Personnel space-time aggregation analysis method and system based on large-scale position data

Abstract

The invention discloses a personnel space-time aggregation analysis method and system based on large-scale position data, and the method comprises the following steps: S1, obtaining the movement data of a mobile device, setting a distance parameter and a duration parameter, setting a starting point, and setting a time interval; comparing the interval between the time data and the adjacent position data with the size of the duration parameter, comparing the interval between the position data and the starting point position data with the size of the distance parameter, and calculating a corresponding geographic space position center point to obtain a mobile equipment landing address set; s2, repeating the step S1 for all the mobile devices to obtain a set of landing addresses of all the mobile devices to form a set of landing addresses; and S3, setting an aggregation parameter and an aggregation distance, and screening the foot address set based on the aggregation parameter and the aggregation distance to obtain a space-time aggregation set of all foot addresses.

Description

Technical Field

[0001] The present invention relates to the technical field of personnel spatio-temporal aggregation analysis, and specifically to a method and system for personnel spatio-temporal aggregation analysis based on large-scale location data. Background Art

[0002] In recent years, with the rapid development of various network technologies such as mobile Internet and mobile communication, a large amount of location data of mobile devices connecting to mobile base stations with spatio-temporal attributes can be obtained. Through these data, the movement trajectories of the device holders can be reflected. Analyzing and depicting the movement trajectories of a group provides important reference significance for judging and analyzing the risks of group personnel. Analyzing the personnel spatio-temporal aggregation behavior has become an important topic in the research of big data intelligent applications.

[0003] Traditional personnel aggregation analysis for large-scale mobile base station location data judges whether the same mobile base stations are connected in the same time interval. However, since mobile phone cards of different operators can only connect to their own mobile base stations, and the coverage range of the base stations is large, users may appear anywhere within the base station coverage area. Judging whether personnel gather at the same location by connecting the same base station results in a large error in the analysis results. Summary of the Invention

[0004] Aiming at the technical problems in the prior art, the present application proposes a method and system for personnel aggregation analysis for large-scale mobile base station location data.

[0005] According to one aspect of the present invention, a method for personnel aggregation analysis for large-scale mobile base station location data is proposed, including:

[0006] S1, obtaining the movement data of the mobile device, where the movement data includes location data and time data, setting a distance parameter and a duration parameter, setting a starting point, and by comparing the interval between adjacent location data of the time data and the duration parameter and comparing the interval between the location data and the starting point location data and the distance parameter and calculating the corresponding geographical space position center point, obtaining the set of the landing addresses of the mobile device;

[0007] S2, repeating S1 for all mobile devices to obtain the set of the landing addresses of all mobile devices, forming the set of landing addresses;

[0008] S3, setting an aggregation parameter and an aggregation distance, and screening the set of landing addresses based on the aggregation parameter and the aggregation distance to obtain the spatio-temporal aggregation set of all landing addresses.

[0009] Preferably, in S1, a starting point is set. By comparing the interval between the time data at adjacent position data and the magnitude of the duration parameter, and comparing the interval between the position data and the starting point position data and the magnitude of the distance parameter, and calculating the corresponding geographical spatial position center point, a set of landing addresses of the mobile device is obtained, which specifically includes:

[0010] S101, set the distance parameter distance and the duration parameter staytime, and reverse the order of the mobile data according to the order of connection occurrence. The mobile data includes the position data Xi (i = 1,..., n) of the mobile device and the corresponding time data Ti (i = 1,..., n), where Xi represents the position data corresponding to the mobile device reaching the ith location, and n represents the time data corresponding to the mobile device reaching the ith location;

[0011] S102, set X1 as the starting point, and traverse the position data in order until the interval between the position data of a certain location Xt and the starting point is greater than the distance parameter distance or the interval between the time data of a certain location Xt and the next location Xt+1 (t≥0) is greater than the duration parameter staytime;

[0012] S103, if the interval between the position data of a certain location Xt and the starting point is greater than the distance parameter distance, use the time interval from the starting point to Xt as the stay period according to the time data, and calculate the geographical spatial position center point of all the position data from the starting point to Xt of the mobile device as the landing address of the mobile device;

[0013] S104, if the interval between the time data of a certain location Xt and the next location Xt+1 (t≥0) is greater than the duration parameter staytime, then use the location Xt as the landing address of the mobile device;

[0014] S105, use Xt as the new starting point, repeat S102 - S104 until all the position data is traversed, use all the obtained geographical spatial position center points as the set of landing addresses of the mobile device, and obtain the corresponding set of stay periods.

[0015] By first calculating the landing behavior data of each individual in the group, through the positions of multiple base stations connected within the landing time, according to the geographical spatial center point calculation algorithm, the position error of the personnel calculated will be greatly reduced.

[0016] Further preferably, calculating the geospatial location center point of all location data of the mobile device from the starting point to Xt specifically includes: obtaining the total number of all location data of the mobile device from the starting point to Xt, converting all the location data into corresponding radian representations (lon, lat) through the radians function, defining x = y = z = 0, calculating the x corresponding to each location data through x = cos(lat)*cos(lon), then summing them up in sequence and dividing by the total number of all location data to obtain x_new, calculating the y corresponding to each location data through y = cos(lat)*sin(lon), then summing them up in sequence and dividing by the total number of all location data to obtain y_new, calculating the z corresponding to each location data through z = sin(lat), then summing them up in sequence and dividing by the total number of all location data to obtain z_new, and calculating to obtain the geospatial location center point (atan2(y_new, x_new)), atan2(z_new, sqrt(x_new*x_new + y_new*y_new))), where sqrt() represents taking the square root of the content in the parentheses, and atan2(y_new, x_new) represents taking the arctangent value of x_new / y_new.

[0017] Preferably, obtaining the movement data of the mobile device in S1 specifically includes: obtaining the access authentication signal data of all mobile device base stations connected by the mobile device through the big data streaming data access processing system, so as to obtain the movement data. Based on the large-scale mobile base station location data, the possible spatio-temporal aggregation behaviors of groups can be analyzed on a large scale.

[0018] Further preferably, the distance parameter distance is set to 700 meters. Setting the distance parameter distance to 700 meters is based on the consideration that the coverage ranges of different base stations are different, and the coverage ranges of the equipment laid by operators in open areas and densely populated areas are also different. It is the average value obtained through multiple experiments and has universality.

[0019] Preferably, setting the aggregation parameter and the aggregation distance, and screening the set of staying addresses based on the aggregation parameter and the aggregation distance to obtain the spatio-temporal aggregation set of all staying addresses, specifically including:

[0020] S301, dividing a day into multiple time periods with the same interval, setting the aggregation parameter, and setting the aggregation distance;

[0021] S302, taking a staying address Q1 in the set of staying addresses, using the staying address Q1 as the center and the aggregation distance as the radius, and querying the staying addresses of mobile devices within the radius;

[0022] S303. Take a certain time period a, traverse all the landing addresses of mobile devices within the above range, compare whether there is a time overlap between the stay period corresponding to each mobile device landing address and the time period a, and retain the corresponding mobile device landing address until all the mobile device landing addresses within the radius range are traversed;

[0023] S304. Compare the number of retained mobile device landing addresses with the size of the aggregation parameter. If the number of retained mobile device landing addresses is greater than or equal to the aggregation parameter, it is considered that spatio-temporal aggregation has occurred, and the retained mobile device landing addresses are used as the spatio-temporal aggregation set for the time period a;

[0024] S305. Traverse all time periods in sequence, repeat S303 - S304 for each time period, obtain the spatio-temporal aggregation sets for all time periods, and perform deduplication and merging to obtain the spatio-temporal aggregation set of the landing address Q1;

[0025] S306. Take all the landing addresses in the landing address set in sequence, repeat S302 - S305 for each landing address, and obtain the spatio-temporal aggregation sets of all landing addresses. By analyzing the landing locations of each individual and then analyzing and calculating the spatio-temporal aggregation behavior of the group, the amount of data to be calculated will be greatly reduced, and the spatio-temporal aggregation analysis efficiency will be improved.

[0026] Further preferably, querying the landing addresses of mobile devices within the radius range specifically includes: setting the radian representation of the landing address Q1 as (lon1, lat1), and taking another landing address Q2(lon2, lat2) in the landing address set, and calculating the distance d between Q1 and Q2 through the following formula:

[0027]

[0028] where R represents the radius of the earth, and the landing addresses of mobile devices within the radius range are confirmed by comparing the size of d with the aggregation distance.

[0029] According to one aspect of the present invention, a spatio-temporal aggregation analysis system for personnel based on large-scale location data is proposed, including the following modules:

[0030] Mobile data processing module: Obtain the mobile data of mobile devices, where the mobile data includes location data and time data, set the distance parameter and duration parameter, set the starting point, and obtain the set of mobile device landing addresses by comparing the interval between adjacent location data in the time data and the size of the duration parameter, comparing the interval between the location data and the starting point location data and the size of the distance parameter, and calculating the corresponding geographical space position center point;

[0031] Landing address set acquisition module: Repeatedly execute the mobile data processing module for all mobile devices to obtain the landing address sets of all mobile devices, and form a landing address set;

[0032] Spatio-temporal aggregation analysis module, set aggregation parameters and aggregation distance, and screen the landing address set based on the aggregation parameters and aggregation distance to obtain the spatio-temporal aggregation set of all landing addresses.

[0033] According to one aspect of the present invention, a computer program product is provided, on which a computer program is stored, and the computer program implements the method according to any one of the first aspects when executed by a processor.

[0034] According to one aspect of the present invention, a computing system is provided, including a processor and a memory, and the processor is configured to execute the method according to any one of the first aspects.

[0035] A method and system for spatio-temporal aggregation analysis of personnel based on large-scale location data proposed by the present invention, based on large-scale mobile base station location data, can analyze the possible spatio-temporal aggregation behaviors of groups on a large scale, provide important reference significance for the judgment and analysis of group personnel risks, and can be widely applied in big data intelligent applications. Brief Description of the Drawings

[0036] The drawings are included to provide a further understanding of the embodiments and are incorporated into and constitute a part of this specification. The drawings illustrate the embodiments and, together with the description, are used to explain the principles of the present invention. Other embodiments and many of the intended advantages of the embodiments will be readily appreciated as they become better understood by reference to the following detailed description. The elements of the drawings are not necessarily to scale with each other. The same reference numerals refer to corresponding like components.

[0037] Figure 1 Shows a schematic flowchart of a method for spatio-temporal aggregation analysis of personnel based on large-scale location data according to the present invention;

[0038] Figure 2 Shows a schematic structural diagram of a system for spatio-temporal aggregation analysis of personnel based on large-scale location data according to the present invention;

[0039] Figure 3 Shows a schematic structural diagram of a computer system of an electronic device suitable for implementing the embodiments of the present application. Detailed Description of the Embodiments

[0040] The present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the relevant invention, rather than limiting the invention. Additionally, it should be noted that for the convenience of description, only the parts related to the relevant invention are shown in the drawings.

[0041] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present application will be described in detail below with reference to the drawings and embodiments.

[0042] Figure 1 A cross-sectional schematic diagram of a bulk acoustic wave resonator according to an embodiment of the present invention is shown, as Figure 1 shown, according to one aspect of the present invention, a method for analyzing personnel aggregation for large-scale mobile base station location data is proposed, including:

[0043] S1, obtaining the movement data of the mobile device, where the movement data includes location data and time data, setting a distance parameter and a duration parameter, setting a starting point, by comparing the interval between adjacent location data of the time data and the size of the duration parameter and comparing the interval between the location data and the starting point location data and the size of the distance parameter and calculating the corresponding geographical space location center point, obtaining the set of the landing addresses of the mobile device;

[0044] S2, repeating S1 for all mobile devices, obtaining the set of the landing addresses of all mobile devices, and forming a set of landing addresses;

[0045] S3, setting an aggregation parameter and an aggregation distance, and screening the set of landing addresses based on the aggregation parameter and the aggregation distance to obtain the spatio-temporal aggregation set of all landing addresses.

[0046] In a specific embodiment, through a big data streaming data access processing system, the access authentication signal data of all mobile device base stations connected by the mobile device is obtained, so that the massive mobile data with spatio-temporal attributes of all mobile device base stations connected by the access mobile device on the calculation date can be obtained. Based on the large-scale mobile base station movement data, the possible spatio-temporal aggregation behaviors of the group can be analyzed on a large scale.

[0047] Preferably, in S1, setting a starting point, by comparing the interval between adjacent location data of the time data and the size of the duration parameter and comparing the interval between the location data and the starting point location data and the size of the distance parameter and calculating the corresponding geographical space location center point, obtaining the set of the landing addresses of the mobile device, specifically includes:

[0048] S101. Set the distance parameter distance and the duration parameter staytime, and arrange the mobile data in reverse order according to the order of connection occurrence. The mobile data includes the position data Xi (i = 1,..., n) of the mobile device and the corresponding time data Ti (i = 1,..., n), where Xi represents the position data corresponding to the mobile device reaching the ith location, and n represents the time data corresponding to the mobile device reaching the ith location;

[0049] S102. Let X1 be the starting point, and traverse the position data in sequence until the interval between the position data of a certain location Xt and the starting point is greater than the distance parameter distance or the interval between the time data of a certain location Xt and the next location Xt+1 (t ≥ 0) is greater than the duration parameter staytime;

[0050] S103. If the interval between the position data of a certain location Xt and the starting point is greater than the distance parameter distance, use the time interval from the starting point to Xt as the stay period according to the time data, and calculate the geographical spatial position center point of all the position data from the starting point to Xt of the mobile device as the landing address of the mobile device;

[0051] S104. If the interval between the time data of a certain location Xt and the next location Xt+1 (t ≥ 0) is greater than the duration parameter staytime, then use the location Xt as the landing address of the mobile device;

[0052] S105. Use Xt as the new starting point, repeat S102 - S104 until all the position data is traversed, use all the obtained geographical spatial position center points as the set of landing addresses of the mobile device, and obtain the corresponding set of stay periods.

[0053] In one embodiment, the distance parameter is default set to 700 meters. Considering that the coverage ranges of different base stations are different, and the coverage ranges of the equipment laid by operators in open areas and densely populated areas are also different, it is the average value with universality obtained after multiple tests. In actual operation, it can also be adjusted according to requirements.

[0054] Taking the first mobile base station data point for calculating the date as the starting point X1, starting from the second point X2, perform spatial interval calculation and time interval calculation with the starting point X1. If the interval is less than the set distance parameter distance, it is considered that the current mobile device holder is in an approximately stationary state. Through sequential traversal, until the spatial interval between a certain point Xt and the starting point X1 is greater than the distance parameter distance, it is considered that the holder of the mobile device starts to change from stationary to moving at this moment. The time interval between the starting point X1 and Xt is considered as the staying duration of the mobile device holder. Form a data set with all the connection data (X1…Xn) in this interval. Through the geographical spatial position center point calculation algorithm, calculate the geographical spatial position center point in this data set, and it can be considered that this point is the temporary staying location of the device holder at this time point, serving as the landing address of the mobile device.

[0055] Among them, calculating the geographical spatial position center point of all the position data of the mobile device from the starting point to Xt specifically includes: obtaining the total number of all the position data of the mobile device from the starting point to Xt, converting all the position data into the corresponding radian representation (lon, lat) through the radians function, defining x = y = z = 0, calculating the corresponding x for each position data through x = cos(lat)*cos(lon) and then summing them up in sequence and dividing by the total number of all the position data to obtain x_new, calculating the corresponding y for each position data through y = cos(lat)*sin(lon) and then summing them up in sequence and dividing by the total number of all the position data to obtain y_new, calculating the corresponding z for each position data through z = sin(lat) and then summing them up in sequence and dividing by the total number of all the position data to obtain z_new, and calculating to obtain the geographical spatial position center point (atan2(y_new, x_new)), atan2(z_new, sqrt(x_new*x_new + y_new*y_new))), where sqrt() represents taking the square root of the content in the brackets, and atan2(y_new, x_new) represents taking the arctangent value of x_new / y_new.

[0056] Among them, when the calculated staying location duration of a certain segment is greater than the set duration parameter stayTime (the default is 30 minutes, and the parameter can be adjusted according to specific business scenarios), it can also be considered that this point is the temporary staying location of the device holder at this time point, serving as the landing address of the mobile device.

[0057] Reset Xt as the new starting point, and continue to calculate the next new temporary staying location according to the above rules until the data iteration calculation of the temporary staying location set ends.

[0058] The comparison of the error between the personnel position calculated by the optimized algorithm proposed in the present invention and the true position and the error between the personnel position calculated by the existing algorithm and the true position is shown in Table 1:

[0059] Table 1

[0060]

[0061] By first calculating the data of the landing behavior of each individual in the group, through the positions of multiple base stations connected during the landing time, according to the geographical space center point calculation algorithm, the error of the calculated personnel position will be greatly reduced.

[0062] In a specific embodiment, a clustering parameter and a clustering distance are set, and based on the clustering parameter and the clustering distance, a spatio-temporal clustering set of all landing addresses is obtained by screening the landing address set, which specifically includes:

[0063] S301, divide a day into multiple time periods with the same interval, set the clustering parameter, and set the clustering distance;

[0064] S302, take a landing address Q1 in the landing address set, take the landing address Q1 as the center, and take the clustering distance as the radius to query the landing addresses of mobile devices within the radius;

[0065] S303, take a certain time period a, traverse all the landing addresses of mobile devices within the above range, compare whether the stay period corresponding to each landing address of the mobile device overlaps with the time period a, and retain the corresponding landing address of the mobile device if there is an overlap until all the landing addresses of mobile devices within the radius are traversed;

[0066] S304, compare the number of the retained landing addresses of mobile devices with the clustering parameter. If the number of the retained landing addresses of mobile devices is greater than or equal to the clustering parameter, it is considered that spatio-temporal clustering occurs, and the retained landing addresses of mobile devices are used as the spatio-temporal clustering set of the time period a;

[0067] S305, traverse all time periods in turn, repeat S303 - S304 for each time period, obtain the spatio-temporal clustering sets of all time periods, and perform deduplication and merging to obtain the spatio-temporal clustering set of the landing address Q1;

[0068] S306, take all the landing addresses in the landing address set in turn, repeat S302 - S305 for each landing address, and obtain the spatio-temporal clustering sets of all landing addresses. By analyzing the landing locations of each individual and then analyzing and calculating the spatio-temporal clustering behavior of the group, the amount of data to be calculated is greatly reduced, and the efficiency of spatio-temporal clustering analysis is improved.

[0069] Further preferably, query the landing addresses of mobile devices within a radius. Specifically, let the radian representation of the landing address Q1 be (lon1, lat1), and let another landing address Q2(lon2, lat2) in the set of landing addresses be taken to calculate the distance d between Q1 and Q2 through the following formula:

[0070]

[0071] where R represents the radius of the earth, and the landing addresses of mobile devices within the radius are confirmed by comparing the size of d with the aggregation distance.

[0072] Finally, we will obtain all the individual information of the group aggregation, the coordinates of the aggregation place, the aggregation location, the duration of the aggregation, etc., for research and judgment analysis.

[0073] According to one aspect of the present invention, a personnel spatio-temporal aggregation analysis system based on large-scale location data is proposed, including the following modules:

[0074] Mobile data processing module 201: Obtain the mobile data of the mobile device. The mobile data includes location data and time data. Set the distance parameter and duration parameter, set the starting point, and calculate the corresponding geographical space position center point by comparing the interval between adjacent location data of the time data and the size of the duration parameter, and comparing the interval between the location data and the starting point location data and the size of the distance parameter, so as to obtain the set of landing addresses of the mobile device;

[0075] Landing address set acquisition module 202: Repeat the execution of the mobile data processing module for all mobile devices to obtain the set of landing addresses of all mobile devices and form a set of landing addresses;

[0076] Spatio-temporal aggregation analysis module 203: Set the aggregation parameter and aggregation distance, and screen the set of landing addresses based on the aggregation parameter and aggregation distance to obtain the spatio-temporal aggregation set of all landing addresses.

[0077] Next, refer to Figure 3 , which shows a schematic structural diagram of a computer system 300 of an electronic device suitable for implementing the embodiments of the present application. Figure 3 The shown electronic device is only an example and should not bring any restrictions to the functions and usage scope of the embodiments of the present application.

[0078] As Figure 3As shown, the computer system 300 includes a central processing unit (CPU) 301 that performs various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage section 309 into a random access memory (RAM) 304. In the RAM 304, various programs and data required for the operation of the system 300 are also stored. The CPU 301, ROM 302, ROM 303, and RAM 304 are connected to each other via a bus 305. An input / output (I / O) interface 306 is also connected to the bus 305.

[0079] The following components are connected to the I / O interface 306: an input section 307 including a keyboard, a mouse, etc.; an output section 308 including, for example, a liquid crystal display (LCD), etc. and a speaker, etc.; a storage section 309 including a hard disk, etc.; and a communication section 310 including a network interface card such as a LAN card, a modem, etc. The communication section 310 performs communication processing via a network such as the Internet. A drive 311 is also connected to the I / O interface 306 as needed. A removable medium 312, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 311 as needed so that a computer program read from it is installed into the storage section 309 as needed.

[0080] In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowchart are implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product that includes a computer program carried on a computer-readable storage medium, and the computer program includes program code for performing the method shown in the flowchart. In such an embodiment, the computer program is downloaded and installed from a network via the communication section 310, and / or installed from the removable medium 312. When the computer program is executed by a central processing unit (CPU) 301, the above-described functions defined in the method of the present application are executed.

[0081] It should be noted that the computer-readable storage medium of the present application is a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. The computer-readable storage medium is, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, the computer-readable storage medium is any tangible medium that contains or stores a program, which is used by or in conjunction with an instruction execution system, apparatus, or device. And in the present application, the computer-readable signal medium includes a data signal propagated in a baseband or as part of a carrier wave, which carries the computer-readable program code. Such a propagated data signal takes various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium is also any computer-readable storage medium other than the computer-readable storage medium, which sends, propagates, or transmits a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable storage medium is transmitted by any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0082] The computer program code for performing the operations of the present application is written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages - such as Java, Smalltalk, C++, and also include conventional procedural programming languages - such as the "C" language or similar programming languages. The program code is executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer is connected to the user's computer through any type of network - including a local area network (LAN) or a wide area network (WAN) - or, connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0083] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagram represents a module, a segment of a program, or a portion of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown 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, as well as combinations of blocks in the block diagram and / or flowchart, are implemented by a dedicated hardware-based system for performing the specified functions or operations, or by a combination of dedicated hardware and computer instructions.

[0084] The modules described in the embodiments of the present application are implemented in software and also in hardware.

[0085] As another aspect, the present application also provides a computer-readable storage medium, which is included in the electronic device described in the above embodiments; it also exists separately and is not assembled into the electronic device. The above computer-readable storage medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device is caused to: S1, obtain the movement data of the mobile device, where the movement data includes location data and time data, set a distance parameter and a duration parameter, set a starting point, calculate the corresponding geographical space position center point by comparing the interval of the time data between adjacent location data with the duration parameter and comparing the interval of the location data from the starting point location data with the distance parameter, and obtain the set of landing addresses of the mobile device; S2, repeat S1 for all mobile devices to obtain the set of landing addresses of all mobile devices and form a set of landing addresses; S3, set an aggregation parameter and an aggregation distance, and filter the set of landing addresses based on the aggregation parameter and the aggregation distance to obtain the spatio-temporal aggregation set of all landing addresses.

[0086] The above description is only a preferred embodiment of the present application and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solutions formed by the specific combination of the above technical features, but also covers other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the technical solutions formed by mutually replacing the above features with the (but not limited to) technical features with similar functions disclosed in the present application.

Claims

1. A personnel spatiotemporal aggregation analysis method based on large-scale location data, characterized in that: The following steps are involved: S1, obtaining mobile data of a mobile device, wherein the mobile data includes location data and time data, setting a distance parameter and a duration parameter, setting a starting point, and obtaining a set of mobile device landing addresses by comparing the interval between the time data and adjacent location data and the size of the duration parameter, and comparing the interval between the location data and the location data of the starting point and the size of the distance parameter, and calculating the corresponding geographic space location center point; S2, repeat S1 for all mobile devices to obtain a set of landing addresses of all mobile devices to form a landing address set; S3, setting a clustering parameter and a clustering distance, and filtering the landing address set based on the clustering parameter and the clustering distance to obtain a spatiotemporal clustering set of all landing addresses.

2. The analysis method according to claim 1, characterized in that In S1, a starting point is set, and a mobile device landing address set is obtained by comparing the interval between the time data and the adjacent location data and the size of the duration parameter, and comparing the interval between the location data and the starting point location data and the size of the distance parameter, and calculating the corresponding geographic space location center point, specifically including: S101, setting the distance parameter distance and the duration parameter staytime, and arranging the mobile data in reverse order according to the order in which the connections occur, wherein the mobile data includes the location data Xi (i=1, ..., n) of the mobile device and the corresponding time data Ti (i=1, ..., n), wherein Xi represents the location data corresponding to the arrival of the mobile device at the i-th location, and n represents the time data corresponding to the arrival of the mobile device at the i-th location; S102, set X1 as the starting point, and traverse the position data in order until the interval between the position data of a certain location Xt and the starting point is greater than the distance parameter distance or the interval between the time data of a certain location Xt and the next location Xt+1 (t is greater than or equal to 0) is greater than the duration parameter staytime; S103, if the distance between the location data of a certain place Xt and the starting point is greater than the distance parameter distance, the time interval from the starting point to Xt is used as the stay period according to the time data, and the geographical spatial location center point of all location data of the mobile device from the starting point to Xt is calculated as the landing address of the mobile device; S104, if the interval between the time data of a certain location Xt and the next location Xt+1 (t is greater than or equal to 0) is greater than the duration parameter staytime, the location Xt is used as the mobile device's landing address; S105, taking Xt as a new starting point, repeating S102-S104 until the location data is traversed, taking all the obtained geographic space location center points as the mobile device landing address set, and obtaining the corresponding stay period set.

3. The analysis method according to claim 2, characterized in that Calculate the geographic spatial position center point of all position data of the mobile device from the starting point to Xt, specifically including: obtaining the total number of all position data of the mobile device from the starting point to Xt, converting all the position data into corresponding radians (lon, lat) through the radians function, defining x=y=z=0, calculating the x corresponding to each position data through x=cos(lat)*cos(lon), summing them up in turn and dividing them by the total number of all position data to obtain x_new, calculating the y corresponding to each position data through y=cos(lat)*sin(lon), and then Sum up and divide by the total number of all position data in turn to get y_new, calculate the z corresponding to each position data through z=sin(lat), and then sum up and divide by the total number of all position data in turn to get z_new, and calculate to obtain the center point of the geographic spatial position (atan2(y_new,x_new)), atan2(z_new,sqrt(x_new*x_new+y_new*y_new))), where sqrt() means taking the square root in the brackets, and atan2(y_new,x_new) means taking the inverse tangent value of x_new / y_new.

4. The analysis method according to claim 1, characterized in that Acquiring the mobile data of the mobile device in S1 specifically includes: acquiring access authentication signal data of all mobile device base stations to which the mobile device has been connected through a large data streaming data access processing system, thereby obtaining the mobile data.

5. The analysis method according to claim 2, characterized in that The distance parameter distance is set to 700 meters.

6. The analysis method according to claim 1, characterized in that Setting a clustering parameter and a clustering distance, and filtering the landing address set based on the clustering parameter and the clustering distance to obtain a spatiotemporal clustering set of all landing addresses, specifically includes: S301, dividing a day into multiple time periods with equal intervals, setting aggregation parameters, and setting aggregation distances; S302, taking a landing address Q1 from the landing address set, taking the landing address Q1 as the center and the clustering distance as the radius, querying the landing addresses of the mobile devices within the radius; S303, taking a certain time period a, traversing all the mobile device landing addresses within the above range, comparing whether the stay period corresponding to each mobile device landing address overlaps with the time period a, and retaining the corresponding mobile device landing address if there is overlap, until all the mobile device landing addresses within the radius are traversed; S304, comparing the number of the reserved mobile device landing addresses with the size of the aggregation parameter, if the number of the reserved mobile device landing addresses is greater than or equal to the aggregation parameter, it is considered that spatiotemporal aggregation occurs, and the reserved mobile device landing addresses are used as the spatiotemporal aggregation set of the time period a; S305, traverse all time periods in sequence, repeat S303-S304 for each time period, obtain the spatiotemporal aggregation sets of all time periods, and perform sorting and merging to obtain the spatiotemporal aggregation set of the landing address Q1; S306, sequentially obtain all the landing addresses in the landing address set, repeat S302-S305 for each landing address, and obtain a spatiotemporal aggregation set of all the landing addresses.

7. The analysis method according to claim 6, characterized in that Querying the landing address of the mobile device within the radius specifically includes: assuming that the arc of the landing address Q1 is represented by (lon1, lat1), assuming that another landing address Q2 (lon2, lat2) in the landing address set is taken, and the distance d between Q1 and Q2 is calculated by the following formula: Where R represents the radius of the earth, and the landing address of the mobile device within the radius is confirmed by comparing d with the aggregation distance.

8. A personnel spatiotemporal aggregation analysis system based on large-scale location data, characterized in that: Includes the following modules: Mobile data processing module: obtain mobile data of mobile devices, the mobile data includes location data and time data, set distance parameters and duration parameters, set the starting point, and obtain the mobile device landing address set by comparing the interval between the time data and the adjacent location data and the size of the duration parameter, and comparing the interval between the location data and the starting point location data and the size of the distance parameter and calculating the corresponding geographic space location center point; A landing address set acquisition module: repeatedly executing the mobile data processing module on all mobile devices to obtain the landing address sets of all mobile devices to form a landing address set; Spatiotemporal aggregation analysis module: sets aggregation parameters and aggregation distance, and screens the landing address set based on the aggregation parameters and aggregation distance to obtain the spatiotemporal aggregation set of all landing addresses.

9. A computer program product having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method according to any one of claims 1 to 7.

10. A computing system comprising a processor and a memory, wherein the processor is configured to execute the method according to any one of claims 1 to 7.

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