Method for enhancing location monitoring with high-precision map for closed space pnt network
By dividing the space into monitoring grids and semantic units, establishing a mapping relationship library, and using PNT network observation information to calculate user location, the problems of large computational load and long response time in traditional methods are solved, achieving efficient user location monitoring and reasonable semantic information generation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINESE ACAD OF SURVEYING & MAPPING
- Filing Date
- 2022-11-25
- Publication Date
- 2026-05-01
AI Technical Summary
In enclosed spaces, traditional methods for monitoring user location involve large computational loads, long response times, and difficulty in fully ensuring the rationality of location semantic information, while user density monitoring also suffers from excessive computational loads.
By dividing the monitoring grid and semantic units, a mapping relationship library between entity objects and semantic information is established. User location is calculated using PNT network observation information, the grid code of semantic units is directly obtained and semantic information is indexed, and constraint relationship judgment is made in combination with physical and user attribute information.
It significantly shortens the generation time of user location monitoring information, improves the rationality of semantic information, and reduces the amount of computation, especially in the efficiency of user location monitoring in large-scale, cross-regional, complex building complexes.
Smart Images
Figure CN115767437B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of wireless positioning, and more particularly to a method for location monitoring using a closed-space PNT network and a high-precision map. Background Technology
[0002] User location is achieved by using PNT network observation information within an enclosed space. The user location results are then transmitted back to the monitoring platform to display the user's location on a map, thus realizing user location monitoring; that is, user location information is displayed through a location point on the map.
[0003] For a single user, extracting the implicit physical and semantic information behind location points on a map can enrich the content of user location monitoring. Traditional methods traverse and search all entity objects within the map based on the user's location, and then use the matched entity objects to obtain the semantic information of that location point. Obviously, traditional methods suffer from high computational cost and long response time. Summary of the Invention
[0004] The purpose of this invention is to provide a method for location monitoring enhanced by a closed-space PNT network and a high-precision map, in order to solve the problems of large computational load and slow speed in the process of generating user location monitoring information in complex building complexes across large areas.
[0005] This invention provides a method for location monitoring using a closed-space PNT network and a high-precision map, comprising:
[0006] Based on the preset monitoring grid side length, a high-precision map of the target closed space is divided to obtain the monitoring grid of the target closed space; based on the preset semantic unit side length, the monitoring grid is divided to obtain the semantic unit grid of the target closed space; a mapping relationship library is established between the semantic information of any entity object element and the location information of its semantic unit.
[0007] Based on the location coordinates of the target entity object, obtain the grid code of the monitoring grid where the target entity object is located;
[0008] Based on the location coordinates of the target entity object and the grid code of the monitoring grid where the target entity object is located, the grid code of the semantic unit where the target entity object is located is obtained;
[0009] Based on the grid encoding of the semantic units and the mapping relationship library, the location semantic information of the target entity object is obtained.
[0010] In the above embodiments of the present invention, preferably, the position coordinates (x, y) of the target entity object are calculated in the following manner:
[0011] Method 1: The target entity obtains observation information from at least three base stations, and calculates the position coordinates of the target entity based on the observation information from the at least three base stations;
[0012] Alternatively, Method 2: The target entity obtains observation information from no more than two base stations, and uses the PNT network observation information to complete the location monitoring of the target entity. Specifically, this involves obtaining the signal strength S of r PNT base stations observed by the target entity. k , k = 1,..,r; for the signal strength S k Sort the PNT base station B corresponding to the maximum signal strength. km coordinates (x) km ,y km ), the coordinates (x km ,y km ) is used as the position coordinates of the target entity object.
[0013] In the above embodiments of the present invention, preferably, based on the position coordinates (x, y) of the target entity object, the grid code (i1, j1) of the monitoring grid where the target entity object is located is calculated using formula (1):
[0014]
[0015] In the formula, (x0, y0) represents the planar coordinates of the lower left corner of the high-precision map boundary of the target entity object's enclosed space; gs represents the side length of the monitoring grid.
[0016] In the above embodiments of the present invention, preferably, the grid code (i2,j2) of the semantic unit where the target entity object is located is calculated using formula (2) based on the location coordinates of the target entity object and the grid code of the monitoring grid where the target entity object is located;
[0017]
[0018] In the formula, (x,y) represents the position coordinates of the target entity object; (x0,y0) represents the planar coordinates of the lower left corner of the high-precision map boundary of the target closed space where the target entity object is located; (i1,j1) represents the grid code of the monitoring grid where the target entity object is located; gs represents the side length of the monitoring grid where the target entity object is located; and ss represents the side length of the semantic unit.
[0019] In the above embodiments of the present invention, preferably, the step of establishing a mapping relationship library between the semantic information of any entity object element and the semantic unit location information of the entity object element specifically includes: establishing a semantic unit encoding and semantic information mapping table; the semantic unit encoding and semantic information mapping table includes the stored value of any semantic unit, the row number and column number of the position of the semantic unit, the stored value being the number of the semantic information table corresponding to the semantic unit, denoted as U(row number, column number); establishing a semantic information table library, the semantic information table library including at least one semantic information table; each semantic information table includes at least the semantic information table number, the entity object element name within the semantic unit, the entity object number within the semantic unit, the spatial location name of the semantic unit, and the spatial attributes of the semantic unit, the spatial attributes including whether personnel are available, whether vehicles are available, and whether logistics are available; establishing an index relationship between the semantic information table library and the semantic unit encoding and semantic information mapping table based on the number of the semantic information table, thus completing the establishment of the mapping relationship library.
[0020] In the above embodiments of the present invention, preferably, after obtaining the position semantic information of the target entity object, the method further includes setting constraints to generate output position semantic information of the target entity object that conforms to the physical scene, specifically as follows:
[0021] S001, using the constraints of formula (3), determine whether the location semantics of the location semantic information is reasonable;
[0022] If so, the location semantic information is used as the output location semantic information of the target entity object and output.
[0023] If not, proceed to S002;
[0024]
[0025] Wherein, U(i2,j2).SpaceName represents the space name where the target entity object is located;
[0026] B km (i2 km j2 km SpaceName represents the base station B with the highest observed signal strength for the target entity. km The name of the space in which the target entity object is located; U(i2,j2).UserTag represents the spatial attribute of the space in which the target entity object is located;
[0027] S002, search for the semantic unit that satisfies formula (3) and is closest to the target entity object, obtain the semantic information table corresponding to the closest semantic unit, and use the semantic information of the semantic information table as the output position semantic information of the target entity object, and output it, specifically as formula (4):
[0028]
[0029] In formula (4), ds represents the distance between the target entity object and the center of any semantic unit in the search grid; the search grid is the set of all semantic units that satisfy the constraints of formula (3); (x,y) represents the position coordinates of the target entity object; (x i3 ,y j3 (i3,j3) represents the center coordinates of any semantic unit in the search grid; (i1,j1) represents the grid code of any semantic unit in the search grid; (x0,y0) represents the planar coordinates of the lower left corner of the high-precision map boundary of the target entity object; gs represents the side length of the monitoring grid; ss represents the side length of the semantic unit; (i2,j2) represents the grid code of the semantic unit where the target entity object is located; based on the grid code (i3,j3) of any semantic unit in the search grid, its semantic information table code U(i3,j3) is obtained; U(i3,j3).SpaceName represents the space name of the semantic unit with grid code (i3,j3); U(i3,j3).UserTag represents the personnel space attribute of the semantic unit with grid code (i3,j3).
[0030] In the above embodiments of the present invention, preferably, the B km (i2 km j2 km The SpaceName is obtained by following these steps: obtaining the signal strength S of the r PNT base stations observed by the target entity object. k , k = 1,..,r; for the signal strength S k Sort the PNT base station B corresponding to the maximum signal strength. km coordinates (x) km ,y km Based on the aforementioned B km coordinates (x) km ,y km ) and the method described in claim 1, to obtain the B from the mapping relation library. km B km (i2 km j2 km ).SpaceName.
[0031] In the above embodiments of the present invention, preferably, the method further includes updating the user density n(i1,j1) within the monitoring grid based on the grid code (i1,j1) of any monitoring grid, specifically: assuming that the user density n of each monitoring grid is initialized to 0; and updating the user density n(i1,j1) within the monitoring grid code (i1,j1) using formula (5):
[0032] n(i1,j1)=n(i1,j1)+1 (5).
[0033] The method for closed-space PNT network and high-precision map-enhanced location monitoring described in this application has the following advantages:
[0034] Beneficial effects:
[0035] (1) Shorten the generation time of user location monitoring information. Traditional methods generate semantic information of a user's location by traversing the geometric relationship between all entity objects within a tile map area and each user's location. This means that generating semantic information of a user's location requires a traversal calculation, resulting in a large amount of computation and a long response time. However, the technical solution of this application only calculates the grid code of the semantic unit where the user is located in a single traversal, and obtains the semantic information by reading the mapping relation library index based on the grid code of the semantic unit, which greatly reduces the amount of computation and response time of user location monitoring information.
[0036] (2) Improving the rationality of user location monitoring information. Traditional methods ignore the spatial relationship features, such as the geometric relationship and transmission space interaction relationship between the user and the PNT base station, implied in the PNT network observation information in the closed space during the matching process. This makes it difficult for traditional methods to fully guarantee the rationality of the extracted location semantic information. The technical solution of this application is based on the constraints of the physical information of the entity object, user attribute information, and PNT network observation information to ensure that the user location is interpreted into semantic units that conform to the constraints, thereby generating reasonable semantic information.
[0037] (3) Reduce the computational load of generating user location density monitoring information. Traditional methods generate area density by traversing and calculating the distance between the area center and each user and determining the range. As the number of users and area centers increases, the computational load increases dramatically, and the computation time increases rapidly. For m areas and n users, at most m×n calculations are required. However, the technical solution of this application directly calculates the area code based on the user location and accumulates the number of users within the area code to complete user density monitoring, requiring at most n calculations. Attached Figure Description
[0038] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0039] Figure 1 This is a schematic diagram of the process of constructing the location semantic relationship of the target closed space high-precision map in the method of enhancing location monitoring with closed space PNT network and high-precision map according to one of the technical solutions of this application;
[0040] Figure 2 This is a schematic diagram illustrating the process of using a closed-space PNT network and a high-precision map to enhance location monitoring, as described in this application, to achieve the output of the semantic location of the target entity object. Detailed Implementation
[0041] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.
[0042] The principle of this application: The technical solution of this application, which uses a closed-space PNT network and high-precision map to enhance location monitoring, calculates the grid code of the semantic unit where the user is located in only one traversal. Based on the grid code of the semantic unit, it reads the index of the mapping relation library to obtain semantic information, which significantly reduces the traversal calculation amount and response waiting time of user location monitoring information. At the same time, the technical solution of this application also ensures that the user's location is interpreted to a semantic unit that conforms to the constraint relationship based on the physical information of the entity object, user attribute information, and the constraint relationship of PNT network observation information, thereby generating reasonable semantic information.
[0043] Example
[0044] Reference Figure 1 and Figure 2 The method for location monitoring using a closed-space PNT network and high-precision map augmentation described in this embodiment includes:
[0045] S1. Based on the preset monitoring grid side length, divide the target closed space into a high-precision map to obtain the monitoring grid of the target closed space; based on the preset semantic unit side length, divide the monitoring grid to obtain the semantic unit grid of the target closed space; establish a mapping relationship library between the semantic information of any entity object element and the location information of its semantic unit.
[0046] In this embodiment, the side length of the monitoring grid is set to gs, typically 2m. The grid code of the monitoring grid is set to (i1, j1), and the user count n within all monitoring grids is set to 0. Based on the boundary of the monitoring service area, the planar coordinates (x0, y0) of the lower left corner of the boundary are obtained.
[0047] In this embodiment, each monitoring grid is further subdivided into semantic units. Specifically, referring to the 3D solid modeling specifications, the semantic unit side length ss is set according to the minimum size of the semantic unit. Generally, the semantic unit side length is 0.2m. The monitoring grid coded as (i1,j1) is divided according to the semantic unit side length to obtain multiple semantic units.
[0048] In this embodiment, a mapping relationship database is established between the semantic information of any entity object element and the location information of the semantic unit in which the entity object element is located. Specifically, a semantic unit encoding and semantic information mapping table is established, as shown in Table 1. The semantic unit encoding and semantic information mapping table includes the stored value of any semantic unit, the row number and column number of the location of the semantic unit, and the stored value is the number of the semantic information table corresponding to the semantic unit, denoted as U(row number i2, column number j2). A semantic information table database is established, which includes at least one semantic information table, as shown in Table 2. The at least one semantic information table includes at least the semantic information table number, the entity object element name in the semantic unit, the entity object number in the semantic unit, the spatial location name of the semantic unit, and the spatial attributes of the semantic unit. The spatial attributes include personnel availability attributes, vehicle availability attributes, and logistics availability attributes. Based on the number of the semantic information table, an index relationship is established between the semantic information table database and the semantic unit encoding and semantic information mapping table, thus completing the establishment of the mapping relationship database.
[0049] Table 1 Semantic Unit Encoding and Semantic Information Mapping Table
[0050]
[0051] Table 2 Semantic Information Table
[0052]
[0053] S2, Based on the location coordinates of the target entity object, obtain the grid code of the monitoring grid where the target entity object is located.
[0054] S201, Regarding the position coordinates of the target entity object
[0055] The closed-space PNT network mainly includes base station positioning devices such as Bluetooth, WIFI, and UWB. The user terminal receives PNT network signals and generates observation information. This observation information mainly includes distance and signal strength. Using this observation information, the terminal performs positioning calculations to obtain the user's location. The user terminal needs to accumulate observation information from at least three base stations before completing the positioning calculation. During periods when the user terminal's observation information is insufficient and positioning calculation conditions are not met, the observation information from the PNT base station network can be used to assist in location monitoring. Therefore, the position coordinates of the target entity are calculated in the following way:
[0056] Method 1: The target entity obtains observation information from more than 3 base stations, and calculates the position coordinates of the target entity based on the observation information from the more than 3 base stations.
[0057] Alternatively, if the target entity obtains observation information from two or fewer base stations, the location monitoring of the target entity can be assisted by the PNT network observation information. Specifically, this involves obtaining the signal strength S of the r PNT base stations observed by the target entity. k , k = 1,..,r; for the signal strength S k Sort the PNT base station B corresponding to the maximum signal strength. km coordinates (x) km ,y km The coordinates are used as the position coordinates of the target entity. The PNT network observation information refers to the PNT network observation information of the enclosed space where the target entity is located.
[0058] S202, based on the position coordinates (x, y) of the target entity, calculate the grid code (i1, j1) of the monitoring grid where the target entity is located using formula (1):
[0059]
[0060] In formula (1), (x0, y0) represents the planar coordinates of the lower left corner of the high-precision map boundary of the target closed space where the target entity object is located; gs represents the side length of the monitoring grid.
[0061] S3, based on the location coordinates of the target entity object and the grid code of the monitoring grid where the target entity object is located, calculate the grid code (i2,j2) of the semantic unit grid where the target entity object is located using formula (2);
[0062]
[0063] In formula (2), ss represents the side length of the semantic unit.
[0064] S4. Based on the semantic unit grid and the mapping relationship library, the location semantic information of the target entity object is obtained. Here, a semantic unit encoding and semantic information mapping table is established, as shown in Table 1; a semantic information table library is established, with each semantic information table shown in Table 2; and a mapping relationship library is established between the semantic unit encoding and semantic information mapping table and the semantic information table library. Based on the mapping relationship library, the semantic information table numbered U(i2,j2) is obtained by indexing the grid encoding (i2,j2) of the semantic unit where the target entity object element is located. According to the number U(i2,j2) of the semantic information table, the space name U(i2,j2).SpaceName of the target entity object is obtained. That is: the semantic information table is obtained by indexing the semantic unit grid encoding; according to the semantic information table, the semantic information of the semantic unit corresponding to the location of each target entity object is interpreted.
[0065] In this embodiment, the method further includes updating the user density n(i1,j1) within a monitoring grid based on the grid code (i1,j1) of any monitoring grid. Specifically, the user density n of each monitoring grid is initialized to 0. Each target entity object is traversed; the grid code (i1,j1) of the monitoring grid where each target entity object is located is calculated based on the position coordinates of each target entity object and formula (1); the user density n(i1,j1) of any monitoring grid is updated using formula (3) according to the grid code (i1,j1) of the monitoring grid where each target entity object is located.
[0066] n(i1,j1)=n(i1,j1)+1 (3)
[0067] User density n(i1,j1) represents the number of users within the monitoring grid (i1,j1).
[0068] In this embodiment, the method further includes: after obtaining the semantic information of the target entity object, setting constraints to generate output semantic information of the target entity object that conforms to the physical scene.
[0069] Taking a regular user as an example, based on user attributes, the available spatial attribute U(i2,j2).UserTag for the semantic unit is obtained. If the actual object of the semantic unit is a wall, column, window sill, or suspended area, the spatial attribute of the user in that semantic unit is set to unavailable. Obviously, a regular user must be located in a semantic unit where the spatial attribute of the user is available. If the spatial attribute of the semantic unit whose index is calculated based on the user location result is unavailable, it indicates a significant deviation in the user location result from the perspective of semantic information of the physical scene. Therefore, it is necessary to make a reasonable judgment and process the semantic information of the obtained target entity object, specifically as follows:
[0070] S001, using the constraints of formula (4), determine whether the location semantics of the location semantic information is reasonable;
[0071] If so, the location semantic information is used as the output location semantic information of the target entity object and output.
[0072] If not, proceed to S002;
[0073]
[0074] Wherein, U(i2,j2).SpaceName represents the name of the space where the target entity object is located; B km (i2 km j2 km SpaceName represents the base station B with the highest observed signal strength for the target entity. km The name of the space in which the target entity object is located; U(i2,j2).UserTag represents the personnel space attribute of the space in which the target entity object is located;
[0075] S002, search for the semantic unit that satisfies formula (4) and is closest to the target entity object, obtain the semantic information table corresponding to the closest semantic unit, and use the semantic information of the semantic information table as the output position semantic information of the target entity object, and output it, specifically as formula (5):
[0076]
[0077] Here, search conditions are set (the search conditions are semantic units that satisfy formula (4)), and a search grid is obtained. The search grid includes at least one semantic unit. The semantic unit closest to the target entity is found within the search grid. That is, according to formula (5), the semantic unit that satisfies the constraints of formula (4) and is closest to the target entity is obtained. The parameters in formula (5) are explained as follows: ds represents the distance between the target entity and the center of any semantic unit in the search grid; the search grid is the set of all semantic units that satisfy the constraints of formula (4); (x,y) represents the position coordinates of the target entity; (x i3 ,y j3(i3,j3) represents the center coordinates of any semantic unit in the search grid; (i1,j1) represents the grid code of any semantic unit in the search grid; (x0,y0) represents the planar coordinates of the lower left corner of the high-precision map boundary of the target entity object; gs represents the side length of the monitoring grid; ss represents the side length of the semantic unit; (i2,j2) represents the grid code of the semantic unit where the target entity object is located; based on the grid code (i3,j3) of any semantic unit in the search grid, its semantic information table code U(i3,j3) is obtained; U(i3,j3).SpaceName represents the space name of the semantic unit with grid code (i3,j3); U(i3,j3).UserTag represents the personnel space attribute of the semantic unit with grid code (i3,j3). Lines 5-6 of Formula (5) indicate the range of the search grid; lines 7-8 of Formula (5), i.e., the last two lines, indicate the physical scene constraints.
[0078] In formulas (4) and (5), the B km (i2 km j2 km The SpaceName is obtained by following these steps: obtaining the signal strength S of the r PNT base stations observed by the target entity object. k , k = 1,..,r; for the signal strength S k Sort the PNT base station B corresponding to the maximum signal strength. km coordinates (x) km ,y km Based on the aforementioned B km coordinates (x) km ,y km The method described in Example 1 is used to obtain the B from the mapping relation library. km B km (i2 km j2 km SpaceName. Specifically: based on the aforementioned B km coordinates (x) km ,y km The method described in Example 1, and the method thereof, are used to obtain the B from the mapping relation library. km B km (i2 km j2 km ).SpaceName, specifically: using formula (1) described in Example 1, calculate the B km The grid code (i1) of the monitored grid km ,j1 km Based on the aforementioned Bkm coordinates (x) km ,y km ) and the B km The grid code (i1) of the monitored grid km ,j1 km Using formula (2) described in Example 1, the B is calculated. km The encoding of the semantic unit (i2) km j2 km Based on the aforementioned B km semantic unit encoding (i2) km j2 km ) and the mapping relationship library, indexed to obtain the encoding as (i2 km j2 km The semantic information table corresponding to the semantic unit of B is obtained; based on the semantic information table, the semantic information table is obtained. km (i2 km j2 km ).SpaceName.
[0079] By adopting the above-disclosed technical solution of this invention, the following beneficial effects are obtained:
[0080] (1) Shorten the generation time of user location monitoring information. Traditional methods generate semantic information of a user's location by traversing the geometric relationship between all entity objects within a tile map area and each user's location. This means that generating semantic information of a user's location requires a traversal calculation, resulting in a large amount of computation and a long response time. However, the technical solution of this application only calculates the grid code of the semantic unit where the user is located in a single traversal, and obtains the semantic information by reading the mapping relation library index based on the grid code of the semantic unit, which greatly reduces the amount of computation and response time of user location monitoring information.
[0081] (2) Improving the rationality of user location monitoring information. Traditional methods ignore the spatial relationship features, such as the geometric relationship and transmission space interaction relationship between the user and the PNT base station, implied in the PNT network observation information in the closed space during the matching process. This makes it difficult for traditional methods to fully guarantee the rationality of the extracted location semantic information. The technical solution of this application is based on the constraints of the physical information of the entity object, user attribute information, and PNT network observation information to ensure that the user location is interpreted into semantic units that conform to the constraints, thereby generating reasonable semantic information.
[0082] (3) Reduce the computational load of generating user location density monitoring information. Traditional methods generate area density by traversing and calculating the distance between the area center and each user and determining the range. As the number of users and area centers increases, the computational load increases dramatically, and the computation time increases rapidly. For m areas and n users, at most m×n calculations are required. However, the technical solution of this application directly calculates the area code based on the user location and accumulates the number of users within the area code to complete user density monitoring, requiring at most n calculations.
[0083] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.
[0084] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.
Claims
1. A method for location monitoring using a closed-space PNT network and a high-precision map, characterized in that, include: Based on the preset monitoring grid side length, a high-precision map of the target enclosed space is divided to obtain the monitoring grid of the target enclosed space; Based on the preset semantic unit side length, the monitoring grid is divided to obtain the semantic unit grid of the target closed space; Establish a mapping database between the semantic information of any entity object element and the location information of its semantic unit; Based on the location coordinates of the target entity object, obtain the grid code of the monitoring grid where the target entity object is located; Based on the location coordinates of the target entity object and the grid code of the monitoring grid where the target entity object is located, the grid code of the semantic unit where the target entity object is located is obtained; Based on the grid encoding of the semantic units and the mapping relationship library, the location semantic information of the target entity object is obtained; The grid code (i2,j2) of the semantic unit where the target entity object is located is calculated using formula (2) based on the location coordinates of the target entity object and the grid code of the monitoring grid where the target entity object is located. （2） In the formula, (x, y) represents the position coordinates of the target entity object; This represents the planar coordinates of the lower left corner of the high-precision map boundary of the target entity object's enclosed space; (i1,j1) represents the grid code of the monitoring grid where the target entity object is located; gs represents the side length of the monitoring grid where the target entity object is located; ss represents the side length of a semantic unit; The establishment of a mapping database between the semantic information of any entity object element and the position information of the semantic unit where the entity object element is located specifically involves: Establish a semantic unit encoding and semantic information mapping table; the semantic unit encoding and semantic information mapping table includes the stored value of any semantic unit, the row number and column number of the position of the semantic unit, and the stored value is the number of the semantic information table corresponding to the semantic unit, denoted as U; Establish a semantic information table library, which includes at least one semantic information table; the semantic information table includes at least the semantic information table number, the entity object element name in the semantic unit, the entity object number in the semantic unit, the spatial location name of the semantic unit, and the spatial attributes of the semantic unit, including whether personnel are available, whether vehicles are available, and whether logistics are available. Based on the numbering of the semantic information table, establish the semantic information table database and the semantic unit encoding and semantic information mapping table index relationship, and complete the establishment of the mapping relationship database.
2. The method for location monitoring using a closed-space PNT network and high-precision map enhancement according to claim 1, characterized in that, The position coordinates (x, y) of the target entity object are calculated in the following way: Method 1: The target entity obtains observation information from at least three base stations, and calculates the position coordinates of the target entity based on the observation information from the at least three base stations; Alternatively, Method 2: The target entity obtains observation information from no more than two base stations, and uses the PNT network observation information to complete the location monitoring of the target entity. Specifically, this involves obtaining the signal strength of r PNT base stations observed by the target entity. k=1,..,r; for the signal strength Sort the PNT base stations corresponding to the highest signal strength. coordinates , coordinate Used as the position coordinates of the target entity object.
3. The method for location monitoring using a closed-space PNT network and high-precision map enhancement according to claim 1, characterized in that, Based on the location coordinates (x, y) of the target entity, the grid code (i1, j1) of the monitoring grid where the target entity is located is calculated using formula (1): （1） In the formula, The coordinates of the lower left corner of the high-precision map boundary of the target entity object are indicated; gs represents the side length of the monitoring grid.
4. The method for location monitoring using a closed-space PNT network and high-precision map enhancement according to any one of claims 1-3, characterized in that, After obtaining the positional semantic information of the target entity object, the method further includes setting constraints to generate output positional semantic information of the target entity object that conforms to the physical scene, specifically: S001, using the constraints of formula (3), determine whether the location semantics of the location semantic information is reasonable; If so, the location semantic information is used as the output location semantic information of the target entity object and output. If not, proceed to S002; （3） in, This indicates the name of the space where the target entity object is located; The base station with the highest observed signal strength for the target entity is indicated. The name of the space it is located in; This represents the spatial attribute of the space in which the target entity object is located; S002, search for the semantic unit that satisfies formula (3) and is closest to the target entity object, obtain the semantic information table corresponding to the closest semantic unit, and use the semantic information of the semantic information table as the output position semantic information of the target entity object, and output it, specifically as formula (4): （4） ds represents the distance between the target entity object and the center of any semantic unit in the search grid; the search grid is the set of all semantic units that satisfy the constraints of formula (3); (x,y) represents the position coordinates of the target entity object; ( This represents the center coordinates of any semantic unit in the search grid; (i3,j3) represents the grid code of any semantic unit in the search grid; (i1,j1) represents the grid code of the monitoring grid where the target entity object is located; This represents the planar coordinates of the lower left corner of the high-precision map boundary of the target entity object's enclosed space; gs represents the side length of the monitoring grid; ss represents the side length of a semantic unit; (i2,j2) represents the grid code of the semantic unit where the target entity object is located; The semantic information table encoding is obtained based on the grid encoding (i3, j3) index of any semantic unit in the search grid. ; The spatial name of the semantic unit with grid encoding (i3,j3); The personnel spatial attributes represent the semantic units with grid encoding (i3,j3).
5. The method for location monitoring using a closed-space PNT network and high-precision map enhancement according to claim 4, characterized in that, The The following steps are followed to obtain the signal strength of the r PNT base stations observed by the target entity object. k=1,..,r; for the signal strength Sort the PNT base stations corresponding to the highest signal strength. coordinates Based on the above coordinates And the method described in claim 1, obtaining the mapping relationship from the mapping relationship library. of .
6. The method for location monitoring using a closed-space PNT network and high-precision map enhancement according to claim 5, characterized in that, The method further includes updating the user density n(i1,j1) within any monitoring grid based on the grid code (i1,j1) of that monitoring grid, specifically: Let the user density n of each monitoring grid be initialized to 0; Update the user density n(i1,j1) within the monitored grid code (i1,j1) using formula (5): （5）。