A high-precision and low-latency positioning system based on 5G signals

Abstract

The present invention relates to the field of 5G positioning technology, and specifically discloses a high-precision, low-latency positioning system based on 5G signals, including: an initial module: intercepting a target area within a preset monitoring area, and gridding the target area to obtain a sub-area; an analysis module: setting sampling points within the sub-area, periodically obtaining the 5G signal strength at the sampling points, and determining a fitting curve; calculating a fluctuation coefficient according to the fitting curve, and when the fluctuation coefficient does not meet a preset condition, executing a preset removal step so that the fluctuation coefficient meets the preset condition; calculating the standard signal strength according to the fitting curve when the fluctuation coefficient meets the preset condition; a deployment module: determining whether the sub-area is an abnormal area according to the standard signal strength at the sampling point, and determining the position of a 5G small base station according to the standard signal strength in the abnormal area. The present invention avoids poor positioning effect caused by weak 5G signals by optimizing the deployment position of 5G small base stations.

Description

Technical Field

[0001] The present invention relates to the field of 5G positioning technology, and in particular to a high-precision and low-latency positioning system based on 5G signals. Background Art

[0002] The fifth generation mobile communication technology (5G for short) is a new generation of broadband mobile communication technology with the characteristics of high speed, low latency and large connection. 5G communication facilities are the network infrastructure for realizing the interconnection between people, machines and things.

[0003] The application of 5G signals in positioning technology has significant advantages. With its ultra-low latency, ultra-high bandwidth and large-scale connection characteristics, it can achieve accurate real-time positioning. This makes 5G show great potential and application prospects in fields such as intelligent navigation. By utilizing 5G's millimeter wave and small base station deployment, positioning accuracy has been greatly improved, even to the centimeter level, providing technical support for various application scenarios that require high-precision positioning.

[0004] However, although 5G signals perform well in many scenarios, their performance is still restricted by some factors. For example, in areas with dense buildings or incomplete signal coverage, 5G signals may be weak, resulting in reduced positioning accuracy, or even positioning errors in extreme cases, affecting the overall positioning effect. Based on this, a high-precision, low-latency positioning system based on 5G signals is provided to optimize the deployment location of small base stations and avoid positioning errors caused by weak 5G signals. Summary of the invention

[0005] The purpose of the present invention is to provide a high-precision and low-latency positioning system based on 5G signals to solve the following technical problems:

[0006] Although 5G signals perform well in many scenarios, their performance is still restricted by some factors. For example, in areas with dense buildings or incomplete signal coverage, 5G signals may be weak, resulting in reduced positioning accuracy and even positioning errors in extreme cases, affecting the overall positioning effect.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A high-precision and low-latency positioning system based on 5G signals, comprising:

[0009] Initial module: a square area is intercepted in the preset monitoring area as the target area, the side length of the target area is the preset length S, the target area is gridded to obtain a sub-area, the width and length of the sub-area are both ε*S, ε is the preset coefficient;

[0010] Analysis module: setting sampling points at preset distance intervals on the route within the sub-area, periodically acquiring the 5G signal strength at the sampling points within a preset sampling period T, and generating data points (i, Di), where Di represents the 5G signal strength at the sampling point acquired for the i-th time, and fitting the data points to obtain a fitting curve f(t);

[0011] Calculate the fluctuation coefficient K according to the fitting curve, the fluctuation coefficient K is used to reflect the fluctuation of the 5G signal strength at the sampling point, set the fluctuation coefficient threshold K', and when the fluctuation coefficient K>K', perform the preset removal step so that the fluctuation coefficient K≤K';

[0012] Determine the fitting curve f'(t) when the fluctuation coefficient K≤K', and calculate the standard signal intensity by the following formula: Wherein [ta, sta, ta, end] represents the domain value range of the a-th segment curve fa'(t) on the fitting curve f'(t), and m represents the total number of segments on the fitting curve f'(t);

[0013] Deployment module: Determine the standard signal strength of all sampling points and obtain the minimum standard signal strength among them. When the minimum standard signal strength is less than the preset optimal positioning signal strength DZJ, the corresponding sub-area is marked as an abnormal area, and the location of the 5G small base station is determined according to the standard signal strength in the abnormal area.

[0014] As a further solution of the present invention: in the analysis module, the process of calculating the fluctuation coefficient K according to the fitting curve specifically includes:

[0015] The starting point, peak point, valley point and end point on the fitting curve f(t) are used as reference points to calculate the signal difference Db'=|Dckb-Dckb-1|, where DckbDck represents the 5G signal strength corresponding to the b-th reference point;

[0016] Calculate the volatility γ is a preset correction coefficient, and N represents the total number of reference points.

[0017] As a further solution of the present invention: in the analysis module, the removal step specifically includes:

[0018] The fitting curve between two adjacent reference points is taken as the target curve g(t), and the deviation value ΔD=maxg(x)-DAVG| is calculated, where

[0019] Generate a deviation value set and ΔDjh=(ΔD1, ΔD2, ..., ΔDn), where ΔDn represents the deviation value corresponding to the nth target curve, and remove the target curve corresponding to the maximum deviation value ΔDmax=max(ΔDjh) from the fitting curve f(t);

[0020] Calculate the fluctuation coefficient again and repeat the above steps until the fluctuation coefficient K≤K' after removing the target curve.

[0021] As a further solution of the present invention: in the deployment module, the process of determining the location of the 5G small base station according to the standard signal strength in the abnormal area specifically includes:

[0022] Determine the minimum circumscribed circle of the area formed by all abnormal points, use it as the signal area, and determine the optimal signal maintenance range X of the 5G small base station;

[0023] When the area of ​​the signal area is Xxh≤X, the center of the signal area is used as the location of the 5G small base station;

[0024] When the area of ​​the signal region Xxh>X, perform the following steps:

[0025] Determine the location information of the outlier points, cluster the outliers by using a K-means clustering algorithm to obtain clusters, and the distance between any two outliers in the clusters is less than 2r, where r is the radius of the optimal signal retention range;

[0026] Determine the minimum circumscribed circle of the area composed of the outliers in the cluster, and use the center of the minimum circumscribed circle as the initial site selection of the 5G small base station;

[0027] Determine the target variable xB. When a 5G small base station is set at the Bth initial position, xB=1; when a 5G small base station is not set at the Bth initial position, xB=0;

[0028] Determine the first function B' represents the total number of initial sites, and the first function satisfies the constraint condition: for any abnormal point, there is at least one 5G small base station such that the distance from the abnormal point to the 5G small base station is less than or equal to r;

[0029] Determine the objective function min(F(x)), solve the objective function through integer linear programming combined with constraints, and determine the location of the 5G small base station.

[0030] As a further solution of the present invention: when the area of ​​the signal area Xxh>X, another method for determining the 5G small base station specifically includes: determining the locations of all 5G small base stations in the abnormal area through a genetic algorithm.

[0031] As a further solution of the present invention: when the number of 5G small base stations in the abnormal area is greater than a preset number, an early warning message is sent to remind the staff.

[0032] As a further solution of the present invention: in the analysis module, the process of calculating the fluctuation coefficient K also includes the following steps:

[0033] When the minimum signal strength min(f(t))≥DZJ, the corresponding sub-region is not regarded as an abnormal region.

[0034] Beneficial effects of the present invention: Compared with the prior art, in the present solution, first, a square area is intercepted in a preset monitoring area as a target area, and the side length of the target area can be set by experience. Then, the target area is gridded to obtain a sub-area, and the size of the sub-area is also determined based on experience; then, sampling points are set on the route in the sub-area, and a curve of the 5G signal strength at the sampling point changing with time is drawn. In the present solution, the above curve is determined by fitting, that is, the fitting curve. The present solution is suitable for navigation positioning, so it is only necessary to consider whether the standard signal strength on the route in the sub-area meets the optimal positioning signal strength; then, the fluctuation coefficient is determined by the fitting curve, and when the fluctuation coefficient does not meet the preset conditions, the preset removal step is performed to make the fluctuation coefficient meet the preset conditions; it can be understood that in actual situations, the 5G signal is not necessarily stable, and there may be large fluctuations. In the present invention, the fluctuation coefficient is used to quantify this fluctuation. For the fitting curve with large fluctuations (that is, the fluctuation coefficient K>K'), the mean value cannot be directly used to replace the average situation within the sampling period. It is necessary to remove those curve parts that cause the fluctuation value to exceed the expected value, that is, to perform the preset removal step. In the removal step, the target curve corresponding to the maximum deviation value is removed, thereby reducing the fluctuation coefficient in the fitting curve, and the above process is repeated many times in this step until the fluctuation coefficient meets the set condition (i.e., the fluctuation coefficient K≤K'); then the standard signal strength is calculated according to the fitting curve when the fluctuation coefficient meets the preset condition; the standard signal strength reflects the average signal strength at the sampling point within the sampling period, which is the basis for determining whether the sub-region is an abnormal region in the subsequent determination; it is worth noting that after the above removal step, the fitting curve when the fluctuation coefficient meets the preset condition becomes a piecewise function, because the target curve corresponding to the maximum deviation value is removed in the preset removal step, and it can be known that the a-th segment curve fa'(t) on the fitting curve f'(t) represents the a-th segment fitting curve f'(t) starting from the starting point of the fitting curve f'(t); finally, whether the sub-region is an abnormal region is determined according to the standard signal strength of the sampling point, and the position of the 5G small base station is determined according to the standard signal strength in the abnormal region; it should be noted that when the minimum standard signal strength in the sub-region is greater than or equal to the optimal positioning signal strength, it is not necessary to set up a 5G small base station in the sub-region, thereby reducing the overall cost. The present invention avoids poor positioning effect caused by weak 5G signals by optimizing the deployment location of 5G small base stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The present invention will be further described below in conjunction with the accompanying drawings.

[0036] Figure 1 It is a flow chart of a high-precision and low-latency positioning system based on 5G signals of the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] See also Figure 1 As shown, the present invention is a high-precision and low-latency positioning system based on 5G signals, comprising:

[0039] Initial module: a square area is intercepted in the preset monitoring area as the target area, the side length of the target area is the preset length S, the target area is gridded to obtain a sub-area, the width and length of the sub-area are both ε*S, ε is the preset coefficient;

[0040] Analysis module: setting sampling points at preset distance intervals on the route within the sub-area, periodically acquiring the 5G signal strength at the sampling points within a preset sampling period T, and generating data points (i, Di), where Di represents the 5G signal strength at the sampling point acquired for the i-th time, and fitting the data points to obtain a fitting curve f(t);

[0041] Calculate the fluctuation coefficient K according to the fitting curve, the fluctuation coefficient K is used to reflect the fluctuation of the 5G signal strength at the sampling point, set the fluctuation coefficient threshold K', and when the fluctuation coefficient K>K', perform the preset removal step so that the fluctuation coefficient K≤K';

[0042] Determine the fitting curve f'(t) when the fluctuation coefficient K≤K', and calculate the standard signal intensity by the following formula: Wherein [ta, sta, ta, end] represents the domain value range of the a-th segment curve fa'(t) on the fitting curve f'(t), and m represents the total number of segments on the fitting curve f'(t);

[0043] Deployment module: Determine the standard signal strength of all sampling points and obtain the minimum standard signal strength among them. When the minimum standard signal strength is less than the preset optimal positioning signal strength DZJ, the corresponding sub-area is marked as an abnormal area, and the location of the 5G small base station is determined according to the standard signal strength in the abnormal area.

[0044] It should be noted that, first, a square area is intercepted in the preset monitoring area as the target area, and the side length of the target area can be set by experience. Then, the target area is gridded to obtain a sub-area, and the size of the sub-area is also determined based on experience; then, sampling points are set on the route in the sub-area, and a curve of the 5G signal strength at the sampling point changing with time is drawn. In this scheme, the above curve is determined by fitting, that is, the fitting curve. This scheme is suitable for navigation positioning, so it is only necessary to consider whether the standard signal strength on the route in the sub-area meets the optimal positioning signal strength; then, the fluctuation coefficient is determined by the fitting curve. When the fluctuation coefficient does not meet the preset conditions, the preset removal step is executed to make the fluctuation coefficient meet the preset conditions; it can be understood that in actual situations, the 5G signal is not necessarily stable, and there may be large fluctuations. In the present invention, the fluctuation coefficient is used to quantify this fluctuation. For the fitting curve with large fluctuations (that is, the fluctuation coefficient K>K'), the mean value cannot be directly used to replace the average situation within the sampling period. It is necessary to remove those curve parts that cause the fluctuation value to exceed the expected value, that is, to execute the preset removal step to remove The target curve corresponding to the maximum deviation value is removed to reduce the fluctuation coefficient in the fitting curve, and the above process is repeated many times in this step until the fluctuation coefficient meets the set condition (i.e., the fluctuation coefficient K≤K'); then the standard signal strength is calculated according to the fitting curve when the fluctuation coefficient meets the preset condition; the standard signal strength reflects the average signal strength at the sampling point within the sampling period, which is the basis for determining whether the sub-region is an abnormal region in the subsequent determination; it is worth noting that after the above removal step, the fitting curve when the fluctuation coefficient meets the preset condition becomes a piecewise function, because the target curve corresponding to the maximum deviation value is removed in the preset removal step, and it can be known that the a-th segment curve fa'(t) on the fitting curve f'(t) represents the a-th segment fitting curve f'(t) starting from the starting point of the fitting curve f'(t); finally, whether the sub-region is an abnormal region is determined according to the standard signal strength of the sampling point, and the location of the 5G small base station is determined according to the standard signal strength in the abnormal region; it should be noted that when the minimum standard signal strength in the sub-region is greater than or equal to the optimal positioning signal strength, it is not necessary to set up a 5G small base station in the sub-region, thereby reducing the overall cost.

[0045] In another preferred embodiment of the present invention, in the analysis module, the process of calculating the fluctuation coefficient K according to the fitting curve specifically includes:

[0046] The starting point, peak point, valley point and end point on the fitting curve f(t) are used as reference points to calculate the signal difference Db'=|Dckb-Dckb-1|, where DckbDck represents the 5G signal strength corresponding to the b-th reference point;

[0047] Calculate the volatility γ is a preset correction coefficient, and N represents the total number of reference points.

[0048] It is worth noting that the larger the signal difference between two adjacent reference points, the higher the amplitude of the fluctuation, which means that the fluctuation value of the fitting curve should be larger. The relationship between the remaining parameters and the fluctuation value can be determined based on the above ideas and will not be repeated here. γ can be set specifically by experience.

[0049] In another preferred embodiment of the present invention, in the analysis module, the removal step specifically includes:

[0050] The fitting curve between two adjacent reference points is taken as the target curve g(t), and the deviation value ΔD=maxg(x)-DAVG| is calculated, where

[0051] Generate a deviation value set and ΔDjh=(ΔD1, ΔD2, ..., ΔDn), where ΔDn represents the deviation value corresponding to the nth target curve, and remove the target curve corresponding to the maximum deviation value ΔDmax=max(ΔDjh) from the fitting curve f(t);

[0052] Calculate the fluctuation coefficient again and repeat the above steps until the fluctuation coefficient K≤K' after removing the target curve.

[0053] It is understandable that the 5G signal strength may fluctuate in actual applications due to various factors such as environment and interference. Such fluctuations will affect the accuracy of the fitting curve, thereby affecting the subsequent calculation of the standard signal strength. If these fluctuations are not handled, it may lead to incorrect judgment of abnormal areas, which in turn affects the deployment of small base stations; by removing the target curve with the largest deviation value, the deviation caused by abnormal fluctuations can be reduced, making the fitting curve more accurate and stable, which helps to more truly reflect the signal strength of the sampling point.

[0054] In another preferred embodiment of the present invention, in the deployment module, the process of determining the location of the 5G small base station according to the standard signal strength in the abnormal area specifically includes:

[0055] Determine the minimum circumscribed circle of the area formed by all abnormal points, use it as the signal area, and determine the optimal signal maintenance range X of the 5G small base station;

[0056] When the area of ​​the signal area is Xxh≤X, the center of the signal area is used as the location of the 5G small base station;

[0057] When the area of ​​the signal region Xxh>X, perform the following steps:

[0058] Determine the location information of the outlier points, cluster the outliers by using a K-means clustering algorithm to obtain clusters, and the distance between any two outliers in the clusters is less than 2r, where r is the radius of the optimal signal retention range;

[0059] Determine the minimum circumscribed circle of the area composed of the outliers in the cluster, and use the center of the minimum circumscribed circle as the initial site selection of the 5G small base station;

[0060] Determine the target variable xB. When a 5G small base station is set at the Bth initial position, xB=1; when a 5G small base station is not set at the Bth initial position, xB=0;

[0061] Determine the first function B' represents the total number of initial sites, and the first function satisfies the constraint condition: for any abnormal point, there is at least one 5G small base station such that the distance from the abnormal point to the 5G small base station is less than or equal to r;

[0062] Determine the objective function min(F(x)), solve the objective function through integer linear programming combined with constraints, and determine the location of the 5G small base station.

[0063] It should be noted that, first, by calculating the minimum circumscribed circle of the area composed of all outliers, the distribution area of ​​these points can be minimized. The purpose of this step is to identify a minimum signal area where the signal strength is poor and requires additional base station support. Next, determine the optimal signal maintenance range X of the 5G small base station. This range defines the maximum area that a base station can effectively cover. If the area Xxh of the signal area is less than or equal to X, a 5G small base station is directly placed at the center of the circle of the area. At this time, one base station is sufficient to cover all abnormal points; if the area Xxh of the signal area is greater than X, it means that one base station is not enough to cover all abnormal points. At this time, the abnormal points are clustered by the K-means clustering algorithm, and these points are divided into several clusters, and it is ensured that the distance between any two abnormal points in each cluster is less than 2r (r is the radius of the optimal signal maintenance range); then, determine the minimum circumscribed circle of each cluster, and use the center of these circles as the initial site selection of the 5G small base station. Finally, through integer linear programming combined with constraints (that is, ensuring that the distance from each abnormal point to the nearest 5G small base station does not exceed r), optimize and determine the final small base station location, so as to minimize the number of small base stations while meeting the signal coverage requirements, so as to reduce deployment costs and improve system efficiency.

[0064] In another preferred embodiment of the present invention, when the area of ​​the signal area Xxh>X, another method for determining the 5G small base station specifically includes: determining the locations of all 5G small base stations in the abnormal area through a genetic algorithm.

[0065] It should be noted that when the signal area is large and the distribution is complex, simple geometric methods (such as minimum circumscribed circle and K-means clustering) may not be sufficient to effectively determine the location of small base stations. The distribution of signal strength, the location of outliers, and the area to be covered may form a highly nonlinear optimization problem. Through genetic algorithms, the optimal combination of small base station locations can be found in large and complex outlier areas. This method can ensure that all outliers are effectively covered while minimizing the number and deployment costs of small base stations. Through repeated iterations and evolution, genetic algorithms can gradually optimize the location arrangement of small base stations, making signal coverage more uniform and effective, thereby improving the service quality and efficiency of the entire 5G network.

[0066] In another preferred embodiment of the present invention, when the number of 5G small base stations in the abnormal area is greater than a preset number, an early warning message is sent to alert the staff.

[0067] Understandably, the deployment and maintenance costs of 5G small base stations are high. When the number of small base stations that need to be deployed exceeds the preset number, the operating costs may increase significantly, which may not be consistent with the initial design or budget, so it is necessary to promptly remind relevant personnel to review and adjust; the dense deployment of small base stations may have an impact on the long-term sustainability of network planning, such as increasing energy consumption, causing signal interference, or reducing overall network efficiency. Therefore, manual intervention is required when the preset number is exceeded to avoid these potential problems.

[0068] In another preferred embodiment of the present invention, in the analysis module, the process of calculating the fluctuation coefficient K also includes the following steps:

[0069] When the minimum signal strength min(f(t))≥DZJ, the corresponding sub-region is not regarded as an abnormal region.

[0070] It is worth noting that if the minimum signal strength of a sub-area has reached or exceeded the set threshold, it means that the signal coverage of the area is already within an acceptable range and no further adjustment or intervention is required, avoiding unnecessary operations in areas with normal signal coverage.

[0071] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A high-precision, low-latency positioning system based on 5G signals, characterized in that: include: Initial module: a square area is intercepted in the preset monitoring area as the target area, the side length of the target area is the preset length S, the target area is gridded to obtain a sub-area, the width and length of the sub-area are both ε*S, ε is the preset coefficient; Analysis module: Set sampling points at preset distance intervals on the route within the sub-area, periodically obtain the 5G signal strength at the sampling points within a preset sampling period T, and generate data points (i, D i ), D i represents the 5G signal strength at the sampling point obtained for the i-th time, and the data points are fitted to obtain a fitting curve f(t); Calculate the fluctuation coefficient K according to the fitting curve, the fluctuation coefficient K is used to reflect the fluctuation of the 5G signal strength at the sampling point, set the fluctuation coefficient threshold K', and when the fluctuation coefficient K>K', perform the preset removal step so that the fluctuation coefficient K≤K'; Determine the fitting curve f'(t) when the fluctuation coefficient K≤K', and calculate the standard signal intensity by the following formula: , where [t a，sta , t a，end ] represents the curve f of the ath segment on the fitting curve f'(t) a '(t) is the value range of the definition domain, and m represents the total number of curve segments on the fitting curve f'(t); The removal step specifically includes: Take the fitting curve between two adjacent reference points as the target curve g(t) and calculate the deviation value ,in ; Generate a set of deviation values ​​and ΔD jh = (ΔD1, ΔD2, …, ΔD n ), ΔD n Indicates the deviation value corresponding to the nth target curve, removing the maximum deviation value ΔDmax=max(ΔD jh ) corresponding to the target curve; Calculate the fluctuation coefficient again and repeat the above steps until the fluctuation coefficient K≤K' after removing the target curve; Deployment module: Determine the standard signal strength of all sampling points and obtain the minimum standard signal strength among them. When the minimum standard signal strength is less than the preset optimal positioning signal strength DZJ, the corresponding sub-area is marked as an abnormal area, and the location of the 5G small base station is determined according to the standard signal strength in the abnormal area.

2. According to claim 1, a high-precision and low-latency positioning system based on 5G signals is characterized in that: In the analysis module, the process of calculating the fluctuation coefficient K according to the fitting curve specifically includes: The starting point, peak point, valley point and end point on the fitting curve f(t) are used as reference points to calculate the signal difference D b '=|Dck b -Dck b-1 |, Dck b Indicates the 5G signal strength corresponding to the b-th reference point; Calculate the volatility , γ is the preset correction coefficient, and N represents the total number of reference points.

3. According to claim 1, a high-precision and low-latency positioning system based on 5G signals is characterized in that: In the deployment module, the process of determining the location of the 5G small base station according to the standard signal strength in the abnormal area specifically includes: Determine the minimum circumscribed circle of the area formed by all abnormal points, use it as the signal area, and determine the optimal signal maintenance range X of the 5G small base station; When the area of ​​the signal area is Xxh≤X, the center of the signal area is used as the location of the 5G small base station; When the area of ​​the signal region Xxh>X, perform the following steps: Determine the location information of the outlier points, cluster the outliers by using a K-means clustering algorithm to obtain clusters, and the distance between any two outliers in the clusters is less than 2r, where r is the radius of the optimal signal retention range; Determine the minimum circumscribed circle of the area composed of the outliers in the cluster, and use the center of the minimum circumscribed circle as the initial site selection of the 5G small base station; Determine the target variable xB. When a 5G small base station is set at the Bth initial position, xB=1; when a 5G small base station is not set at the Bth initial position, xB=0; Determine the first function , B' represents the total number of initial sites, and the first function satisfies the constraint condition: for any abnormal point, there is at least one 5G small base station such that the distance from the abnormal point to the 5G small base station is less than or equal to r; Determine the objective function min(F(x)), solve the objective function through integer linear programming combined with constraints, and determine the location of the 5G small base station.

4. According to claim 3, a high-precision and low-latency positioning system based on 5G signals is characterized in that: When the area of ​​the signal area Xxh>X, another method for determining the 5G small base station specifically includes: determining the locations of all 5G small base stations in the abnormal area through a genetic algorithm.

5. According to claim 3, a high-precision and low-latency positioning system based on 5G signals is characterized in that: When the number of 5G small base stations in the abnormal area is greater than the preset number, an early warning message is sent to remind the staff.

6. A high-precision, low-latency positioning system based on 5G signals according to claim 1, characterized in that: In the analysis module, the process of calculating the fluctuation coefficient K also includes the following steps: When the minimum signal strength min(f(t))≥DZJ, the corresponding sub-region is not regarded as an abnormal region.

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