Positioning method, device and system

Abstract

The invention provides a positioning method, device and system, and relates to the field of communication. According to the method, credibility screening based on telecommunication characteristic indexes is carried out on multiple pieces of measurement report MR data of a terminal in a historical time period, one or more pieces of credible MR data are obtained, positioning deviation correction is carried out on the MR data, and then a building where the terminal is located is positioned based on the credible MR data. Thus, the problem that the precision cannot meet the requirement of building positioning due to the poor positioning precision of the MR data is avoided, accurate building positioning can be performed on the terminal by using the data with a wider coverage range in the terminal of the user, and the universality of performing building positioning on the terminal is improved.

Description

Technical Field

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

[0002] With the development of communication technology and its applications, the positioning information of terminals is the basis of services and applications in the fields of security, navigation, communication management, etc. In the field of communication, most voice and data services occur indoors, that is, inside buildings. Therefore, the positioning information of the terminal in the building can indicate the specific building to which the terminal belongs, and is an important part of the terminal's positioning information.

[0003] Currently, the location information of the terminal in the building is mainly determined by minimization of drive-tests (MDT) data. However, due to user privacy reasons, communication protocol support, and data collection workload, service providers (such as operators, etc.) can only obtain the MDT data of a small number of users' terminals. Therefore, there is an urgent need for a solution that does not rely on MDT data to accurately locate the building where the terminal is located. Summary of the invention

[0004] The present application provides a positioning method, device and system, which can perform positioning correction on positioning data of measurement report (MR) data of a terminal, thereby accurately locating the building where the terminal is located.

[0005] In a first aspect, a positioning method is provided. First, multiple MR data of a terminal in a historical period are obtained. Then, one or more credible MR data are selected from the multiple MR data according to telecommunication characteristic indicators. Finally, a building where the terminal is located in the historical period is located from multiple buildings based on the one or more credible MR data. The telecommunication characteristic indicators include at least one indicator in the MR data that is positively correlated with the positioning accuracy.

[0006] Based on the above positioning method, one or more credible MR data are selected from multiple MR data according to telecommunication characteristic indicators, and the positioning of the MR data is corrected. Then, the building where the terminal is located is located based on the credible MR data, thereby avoiding the problem that the positioning accuracy of the MR data is poor and cannot meet the requirements of building positioning. In this way, the terminal can also be positioned with a high degree of accuracy in the building using the MR data of the terminal, that is, the building where the terminal is located, and no longer relies on the minimized drive test data. The terminal can be accurately positioned in the building using data with a wider coverage in the user's terminal, thereby improving the universality of building positioning for the terminal.

[0007] The present application does not limit the execution subject of the positioning method, for example, the steps of the positioning method are executed by a computing device capable of acquiring MR data of the base station to the terminal. The computing device may be a computing device deployed in the base station, or deployed outside the base station and connected to the base station in communication.

[0008] As a possible implementation method, when screening credible MR data, the computing device first forms a resident data set with MR data whose positioning distance difference in a historical period is less than a first threshold value among multiple MR data, and then selects a credible resident data subset from the resident data set according to the telecommunication characteristic index. Among them, the credible resident data subset includes one or more credible MR data. In this way, according to the restrictions of the historical period and the positioning distance difference, MR data representing that the position of the terminal has been in a certain local space range within a certain time period are selected from multiple MR data to form a resident data set. The resident data set represents that the terminal remains relatively static in the historical period and is likely to be in the building. Then, the resident data set is screened for credibility according to the telecommunication characteristic index, and one or more credible MR data with high positioning reliability and accuracy are selected from the resident data set, thereby reducing the impact of the positioning error of the MR data on the subsequent building positioning.

[0009] Optionally, the computing device forms a mobile chain of multiple MR data according to the order of timestamps of the multiple MR data, and then splits the chain into one or more mobile sub-chains according to the timestamp difference and positioning distance difference of the multiple MR data in the mobile chain, and finally forms a resident data set with the MR data in a mobile sub-chain that are in a resident state and in an indoor state. Taking the first mobile sub-chain in the one or more mobile sub-chains as an example, the timestamp difference of the MR data included in the first mobile sub-chain is less than the length of the historical period, and the positioning distance difference is less than the first threshold.

[0010] As a possible implementation, the telecommunication characteristic index may include a first index and a second index, wherein the first index may include signal delay, and the second index may include at least one of average signal strength, average number of adjacent cells, and total number of signal points.

[0011] Optionally, the computing device calculates the inverse distance weight of any building among the multiple buildings to the positioning center of the trusted resident data subset, and determines that the building where the terminal is located in the historical period is the building with the largest inverse distance weight among the multiple buildings. In this way, when the positioning error of the trusted resident data subset is low, the building with the largest inverse distance weight to the positioning center of the trusted resident data subset among the multiple buildings is determined as the building where the terminal is located in the historical period, thereby ensuring the accuracy of the building positioning based on the MR data of the terminal.

[0012] As a possible implementation method, the computing device can also consider the influence of points of interest (POI) in geographic information on the building where the user resides, and comprehensively correct the building positioning of the terminal based on the user's behavioral characteristics, based on the positioning result of the terminal obtained by the positioning method of the first aspect. In this way, the MR data of the terminal at multiple times and the building attribute characteristics based on the points of interest are fully utilized to comprehensively determine the user's building positioning, so as to achieve more accurate building positioning, and obtain building positioning results that match the scene in different building densities and types and at different times, so as to meet the actual business requirements for fine-grained building positioning in multiple scenarios.

[0013] Optionally, the computing device first determines the first building membership rate of the terminal's location in the historical period according to the inverse distance weight of the location center of any building in the multiple buildings to the trusted resident data subset, and then determines the second building membership rate of the terminal's location in the historical period according to the membership of the trusted resident data subset to buildings with different building attribute characteristics in the multiple buildings, and finally locates the building where the terminal is located in the historical period from the multiple buildings by combining the first building membership rate and the second building membership rate. The time index includes at least one of the residence duration, residence time and residence date, and the building attribute characteristics include at least one of work, residence, life and shopping. The building membership rate includes the probability that the terminal is located in any building in the multiple buildings.

[0014] Optionally, the multiple buildings include multiple buildings within an area with a location center (location cluster center) of the trusted resident data subset as the center and a radius of a preset radius.

[0015] In a second aspect, a positioning device is provided, which includes various modules for executing the positioning method in the first aspect or any possible implementation of the first aspect. The positioning device in the present application may include a transceiver module, a data processing module, and a positioning calculation module. The transceiver module is used to obtain multiple MR data of the terminal in a historical period. The data processing module is used to select one or more credible MR data from multiple MR data according to telecommunication characteristic indicators. The positioning calculation module is used to locate the building where the terminal is located in the historical period from multiple buildings based on one or more credible MR data. Among them, the telecommunication characteristic indicator includes at least one indicator in the MR data that is positively correlated with the positioning accuracy.

[0016] In a third aspect, a computing device is provided, including a memory and a processor, wherein the memory is used to store a set of computer instructions, and when the processor executes the set of computer instructions, it is used to execute the operating steps of the positioning method in the first aspect or any possible implementation of the first aspect.

[0017] In a fourth aspect, a communication system is provided, the system comprising multiple terminals, multiple base stations and the computing device described in the third aspect, the multiple base stations are connected to the multiple terminals, the multiple base stations are used to collect MR data of the multiple terminals, and the computing device is used to execute the operating steps of the positioning method in the first aspect or any possible implementation of the first aspect.

[0018] In addition, the technical effects of the second, third and fourth aspects can refer to the technical effects of the positioning method described in the first aspect and will not be repeated here.

[0019] In a fifth aspect, a readable storage medium is provided, which includes: a computer program or instructions; when the computer program or instructions are executed on a computer, the computer executes the positioning method described in any possible implementation manner of the first aspect.

[0020] In a sixth aspect, a computer program product is provided, which includes a computer program or instructions, and when the computer program or instructions are executed on a computer, the computer executes the positioning method described in any possible implementation manner of the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A schematic diagram of the structure of a communication system provided for this application;

[0022] Figure 2 A flowchart of a positioning method provided by this application;

[0023] Figure 3 A schematic diagram of building division provided for this application;

[0024] Figure 4 A schematic diagram of a building belonging probability based on telecommunication characteristics provided for this application;

[0025] Figure 5 A schematic diagram of a flow chart of a resident data extraction step provided in this application;

[0026] Figure 6 A flow chart of a credibility screening step provided for this application;

[0027] Figure 7 A flowchart of another positioning method provided by the present application;

[0028] Figure 8 A schematic diagram of the structure of a positioning device provided in this application;

[0029] Fig. 9 A schematic diagram of the structure of a computing device provided in this application. DETAILED DESCRIPTION

[0030] The positioning method, device and system provided in the embodiments of the present application can be applied to mobile communication scenarios. The following is a brief introduction to the technologies that may be involved in the present application.

[0031] (1) Measurement report

[0032] The measurement report is the original network data measured by the user terminal. The terminal sends a measurement report to the network side equipment such as the base station through the service channel every 480 milliseconds (every 470 milliseconds on the information channel). The measurement report carries the relevant signals of the uplink and downlink wireless links. The downlink signal of the downlink wireless link is measured and collected by the terminal and reported to the network side equipment, and the uplink signal of the uplink wireless link is measured and collected by the base transceiver station (BTS) on the network side and reported to the base station controller (BCS).

[0033] The uplink information in the measurement report includes the level strength and quality of the serving cell, the terminal's transmit power, the use of discontinuous transmission (DTX), the strongest signal strength of multiple neighboring cells, and the base station identity code (BSIC).

[0034] The downlink information in the measurement report includes the uplink level strength and quality of the terminal, the transmission power of the base transceiver station, the use status of discontinuous transmission, the maximum time advance (TA), etc.

[0035] The level strength and quality of the uplink of the serving cell or the terminal may include signal delay, reference signal received power (RSRP), signal to interference plus noise ratio (SINR), etc.

[0036] (2) Fuzzy comprehensive evaluation method

[0037] Fuzzy comprehensive evaluation method is a comprehensive evaluation method based on fuzzy mathematics. Fuzzy comprehensive evaluation method transforms qualitative evaluation into quantitative evaluation according to the membership theory of fuzzy mathematics, that is, using fuzzy mathematics to make an overall evaluation of things or objects that are constrained by multiple factors. Among them, fuzzy mathematics, also known as Fuzzy mathematics, is a mathematical theory and method for studying and dealing with fuzzy phenomena.

[0038] The basic steps of the fuzzy comprehensive evaluation method include: a. Determine the factor (indicator) set and evaluation (grade) set of the object to be evaluated; b. Determine the weight of each factor and their membership degree to obtain the fuzzy evaluation matrix; c. Perform fuzzy operations on the fuzzy evaluation matrix and the weight vector of the factors and normalize them to obtain the fuzzy evaluation comprehensive result.

[0039] Membership refers to the numerical representation of the degree to which an element belongs to a set, usually expressed as a real number between 0 and 1. It is a basic concept used to describe fuzzy sets and can be used to represent the position and degree of belonging of an element in a set. In fuzzy set theory, membership is defined as a similarity measure between an element and a set, that is, a numerical value representing the degree to which an element belongs to a set.

[0040] (3) Points of interest

[0041] Points of interest generally refer to point data in Internet electronic maps, which basically include four attributes: name, address, coordinates, and type. For example, on an electronic map, a point of interest can represent different types of positioning points such as restaurants, supermarkets, tourist attractions, and transportation facilities.

[0042] The present application provides a positioning method, specifically a positioning method of "combining user behavior characteristics and telecommunication characteristics at different time and space to perform building positioning". First, multiple MR data of a terminal in a historical period are obtained. Then, one or more credible MR data are selected from the multiple MR data according to telecommunication characteristic indicators. Finally, the building where the terminal is located in the historical period is located from multiple buildings based on one or more credible MR data. Among them, the telecommunication characteristic indicator includes at least one indicator in the MR data that is positively correlated with the positioning accuracy. Based on the above positioning method, one or more credible MR data are selected from multiple MR data according to the telecommunication characteristic indicator, so as to realize the correction of the positioning of the MR data, and then the building where the terminal is located is located based on the credible MR data, thereby avoiding the problem that the positioning accuracy of the MR data is poor and the accuracy cannot meet the requirements of building positioning. In this way, the terminal can also be positioned in a building with high accuracy using the MR data of the terminal, and no longer relies on the minimized drive test data. The terminal can be accurately positioned in a building using data with a wider coverage range in the user's terminal, thereby improving the universality of building positioning for the terminal.

[0043] To facilitate understanding of the embodiments of the present application, first Figure 1 The communication system shown in FIG. 1 is used as an example to describe in detail a communication system 100 applicable to an embodiment of the present application.

[0044] Figure 1 A schematic diagram of the architecture of a communication system to which the beam measurement method provided in the embodiment of the present application is applicable. Figure 1As shown, the communication system 100 includes: a terminal 110 , a network device 120 and a computing device 130 .

[0045] The number of terminals 110 can be one or more (only 5 are shown in the figure), each terminal 110 is connected to the same or different network device 120, and reports MR data to the network device 120, such as the signal delay of the service cell, the reference signal receiving power, the signal to interference plus noise ratio, etc.

[0046] The terminal 110 may be a terminal with transceiver functions, or a chip or chip system that can be set in the terminal. The terminal 110 may also be called user equipment (UE), access terminal, subscriber unit, user station, mobile station (MS), mobile station, remote station, remote terminal, mobile device, user terminal, terminal, wireless communication device, user agent or user device. The terminal 110 in the embodiments of the present application can be a mobile phone, a cellular phone, a smart phone, a tablet computer, a wireless data card, a personal digital assistant (PDA), a wireless modem, a handset, a laptop computer, a machine type communication (MTC) terminal, a computer with wireless transceiver function, a virtual reality (VR) terminal, an augmented reality (AR) terminal, a wireless terminal in industrial control, a wireless terminal in self driving, a wireless terminal in remote medical, a wireless terminal in smart grid, a wireless terminal in transportation safety, a wireless terminal in a smart city, a wireless terminal in a smart home, a vehicle-mounted terminal, a road side unit (RSU) with terminal function, etc. The terminal 110 of the present application may also be a vehicle-mounted module, a vehicle-mounted module, a vehicle-mounted component, a vehicle-mounted chip or a vehicle-mounted unit built into the vehicle as one or more components or units. Alternatively, the terminal 110 may also be a customer-premises equipment (CPE).

[0047] The number of network devices 120 can be one or more ( Figure 1 Only two are shown in the figure), each network device 120 is connected to one or more terminals 110, collects MR data of the terminals 110, and sends the collected MR data to the computing device 130. The MR data of the terminals 110 collected by the network device 120 includes the MR data reported by the terminals 110, and the MR data measured by the network device 120 on the terminals 110, such as the signal delay of the terminal uplink, the reference signal received power, the signal to interference plus noise ratio, etc.

[0048] The network device 120 may be an access network (AN) device, or may be referred to as a radio access network (RAN) device. The RAN device may provide access functions for the terminal 110, and is responsible for functions such as radio resource management, quality of service (QoS) management, data compression and encryption on the air interface side. The RAN device may include the fifth generation of broadband cellular network (5G), such as a gNB in ​​an NR system, or one or a group of antenna panels (including multiple antenna panels) of a base station in a 5G, or a network node constituting a gNB, a transmission point (TRP or transmission point, TP) or a transmission measurement function (TMF), such as a baseband unit (BBU), or a centralized unit (CU) or a distributed unit (DU), an RSU with a base station function, or a wired access gateway, or a core network element of a 5G. Alternatively, the RAN device may also include an access point (AP) in a wireless fidelity (WiFi) system, a wireless relay node, a wireless backhaul node, various forms of macro base stations, micro base stations (also called small stations), relay stations, access points, wearable devices, vehicle-mounted devices, etc. Alternatively, the RAN device may also include a next-generation mobile communication system, such as a 6G access network device, such as a 6G base station, or in the next-generation mobile communication system, the network device 120 may also have other naming methods, which are all covered within the protection scope of the embodiments of the present application, and the present application does not make any limitation on this.

[0049] The number of computing devices 130 can be one or more ( Figure 1 Only one is shown in the figure), the computing device 130 is connected to each network device 120, obtains multiple MR data of the terminal 110 in the historical period sent by the network device 120, and selects one or more credible MR data from the multiple MR data according to the telecommunication characteristic index, and then locates the building where the terminal is located in the historical period from multiple buildings based on the one or more credible MR data. Among them, the telecommunication characteristic index includes at least one index in the MR data that is positively correlated with the positioning accuracy.

[0050] The computing device 130 may be a terminal device or a network device, or a computer, a robot, a car computer, etc. It may also be a chip (system) or other parts or components that can be set in a terminal device or a network device, such as a graphic processing unit (GPU), a central processing unit (CPU), other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. For example, the computing device 130 is a computing device integrated on the network device 120. For another example, the computing device 130 is a computing device deployed outside the network device 120 and connected to the network device 120.

[0051] Understandably, Figure 1 The simplified schematic diagram is provided for ease of understanding. The communication system may also include other network devices, other computing devices, and / or other terminal devices. Figure 1 Not drawn.

[0052] In combination with the communication system 100, the positioning method provided in this embodiment can be applied to building positioning scenarios in mobile communications, such as scenarios in which mobile networks such as 4G networks and 5G networks perform building positioning on user terminals.

[0053] Next, the flow prediction method provided by this embodiment is described in detail with reference to the accompanying drawings.

[0054] In the mobile network scenario, the above Figure 1 The terminal 110, the network device 120 and the computing device 130 in the communication system 100 shown may be the execution device of the positioning method provided by the present application. Figure 2 Taking steps 210 to 240 as an example, the positioning method is generally described.

[0055] Step 210 : The network device 120 collects MR data of the terminal 110 .

[0056] As a possible example, the network device 120 collects uplink information in the MR data of the terminal 110 , and measures downlink data in the MR data of the terminal 110 , and obtains complete MR data of the terminal 110 according to the uplink information and the downlink information.

[0057] As a possible implementation, the MR data may be MR data of multiple terminals 110 at multiple times. This embodiment is described by taking MR data of one terminal 110 at multiple times as an example, that is, the terminal 110 described in steps 210 to 240 is the same terminal.

[0058] Step 220 : The computing device 130 obtains a plurality of MR data of the terminal 110 within a historical period.

[0059] As a possible example, the computing device 130 receives a plurality of MR data of the terminal 110 within a historical period sent by the network device 120 .

[0060] Step 230 : The computing device 130 selects one or more credible MR data from the plurality of MR data according to the telecommunication characteristic indicator.

[0061] As a possible example, the computing device 130 selects one or more reliable MR data from multiple MR data according to telecommunication characteristic indicators based on the reliability of the positioning accuracy of the MR data, wherein the telecommunication characteristic indicator includes at least one indicator in the MR data that is positively correlated with the positioning accuracy.

[0062] As a possible implementation, the computing device 130 forms a resident data set from the plurality of MR data whose positioning distance difference in the historical period is less than a first threshold, and then selects a trusted resident data subset from the resident data set according to the telecommunication characteristic indicator. The trusted resident data subset includes one or more trusted MR data.

[0063] Optionally, the positioning distance differences in the plurality of MR data within the historical period are achieved based on position estimation of the MR data.

[0064] For example, the computing device 130 performs fingerprint feature matching in a fingerprint feature library based on assisted global positioning system (AGPS) data in the MR data to estimate the position. The fingerprint feature library is trained using MR data containing position information, and can perform fingerprint matching on MR data that does not contain position information to generate position information of the MR data.

[0065] Optionally, when the computing device 130 selects the resident data set from the plurality of MR data, it is necessary to determine that the location corresponding to the MR data is indoors. For specific steps, please refer to Figure 5 Steps 510 to 530 are shown and will not be described in detail here.

[0066] Optionally, the computing device 130 uses data content that is positively correlated with positioning accuracy (reliability and / or concentration) as a telecommunication feature indicator, performs credibility screening on the resident data set, and obtains a credible resident data subset.

[0067] First, the computing device 130 divides the multiple MR data included in the resident data set into multiple point clusters based on the base station cell. For example, one or more MR data with a base station cell identifier (ID) of 123 form one point cluster, and one or more MR data with a base station cell identifier of 124 form another point cluster.

[0068] Then, the computing device 130 uses the data content that is positively correlated with the reliability and / or concentration of the positioning as a multi-level indicator, performs multi-level credibility screening on multiple point clusters, and obtains the optimal credible point cluster, that is, the credible resident data subset. For example, the first-level indicator (also called the first indicator) includes signal delay, and the second-level indicator (also called the second indicator) includes average signal field strength, average number of adjacent cells, and total number of signal points. Among them, the average signal field strength is the average value of the reference signal receiving power of all resident data contained in the point cluster. The average number of adjacent cells is the average value of the number of adjacent cells of all resident data contained in the point cluster. The total number of signal points refers to the total number of points of the MR data contained in the point cluster, and the signal delay is the communication delay between the terminal 110 and the network device 120.

[0069] This application does not limit the credibility screening method of the computing device 130. For example, the computing device 130 generates a credibility model based on the primary index and the secondary index using numerical fitting, regression analysis or machine learning, and uses the credibility model to calculate the weights of multiple point clusters respectively. The higher the weight, the higher the credibility of the point cluster. The trusted resident data subset may include the top n point clusters in credibility among multiple point clusters, where n is a positive integer. For example, when n is 1, the optimal trusted point cluster includes the point cluster with the highest credibility among multiple point clusters. For another example, when n is 3, the optimal trusted point cluster includes the top 3 point clusters in credibility among multiple point clusters.

[0070] For the specific steps of the above multi-level credibility screening, please refer to Figure 6 Steps 610 to 620 are shown and will not be described in detail here.

[0071] Step 240 : The computing device 130 locates the building where the terminal was located during the historical period from among multiple buildings based on one or more reliable MR data.

[0072] As a possible example, the computing device 130 respectively calculates the inverse distance weight between any building among the multiple buildings and the positioning center of one or more trusted MR data, uses the inverse distance weight as the probability that the terminal is located in any building among the multiple buildings, and determines that the building where the terminal is located in the historical period is the building with the largest inverse distance weight among the multiple buildings.

[0073] As a possible implementation, the computing device 130 calculates the average longitude and latitude values ​​of the locations of the MR data included in the trusted resident data subset as the location center of the trusted resident data subset, also known as the cluster centroid or cluster center.

[0074] Optionally, the location center of the trusted resident data subset is calculated as shown in formula (1).

[0075]

[0076] Among them, X j Indicates the horizontal coordinate of the positioning center, Y j represents the ordinate of the positioning center, n represents the total number of signal points in the trusted resident data subset, x ij The horizontal coordinate of the signal point location of the i-th MR data in the trusted resident data subset j, y ij The ordinate represents the signal point location of the i-th MR data in the trusted resident data subset j.

[0077] As a possible implementation method, the computing device 130 uses an inverse distance weighted method to respectively calculate the positioning center of the trusted resident data subset and the inverse distance weight of each building. The product of the inverse distance weight and the building weight represents the probability that the terminal device 110 is located in each building. The building with the highest corresponding inverse distance weight among multiple buildings is determined as the building where the terminal was located during the historical period.

[0078] Optionally, the inverse distance weighted method is specifically shown in formula (2).

[0079]

[0080] Among them, p i represents the inverse distance weight between the location center of the trusted resident data subset and the i-th building, d ij Represents the distance between the location center of the trusted resident data subset and the i-th building.

[0081] For example Figure 3As shown, the computing device 130 determines a buffer zone in an electronic map with the location center of the trusted resident data subset as the center of the circle according to a preset radius, and the multiple buildings include buildings included in the buffer zone or buildings intersecting with the buffer zone. The center of the circle is marked as a solid dot, the preset radius may be 150 meters, and the multiple buildings include buildings 1 to 23.

[0082] by Figure 3 Taking the multiple buildings shown in FIG. 1 as an example, the probability of the terminal being located in any of the multiple buildings output by the computing device 130 can be as follows: Figure 4 shown.

[0083] Based on the above steps 210 to 240, the computing device 130 uses the credibility model to screen the MR data of the terminal 110, and determines the probability that the terminal 110 is located in the building based on the screened one or more credible MR data that can accurately characterize the position of the terminal 110. Compared with the building positioning that relies on minimized drive test data in the 5G network scenario, which lacks minimized drive test data due to privacy issues, or requires users to sign relevant agreements with operators and terminal support to collect minimized drive test data, MR data has higher universal coverage and can be widely collected in the 5G network scenario. The positioning method of the present application provides a solution for accurately positioning the terminal in the building without relying on minimized drive test data, realizes accurate building positioning based on MR data, and improves the universality of building positioning for the terminal.

[0084] Combination of the above Figure 2 The positioning method of this application is described in general. Figure 5 and Figure 6 , the steps of resident data set division and credibility screening are explained in detail.

[0085] like Figure 5 As shown, the resident data set partitioning step may include the following steps 510 to 530.

[0086] Step 510 : The computing device 130 organizes the plurality of MR data into a movement chain.

[0087] As a possible example, the computing device 130 groups the multiple MR data into a movement chain (trajectory) according to the sequence of timestamps of the MR data of the terminal 110 at multiple moments.

[0088] For example, the MR data of terminal 110 includes MR data 1, MR data 2, MR data 3, MR data 4, and MR data 5, and the timestamps are "2020-01-01 00:00:01.000000", "2020-01-01 00:11:12.0003645", "2020-01-01 00:17:42.100031", "2020-01-01 16:36:42.100031", and "2020-01-01 16:42:11.102341" respectively. The computing device 130 organizes MR data 1, MR data 2, MR data 3, MR data 4, and MR data 5 into a mobile chain in the order of timestamps, i.e., MR data 1-MR data 2-MR data 3-MR data 4-MR data 5.

[0089] Step 520: The computing device 130 performs denoising on the mobile chain.

[0090] As a possible example, the computing device 130 removes or corrects significant drift points in the moving chain to implement denoising processing on the moving chain.

[0091] As a possible implementation manner, the computing device 130 uses ping-pong denoising and outlier correction to remove or correct significant drift points in the moving chain.

[0092] Optionally, the denoising process includes dividing the MR data according to adjacent positioning in the mobile chain. For example, the computing device 130 divides the mobile chain into one or more mobile sub-chains according to the time interval and spatial interval of each MR data in the mobile chain. The computing device 130 divides the mobile chain into one or more mobile sub-chains according to the timestamp difference and positioning distance difference of multiple MR data in the mobile chain, and the timestamp difference of the MR data contained in each mobile sub-chain is less than the length of the historical period, and the positioning distance difference is less than the first threshold.

[0093] For example, the computing device 130 splits the mobile chain into multiple mobile sub-chains according to the timestamp and position information of each MR data in the mobile chain. The computing device 130 splits the mobile chain into two mobile sub-chains when the timestamp difference or distance between each two adjacent points in the mobile chain is greater than a preset threshold. Taking MR data 1-MR data 2-MR data 3-MR data 4-MR data 5 as an example, if the second threshold is 20 minutes, and the timestamp between MR data 3 and MR data 4 is greater than 20 minutes, the computing device 130 splits MR data 1-MR data 2-MR data 3-MR data 4-MR data 5 into two mobile sub-chains, namely MR data 1-MR data 2-MR data 3 and MR data 4-MR data 5.

[0094] Step 530 : The computing device 130 combines the MR data in the mobile sub-chain that are in the resident state and in the indoor state into a resident data set.

[0095] As a possible example, for each mobile sub-chain, the computing device 130 splits the mobile sub-chain into resident segments, and assembles the MR data located indoors in the resident segments into a resident data set.

[0096] As a possible implementation, the computing device 130 uses a clustering algorithm to identify the residence features of each mobile sub-chain, further splits the mobile sub-chain into a residence segment and a motion segment, and composes the MR data located indoors in the residence segment into a residence data set.

[0097] Optionally, the computing device 130 may identify the MR data located indoors in the resident segment based on the “indoor / outdoor” tag of the MR data. The “indoor / outdoor” tag may be added by the computing device 130 based on data representing the signal strength of the terminal 110 in the MR data. The specific method may refer to the existing indoor and outdoor scene recognition based on MR data, which will not be described in detail here.

[0098] This application does not limit the type of clustering algorithm, such as density-based spatial clustering of applications with noise (DBSCAN) and the like.

[0099] Based on the above steps 510 to 530, the temporal and spatial continuity of the MR data is considered in the process of dividing the resident data set, thereby introducing spatiotemporal context information, which can reduce the impact of the positioning error based on MR data on the accuracy of building positioning, thereby achieving subsequent accurate building positioning in scenarios at different times and time spans.

[0100] like Figure 6 As shown, Figure 2 In step 230 shown, the step of selecting a trusted resident data subset from the resident data set by the computing device 130 according to the telecommunication characteristic indicator may include the following steps 610 to 620.

[0101] Step 610: The computing device 130 performs a first-level screening of the resident data set using signal delay as a primary indicator.

[0102] As a possible example, the computing device 130 respectively counts the signal delay distribution of the MR data of any point cluster in the resident data set, and uses the delay distribution interval with the highest concentration as the credible interval to filter the MR data of the point cluster.

[0103] For example, multiple MR data of a base station cell with a base station identifier of 123 are divided into a point cluster, and the dots of the MR data contained in the point cluster are shown as a circular icon. Multiple resident data of a base station cell with a base station identifier of 124 are divided into a point cluster, and the dots of the MR data contained in the point cluster are shown as a triangular icon. Taking the point cluster of the base station cell with a base station identifier of 123 as an example, the computing device 130 determines the delay interval with the highest concentration in a coordinate system with signal delay as the horizontal coordinate and the distance between the positioning of the MR data and the geometric center of the building as the vertical coordinate. Then, the computing device 130 marks the multiple MR data contained in the delay interval with the highest concentration to obtain the spatial distribution of the first-level screening results.

[0104] Step 620 , the computing device 130 performs a second-level screening of the resident data set using the average signal strength, the average number of adjacent cells, and the total number of signal points as secondary indicators.

[0105] As a possible example, the computing device 130 performs a second-level screening on the resident data set based on the credibility model, taking the average signal strength, the average number of adjacent cells and the total number of signal points as secondary indicators to obtain a credible resident data subset.

[0106] Among them, the credibility model is based on the correlation between different telecommunication characteristic indicators and positioning errors in actual test data, and determines the indicators with strong correlation with positioning errors as secondary indicators, and generates the model by numerical fitting, regression analysis or machine learning. Telecommunication characteristic indicators include reference signal received power, signal to interference plus noise ratio, number of adjacent cells and other characteristics related to network quality in MR data.

[0107] For example, the credibility model is constructed based on the average signal field strength, the average number of adjacent cells and the total number of signal points as secondary indicators, and the computing device 130 counts each point cluster (such as Figure 6 The average signal strength, average number of adjacent cells and total number of signal points of point clusters 1 to 4 are used to calculate the credibility score of each point cluster according to the following formula (3).

[0108]

[0109] Among them, score i Represents the credibility score of the i-th point cluster. w(RSRP), w(NoCellID), and w(NoPoint) represent the weights of the average signal strength, the average number of adjacent cells, and the total number of signal points, respectively. RSRP meani Represents the average signal strength of the i-th point cluster, RSRP mean Indicates the average signal strength of the first-level screening results. meaniIndicates the average number of adjacent cells of the i-th point cluster, NoCellID mean Indicates the average number of adjacent cells in the first-level screening results. i The total number of signal points of the i-th point cluster, NoPoint represents the total number of signal points of multiple point clusters in the first-level screening results.

[0110] Based on the above steps 610 to 620, the resident data set is credibility screened according to indicators related to the reliability and / or concentration of positioning in the MR data, so as to screen out the MR data that can accurately characterize the positioning of the terminal to perform subsequent positioning steps, providing accurate basic data for the building positioning of the terminal, which is conducive to improving the accuracy of building positioning.

[0111] In combination with the above Figure 2-Figure 6 How to calculate which building of the terminal is located among multiple buildings based on the telecommunication features of MR data to complete the building positioning of the terminal is described in detail. Considering that in addition to the spatiotemporal context information contained in the telecommunication features, etc., which can support accurate building positioning, the user's behavioral characteristics usually also affect which building the user enters, the present application can also calculate the comprehensive probability of the user's terminal being in any building by integrating the user's behavioral characteristics after the above steps 210 to 240.

[0112] like Figure 7 As shown, after the above steps 210 to 240, the positioning method further includes a comprehensive probability calculation step, which includes the following steps 250 to 270.

[0113] Step 250: The computing device 130 determines the first building membership rate of the terminal's location in the historical period according to the inverse distance weight of the location center of any building among the multiple buildings for the trusted resident data subset.

[0114] As a possible example, for the specific method of step 250, please refer to the relevant content of determining the probability of the terminal being located in any building among multiple buildings according to the inverse distance weight in the above step 240, which will not be repeated here.

[0115] Step 260: The computing device 130 determines a second building membership rate of the terminal's location in the historical period based on the membership of the trusted resident data subset to buildings with different building attribute characteristics among the multiple buildings.

[0116] As a possible example, the membership degree of the trusted resident data subset to buildings with different building attribute characteristics among multiple buildings is calculated based on the time index of the trusted resident data subset.

[0117] As a possible example, the computing device 130 determines building attribute characteristics of different buildings among the multiple buildings according to the point of interest data, and calculates the membership of the trusted resident data subset to the buildings with different building attribute characteristics based on the time index.

[0118] As a possible implementation, the computing device 130 uses the boundary of each building among the multiple buildings as a buffer zone, counts the number of different types of points of interest within the buffer zone, and uses the type of the points of interest with the largest number among the different types of points of interest as the building attribute feature.

[0119] Optionally, the type of the point of interest may include at least one of work, residence, shopping, and life.

[0120] As a possible implementation method, the computing device 130 constructs a membership model between the time index and different building attribute characteristics based on prior knowledge and inference rules, and calculates the membership of the trusted resident data subset to the buildings with four building attribute characteristics of work, residence, shopping, and life based on the membership model.

[0121] Optionally, the time indicator includes at least one of a residence duration, a residence time and a residence date.

[0122] Optionally, the membership model is constructed based on a linear piecewise function form, such as an ascending semi-trapezoidal function, a descending semi-trapezoidal function, etc. For example, this embodiment uses a trapezoidal function and discrete points to construct a membership function and establish a membership model.

[0123] Next, the computing device 130 performs fuzzy operation on the membership degree and normalizes it to obtain a second building membership rate, that is, the probability that the terminal is located in a building with different building attribute characteristics during the historical period.

[0124] As a possible example, the computing device 130 performs fuzzy operation on the weight and membership of the time index and normalizes them to obtain the second building membership rate.

[0125] In this embodiment, the time index can be regarded as a factor set in the fuzzy comprehensive evaluation method, the building attribute characteristics can be regarded as an evaluation set in the fuzzy comprehensive evaluation method, the computing device 130 calculates the membership degree, and the specific method of performing fuzzy operation and normalization on the membership degree can refer to the conventional process of the fuzzy comprehensive evaluation method, which will not be repeated here.

[0126] Step 270: The computing device 130 combines the first building membership rate and the second building membership rate to locate the building where the terminal was located during the historical period from among multiple buildings.

[0127] As a possible example, the computing device 130 performs a weighted operation on the first building membership rate and the second building membership rate to obtain a comprehensive probability that the terminal is located in any of the multiple buildings, and uses the building with the highest comprehensive probability as the building where the terminal was located in the historical period.

[0128] As a possible implementation method, the weight values ​​of the first building membership rate and the second building membership rate are dynamically adjusted according to the actual application scenario of building matching, thereby realizing a building matching result generation scheme with a dynamic weight allocation function and achieving a more accurate correction effect.

[0129] Optionally, the comprehensive probability may be calculated as shown in formula (4).

[0130] p i =w 1 *p 1 +w 2 *p 2 , and w 1 +w 2 =1 (4)

[0131] Among them, p i represents the comprehensive probability that the terminal is located in the i-th building among multiple buildings, p 1 is the probability that the terminal is located in the i-th building among multiple buildings in the first building membership rate, p 2 is the probability that the terminal is located in the i-th building among multiple buildings in the second building membership rate, ω 1 ,ω 2 are the building matching weights of the two respectively.

[0132] Optionally, the computing device 130 may also correct the position of the MR data of the building positioning result. For example, when the positioning of the MR data is not within the building indicated by the building positioning result, the positioning of the MR data is corrected to be within the building indicated by the building positioning result.

[0133] Based on the above steps 250 to 270, without relying on the minimization of drive test data, the user's behavior characteristics are introduced for comprehensive fuzzy evaluation, so that the positioning method of the present application can also be assisted by the user's behavior characteristics to perform building positioning in the absence of residence data, thereby improving the accuracy of building positioning. At the same time, in the overall process of building positioning, the MR data is screened for credibility and membership is calculated, the serial precision decay chain from indoor residence recognition to building membership calculation is controlled, and the robustness of building positioning is improved.

[0134] In order to cooperate with the positioning method in the above embodiment, the present application also provides a positioning device for executing the positioning method provided by the present application. Figure 8Schematic diagram of the structure of a possible positioning device provided in this embodiment. The positioning device can be used to implement the functions of the computing device 130 in the above method embodiment, and thus also has the beneficial effects possessed by the above method embodiment. In this embodiment, the positioning device can be as follows Figure 1 The computing device 130 or other device with computing functions shown may also be a module (such as a chip) applied to a server.

[0135] like Figure 8 As shown, the positioning device 800 includes a transceiver module 810 , a data processing module 820 and a positioning calculation module 830 .

[0136] The transceiver module 810 is used to obtain multiple MR data of the terminal in the historical period. For example, the transceiver module 810 is used to perform the following steps: Figure 2 Step 220 is shown.

[0137] The data processing module 820 is used to select one or more credible MR data from a plurality of MR data according to the telecommunication characteristic index. For example, the data processing module 820 is used to perform the following steps: Figure 2 Step 230 is shown.

[0138] The positioning calculation module 830 is used to locate the building where the terminal is located in the historical period from multiple buildings based on one or more reliable MR data, and the telecommunication characteristic index includes at least one index in the MR data that is positively correlated with the positioning accuracy. For example, the positioning calculation module 830 is used to perform the following steps: Figure 2 Step 240 is shown.

[0139] As a possible implementation method, the data processing module 820 is specifically used to: form a resident data set from MR data whose positioning distance difference within a historical period is less than a first threshold among multiple MR data; select a trusted resident data subset from the resident data set according to telecommunication characteristic indicators, and the trusted resident data subset includes one or more trusted MR data.

[0140] Optionally, the data processing module 820 is specifically used to: organize multiple MR data into a mobile chain according to the order of timestamps of the multiple MR data; split the mobile chain into one or more mobile sub-chains according to the timestamp difference and positioning distance difference of the multiple MR data in the mobile chain, the one or more mobile sub-chains including a first mobile sub-chain, the timestamp difference of the MR data contained in the first mobile sub-chain is less than the length of the historical period, and the positioning distance difference is less than a first threshold; organize the MR data in the first mobile sub-chain that are in a resident state and in an indoor state into a resident data set.

[0141] As a possible implementation method, the data processing module 820 is specifically used to: divide the resident data set into one or more resident data subsets according to the base station to which the resident data belongs, each resident data subset includes at least one MR data belonging to a base station cell; based on the first indicator, perform trusted point cluster screening on one or more resident data subsets to obtain one or more trusted point clusters, and the first indicator is used to characterize the network quality; based on the second indicator, perform credibility screening on one or more trusted point clusters to obtain trusted resident data subsets.

[0142] Optionally, the first indicator includes signal delay.

[0143] Optionally, the second indicator includes at least one of average signal strength, average number of adjacent cells and total number of signal points.

[0144] As a possible implementation method, the positioning calculation module 830 is specifically used to: calculate the inverse distance weight of the positioning center of any building among multiple buildings for the trusted resident data subset; determine that the building where the terminal is located during the historical period is the building with the largest inverse distance weight among the multiple buildings.

[0145] As a possible implementation method, the positioning calculation module 830 is also used to: determine a first building membership rate of the terminal's positioning in the historical period according to an inverse distance weight of the positioning center of any building among the multiple buildings to the trusted resident data subset, the first building membership rate including the probability that the terminal is located in any building among the multiple buildings; determine a second building membership rate of the terminal's positioning in the historical period according to the membership of the trusted resident data subset to buildings with different building attribute characteristics among the multiple buildings; and locate the building where the terminal was located in the historical period from among the multiple buildings by combining the first building membership rate and the second building membership rate.

[0146] It should be understood that the positioning device 800 of the embodiment of the present invention can be implemented by a CPU, or by a dedicated integrated circuit, or by a programmable logic device (PLD), wherein the PLD can be a complex programmable logical device (CPLD), a field programmable gate array, a generic array logic (GAL), or any combination thereof. The positioning device 800 is implemented by software. Figure 2 In the positioning method shown in FIG. 8 , the positioning device 800 and its various modules may also be software modules.

[0147] It should be understood that the Figure 1 or Figure 2The computing device 130 shown in the figure may correspond to the positioning device 800 in the embodiment of the application, and may correspond to the corresponding subject in the method according to the embodiment of the application, and the above and other operations and / or functions of each module in the positioning device 800 are respectively to realize Figure 2 For the sake of brevity, the corresponding process of the method in will not be repeated here.

[0148] For example, Fig. 9 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. The computing device may be a terminal device, a network device, a robot, an onboard computer, etc., or may be a chip (system) or other components or assemblies that can be provided in a terminal device, a network device, a robot, an onboard computer, for example, the computing device is Figure 1 or Figure 2 The computing device 130 shown in FIG. Fig. 9 As shown, the computing device 900 may include a processor 901. Optionally, the computing device 900 may also include a memory 902 and / or a transceiver 903. The processor 901 is coupled with the memory 902 and the transceiver 903, such as being connected via a communication bus.

[0149] Combine the following Fig. 9 The components of the computing device 900 are described in detail:

[0150] The processor 901 is the control center of the computing device 900, and may be a processor or a general term for multiple processing elements. For example, the processor 901 is one or more central processing units, or may be a dedicated integrated circuit, or may be configured to implement one or more integrated circuits of the embodiments of the present application, such as one or more microprocessors (digital signal processor, DSP), or one or more field programmable gate arrays.

[0151] Optionally, the processor 901 may execute various functions of the computing device 900 by running or executing software programs stored in the memory 902 and calling data stored in the memory 902, such as Figure 2 Steps 220 to 240 shown in the figure or the functions implemented by the transceiver module 810, the data processing module 820 and the positioning calculation module 830 in the positioning device 800.

[0152] In a specific implementation, as an embodiment, the processor 901 may include one or more CPUs, such as Fig. 9 CPU0 and CPU1 are shown in FIG.

[0153] In a specific implementation, as an embodiment, the computing device 900 may also include multiple processors, such as Fig. 9901 and processor 904 are shown in FIG. Each of these processors may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). A processor herein may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0154] The memory 902 is used to store a software program for executing the solution of the present application, for example, to enable the processor 901 to execute the following Figure 2 The software program for the functions implemented by the modules such as the transceiver module 810, the data processing module 820 and the positioning calculation module 830 in the shown steps 220-240 or the positioning device 800 is controlled and executed by the processor 901. The specific implementation method can refer to the above method embodiment and will not be repeated here.

[0155] Optionally, the memory 902 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 902 may be integrated with the processor 901, or may exist independently and accessed through the interface circuit ( Fig. 9 (not shown) is coupled to the processor 901, which is not specifically limited in the embodiment of the present application.

[0156] The transceiver 903 is used for communication with other devices. For example, if the computing device 900 is a server, the transceiver 903 can be used for communication with a base station or another server.

[0157] Optionally, the transceiver 903 may include a receiver and a transmitter ( Fig. 9 The receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0158] Optionally, the transceiver 903 may be integrated with the processor 901, or may exist independently and be connected to the processor 901 through the interface circuit ( Fig. 9 (not shown) is coupled to the processor 901, which is not specifically limited in the embodiment of the present application.

[0159] It should be noted that Fig. 9 The structure of the computing device 900 shown in the figure does not constitute a limitation on the device, and an actual device may include more or less components than those shown in the figure, or combine certain components, or arrange the components differently.

[0160] In addition, the technical effects of the computing device 900 can refer to the technical effects of the positioning method described in the above method embodiment, which will not be repeated here.

[0161] It should be understood that the processor in the embodiments of the present application may be a central processing unit, and the processor may also be other general-purpose processors, digital signal processors, application-specific integrated circuits, field programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0162] It should also be understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0163] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0164] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0165] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system and device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0166] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0167] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0168] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0169] If the 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 this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk.

[0170] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A positioning method, characterized in that: The method comprises: Obtain multiple MR data of the terminal in the historical period; Selecting one or more credible MR data from the plurality of MR data according to the telecommunication characteristic indicator; The building where the terminal is located during the historical period is located from a plurality of buildings based on the one or more reliable MR data; the telecommunication characteristic index includes at least one index in the MR data that is positively correlated with the positioning accuracy.

2. The method according to claim 1, characterized in that The step of selecting one or more credible MR data from the plurality of MR data according to the telecommunication characteristic indicator comprises: MR data whose positioning distance difference in the historical period is less than a first threshold among the plurality of MR data are formed into a resident data set; A trusted resident data subset is selected from the resident data set according to the telecommunication characteristic indicator, wherein the trusted resident data subset includes one or more trusted MR data.

3. The method according to claim 2, characterized in that The step of forming a resident data set from the plurality of MR data, the MR data having a positioning distance difference within a historical period being less than a first threshold, comprises: According to the sequence of timestamps of the multiple MR data, the multiple MR data are combined into a mobile chain; Splitting the mobile chain into one or more mobile sub-chains according to timestamp differences and positioning distance differences of multiple MR data in the mobile chain, wherein the one or more mobile sub-chains include a first mobile sub-chain, wherein the timestamp difference of the MR data included in the first mobile sub-chain is less than the length of the historical period, and the positioning distance difference is less than the first threshold; The MR data in the first mobile sub-chain that are in the resident state and in the indoor state are combined into the resident data set.

4. The method according to claim 2 or 3, characterized in that: The telecommunication characteristic indicator includes a first indicator and a second indicator, and selecting a trusted resident data subset from the resident data set according to the telecommunication characteristic indicator includes: According to the base station to which the resident data belongs, the resident data set is divided into one or more resident data subsets, each resident data subset including at least one MR data belonging to a base station cell; Based on the first indicator, the one or more resident data subsets are respectively screened for trusted point clusters to obtain one or more trusted point clusters, wherein the first indicator is used to characterize network quality; Based on the second indicator, the one or more trusted point clusters are respectively screened for credibility to obtain the trusted resident data subset.

5. The method according to claim 4, characterized in that The first indicator includes signal delay.

6. The method according to claim 4 or 5, characterized in that: The second indicator includes at least one of an average signal strength, an average number of adjacent cells, and a total number of signal points.

7. The method according to any one of claims 2 to 6, characterized in that: The locating the building where the terminal is located in the historical period from among multiple buildings based on the one or more reliable MR data includes: Calculating an inverse distance weight of a location center of any building in the plurality of buildings with respect to the trusted resident data subset; It is determined that the building where the terminal is located during the historical period is the building with the largest inverse distance weight among the multiple buildings.

8. The method according to any one of claims 2 to 6, characterized in that: The locating the building where the terminal is located in the historical period from among multiple buildings based on the one or more reliable MR data includes: Determine, according to the inverse distance weight of the location center of any building of the multiple buildings to the trusted resident data subset, a first building membership rate of the location of the terminal in the historical period, wherein the first building membership rate includes a probability that the location of the terminal is in any building of the multiple buildings; Determining a second building membership rate of the terminal's location in the historical period according to the membership of the trusted resident data subset to buildings with different building attribute characteristics among the multiple buildings; The first building membership rate and the second building membership rate are combined to locate the building where the terminal is located during the historical period from among multiple buildings.

9. The method according to claim 8, characterized in that The time index includes at least one of the residence duration, residence time and residence date, and the building attribute characteristics include at least one of work, residence, life and shopping.

10. A positioning device, characterized in that: A module comprising the steps of executing the method according to any one of claims 1 to 9.

11. A computing device, characterized in that: The computing device includes a memory and a processor, wherein the memory is used to store a set of computer instructions; when the processor executes the set of computer instructions, the operation steps of any one of the methods described in claims 1-9 are performed.

12. A communication system, characterized in that: The system includes multiple terminals, multiple base stations and the computing device described in claim 11, wherein the multiple base stations are connected to the multiple terminals, the multiple base stations are used to collect MR data of the multiple terminals, and the computing device is used to execute the operating steps of the method described in any one of claims 1-9.

13. A readable storage medium, characterized in that: The readable storage medium includes a computer program or instructions. When the computer program or instructions are executed on a computer, the computer is caused to execute the operation steps of the method described in any one of claims 1 to 9.

Images