becomes the current activity area of ​​the target user. The setting of the threshold can be adjusted according to the actual site usage, user behavior patterns, and the time required for charging to complete. If it is found in the video recording that the target user's activity time does not exceed the threshold, it means that the area is not the target user's activity area for the time being. If the user's activity time does not reach the threshold, you can consider not proceeding to the next step of screening, or choose to extend the time before making a judgment.

[0115] Several video segments related to the target user's current activity area are screened out from the activity video records, and secondary screening is performed to find several historical local videos of the target user returning from the current activity area to the charging area. This process first involves identifying the target user's current area, and then extracting video segments related to activities in the area from historical videos through video parsing technology. The purpose of the secondary screening is to ensure that these video segments contain the target user without additional stops or activities during the process of returning to the charging area. The screening criteria should ensure that the target user's trajectory and behavior meet the conditions of "returning from the current activity area to the charging area", and that there is no deviation or other activities during the return process.

[0116] During the implementation process, for the condition of "returning from the current activity area to the charging area", a special algorithm can be set to exclude the possible deviation of the user's activity trajectory during the journey. For example, if the target user enters other areas or stays for too long on the way back, the video segment will be excluded to ensure that the screened video segment accurately reflects the user's trajectory of returning directly from the current activity area to the charging area.

[0117] Furthermore, the parking space anti-occupancy system further includes:

[0118] A line graph generation module 300 is used to intelligently analyze each historical partial video, calculate the return time of the target user in each historical partial video, evaluate the load condition of the target user in each historical partial video based on a preset load reference model, and associate the return time with the load condition to generate a reference line graph;

[0119] The preset weight reference model refers to a standard model used to set weight values ​​for different items based on the type, weight, and volume of the items. The model is specifically used to evaluate the weight of items carried by users.

[0120] Specifically, Figure 7It shows a structural block diagram of the line graph generating module 300 in the system provided by the embodiment of the present invention.

[0121] Among them, in the preferred embodiment provided by the present invention, the line graph generating module 300 specifically includes:

[0122] The video parsing unit 301 is used to perform intelligent analysis on each historical local video, extract the movement trajectory and time information of the target user, and calculate the return time required for the target user to return from the current activity area to the charging area through the timestamp and the movement trajectory of the user;

[0123] The load value evaluation unit 302 is used to capture images of the target user carrying items in the current activity area from the historical local video, and input the images into a preset load reference model to evaluate the load value of the target user;

[0124] The reference line graph generating unit 303 is used to associate the return duration and the load value of all historical local videos, and generate a reference line graph by drawing data points.

[0125] In an embodiment of the present invention, the design of the preset load reference model can make full use of existing technologies, and establish a corresponding database or data set based on the multi-dimensional characteristics of the item, such as the type, weight, and volume. On this basis, the load value of the item can be obtained by manual marking or by algorithm calculation. This model is usually combined with an item classification system, and uses various parameters of the item to calculate and evaluate the load, thereby providing accurate load assessment for different items. The core of this method is to achieve quantitative assessment of the load through the physical properties of the item.

[0126] When processing historical local videos, we first apply computer vision technology to analyze the video frames and extract the spatial movement information of the target user by tracking his / her movement trajectory. As each frame of the image is processed step by step, combined with the timestamp data, the user's position change can be accurately recorded. This data allows us to calculate the time required for the target user to return to the charging area from the current activity area, and further accurately measure the return time.

[0127] At the same time, in the case of users carrying items, image recognition technology can be used to extract images of the items carried by the target user from the video. In this process, the object recognition algorithm will analyze the characteristics of the items in the video, and match the items with the corresponding weight data according to the preset object classification system, and then evaluate the user's weight. This step requires the support of image processing technology to ensure that clear images of objects are extracted from the video of the activity area, and the weight is evaluated by combining it with the weight reference model.

[0128] Finally, after associating the return time of all historical partial videos with the load value, a reference line graph is generated. This graph uses a standard XY axis structure, where the X axis represents the return time of the target user (usually in seconds or minutes), and the Y axis represents the corresponding load value. By plotting the return time and load value of each historical partial video in the graph, and connecting these data points to form a line graph, the relationship between the user's load value and return time can be intuitively displayed.

[0129] Furthermore, the parking space anti-occupancy system further includes:

[0130] The instruction generation module 400 is used to monitor the changes in the target user's load and the remaining charging time of the new energy vehicle in real time. When the target user's load and the remaining charging time are detected to match a point in the reference line graph, a moving instruction is issued to the target user.

[0131] Specifically, Figure 8 It shows a structural block diagram of the instruction generation module 400 in the system provided by the embodiment of the present invention.

[0132] In a preferred embodiment of the present invention, the instruction generation module 400 specifically includes:

[0133] The load value real-time acquisition unit 401 is used to monitor the load changes of the target user in the current activity area in real time and continuously acquire the load value of the target user;

[0134] The remaining charging time real-time tracking unit 402 is used to track the remaining charging time of the new energy vehicle in real time while monitoring the change of the target user's load;

[0135] The vehicle moving instruction issuing unit 403 is used to determine that the remaining charging time at this time is consistent with the return time required by the target user based on the current load situation when the real-time load level value and the real-time remaining charging time of the target user are monitored to match the load level value and return time corresponding to a certain point in the reference line graph, and issue a vehicle moving instruction to the target user.

[0136] When monitoring whether the target user's load condition and remaining charging time match a certain point in the reference line graph, the point may be an interpolation result of a line connecting two data points in the reference line graph, and is not limited to the actual data points.

[0137] In an embodiment of the present invention, during the real-time monitoring process, the target user's weight change is evaluated by analyzing the image of the items he carries through video monitoring, and combined with a preset weight reference model. The monitoring system first identifies the items carried by the user through image processing technology, and then calculates the user's weight value based on the type, weight, and volume of the items. These data will be transmitted to the processing system in real time to be synchronized with the remaining charging time data of the new energy vehicle.

[0138] The remaining charging time of new energy vehicles is obtained by connecting to the real-time information system of the charging station and obtaining charging progress data from the charging pile or vehicle communication interface. The system will continuously track and record the charging progress to ensure that the remaining charging time is always accurate.

[0139] Once the system obtains the target user's load value and remaining charging time data, this information will be input into the model and compared with the data in the reference line graph. The reference line graph shows the relationship between different load values ​​and corresponding return times, helping the system determine whether the user's load and charging time at the current moment match the data at a certain point. If the real-time monitoring shows that the user's load value and charging time match a certain data point, the system will calculate the return time required by the user.

[0140] For example, when the reference line chart shows that the load value is 20, the user needs 10 minutes to return to the charging area, and the system monitors the target user's load value in real time as 20, and the remaining charging time is 10 minutes, then the system can accurately calculate that the target user can reach the charging area 10 minutes before charging is completed. At this time, the system will issue a move command to ensure that the user can move the car in time to avoid occupying the charging parking space.

[0141] In the absence of a load, the system will refer to the return time when the load is 0 in the line graph. By monitoring the target user's load changes in real time, when the system recognizes that the user is not carrying anything, it will compare this state with the return time when the load is 0 in the line graph. If the remaining charging time at this time is consistent with the return time when the load is 0, the system can accurately determine the user's return time and issue a move instruction to the user.

[0142] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0143] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0144] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0145] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0146] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A parking space anti-occupancy method, characterized in that: The method comprises: When the remaining charging time of the target user's new energy vehicle in the charging area is lower than a first preset threshold, obtaining an activity video record of a designated site to which the charging area belongs; Analyze the activity video records, determine the current activity area of ​​the target user, and filter out several historical partial videos of the target user returning from the current activity area to the charging area; Intelligently analyze each historical partial video, calculate the return time of the target user in each historical partial video, evaluate the load situation of the target user in each historical partial video based on the preset load reference model, and associate the return time with the load situation to generate a reference line chart; The target user's load changes and the remaining charging time of the new energy vehicle are monitored in real time. When the target user's load and the remaining charging time are detected to match a point in the reference line graph, a moving instruction is issued to the target user.

2. The parking space anti-occupancy method according to claim 1, characterized in that: The steps of parsing the activity video record, determining the current activity area of ​​the target user, and filtering out a number of historical partial videos of the target user returning from the current activity area to the charging area include: Analyze the activity video records of the designated venue and identify the area where the target user is currently located; Determine whether the activity time of the target user in the current area exceeds a second preset threshold, and if so, determine that the area is the current activity area of ​​the target user; Several video segments related to the current activity area of ​​the target user are screened out from the activity video records, and these video segments are screened again to find several historical partial videos of the target user returning from the current activity area to the charging area.

3. The parking space anti-occupancy method according to claim 1, characterized in that: The preset weight reference model refers to a standard model used to set weight values ​​for different items based on the type, weight, and volume of the items. The model is specifically used to evaluate the weight of items carried by users.

4. The parking space anti-occupancy method according to claim 3, characterized in that: Intelligently analyze each historical partial video, calculate the return time of the target user in each historical partial video, evaluate the load situation of the target user in each historical partial video based on the preset load reference model, and associate the return time with the load situation. The steps of generating a reference line graph include: Intelligently analyze each historical local video to extract the target user's movement trajectory and time information, and calculate the return time required for the target user to return from the current activity area to the charging area based on the timestamp and the user's movement trajectory; Capturing images of the target user carrying items in the current activity area from the historical local video, and inputting the images into a preset weight reference model to evaluate the weight value of the target user; The return duration and load value of all historical local videos are associated, and the data points are plotted to generate a reference line graph.

5. The parking space anti-occupancy method according to claim 4, characterized in that: The steps of monitoring the load change of the target user and the remaining charging time of the new energy vehicle in real time, and issuing a vehicle moving instruction to the target user when the load condition and the remaining charging time of the target user match a certain point in the reference line graph include: Monitor the weight changes of the target user in the current activity area in real time, and continuously obtain the weight value of the target user; While monitoring the target user's load changes, it also tracks the remaining charging time of new energy vehicles in real time; When the real-time load value and the real-time remaining charging time of the target user are monitored to match the load value and return time corresponding to a point in the reference line graph, it is determined that the remaining charging time at this time is consistent with the return time required by the target user based on the current load situation, and a moving instruction is issued to the target user.

6. The parking space anti-occupancy method according to claim 5, characterized in that: When monitoring whether the target user's load condition and remaining charging time match a certain point in the reference line graph, the point may be an interpolation result of a line connecting two data points in the reference line graph, and is not limited to the actual data points.

7. A parking space anti-occupancy system, characterized in that: The system comprises: a data acquisition module, a video screening module, a line graph generation module and an instruction generation module, wherein: A data acquisition module, configured to acquire an activity video record of a designated site to which the charging area belongs when the remaining charging time of the target user's new energy vehicle in the charging area is lower than a first preset threshold; A video screening module is used to parse the activity video records, determine the current activity area of ​​the target user, and screen out several historical partial videos of the target user returning from the current activity area to the charging area; A line graph generation module is used to intelligently analyze each historical partial video, calculate the return time of the target user in each historical partial video, evaluate the load situation of the target user in each historical partial video based on a preset load reference model, and associate the return time with the load situation to generate a reference line graph; The preset load reference model refers to a standard model used to set load values ​​for different items according to the type, weight, and volume of the items. The model is specifically used to evaluate the load of the items carried by the user. The instruction generation module is used to monitor the changes in the target user's load and the remaining charging time of the new energy vehicle in real time. When the target user's load and remaining charging time are detected to match a point in the reference line graph, a vehicle moving instruction is issued to the target user.

8. The parking space anti-occupancy system according to claim 7, characterized in that: The video screening module specifically includes: A region identification unit is used to analyze the activity video records of a specified venue and identify the region where the target user is currently located; an activity area determination unit, used to determine whether the activity time of the target user in the current area exceeds a second preset threshold, and if so, determine the area as the current activity area of ​​the target user; The video screening unit is used to screen out several video segments related to the current activity area of ​​the target user from the activity video record, and perform secondary screening on these video segments to find out several historical local videos of the target user returning from the current activity area to the charging area.

9. The parking space anti-occupancy system according to claim 8, characterized in that: The line graph generation module specifically includes: The video analysis unit is used to intelligently analyze each historical local video, extract the target user's movement trajectory and time information, and calculate the return time required for the target user to return from the current activity area to the charging area based on the timestamp and the user's movement trajectory; A load value evaluation unit is used to capture images of the target user carrying items in the current activity area from the historical local video, and input the images into a preset load reference model to evaluate the load value of the target user; The reference line graph generation unit is used to associate the return duration and load value of all historical local videos, and generate a reference line graph by drawing data points.

10. The parking space anti-occupancy system according to claim 9, characterized in that: The instruction generation module specifically includes: A load value real-time acquisition unit is used to monitor the load changes of the target user in the current activity area in real time and continuously acquire the load value of the target user; A real-time tracking unit for remaining charging time is used to track the remaining charging time of new energy vehicles in real time while monitoring the load changes of the target user; The vehicle moving instruction issuing unit is used to determine that the remaining charging time at this time is consistent with the return time required by the target user according to the current load situation when the real-time load level value and the real-time remaining charging time of the target user match the load level value and the return time corresponding to a certain point in the reference line graph, and issue a vehicle moving instruction to the target user; When monitoring whether the target user's load condition and remaining charging time match a certain point in the reference line graph, the point may be an interpolation result of a line connecting two data points in the reference line graph, and is not limited to the actual data points.

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