Positioning method and system

Abstract

The invention provides a positioning method and system, and the method comprises the steps: obtaining the base station positions of a plurality of positioning base stations, and the distance information, collected by the positioning base stations, of positioning tags; generating a base station combination based on the base station positions of the plurality of positioning base stations; wherein the base station combination comprises a specified number of positioning base stations in the plurality of positioning base stations; generating alternative label positions based on distance information collected by positioning base stations in the base station combination; performing clustering processing on the alternative label positions to obtain a clustering result; based on a clustering result, screening from the base station combinations to obtain a target combination; and determining the position of the positioning tag based on the distance information collected by the positioning base station in the target combination. According to the method, the base station position of the positioning base station in the positioning calculation process is considered, the positioning base station and the positioning data are determined according to the clustering result, and the positioning accuracy is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing, and in particular to a positioning method and system. Background Art

[0002] The wireless positioning system includes multiple positioning base stations and positioning tags, and information transmission between the positioning base stations and positioning tags is achieved through wireless signals. In the related art, for the same positioning tag, multiple positioning base stations respectively obtain the distance parameters between themselves and the positioning tag, and determine the position of the positioning tag through the distance parameters obtained by multiple positioning base stations. This method has low positioning accuracy. Summary of the invention

[0003] In view of this, an object of the present disclosure is to provide a positioning method and system to improve positioning accuracy.

[0004] In a first aspect, an embodiment of the present disclosure provides a positioning method, which includes: obtaining base station positions of multiple positioning base stations and distance information of positioning tags collected by the positioning base stations; generating a base station combination based on the base station positions of the multiple positioning base stations; wherein the base station combination includes: a specified number of positioning base stations from multiple positioning base stations; generating candidate tag positions based on the distance information collected by the positioning base stations in the base station combination; clustering the candidate tag positions to obtain clustering results; based on the clustering results, screening a target combination from the base station combination; and determining the position of the positioning tag based on the distance information collected by the positioning base stations in the target combination.

[0005] The step of generating a base station combination based on the base station positions of multiple positioning base stations includes: traversing multiple positioning base stations to generate multiple base station combinations; wherein the multiple base station combinations include: any group of a specified number of positioning base stations among the multiple positioning base stations.

[0006] The above-mentioned step of generating candidate tag positions based on the distance information collected by the positioning base station in the base station combination includes: generating multiple curves based on the base station positions of the positioning base stations in the base station combination and the distance information collected by the positioning base stations; determining the intersection positions of the multiple curves as candidate tag positions.

[0007] The above-mentioned step of generating multiple curves based on the base station positions of the positioning base stations in the base station combination and the distance information collected by the positioning base stations includes: when the base station combination includes two positioning base stations, two circles are generated based on the base station positions of the positioning base stations in the base station combination and the distance information collected by the positioning base stations; when the base station combination includes three positioning base stations, two hyperbolas are generated based on the base station positions of the positioning base stations in the base station combination and the distance information collected by the positioning base stations.

[0008] The above-mentioned step of clustering the alternative label positions to obtain the clustering results includes: determining the initial cluster center from the alternative label positions; classifying the alternative label positions based on the distance between the alternative label positions and the cluster center to obtain at least one position class; wherein each cluster center corresponds to a position class; the position class contains at least one alternative label position; and determining the position class containing the largest number of alternative label positions as the clustering result.

[0009] The above-mentioned step of classifying the candidate label positions based on the distance between the candidate label positions and the cluster center to obtain at least one position class includes: determining the distance parameter between the candidate label positions and the cluster center, determining the target cluster center corresponding to the minimum distance parameter; and classifying the candidate label positions into the position class corresponding to the target cluster center.

[0010] Before the above-mentioned step of determining the location class containing the largest number of candidate label positions as the clustering result, the method also includes: updating the cluster center corresponding to the location class based on the candidate label positions in the location class; determining the center distance between the initial cluster center and the updated cluster center; based on the center distance and the current number of clustering times, determining whether to continue to execute the following target steps: classifying the candidate label positions based on the distance between the candidate label positions and the cluster center to obtain at least one location class.

[0011] The step of updating the cluster center corresponding to the location class based on the candidate label positions in the location class includes: determining the average position of the candidate label positions in the location class, and using the average position only as the cluster center of the location class.

[0012] The above step of determining the center distance between the initial cluster center and the updated cluster center includes: for each location class, determining the center distance between the initial cluster center and the updated cluster center corresponding to the location class; and determining the sum of the center distances corresponding to each location class as the final center distance.

[0013] The above step of determining whether to continue to execute the following target step based on the center distance and the current classification number includes: when the center distance is greater than a preset distance threshold and the current clustering number is less than or equal to a first number threshold, continuing to execute the target step.

[0014] The above clustering results include: alternative label positions in a position class; the above step of screening the target combination from the base station combination based on the clustering results includes: determining whether the clustering result is a valid result based on the number of alternative label positions in the clustering results and the clustering radius corresponding to the alternative label positions in the clustering results; if the clustering result is a valid result, updating the number of valid results and updating the number of selections of the alternative label positions in the clustering results; wherein the number of selections includes: the number of times the alternative label position is located in the clustering result; based on the number of selections and the number of valid results, determining the selection probability of the alternative label position; determining the base station combination where the alternative label positions whose selection probability is greater than a preset probability threshold are located as the target combination.

[0015] The above-mentioned step of determining whether the clustering result is a valid result based on the number of alternative label positions in the clustering result and the clustering radius corresponding to the alternative label positions in the clustering result includes: if the number of alternative label positions in the clustering result is greater than or equal to a preset number threshold, and the clustering radius corresponding to the alternative label positions in the clustering result is less than or equal to a preset radius threshold, determining that the clustering result is a valid result.

[0016] Before the above-mentioned step of determining whether the clustering result is a valid result based on the number of alternative label positions in the clustering result and the clustering radius corresponding to the alternative label positions in the clustering result, the method also includes: updating the total number of clustering times based on the current number of clustering times corresponding to the clustering result; the method also includes: if the clustering result is not a valid result, and the total number of clustering times is less than or equal to the second number threshold, continuing to perform clustering processing on the alternative label positions to obtain the clustering result; if the clustering result is a valid result, and the total number of clustering times is less than or equal to the second number threshold, after updating the number of valid results and the number of selections, continuing to perform clustering processing on the alternative label positions to obtain the clustering result; if the total number of clustering times is greater than the second number threshold, executing the following steps: determining the selection probability of the alternative label position based on the number of selections and the number of valid results; and determining the base station combination where the alternative label positions whose selection probability is greater than the preset probability threshold are located as the target combination.

[0017] The step of determining the selection probability of the candidate label position based on the number of selections and the number of valid results includes: determining the selection probability of the candidate label position as the ratio of the number of selections and the number of valid results.

[0018] In a second aspect, an embodiment of the present disclosure provides a positioning system, which includes: a position and distance acquisition module, which is used to acquire the base station positions of multiple positioning base stations and the distance information of the positioning tags collected by the positioning base stations; a base station combination generation module, which is used to generate a base station combination based on the base station positions of multiple positioning base stations; wherein the base station combination includes: a specified number of positioning base stations from multiple positioning base stations; an alternative tag position generation module, which is used to generate alternative tag positions based on the distance information collected by the positioning base stations in the base station combination; a clustering result screening module, which is used to cluster the alternative tag positions to obtain clustering results; based on the clustering results, a target combination is screened from the base station combination; a positioning tag position determination module, which is used to determine the position of the positioning tag based on the distance information collected by the positioning base stations in the target combination.

[0019] The embodiments of the present disclosure bring the following beneficial effects:

[0020] The above provides a positioning method and system, wherein the method includes: obtaining the base station positions of multiple positioning base stations and the distance information of the positioning tags collected by the positioning base stations; generating a base station combination based on the base station positions of multiple positioning base stations; wherein the base station combination includes: a specified number of positioning base stations among multiple positioning base stations; generating candidate tag positions based on the distance information collected by the positioning base stations in the base station combination; clustering the candidate tag positions to obtain clustering results; based on the clustering results, screening the target combination from the base station combination; determining the position of the positioning tag based on the distance information collected by the positioning base station in the target combination. In this method, a base station combination is generated based on the base station positions of multiple positioning base stations, clustering is performed on the candidate tag positions generated based on the distance information, and the target combination is screened from the base station combination based on the clustering results, thereby determining the position of the positioning tag. This method takes into account the base station position of the positioning base station in the positioning calculation process, determines the positioning base station and positioning data based on the clustering results, and improves the positioning accuracy.

[0021] Other features and advantages of the present disclosure will be described in the following description, and partly become apparent from the description, or understood by practicing the present disclosure. The purpose and other advantages of the present disclosure are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0022] In order to make the above-mentioned objectives, features and advantages of the present disclosure more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the specific embodiments of the present disclosure or the technical solutions in the prior art, the drawings required for use in the specific embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0024] Figure 1 A schematic diagram of a positioning method provided by an embodiment of the present disclosure;

[0025] Figure 2 A logical diagram of a main process provided by an embodiment of the present disclosure;

[0026] Figure 3 A logical schematic diagram of obtaining a target combination through clustering processing provided by an embodiment of the present disclosure;

[0027] Figure 4 A logical schematic diagram of clustering processing provided by an embodiment of the present disclosure;

[0028] Figure 5 A schematic diagram of a practical application scenario provided by an embodiment of the present disclosure;

[0029] Figure 6 A schematic diagram of a positioning system provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present disclosure, rather than all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present disclosure.

[0031] Currently, a UWB (Ultra Wide Band) wireless positioning system includes multiple positioning base stations and positioning tags. Information transmission between the positioning base stations and the positioning tags is achieved through wireless signals to determine the positions of the positioning tags.

[0032] In related technologies, for the same positioning tag, multiple positioning base stations respectively obtain the distance parameters between themselves and the positioning tag, and determine the position of the positioning tag through the distance parameters obtained by multiple positioning base stations. In other words, for a two-dimensional positioning scenario, there are three distance parameters from the positioning tag to the positioning base station, which can complete the determination of the position of the positioning tag; in actual implementation, in order to ensure coverage of the positioning area, in most scenarios, the number of distance parameters will be more than three. At this time, these distance parameters can be integrated and reorganized to maximize the use of these distance parameters to improve positioning accuracy. However, the positioning accuracy of this method is low.

[0033] In addition to the fact that distance accuracy affects positioning accuracy, the location of the positioning base station involved in the positioning calculation will also affect positioning accuracy. Most existing algorithms solve the location of the positioning tag based on the residual error in the positioning calculation process, and fail to take into account the location of the positioning base station involved in the positioning calculation. Some methods use the angle measurement parameters of the positioning tag and the positioning base station to correct the distance parameters, but on the one hand, angle measurement will increase hardware costs and system complexity; on the other hand, angle measurement is more susceptible to reflection and occlusion, so it has little effect in engineering practice.

[0034] Based on the above, the embodiments of the present disclosure provide a positioning method and system, which can be applied before the positioning calculation process of determining the position of the positioning tag.

[0035] To facilitate understanding of this embodiment, a specific process of the embodiment of the present disclosure is described below. Figure 1 As shown, an embodiment of a positioning method in an embodiment of the present disclosure includes:

[0036] Step S101, obtaining base station positions of multiple positioning base stations and distance information of positioning tags collected by the positioning base stations;

[0037] The execution subject of the present disclosure may be a terminal or a server, in which an intelligent positioning system is running, and the positioning system can determine the positioning base station and positioning data through functions such as candidate tag position generation and clustering processing. The following execution subject is described by taking a terminal as an example, and the terminal is usually an intelligent management terminal.

[0038] Here, the terminal obtains the base station positions of multiple positioning base stations, such as the coordinates of the positioning base stations; and obtains the distance information of the positioning tags collected by the multiple positioning base stations. For the same positioning tag, multiple positioning base stations measure the distance between the positioning tag and the positioning base station to obtain the distance information of the multiple positioning tags.

[0039] Step S102, generating a base station combination based on the base station positions of the plurality of positioning base stations; wherein the base station combination includes: a specified number of positioning base stations among the plurality of positioning base stations;

[0040] The specified number can be two or three.

[0041] Specifically, multiple positioning base stations are traversed, and the base station positions of the multiple positioning base stations are grouped according to the minimum solution granularity to generate multiple base station combinations, each of which may include two or three positioning base stations from the multiple positioning base stations, wherein the number of base station combinations can be set according to the total number of positioning base stations.

[0042] Step S103, generating candidate tag positions based on distance information collected by the positioning base stations in the base station combination;

[0043] Specifically, each base station combination may generate multiple curves according to the base station positions of the positioning base stations in the base station combination and the distance information collected by the positioning base stations, and determine the intersection positions of the multiple curves as candidate tag positions.

[0044] In one example, if the base station combination includes two positioning base stations, two circles are generated according to the base station positions of the positioning base stations and the distance information collected by the positioning base stations, and then the intersection position of the two circles is determined as the candidate label position.

[0045] In another example, if the base station combination includes three positioning base stations, two hyperbolas are generated according to the base station positions of the positioning base stations and the distance information collected by the positioning base stations, and then the intersection position of the two hyperbolas is determined as the candidate tag position.

[0046] Step S104, clustering the candidate tag positions to obtain a clustering result; based on the clustering result, screening the target combination from the base station combination;

[0047] That is to say, according to the aggregation characteristics of multiple candidate tag positions, aggregated candidate tag positions can be screened out, and the positioning base stations involved are the screened out positioning base stations.

[0048] Specifically, the initial cluster center is determined from the candidate label positions, and the candidate label positions are classified according to the distance between the candidate label positions and the cluster center to obtain at least one position class, which is one clustering process; a position class contains at least one candidate label position, each cluster center corresponds to a position class, and the candidate label positions are not repeated in different position classes.

[0049] Next, the cluster center corresponding to the location class can be updated according to the candidate label positions in the location class, and the center distance between the initial cluster center and the updated cluster center can be determined. Then, according to the center distance and the current number of clustering processes, it is determined whether the candidate label positions need to be classified again according to the distance between the candidate label positions and the cluster center to obtain at least one location class. After the clustering process is completed, the location class containing the largest number of candidate label positions is determined from at least one location class, which is the clustering result.

[0050] Here, for multiple clustering results, their validity can be checked. Whether the clustering result is a valid result can be determined based on the number of candidate label positions in the clustering result and the cluster radius corresponding to the candidate label position. If the clustering result is a valid result, the number of valid results is updated, and the number of selected times of the candidate label position in the clustering result is updated. The selection probability of the candidate label position is determined based on the number of selected times and the number of valid results. When the selection probability is greater than the preset probability threshold, the base station combination where the candidate label position is located is the target combination.

[0051] In addition, the total number of clustering times can also be updated according to the number of clustering processing times corresponding to the clustering results. When the total number of clustering times is less than or equal to the clustering processing times threshold, the step of clustering the candidate label positions to obtain the clustering results is performed; when the total number of clustering times is greater than the clustering processing times threshold, the target combination is determined according to the selection probability of the candidate label positions.

[0052] The above clustering process can be implemented by various algorithms, for example, K-means algorithm, hierarchical clustering algorithm, spectral clustering algorithm, etc.

[0053] Step S105, determining the position of the positioning tag based on the distance information collected by the positioning base station in the target combination.

[0054] The positioning base station in the target combination is a positioning base station that meets the positioning accuracy. The position of the positioning tag is calculated using a conventional algorithm based on the distance information collected by the positioning base station.

[0055] The above positioning method obtains the base station positions of multiple positioning base stations and the distance information of the positioning tags collected by the positioning base stations; generates a base station combination based on the base station positions of the multiple positioning base stations; wherein the base station combination includes: a specified number of positioning base stations from the multiple positioning base stations; generates candidate tag positions based on the distance information collected by the positioning base stations in the base station combination; clusters the candidate tag positions to obtain clustering results; based on the clustering results, screens a target combination from the base station combination; and determines the position of the positioning tag based on the distance information collected by the positioning base stations in the target combination.

[0056] In this method, a base station combination is generated based on the base station positions of multiple positioning base stations, and the candidate tag positions generated based on the distance information are clustered. The target combination is screened from the base station combination based on the clustering results, and then the position of the positioning tag is determined. This method takes into account the base station position of the positioning base station in the positioning calculation process, determines the positioning base station and positioning data based on the clustering results, and improves the positioning accuracy.

[0057] In one method, multiple positioning base stations are traversed to generate multiple base station combinations; wherein the multiple base station combinations include: any group of a specified number of positioning base stations among the multiple positioning base stations.

[0058] Specifically, after traversing multiple positioning base stations, the terminal groups the base station positions of the multiple positioning base stations according to the minimum solution granularity to generate multiple base station combinations. There may be overlapping positioning base stations between different base station combinations, and each base station combination may include any two or three positioning base stations from the multiple positioning base stations.

[0059] An example of generating candidate label positions is provided below.

[0060] In one method, multiple curves are generated based on the base station positions of the positioning base stations in the base station combination and the distance information collected by the positioning base stations; and the intersection positions of the multiple curves are determined as candidate label positions.

[0061] Specifically, multiple curves are generated according to the base station positions of the specified number of positioning base stations in the base station combination and the distance information collected by the positioning base stations. When the specified number of positioning base stations in the base station combination is different, the shapes of the multiple curves are usually different. Here, the intersection positions of the multiple curves are obtained and the intersection positions are determined as candidate label positions.

[0062] Furthermore, when the base station combination includes two positioning base stations, two circles are generated based on the base station positions of the positioning base stations in the base station combination and the distance information collected by the positioning base stations; when the base station combination includes three positioning base stations, two hyperbolas are generated based on the base station positions of the positioning base stations in the base station combination and the distance information collected by the positioning base stations.

[0063] Specifically, if the specified number of positioning base stations in the base station combination is two, then two circles are generated based on the base station positions of the two positioning base stations in the base station combination and the distance information collected by the two positioning base stations; if the specified number of positioning base stations in the base station combination is three, then two hyperbolas are generated based on the base station positions of the three positioning base stations in the base station combination and the distance information collected by the three positioning base stations.

[0064] The following provides an embodiment of performing clustering processing on candidate label positions to obtain clustering results.

[0065] In one method, an initial cluster center is determined from the candidate label positions; based on the distance between the candidate label positions and the cluster center, the candidate label positions are classified to obtain at least one position class; wherein each cluster center corresponds to a position class; a position class contains at least one candidate label position; and the position class containing the largest number of candidate label positions is determined as the clustering result.

[0066] Specifically, K candidate label positions are randomly selected from M candidate label positions as the initial cluster centers, and then the distance parameters between each candidate label position and the K cluster centers are calculated respectively. The candidate label positions are classified according to the distance parameters to obtain one or more position classes, which is one clustering number. The K cluster centers correspond to K position classes, each position class contains one or more candidate labels, and the candidate label positions are not repeated in different position classes.

[0067] Here, the location class containing the largest number of candidate label positions is obtained from the K location classes and determined as the clustering result.

[0068] Further, the distance parameter between the candidate label position and the cluster center is determined, and the target cluster center corresponding to the minimum distance parameter is determined; the candidate label position is classified into the position class corresponding to the target cluster center.

[0069] Specifically, the distance parameter between the mth candidate label position and the K cluster centers is obtained by calculation, and then the target cluster center corresponding to the smallest distance parameter is selected, and the mth candidate label position is classified into the position class corresponding to the target cluster center.

[0070] In one method, before obtaining the clustering result, the cluster center corresponding to the location class can be updated based on the candidate label position in the location class; the center distance between the initial cluster center and the updated cluster center can be determined; based on the center distance and the current number of clustering times, it is determined whether to continue to perform the following target steps: based on the distance between the candidate label position and the cluster center, the candidate label position is classified to obtain at least one location class.

[0071] Specifically, according to the candidate label positions in the location class, the cluster center corresponding to the location class is updated. For example, the average position of the candidate label positions in the location class is used as the cluster center of the location class. The sum of the differences between the updated cluster center and the initial cluster center is calculated to obtain the center distance.

[0072] Next, when the center distance and the current clustering number c both meet the target value range, continue to execute the target step, that is, the aforementioned step of classifying the candidate label positions based on the distance between the candidate label positions and the cluster center to obtain at least one position class.

[0073] In an optional manner, the average position of the candidate label positions in the position class is determined, and the average position is used as the cluster center of the position class.

[0074] Specifically, the average position is calculated according to the position coordinates of the candidate label positions in each position class to obtain the cluster center Ω of the position class. c,k , where k is in the interval from 1 to the total number of location classes K.

[0075] In an optional manner, for each location class, the center distance between the initial cluster center and the updated cluster center corresponding to the location class is determined; and the sum of the center distances corresponding to each location class is determined as the final center distance.

[0076] Specifically, in each location class, the center distance δ between the initial cluster center and the updated cluster center corresponding to the location class is calculated c,k =||Ω c,k -Ω c-1,k ||; Add the center distances corresponding to each location class to obtain the final center distance.

[0077] In an optional manner, when the center distance is greater than a preset distance threshold and the current number of clustering times is less than or equal to a first number threshold, the target step continues to be executed.

[0078] Specifically, when the center distance δ c,k When the distance is greater than the preset distance threshold and the current clustering number c is less than or equal to the first number threshold, continue to execute the target step, that is, the aforementioned step of classifying the candidate label positions based on the distance between the candidate label positions and the cluster center to obtain at least one position class.

[0079] The following provides an embodiment of screening a target combination from a base station combination.

[0080] The above clustering results include: candidate label positions in a position class.

[0081] In one method, based on the number of alternative label positions in the clustering result and the clustering radius corresponding to the alternative label positions in the clustering result, it is determined whether the clustering result is a valid result; if the clustering result is a valid result, the number of valid results is updated, and the number of selections of the alternative label positions in the clustering result is updated; wherein the number of selections includes: the number of times the alternative label position is located in the clustering result; based on the number of selections and the number of valid results, the selection probability of the alternative label position is determined; and the base station combination where the alternative label positions whose selection probability is greater than a preset probability threshold are located is determined as the target combination.

[0082] The preset probability threshold can be used to set the probability of filtering out abnormal data. The higher the screening probability, the easier it is to filter out abnormal data, but it is also easy to mistakenly delete Zhang Chang's data. Therefore, a balanced consideration is required when setting it. It can usually be set to 0.25.

[0083] Specifically, after the clustering result is obtained through clustering processing, the clustering result contains all candidate label positions in a position class. Here, whether the clustering result is a valid result is determined based on whether the number of candidate labels J in the clustering result and the cluster radius r corresponding to the candidate labels fall within the preset threshold range. When the clustering result is a valid result, the number of valid results Z is updated, and the number of selected candidate label positions z in the clustering result is updated. m , the number of selections specifically includes the number of times the candidate label position is located in the clustering result z mj , where m represents the number of candidate label positions, and j represents the number of candidate label positions in the clustering results.

[0084] Here, according to the number of selections z m Calculate the probability p of selecting the candidate label position by the number of valid results Z m , and then the selection probability p m The probability is compared with a preset threshold, and the base station combination where the candidate tag positions with probability greater than the preset threshold are located is taken as the target combination.

[0085] Further, if the number of candidate label positions in the clustering result is greater than or equal to a preset number threshold, and the clustering radius corresponding to the candidate label positions in the clustering result is less than or equal to a preset radius threshold, the clustering result is determined to be a valid result.

[0086] The larger the above preset number threshold is, the higher the screening probability is, and it is easier to screen out abnormal data, but it is also easy to mistakenly delete normal data. Therefore, a balanced consideration needs to be made when setting it, and it can usually be set to 3; the preset radius threshold is related to the distance accuracy. The higher the distance accuracy, the smaller the preset radius threshold. The preset radius threshold is usually set to 1 meter.

[0087] Specifically, when the number j of candidate label positions in the clustering result is greater than or equal to a preset number threshold, such as 3, and the clustering radius r corresponding to the candidate label positions in the clustering result is less than or equal to a preset radius threshold, such as 1 meter, the clustering result is determined to be a valid result.

[0088] In one method, before determining whether the clustering result is a valid result, the total number of clustering is updated based on the current number of clustering corresponding to the clustering result. Specifically, the total number of clustering is increased by one each time a clustering result is obtained, and the total number of clustering is updated according to the current number of clustering corresponding to the clustering result, and the following result example is obtained.

[0089] Example 1: If the clustering result is not a valid result and the total number of clustering times is less than or equal to the second number threshold, continue to perform clustering processing on the candidate label positions to obtain the clustering result; the smaller the second number threshold, the better the screening performance, but the longer the time consumption, and the second number threshold can usually be set to 10,000 times. Specifically, when it is determined that the clustering result is not a valid result and the total number of clustering times is less than or equal to the second number threshold, continue to perform the aforementioned clustering processing on the candidate label positions to obtain the clustering result.

[0090] Example 2: If the clustering result is a valid result and the total number of clustering times is less than or equal to the second number threshold, after updating the number of valid results and the number of selections, continue to perform clustering processing on the alternative label positions to obtain the clustering result step; specifically, when it is determined that the clustering result is a valid result and the total number of clustering times is less than or equal to the second number threshold, after updating the number of valid results and the number of selections, continue to perform the aforementioned clustering processing on the alternative label positions to obtain the clustering result step.

[0091] Example 2: If the total number of clustering is greater than the second number threshold, perform the following steps: determine the selection probability of the candidate tag position based on the number of selections and the number of valid results; determine the base station combination where the candidate tag position with a selection probability greater than the preset probability threshold is located as the target combination. Specifically, when the total number of clustering is greater than the second number threshold, according to the number of selections z m And the number of valid results Z calculates the probability p of selecting the candidate label position m , filter out the selected probability p m The base station combination where the candidate tag positions with probability greater than the preset threshold are located is the target combination.

[0092] In one approach, the ratio of the number of selections to the number of valid results is determined as the selection probability of the candidate label position.

[0093] Specifically, for the number of selections z m Perform a ratio operation with the number of valid results Z to obtain the probability p of selecting the candidate label position m =z m / Z.

[0094] An implementation example Figure 2 As shown, the specific implementation method of the main process of this disclosure is introduced.

[0095] Step S201, obtaining the positions of multiple positioning base stations and distance information of positioning tags;

[0096] Specifically, the position coordinates Xi of the positioning base station and the distance information of the positioning tags collected by each positioning base station are input, where i is in the range of 1 to I, and the number of positioning base stations is I.

[0097] Step S202, determining whether to group according to a specified number; the specified number is two;

[0098] If the grouping is performed according to the specified number, then step S203 is executed; if the grouping is not performed according to the specified number, then step S204 is executed.

[0099] Step S203, every two positioning base stations generate a base station combination, and generate candidate tag positions based on the distance information collected in each combination; and execute the next step S205;

[0100] Step S204, generating a base station combination for every three positioning base stations, and generating candidate tag positions based on the distance information collected in each combination; executing the next step S205;

[0101] Step S205, clustering the candidate tag positions to obtain a clustering result, and screening the target combination from the base station combination based on the clustering result;

[0102] Specifically, clustering is performed on the M candidate label positions to obtain a clustering result, the candidate label positions that can be clustered are screened out, and the target combination is determined according to the clustering result.

[0103] Step S206, determining the position of the positioning tag based on the distance information collected in the target combination;

[0104] Specifically, the position of the positioning tag is calculated based on the distance information collected by the positioning base station in the target combination.

[0105] Step S206, end.

[0106] An implementation example Figure 3 As shown, the specific implementation method of the above S205 step of the present disclosure is introduced.

[0107] Step S301, input N base station combinations and corresponding M candidate tag positions;

[0108] Specifically, N base station combinations and corresponding M candidate tag positions are input, where N is less than M.

[0109] Step S302, initialize the total number of clustering times C = 0, the number of clustering results that are valid results Z = 0, and the number of candidate label positions selected z m =0;

[0110] Specifically, the total number of clusterings C is initialized to 0, the number of valid clustering results Z is 0, and the number of times each candidate label position is selected is z m is 0, where m is in the range of 1 to M.

[0111] Step S303, determining whether the total number of clustering times C is less than or equal to the second number threshold T1;

[0112] If the number C is less than or equal to T1, then execute step S304; if C is greater than T1, then execute step S309; ​​the larger the upper limit value T1 is, the better the screening performance is, but the longer it takes, and the general value is 10,000 times.

[0113] Step S304, clustering the candidate tag positions, and recording the clustering results: number of elements J, element mj, clustering radius r, current clustering times c; and executing the next step S305;

[0114] Step S305, updating the total number of clustering times C=C+c;

[0115] Step S306, determining whether the number J of candidate label positions in the clustering result is greater than or equal to a preset number threshold T2, and whether the cluster radius r corresponding to the candidate label positions in the clustering result is less than or equal to a preset radius threshold T3;

[0116] If J is less than or equal to T2, and r is less than or equal to T3, then execute step S307; if J is greater than T2, or r is greater than T3, then execute step S303; the larger the lower limit value T2 is, the higher the screening ratio is, and it is easier to screen out abnormal data, but it is also easy to mistakenly delete normal data, so a balanced consideration should be made when setting it, and it is generally set to 3; the upper limit value T3 is related to the distance accuracy. The higher the distance accuracy, the smaller the value is, and it is generally set to 1 meter.

[0117] Step S307, the number of times the clustering result is a valid result Z=Z+1;

[0118] Step S308, the number of times the candidate label position in the clustering result is selected is increased by 1; and the next step S303 is executed;

[0119] Step S309: Calculate the probability p of each candidate label position being selected m =z m / Z;

[0120] Specifically, the selection probability p is calculated for each candidate label position m =z m / Z, where m is in the range of 1 to M.

[0121] Step S310, determining the base station combination where the candidate tag position with a selection probability greater than a preset probability threshold T4 is located as the target combination;

[0122] Specifically, the larger the value of the screening threshold T4 is, the higher the screening ratio is, and it is easier to screen out abnormal data, but it is also easy to mistakenly delete normal data. Therefore, a balance needs to be considered when setting it, and it is generally set to 0.25.

[0123] Step S311, outputting a target combination; the target combination specifically includes a positioning base station and corresponding positioning data, and the number of the positioning base stations is at least three.

[0124] Step S312, end.

[0125] An implementation example Figure 4 As shown, the specific implementation method of the above S304 step of the present disclosure is introduced.

[0126] Step S401, input M candidate label positions;

[0127] Step S402, initialize the number of clustering times c=1, the number of candidate label positions m=1, and randomly select K candidate label positions as initial cluster centers;

[0128] Step S403, classifying the candidate label positions based on the distance between the candidate label positions and the cluster center to obtain at least one position class;

[0129] Specifically, the distances from the mth candidate label position to the K cluster centers are calculated respectively, the cluster center corresponding to the minimum value is extracted, and the candidate label position is classified into the position class corresponding to the cluster center.

[0130] Step S404, update the number of candidate tag positions m=m+1;

[0131] Step S405, determining the magnitude relationship between m and M;

[0132] If m is greater than M, then execute step S406; if m is less than or equal to M, then execute step S403;

[0133] Step S406, updating the clustering times c=c+1, and calculating the average position of the candidate label positions in the position class to obtain a new cluster center;

[0134] Step S407, calculating the center distance of the cluster center before and after each location class is updated, and obtaining the final center distance δc through summation operation;

[0135] Specifically, for each location class, the center distance between the initial cluster center and the updated cluster center is calculated, and the center distances of all location classes are summed to obtain the final center distance δc.

[0136] Step S408, determining whether the center distance δc is greater than a preset distance threshold T5, and the current clustering number c is less than or equal to the first number threshold T0;

[0137] If δc is greater than T5, and c is less than or equal to T0, then execute step S409; if δc is less than or equal to T5, or c is greater than T0, then execute step S410; the smaller the upper limit value T5, the better the screening performance, but the longer it takes, and the general value is 0.01 meters.

[0138] Step S409, initialize the number of candidate tag positions m=1; execute the next step S403;

[0139] Step S410, determining the position cluster containing the largest number of candidate label positions as the clustering result, and recording: the number of elements J, the element mj, the clustering radius r, and the current clustering number c;

[0140] Step S411, outputting clustering results;

[0141] Step S412, end.

[0142] This embodiment groups the base stations according to the minimum solution granularity and solves the intersection coordinates, and based on the clustering characteristics of the intersections, screens out base station combinations whose intersection coordinates can be clustered together. The base stations and corresponding data involved in these base station combinations are the filtered positioning data.

[0143] An implementation example Figure 5 As shown, a specific implementation method of filtering out abnormal values ​​in positioning data to a certain extent through the solution of the present disclosure is introduced.

[0144] In an example, the actual distances and measured distances corresponding to the six base stations are shown in the following table, and the measured distances are plotted as shown in the following Table 1.

[0145] Table 1

[0146]

[0147] Focus the table on the range of true distance X-axis between 0 and 13 and measured distance Y-axis between 0 and 8, as shown in Figure 5 As shown, the dark blue curve, i.e., the measurement data of base station 3, is far away from the intersection screening result. That is to say, the positioning data corresponding to base station 3 does not pass through these clustering results, so the measurement data of base station 3 is abnormal.

[0148] Corresponding to the above method embodiment, see Figure 6 A schematic diagram of a positioning system is shown, the system comprising:

[0149] The position and distance acquisition module 601 is used to acquire the base station positions of multiple positioning base stations and the distance information of the positioning tags collected by the positioning base stations;

[0150] The base station combination generating module 602 is used to generate a base station combination based on the base station positions of the multiple positioning base stations; wherein the base station combination includes: a specified number of positioning base stations among the multiple positioning base stations;

[0151] A candidate tag position generating module 603 is used to generate a candidate tag position based on the distance information collected by the positioning base station in the base station combination;

[0152] The clustering result screening module 604 is used to perform clustering processing on the candidate tag positions to obtain clustering results; based on the clustering results, a target combination is screened from the base station combination;

[0153] The positioning tag position determination module 605 is used to determine the position of the positioning tag based on the distance information collected by the positioning base station in the target combination.

[0154] The above-mentioned positioning system obtains the base station positions of multiple positioning base stations and the distance information of the positioning tags collected by the positioning base stations; generates a base station combination based on the base station positions of multiple positioning base stations; wherein the base station combination includes: a specified number of positioning base stations from multiple positioning base stations; generates candidate tag positions based on the distance information collected by the positioning base stations in the base station combination; clusters the candidate tag positions to obtain clustering results; based on the clustering results, screens a target combination from the base station combination; and determines the position of the positioning tag based on the distance information collected by the positioning base stations in the target combination.

[0155] In this method, a base station combination is generated based on the base station positions of multiple positioning base stations, and the candidate tag positions generated based on the distance information are clustered. The target combination is screened from the base station combination based on the clustering results, and then the position of the positioning tag is determined. This method takes into account the base station position of the positioning base station in the positioning calculation process, determines the positioning base station and positioning data based on the clustering results, and improves the positioning accuracy.

[0156] The base station combination generation module is further used to traverse multiple positioning base stations to generate multiple base station combinations; wherein the multiple base station combinations include: any group of a specified number of positioning base stations among the multiple positioning base stations.

[0157] The candidate tag position generating module is further used to generate multiple curves based on the base station positions of the positioning base stations in the base station combination and the distance information collected by the positioning base stations; and determine the intersection positions of the multiple curves as the candidate tag positions.

[0158] The above-mentioned alternative label position generation module is also used to generate two circles when the base station combination includes two positioning base stations based on the base station positions of the positioning base stations in the base station combination and the distance information collected by the positioning base stations; when the base station combination includes three positioning base stations, two hyperbolas are generated based on the base station positions of the positioning base stations in the base station combination and the distance information collected by the positioning base stations.

[0159] The above-mentioned clustering result screening module is also used to determine the initial cluster center from the alternative label positions; based on the distance between the alternative label positions and the cluster center, the alternative label positions are classified to obtain at least one position class; wherein each cluster center corresponds to a position class; the position class contains at least one alternative label position; and the position class containing the largest number of alternative label positions is determined as the clustering result.

[0160] The above clustering result screening module is also used to determine the distance parameter between the candidate label position and the cluster center, determine the target cluster center corresponding to the minimum distance parameter; and classify the candidate label position into the position class corresponding to the target cluster center.

[0161] The above-mentioned device also includes a center distance determination module, which is used to update the cluster center corresponding to the location class based on the alternative label position in the location class; determine the center distance between the initial cluster center and the updated cluster center; based on the center distance and the current number of clustering times, determine whether to continue to execute the following target steps: based on the distance between the alternative label position and the cluster center, classify the alternative label position to obtain at least one location class.

[0162] The above-mentioned center distance determination module is also used to determine the average position of the candidate label positions in the position class, and use the average position as the cluster center of the position class.

[0163] The center distance determination module is also used to determine the center distance between the initial cluster center and the updated cluster center corresponding to each location class for each location class; and determine the sum of the center distances corresponding to each location class as the final center distance.

[0164] The above-mentioned center distance determination module is also used to continue executing the target step when the center distance is greater than a preset distance threshold and the current clustering number is less than or equal to the first number threshold.

[0165] The above clustering results include: alternative label positions in a position class; the above clustering result screening module is also used to determine whether the clustering result is a valid result based on the number of alternative label positions in the clustering results and the clustering radius corresponding to the alternative label positions in the clustering results; if the clustering result is a valid result, the number of valid results is updated, and the number of selections of the alternative label positions in the clustering results is updated; wherein the number of selections includes: the number of times the alternative label positions are located in the clustering results; based on the number of selections and the number of valid results, the selection probability of the alternative label positions is determined; the base station combination where the alternative label positions whose selection probability is greater than a preset probability threshold are located is determined as the target combination.

[0166] The above clustering result screening module is also used to determine that the clustering result is a valid result if the number of candidate label positions in the clustering result is greater than or equal to a preset number threshold, and the cluster radius corresponding to the candidate label positions in the clustering result is less than or equal to a preset radius threshold.

[0167] The above-mentioned clustering result screening module is also used to update the total number of clustering times based on the current number of clustering times corresponding to the clustering result; the above-mentioned device also includes a first judgment module, which is used to, if the clustering result is not a valid result and the total number of clustering times is less than or equal to the second number threshold, continue to perform clustering processing on the alternative label positions to obtain the clustering result step; if the clustering result is a valid result and the total number of clustering times is less than or equal to the second number threshold, after updating the number of valid results and the number of selections, continue to perform clustering processing on the alternative label positions to obtain the clustering result step; if the total number of clustering times is greater than the second number threshold, perform the following steps: based on the number of selections and the number of valid results, determine the selection probability of the alternative label position; determine the base station combination where the alternative label position with a selection probability greater than the preset probability threshold is located as the target combination.

[0168] The clustering result screening module is further used to determine the selection probability of the candidate label position by the ratio of the number of selections to the number of valid results.

[0169] 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.

[0170] In addition, in the description of the embodiments of the present disclosure, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present disclosure can be understood according to specific circumstances.

[0171] If the functions are implemented in the form of software functional units 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 disclosure, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which 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 method described in each embodiment of the present disclosure. 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 (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0172] In the description of the present disclosure, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present disclosure and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present disclosure. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0173] Finally, it should be noted that the above embodiments are only specific implementation methods of the present disclosure, which are used to illustrate the technical solutions of the present disclosure, rather than to limit them. The protection scope of the present disclosure is not limited thereto. Although the present disclosure is described in detail with reference to the above embodiments, those skilled in the art should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above embodiments within the technical scope disclosed in the present disclosure, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure, and should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A positioning method, characterized in that: The method comprises: Acquire base station locations of multiple positioning base stations and distance information of positioning tags collected by the positioning base stations; Based on the base station positions of the multiple positioning base stations, a base station combination is generated; wherein the base station combination includes: a specified number of positioning base stations among the multiple positioning base stations; Generate candidate tag positions based on distance information collected by positioning base stations in the base station combination; Performing clustering processing on the candidate tag positions to obtain a clustering result; and screening a target combination from the base station combination based on the clustering result; The position of the positioning tag is determined based on the distance information collected by the positioning base station in the target combination.

2. The method according to claim 1, characterized in that The step of generating a base station combination based on the base station positions of the multiple positioning base stations comprises: The multiple positioning base stations are traversed to generate multiple base station combinations; wherein the multiple base station combinations include: any group of a specified number of positioning base stations among the multiple positioning base stations.

3. The method according to claim 1, characterized in that The step of generating candidate tag positions based on the distance information collected by the positioning base station in the base station combination includes: Generate a plurality of curves based on the base station positions of the positioning base stations in the base station combination and the distance information collected by the positioning base stations; The intersection positions of the multiple curves are determined as candidate label positions.

4. The method according to claim 3, characterized in that The step of generating a plurality of curves based on the base station positions of the positioning base stations in the base station combination and the distance information collected by the positioning base stations comprises: When the base station combination includes two positioning base stations, two circles are generated based on the base station positions of the positioning base stations in the base station combination and the distance information collected by the positioning base stations; When the base station combination includes three positioning base stations, two hyperbolas are generated based on the base station positions of the positioning base stations in the base station combination and the distance information collected by the positioning base stations.

5. The method according to claim 1, characterized in that The step of clustering the candidate label positions to obtain a clustering result includes: Determining an initial cluster center from the candidate label positions; Based on the distance between the candidate label position and the cluster center, the candidate label position is classified to obtain at least one position class; wherein each cluster center corresponds to a position class; and the position class contains at least one candidate label position; The location class containing the largest number of candidate label locations is determined as the clustering result.

6. The method according to claim 5, characterized in that The step of classifying the candidate label positions based on the distance between the candidate label positions and the cluster center to obtain at least one position class includes: Determine a distance parameter between the candidate label position and the cluster center, and determine a target cluster center corresponding to the minimum distance parameter; The candidate label positions are classified into the position class corresponding to the target cluster center.

7. The method according to claim 5, characterized in that Before the step of determining the position class containing the largest number of candidate label positions as the clustering result, the method further includes: Based on the candidate label positions in the position class, updating the cluster center corresponding to the position class; Determining the center distance between the initial cluster center and the updated cluster center; Based on the center distance and the current number of clustering times, determine whether to continue to perform the following target step: classify the candidate label positions based on the distance between the candidate label positions and the cluster center to obtain at least one position class.

8. The method according to claim 7, characterized in that The step of updating the cluster center corresponding to the location class based on the candidate label position in the location class includes: An average position of the candidate label positions in the position class is determined, and the average position is used as the cluster center of the position class.

9. The method according to claim 7, characterized in that: The step of determining the center distance between the initial cluster center and the updated cluster center comprises: For each location class, determining the center distance between the initial cluster center and the updated cluster center corresponding to the location class; The sum of the center distances corresponding to each of the position classes is determined as the final center distance.

10. The method according to claim 7, characterized in that The step of determining whether to continue to perform the following target step based on the center distance and the current number of clustering times includes: When the center distance is greater than the preset distance threshold and the current clustering number is less than or equal to the first number threshold, continue to execute the target step.

11. The method according to claim 1, characterized in that: The clustering result includes: candidate tag positions in a position class; the step of screening a target combination from the base station combination based on the clustering result includes: Determining whether the clustering result is a valid result based on the number of candidate label positions in the clustering result and the clustering radius corresponding to the candidate label positions in the clustering result; If the clustering result is a valid result, update the number of valid results and update the number of times the candidate label position in the clustering result is selected; wherein the number of times the candidate label position is located in the clustering result; Determining the probability of selection of the candidate label position based on the number of selections and the number of valid results; The base station combination where the candidate tag positions with the selected probability greater than the preset probability threshold are located is determined as the target combination.

12. The method according to claim 11, characterized in that The step of determining whether the clustering result is a valid result based on the number of candidate label positions in the clustering result and the clustering radius corresponding to the candidate label positions in the clustering result includes: If the number of candidate label positions in the clustering result is greater than or equal to a preset number threshold, and the cluster radius corresponding to the candidate label positions in the clustering result is less than or equal to a preset radius threshold, the clustering result is determined to be a valid result.

13. The method according to claim 11, characterized in that Before the step of determining whether the clustering result is a valid result based on the number of candidate label positions in the clustering result and the clustering radius corresponding to the candidate label positions in the clustering result, the method further includes: updating the total number of clustering times based on the current number of clustering times corresponding to the clustering result; The method further comprises: if the clustering result is not a valid result and the total number of clustering times is less than or equal to a second number threshold, continuing to perform clustering processing on the candidate label positions to obtain a clustering result; If the clustering result is a valid result, and the total number of clustering times is less than or equal to the second number threshold, after updating the number of valid results and the number of selections, continue to perform clustering processing on the candidate label positions to obtain a clustering result; If the total number of clustering times is greater than the second number threshold, perform the following steps: determine the selection probability of the candidate tag position based on the number of selections and the number of valid results; and determine the base station combination where the candidate tag position whose selection probability is greater than the preset probability threshold is located as the target combination.

14. The method according to claim 11, characterized in that The step of determining the selection probability of the candidate label position based on the number of selections and the number of valid results includes: The ratio of the number of selections to the number of valid results is determined as the selection probability of the candidate label position.

15. A positioning system, characterized in that: The system comprises: A position and distance acquisition module, used to acquire the base station positions of multiple positioning base stations and the distance information of the positioning tags collected by the positioning base stations; A base station combination generating module, configured to generate a base station combination based on the base station positions of the plurality of positioning base stations; wherein the base station combination includes: a specified number of positioning base stations among the plurality of positioning base stations; A candidate tag position generating module, used to generate a candidate tag position based on the distance information collected by the positioning base station in the base station combination; A clustering result screening module, used for performing clustering processing on the candidate tag positions to obtain clustering results; based on the clustering results, screening a target combination from the base station combination; The positioning tag position determination module is used to determine the position of the positioning tag based on the distance information collected by the positioning base station in the target combination.

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