Path prediction method and device

Abstract

The present application provides a path prediction method and device. The path prediction method includes: obtaining first signal measurement data of a target terminal communicating with at least two base stations during a trip; determining second signal measurement data of each geographical grid in a target coverage area corresponding to the at least two base stations; and determining a predicted path of the target terminal based on a matching relationship between the first signal measurement data and the second signal measurement data. The method of the present application can improve the accuracy of path prediction.

Description

Technical Field

[0001] The present application relates to the field of positioning technology, and in particular to a path prediction method and device. Background Art

[0002] At present, when restoring the user's travel path, that is, predicting the historical travel path that the user may take, the historical position of the user's terminal device can be located through the base station, and based on the historical position of the terminal device, the historical travel path that the user may take is predicted. However, due to factors such as the distance between base stations and signal transmission delays, the positioning accuracy of the terminal device through the base station is low, which in turn leads to the problem of low accuracy of the user's historical travel path predicted based on base station positioning. Therefore, how to improve the accuracy of path prediction is an urgent problem to be solved. Summary of the invention

[0003] The present application provides a path prediction method and device, which can improve the accuracy of path prediction.

[0004] In a first aspect, the present application provides a path prediction method, the method comprising:

[0005] Acquire first signal measurement data of communication interaction between the target terminal and at least two base stations during the travel process;

[0006] Determine second signal measurement data of each geographical grid within the target coverage area corresponding to the at least two base stations;

[0007] The predicted path of the target terminal is determined according to a matching relationship between the first signal measurement data and the second signal measurement data.

[0008] In a second aspect, the present application provides a path prediction device, the device comprising:

[0009] An acquisition module, used to acquire first signal measurement data of communication interaction between the target terminal and at least two base stations during the travel process;

[0010] A determination module, configured to determine second signal measurement data of each geographical grid within a target coverage area corresponding to the at least two base stations;

[0011] A processing module is used to determine the predicted path of the target terminal according to the matching relationship between the first signal measurement data and the second signal measurement data.

[0012] In a third aspect, the present application provides an electronic device, comprising: a processor and a memory; the processor is communicatively connected to the memory;

[0013] The memory stores computer-executable instructions;

[0014] The processor executes the computer-executable instructions stored in the memory to implement the method as described in any one of the first aspects.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the method as described in any one of the first aspects.

[0016] In a fifth aspect, the present application provides a computer program product, including a computer program, which implements the method described in any one of the first aspects when executed by a processor.

[0017] The path prediction method and device provided by the present application obtain the first signal measurement data of the target terminal in the communication and interaction with at least two base stations during the travel process. Determine the second signal measurement data of each geographical grid in the target coverage area corresponding to at least two base stations, and determine the predicted path of the target terminal based on the matching relationship between the first signal measurement data and the second signal measurement data. This method can predict the geographical grids that the target terminal may pass through around each base station through the first signal measurement data during the communication and interaction between the target terminal and the base station, and the second signal measurement data of each geographical grid, and determine the predicted path of the target terminal based on the geographical grids that may be passed through. Compared with the predicted path of the target terminal generated directly based on the location of the base station passed, the waypoints on the predicted path generated by the method of the present application are further refined to a smaller geographical grid, thereby improving the accuracy of determining the predicted path of the target terminal. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present application or the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0019] Figure 1 A schematic diagram of a path prediction scenario provided for this application;

[0020] Figure 2 A schematic diagram of a flow chart of a path prediction method provided in an embodiment of the present application;

[0021] Figure 3 A schematic diagram of a target coverage area provided in an embodiment of the present application;

[0022] Figure 4 A schematic diagram of a flow chart of another example of a path prediction method provided in an embodiment of the present application;

[0023] Figure 5 A schematic diagram of a flow chart of another path prediction method provided in an embodiment of the present application;

[0024] Figure 6 A schematic diagram of a flow chart of another path prediction method provided in an embodiment of the present application;

[0025] Figure 7 A schematic diagram of a flow chart of another path prediction method provided in an embodiment of the present application;

[0026] Figure 8 A schematic diagram of the structure of a path prediction device provided in an embodiment of the present application;

[0027] Fig. 9 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0028] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0030] At present, with the rapid development of science and technology, positioning technology has been widely used in all walks of life. For example, positioning technology can be used to help autonomous driving vehicles drive accurately, plan routes for intelligent logistics distribution, and provide strong support for rescue operations.

[0031] Among the many application scenarios related to positioning technology, restoring the user's historical travel path based on the user's historical location is an important application scenario of positioning technology. For example, in the field of public safety, key clues can be obtained by restoring the user's historical travel path; in traffic management, the user's historical travel path can be restored to assist in traffic planning and flow control; in the commercial field, the consumer's historical travel path can be restored to study consumer behavior, etc.

[0032] At present, the historical travel path of the target user can be obtained by obtaining the historical positioning data of the target user's target terminal (such as mobile terminal devices such as mobile phones, tablets, smart wearable devices, and vehicle-mounted terminals). However, in some scenarios, the user's historical positioning data cannot be obtained. For example, if the user's target terminal has turned off the positioning function, or has set a setting that does not allow third parties to obtain its positioning data, the historical positioning data of the target user's target terminal cannot be obtained, resulting in the inability to determine the target user's historical travel path based on the historical positioning data. However, in these scenarios, there is still a need to determine the target user's historical travel path. For example, in the field of public safety, even if the historical positioning data of the target user's target terminal cannot be obtained, it is still necessary to determine the user's historical travel path to obtain key clues.

[0033] In these scenarios, existing methods mainly rely on base station positioning to predict the user's historical travel path based on the base station location (in this scenario, the historical travel path cannot be accurately determined, and the historical travel path can only be predicted based on the information obtained from the base station side). That is, based on the communication interaction between the target user's target terminal and the base station, the base stations that the target terminal passes through during the target user's travel are obtained. The base station location of each base station that the target terminal passes through during the process is connected, and the connected path is used as the predicted historical travel path of the target user.

[0034] For example, Figure 1 A schematic diagram of a path prediction scenario provided by this application. Figure 1 As shown, the base stations that the target terminal passes through during the target user's travel include base station 1, base station 2, and base station N. The current method for predicting the travel path is to obtain the base station position corresponding to each base station from base station 1 to base station N, and connect the base station positions in chronological order as the predicted historical travel path of the target user.

[0035] However, due to the distance between base stations and signal transmission delay, the positioning accuracy of terminal devices through base stations is low. In addition, in complex environments such as cities, objects such as terrain and buildings may affect the propagation and reception quality of base station signals, resulting in inaccurate positioning or failure to obtain positioning. Figure 1 The travel path of the target user is an ideal route, but the path that can support travel in real scenarios is usually quite different from the ideal route. For example, the target user cannot usually move from the base station location of base station 1 to the base station location of base station 2 via a straight path, but needs to move along the existing road (i.e. Figure 1 Therefore, the current prediction of the target user's travel path based on base station positioning has the problem of low accuracy.

[0036] In view of this, the present application provides a path prediction method. By obtaining the positions of at least two base stations that the target terminal passes through during its travel, and the first signal measurement data when the target terminal communicates and interacts with these base stations. Based on the positions of these base stations, the target coverage area located around these base stations is determined, and the second signal measurement data and the first signal measurement data of the geographic grid belonging to the target coverage area are matched to obtain the predicted path of the target terminal. Compared with the prior art that predicts the travel path only based on the base station position, the present method can predict the geographic grids that the target terminal may pass through around each base station through the first signal measurement data when the target terminal communicates and interacts with the base station, and the second signal measurement data of each geographic grid, and determine the predicted path of the target terminal based on the possible geographic grids passed through, so as to obtain a more accurate predicted path, thereby improving the accuracy of the path prediction.

[0037] The execution subject of the path prediction method may be an electronic device with computing capabilities, such as a smart phone, a computer, a tablet computer, a server, etc. The electronic device may be deployed with software or program code for running the path prediction method, and the travel path corresponding to the travel process of the target terminal may be predicted through the software or program code.

[0038] The technical solution of the present application is described in detail below in conjunction with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0039] Figure 2 A schematic diagram of a path prediction method provided in an embodiment of the present application. Figure 2 As shown, the method may include the following steps:

[0040] S201. Acquire first signal measurement data of a target terminal communicating with at least two base stations during a trip.

[0041] During the communication interaction between the target terminal (such as a mobile phone) and the base station, the operator's base station equipment will measure and record the signal measurement data when communicating with the target terminal in real time, such as transmit power adjustment information, uplink and downlink signal quality parameters, etc. Alternatively, the target terminal will also measure and record signal measurement data in real time during the interaction process, such as received signal strength indicator (RSSI), signal quality, etc. The target terminal will report the signal measurement data to the operator's network-side server or application background server periodically or when a specific event is triggered. Therefore, the first signal measurement data of the communication interaction between the target terminal and each base station can be obtained from the operator of the base station.

[0042] Among them, the first signal measurement data includes at least one of the data such as signal strength and signal frequency, which is used for subsequent comparison with the second signal measurement data of each geographic grid within the coverage area of ​​the base station, so as to further narrow the range of the specific location of the target terminal when passing through the base station based on the signal strength of different areas or different locations within the coverage area of ​​the base station.

[0043] Optionally, the measurement time of the first signal measurement data, the base station identifiers (base station name, base station number, etc.) of each base station that communicates and interacts with the target terminal, etc., can also be obtained. The base station identifiers can be used to determine which at least two base stations the target terminal passes through during its travel; the measurement time can be used to determine the time sequence (i.e., sequence) of at least two base stations that the target terminal passes through during its travel, so as to preliminarily determine the travel path of the target terminal. In this step, the base stations with which the target terminal has communicated and interacted during its travel can also be determined based on the historical records of communication interactions between the target terminal and the base stations. After determining the base stations with which the target terminal has communicated and interacted during its travel, the location of the base stations with which the target terminal has communicated and interacted during its travel can be obtained through the operator base station information.

[0044] S202: Determine second signal measurement data of each geographical grid within a target coverage area corresponding to at least two base stations.

[0045] Among them, based on the above, since the actual path of the target terminal during the trip is usually along the existing road, and since the target terminal passes through the at least two base stations, it can be inferred that the actual path of the target terminal is in the area near each base station it passes through. Therefore, the target coverage area corresponding to the base station refers to the area near the base station, and the target coverage area is used to divide the range to which the actual path of the target terminal belongs, so as to narrow the prediction range for the subsequent determination of the predicted path.

[0046] The second signal measurement data of each geographic grid within the target coverage area is used to compare with the first signal measurement data of the target terminal communicating with the base station during the travel process, so as to determine which geographic grids the target terminal may have passed through, so as to further narrow the prediction range for the subsequent determination of the predicted path. For example, if the first signal measurement data of the target terminal at base station A is the same or similar to the second signal measurement data corresponding to geographic grid 1, geographic grid 2, and geographic grid 3 near base station A in the target coverage area, it can be determined that the target terminal may have passed through geographic grid 1, and / or, geographic grid 2, and / or, geographic grid 3 when passing through base station A, thereby narrowing the area passed by the target terminal near base station A when the predicted path is subsequently determined.

[0047] In one possible implementation, the target coverage area can be determined based on the location of each base station in the target terminal's path and the coverage of each base station. For example, taking the two-dimensional coverage on the ground as an example, if the coverage of each base station is a circular coverage with the location of each base station as the center and a radius of 1 kilometer, the target coverage area includes the circular coverage of each base station. Alternatively, the location of each base station can be used as the center of the circle, and a preset radius can be set within the coverage of each base station to determine the circular coverage (for example, the maximum radius is 1 kilometer, and 500 meters can be set as the radius). The target coverage area includes the circular coverage of each base station based on the preset radius. The preset radius can be determined according to actual needs, and this application does not limit this.

[0048] In another possible implementation, the positions of at least two base stations that the target terminal passes through during the travel process may be obtained. Based on the interaction sequence of the target terminal communicating with the at least two base stations during the travel process, the positions of the at least two base stations are sequentially connected to obtain a reference line of the target coverage area. Based on a preset width, the width is expanded on at least one side of the reference line to obtain the target coverage area.

[0049] Specifically, it can be based on Figure 1 In the prior art, first, according to the positions of at least two base stations along the route, the positions of all base stations along the route are connected in time sequence (for example, the time sequence is determined according to the measurement time of the first signal measurement data of the communication interaction between the target terminal and each base station), as a reference line of the target coverage area (i.e., equivalent to Figure 1 The target coverage area is determined based on the reference line and the preset width value. Compared with the target coverage area determined directly based on the coverage range of each base station, the target coverage area determined in this way can cover the missing part between the circular coverage ranges of the two base stations (that is, the part of the area outside the intersection and inside the tangent line of the two circles when the two circles intersect), thereby increasing the scope of the target coverage area and thus improving the accuracy of the subsequent predicted path based on the target coverage area.

[0050] In this implementation, optionally, the target coverage area can be determined by expanding on one side of the reference line based on a preset width value, or by expanding on both sides of the reference line based on a preset width value. Figure 3 A schematic diagram of a target coverage area provided in an embodiment of the present application. Figure 3 As shown in FIG. 1 , assuming that the preset width value is 500 meters, it is necessary to expand the reference line on both sides based on the preset width value to determine the target coverage area. The target coverage area finally determined is as follows: Figure 3 Target coverage area shown in .

[0051] Among them, the geographic grids within the target coverage area can be determined based on the geographic grids pre-divided in the map (for example, the target coverage area can be drawn on the map with the geographic grids pre-divided to obtain the target coverage area divided into several geographic grids), or after the target coverage area is determined, the target coverage area can be divided into several geographic grids according to a preset geographic grid size (for example, according to a preset geographic grid size of 10 meters long and 10 meters wide, the target coverage area is divided into an area consisting of several such geographic grids).

[0052] For each geographic grid within the target coverage area, the second signal measurement data corresponding to each geographic grid can be obtained from the operator corresponding to the base station; or, the second signal measurement data corresponding to each geographic grid can be obtained through field measurement; or, the existing signal propagation model and algorithm can be used, combined with the base station location, transmission power and topographic information of the target coverage area, to perform virtual prediction and acquisition of the signal measurement data of each geographic grid.

[0053] S203: Determine a predicted path of the target terminal according to a matching relationship between the first signal measurement data and the second signal measurement data.

[0054] For each geographical grid in the target coverage area, due to factors such as different adjacent base stations, different locations, and different surrounding environments, even if different geographical grids belong to the same base station providing communication services, their corresponding second signal measurement data are different. Therefore, different second signal measurement data can represent different geographical grids.

[0055] According to the matching relationship between the first signal measurement data corresponding to each base station and the second signal measurement data corresponding to each geographical grid within the coverage of each base station, the second signal measurement data identical or similar to the first signal measurement data can be determined. Based on the second signal measurement data identical or similar to the first signal measurement data, the geographical grid corresponding to the second signal measurement data can be determined within the coverage of each base station.

[0056] For example, taking the signal measurement data as signal strength, assuming that the first signal measurement data is -80dBm, then within the target coverage area, determine the coverage range near the base station corresponding to the first signal measurement data, and within the coverage range, determine the geographical grid near -80dBm for the second signal measurement data, or the geographical grid where the second signal measurement data is -80dBm. Since the second signal measurement data is the same as the first signal measurement data or the difference is small, it can be inferred that the target terminal is more likely to pass through the geographical grid when passing through the base station, and the geographical grid can be used as a passing point on the predicted path of the target terminal.

[0057] Optionally, the matching relationship between the first signal measurement data and the second signal measurement data may be determined based on the similarity between the first signal measurement data and the second signal measurement data, or the matching relationship may be determined based on the difference between the first signal measurement data and the second signal measurement data.

[0058] When the matching relationship is determined based on the similarity between the first signal measurement data and the second signal measurement data, the higher the similarity, the higher the matching degree of the first signal measurement data and the second signal measurement data, and the greater the possibility that the target terminal passes through the geographical grid corresponding to the second signal measurement data when passing through the base station;

[0059] When the matching relationship is determined based on the difference between the first signal measurement data and the second signal measurement data, the smaller the difference is, the higher the degree of matching between the first signal measurement data and the second signal measurement data is, and the more likely the target terminal is to pass through the geographic grid corresponding to the second signal measurement data when passing through the base station.

[0060] Through the above method, the geographical grids with a greater possibility of being passed through each base station passed by the target terminal during the travel process are determined, and based on these geographical grids, the predicted path of the target terminal is determined.

[0061] Optionally, the predicted path of the target terminal can be determined directly based on the connection lines of the geographical grids near each base station where the target terminal has a greater possibility of passing. Alternatively, at least one candidate travel path can be obtained in the target coverage area based on the position of the first base station and the position of the last base station passed by the target terminal, and the predicted path can be determined from the candidate travel paths based on the matching degree between each candidate travel path and the geographical grid where the target terminal has a greater possibility of passing.

[0062] The method provided by the embodiment of the present application obtains the first signal measurement data of the target terminal in the communication and interaction with at least two base stations during the travel process. Determine the second signal measurement data of each geographical grid in the target coverage area corresponding to at least two base stations, and determine the predicted path of the target terminal based on the matching relationship between the first signal measurement data and the second signal measurement data. This method can predict the geographical grids that the target terminal may pass through around each base station through the first signal measurement data during the communication and interaction between the target terminal and the base station, and the second signal measurement data of each geographical grid, and determine the predicted path of the target terminal based on the geographical grids that may be passed through. Compared with the predicted path of the target terminal generated directly based on the location of the base station passed, the waypoints on the predicted path generated by the method of the present application are further refined to a smaller geographical grid, thereby improving the accuracy of determining the predicted path of the target terminal.

[0063] In a possible implementation, the method may further include displaying the predicted path of the target terminal. The predicted path may be displayed on a display screen of an electronic device executing the path prediction method, or may be output to a third-party electronic device for display, etc. For example, the predicted path may be displayed in a map interface on a display screen of an electronic device executing the path prediction method, so as to facilitate observation of the predicted path of the target terminal.

[0064] Optionally, if the predicted paths of the displayed target terminal include multiple paths, the predicted paths to be displayed may be determined according to the display configuration. The display configuration may be pre-configured or may be adjusted in real time according to manual operation during the display process. For example, at least one of the number of predicted paths displayed, the predicted paths of a specific travel mode, etc. may be adjusted.

[0065] Next, how to determine the predicted path of the target terminal according to the matching relationship between the first signal measurement data and the second signal measurement data in the aforementioned step S203 is described in detail.

[0066] Implementation method A: The matching relationship is determined based on similarity.

[0067] Figure 4 A schematic diagram of another path prediction method provided in an embodiment of the present application. Figure 4 As shown, the aforementioned step S203 may specifically include the following steps:

[0068] S401. Determine a first geographic grid set matching the first signal measurement data according to similarities between the first signal measurement data and the second signal measurement data.

[0069] The similarity between the second signal measurement data of the first geographic grid in the first geographic grid set and the first signal measurement data is greater than or equal to a preset similarity threshold.

[0070] Optionally, the similarity of the first signal measurement data and the second signal measurement data can be determined based on the difference between the first signal measurement data and the second signal measurement data. For example, taking the signal measurement data as signal strength as an example, the difference between the first signal strength and the second signal strength can be obtained by subtracting the first signal strength from the second signal strength. The similarity between the two is determined according to the size of the difference between the two. For example, the larger the difference, the smaller the similarity; the smaller the difference, the greater the similarity. Alternatively, a difference threshold can be set, and when the difference between the two is greater than or equal to the difference threshold, the similarity is less than a preset similarity threshold; when the difference between the two is less than the difference threshold, the similarity is greater than or equal to the preset similarity threshold.

[0071] Alternatively, when the first signal measurement data and the second signal measurement data are time series data, the similarity between the first signal measurement data and the second signal measurement data may be determined based on the similarity between the distribution of the time series of the first signal measurement data and the second signal measurement data.

[0072] After the similarity is determined, the second signal measurement data whose similarity to the first signal measurement data is greater than or equal to the similarity threshold can be screened out from the second signal measurement data according to the preset similarity threshold. And the geographic grids corresponding to all the second signal measurement data whose similarity to the first signal measurement data is greater than or equal to the similarity threshold are determined as the first geographic grid set. The first geographic grid set represents the geographic grids that the target terminal has a high probability of passing through during its travel.

[0073] It should be understood that when calculating the similarity, the base station corresponding to the first signal measurement data and the base station to which the geographical grid corresponding to the second signal measurement data belongs are the same base station. For each base station, the similarity comparison is performed, and the first geographical grid set is determined based on the geographical grids corresponding to the second signal measurement data determined by all base stations and having a similarity greater than or equal to the similarity threshold with the first signal measurement data.

[0074] S402: Determine a predicted path of a target terminal according to a first geographic grid set.

[0075] A possible implementation method is to directly determine the predicted path of the target terminal based on the connection lines of the geographical grids near each base station that the target terminal is likely to pass through. This implementation method can be implemented based on the following sub-steps:

[0076] S4021. Based on the interaction sequence of the target terminal communicating with at least two base stations during the travel process, the first geographic grids corresponding to the base stations in the first geographic grid set are connected in sequence to obtain at least one candidate predicted path of the target terminal.

[0077] Based on the first geographical grids included in the coverage of each base station and the interaction sequence of the target terminal with each base station during the travel, a first geographical grid can be selected within the coverage of each base station each time, and multiple first geographical grids corresponding to different base stations can be connected according to the interaction sequence of the target terminal with each base station during the travel to obtain a candidate predicted path for the target terminal.

[0078] Then, by means of permutations and combinations, at least one selected first geographical grid is replaced with a first geographical grid that has not been selected, and the above steps are repeated multiple times to obtain multiple candidate predicted paths of the target terminal.

[0079] Exemplarily, it is assumed that the target terminal passes through three base stations, base station 1, base station 2 and base station 3, and the coverage range of base station 1 (i.e., the vicinity) includes the first geographical grid 1, the first geographical grid 2 and the first geographical grid 3, the coverage range of base station 2 includes the first geographical grid 4, and the coverage range of base station 3 includes the first geographical grid 5 and the first geographical grid 6. Then, in this implementation, the candidate predicted path includes: first geographical grid 1-first geographical grid 4-first geographical grid 5; first geographical grid 1-first geographical grid 4-first geographical grid 6; first geographical grid 2-first geographical grid 4-first geographical grid 5; first geographical grid 2-first geographical grid 4-first geographical grid 6; first geographical grid 3-first geographical grid 4-first geographical grid 5; first geographical grid 3-first geographical grid 4-first geographical grid 6. Taking the candidate predicted path first geographical grid 1-first geographical grid 4-first geographical grid 5 as an example, its representative candidate predicted path is the path corresponding to the line connecting the first geographical grid 1, the first geographical grid 4, and the first geographical grid 5 in sequence.

[0080] S4022: Determine a predicted path of the target terminal from at least one candidate predicted path.

[0081] When only one candidate prediction path is obtained, the candidate prediction path is directly determined as the prediction path of the target terminal. When multiple candidate prediction paths are obtained, the multiple candidate prediction paths can all be used as the prediction paths of the target terminal. Alternatively, based on specific screening rules, a prediction path that meets actual needs can be screened out from multiple candidate prediction paths.

[0082] When selecting a predicted path that meets actual needs from multiple candidate predicted paths based on specific screening rules, for example, the screening can be performed based on the distance of the candidate predicted paths (such as selecting a candidate predicted path with the shortest distance or a distance less than a preset distance threshold as a predicted path), or based on the degree of matching between the candidate predicted paths and the actual road network (such as selecting a candidate predicted path that includes or is close to more actual passable roads as a predicted path), or based on the area through which the candidate predicted paths pass (for example, a candidate predicted path that passes through more buildings can be selected as a predicted path), etc. This application does not specifically limit how to perform the screening, and it can be selected according to actual needs.

[0083] Compared with the prior art which directly generates the predicted path of the target terminal according to the locations of the base stations along the way, the implementation method of this sub-step generates the waypoints on the predicted path of the target terminal based on the first geographical grid with a higher probability of the target terminal's path, so that the accuracy of the predicted waypoints of the target terminal is higher, thereby improving the accuracy of determining the predicted path of the target terminal.

[0084] Another possible implementation method is to obtain multiple commonly used candidate travel paths in the actual road network based on the starting point and end point corresponding to the travel process of the target terminal after obtaining the first geographic grid set. The first geographic grid set is matched with each candidate travel path, and the candidate travel path with a higher matching degree with the first geographic grid set is determined as the predicted path of the target terminal.

[0085] Implementation method B: The matching relationship is determined based on the difference.

[0086] Figure 5 A schematic diagram of a flow chart of another path prediction method provided in an embodiment of the present application. Figure 5 As shown, the aforementioned step S203 may specifically include the following steps:

[0087] S501. Determine a first geographic grid set matching the first signal measurement data according to a difference between the first signal measurement data and the second signal measurement data.

[0088] Taking the first signal measurement data and the second signal measurement data as signal strength as an example, the first signal strength is the first signal measurement data, and the second signal strength is the second signal measurement data. By subtracting the first signal strength from the second signal strength, the difference between the first signal measurement data and the second signal measurement data can be obtained.

[0089] After obtaining the difference, the first set of geographic grids matching the first signal measurement data can be determined by presetting the difference threshold. The preset difference threshold can be set according to actual needs, for example, it can be set to 1dBm, 2dBm, etc. By presetting the difference threshold, it can be determined whether the second signal strength of each geographic grid and the difference between the first signal strength between the base station corresponding to each geographic grid and the target terminal are within the preset difference threshold. If the difference is within the preset difference threshold, it is characterized that the geographic grids corresponding to these second signal strengths are most likely the geographic grids passed by the target terminal. Therefore, based on the second signal strengths corresponding to all geographic grids, multiple first geographic grids corresponding to the third signal strength whose difference with the first signal strength is less than or equal to the preset difference threshold can be screened out, and based on all the first geographic grids, the first set of geographic grids matching the first signal measurement data can be determined.

[0090] For example, assuming that when the target terminal passes through base station 3, its corresponding first signal strength is -70dBm, and there are three geographical grids with a second signal strength of -70dBm in the geographical grid near base station 3, which means that the difference between the three geographical grids with a second signal strength of -70dBm and the first signal strength is 0, indicating that when the target terminal passes through base station 3, the target terminal is likely to pass through at least one of the three geographical grids with a second signal strength of -70dBm. Therefore, the three geographical grids are the first geographical grids.

[0091] S502: Determine a predicted path of a target terminal according to a first geographic grid set.

[0092] This step may refer to the aforementioned step S402 and will not be described in detail here.

[0093] The method provided in the embodiment of the present application determines a first geographic grid set matching the first signal measurement data based on the similarity or difference between the first signal measurement data and the second signal measurement data, and determines a predicted path of the target terminal based on the first geographic grid set. The method generates waypoints on the predicted path of the target terminal based on the first geographic grid with a higher probability of the target terminal passing through, so that the accuracy of the predicted waypoints of the target terminal is higher, thereby improving the accuracy of determining the predicted path of the target terminal.

[0094] Next, taking the determination of the predicted path of the target terminal based on the candidate travel paths as an example, how to determine the predicted path of the target terminal based on the first geographic grid set in the aforementioned step S402 is described in detail. Figure 6 A schematic diagram of a flow chart of another path prediction method provided in an embodiment of the present application. Figure 6 As shown, the aforementioned step S402 may specifically include the following steps:

[0095] S601: Acquire at least one candidate travel path of a target terminal in a target coverage area.

[0096] In this step, a travel path from the starting point corresponding to the travel process of the target terminal to the end point corresponding to the travel process can be generated within the target coverage area based on the existing path planning engine as a candidate travel path for the target terminal.

[0097] Specifically, map data of the target coverage area may be collected, including road network information (such as the location, type, and connection relationship of the road), points of interest, etc. These data may be obtained from professional map data providers, or obtained by collecting and processing geographic information of the target area. The starting point and the end point corresponding to the travel process of the target terminal may be determined by at least two base stations along the travel process of the target terminal. In one embodiment, for example, the position of the base station of the first path during the travel process may be used as the starting point position, and the position of the base station of the last path during the travel process may be used as the end point position.

[0098] The map data, starting point location, and end point location of the target coverage area are input into the selected path planning engine. The path planning engine searches or plans all possible paths from the starting point to the end point in the road network based on existing path planning algorithms (such as Dijkstra algorithm, A algorithm, etc.). These paths are candidate travel paths. The generated candidate travel paths can include different road options, number of turns, etc. to meet different travel needs and scenarios.

[0099] S602: Determine the matching degree between the first geographic grid set and each candidate travel path.

[0100] Among them, the first geographic grid included in the first geographic grid set represents a geographic grid that the target terminal has a high probability of passing through during the travel process. However, in the target coverage area, there may be multiple geographic grids with similar second signal measurement data, that is, there may also be multiple first geographic grids with similar second signal measurement data in the first geographic grid set. The path options available for the target user to travel are usually limited. Therefore, by combining the candidate travel path and the first geographic grid set, the first geographic grids that some users usually do not pass through during the travel process can be excluded to further improve the accuracy of the predicted path. For example, if a candidate travel path matches a large number of first geographic grids, the possible travel path represented by the signal measurement data has a high degree of overlap with the candidate travel path, that is, the target user is more likely to adopt the candidate travel path.

[0101] In a possible implementation, the number of target geographic grids that each candidate travel path passes through can be determined in the candidate geographic grids, and the matching degree between the candidate geographic grids and each candidate travel path can be determined according to the number of target geographic grids. The more target geographic grids there are, the higher the matching degree between the candidate travel path and the candidate geographic grids. For example, if one candidate travel path passes through 10 target geographic grids, while another passes through only 5, the matching degree of the candidate travel path that passes through 10 target geographic grids is higher.

[0102] Another possible implementation method is to determine the target geographical grids that each candidate travel path passes through in the candidate geographical grids, and determine the matching degree between the candidate geographical grids and each candidate travel path according to the proportion of the target geographical grids in all the geographical grids that the candidate travel path passes through. The proportion can be a number proportion or an area proportion. For example, assuming that the number of target geographical grids accounts for 60% of all the geographical grids that a candidate travel path passes through, and the number of target geographical grids accounts for 80% in another candidate travel path, the candidate travel path with the number of target geographical grids accounting for 80% has a higher matching degree.

[0103] In another possible implementation, the range of the geographic grid of each candidate travel path may be expanded. For example, the geographic grid of each candidate travel path may include not only the geographic grid of the actual path, but also the geographic grid near the geographic grid of the actual path (e.g., adjacent geographic grid). Within the expanded geographic grid range of the candidate travel path, the matching degree between the candidate geographic grid and each candidate travel path is determined based on the proportion of the target geographic grid of the candidate travel path within the expanded geographic grid range of the candidate travel path.

[0104] S603: Determine a predicted path of the target terminal according to the matching degree corresponding to each candidate travel path.

[0105] In one possible implementation, the candidate travel path with the highest matching degree may be determined as the predicted path of the target terminal. Since the higher the matching degree, the higher the probability that the travel path taken by the target terminal during the travel process is the corresponding candidate travel path, the candidate travel path with the highest matching degree may be determined as the predicted path of the target terminal.

[0106] Another possible implementation method may determine the ranking result of the candidate travel paths according to the matching degree corresponding to each candidate travel path. According to the ranking result, the predicted path of the target terminal is determined. In this implementation method, the ranking result of the candidate travel paths may be generated according to the matching degree corresponding to each candidate travel path, and in the ranking result, according to the preset selection rule, multiple candidate travel paths with the highest matching degree ranking are selected, and the multiple candidate travel paths are used as the predicted path of the target terminal. Then, based on other positioning technologies or survey methods, the multiple determined predicted paths are processed to comprehensively determine the actual path of the target terminal.

[0107] The method provided in the embodiment of the present application obtains at least one candidate travel path of the target terminal in the target coverage area, determines the matching degree between the first geographic grid set and each candidate travel path, and determines the predicted path of the target terminal according to the matching degree of each candidate travel path. The method selects the candidate travel path with a higher matching degree with the first geographic grid as the predicted path of the target terminal according to the matching degree between the first geographic grid and the candidate travel path that the target terminal may pass through. Compared with the prior art that directly generates the predicted path according to the base station position of the target terminal's path, the method further improves the positioning accuracy of the target terminal through signal measurement data, thereby improving the accuracy of the generated predicted path.

[0108] Next, how to obtain the candidate travel path of the target terminal in the target coverage area in the aforementioned step S601 is described in detail. Figure 7 A schematic diagram of a flow chart of another path prediction method provided in an embodiment of the present application. Figure 7 As shown, the aforementioned step S601 may specifically include the following steps:

[0109] S701. Determine a starting base station and an ending base station according to a measurement time of first signal measurement data.

[0110] In this step, the first signal measurement data is obtained by measuring the communication interaction between the target terminal and at least two base stations along the way of the target terminal during the travel process. According to the measurement behavior, the measurement time of measuring the first signal measurement data corresponding to each base station can be determined, and the measurement time represents the time point when the target terminal is near each base station. Based on the measurement time, the path sequence of at least two base stations along the way of the target terminal during the travel process can be determined. Therefore, the starting base station and the end base station of the target terminal during the travel process can be determined according to the measurement time.

[0111] For example, the base station with the earliest measurement time is the starting base station, and the base station with the latest measurement time is the ending base station.

[0112] S702: Determine the starting point position of the candidate travel path according to the position of the starting base station.

[0113] In a possible implementation, the location of the starting base station may be used as the starting location of the candidate travel path.

[0114] Another possible implementation manner is to use any location within the coverage area of ​​the starting base station as the starting location of the candidate travel path.

[0115] S703: Determine the terminal position of the candidate travel path according to the position of the terminal base station.

[0116] In a possible implementation, the location of the terminal base station may be used as the terminal location of the candidate travel path.

[0117] Another possible implementation manner is to use any location within the coverage of the destination base station as the destination location of the candidate travel path.

[0118] S704: Acquire at least one candidate travel path of the target terminal in the target coverage area according to the starting point position and the ending point position.

[0119] In this step, map data of the target coverage area may be collected, including road network information (such as the location, type, and connection relationship of the road), points of interest, etc. These data may be obtained from a professional map data provider, or obtained by collecting and processing geographic information of the target area. The starting point and the end point corresponding to the travel process of the target terminal may be determined by at least two base stations along the travel process of the target terminal. In one embodiment, for example, the position of the base station of the first path during the travel process may be used as the starting point position, and the position of the base station of the last path during the travel process may be used as the end point position.

[0120] The map data, starting point location, and end point location of the target coverage area are input into the selected path planning engine. The path planning engine searches or plans all possible paths from the starting point to the end point in the road network based on existing path planning algorithms (such as Dijkstra algorithm, A algorithm, etc.). These paths are candidate travel paths. The generated candidate travel paths can include different road options, number of turns, etc. to meet different travel needs and scenarios.

[0121] Optionally, the travel paths may be different due to different travel modes. For example, travel modes may include driving, walking, cycling, public transportation (bus travel, subway travel), and other travel modes. For driving, the corresponding candidate travel paths are mainly on motor vehicle roads; for walking, the corresponding candidate travel paths may include some narrow roads that cannot be used when driving; for public transportation, the candidate travel paths are based on fixed public transportation routes. Therefore, the impact of different travel modes on the generation of candidate travel paths by the path planning engine can be further considered. In this implementation, this step can be implemented through the following sub-steps:

[0122] S7041. Determine the travel mode of the target terminal.

[0123] In this step, the specific travel modes and the number of travel modes to be selected can be determined according to actual needs. For example, it can be selected by default to obtain candidate travel paths corresponding to all travel modes; or the candidate travel paths corresponding to some travel modes can be selected according to actual needs. Optionally, when the travel mode is a non-fixed route travel mode such as driving, walking, cycling, etc., for each travel mode, when obtaining the corresponding candidate travel path according to the starting point location and the end point location, one or more candidate travel paths corresponding to the travel mode can be obtained.

[0124] Alternatively, the travel mode of the target terminal may also be determined based on the distances between the base stations that the target terminal passes through and the time it takes to pass through these base stations.

[0125] Taking the example that the travel mode of the target terminal is determined based on the distance between the base stations that the target terminal passes through and the time when passing through these base stations, the measurement time difference of the adjacent base stations in at least two base stations can be first obtained according to the measurement time of the first signal measurement data. Among them, the measurement time corresponding to each base station is the measurement time in the first signal measurement data obtained by the interactive measurement of the communication with each base station when the target terminal passes through each base station during the travel process. The measurement time can characterize the time when the target terminal passes through each base station, that is, the time when the target terminal is in different locations. The time spent by the target terminal from the vicinity of a base station to the vicinity of the adjacent base station of the base station can be determined by the time difference between the measurement times of adjacent base stations. Specifically, the time difference between adjacent base stations can be determined by subtracting the measurement times corresponding to each adjacent base station.

[0126] Then, the distance between adjacent base stations is obtained. The distance between adjacent base stations can be determined according to the positions corresponding to the adjacent base stations. The positions corresponding to the adjacent base stations can be, for example, the positions where the base stations are located, or the preset positions of the base stations (for example, a reference position near the base stations). For example, taking the position where the positions corresponding to the adjacent base stations are the positions where the base stations are located as an example, the distance between adjacent base stations is the straight-line distance between the positions of the two base stations obtained by calculating the positions of the two adjacent base stations; or the distance between adjacent base stations is the distance of a preset path between the positions of the two base stations obtained by calculating the positions of the two adjacent base stations.

[0127] Finally, the candidate travel modes related to the measured time difference and distance of each adjacent base station are determined as the travel mode of the target terminal. Based on the time difference and distance, the movement of the target terminal from one base station to the adjacent base station of the base station can be determined. In addition, the movement conditions of different travel modes are usually quite different. For example, the movement conditions of public transportation are usually consistent, such as the time required for movement is relatively stable and the distance of movement is also fixed; the speed of driving is usually greater than the speed of cycling, and the speed of cycling is greater than the speed of walking, etc. Therefore, the travel mode of the target terminal can be predicted and determined based on the movement of the target terminal from one base station to the adjacent base station of the base station to improve the accuracy of determining the travel mode.

[0128] A possible implementation method is to determine the travel mode of the target terminal according to the mapping relationship between the time difference, distance and travel mode. The mapping relationship between the time difference, distance and travel mode can be pre-configured, and different time differences and distances correspond to different travel modes. After the time difference and distance are obtained, the corresponding travel mode can be determined through the mapping relationship. For example, if the time difference and distance are close to the time and distance required for traveling by public transportation, the travel mode can be determined to be public transportation based on the time difference, distance, and the mapping relationship between the time difference, distance and travel mode.

[0129] Another possible implementation method is to obtain the predicted speed of the target terminal according to the time difference and the distance. According to the predicted speed and the mapping relationship between the predicted speed and the travel mode, the travel mode is obtained. As mentioned above, due to different travel modes, the speed may be different, for example, for driving, cycling, and walking, the speeds are all different. Therefore, the predicted speed of the target terminal can be obtained according to the time difference and the distance, and it can be determined whether the predicted speed is close to the speed corresponding to the travel mode of driving, cycling, or walking, so as to predict the travel mode of the target terminal. Specifically, for example, according to the speed ranges corresponding to driving, cycling, and walking, respectively, it can be determined in which speed interval the predicted speed falls.

[0130] In this implementation, the travel mode of the target terminal is determined based on the measurement time of the first signal measurement data, and the starting base station and the end base station are determined, and the speed of the target terminal when passing through each base station can be calculated. Since the speeds of different travel modes are different, the travel mode with a speed close to that of the target terminal during the travel process can be further screened out according to the different speeds of the target terminal, thereby improving the accuracy of determining the travel mode, and further reducing the number of generated candidate travel paths by controlling the travel mode, thereby improving the efficiency of determining the predicted path.

[0131] S7042: Acquire at least one candidate travel path of the target terminal in the target coverage area according to the starting point location, the ending point location, and at least one travel mode.

[0132] Specifically, the starting point location, the end point location, and at least one travel mode may be input into the path planning engine to obtain a candidate travel path of the target terminal within the target coverage area. For example, the target coverage area may be simultaneously input into the path planning engine to obtain a candidate travel path of the target terminal within the target coverage area; or, after obtaining multiple travel paths corresponding to the starting point location, the end point location, and at least one travel mode through the path planning engine, a travel path within the target coverage area may be selected as a candidate travel path.

[0133] The method provided in the embodiment of the present application determines the starting base station and the ending base station according to the measurement time of the first signal measurement data. The starting position of the candidate travel path is determined according to the position of the starting base station, and the ending position of the candidate travel path is determined according to the position of the ending base station. According to the starting position and the ending position, at least one candidate travel path of the target terminal is obtained within the target coverage area. The method of this embodiment utilizes the starting position and the ending position of the target terminal during the travel process to plan multiple commonly used candidate travel paths, which are convenient for subsequent matching with the first geographic grid set to determine the predicted path of the target terminal. Using the candidate travel path, inaccurate candidate travel modes (such as uncommon paths) can be reduced to remove redundant calculations, thereby improving the efficiency of predicting travel paths.

[0134] Figure 8 This is a schematic diagram of the structure of a path prediction device provided in an embodiment of the present application. Figure 8 As shown, the device may include: an acquisition module 11, a determination module 12, and a processing module 13.

[0135] The acquisition module 11 is used to acquire first signal measurement data of the target terminal communicating with at least two base stations during the travel process.

[0136] The determination module 12 is configured to determine second signal measurement data of each geographical grid within a target coverage area corresponding to at least two base stations.

[0137] The processing module 13 is used to determine the predicted path of the target terminal according to the matching relationship between the first signal measurement data and the second signal measurement data.

[0138] Optionally, the processing module 13 is specifically configured to determine a first geographic grid set matching the first signal measurement data based on the similarity between the first signal measurement data and the second signal measurement data. Determine a predicted path of the target terminal based on the first geographic grid set. The similarity between the second signal measurement data of the first geographic grid in the first geographic grid set and the first signal measurement data is greater than or equal to a preset similarity threshold.

[0139] Optionally, the acquisition module 11 is specifically used to acquire at least one candidate travel path of the target terminal in the target coverage area. The processing module 13 is specifically used to determine the matching degree between the first geographic grid set and each candidate travel path. According to the matching degree of each candidate travel path, the predicted path of the target terminal is determined.

[0140] Optionally, the processing module 13 is specifically configured to determine the number of target geographical grids that each candidate travel path passes through in the first geographical grid set, and determine the matching degree between the first geographical grid and each candidate travel path according to the number of target geographical grids.

[0141] Optionally, the processing module 13 is specifically configured to determine a starting base station and an ending base station according to a measurement time of the first signal measurement data. Determine a starting position of a candidate travel path according to a position of the starting base station. Determine an ending position of the candidate travel path according to a position of the ending base station. According to the starting position and the ending position, obtain at least one candidate travel path of the target terminal in the target coverage area.

[0142] Optionally, the processing module 13 is specifically configured to determine the travel mode of the target terminal and obtain at least one candidate travel path of the target terminal in the target coverage area according to the starting point location, the end point location, and the travel mode.

[0143] Optionally, the processing module 13 is specifically configured to obtain, according to the measurement time of the first signal measurement data, a measurement time difference of adjacent base stations among at least two base stations, obtain a distance between adjacent base stations, and determine a candidate travel mode related to the measurement time difference and distance of each adjacent base station as a travel mode of the target terminal.

[0144] Optionally, the processing module 13 is specifically configured to sequentially connect the first geographic grids corresponding to the base stations in the first geographic grid set based on the interaction sequence of the target terminal communicating with at least two base stations during the travel process, and obtain at least one candidate predicted path of the target terminal. Determine the predicted path of the target terminal from the at least one candidate predicted path.

[0145] Optionally, the acquisition module 11 is further used to acquire the positions of at least two base stations that the target terminal passes through during the travel process. The processing module 13 is further used to connect the positions of at least two base stations in sequence based on the interaction sequence of the target terminal communicating with the at least two base stations during the travel process to obtain a reference line of the target coverage area. Based on a preset width, the width of at least one side of the reference line is expanded to obtain the target coverage area.

[0146] Optionally, the signal measurement data includes at least one of signal strength and signal frequency.

[0147] The path prediction device provided in the embodiment of the present application can execute the path prediction method in the above method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0148] Fig. 9 The structure diagram of an electronic device provided in an embodiment of the present application is shown in FIG. 1 . The electronic device can be used to execute the path prediction method mentioned above. Fig. 9 As shown, the electronic device 900 may include: at least one processor 901 and a memory 902. In a possible implementation, a communication interface 903 may also be included.

[0149] The memory 902 is used to store programs. Specifically, the programs may include program codes, and the program codes include computer operation instructions.

[0150] The memory 902 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0151] The processor 901 is used to execute the computer-executable instructions stored in the memory 902 to implement the method described in the above method embodiment. The processor 901 may be a CPU, or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0152] The processor 901 can communicate and interact with external devices through the communication interface 903. The external devices can be, for example, the target terminal, base station, operator's server, etc. mentioned above. In specific implementation, if the communication interface 903, the memory 902 and the processor 901 are implemented independently, the communication interface 903, the memory 902 and the processor 901 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc., but it does not mean that there is only one bus or one type of bus.

[0153] Optionally, in a specific implementation, if the communication interface 903, the memory 902 and the processor 901 are integrated on a chip, the communication interface 903, the memory 902 and the processor 901 can communicate through an internal interface.

[0154] The present application also provides a computer-readable storage medium, which may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, and other media that can store program codes. Specifically, the computer-readable storage medium stores program instructions, and the program instructions are used for the methods in the above embodiments.

[0155] The present application also provides a program product, which includes an execution instruction, which is stored in a readable storage medium. At least one processor of an electronic device can read the execution instruction from the readable storage medium, and at least one processor executes the execution instruction so that the electronic device implements the path prediction method provided by the various embodiments described above.

[0156] The term "plurality" in this article refers to two or more than two. The term "and / or" in this article is only a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the previous and next associated objects are in an "or" relationship; in the formula, the character " / " indicates that the previous and next associated objects are in a "division" relationship. In addition, it should be understood that in the description of this application, words such as "first" and "second" are only used for the purpose of distinguishing descriptions, and cannot be understood as indicating or implying relative importance, nor can they be understood as indicating or implying order.

[0157] It should be understood that the various numerical numbers involved in the embodiments of the present application are only used for the convenience of description and are not used to limit the scope of the embodiments of the present application.

[0158] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A path prediction method, characterized in that: The method comprises: Acquire first signal measurement data of communication interaction between the target terminal and at least two base stations during the travel process; Acquire the positions of the at least two base stations that the target terminal passes through during its travel; Based on the interaction sequence of the target terminal communicating with the at least two base stations during the travel process, the positions of the at least two base stations are sequentially connected to obtain a reference line of the target coverage area; Based on a preset width, expanding the width on at least one side of the reference line to obtain the target coverage area; Determine second signal measurement data of each geographical grid within the target coverage area corresponding to the at least two base stations; The predicted path of the target terminal is determined according to a matching relationship between the first signal measurement data and the second signal measurement data.

2. The method according to claim 1, characterized in that The determining the predicted path of the target terminal according to the matching relationship between the first signal measurement data and the second signal measurement data includes: Determine, according to the similarity between the first signal measurement data and the second signal measurement data, a first geographic grid set matching the first signal measurement data, wherein the similarity between the second signal measurement data of a first geographic grid in the first geographic grid set and the first signal measurement data is greater than or equal to a preset similarity threshold; A predicted path of the target terminal is determined according to the first geographic grid set.

3. The method according to claim 2, characterized in that The determining, according to the first geographic grid set, a predicted path of the target terminal includes: Acquire at least one candidate travel path of the target terminal in the target coverage area; Determining a matching degree between the first geographic grid set and each of the candidate travel paths; The predicted path of the target terminal is determined according to the matching degree corresponding to each of the candidate travel paths.

4. The method according to claim 3, characterized in that The determining the matching degree between the first geographic grid set and each of the candidate travel paths includes: Determining the number of target geographical grids passed by each of the candidate travel paths in the first geographical grid set; According to the number of the target geographical grids, the matching degree between the first geographical grid and each of the candidate travel paths is determined.

5. The method according to claim 3, characterized in that: The acquiring at least one candidate travel path of the target terminal in the target coverage area includes: Determine a starting base station and an ending base station according to the measurement time of the first signal measurement data; Determine the starting point position of the candidate travel path according to the position of the starting base station; Determining the terminal position of the candidate travel path according to the position of the terminal base station; At least one candidate travel path of the target terminal is acquired in the target coverage area according to the starting point position and the end point position.

6. The method according to claim 5, characterized in that The acquiring at least one candidate travel path of the target terminal in the target coverage area according to the starting point position and the end point position includes: Determining a travel mode of the target terminal; At least one candidate travel path of the target terminal is acquired in the target coverage area according to the starting point position, the end point position, and the travel mode.

7. The method according to claim 2, characterized in that The determining, according to the first geographic grid set, a predicted path of the target terminal includes: Based on the interaction sequence of the target terminal communicating with the at least two base stations during the travel process, sequentially connect the first geographical grids corresponding to the base stations in the first geographical grid set to obtain at least one candidate predicted path for the target terminal; The predicted path of the target terminal is determined from the at least one candidate predicted path.

8. The method according to any one of claims 1 to 7, characterized in that The signal measurement data includes at least one of signal strength and signal frequency.

9. A path prediction device, characterized in that: The device comprises: An acquisition module, used to acquire first signal measurement data of communication interaction between the target terminal and at least two base stations during the travel process; A determination module, configured to determine second signal measurement data of each geographical grid within a target coverage area corresponding to the at least two base stations; a processing module, configured to determine a predicted path of the target terminal according to a matching relationship between the first signal measurement data and the second signal measurement data; The acquisition module is further used to acquire the positions of the at least two base stations along which the target terminal passes during its travel; The processing module is also used to connect the positions of the at least two base stations in sequence based on the interaction order of the target terminal communicating with the at least two base stations during travel to obtain a reference line of the target coverage area; and based on a preset width, expand the width on at least one side of the reference line to obtain the target coverage area.

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