Target device searching method and system, electronic equipment and computer storage medium

Abstract

The invention provides a target device searching method and system, electronic equipment and a computer storage medium, and the method comprises the steps: obtaining second equipment associated with first equipment under the condition that the first equipment is detected; the first equipment is equipment carried by one or more users, and the second equipment is one or more equipment arranged on a target device and / or the target device; determining the position of the second equipment as a target position; planning a moving path according to the current position and the target position of the first equipment; the moving path is a path from the current position to the target position. By associating the first equipment with the second equipment, the second equipment associated with the first equipment can be directly acquired, the target position of the target device can be determined, and the moving path can be automatically planned, so that a user does not need to specially memorize the placement position of the target device and does not need to install other software; therefore, the placement position of the target device can be recorded without feeling, and the user experience is improved.

Description

Technical Field

[0001] The present application relates to the field of path planning, and more specifically, to a method, system, electronic device and computer storage medium for finding a target device. Background Art

[0002] In existing target device placement management systems, a significant problem is that users often need to rely on memory or manually taken photos of the path or drawn paths to find the placed target device. This situation greatly affects the user experience. For example, in a parking management system, especially in large parking lots or multi-storey parking lots, due to the large number of parking spaces and complex layout, it is often difficult for users to quickly locate their vehicles based on memory alone. This not only increases the user's parking time cost, but may also cause unnecessary anxiety and frustration, thereby reducing the user experience. Summary of the invention

[0003] In view of this, the purpose of the embodiments of the present application is to provide a target device search method, system, electronic device and computer storage medium, which can realize the non-sensitive recording of the placement position of the target device and improve the user experience.

[0004] In a first aspect, an embodiment of the present application provides a method for searching a target device, comprising: when a first device is detected, obtaining a second device associated with the first device; wherein the first device is a device carried by one or more users, and the second device is one or more devices and / or the target device set on the target device; determining the location of the second device as the target location; planning a moving path based on the current location of the first device and the target location; wherein the moving path is the path between the current location and the target location.

[0005] In the above implementation process, by associating the first device with the second device, when the first device is obtained, the second device associated with the first device can be directly obtained, and then the target position of the target device is determined, and the moving path is automatically planned according to the current position and target position of the first device. The user no longer needs to specifically remember the placement position of the target device, nor does he need to install other software on the first device, so that the placement position of the target device can be recorded without feeling, thereby improving the user experience. In addition, since the first device is a device carried by the user, and there can be multiple first devices with the same motion trajectory as the second device, when the first device of any user in the same group is detected, the second device associated with it can be obtained, and then the moving path can be planned. The method of searching for the target device can also identify and record the companions, and does not limit the same user to enter and exit the target area, which increases the application scenarios of the method of searching for the target device.

[0006] In one embodiment, before obtaining a second device associated with the first device when the first device is detected, the method further includes: calculating the temporal correlation and spatial correlation of the devices to be associated detected in the target area; where the devices to be associated include one or more of the first device and the second device; determining the trajectory similarity of each of the devices to be associated according to the trajectory of the devices to be associated; and determining the association relationship of each of the devices to be associated according to the temporal correlation, the spatial correlation, and the trajectory similarity.

[0007] In the above implementation process, by calculating the temporal correlation, spatial correlation, and trajectory correlation of the devices to be associated, and jointly determining the association relationship of the devices to be associated according to multi-dimensional information such as temporal correlation, spatial correlation, and trajectory correlation, the accuracy of determining the association relationship can be improved. Additionally, since the association relationship is determined according to the temporal correlation, spatial correlation, and trajectory correlation, one or more first devices and second devices on the same target device can be associated, and thus the people traveling together can also be identified and recorded. It is not limited that the user entering and leaving the target area is the same user, which can increase the application scenarios of the method for finding the target device.

[0008] In one embodiment, before obtaining a second device associated with the first device when the first device is detected, the method further includes: tracking the device movement information of the target device; when it is determined that the target device is in a stationary state according to the device movement information, tracking the associated device movement information of each associated device associated with the target device; and determining the first device and the second device associated with the target device according to the associated device movement information.

[0009] In the above implementation process, by analyzing the movement information of each associated device to classify the associated devices, the automatic separation of the first device and the second device associated with the same target device can be realized, improving the ability to identify the behaviors of different devices, and further enhancing the management security and efficiency of the target area. Additionally, the user does not need to participate in the whole process, realizing the non-intrusive recording of the position of the target device and improving the user experience.

[0010] In one embodiment, the determination method of the devices to be associated includes: identifying one or more of the target device, the first device, and the second device that enter the target area through wireless detection technology; and determining one or more of the target device, the first device, and the second device that enter the target area as the devices to be associated.

[0011] In the above implementation process, one or more of the first device and the second device entering the target area are identified through wireless detection technology. When one or more of the first device and the second device entering the target area are identified, the identified first device and / or second device entering the target area are determined as devices to be associated, so as to perform device association, realizing automatic association of devices. The user does not need to operate, improving the user experience.

[0012] In one embodiment, for any two devices to be associated among the devices to be associated, calculating the time correlation and space correlation of the devices to be associated detected in the target area includes: recording the wireless signal sampling sequence of the devices to be associated within a preset time window; determining the time correlation of the corresponding two devices to be associated according to the wireless signal sampling sequence; obtaining the current position coordinates of the devices to be associated; calculating the space distance sequence between the corresponding two devices to be associated according to the current position coordinates; and determining the space correlation of the corresponding two devices to be associated according to the space distance sequence.

[0013] In the above implementation process, by determining the time correlation of two devices to be associated according to the sampling sequence of wireless signals of the devices to be associated within a preset time window, and determining the space correlation of two devices to be associated according to the current position coordinates of the devices to be associated, the time correlation and space correlation of the devices to be associated are further determined, and the first device and / or the second device on the same target device are bound. Furthermore, the position of the target device and the position of the user can be directly determined according to the device information, without the participation of other devices and users, with low cost and good user experience.

[0014] In one embodiment, before planning the movement path according to the current position of the first device and the target position, the method further includes: determining the time difference according to the timestamps of the management frames received by the target device from the reference wireless access point and the management frames of other wireless access points; determining the distance difference between the target device and the reference wireless access point and / or the other wireless access points according to the time difference; and determining the current position of the target device according to the distance difference; where the target device is one or more of the first device and the second device that need to determine the current position.

[0015] In the above implementation process, by converting the time difference into a distance difference and determining the current position of the target device through multiple pairs of hyperbolas, a higher positioning accuracy can be obtained, improving the accuracy of the current position.

[0016] In one embodiment, planning a movement path according to the current position of the first device and the target position includes: discretizing the environment where the target device is located into a grid map; calculating the initial total movement cost of each node in the grid map; determining the node with the minimum initial total movement cost as the current node; determining the new total movement cost of multiple adjacent nodes of the current node; updating the current node through the node with the minimum new total movement cost; continuing to determine the new total movement cost of multiple adjacent nodes of the current node until the current node reaches the target position; generating the movement path according to all the current nodes and the target position during the iteration process.

[0017] In the above implementation process, by evaluating the total cost of nodes, nodes close to the target position can be preferentially expanded, thereby reducing unnecessary search and expansion operations and improving the efficiency of determining the movement path. Additionally, when the heuristic cost value is the shortest path cost from the current node to the target position, the shortest path from the current node to the target position can be determined, shortening the total distance of the movement path and improving the user experience.

[0018] In a second aspect, an embodiment of the present application further provides a target device searching device, including: an acquisition module, configured to acquire a second device associated with the first device when detecting the first device; wherein the first device is a device carried by one or more users, and the second device is one or more devices provided on the target device and / or the target device; a determination module, configured to determine the location where the second device is located as the target position; a planning module, configured to plan a movement path according to the current position of the first device and the target position; wherein the movement path is the path between the current position and the target position.

[0019] In a third aspect, an embodiment of the present application further provides an electronic device, including: a processor and a memory, where the memory stores machine-readable instructions executable by the processor, and when the electronic device runs, the machine-readable instructions, when executed by the processor, execute the steps of the method in the first aspect or any possible implementation manner of the first aspect.

[0020] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is run by a processor, it executes the steps of the target device searching method in the first aspect or any possible implementation manner of the first aspect.

[0021] Fifth aspect, an embodiment of the present application further provides a target device search system, including: a wireless detection device and the electronic device described in the third aspect; the wireless detection device is connected to the electronic device; the wireless detection device is configured to detect one or more of a first device and a second device entering a target area, and transmit the detected detection information to the electronic device; the electronic device is configured to establish an association relationship between the target device and one or more of the first device and the second device according to the detection information, and store it; the electronic device is further configured to search for the target device according to the detection information.

[0022] To make the above objects, features, and advantages of the present application more obvious and understandable, specific embodiments are hereinafter given and described in detail in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] To more clearly illustrate the technical solutions of the embodiments of the present application, the accompanying drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present application and should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0024] Figure 1 A schematic diagram of the interaction between the wireless detection device and the electronic device in the target device search system provided by the embodiment of the present application;

[0025] Figure 2 A block diagram of the electronic device provided by the embodiment of the present application;

[0026] Figure 3 A flowchart of the target device search method provided by the embodiment of the present application;

[0027] Figure 4 A functional module diagram of the target device search device provided by the embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0029] It should be noted that: similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the present application, the terms "first", "second", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0030] In the existing parking lot management system, car owners often need to rely on active memory to remember the parking location of their vehicles. This traditional method not only increases the memory burden on car owners but also may cause difficulties in finding their vehicles in large or multi-level parking lots. In addition, some advanced parking lot management systems require users to pre-install a dedicated APP application to reserve and manage parking spaces. Although this improves efficiency and convenience to a certain extent, it still cannot completely solve the problem of car owners forgetting the parking location of their vehicles.

[0031] When car owners return to the parking lot to pick up their vehicles, they may need to retrieve the information from their memory or find the location of their vehicles in the pre-downloaded application. However, due to the limitations of human memory and various interference factors in daily life, car owners are easily forget the specific parking space number or area, resulting in users getting lost in the parking lot and reducing the overall service quality and user experience of the parking lot.

[0032] In view of this, the present application proposes a method for finding a target device. By associating a first device with a second device, when the first device is acquired, the second device associated with the first device can be directly acquired, and then the target location where the target device is located can be determined. Automatically plan a movement path based on the current location and the target location of the first device. Users no longer need to specifically remember or record the placement location of the target device, nor do they need to install other software on the first device, and can realize the non-intrusive recording of the placement location of the target device, improving the user experience. In addition, since the first device is a device carried by the user, and there can be multiple first devices with the same movement trajectory as the second device, when the first device of any user in the same group is detected, the second device associated with it can be acquired, and then a movement path can be planned. The method for finding the target device can also identify and record the people in the same group, and does not limit that the same user enters and exits the target area, increasing the application scenarios of the method for finding the target device.

[0033] To facilitate the understanding of this embodiment, first, a target device finding system for implementing a method for finding a target device disclosed in the embodiments of the present application will be introduced in detail.

[0034] As Figure 1 shown, it is a schematic diagram of the interaction between various devices in the target device finding system provided by the embodiments of the present application, including: a wireless detection device 200 and an electronic device 100.

[0035] Among them, the wireless detection device 200 is communicatively connected to the electronic device 100 for data communication or interaction.

[0036] Here, the wireless detection device 200 is configured to detect the wireless information of the target area, and the wireless detection device 200 can be used to detect one or more of the first device and the second device entering the target area.

[0037] It should be understood that when one or more of the first device and the second device enter the target area, one or more of the first device and the second device entering the target area enter the wireless detection range of the wireless detection device 200. Furthermore, the wireless detection device 200 can detect the wireless information of one or more of the first device and the second device entering the target area, and generate corresponding identity information according to the wireless information. Wherein, the identity information is used to record the corresponding target device, first device or second device. After one or more of the first device and the second device leave the target area, one or more of the first device and the second device leaving the target area also leave the wireless detection range of the wireless detection device 200, and the wireless detection device 200 can detect the wireless information of one or more of the first device and the second device leaving the target area. At this time, the corresponding identity information can be marked, so that in the case of detecting the corresponding identity information again, the target device can be found according to the identity information. Or, when the leaving time of the identity information exceeds the set time, the stored identity information is eliminated.

[0038] In one embodiment, the wireless detection device 200 is further configured to transmit the detected detection information to the electronic device 100.

[0039] Wherein, the detection information may include wireless information, identity information, entry time, departure time, movement trajectory, etc. detected by the wireless detection device 200. The detection information can be adjusted according to the actual situation.

[0040] Optionally, one or more wireless detection devices 200 may be set in the target area. The wireless detection device 200 may be a WIFI detection device, a Bluetooth detection device, etc., and the number and type of the wireless detection device 200 can be selected according to the actual situation.

[0041] The above-mentioned electronic device 100 is used to establish an association relationship between one or more of the first device and the second device according to the detection information.

[0042] It should be understood that the electronic device 100 can determine whether there is an association relationship between the target device, the first device and the second device according to the obtained detection information. If there is an association relationship, the target device, the first device and the second device with the association relationship are associated. For example, if the target device is a vehicle, the first device is a mobile device carried by a user sitting in the vehicle, and the second device is an in-vehicle device installed on the vehicle, then the first device and the second device associated with the same target device can be determined according to the movement trajectories of these devices.

[0043] In addition, after obtaining the detection information, the electronic device 100 can also store the obtained detection information.

[0044] The electronic device 100 here is also used to find the target device according to the detection information.

[0045] Understandably, when the target device stops at the set position, the user on the target device may carry the first device and leave the target device. In the case where the user enters the target area again, the wireless detection device 200 can detect the wireless information corresponding to the first device again. At this time, the electronic device 100 can determine the second device or the target device associated with the first device according to the stored association relationship, and determine the target position of the user carrying the first device according to the second device or the target device, so as to automatically find the target position.

[0046] Optionally, the electronic device 100 may be a network server, a database server, a personal computer (PC), a tablet computer, a smart phone, a personal digital assistant (PDA), etc. The electronic device 100 can be selected according to the actual situation. Optionally, the first device is a device carried by one or more users. For example, a mobile phone, an earphone, a bracelet, etc. The first device can be selected according to the actual situation.

[0047] Optionally, the second device is one or more devices and / or the target device set on the target device. For example, an in-vehicle computer, an in-vehicle navigator, a vehicle body, etc. The second device can be selected according to the actual situation.

[0048] For the convenience of understanding this embodiment, the electronic device 100 that executes the target device finding method disclosed in the embodiments of the present application will be introduced in detail below.

[0049] As Figure 2 shown, it is a block diagram of the electronic device. The electronic device 100 may include a memory 111 and a processor 113. Those of ordinary skill in the art can understand that Figure 2 the structure shown is only schematic, and it does not limit the structure of the electronic device 100. For example, the electronic device 100 may further include more or fewer components than Figure 2 shown, or have a different configuration from Figure 2 shown.

[0050] The above-mentioned memory 111 and processor 113 are directly or indirectly electrically connected to each other to achieve data transmission or interaction. For example, these elements may be electrically connected to each other through one or more communication buses or signal lines. The above-mentioned processor 113 is used to execute the executable module stored in the memory.

[0051] Among them, the memory 111 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), an Electric Erasable Programmable Read-Only Memory (EEPROM), etc. Among them, the memory 111 is used to store a program, and after receiving an execution instruction, the processor 113 executes the program. The method executed by the electronic device 100 defined by the process disclosed in any embodiment of the present application can be applied to the processor 113 or implemented by the processor 113.

[0052] The above-mentioned processor 113 may be an integrated circuit chip with signal processing capabilities. The above-mentioned processor 113 may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a digital signal processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0053] The electronic device 100 in this embodiment can be used to execute each step in the various methods provided by the embodiments of the present application. The implementation process of the target device search method will be described in detail through several embodiments below.

[0054] Please refer to Figure 3 which is a flowchart of the target device search method provided by the embodiments of the present application. The following will elaborate in detail on the Figure 3 specific process shown.

[0055] Step S201, when a first device is detected, obtain a second device associated with the first device.

[0056] Among them, the first device is a device carried by one or more users. For example, a mobile phone, headphones, a smart bracelet, etc. The first device can be selected according to the actual situation.

[0057] The user here refers to one or more users whose movement trajectories are the same as those of the target device. For example, if the target device is a vehicle, the user can be a user located in the vehicle. For another example, if the target device is goods, the user can be the person carrying the goods.

[0058] It can be understood that if the movement trajectories of the user and the target device are basically the same, it can be basically determined that there is an association between the user and the target device, and then the first device carried by the user can be associated with the second device. After the user enters the target area again, the second device associated with it can be directly determined according to the first device carried by the user, thus realizing the automatic search for the target device without the user having to remember the location of the target device again.

[0059] The above-mentioned second device is one or more devices and / or the target device set on the target device. For example, an in-vehicle computer, a car navigator, a vehicle body, etc. The second device can be selected according to the actual situation.

[0060] It can be understood that since one or more devices set on the target device are usually fixed on the target device, their movement trajectories are basically the same as those of the target device. And after the target device stops, one or more devices set on the target device also stop at the same position. Therefore, the parking position of the target device can be determined according to the position of any one of the one or more devices set on the target device and the target device.

[0061] Optionally, obtaining the second device associated with the first device can be obtaining one second device associated with the first device or obtaining multiple second devices associated with the first device. The number of second devices associated with the first device can be selected according to the actual situation.

[0062] The above-mentioned first device can be detected by a wireless detection device.

[0063] It can be understood that when the user carrying the first device enters the target area, the wireless detection device detects the wireless information corresponding to the first device and determines the identity information of the first device according to the wireless information. Based on the identity information, it is matched with the stored second device to determine whether there is a second device associated with the first device. If there is a second device associated with the first device, the second device associated with the first device is obtained.

[0064] It should be understood that in the actual usage scenario, the number of users with the same movement trajectory as the same target device can be one or more.

[0065] For example, if the target device is a vehicle and there can be multiple users riding in the vehicle, and all of the multiple users carry a first device, then when associating the first device with the second device, the first device carried by each user on the vehicle can be associated with the second device. When any user riding in the vehicle enters the target area again while carrying the first device, the second device associated with the first device can be determined based on the first device, thereby helping the user find the corresponding vehicle. The search method for this target device can also identify and record fellow travelers, and does not limit that the same user enters and exits the target area, so the usage scenario is wider.

[0066] Step S202: Determine the location of the second device as the target location.

[0067] The target location here refers to the location where the target device that the user needs to find is located.

[0068] It should be understood that in the case where the second device associated with the first device is determined, the location information of the second device can be further obtained, and the location of the second device can be determined based on the location information of the second device.

[0069] Since the second device is one or more devices provided on the target device and / or the target device, the location of the second device is the location of the target device. Therefore, it can be determined that the location of the second device is the target location where the target device is located.

[0070] Step S203: Plan a movement path according to the current location of the first device and the target location.

[0071] Among them, the movement path is the path between the current location and the target location.

[0072] In one embodiment, when the first device is detected, the location information of the first device can be further obtained, and the current location of the first device can be determined according to the location information of the first device.

[0073] Optionally, the location information of the first device can be determined by algorithms such as the TDOA algorithm and the signal strength algorithm, or can be obtained by the positioning unit on the first device. The acquisition method of the location information of the first device can be selected according to the actual situation.

[0074] It should be understood that after determining the current location of the first device, a movement path can be planned based on the current location of the first device and the target location of the target device for the user to find the corresponding target device.

[0075] Optionally, the planning of the moving path can be implemented by algorithms such as A* algorithm, Dijkstra algorithm, bidirectional search algorithm, Voronoi Diagram algorithm, ant colony algorithm, etc. The planning of the moving path can be selected according to actual conditions.

[0076] In one embodiment, the moving path may be planned in real time according to the current location of the first device and the target location.

[0077] Exemplarily, the current position of the first device is acquired in each time frame, and a moving path is planned according to the current position of the first device in each time frame, thereby achieving real-time planning of the moving path.

[0078] In the above implementation process, by associating the first device with the second device, when the first device is obtained, the second device associated with the first device can be directly obtained, and then the target position of the target device is determined, and the moving path is automatically planned according to the current position and target position of the first device. The user no longer needs to specifically remember the placement position of the target device, nor does he need to install other software on the first device, so that the placement position of the target device can be recorded without feeling, thereby improving the user experience. In addition, since the first device is a device carried by the user, and there can be multiple first devices with the same motion trajectory as the second device, when the first device of any user in the same group is detected, the second device associated with it can be obtained, and then the moving path can be planned. The method of searching for the target device can also identify and record the companions, and does not limit the same user to enter and exit the target area, which increases the application scenarios of the method of searching for the target device.

[0079] In a possible implementation, before step S201, the method further includes: calculating the time correlation and spatial correlation of the devices to be associated detected in the target area; determining the trajectory similarity of each device to be associated according to the trajectory of the device to be associated; and determining the association relationship of each device to be associated according to the time correlation, spatial correlation and trajectory similarity.

[0080] The device to be associated may be understood as a device that has just entered the target area and has not yet been associated. The device to be associated includes one or more of the first device and the second device.

[0081] The time correlation here refers to the similarity or proximity of the devices to be associated in the time dimension.

[0082] In one embodiment, the time correlation may be calculated using a sliding time window method, wherein the sliding time window method refers to a method for determining the time correlation of a device according to a wireless signal sampling sequence of the device within a set time window.

[0083] Optionally, this time correlation can be used to reflect the correlation of information such as the time when each device to be associated enters the target area, the time when it leaves the target area, and the time when it moves in the target area. The specific information reflected by this time correlation can be adjusted according to the actual situation.

[0084] The above-mentioned spatial correlation is the similarity or proximity presented by the devices to be associated in the spatial dimension.

[0085] In one embodiment, this spatial correlation can be determined by algorithms such as the DOA algorithm and the signal strength algorithm.

[0086] Optionally, this spatial correlation can be used to reflect the correlation of information such as the positions of each device to be associated in each time frame, the stop positions, and the positions where they enter the target area. The specific information reflected by this spatial correlation can be adjusted according to the actual situation.

[0087] The trajectory of the device to be associated here can be determined based on the position information obtained by positioning each device to be associated.

[0088] In one embodiment, the trajectory similarity of each device to be associated can be determined in the following manner: preprocess the trajectory of the device to be associated; calculate the similarity of the preprocessed devices to be associated.

[0089] Among them, preprocessing the trajectory of the device to be associated may include: representing the trajectory of the device to be associated as a time series: TR i ={(t1,x1,y1),(t2,x2,y2),…,(t n ,x n ,y n )}; resample the trajectory to ensure that the sampling point intervals are uniform.

[0090] Optionally, this trajectory resampling can be implemented by the linear interpolation method.

[0091] Calculating the similarity of the preprocessed devices to be associated may include:

[0092] For two trajectories of devices to be associated TR i and TR j , calculate their dynamic time warping distance: DTW(TR i ,TR j ) = min{∑d(w k )};

[0093] Among them, DTW(TR i ,TR j ) is the dynamic time warping distance between the trajectories of the devices to be associated TR i and TR j , and w kis a corresponding point pair, d(w k ) is the Manhattan distance when only considering the four orthogonal directions of up, down, left, and right in the vehicle scenario.

[0094] Here, d(w k ) = |x i - x j | + |y i - y j |. Among them, d(w k ) is the Manhattan distance between point i and point j, (x i , y i ) are the abscissa and ordinate of point i respectively, (x j , y j ) are the abscissa and ordinate of point j respectively.

[0095] Calculate the direction similarity of two device trajectories TR i and TR j through cosine similarity:

[0096]

[0097] Among them, DIR(TR i , TR j ) is the direction similarity of the device trajectories TR i and TR j , θ k is the direction angle between the device trajectories TR i and TR j , and K is the total number of trajectory segments of each trajectory.

[0098] Here, the cosine similarity is used to measure the similarity of direction vectors, and its range can be [-1, 1]. The closer its value is to 1, the more consistent the directions are.

[0099] Calculate the speed similarity of two device trajectories TR i and TR j using the Pearson correlation coefficient:

[0100] VEL(TR i , TR j ) = Pearson(V i , V j );

[0101] Among them, V i and V j are the speed sequences of the device trajectories TR i and TR j respectively.

[0102] The speed sequences here can be obtained in the following way: from the trajectories TR of the devices to be associated i and TR j extract the speed sequences V i and V j :

[0103] V i ={v i1 ,v i2 ,…,v iK};

[0104] V j ={v j1 ,v j2 ,…,v jK};

[0105] where K is the total number of trajectory segments of each trajectory, and v iK and v jK are the speeds of the trajectories TR of the devices to be associated i and TR j at the Kth trajectory segment respectively.

[0106] The mean value of the speed sequence can be calculated by the following formula:

[0107]

[0108] Calculate the covariance:

[0109]

[0110] Calculate the variances of the trajectories of each device to be associated:

[0111]

[0112] Then the Pearson correlation coefficient can be:

[0113]

[0114] where r is the Pearson correlation coefficient, which is used to represent the speed similarity between the trajectories TR i and TR j of the devices to be associated. The value range of this Pearson correlation coefficient is [-1, 1], and the closer its value is to 1, the more consistent the speeds are.

[0115] It can be understood that after determining the temporal correlation, spatial correlation, and trajectory correlation of the device to be associated, the association relationship of the device to be associated can be further determined based on this temporal correlation, spatial correlation, and trajectory correlation.

[0116] The association relationship here can be determined by calculating the comprehensive similarity of each trajectory.

[0117] The comprehensive similarity score can be determined by the following formula:

[0118] SIM(D i ,D j ) = α1·ST + α2·SS + α3·DTW + α4·DIR + α5·VEL;

[0119] Where α1, α2, α3, α4, and α5 are the weight coefficients of temporal correlation, spatial correlation, time warping distance, direction similarity, and speed similarity respectively. ST is the temporal correlation score, SS is the spatial correlation score, DTW is the time warping distance score, DIR is the direction similarity score, and VEL is the speed similarity score.

[0120] The initial values of α1, α2, α3, α4, and α5 here can be set to 0.25, 0.25, 0.20, 0.15, and 0.15 respectively. Since time and space are the most fundamental and important features in target device search and can be used to directly reflect the physical location and time synchronization of the device. Therefore, the configuration of each weight coefficient can also be adjusted according to the specific situation of the target area, and the optimal configuration of each weight coefficient can be set through actual testing. The specific values of α1, α2, α3, α4, and α5 can be adjusted according to the actual situation.

[0121] Determining the association relationship between devices to be associated according to the above comprehensive similarity may include: setting a similarity threshold; determining the association relationship between devices to be associated according to the relationship between the comprehensive similarity score and the similarity threshold.

[0122] In one embodiment, the set similarity threshold can be set to: θ;

[0123] When SIM(D i ,D j ) > θ, it is determined that the devices to be associated D i and D j have an association relationship.

[0124] It can be understood that when multiple devices to be associated all have an association relationship, the multiple devices to be associated with an association relationship can be divided into the same device group. For example, if device A is associated with device B and device B is associated with device C, then device A, device B, and device C can be divided into the same device group.

[0125] Optionally, after determining the association relationship of each device to be associated according to temporal correlation, spatial correlation, and trajectory similarity, the method may further include:

[0126] Update the similarity score between devices. When the similarity score between devices is lower than the similarity threshold for more than a set time interval, the device association is terminated.

[0127] In the above implementation process, by calculating the time correlation, space correlation and trajectory correlation of the devices to be associated, and jointly determining the association relationship of the devices to be associated based on multi-dimensional information such as time correlation, space correlation and trajectory correlation, the accuracy of determining the association relationship can be improved. In addition, since the association relationship is determined based on time correlation, space correlation and trajectory correlation, one or more first devices and second devices on the same target device can be associated, and then the companions can also be identified and recorded, without limiting the same user entering and leaving the target area, and the application scenarios of the target device search method can be increased.

[0128] In a possible implementation, before step S201, the method further includes: tracking device motion information of the target device; when it is determined that the target device is in a stationary state based on the device motion information, tracking associated device motion information of each associated device associated with the target device; and determining a first device and a second device associated with the target device based on the associated device motion information.

[0129] The motion information may include: trajectory information, position information, speed information, acceleration information, etc. The motion information may be selected according to actual conditions.

[0130] In one embodiment, the motion trajectory can be tracked through the following steps: constructing a state space model; initializing the system; state prediction; state update and output processing.

[0131] The state space model can be constructed by using a four-dimensional state vector to represent the motion state of the target device and constructing a system matrix, wherein the state components include position coordinates and velocity components. The system matrix may include a state transfer matrix, a measurement matrix, a process noise covariance matrix, a measurement noise covariance matrix, and an error covariance matrix.

[0132] Here, the state transfer matrix is ​​used to describe the evolution law of the state over time, the measurement matrix is ​​used to map the state space to the observation space, the process noise covariance matrix is ​​used to characterize the system dynamic error, the measurement noise covariance matrix is ​​used to characterize the observation error, and the error covariance matrix is ​​used to describe the uncertainty of state estimation.

[0133] Initializing the system may include the following steps: parameter setting and noise model construction.

[0134] The parameter setting may include setting the time step, initializing the state vector, the state transfer matrix, the measurement matrix and other parameters.

[0135] In one embodiment, the set time step can be set to 0.1 second, and the initial state vector can be a zero vector.

[0136] The state transition matrix can be:

[0137]

[0138] The measurement matrix can be:

[0139]

[0140] where Δt is the set time step, F is the state transition matrix, and H is the measurement matrix.

[0141] The construction of the noise model here can be achieved through the following steps: establishing the process noise covariance matrix and establishing the measurement noise covariance matrix.

[0142] The process noise covariance matrix can be:

[0143]

[0144] The measurement noise covariance matrix can be:

[0145]

[0146] where the process noise covariance matrix is used to characterize the system uncertainty, and the measurement noise covariance matrix is used to describe the characteristics of the observation error.

[0147] In one embodiment, the state estimation error covariance matrix can also be initialized.

[0148] The above state prediction can include the following steps: updating the time and error covariance prediction.

[0149] where updating the time can include predicting the state at the next moment based on the state transition matrix.

[0150] Optionally, the state prediction formula can be:

[0151] x(k|k - 1) = F · x(k - 1|k - 1);

[0152] where F is the state transition matrix and x is the state vector.

[0153] The error covariance prediction here can include: updating the uncertainty of the state estimation.

[0154] Optionally, the covariance prediction formula can be:

[0155] P(k|k - 1) = F · P(k - 1|k - 1) · F ^T + Q;

[0156] Wherein, P is the error covariance matrix and Q is the process noise covariance matrix.

[0157] The state update here can be achieved through the following steps: calculating the Kalman gain; updating the state estimate; updating the error covariance.

[0158] Calculating the Kalman gain includes: calculating the optimal gain based on the prediction error and the measurement error. Wherein, the gain calculation formula can be:

[0159] K = P(k|k - 1)·H ^ T·[H·P(k|k - 1)·H ^ T + R] ^ (-1);

[0160] Wherein, H is the measurement matrix and R is the measurement noise covariance matrix.

[0161] Updating the state estimate includes: correcting the predicted state in combination with the observation data. Wherein, the state update calculation formula can be:

[0162] x(x|k) = x(x|k - 1) + k·[z(k) - H·x(x|k - 1)];

[0163] Wherein, z(k) is the observation vector.

[0164] Updating the error covariance may include: updating the uncertainty evaluation of the state estimate. Wherein, the covariance update formula can be:

[0165] P(k|k) = [I - k·H]·P(k|k - 1);

[0166] Wherein, I is the identity matrix.

[0167] The above output processing can be achieved in the following way: obtaining the estimated structure of the latest position and outputting the trajectory point coordinates after filtering.

[0168] Wherein, by evaluating the error change in the estimation process, the process noise and the measurement noise covariance matrix can be dynamically adjusted.

[0169] Exemplarily, if the observation error increases, the measurement noise covariance matrix is appropriately increased. If the system dynamic changes violently, the process noise covariance matrix is appropriately increased.

[0170] It should be understood that after tracking the movement trajectories of each first device and / or second device through the above method, information such as whether the target device stops and the parking position of the second device can be determined according to the change of the movement trajectory at each moment.

[0171] In one embodiment, to determine which devices are the first devices, when it is determined that the target device has stopped, the first device and the second device associated with the target device can be separated.

[0172] The separation of the first device and the second device here can be achieved according to the motion information of the associated devices. The specific methods can include: extracting motion features; defining feature thresholds; determining device types, and outputting classification results, etc.

[0173] The extraction of the motion features here can be performed by extracting the trajectory sequence of the associated devices, including: calculating the average moving speed of the associated devices within the observation time window, calculating the maximum acceleration value of the associated devices, and determining the direction change frequency of the associated devices.

[0174] The above-mentioned feature thresholds can be defined by key threshold parameters.

[0175] Exemplarily, the maximum speed threshold of the first device can be a first set speed (e.g., 5.0 km / h), the maximum acceleration threshold of the first device can be a set acceleration (e.g., 2.0 m / s²), and the speed range threshold of the second device can be a second set speed range (e.g., 3.0 to 20.0 km / h).

[0176] The device type here can be determined based on the previously extracted motion features.

[0177] Exemplarily, the determination method of the first device can be as follows: If the average moving speed of the associated device is lower than the first set speed and the maximum acceleration value is lower than the set acceleration, then it is determined that the associated device is the first device.

[0178] The determination method of the second device can be as follows: If the average moving speed of the associated device is within the second set speed range, then it is determined that the associated device is the second device.

[0179] If the motion of the associated device does not meet the determination conditions of the above-mentioned first device and second device, then it is determined that the associated device is an unknown device.

[0180] It should be understood that after determining the corresponding device types of each associated device, the corresponding classification results can be output.

[0181] In the above implementation process, the associated devices are classified by analyzing the motion information of each associated device, thereby realizing the automatic separation of the first device and the second device associated with the same target device, improving the recognition ability of the behaviors of different devices, and further enhancing the management security and efficiency of the target area. In addition, the user does not need to participate in the whole process, realizing the non-intrusive recording of the position of the target device and improving the user experience.

[0182] In a possible implementation, the method for determining the device to be associated includes: identifying one or more of the first device and the second device that enter the target area through wireless detection technology; and determining one or more of the first device and the second device that enter the target area as the device to be associated.

[0183] The wireless detection technology here can be WIFI detection technology or Bluetooth detection technology, and the wireless detection technology can be selected according to the actual situation.

[0184] Optionally, the first device and / or the second device in the target area can be identified in real time through wireless detection technology, or the first device and / or the second device in the target area can be identified when triggered by set information (such as when an object appears at the entrance or exit of the target area). The identification method of the first device and / or the second device in the target area can be selected according to the actual situation.

[0185] It should be understood that when one or more of the first device and the second device enter the target area, the wireless detection device can detect the first device and / or the second device that enter the target area. Since these devices have not had time to be associated with other devices when they first enter the target area, one or more of the first device and the second device that enter the target area can be determined as the device to be associated.

[0186] When the electronic device performs device association, the time correlation, spatial correlation, and trajectory correlation between the devices to be associated are determined according to the device movement information of the devices to be associated, and the devices to be associated are associated according to the time correlation, spatial correlation, and trajectory correlation.

[0187] In the above implementation process, one or more of the first device and the second device that enter the target area are identified through wireless detection technology. When one or more of the first device and the second device that enter the target area are identified, the first device and / or the second device that enter the target area and are identified are determined as the devices to be associated for device association, realizing automatic device association without user operation, thereby improving the user experience.

[0188] In a possible implementation, for any two devices to be associated among the devices to be associated, calculating the time correlation and spatial correlation of the devices to be associated detected in the target area includes: recording the wireless signal sampling sequence of the devices to be associated within a preset time window; determining the time correlation of the corresponding two devices to be associated according to the wireless signal sampling sequence; obtaining the current position coordinates of the devices to be associated; calculating the spatial distance sequence between the corresponding two devices to be associated according to the current position coordinates; and determining the spatial correlation of the corresponding two devices to be associated according to the spatial distance sequence.

[0189] The time - line correlation here can be calculated using a sliding time - window method.

[0190] In one embodiment, the set time - window can be set to 30 seconds, and the window - sliding step - length can be set to 5 seconds.

[0191] The wireless signal sampling sequence of the device to be associated within the preset time - window can be implemented in the following way:

[0192] For any two devices D i and D j in the target area, within the time - window [t, t + T]:

[0193] Record the wireless signal sampling sequence of device D i :

[0194] Si = {(ti1, ri1), (ti2, ri2), …, (tin, rin)};

[0195] Record the wireless signal sampling sequence of device D j :

[0196] Sj = {(tj1, rj1), (tj2, rj2), …, (tjn, rjn)};

[0197] where t represents the sampling timestamp, and r represents the signal strength.

[0198] The time correlation here can be calculated by the following formula:

[0199]

[0200] where w(tik, tjl) is the time - weight function, and sim(rik, rjl) is the signal - strength similarity function.

[0201] Here, the time - weight function decays exponentially as the time - difference increases, and the signal - strength similarity function can be the cosine similarity.

[0202] w(tik, tjl)=e -β|tik-tj1| ;

[0203]

[0204] where β is the decay coefficient, and N is the number of effective paired sampling points.

[0205] The current position coordinates of the device to be associated can be determined by algorithms such as the DOA algorithm or the signal - strength algorithm.

[0206] In one embodiment, the current position coordinates of the device to be associated can be expressed as:

[0207] P i (t) = (x i (t), y i (t));

[0208] P j (t) = (x j (t), y j (t));

[0209] Among them, P i (t) is the current position coordinate of device D i , and P j (t) is the current position coordinate of device D j .

[0210] The spatial distance sequence between the devices to be associated here can be determined by the following formula:

[0211]

[0212] The spatial correlation can be determined by the following formula:

[0213] SS(D i , D j ) = e -λ·avg(D(t)) ;

[0214] Among them, λ is the distance attenuation factor, and avg(D(t)) is the average value of all distances within a preset time window.

[0215] It should be understood that for any two devices to be associated, their temporal correlation and spatial correlation can be determined in the above manner.

[0216] In the above implementation process, by determining the temporal correlation of two devices to be associated according to the sampling sequence of the wireless signal of the devices to be associated within a preset time window, and determining the spatial correlation of two devices to be associated according to the current position coordinates of the devices to be associated, the temporal correlation and spatial correlation of the devices to be associated are determined, and the first device and / or the second device on the same target device are bound. Furthermore, the position of the target device and the position of the user can be directly determined according to the device information, without the participation of other devices and users, with low cost and good user experience.

[0217] In a possible implementation manner, before step S203, the method further includes: determining the time difference according to the timestamps of the management frames received by the target device from the reference wireless access point and other wireless access points; determining the distance difference between the target device and the reference wireless access point and / or other wireless access points according to the time difference; determining the current position of the target device according to the distance difference.

[0218] Among them, the target device is one or more of the first device and the second device that need to determine the current location.

[0219] The reference wireless access point here can be any wireless access point in the target area, and the other wireless access points are the other wireless access points in the target area except the reference wireless access point.

[0220] Among them, when calculating the time difference, the time difference of the management frames received by the target device from at least 3 wireless access points can be calculated.

[0221] Optionally, the above reference wireless access point can be 1, and the other wireless access points are at least 2.

[0222] It can be understood that the timestamps of the management frames received by the target device from the reference wireless access point and the timestamps of the management frames received by the target device from the other wireless access points can be obtained respectively, and the time difference of the signal propagating from each wireless access point to the target device can be determined according to the timestamps of the management frames received by the target device from the reference wireless access point and the timestamps of the management frames received by the target device from the other wireless access points.

[0223] The above distance difference can be determined by the following formula:

[0224] r j1 = c·t j1 ;

[0225] Among them, r j1 is the distance difference between the target device and the j-th other wireless access point and the reference access point, t j1 is the time difference between the target device and the j-th other wireless access point and the reference access point, and c is the speed of light.

[0226] The formula for converting the time difference into the distance difference here is:

[0227] d i = c·(t i - t ref );

[0228] Among them, d i is the distance between the target device and the i-th other wireless access point, t i is the time when the target device receives the management frame of the i-th other wireless access point, and t ref is the time when the target device receives the management frame of the reference wireless access point.

[0229] It can be understood that through the above steps, for each other wireless access point, a hyperbola equation centered on the wireless access point and the reference wireless access point can be obtained, representing the possible position of the target device. In a two-dimensional space, the hyperbola equation can be expressed as:

[0230]

[0231] where (x i , y i ) are the coordinates of the i-th other wireless access point, (x ref , y ref ) are the coordinates of the reference wireless access point, and (x, y) is the current position of the target device.

[0232] It should be understood that at least 3 wireless access points can be set. Each wireless access point provides a hyperbola equation, and the current position of the target device can be determined by solving 3 hyperbola equations.

[0233] Optionally, the hyperbola equation can be solved by algorithms such as the least squares method and the gradient descent method, and the solution method of the hyperbola equation can be selected according to the actual situation.

[0234] In one embodiment, each wireless access point in the target area can periodically send management frames.

[0235] In the above implementation process, by converting the time difference into a distance difference and determining the current position of the target device through multiple pairs of hyperbolas, a relatively high positioning accuracy can be obtained, and the accuracy of the current position can be improved.

[0236] In one possible implementation manner, step S203 includes: discretizing the environment where the target device is located into a grid map; calculating the initial total movement cost of each node in the grid map; determining the node with the minimum initial total movement cost as the current node; determining the new total movement cost of multiple adjacent nodes of the current node; updating the current node through the node with the minimum new total movement cost; continuing to determine the new total movement cost of multiple adjacent nodes of the current node until the current node reaches the target position; generating a movement path according to all the current nodes and the target position in the iterative process.

[0237] The grid map here can be a two-dimensional space grid. Among them, the grid map supports the movement of the target device in multiple directions. For example, the four orthogonal directions of up, down, left, and right.

[0238] Each grid cell in the grid map represents a possible position node, and obstacle information can be recorded in the grid map to ensure the effectiveness of the path in the grid map.

[0239] In one embodiment, after discretizing the environment where the target device is located into a grid map, the method further includes: search initialization and node evaluation.

[0240] Among them, the search initialization includes: establishing an open node set, a closed node set, a node association mapping, and assigning a cost value of zero to the initialization starting point.

[0241] The open node set here is used to store the nodes to be evaluated, the closed node set is used to store the evaluated nodes, and the node association mapping is used to record the predecessor nodes of the optimal path.

[0242] The above node evaluation method is as follows: for each node to be evaluated, calculate the following cost values:

[0243] Actual cost value: that is, the actual movement cost from the starting point to the current node;

[0244] Heuristic cost value: the estimated cost from the current node to the target position. It can be determined by the Manhattan distance:

[0245] h(n) = |x 目标 - x n | + |y 目标 - y n |;

[0246] where, (x 目标 , y 目标 ) are the coordinates of the target position point, and (x n , y n ) are the coordinates of the current node.

[0247] Total cost value: the sum of the actual cost value and the heuristic cost value.

[0248] In one embodiment, the actual cost value can be determined according to the number of grids between the starting point and the current node, and the heuristic cost value can also be determined according to the number of grids between the current node and the target position.

[0249] It can be understood that after determining the total cost values of each node in the grid map, the node with the minimum total cost can be determined as the current node, and the new movement total costs of multiple nodes adjacent to the current node (such as the nodes located above, below, left, and right of the current node respectively) can be determined. Then continue to determine the node with the minimum new movement total cost as the new current node, and continue to determine the new movement total costs of multiple nodes adjacent to the new current node, and iterate the above steps until the current position reaches the target position and stop the iteration.

[0250] After the iteration is completed, all the current nodes and the target position during the iteration process are connected to generate a movement path.

[0251] In one embodiment, before calculating the total cost of each node, the method further includes: checking the validity of the node.

[0252] Optionally, the validity check may include: checking whether the adjacent nodes of the node are within the map range, checking whether the node is an obstacle, detecting whether the node is already in the closed set, etc. The validity check can be selected according to the actual situation.

[0253] In the above implementation process, by evaluating the total cost of the nodes, the nodes close to the target position can be preferentially expanded, thereby reducing unnecessary search and expansion operations and improving the efficiency of determining the movement path. In addition, when the heuristic cost value is the shortest path cost from the current node to the target position, the shortest path from the current node to the target position can be determined, shortening the total distance of the movement path and improving the user experience.

[0254] Based on the same inventive concept, an embodiment of the present application also provides a target device searching device corresponding to the target device searching method. Since the principle of solving problems by the device in the embodiment of the present application is similar to that of the foregoing target device searching method embodiment, the implementation of the device in this embodiment can refer to the description in the embodiment of the above method, and the repeated parts will not be elaborated.

[0255] Please refer to Figure 4 , which is a schematic diagram of the functional modules of the target device searching device provided by the embodiment of the present application. Each module in the target device searching device in this embodiment is used to execute each step in the above method embodiment. The target device searching device includes an acquisition module 301, a determination module 302, and a planning module 303; wherein,

[0256] The acquisition module 301 is configured to acquire a second device associated with the first device when the first device is detected; wherein, the first device is a device carried by one or more users, and the second device is one or more devices provided on the target device and / or the target device.

[0257] The determination module 302 is configured to determine the location of the second device as the target location.

[0258] The planning module 303 is configured to plan a movement path according to the current location of the first device and the target location; wherein, the movement path is the path between the current location and the target location.

[0259] In a possible implementation manner, the target device searching device further includes: a calculation module, configured to calculate the time correlation and spatial correlation of the devices to be associated detected in the target area; wherein, the devices to be associated include one or more of the first device and the second device; determine the trajectory similarity of each of the devices to be associated according to the trajectories of the devices to be associated; and determine the association relationship of each of the devices to be associated according to the time correlation, the spatial correlation, and the trajectory similarity.

[0260] In a possible implementation, the target device searching device further includes: a tracking module, configured to track the device movement information of the target device; when it is determined according to the device movement information that the target device is in a stationary state, track the associated device movement information of each associated device associated with the target device; and determine the first device and the second device associated with the target device according to the associated device movement information.

[0261] In a possible implementation, the determining module 302 is further configured to: identify one or more of the target device, the first device, and the second device that enter the target area through wireless detection technology; and determine that one or more of the target device, the first device, and the second device that enter the target area are the devices to be associated.

[0262] In a possible implementation, the calculation module is further configured to: record the wireless signal sampling sequence of the device to be associated within a preset time window; determine the time correlation between two corresponding devices to be associated according to the wireless signal sampling sequence; obtain the current position coordinates of the device to be associated; calculate the spatial distance sequence between two corresponding devices to be associated according to the current position coordinates; and determine the spatial correlation between two corresponding devices to be associated according to the spatial distance sequence.

[0263] In a possible implementation, the determining module 302 is specifically configured to: determine the time difference according to the timestamps of the management frames received by the target device from the reference wireless access point and other wireless access points; determine the distance difference between the target device and the reference wireless access point and / or the other wireless access points according to the time difference; and determine the current position of the target device according to the distance difference, where the target device is one or more of the first device and the second device for which the current position needs to be determined.

[0264] In a possible implementation, the planning module 303 is further configured to: discretize the environment where the target device is located into a grid map; calculate the initial total movement cost of each node in the grid map; determine the node with the minimum initial total movement cost as the current node; determine the new total movement cost of multiple adjacent nodes of the current node; update the current node through the node with the minimum new total movement cost; continue to determine the new total movement cost of multiple adjacent nodes of the current node until the current node reaches the target position; and generate the movement path according to all the current nodes and the target position in the iterative process.

[0265] In addition, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, it executes the steps of the target device search method described in the above method embodiment.

[0266] A computer program product for the target device search method provided by an embodiment of the present application includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the steps of the target device search method described in the above method embodiment. For details, please refer to the above method embodiment and will not be elaborated here.

[0267] In several embodiments provided by the present application, it should be understood that the disclosed device and method can also be implemented in other ways. The device embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions, and operations of devices, methods, and computer program products according to multiple embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the module, program segment, or part of code includes one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order from that marked in the accompanying drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0268] In addition, in each embodiment of the present application, the various functional modules may be integrated together to form an independent part, or each module may exist alone, or two or more modules may be integrated to form an independent part.

[0269] When the above-mentioned functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes. It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprise", "include" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, the elements defined by the statement "including..." do not exclude the existence of additional identical elements in the process, method, article or device including the said elements.

[0270] The above are only the preferred embodiments of this application and are not used to limit this application. For those skilled in the art, this application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this application shall be included within the protection scope of this application. It should be noted that similar reference numerals and letters indicate similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0271] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of changes or replacements, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application shall be subject to the protection scope of the claims.

Claims

1. A method for finding a target device, characterized in that: include: When a first device is detected, a second device associated with the first device is acquired; wherein the first device is a device carried by one or more users, and the second device is one or more devices set on a target device and / or the target device; Determining the location of the second device as a target location; Planning a moving path according to the current location of the first device and the target location; The moving path is a path from the current position to the target position.

2. The method according to claim 1, characterized in that Before acquiring the second device associated with the first device when the first device is detected, the method further includes: Calculating the time correlation and spatial correlation of the devices to be associated detected in the target area; wherein the devices to be associated include one or more of the first device and the second device; Determining the trajectory similarity of each of the devices to be associated according to the device trajectories to be associated of the devices to be associated; The association relationship of each of the to-be-associated devices is determined according to the time correlation, the space correlation and the trajectory similarity.

3. The method according to claim 2, characterized in that Before acquiring the second device associated with the first device when the first device is detected, the method further includes: Tracking device motion information of the target device; In a case where it is determined according to the device motion information that the target device is in a stationary state, tracking associated device motion information of each associated device associated with the target device; The first device and the second device associated with the target apparatus are determined according to the associated device motion information.

4. The method according to claim 2, characterized in that: in, The method for determining the device to be associated includes: identifying one or more of the target device, the first apparatus, and the second apparatus entering the target area through wireless detection technology; It is determined that one or more of the target device, the first device, and the second device entering the target area is the device to be associated.

5. The method according to claim 2, characterized in that: For any two devices to be associated among the devices to be associated, calculating the time correlation and spatial correlation of the devices to be associated detected in the target area includes: Recording a wireless signal sampling sequence of the device to be associated within a preset time window; Determining the time correlation of the two devices to be associated according to the wireless signal sampling sequence; Obtaining the current location coordinates of the device to be associated; Calculating a spatial distance sequence between two corresponding devices to be associated according to the current position coordinates; The spatial correlation between the two devices to be associated is determined according to the spatial distance sequence.

6. The method according to any one of claims 1 to 5, characterized in that: Before planning the moving path according to the current position of the first device and the target position, the method further includes: Determine a time difference based on timestamps of management frames of the reference wireless access point and other wireless access points received by the target device; Determine, according to the time difference, a distance difference between the target device and the reference wireless access point and / or the other wireless access points; Determining a current location of the target device according to the distance difference; The target device is one or more of the first device and the second device for which the current position needs to be determined.

7. The method according to any one of claims 1 to 5, characterized in that: The planning of a moving path according to the current position of the first device and the target position includes: Discretize the environment where the target device is located into a grid map; Calculating the total initial movement cost of each node in the grid map; Determine the node with the smallest total cost of the initial movement as the current node; Determine a new total cost of movement of multiple nodes adjacent to the current node; Update the current node by the node with the smallest total cost of new movement; Continue to determine the new total movement cost of multiple nodes adjacent to the current node until the current node is at the target location; The moving path is generated according to all the current nodes and the target positions in the iterative process.

8. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the machine-readable instructions are executed by the processor to perform the steps of any method described in claims 1 to 7.

9. A target device search system, characterized in that: include: A wireless detection device and the electronic device according to claim 8; the wireless detection device is connected to the electronic device; The wireless detection device is used to detect one or more of the first device and the second device entering the target area, and transmit the detected detection information to the electronic device; The electronic device is used to establish an association relationship between one or more of the target device, the first device and the second device according to the detection information, and store the association relationship; The electronic device is further used to find the target device according to the detection information.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are executed.

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