Movement trajectory verification method and device

Abstract

The present application discloses a method and apparatus for verifying a mobile trajectory. The method includes: determining a plurality of continuous trajectory points in a target object's mobile trajectory, selecting a preset number of trajectory points from the plurality of continuous trajectory points and inputting them into a preset model, determining a predicted distance between a first target point and a next adjacent trajectory point, wherein the first target point is the last of the preset number of trajectory points; determining an actual distance between the first target point and the adjacent trajectory point, and comparing the actual distance with the predicted distance; if the actual distance is greater than the predicted distance, determining the difference between the actual distance and the predicted distance as an error value, and determining an error value between each of the plurality of continuous trajectory points and the adjacent trajectory point; determining the sum of the error values ​​between each trajectory point and the adjacent trajectory point, and determining that the mobile trajectory fails verification if the ratio of the sum of the error values ​​to the total length of the mobile trajectory is greater than a preset threshold.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a method and device for verifying a movement trajectory. Background Art

[0002] With the widespread use of travel cards, some users' movement patterns are clearly illogical, with issues such as longitude and latitude jumps. Previously, manual verification of the reasonableness of the trajectory was required after users reported it. This approach not only resulted in low recognition efficiency and high latency, but also lacked a standardized standard for what constituted a reasonable jump.

[0003] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0004] The embodiments of the present application provide a method and apparatus for verifying a movement trajectory, so as to at least solve the technical problem of low efficiency in movement trajectory verification.

[0005] According to one aspect of an embodiment of the present application, a method for verifying a movement trajectory is provided, comprising: determining a plurality of continuous trajectory points in a movement trajectory of a target object, selecting a preset number of trajectory points from the plurality of continuous trajectory points and inputting the selected trajectory points into a preset model, determining a predicted distance between a first target point and a next adjacent trajectory point, wherein the first target point is the last of the preset number of trajectory points; determining an actual distance between the first target point and the adjacent trajectory point, and comparing the actual distance with the predicted distance; if the actual distance is greater than the predicted distance, determining a difference between the actual distance and the predicted distance as an error value, and determining an error value between each trajectory point in the plurality of continuous trajectory points and the adjacent trajectory point; determining a sum of the error values ​​between each trajectory point and the adjacent trajectory point, and determining that the movement trajectory fails verification if a ratio of the sum of the error values ​​to the total length of the movement trajectory is greater than a preset threshold.

[0006] Optionally, the preset model is determined by the following method, including: obtaining historical movement trajectories of multiple target objects, and determining multiple continuous historical trajectory points in the historical movement trajectories of the multiple target objects; using a sliding window algorithm to extract multiple historical trajectory point sets from the multiple continuous historical trajectory points, wherein the historical trajectory point set includes a preset number of historical trajectory points; using the multiple historical trajectory point sets as training data sets, and the actual distance between the second target point in the multiple historical trajectory point sets and the next adjacent historical trajectory point as a label, training to obtain the preset model, wherein the second target point is the last historical trajectory point in the trajectory point set.

[0007] Optionally, determining multiple continuous historical trajectory points in multiple historical movement trajectories of target objects includes: determining parameter information of each historical trajectory point, the parameter information including at least: a position of each historical trajectory point, a distance between each historical trajectory point and an adjacent historical trajectory point, and a time interval between each historical trajectory point and an adjacent historical trajectory point, wherein the position of the historical trajectory point includes: the latitude and longitude of the historical trajectory point.

[0008] Optionally, the preset model includes: a linear regression model.

[0009] Optionally, a plurality of historical trajectory point sets are used as training data sets, and the actual distance between the second target point and the next adjacent historical trajectory point in the plurality of historical trajectory point sets is used as a label to train a preset model, including: determining parameter information of each historical trajectory point in the plurality of historical trajectory point sets; using the parameter information of each historical trajectory point in the plurality of historical trajectory point sets as input of the preset model, and using the actual distance between the second target point and the next adjacent historical trajectory point as output, to train the preset model.

[0010] Optionally, determining the actual distance between the first target point and the adjacent trajectory point includes: determining the longitude and latitude of the first target point and the longitude and latitude of the adjacent trajectory point; and determining the actual distance between the first target point and the adjacent trajectory point based on the longitude and latitude of the first target point and the longitude and latitude of the adjacent trajectory point.

[0011] Optionally, after determining that the movement trajectory fails to pass the verification, the method further includes: reacquiring the movement trajectory of the target object, and replacing the movement trajectory that fails to pass the verification with the reacquired movement trajectory.

[0012] According to another aspect of an embodiment of the present application, a mobile trajectory verification device is further provided, comprising: a determination module, configured to determine a plurality of continuous trajectory points in a target object's mobile trajectory, select a preset number of trajectory points from the plurality of continuous trajectory points and input them into a preset model, and determine a predicted distance between a first target point and a next adjacent trajectory point, wherein the first target point is the last of the preset number of trajectory points; a comparison module, configured to determine an actual distance between the first target point and the adjacent trajectory point, and compare the actual distance with the predicted distance; a calculation module, configured to determine, when the actual distance is greater than the predicted distance, a difference between the actual distance and the predicted distance as an error value, and determine an error value between each trajectory point in the plurality of continuous trajectory points and the adjacent trajectory point; and a verification module, configured to determine a sum of the error values ​​between each trajectory point and the adjacent trajectory point, and determine that the mobile trajectory fails verification when a ratio of the sum of the error values ​​to the total length of the mobile trajectory is greater than a preset threshold.

[0013] According to another aspect of an embodiment of the present application, a non-volatile storage medium is further provided. The non-volatile storage medium includes a stored program, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute the above-mentioned movement trajectory verification method.

[0014] According to another aspect of the embodiments of the present application, an electronic device is provided, including a memory and a processor, wherein the processor is configured to run a program, wherein the above-mentioned movement trajectory verification method is executed when the program is run.

[0015] In an embodiment of the present application, a plurality of continuous trajectory points in a target object's movement trajectory are determined, a preset number of trajectory points are selected from the plurality of continuous trajectory points and input into a preset model, a predicted distance between a first target point and a next adjacent trajectory point is determined, wherein the first target point is the last of the preset number of trajectory points; an actual distance between the first target point and the adjacent trajectory point is determined, and the actual distance is compared with the predicted distance; if the actual distance is greater than the predicted distance, the difference between the actual distance and the predicted distance is determined as an error value, and an error value is determined between each trajectory point in the plurality of continuous trajectory points and the adjacent trajectory point; the sum of the error values ​​between each trajectory point and the adjacent trajectory point is determined, and if the ratio of the sum of the error values ​​to the total length of the movement trajectory is greater than a preset threshold, the movement trajectory is determined to have failed verification. By using a preset model to determine the predicted distance between each trajectory point and the adjacent trajectory point, then comparing the predicted distance with the actual distance to determine the error value, and finally verifying the movement trajectory by accumulating the error values, the purpose of quickly determining whether the movement trajectory is abnormal is achieved, thereby achieving the technical effect of improving the efficiency of movement trajectory verification, and thereby solving the technical problem of low movement trajectory verification efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0017] Figure 1 This is a hardware structure block diagram of a computer terminal (or mobile device) for a mobile trajectory verification method according to an embodiment of the present application;

[0018] Figure 2 is a schematic diagram of a movement trajectory verification method according to the present application;

[0019] Figure 3 A schematic diagram of an optional target object movement trajectory according to an embodiment of the present application;

[0020] Figure 4 This is a schematic diagram of an optional movement trajectory verification device according to the present application. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0022] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in a sequence other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0023] According to an embodiment of the present application, an embodiment of a method for verifying a movement trajectory is also provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0024] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal, a cloud server or a similar computing device. Figure 1 The hardware structure block diagram of a computer terminal (or mobile device) for implementing a method for verifying a moving trajectory is shown. Figure 1 As shown, the computer terminal 10 (or mobile device 10) may include one or more (illustrated as 102a, 102b, ..., 102n) processors 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission module 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a power supply and / or a camera. It will be understood by those skilled in the art that Figure 1The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0025] It should be noted that the one or more processors 102 and / or other data processing circuits described above may generally be referred to herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuitry may be a single independent processing module, or may be incorporated in whole or in part into any of the other components of the computer terminal 10 (or mobile device). As described in the embodiments of the present application, the data processing circuitry serves as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0026] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the mobile trajectory verification method in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, to implement the above-mentioned bill push method. The memory 104 may include a high-speed random access memory and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely located relative to the processor 102, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0027] The transmission module 106 is configured to receive or transmit data via a network. A specific example of the aforementioned network may include a wireless network provided by the communications provider of the computer terminal 10. In one embodiment, the transmission module 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, the transmission module 106 may be a radio frequency (RF) module, which is configured to communicate with the Internet wirelessly.

[0028] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 (or mobile device).

[0029] According to an embodiment of the present application, an embodiment of a method for verifying a movement trajectory is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0030] Figure 2 is a flow chart of a method for verifying a movement trajectory according to an embodiment of the present application, such as Figure 2 As shown, the method includes the following steps:

[0031] Step S202: determining a plurality of continuous trajectory points in the target object's movement trajectory, selecting a preset number of trajectory points from the plurality of continuous trajectory points and inputting them into a preset model, and determining a predicted distance between a first target point and a next adjacent trajectory point, wherein the first target point is the last of the preset number of trajectory points;

[0032] Step S204, determining the actual distance between the first target point and the adjacent trajectory points, and comparing the actual distance with the predicted distance;

[0033] Step S206: if the actual distance is greater than the predicted distance, the difference between the actual distance and the predicted distance is determined as an error value, and the error value between each trajectory point and the adjacent trajectory point in the plurality of continuous trajectory points is determined;

[0034] Step S208 : determining the sum of the error values ​​between each trajectory point and its adjacent trajectory points, and determining that the trajectory fails verification when the ratio of the sum of the error values ​​to the total length of the trajectory is greater than a preset threshold.

[0035] Through the above steps, it is possible to determine the predicted distance between each trajectory point and the adjacent trajectory point by using a preset model, then determine the error value by comparing the predicted distance with the actual distance, and finally verify the movement trajectory by accumulating the size of the error value, thereby achieving the purpose of quickly determining whether the movement trajectory is abnormal, thereby achieving the technical effect of improving the efficiency of movement trajectory verification, and further solving the technical problem of low efficiency of movement trajectory verification.

[0036] It should be noted that currently, determining whether a movement trajectory is abnormal is done manually, which is inefficient. The method provided in this application can automatically learn the movement trajectory of the target object in the area by the model, and obtain the predicted distance between each trajectory point, thereby improving the verification efficiency.

[0037] In step S202, the target object may be a user's terminal device. Each terminal device corresponds to a unique number, such as a mobile phone, an electronic watch, etc. The moving track of the target object is continuous, such as Figure 3As shown, if time T1 appears at point A and time T2 appears at point B, then the longitude and latitude of point B must appear within a circular area with point A as the center and a radius of R. The length of R is related to the interval between point A and point B and the moving speed of the target object.

[0038] It should be further explained that the continuous trajectory points are sorted in chronological order. The last one of the preset number of trajectory points refers to the trajectory point with the latest appearance time among the preset number of trajectory points. For example, three trajectory points are input into the preset model, namely point C, point D and point E. Point C, point D and point E are sorted in chronological order, then point E is the first target point; the target object's record of moving to a certain target position point is the trajectory point.

[0039] In step S206 , when the actual distance is smaller than the predicted distance, the error value is determined to be zero; when the actual distance is larger than the predicted distance, the error value is determined to be the difference between the actual distance and the predicted distance.

[0040] The above steps S202 to S208 are described in detail below through a specific embodiment.

[0041] In step S202, the preset model is determined by the following method, including: obtaining historical movement trajectories of multiple target objects and determining multiple continuous historical trajectory points in the historical movement trajectories of the multiple target objects; extracting multiple trajectory point sets from the multiple continuous historical trajectory points using a sliding window algorithm, wherein the historical trajectory point sets include a preset number of historical trajectory points; using the multiple historical trajectory point sets as a training data set, and the actual distance between the second target point in the multiple historical trajectory point sets and the next adjacent historical trajectory point as a label, to train to obtain the preset model, wherein the second target point is the last historical trajectory point in the historical trajectory point set.

[0042] It should be noted that the width of the sliding window can be set to a preset number. For example, if the preset number is 3, the width of the sliding window can be set to 3.

[0043] By extracting training data sets through sliding windows, a trajectory can be divided into multiple data sets, which expands the number of data sets and further improves the accuracy of the preset model.

[0044] Optionally, the parameter information of each historical trajectory point includes at least: the position of each historical trajectory point, the distance between each historical trajectory point and an adjacent historical trajectory point, and the interval time between each historical trajectory point and an adjacent historical trajectory point, wherein the position of the historical trajectory point includes: the latitude and longitude of the historical trajectory point;

[0045] It should be noted that the interval distance between the track points is the actual distance between the track points.

[0046] Specifically, as shown in Table 1, the table shows the parameter information of three consecutive historical trajectory points: latitude, longitude, interval distance, and interval duration. In Table 1, latitude 3, longitude 3, interval distance 3, and interval duration 3 respectively show the parameter information of the third historical trajectory point. The third historical trajectory point is the second target point. The interval distance between the second target point and the next trajectory point adjacent to the second target point is determined as the label of the training data set, which eliminates the manual labeling process and improves the rate of model training.

[0047]

[0048]

[0049] Table 1

[0050] It should be noted that the units of longitude and latitude in Table 1 are degrees, the unit of interval time is minutes, and the unit of interval distance is kilometers. The above method directly determines the actual distance between the second target point and the next trajectory point adjacent to the second target point as the label of the predicted distance training set. At the same time, the historical movement trajectory includes trajectories using various means of transportation, fully considering the influence of transportation on the length of the movement trajectory, making the training results more accurate.

[0051] In an optional manner, the preset model can be a linear regression model, which is obtained by training by determining parameter information of each historical trajectory point in a set of multiple historical trajectory points; using the parameter information of each historical trajectory point in the set of multiple historical trajectory points as input to the linear regression model, and using the actual distance between the second target point and the next adjacent historical trajectory point as output.

[0052] In some embodiments of the present application, the actual distance between the first target point and the adjacent trajectory point can be determined in the following manner, including: determining the longitude and latitude of the first target point and the longitude and latitude of the adjacent trajectory point; determining the actual distance between the first target point and the adjacent trajectory point based on the longitude and latitude of the first target point and the longitude and latitude of the adjacent trajectory point.

[0053] Specifically, the actual distance between two points can be determined using the law of cosines.

[0054] In some embodiments of the present application, after determining that the movement trajectory fails verification, the method further includes: reacquiring the movement trajectory of the target object, and replacing the movement trajectory that fails verification with the reacquired movement trajectory.

[0055] The mobile trajectory verification method provided in the embodiment of the present application is also applied to a mobile trajectory verification device provided in the embodiment of the present application, such as Figure 4As shown, it includes: a determination module 50, which is used to determine multiple continuous trajectory points in the target object's movement trajectory, select a preset number of trajectory points from the multiple continuous trajectory points and input them into a preset model, and determine the predicted distance between the first target point and the next adjacent trajectory point, wherein the first target point is the last of the preset number of trajectory points; a comparison module 52, which is used to determine the actual distance between the first target point and the adjacent trajectory point, and compare the actual distance with the predicted distance; a calculation module 54, which is used to determine the difference between the actual distance and the predicted distance as an error value when the actual distance is greater than the predicted distance, and determine the error value between each trajectory point in the multiple continuous trajectory points and the adjacent trajectory point; a verification module 56, which is used to determine the sum of the error values ​​between each trajectory point and the adjacent trajectory point, and determine that the movement trajectory fails the verification when the ratio of the sum of the error values ​​to the total length of the movement trajectory is greater than a preset threshold.

[0056] The determination module 50 includes: a training submodule, the training submodule being configured to obtain a plurality of historical movement trajectories of target objects and determine a plurality of continuous historical trajectory points in the historical movement trajectories of the target objects; extracting a plurality of trajectory point sets from the plurality of continuous historical trajectory points using a sliding window algorithm, wherein the historical trajectory point sets include a preset number of historical trajectory points; using the plurality of historical trajectory point sets as training data sets and the actual distance between a second target point in the plurality of trajectory point sets and the next adjacent historical trajectory point as a label, training to obtain a preset model, wherein the second target point is the last historical trajectory point in the trajectory point set, wherein the preset model includes: a linear regression model;

[0057] The training submodule includes: a determination unit and a training unit, the determination unit is used to determine parameter information of each historical trajectory point, the parameter information includes at least: the position of each historical trajectory point, the distance between each historical trajectory point and an adjacent historical trajectory point, and the interval between each historical trajectory point and an adjacent historical trajectory point, wherein the position of the historical trajectory point includes: the latitude and longitude of the historical trajectory point;

[0058] The training unit is used to determine parameter information of each historical trajectory point in the plurality of trajectory point sets; the parameter information of each historical trajectory point in the plurality of trajectory point sets is used as input to a linear regression model, and the actual distance between the second target point and the next adjacent historical trajectory point is used as output to train a preset model;

[0059] The calculation module 54 includes: a calculation submodule, which is used to determine the longitude and latitude of the first target point and the longitude and latitude of the adjacent track points; and determine the actual distance between the first target point and the adjacent track points based on the longitude and latitude of the first target point and the longitude and latitude of the adjacent track points.

[0060] The verification module 56 includes a replacement submodule, which is used to reacquire the movement trajectory of the target object and replace the movement trajectory that fails the verification with the reacquired movement trajectory.

[0061] According to another aspect of an embodiment of the present application, a non-volatile storage medium is provided, including a stored program, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute the above-mentioned movement trajectory verification method.

[0062] According to another aspect of an embodiment of the present application, an electronic device is provided, including a memory and a processor, wherein the processor is configured to run a program, wherein the above-mentioned movement trajectory verification method is executed when the program is run.

[0063] The above trajectory verification method can predict the rationality of trajectory data within any latitude and longitude range. It is applicable to trajectory data at any latitude and longitude. The training data contains a variety of scenarios, and can consider movement trajectories under various conditions. Traditional methods can only calculate based on fixed formulas and cannot analyze based on geographical characteristics of longitude and latitude.

[0064] The prediction model used in the method provided in this application is a pre-trained model, which only requires a single training session for long-term use in trajectory prediction. After training, the pre-set model can be deployed on a server, and a database can be used to batch search for trajectory data that has not passed verification. In some embodiments of this application, the prediction results can be visualized to facilitate timely access to verification results.

[0065] The above method uses a preset model to determine the predicted distance between each trajectory point and the adjacent trajectory points, then compares the predicted distance with the actual distance to determine the error value, and finally verifies the movement trajectory by accumulating the size of the error value. This achieves the purpose of quickly determining whether the movement trajectory is abnormal, thereby achieving the technical effect of improving the efficiency of movement trajectory verification and solving the technical problem of low movement trajectory verification efficiency.

[0066] The serial numbers of the above embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0067] In the above embodiments of the present application, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0068] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0069] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected to achieve the purpose of the present embodiment according to actual needs.

[0070] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0071] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program code.

[0072] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for verifying a movement trajectory, characterized in that: include: Determine a plurality of continuous trajectory points in a target object's movement trajectory, select a preset number of trajectory points from the plurality of continuous trajectory points and input them into a preset model, and determine a predicted distance between a first target point and a next adjacent trajectory point, wherein the first target point is the last of the preset number of trajectory points; determining an actual distance between the first target point and the next adjacent trajectory point, and comparing the actual distance with the predicted distance; In a case where the actual distance is greater than the predicted distance, determining a difference between the actual distance and the predicted distance as an error value, and determining the error value between each trajectory point in the plurality of consecutive trajectory points and the next adjacent trajectory point; The sum of the error values ​​between each trajectory point and the next adjacent trajectory point is determined, and when a ratio of the sum of the error values ​​to the total length of the movement trajectory is greater than a preset threshold, it is determined that the movement trajectory fails verification.

2. The method according to claim 1, characterized in that The preset model is determined by the following methods, including: Acquire multiple historical movement trajectories of target objects, and determine multiple continuous historical trajectory points in the multiple historical movement trajectories of the target objects; Extracting a plurality of historical trajectory point sets from the plurality of continuous historical trajectory points using a sliding window algorithm, wherein the historical trajectory point sets include a preset number of historical trajectory points; The plurality of historical trajectory point sets are used as a training data set, and the actual distance between a second target point in the plurality of historical trajectory point sets and the next adjacent historical trajectory point is used as a label to train the preset model, wherein the second target point is the last historical trajectory point in the trajectory point set.

3. The method according to claim 2, characterized in that The determining of a plurality of continuous historical trajectory points in the plurality of historical movement trajectories of the target objects includes: Determine parameter information for each historical trajectory point, the parameter information including at least: a position of each historical trajectory point, a distance between each historical trajectory point and its next adjacent historical trajectory point, and a duration between each historical trajectory point and its next adjacent historical trajectory point, wherein the position of the historical trajectory point includes the latitude and longitude of the historical trajectory point.

4. The method according to claim 2, characterized in that The preset model includes: a linear regression model.

5. The method according to claim 2, characterized in that The preset model is obtained by training the plurality of historical trajectory point sets as a training data set and the actual distance between the second target point and the next adjacent historical trajectory point in the plurality of historical trajectory point sets as a label, including: Determining parameter information of each historical trajectory point in the plurality of historical trajectory point sets; The preset model is obtained by training by taking parameter information of each historical trajectory point in the set of multiple historical trajectory points as input of the preset model and taking the actual distance between the second target point and the next adjacent historical trajectory point as output.

6. The method according to claim 1, wherein Determining the actual distance between the first target point and the next adjacent trajectory point includes: Determining the longitude and latitude of the first target point and the longitude and latitude of the next adjacent trajectory point; The actual distance between the first target point and the next adjacent track point is determined based on the longitude and latitude of the first target point and the longitude and latitude of the next adjacent track point.

7. The method according to claim 1, characterized in that After determining that the movement trajectory fails verification, the method further includes: The movement trajectory of the target object is reacquired, and the movement trajectory that fails verification is replaced with the reacquired movement trajectory.

8. A mobile trajectory verification device, characterized in that: include: a determination module, configured to determine a plurality of continuous trajectory points in a target object's movement trajectory, select a preset number of trajectory points from the plurality of continuous trajectory points and input them into a preset model, and determine a predicted distance between a first target point and a next adjacent trajectory point, wherein the first target point is the last of the preset number of trajectory points; a comparison module, configured to determine an actual distance between the first target point and the next adjacent trajectory point, and compare the actual distance with the predicted distance; a calculation module, configured to, when the actual distance is greater than the predicted distance, determine a difference between the actual distance and the predicted distance as an error value, and determine the error value between each trajectory point in the plurality of consecutive trajectory points and its next adjacent trajectory point; The verification module is configured to determine the sum of the error values ​​between each trajectory point and its next adjacent trajectory point, and determine that the movement trajectory fails verification if a ratio of the sum of the error values ​​to the total length of the movement trajectory is greater than a preset threshold.

9. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored program, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute the movement trajectory verification method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the processor is used to run a program, wherein the program executes the movement trajectory verification method according to any one of claims 1 to 7 when running.

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