Electronic fence entering and leaving judgment method

Abstract

The invention relates to the technical field of electronic fences, in particular to an electronic fence entry and exit determination method, which comprises the following steps: acquiring a positioning coordinate of a target vehicle in a target time period, establishing a moving track route of the target vehicle based on the positioning coordinate, and smoothing the moving track route; setting the initial values of the target electronic fence and the in-out state of the target vehicle as 0, calculating the number of intersection points of the moving track route and the target electronic fence, and defining the number as a first number; on the basis of the first number, obtaining the in-and-out state change of the target vehicle in the target time period, and visually representing the in-and-out state; acquiring historical in-and-out data of the target vehicle, establishing an anomaly analysis model based on the historical in-and-out data, inputting the first number and the moving track route into the anomaly analysis model, judging whether the target vehicle is abnormal or not, and if yes, generating an alarm signal; according to the invention, the accuracy and processing efficiency of electronic fence entry and exit judgment are improved.

Description

Technical Field

[0001] This application relates to the technical field of electronic fences, and particularly to a method for determining entry and exit of an electronic fence. Background Art

[0002] In the fields of modern logistics, intelligent transportation, security monitoring, etc., the demand for precise management of vehicle entry and exit from specific areas is increasing day by day. With the rapid development of the Internet of Things, positioning technology, and geographic information systems, the electronic fence technology has emerged. The electronic fence uses a positioning system to obtain the real-time position information of vehicles, realizing intelligent monitoring of vehicle entry and exit from specific areas.

[0003] Existing methods for determining vehicle entry and exit of an electronic fence, such as the Chinese patent application with the publication number CN112218232A, propose a method, device, storage medium, and electronic device for judging entry and exit of an electronic fence. Among them, the method includes: obtaining a first positioning coordinate at the start of movement and a second positioning coordinate at the end of movement of a monitored object. When it is determined that an event of entering or exiting the electronic fence occurs based on the first positioning coordinate and the second positioning coordinate, obtaining a first movement direction and a first movement distance collected by a sensor, calculating a second movement direction and a second movement distance based on the first positioning coordinate and the second positioning coordinate. If the first movement direction does not match the second movement direction, and / or the first movement distance does not match the second movement distance, the event of entering or exiting the electronic fence is not reported to the server; also, for example, the Chinese patent application with the publication number CN118828411A proposes a monitoring method, device, electronic device, and storage medium. The method includes: obtaining a monitoring image of the boundary area of a target electronic fence in real time; confirming whether a target object in the electronic fence exhibits abnormal behavior of leaving the fence based on the monitoring image; obtaining the positioning information of the target object in the case where the target object exhibits abnormal behavior of leaving the fence; verifying the abnormal behavior of leaving the fence of the target object based on the positioning information to obtain a verification result of whether the target object abnormally leaves the fence.

[0004] However, in the above-mentioned existing technologies, it is necessary to rely on sensor data for judgment. Detecting the movement direction, movement distance, and monitoring image using sensors is prone to errors, reducing the accuracy of the judgment result, and the system needs to have high computing power to quickly process a large amount of sensor data, thereby reducing the data processing efficiency. Therefore, a method for determining entry and exit of an electronic fence is needed to improve the accuracy of the judgment result of target vehicle entry and exit, and at the same time improve the computing efficiency of data processing. Summary of the Invention

[0005] This application provides a method for determining entry and exit of an electronic fence to solve the problems raised in the above background art.

[0006] To achieve the above-mentioned invention objectives, the present invention proposes an electronic fence access determination method, including: S1: Obtain the positioning coordinates of the target vehicle within the target time period, establish the moving trajectory route of the target vehicle based on the positioning coordinates, and smooth the moving trajectory route. S2: Set the target electronic fence, and set the initial value of the access status of the target vehicle to 0. Calculate the number of intersection points between the moving trajectory route and the target electronic fence, which is defined as the first quantity. S3: Obtain the change in the access status of the target vehicle within the target time period based on the first quantity, and visually represent the access status. S4: Obtain the historical access data of the target vehicle, establish an anomaly analysis model based on the historical access data, input the first quantity and the moving trajectory route into the anomaly analysis model, and determine whether there is an anomaly in the target vehicle. If so, generate an alarm signal.

[0007] Further, establishing the moving trajectory route of the target vehicle based on the positioning coordinates includes the following steps: Divide the target time period into multiple time points. There are corresponding positioning coordinates of the target vehicle at each time point, and eliminate the abnormal positioning coordinates. Connect the positioning coordinates at adjacent time points among the remaining positioning coordinates to generate the first line segment, and connect the first line segments within the target time period to obtain the moving trajectory route.

[0008] Further, smoothing the moving trajectory route includes the following steps: Perform fitting processing on the positioning coordinates based on the curve fitting algorithm to generate a fitting curve, optimize the fitting curve, remove the noise points and mutation points on the fitting curve, and use the optimized fitting curve as the smoothed moving trajectory route.

[0009] Further, eliminating the abnormal positioning coordinates includes the following steps: Calculate the distance between adjacent positioning coordinates. If the distance between a positioning coordinate and the previous positioning coordinate is greater than the first threshold, then determine that this positioning coordinate is an abnormal positioning coordinate and eliminate the abnormal positioning coordinate.

[0010] Further, setting the target electronic fence includes the following steps: Set multiple position points within the preset area, obtain the coordinates of each position point, and connect the coordinates of the position points in the preset order to form the boundary of the target electronic fence.

[0011] Further, calculating the number of intersection points between the moving trajectory route and the target electronic fence includes the following steps: Obtain all the boundary line segments of the target electronic fence, calculate the number of intersection points between the moving trajectory route and each boundary line segment, and count the total number of all intersection points, which is defined as the first quantity.

[0012] Further, obtaining the change in the in-out state of the target vehicle within the target time period based on the first quantity includes the following steps: If the first quantity is odd, set the value of the in-out state to 1. If the first quantity is even, keep the initial value unchanged, and associate the first time point when the value of the in-out state changes with the positioning coordinates of the target vehicle.

[0013] Further, visually representing the in-out state includes the following steps: Draw the moving trajectory route of the target vehicle and the target electronic fence on the electronic map. Use the first color to represent that the target vehicle is inside the target electronic fence, use the second color to represent that the target vehicle is outside the target electronic fence, and mark the corresponding positioning coordinates at the first time point on the electronic map.

[0014] Further, establishing an anomaly analysis model based on the historical in-out data includes the following steps: The historical in-out data includes the trajectory routes of vehicles marked as abnormal within a historical time period and the number of changes in the in-out state. Establish an anomaly analysis model based on a neural network, and input the historical in-out data as training data into the anomaly analysis model.

[0015] Further, determining whether there is an anomaly in the target vehicle includes the following steps: The anomaly analysis model learns the number of changes in the in-out state in the historical in-out data, calculates the average value of the number of changes. If the first quantity is greater than the average value, the anomaly analysis model outputs that there is an anomaly in the target vehicle.

[0016] Compared with the prior art, the beneficial effects of the present invention are at least as follows: By obtaining the positioning coordinates of the target vehicle and establishing a moving trajectory route, the present invention can accurately reflect the driving path of the target vehicle, reduce the positioning error through smoothing processing, and improve the accuracy of the moving trajectory route; by setting a target electronic fence to define the determination range and calculating the number of intersection points between the moving trajectory route and the target electronic fence, it provides an accurate quantitative basis for judging the entry and exit status of the target vehicle; based on the number of intersection points to judge the change of the entry and exit status, the algorithm logic is clear, which improves the processing efficiency and judgment accuracy of the system, and enhances the intuitiveness of monitoring through visual representation; by establishing an abnormal analysis model based on historical entry and exit data and combining the first quantity and the moving trajectory route, it can timely detect abnormal vehicle behaviors and trigger alarms, improving the intelligence level of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flowchart of the steps of a method for determining entry and exit of an electronic fence according to the present invention; Figure 2 is a flowchart of the steps of establishing a moving trajectory route according to the present invention; Figure 3 is a flowchart of a method for determining the entry and exit status of a target vehicle according to the present invention; Figure 4 is a schematic diagram of a target vehicle entering and exiting a target electronic fence according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] The embodiments of the present application provide a method for determining entry and exit of an electronic fence. The terms "first", "second", "third", "fourth", etc. (if any) in the specification, claims and drawings of the present application are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" or "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily limit to those clearly listed steps or units, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0019] As Figure 1 shown, a method for determining entry and exit of an electronic fence includes: Step S1: Obtain the positioning coordinates of the target vehicle within a target time period, establish a moving trajectory route of the target vehicle based on the positioning coordinates, and perform smoothing processing on the moving trajectory route; Specifically, first, through satellite positioning devices such as GPS and Beidou, obtain the positioning coordinates of the target vehicle within the target time period. The target time period can be set as the daily working time period of the preset area. These positioning coordinate data include geographical location information such as longitude and latitude. Then, connect these positioning coordinates in chronological order to visually depict the driving trajectory route of the vehicle. Next, perform smoothing processing on the moving trajectory route to eliminate noise and position jitter in the positioning data, providing a data basis for the subsequent determination of the number of intersection points.

[0020] Step S2: Set the target electronic fence, and set the initial value of the entry / exit status of the target vehicle to 0. Calculate the number of intersection points between the moving trajectory route and the target electronic fence, defined as the first quantity. Specifically, demarcate the area of the target electronic fence on the electronic map, and at the same time set an initial value for the entry / exit status of the target vehicle. When the target vehicle has not entered the target electronic fence initially, the initial value is marked as 0. Then the system will automatically monitor the moving trajectory route of the target vehicle. When the moving trajectory route intersects with the boundary of the target electronic fence, a counting is triggered, and then the number of intersection points is incremented by 1. The number of intersection points is the first quantity, providing a basis for determining the change in the entry / exit status.

[0021] Step S3: Obtain the change in the entry / exit status of the target vehicle within the target time period based on the first quantity, and visually represent the entry / exit status. Specifically, according to the number of intersections between the moving trajectory route and the target electronic fence, the actual number of times the target vehicle enters and exits the target electronic fence during this period can be determined. Whenever the target vehicle completes an entry or exit action, the system will automatically update its entry / exit status mark. To enable the staff to more intuitively grasp the dynamics of the target vehicle, the change process of the entry / exit status is displayed in the form of an image through a visualization platform.

[0022] Step S4: Obtain the historical entry / exit data of the target vehicle, establish an anomaly analysis model based on the historical entry / exit data, input the first quantity and the moving trajectory route into the anomaly analysis model, and determine whether there is an anomaly in the target vehicle. If so, generate an alarm signal and transmit it to the monitoring center.

[0023] Specifically, historical access and departure data of the target vehicle are retrieved from the database. These data include information such as the complete moving trajectory route of the target vehicle and the number of changes in the access and departure status. A neural network algorithm is used to establish an anomaly analysis model. By learning the historical access and departure data, the model can identify abnormal behaviors such as frequent access and departure and long-term stay. When it is necessary to monitor the target vehicle, the first quantity and the moving trajectory route of the target vehicle are input into the anomaly analysis model. The model will quickly judge by comparing the current behavior. Once an abnormal behavior is detected, an alarm mechanism will be immediately triggered to generate an alarm signal. The alarm signal can be sent by means of text message, email, or directly transmitted to the monitoring center, helping the monitoring center to quickly respond to potential security problems and effectively reducing the workload of manual inspections.

[0024] As a preferred technical solution of the present invention, establishing the moving trajectory route of the target vehicle based on the positioning coordinates includes the following steps: The target time period is divided into multiple time points. At each time point, there is a corresponding positioning coordinate of the target vehicle, and abnormal positioning coordinates are removed. The positioning coordinates at adjacent time points among the remaining positioning coordinates are connected to generate a first line segment, and the first line segments within the target time period are connected to obtain the moving trajectory route.

[0025] Specifically, as Figure 2 shown, it is a flow chart of the steps for establishing the moving trajectory route. First, the target time period is determined as the daily working time period of the preset area, and then the daily working time period is divided into multiple time points at a certain time interval (such as every 10 seconds, etc.). The positioning coordinates corresponding to each time point are extracted from the database. Then, the abnormal positioning coordinate points are removed from the data set to ensure the accuracy of subsequent trajectory generation. The remaining valid positioning coordinates are arranged in chronological order, and the positioning coordinates at adjacent two time points are connected by a straight line to form a first line segment. Finally, all the first line segments generated within the time periods are connected in sequence, and the moving trajectory route of the target vehicle can be completely restored. This method is applicable to positioning data with different time intervals and can flexibly meet the requirements of different scenarios.

[0026] Smoothing the moving trajectory route includes the following steps: Based on the curve fitting algorithm, the positioning coordinates are fitted to generate a fitting curve. The fitting curve is optimized to remove the noise points and mutation points on the fitting curve, and the optimized fitting curve is used as the smoothed moving trajectory route.

[0027] Specifically, after obtaining the positioning coordinates of the target vehicle, a curve fitting algorithm is used to fit these positioning coordinates. Further, a cubic spline interpolation algorithm is applied to fit the positioning coordinates to generate a smooth fitting curve. The generated fitting curve is analyzed in depth to detect and remove the noise points and mutation points therein. The optimized fitting curve is used as the moving trajectory route after smoothing processing, which not only improves the accuracy and readability of the moving trajectory route, but also provides a reliable data basis for intersection calculation.

[0028] The elimination of abnormal positioning coordinates includes the following steps: Calculate the distance between adjacent positioning coordinates. If the distance between a positioning coordinate and the previous positioning coordinate is greater than the first threshold, then determine that the positioning coordinate is an abnormal positioning coordinate and remove the abnormal positioning coordinate from the moving trajectory route.

[0029] Specifically, for the positioning coordinates at each time point, use the Euclidean distance formula to calculate the distance between it and the positioning coordinate at the previous time point, and set the first threshold in combination with the maximum reasonable speed of the target vehicle. This threshold is used as the distance critical value between adjacent positioning coordinates of the target vehicle. The first threshold can meet the requirements of different target vehicle types and driving scenarios, and can effectively improve the accuracy of identifying abnormal positioning coordinates. If it is detected that the distance between adjacent positioning coordinates exceeds the first threshold, then determine that the current positioning coordinate is an abnormal point and remove the abnormal point from the moving trajectory route, so as to ensure the integrity and reliability of the generated moving trajectory route.

[0030] Setting the target electronic fence includes the following steps: Set multiple position points within the preset area, obtain the coordinates of each position point, and connect the coordinates of the position points in the preset order to form the boundary of the target electronic fence.

[0031] Specifically, according to the actual business requirements and monitoring targets, determine the preset area where the target electronic fence needs to be set. On the boundary of the preset area, the staff needs to reasonably arrange multiple position points and collect their accurate coordinate data, and then connect these position points in a clockwise or counterclockwise direction in sequence to form the closed boundary of the target electronic fence. In practical applications, according to the terrain characteristics and monitoring requirements, the number and distribution density of the position points can be flexibly adjusted to construct the boundary of the target electronic fence that adapts to different scenario requirements.

[0032] Calculating the number of intersection points between the moving trajectory route and the target electronic fence includes the following steps: Obtain all the boundary line segments of the target electronic fence, calculate the number of intersection points between the moving trajectory route and each boundary line segment, and count the total number of all intersection points, which is defined as the first quantity.

[0033] Specifically, as Figure 3As shown in the figure, it is a flowchart of the method for determining the state of a target vehicle entering and leaving a fence. First, the coordinates of each position point of the target electronic fence are stored in a list in sequence. The position point list is traversed, and two adjacent points are taken out in turn and combined into a line segment. At the same time, the last point and the first point in the list are also combined into a line segment to form a set of line segments of the boundary of the target electronic fence. Then, the positioning coordinates on the moving trajectory route are also stored in a coordinate list, and the adjacent positioning coordinates are connected to form a set of line segments of the moving trajectory route. Next, the method of vector cross product is used to determine whether each line segment on the moving trajectory route intersects with each boundary line segment of the target electronic fence. Traverse the set of line segments of the moving trajectory route and the set of boundary line segments of the target electronic fence one by one. If there is an intersection, the number of intersection points is incremented by 1. Finally, all the calculated numbers of intersection points are accumulated to obtain the final total number of intersection points, which is defined as the first quantity. By calculating the number of intersection points between the moving trajectory route and the boundary line segments of the target electronic fence, it can provide an accurate quantitative basis for judging the state of the target vehicle entering and leaving the fence. Moreover, both the moving trajectory route and the target electronic fence are split into line segments for comparison to calculate the intersection points. The algorithm has a clear logic and a low computational complexity. Even in the face of a large amount of trajectory data and complex fence shapes, it can complete the calculation of the number of intersection points within a reasonable time, ensuring the processing efficiency of the system.

[0034] Obtaining the change in the state of the target vehicle entering and leaving the fence during the target time period based on the first quantity includes the following steps: If the first quantity is odd, set the value of the entering and leaving fence state to 1. If the first quantity is even, keep the initial value unchanged and associate the first time point when the value of the entering and leaving fence state changes with the positioning coordinates of the target vehicle.

[0035] Specifically, as Figure 4 shown in the figure, it is a schematic diagram of the target vehicle entering and leaving the target electronic fence. After obtaining the first quantity, judge whether the state of the target vehicle entering and leaving the fence has changed according to its parity. If the first quantity is odd, it indicates that the state of the target vehicle entering and leaving the fence has changed, specifically from the outside to the inside of the target electronic fence. At this time, set the value of the entering and leaving fence state to 1. If the first quantity is even, it indicates that the state of the target vehicle entering and leaving the fence has not changed, that is, the target vehicle has moved from the inside to the outside of the target electronic fence. At this time, keep the initial state value of 0 unchanged. Using the parity of the number of intersection points to judge the change in the value of the entering and leaving fence state, this method has a simple logic and high computational efficiency, and associates the first time point with the positioning coordinates of the target vehicle at this time point to form a time-space record of the change in the entering and leaving fence state, providing accurate data support for subsequent abnormal analysis and realizing visual display.

[0036] Visualizing the in-and-out status of the fence includes the following steps: Draw the moving trajectory of the target vehicle and the target electronic fence on the electronic map. Use the first color to indicate that the target vehicle is inside the target electronic fence, and use the second color to indicate that the target vehicle is outside the target electronic fence. Mark the corresponding positioning coordinates at the first time point on the electronic map.

[0037] Specifically, when visualizing the in-and-out status of the fence, accurately draw the moving trajectory of the target vehicle and the target electronic fence on the electronic map. To enable the staff to more intuitively distinguish the position of the target vehicle, select the first color, such as green, to indicate that the target vehicle is currently inside the target electronic fence, and the second color, such as red, to represent that the target vehicle is already outside the target electronic fence. At the same time, clearly mark the positioning coordinates corresponding to the first time point on the electronic map. The staff can clearly and quickly grasp the real-time position of the target vehicle and the specific positioning coordinates corresponding to the in-and-out of the target vehicle just by looking at the electronic map. This visual display method greatly improves the intuitiveness and convenience of monitoring the target vehicle.

[0038] Establishing an anomaly analysis model based on historical in-and-out data of the fence includes the following steps: The historical in-and-out data of the fence includes the trajectory of vehicles marked as abnormal within a historical time period and the number of changes in the in-and-out status. An anomaly analysis model is established based on a neural network, and the historical in-and-out data is input into the anomaly analysis model as training data.

[0039] Specifically, first collect the historical in-and-out data, which contains the moving trajectories of vehicles marked as having abnormal behaviors within a historical time period and the number of changes in their in-and-out status. Then select a suitable neural network architecture and establish an anomaly analysis model based on this. Input the organized historical in-and-out data as training data into the established anomaly analysis model for training. Finally, the trained anomaly analysis model can be applied to the monitoring scenario of the in-and-out of the target vehicle. This model can effectively identify the abnormal behaviors of the target vehicle during in-and-out, realizing intelligent prediction and judgment of the abnormal behaviors of the target vehicle, and greatly improving the accuracy and efficiency of abnormal behavior detection.

[0040] Determining whether there is an anomaly in the target vehicle includes the following steps: The anomaly analysis model learns the number of changes in the in-and-out status in the historical in-and-out data, calculates the average value of the number of changes. If the first quantity is greater than the average value, the anomaly analysis model outputs that there is an anomaly in the target vehicle.

[0041] Specifically, the number of changes in the status of the target vehicle entering and leaving the railing is input into the already trained anomaly analysis model. The model will first calculate the average value of the number of changes in the entering and leaving railing status, and then compare the first quantity corresponding to the target vehicle with the average value. If the first quantity of the target vehicle is larger than the average value, it indicates that there is an abnormal situation with the target vehicle, which can provide strong support for safety monitoring and management.

[0042] Compared with the prior art, the beneficial effects of the present invention are at least as follows: By obtaining the positioning coordinates of the target vehicle and establishing a moving trajectory route, the present invention can accurately reflect the driving path of the target vehicle, and reduce the positioning error through smoothing processing to improve the accuracy of the moving trajectory route; by setting a target electronic fence to clarify the determination range and calculating the number of intersections between the moving trajectory route and the target electronic fence, it provides an accurate quantitative basis for judging the status of the target vehicle entering and leaving the railing; judging the change in the entering and leaving railing status based on the number of intersections, the algorithm logic is clear, which improves the processing efficiency and judgment accuracy of the system, and enhances the intuitiveness of monitoring through visual representation; by establishing an anomaly analysis model based on historical entering and leaving railing data and combining the first quantity and the moving trajectory route, it can timely detect abnormal vehicle behaviors and trigger alarms, improving the intelligence level of the system.

[0043] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. A method for determining entry and exit of an electronic fence, characterized in that The method includes the following steps: S1: Obtain the positioning coordinates of the target vehicle within the target time period, establish the movement trajectory route of the target vehicle based on the positioning coordinates, and smooth the movement trajectory route. S2: Set a target electronic fence, and set the initial value of the entry / exit state of the target vehicle to 0. Calculate the number of intersection points between the movement trajectory route and the target electronic fence, which is defined as the first quantity. S3: Obtain the change in the entry / exit state of the target vehicle within the target time period based on the first quantity, and visually represent the entry / exit state. S4: Obtain the historical entry / exit data of the target vehicle, establish an anomaly analysis model based on the historical entry / exit data, input the first quantity and the movement trajectory route into the anomaly analysis model, and determine whether there is an anomaly in the target vehicle. If so, generate an alarm signal.

2. The method according to claim 1, characterized in that, Establishing the movement trajectory route of the target vehicle based on the positioning coordinates includes the following steps: Divide the target time period into multiple time points. There are corresponding positioning coordinates of the target vehicle at each time point, and eliminate abnormal positioning coordinates. Connect the positioning coordinates at adjacent time points among the remaining positioning coordinates to generate a first line segment, and connect the first line segments within the target time period to obtain the movement trajectory route.

3. The method according to claim 1, wherein Smoothing the movement trajectory route includes the following steps: Perform fitting processing on the positioning coordinates based on the curve fitting algorithm to generate a fitting curve. Optimize the fitting curve to remove noise points and mutation points on the fitting curve, and use the optimized fitting curve as the smoothed movement trajectory route.

4. The method according to claim 2, wherein And eliminating abnormal positioning coordinates includes the following steps: Calculate the distance between adjacent positioning coordinates. If the distance between a positioning coordinate and the previous positioning coordinate is greater than a first threshold, then determine that this positioning coordinate is an abnormal positioning coordinate and eliminate the abnormal positioning coordinate.

5. The method according to claim 1, characterized in that, Setting the target electronic fence includes the following steps: Set multiple position points within a preset area, obtain the coordinates of each position point, and connect the coordinates of the position points in a preset order to form the boundary of the target electronic fence.

6. The method according to claim 5, characterized in that Calculating the number of intersection points between the movement trajectory route and the target electronic fence includes the following steps: Obtain all the boundary line segments of the target electronic fence, calculate the number of intersection points between the movement trajectory route and each boundary line segment, and count the total number of all intersection points, which is defined as the first quantity.

7. The method according to claim 6, characterized in that, Obtaining the change in the entry / exit state of the target vehicle within the target time period based on the first quantity includes the following steps: If the first quantity is odd, set the value of the entry / exit state to 1. If the first quantity is even, keep the initial value unchanged, and associate the first time point when the value of the entry / exit state changes with the positioning coordinates of the target vehicle.

8. The method according to claim 7, wherein And visually representing the entry / exit state includes the following steps: Draw the moving trajectory route of the target vehicle and the target electronic fence on the electronic map, use the first color to indicate that the target vehicle is inside the target electronic fence, use the second color to indicate that the target vehicle is outside the target electronic fence, and mark the corresponding positioning coordinates at the first time point on the electronic map.

9. The method according to claim 1, wherein Establishing an anomaly analysis model based on the historical in-and-out data includes the following steps: The historical in-and-out data includes the trajectory routes of vehicles marked as abnormal within a historical time period and the number of changes in the in-and-out status. An anomaly analysis model is established based on a neural network, and the historical in-and-out data is input into the anomaly analysis model as training data.

10. The method according to claim 9, wherein Judging whether the target vehicle has an anomaly includes the following steps: The anomaly analysis model learns the number of changes in the in-and-out status in the historical in-and-out data, calculates the average value of the number of changes. If the first quantity is greater than the average value, the anomaly analysis model outputs that the target vehicle has an anomaly.

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