A path statistics method and system based on intelligent wearable devices

Abstract

The present invention relates to the technical field of path statistics, and specifically discloses a path statistics method based on a smart wearable device. The method includes updating user points in a map according to the position information and the motion information to obtain maps containing user points at different times, and constructing a map group; splitting the map group according to a preset time period to obtain sub-map groups, querying the user points corresponding to each user in the sub-map groups, converting them into three-dimensional coordinates, and fitting the spatio-temporal path of the user based on the three-dimensional coordinates; comparing the spatio-temporal paths of all users in different time periods to determine the degree of abnormality of each time period. Based on the smart wearable device, the present invention obtains the position of the user, obtains a map at each moment, obtains the coordinates of the same user in the map at each moment and connects them in series, fits out the path, obtains a path set, and compares and identifies the path set, so as to determine whether there is a group abnormal phenomenon from a macroscopic perspective.

Description

Technical Field

[0001] The present invention relates to the technical field of path statistics, and specifically to a path statistics method and system based on intelligent wearable devices. Background Art

[0002] In some special occasions, it is necessary to manage personnel, such as in nursing homes; on the basis of user authorization, intelligent wearable devices are equipped for users to obtain their location information and movement information, and then manage and assist users. In more extreme cases, such as in places like psychiatric hospitals, there are potential safety hazards for the personnel, and management is even more necessary. Intelligent wearable devices are common management facilities.

[0003] Through intelligent wearable devices, the path information of personnel in the process of life can be obtained. Existing management rules are mostly from a micro perspective, that is, individual management of each person. When a person exceeds the scope or has other abnormal movements, a prompt message will be sent; at the macro level, that is, from the perspective of the group, there is no risk determination scheme. Providing a macro-level group path recognition scheme is the technical problem to be solved by the technical solution of the present invention. Summary of the Invention

[0004] The purpose of the present invention is to provide a path statistics method and system based on intelligent wearable devices to solve the problems raised in the above background art.

[0005] To achieve the above purpose, the present invention provides the following technical solutions:

[0006] A path statistics method based on intelligent wearable devices, the method includes:

[0007] Receiving the location sharing permission granted by the user, and regularly obtaining the location information and movement information based on the location sharing permission;

[0008] Updating the user points in the map according to the location information and the movement information to obtain maps containing user points at different times, and constructing a map group;

[0009] Splitting the map group according to a preset time period to obtain sub-map groups, querying the user points corresponding to each user in the sub-map groups, converting them into three-dimensional coordinates, and fitting the spatio-temporal paths of the users based on the three-dimensional coordinates;

[0010] Comparing the spatio-temporal paths of all users in different time periods to determine the abnormal degree of each time period.

[0011] As a further scheme of the present invention: the step of receiving the location sharing permission granted by the user and regularly obtaining the location information and movement information includes:

[0012] Receive the location sharing permission granted by the user;

[0013] Obtain the user's location in real time based on the location sharing permission;

[0014] Obtain the number of steps according to the user's vibration information, and determine the movement distance within a unit time based on the number of steps as the movement speed;

[0015] Obtain the change amount of the movement speed within a unit time as the acceleration value;

[0016] Adjust the acquisition frequencies of the location and the number of steps according to the movement speed and the acceleration value; the acquisition frequency is proportional to the movement speed and is proportional to the acceleration value.

[0017] As a further solution of the present invention: the step of updating the user point in the map according to the location information and the movement information to obtain a map containing the user point at different times and constructing a map group includes:

[0018] Copy the map containing the user point at the previous moment every preset duration;

[0019] Read the location of the user obtained, and update the user point in the copied map according to the location;

[0020] Overlay the maps on the time axis to obtain a map group; wherein, the maps in the map group are sorted according to the time sequence.

[0021] As a further solution of the present invention: the step of splitting the map group according to a preset time period to obtain a sub-map group, querying the user points corresponding to each user in the sub-map group, converting them into three-dimensional coordinates, and fitting the spatio-temporal path of the user based on the three-dimensional coordinates includes:

[0022] Split the map group according to a preset time period to obtain a sub-map group;

[0023] Query the user labels of the user points in each sub-map in the sub-map group, and classify the user points according to the user labels;

[0024] For the same type of user points, read the coordinates and the moment of the user points to construct three-dimensional coordinates; the moment is the relative moment based on the time period;

[0025] Fit the spatio-temporal path of the user based on the three-dimensional coordinates.

[0026] As a further solution of the present invention: the step of comparing the spatio-temporal paths of all users in different time periods and determining the abnormal degree of each time period includes:

[0027] Count the spatio-temporal paths of all users in different time periods;

[0028] Select path points on all spatio-temporal paths according to a preset granularity;

[0029] Taking a preset unified origin as the starting point and the path points as the ending points, a vector cluster is obtained;

[0030] Compare the vector clusters corresponding to different time periods and determine the abnormality degree of each time period.

[0031] As a further solution of the present invention: the content of comparing the vector clusters corresponding to different time periods and determining the abnormality degree of each time period includes:

[0032] ; where is the abnormality degree of a certain time period, is the th vector in the vector cluster corresponding to this time period, is the total number of vectors in the vector cluster corresponding to this time period; is the th vector in the vector cluster corresponding to the th time period, is the th time period corresponding to the total number of vectors in the vector cluster; is and similarity, represents obtaining and the th time period corresponding to the maximum value of the similarity of all vectors in the vector cluster; is the total number of time periods to be determined; is an exponential function.

[0033] The technical solution of the present invention also provides a path statistics system based on an intelligent wearable device, and the system includes:

[0034] An information acquisition module, configured to receive the location sharing permission granted by the user and regularly acquire location information and motion information based on the location sharing permission;

[0035] A map group construction module, configured to update user points in the map according to the location information and the motion information, obtain maps containing user points at different times, and construct a map group;

[0036] A spatio-temporal path fitting module, configured to split the map group according to a preset time period to obtain a sub-map group, query the user points corresponding to each user in the sub-map group, convert them into three-dimensional coordinates, and fit the spatio-temporal path of the user based on the three-dimensional coordinates;

[0037] Anomaly recognition module, which is used to compare the spatio-temporal paths of all users in different time periods and determine the degree of anomaly in each time period.

[0038] As a further solution of the present invention: The information acquisition module includes:

[0039] Permission receiving unit, which is used to receive the location sharing permission granted by the user;

[0040] Location acquisition unit, which is used to acquire the location of the user in real time based on the location sharing permission;

[0041] Speed calculation unit, which is used to obtain the number of steps according to the vibration information of the user, and determine the movement distance within a unit time according to the number of steps as the movement rate;

[0042] Acceleration value calculation unit, which is used to obtain the change amount of the movement rate within a unit time as the acceleration value;

[0043] Frequency adjustment unit, which is used to adjust the acquisition frequency of the location and the number of steps according to the movement rate and the acceleration value; the acquisition frequency is directly proportional to the movement speed, and the acquisition frequency is directly proportional to the acceleration value.

[0044] As a further solution of the present invention: The map group construction module includes:

[0045] Map copy unit, which is used to copy the map containing user points at the previous moment every preset time period;

[0046] User point update unit, which is used to read the location of the acquired user and update the user points in the copied map according to the location;

[0047] Map overlay unit, which is used to overlay the maps on the time axis to obtain a map group; wherein, the maps in the map group are sorted according to the time sequence.

[0048] As a further solution of the present invention: The spatio-temporal path fitting module includes:

[0049] Map group splitting unit, which is used to split the map group according to a preset time period to obtain a sub-map group;

[0050] User point classification unit, which is used to query the user labels of the user points in each sub-map in the sub-map group and classify the user points according to the user labels;

[0051] Three-dimensional coordinate generation unit, which is used to read the coordinates and the moment of the user points for the same type of user points and construct three-dimensional coordinates; the moment is the relative moment based on the time period;

[0052] Fitting execution unit, which is used to fit the spatio-temporal path of the user based on the three-dimensional coordinates.

[0053] Compared with the prior art, the beneficial effects of the present invention are as follows: Based on the intelligent wearable device, the present invention obtains the user's location, obtains a map for each moment, obtains the coordinates of the same user in the map of each moment and connects them in series, and then fits out a path to obtain a path set as a group feature. By comparing and identifying the path set, it is possible to determine whether a group abnormal phenomenon occurs from a macroscopic perspective. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0055] Figure 1 It is a flowchart of a path statistics method based on an intelligent wearable device.

[0056] Figure 2 It is a structural block diagram of a path statistics system based on an intelligent wearable device. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0057] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the following further details the present invention in conjunction with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0058] Figure 1 It is a general flowchart of a path statistics method based on an intelligent wearable device. In an embodiment of the present invention, a path statistics method based on an intelligent wearable device includes:

[0059] Step S100: Receive the location sharing permission granted by the user, and regularly obtain the location information and motion information based on the location sharing permission;

[0060] Receive the location sharing permission granted by the user, and regularly obtain the location information and motion information based on the location sharing permission; this process occurs in the intelligent wearable device equipped on the user. The user grants the permission through the intelligent wearable device, and the execution entity of this method obtains the location information and motion information of the user in real time through the intelligent wearable device; the location information is the location of the user, and the motion information is the vibration signal generated by the user during the movement. It should be noted that the location information and motion information are obtained regularly, and there is an acquisition frequency, that is, the data upload frequency of the intelligent wearable device.

[0061] Step S200: Update the user points in the map according to the location information and the motion information to obtain maps containing user points at different times, and construct a map group;

[0062] By analyzing the location information and motion information, the user point can be determined on the map. Each time the location information and motion information are obtained, the user point is synchronously updated on the map. By counting the maps containing the user point obtained at each moment (sorted by time), a map set can be obtained, which is called a map group.

[0063] Step S300: Split the map group according to a preset time period to obtain sub-map groups. Query the user points corresponding to each user in the sub-map groups, convert them into three-dimensional coordinates, and fit the spatio-temporal path of the user based on the three-dimensional coordinates.

[0064] Split the map group according to a preset time period. The time period can be one day, half a day, or even shorter. According to the preset time period, the map group can be split into multiple small map groups, which are called sub-map groups. In the sub-map groups, the user points of the same user are classified into one category. Read the coordinates and the corresponding moment of the map to obtain a three-dimensional coordinate, which includes spatial position and time information. Fit the three-dimensional coordinate, and the obtained path is called the spatio-temporal path. The spatio-temporal path can continuously represent the various spatial positions of the user within a time period.

[0065] Step S400: Compare the spatio-temporal paths of all users in different time periods and determine the degree of abnormality of each time period.

[0066] By comparing the spatio-temporal paths of all users in different time periods, the degree of abnormality of each time period can be judged. The more similar the spatio-temporal path is to those in other time periods, the more common the corresponding time period is and the lower the degree of abnormality. The actual function of this comparison process is mainly to perform abnormality assessment on the latest time period to determine whether there is an abnormality in the latest user motion state.

[0067] Regarding the application scenarios of the technical solution of the present invention, examples need to be given. The application scenarios of this application mainly include areas where real-time supervision of personnel is required, such as a nursing home. Let the elderly wear smart wearable devices. The management side can discover the problems of the elderly in the first time. If the elderly refuse to share their locations, it is of course also feasible.

[0068] As a preferred embodiment of the technical solution of the present invention, the steps of receiving the location sharing permission granted by the user and periodically obtaining the location information and motion information based on the location sharing permission include:

[0069] Receive the location sharing permission granted by the user;

[0070] Obtain the location of the user in real time based on the location sharing permission;

[0071] Obtain the number of steps according to the user's vibration information, and determine the movement distance within a unit time based on the number of steps as the movement speed;

[0072] Obtain the change amount of the movement speed within a unit time as the acceleration value;

[0073] Adjust the acquisition frequencies of the position and the number of steps according to the movement speed and the acceleration value; the acquisition frequencies are proportional to the movement speed and are proportional to the acceleration value.

[0074] The above content specifically defines the acquisition process of the position information and the movement information. On the basis of receiving the position sharing permission granted by the user, the process of obtaining the position information and the movement information is very simple and is the prior art; the key point of the above content is that the acquisition process of the user's position and the acquisition process of the movement information are separated. The acquisition process of the user's position is obtained through the positioning module installed in the smart wearable device, which is not complicated. In the prior art solutions, the position can be differentiated multiple times to obtain the movement speed and the acceleration, and the obtained is a vector, which is separated from the actual situation. The solution adopted in this application is to calculate the movement speed and the acceleration value according to the number of steps, which is equivalent to calculating the movement speed and the acceleration value according to the distance traveled, and the obtained is a scalar, which is more in line with the actual situation.

[0075] Adjust the acquisition frequencies of the position and the number of steps according to the movement speed and the acceleration value. The smaller the acceleration value, the smoother the user's movement process. At this time, reduce the acquisition frequency to reduce the transmission energy consumption and thus improve the battery life; the lower the movement speed, the smoother the user's movement process. At this time, reduce the acquisition frequency to improve the battery life.

[0076] As a preferred embodiment of the technical solution of the present invention, the step of updating the user point in the map according to the position information and the movement information to obtain the map containing the user point at different times and constructing a map group includes:

[0077] Copy the map containing the user point at the previous moment every preset time period;

[0078] Read the position of the user obtained, and update the user point in the copied map according to the position;

[0079] Overlay the maps on the time axis to obtain a map group; wherein, the maps in the map group are sorted according to the time sequence.

[0080] The above content specifically defines the construction process of the map group. Since the positions of each user at different times are different, which means that the user points in the map are all moving points and need to be updated in real time. Therefore, the above content actually provides a real-time update solution.

[0081] At every preset time interval, copy the map containing the user points at the previous moment. This time interval is different from the data acquisition frequency of each smart wearable device. For example, this time interval may be one second, while the data acquisition frequency of the smart wearable devices may be once every two seconds for some and once every three seconds for others. This will result in some discrepancies. However, for the update scheme provided in this application, such discrepancies do not matter. At every preset time interval, copy the map containing the user points at the previous moment. If the user's position has changed within this time interval, update the user points according to the user's position to obtain a map; it should be noted that each update is performed on the previous map.

[0082] Finally, arrange the maps in order on the time axis to obtain a map group.

[0083] As a preferred embodiment of the technical solution of the present invention, the steps of splitting the map group according to a preset time period to obtain sub-map groups, querying the user points corresponding to each user in the sub-map groups, converting them into three-dimensional coordinates, and fitting the spatio-temporal path of the user based on the three-dimensional coordinates include:

[0084] Split the map group according to a preset time period to obtain sub-map groups;

[0085] Query the user labels of the user points in each sub-map in the sub-map groups, and classify the user points according to the user labels;

[0086] For the same type of user points, read the coordinates and the corresponding moments of the user points to construct three-dimensional coordinates; the moment is the relative moment based on the time period;

[0087] Fit the spatio-temporal path of the user based on the three-dimensional coordinates.

[0088] In the above content, a specific spatio-temporal path fitting scheme is provided. From the foregoing content, it can be known that the map group is a map arranged in chronological order. Splitting the map group according to a preset time period can obtain subsets of the map group, which are called sub-map groups.

[0089] Analyzing each sub-map group is to analyze each time period. Query the user labels of the user points in each sub-map in the sub-map groups, classify the user points according to the user labels, and for the same type of user points, read the coordinates and the corresponding moments of the user points to construct three-dimensional coordinates ; fitting the three-dimensional coordinates can obtain the spatio-temporal path; the process of fitting the three-dimensional coordinates can adopt existing technologies, such as the fitting process of Bezier curves; actually, the broken line obtained by connecting multiple coordinates can also be used as a kind of spatio-temporal path, but the broken line itself is a piecewise function and is not easy to analyze. After being fitted into a curve, the path can be represented by a function expression.

[0090] As a preferred embodiment of the technical solution of the present invention, the step of comparing the spatio-temporal paths of all users in different time periods and determining the degree of abnormality of each time period includes:

[0091] Statistical spatio-temporal paths of all users in different time periods;

[0092] Select path points on all spatio-temporal paths according to a preset granularity;

[0093] Taking a preset unified origin as the starting point and the path points as the ending points, a vector cluster is obtained;

[0094] Compare the vector clusters corresponding to different time periods and determine the degree of abnormality of each time period.

[0095] The above provides a specific spatio-temporal path comparison process. Statistical spatio-temporal paths of all users in different time periods, and the comparison process is a comparison process of multiple paths, which is actually a difficult thing; the technical solution of the present invention provides a simplified solution. Select path points on all spatio-temporal paths according to a preset granularity. Taking a preset unified origin as the starting point and the path points as the ending points, a vector cluster determined by multiple spatio-temporal paths can be obtained, and each path point corresponds to a vector; at this time, the comparison process of multiple paths is converted into a comparison process of vector clusters, simplifying the calculation process.

[0096] It is worth mentioning that the time path itself is composed of multiple user points fitted together. Adjacent user points correspond to adjacent maps, and the distance is the adjacent time duration; if the preset granularity is determined based on the time duration of adjacent maps, then the selected path points are the user points in each map. However, in actual applications, the path points are completely selected based on a higher density (selecting more points) or a lower density (selecting fewer points). The higher the density, the higher the accuracy of the comparison result, but the more resource consumption and the longer the time-consuming. The lower the density, the lower the accuracy of the comparison result, but the less resource consumption and the shorter the time-consuming; this also provides a broader free selection space for users.

[0097] Specifically, the content of comparing the vector clusters corresponding to different time periods and determining the degree of abnormality of each time period includes:

[0098] ; where is the degree of abnormality of a certain time period, is the th vector in the vector cluster corresponding to this time period, is the total number of vectors in the vector cluster corresponding to this time period; is the th vector in the vector cluster corresponding to the a vector, is the total number of vectors in the vector cluster corresponding to the is and similarity, denotes obtaining and the maximum similarity among the similarities of all vectors in the vector cluster corresponding to the total number of time periods to be determined; is an exponential function.

[0099] The explanation of the above content is as follows:

[0100] For a certain time period, select a time period to be compared, obtain the vector clusters corresponding to the two time periods, and calculate the similarity between the two vector clusters; the calculation process is as follows:

[0101] Select vectors from a certain vector cluster in sequence, calculate its similarity with each vector in the other vector cluster, and take the maximum similarity as the similarity between the selected vector and the other vector cluster; perform this operation on all vectors in the certain vector cluster, obtain the similarity between each vector and the other vector cluster, and calculate the average value as the similarity between the two vector clusters.

[0102] For the vector cluster corresponding to a certain time period, count its similarity with the vector clusters corresponding to all other time periods, and calculate the average value again to obtain its average similarity with other time periods; based on the average similarity, introduce a decreasing function, and the function value obtained can be used as the degree of abnormality.

[0103] Figure 2 is the structural block diagram of the path statistics system based on the intelligent wearable device. In a preferred embodiment of the technical solution of the present invention, a path statistics system based on the intelligent wearable device is further provided. The system 10 includes:

[0104] An information acquisition module 11, configured to receive the location sharing permission granted by the user, and regularly acquire location information and motion information based on the location sharing permission;

[0105] A map group construction module 12, configured to update user points in the map according to the location information and the motion information, obtain maps containing user points at different times, and construct a map group;

[0106] A spatio-temporal path fitting module 13, configured to split the map group according to a preset time period to obtain sub-map groups, query the user points corresponding to each user in the sub-map groups, convert them into three-dimensional coordinates, and fit the spatio-temporal path of the user based on the three-dimensional coordinates;

[0107] Anomaly recognition module 14 is used to compare the spatio-temporal paths of all users in different time periods and determine the degree of anomaly in each time period.

[0108] Further, the information acquisition module 11 includes:

[0109] Permission receiving unit, which is used to receive the location sharing permission granted by the user;

[0110] Location acquisition unit, which is used to acquire the location of the user in real time based on the location sharing permission;

[0111] Speed calculation unit, which is used to obtain the number of steps according to the vibration information of the user, and determine the moving distance within a unit time based on the number of steps as the movement rate;

[0112] Acceleration value calculation unit, which is used to obtain the change amount of the movement rate within a unit time as the acceleration value;

[0113] Frequency adjustment unit, which is used to adjust the acquisition frequency of the location and the number of steps according to the movement rate and the acceleration value; the acquisition frequency is proportional to the movement speed, and the acquisition frequency is proportional to the acceleration value.

[0114] Specifically, the map group construction module 12 includes:

[0115] Map copying unit, which is used to copy the map containing user points at the previous moment every preset duration;

[0116] User point update unit, which is used to read the acquired location of the user and update the user points in the copied map according to the location;

[0117] Map overlay unit, which is used to overlay maps on the time axis to obtain a map group; among them, the maps in the map group are sorted according to the time sequence.

[0118] Furthermore, the spatio-temporal path fitting module 13 includes:

[0119] Map group splitting unit, which is used to split the map group according to a preset time period to obtain a sub-map group;

[0120] User point classification unit, which is used to query the user tags of the user points in each sub-map in the sub-map group and classify the user points according to the user tags;

[0121] Three-dimensional coordinate generation unit, which is used to read the coordinates and the moment of the user points for the same type of user points and construct three-dimensional coordinates; the moment is the relative moment based on the time period;

[0122] Fitting execution unit, which is used to fit the spatio-temporal path of the user based on the three-dimensional coordinates.

[0123] All functions that can be achieved by the path statistics method based on the smart wearable device are completed by a computer device, which includes one or more processors and one or more memories. At least one program code is stored in the one or more memories, and the program code is loaded and executed by the one or more processors to implement the path statistics method based on the smart wearable device.

[0124] The processor fetches instructions one by one from the memory, analyzes the instructions, and then completes corresponding operations according to the requirements of the instructions, generating a series of control commands to make all parts of the computer operate automatically, continuously and coordinately, becoming an organic whole, and realizing the input of the program, the input of data, and the operation and output of results. All arithmetic operations or logical operations generated in this process are completed by the arithmetic unit; the memory includes a read-only memory (ROM), which is used to store computer programs, and a protection system is provided outside the memory.

[0125] Exemplarily, a computer program can be divided into one or more modules, and one or more modules are stored in the memory and executed by the processor to complete the present invention. One or more modules can be a series of computer program instruction segments that can complete specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device.

[0126] Those skilled in the art can understand that the above description of the service device is only an example and does not constitute a limitation on the terminal device. It may include more or fewer components than the above description, or combine some components, or different components. For example, it may include input / output devices, network access devices, buses, etc.

[0127] The so-called processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The above processor is the control center of the above terminal device, and uses various interfaces and lines to connect all parts of the entire staff terminal.

[0128] The above-mentioned memory can be used to store computer programs and / or modules. By running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory, the above-mentioned processor realizes various functions of the terminal device. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as the function of displaying information collection templates, the function of publishing product information, etc.); the data storage area can store data created according to the use of the berth status display system (such as product information collection templates corresponding to different product types, product information that different product providers need to publish, etc.). In addition, the memory can include high-speed random access memory, and can also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0129] If the modules / units integrated in the terminal device are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable medium. Based on such an understanding, to implement all or part of the modules / units in the above-mentioned embodiment system of the present invention, it can also be completed by instructing relevant hardware through a computer program. The above-mentioned computer program can be stored in a computer-readable medium. When the computer program is executed by a processor, it can realize the functions of the above-mentioned various system embodiments. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or system that can carry computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disc, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0130] It should be noted that in this article, the term "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or system 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 a process, method, article or system. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of another identical element in the process, method, article or system including the element.

[0131] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be similarly included in the patent protection scope of the present invention.

Claims

1. A path statistics method based on a smart wearable device, characterized in that, The method includes: Receiving the location sharing permission granted by the user, and periodically obtaining location information and motion information based on the location sharing permission; Updating the user point in the map according to the location information and the motion information, obtaining maps containing user points at different times, and constructing a map group; Splitting the map group according to a preset time period to obtain sub-map groups, querying the user points corresponding to each user in the sub-map groups, converting them into three-dimensional coordinates, and fitting the spatio-temporal path of the user based on the three-dimensional coordinates; Counting the spatio-temporal paths of all users in different time periods; Selecting path points on all spatio-temporal paths according to a preset granularity; Taking a preset unified origin as the starting point and the path points as the ending points to obtain a vector cluster; Comparing the vector clusters corresponding to different time periods to determine the degree of abnormality of each time period; The content of comparing the vector clusters corresponding to different time periods to determine the degree of abnormality of each time period includes: ; In the formula, is the degree of abnormality in a certain time period, is the th vector in the vector cluster corresponding to this time period, is the total number of vectors in the vector cluster corresponding to this time period; is the th vector in the vector cluster corresponding to the th time period, is the th time period, and the total number of vectors in the vector cluster corresponding to this time period; is and similarity, represents finding the maximum value of the similarities between and all vectors in the vector cluster corresponding to the th time period; is the total number of time periods to be determined; is an exponential function; The acquisition frequency is proportional to the motion speed, and the acquisition frequency is proportional to the acceleration value; The creation process of the map group includes: copying the map containing the user point at the previous moment every preset time duration. If the user's location has changed during this time duration, update the user point according to the user's location to obtain a map, and arrange the maps in order on the time axis to obtain the map group; The fitting process of the spatio-temporal path includes: classifying the user points according to the user label. For the same-class user points, read the coordinates and the time of the user points to construct three-dimensional coordinates (x, y, t); fitting the three-dimensional coordinates can obtain the spatio-temporal path.

2. The path statistics method based on the smart wearable device according to claim 1, wherein The steps of receiving the location sharing permission granted by the user and periodically obtaining location information and motion information based on the location sharing permission include: Receiving the location sharing permission granted by the user; Obtaining the user's location in real time based on the location sharing permission; Obtaining the number of steps according to the user's vibration information, and determining the motion distance per unit time as the motion speed; Obtaining the change amount of the motion speed per unit time as the acceleration value; Adjusting the acquisition frequency of the location and the number of steps according to the motion speed and the acceleration value.

3. The path statistics method based on the smart wearable device according to claim 2, wherein, The steps of updating the user point in the map according to the location information and the motion information, obtaining maps containing user points at different times, and constructing a map group include: Copying the map containing the user point at the previous moment every preset time duration; Reading the obtained user's location and updating the user point in the copied map according to the location; Stacking the maps on the time axis to obtain the map group; wherein, the maps in the map group are sorted according to the time sequence.

4. The path statistics method based on the intelligent wearable device according to claim 1, wherein The steps of splitting the map group according to a preset time period to obtain sub-map groups, querying the user points corresponding to each user in the sub-map groups, converting them into three-dimensional coordinates, and fitting the spatio-temporal path of the user based on the three-dimensional coordinates include: Splitting the map group according to a preset time period to obtain sub-map groups; Querying the user labels of the user points in each sub-map in the sub-map groups, and classifying the user points according to the user labels; For the same-class user points, reading the coordinates and the time of the user points to construct three-dimensional coordinates; the time is the relative time based on the time period; Fit the spatio-temporal path of the user based on three-dimensional coordinates.

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