A method and system for quantifying and representing positioning signals in large underground spaces

Abstract

This invention proposes a method and system for quantitative characterization of positioning signals in large underground spaces. It introduces a sliding window and defines a spatial non-uniformity index from the perspective of spatial heterogeneity. By calculating the non-uniformity of voxel signal distribution within the sliding window, signal distribution heterogeneity is quantified. The distribution characteristics of the spatial non-uniformity index are analyzed, and an optimal threshold is determined based on the sensitivity of connected components to the threshold. Signal blind zone detection is achieved under the optimal threshold. The introduction of the spatial non-uniformity index makes anomaly detection more scientific and accurate. This invention can comprehensively capture the non-uniform characteristics of overall and local signal distribution in large underground spaces from a three-dimensional perspective, achieving blind zone detection and significantly expanding the application scenarios of signal spatial distribution characterization methods.

Description

Technical Field

[0001] This invention belongs to the field of signal quantization technology, and in particular relates to a method and system for quantizing and representing positioning signals in large underground spaces. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] The spatial distribution of signals determines the coverage and signal quality of a network. Quantifying the spatial distribution characteristics of signals can help identify signal blind spots, thereby optimizing the location of routers or access points and ensuring stable signal coverage throughout the area. This is of great significance for research on wireless network planning and optimization, indoor positioning accuracy, and wireless spectrum management.

[0004] Underground spaces typically include underground pedestrian streets, underground sidewalks, subways, urban roads and tunnels, underground commercial facilities, underground pipe networks, and civil defense facilities. Underground spaces are the main spaces for human activities, but due to the enclosed nature of underground spaces, traditional GNSS systems struggle to provide positioning information.

[0005] Quantifying the three-dimensional spatiotemporal differentiation characteristics of signal strength in large spaces, especially underground spaces, and detecting signal blind zones have become pressing challenges. These challenges are also crucial for building resilient cities, mitigating disaster losses, and rationally allocating emergency rescue resources. Current signal quantification methods include: quantifying the spatial distribution characteristics of the signal field using a series of statistical indicators based on RSSI eigenvalues. Some technologies attempt to obtain signal strength at different locations within a space by simulating signal propagation characteristics based on signal transmission characteristics and the indoor environment. In recent years, deep learning technology has been introduced into signal distribution representation; for example, convolutional neural networks (CNNs) and fusion capsule networks (Caps Nets) are used to capture the spatiotemporal distribution characteristics of signals, enabling the representation of indoor spatial signal strength.

[0006] The aforementioned existing technologies focus on mining the overall signal distribution pattern within a space, relying on various factors such as base station location parameters and the spatial environment. They lack the ability to quantify the spatial non-uniformity of distribution within local areas and do not quantify signal distribution patterns in large underground spaces to achieve accurate detection of blind spots. Furthermore, in large underground spaces, the unique physical structure and complex propagation environment, such as curves and branches in subways and tunnels, and numerous partitions and obstacles in underground commercial facilities, severely affect signal propagation, leading to more complex and diverse signal anomalies. Compared to traditional ground-level or simple indoor environments, the signal reflection, scattering, and attenuation patterns in large underground spaces are unique. Most existing methods are designed for simple scenarios, and the simulation conditions are too idealistic to be applicable to the real characteristics of large spaces, failing to fully represent the true spatial distribution characteristics of signals.

[0007] In summary, existing methods for quantizing signals suffer from limited characterization capabilities, poor adaptability to complex environments, high costs, and low efficiency. Summary of the Invention

[0008] To overcome the shortcomings of the prior art, this invention provides a method and system for quantitative characterization of positioning signals in large underground spaces. This method can comprehensively capture the non-uniform characteristics of signal distribution in large underground spaces from a three-dimensional perspective, enabling blind zone detection and significantly expanding the application scenarios of signal spatial distribution characterization methods.

[0009] To achieve the above objectives, the present invention adopts the following technical solution:

[0010] In a first aspect, the present invention provides a method for quantifying and characterizing positioning signals in large underground spaces, comprising:

[0011] Set a threshold selection range, and based on different thresholds within the threshold selection range, perform voxel discretization processing on the signal sampling data to obtain standard signal voxel data corresponding to different thresholds;

[0012] Based on spatial heterogeneity, the standard signal voxel data is processed through a sliding window to obtain a connected component matrix, and the spatial non-uniformity within the sliding window range is calculated using the generated connected component matrix.

[0013] Based on the spatial non-uniformity corresponding to different thresholds, the optimal threshold is determined, and blind zone detection is performed according to the spatial non-uniformity corresponding to the optimal threshold.

[0014] Secondly, the present invention provides a quantitative characterization system for positioning signals in large underground spaces, comprising:

[0015] The discretization module is configured to: set a threshold selection range, and perform voxel discretization processing on the signal sampling data based on different thresholds within the threshold selection range to obtain standard signal voxel data corresponding to different thresholds;

[0016] The calculation module is configured to: perform calculations on the standard signal voxel data through a sliding window based on spatial heterogeneity to obtain a connected component matrix, and use the generated connected component matrix to calculate the spatial non-uniformity within the sliding window range;

[0017] The blind spot detection module is configured to: determine the optimal threshold based on the spatial non-uniformity corresponding to different thresholds, and perform blind spot detection based on the spatial non-uniformity corresponding to the optimal threshold.

[0018] Thirdly, the present invention provides an electronic device including a memory and a processor, and computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, perform the method described in the first aspect.

[0019] Fourthly, the present invention provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the method described in the first aspect.

[0020] Fifthly, the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the method described in the first aspect.

[0021] The above one or more technical solutions have the following beneficial effects:

[0022] This invention introduces a sliding window to define a spatial non-uniformity index from the perspective of spatial heterogeneity. By calculating the non-uniformity of voxel signal distribution within the sliding window range, signal distribution heterogeneity is quantified. By analyzing the distribution characteristics of the spatial non-uniformity index, an optimal threshold is determined based on the sensitivity of connected components to the threshold. Signal blind zone detection is achieved under the optimal threshold. The introduction of the spatial non-uniformity index makes anomaly detection more scientific and accurate. This invention overcomes the limitations of traditional two-dimensional signal representation, enabling comprehensive three-dimensional capture of the non-uniformity characteristics of overall and local signal distribution in large underground spaces, achieving blind zone detection and significantly expanding the application scenarios of signal spatial distribution representation methods.

[0023] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0024] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0025] Figure 1 This is a block diagram of the underground large-space positioning signal quantization and characterization method in Embodiment 1 of the present invention;

[0026] Figure 2 This is a flowchart of the underground large-space positioning signal quantization and characterization method in Embodiment 1 of the present invention;

[0027] Figure 3 This is a schematic diagram of the sliding window in Embodiment 1 of the present invention;

[0028] Figure 4 This is a three-dimensional model of the test area in Embodiment 1 of the present invention;

[0029] Figure 5 This is a schematic diagram of the variance results of the global signal field spatial non-uniformity algorithm under different thresholds in Embodiment 1 of the present invention;

[0030] Figure 6 The results of the algorithm operation at a threshold of 1.5 dBm in Embodiment 1 of the present invention are shown.

[0031] Figure 7 This is the result of blind zone detection in the algorithm of Embodiment 1 of the present invention. Detailed Implementation

[0032] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0033] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0034] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0035] Terminology Explanation:

[0036] Voxel: A combination of "volume" and "pixel," it is a basic unit in three-dimensional space, similar to a pixel in a two-dimensional image. Voxels are used to represent discrete volume elements in three-dimensional space.

[0037] Spatial Inhomogeneity Index (SI)

[0038] Example 1

[0039] This embodiment discloses a method for quantifying and representing positioning signals in large underground spaces, including:

[0040] Set a threshold selection range, and based on different thresholds within the threshold selection range, perform voxel discretization processing on the signal sampling data to obtain standard signal voxel data corresponding to different thresholds;

[0041] Based on spatial heterogeneity, the standard signal voxel data is processed through a sliding window to obtain a connected component matrix, and the spatial non-uniformity within the sliding window range is calculated using the generated connected component matrix.

[0042] Based on the spatial non-uniformity corresponding to different thresholds, the optimal threshold is determined, and blind zone detection is performed according to the spatial non-uniformity corresponding to the optimal threshold.

[0043] The global signal field spatial non-uniformity algorithm proposed in this embodiment aims to characterize the spatial distribution of the signal field and accurately detect signal blind zones. This embodiment uses WiFi signals as an example, employing a sliding window-based spatial non-uniformity calculation method. By combining this method with on-site measurement data, it achieves real-time characterization of the WiFi signal field distribution and blind zone detection.

[0044] like Figure 1 As shown, it is mainly divided into four parts: signal voxel construction, spatial non-uniformity calculation, optimal threshold determination, and blind zone identification.

[0045] S1: Set the threshold selection range. First, try selecting a larger value to calculate the global spatial non-uniformity index for all data. If the result is less than 0.05, then determine this value as the maximum threshold value w. max The threshold selection range is determined to be (0, w max The threshold selection interval is determined based on the amount of sampled data and the threshold range, and the default setting is 0.1.

[0046] Based on different thresholds within the threshold selection range, the signal sampling data is discretized into voxels to obtain standard signal voxel data corresponding to different thresholds. That is, the value of the voxel data after discretization is the standard value.

[0047] First, the signal sampling data needs to be preprocessed. After filtering and removing outliers, descriptive statistical analysis is performed to initially obtain the statistical characteristics of the WiFi signal field. After preprocessing, the threshold selection range WS is determined based on the signal field statistical characteristics. The threshold selection range WS is traversed, and a value is selected as the threshold at each step. The threshold is then used to discretize the sampled data into voxels to generate a set of standard signal voxel data sd.

[0048] The calculation formula for the discretization process is as follows:

[0049]

[0050] Among them, S i Z represents the value of the standard signal voxel data sd at spatial index i. min Z represents the minimum value of the preprocessed sampled data. i w represents the value of the preprocessed sampled data at the i-th sample point, where the spatial index is [i]. j Let be the j-th threshold selected from the threshold set.

[0051] S2: Sliding window calculation of exponent. A window with dimensions (d, h, w) is set to determine the calculation range of local signal field heterogeneity. The spatial non-uniformity within the window is calculated each time by sliding the sliding window.

[0052] Let the WiFi signal field be a three-dimensional data X with dimensions (D, H, W), and the sliding window K be a three-dimensional filter with dimensions (d, h, w). K(i, j, k) represents the spatial index (i, j, k) of the window center point in the three-dimensional data X. Each sliding operation of the method is expressed as follows: Figure 3 The process shown calculates and outputs the result SI(i,j,k) within the range of K(i,j,k). Then, (i,j,k) is changed to perform the next sliding operation until the signal field is traversed.

[0053] In three-dimensional space, spatial heterogeneity can manifest as differences in the spatial distribution of clustered regions with spatial autocorrelation. The overall size and distribution of these regions in space reflect the differences in spatial heterogeneity, which are represented in the standard voxel model as continuous regions with the same standard value and spatial proximity. In this embodiment, such regions are defined as connected domains. Connected domains can be used as estimates of WiFi signal field clustered regions within the standard value field, and the size of a connected domain is the number of voxels it contains.

[0054] Based on the concept of connected components, this embodiment proposes an exponential spatial non-uniformity SI that can quantify the spatial heterogeneity of three-dimensional WiFi signal fields. The calculation process of this exponent is as follows: First, set the sliding window size, and set a mask for the area outside the sliding window each time; statistically analyze the connected component information of the area within the sliding window, and store the number of connected components with the same scale for each standard value within the sliding window area into the connected component matrix CM. The connected component matrix CM(c n ,a m The value of ) is equal to the size of c. n The standard value is a mThe number of connected components; considering the negative contribution of large-scale connected components to heterogeneity, matrix CM is used to assign scale weights to all large-scale connected components in the space. Then, the summation result is divided by the sum of the scales of all unweighted connected components, and the result range is mapped to [0,1]. SI is calculated using the following formula:

[0055]

[0056] Where i is the standard value; j is the size of the connected component; t m N represents the maximum standard value of the standard signal voxel data. s CM(i,j) represents the maximum size of the connected components; CM(i,j) represents the number of connected components with standard value i and size j.

[0057] SI quantifies the uniformity of the spatial distribution of connected components of different sizes, serving as a measure of spatial heterogeneity with a value range of [0,1]. A decrease in SI indicates a greater tendency for the spatial distribution to consist of large-scale connected components. Based on the above steps, a sliding window operation is performed to generate a global spatial non-uniformity calculation result for each threshold width.

[0058] S3: Threshold selection aims to accurately determine the threshold through quantitative analysis.

[0059] Setting the threshold is crucial for uncovering WiFi signal distribution characteristics and detecting blind spots. The optimal threshold width_best needs to be determined based on the algorithm results under different threshold widths.

[0060] This embodiment presents a method for calculating the selected threshold. The variance of the algorithm results is calculated for each threshold to determine the difference in sensitivity of the WiFi signal field connectivity to different threshold selections. Sensitivity increases with increasing threshold and then tends to stabilize; the optimal threshold, width_best, is the minimum threshold in the stable state.

[0061] S4: Identify blind spots based on the algorithm results under width_best.

[0062] WiFi dead zones are areas where WiFi signals cannot effectively cover the network due to distance from base stations or physical obstacles causing interference, resulting in weak signal strength. Based on the distance-based attenuation characteristics of WiFi signals, dead zones typically exhibit very low non-uniformity. Generally, areas where the spatial non-uniformity algorithm output is less than 0.2 are identified as dead zones.

[0063] To verify the universality and reliability of the technical solution, this embodiment selects an underground space with signal coverage as the test area. This signal coverage area is located underground and is a non-public area. Figure 2As shown, the test area is a long, narrow underground corridor with three WiFi signal base stations and several signal-blocking walls. Signal data was collected at sampling points within the test area. Partial sampling data from the test area is shown in Table 1. Three base stations were arranged sequentially along the length of the corridor. The horizontal resolution of the measurement data was 1m x 1m, and the vertical resolution was 0.4m. The data dimensions were (4, 75, 9), representing a total of 2700 signal strength sample points in the width, length, and height directions. Considering that the width of the experimental space is much smaller than its length in the horizontal dimension, this embodiment focuses on the spatial distribution characteristics along the length and height directions. Therefore, the sliding window size was set to (4, 6, 3), ensuring that the window width covers the width of the experimental space and slides along the length and height directions.

[0064] Table 1. Data from some WiFi sampling points in the test area.

[0065]

[0066] After statistical analysis of the experimental data, the threshold selection range was determined to be [0.1, 4.0] based on the value range and distribution characteristics of the sample points. Each threshold width interval was 0.1, resulting in 40 thresholds to be selected across the Widths. The Widths were iterated, and a convolution-based global signal field spatial non-uniformity algorithm was run on the signal strength dataset for each threshold. First, the variance of the algorithm results was calculated for each threshold to preliminarily determine the sensitivity differences of the narrow WiFi signal field connected components to different threshold selections. The calculation results are shown below. Figure 3 As shown.

[0067] Running the algorithm with different thresholds reveals that when the threshold is less than or equal to 1.5 dBm, the variance of the WiFi signal field non-uniformity convolution result increases rapidly from its minimum value, and the size of the connected components changes significantly with increasing threshold, making the selected threshold ineffective for characterization. When the threshold is greater than 1.5 dBm, the variance exhibits relatively stable fluctuations around 0.02, remaining around 0.02 even when the threshold increases to 4.0 dBm. This indicates that when the threshold is greater than 1.5 dBm, a considerably large connected component has formed within the signal field, and the distribution of this large connected component is relatively stable, no longer changing drastically with increasing threshold. A threshold of 1.5 dBm can detect both relatively stable and large-scale connected components in the algorithm results, as well as regions with significant local non-uniformity.

[0068] Figure 4 The algorithm results with a threshold of 1.5 dBm are shown for subsequent analysis of the distribution characteristics of different regions and dimensions in space. The results of local spatial non-uniformity are restored to the spatial location by spatial index after the algorithm is run. The red dots in the figure represent the locations of known WiFi base stations.

[0069] pass Figure 6 It can be observed that significant spatial non-uniformity exists at different locations within the experimental space, with a minimum non-uniformity of 0.2 and a maximum of 0.7. Three high-non-uniformity regions and one low-non-uniformity region were identified. The high-non-uniformity region completely overlaps with the Wi-Fi base station location, and the non-uniformity increases with closer proximity to the signal source. This is because the signal strength difference is significant; near the signal source, the 1.5dBm threshold subdivides the region into more small connected domains, reflecting the rapid attenuation characteristics of Wi-Fi signals. The low-non-uniformity region appears in the 22-28 meter range, indicating a relatively uniform signal distribution. During convolution, the signal differences are small, forming a larger connected domain. This region is far from the two Wi-Fi signal sources, indicating that it is an intersection area between the two sources, with a uniform signal distribution. A similar low-non-uniformity region also appears at 55 meters, but due to its closer proximity to the signal source, the non-uniformity is 0.4, indicating a higher overall level. Therefore, the algorithm can effectively reflect the rapid attenuation characteristics of Wi-Fi signals and accurately identify the distribution characteristics of signal sources and intersection areas.

[0070] Meanwhile, the signal distribution heterogeneity differed along the length and height directions within the experimental space. Along the length direction, the Wi-Fi signal exhibited significant spatial heterogeneity, with marked signal attenuation and fluctuating non-uniformity within the region. Along the height direction, the non-uniformity varied less, showing a slight decrease only in the Wi-Fi signal source region, but remaining at a relatively high level overall.

[0071] WiFi blind spots are areas where WiFi signals cannot effectively cover the network due to distance from base stations or physical obstacles causing interference, resulting in weak signal strength. Considering the distance-based attenuation characteristics of WiFi signals, blind spots are characterized by low signal strength, minimal variation, and uniform distribution; therefore, they typically exhibit very low non-uniformity. In this experiment, areas where the spatial non-uniformity algorithm output is less than 0.2 are identified as blind spots. The detected blind spot locations are shown below. Figure 5 As shown, the red dots represent the locations of WiFi base stations.

[0072] Blind spot detection results show that a large blind spot exists within a 22-28m length range in the underground experimental space. The algorithm detects that the blind spot is far from the two nearest base stations, resulting in generally weak signal strength within the blind spot. This causes devices to be unable to connect stably to the network, affecting the stability of the communication and positioning functions. The distance between the two nearest WiFi base stations to the blind spot reaches 33m along the length direction. This excessive distance results in weak signal strength from both base stations reaching the blind spot, causing the blind spot to appear. In contrast, the distance between two adjacent base stations is 21m, and the blind spot detection results show only a small, localized blind spot between these two base stations. This indicates that reducing the distance between signal strength points can prevent the occurrence of blind spots. Therefore, to avoid signal blind spots in the underground experimental space, the distance between adjacent WiFi base stations should be reduced to within 20m along the length direction.

[0073] Example 2

[0074] The purpose of this embodiment is to provide a quantitative characterization system for positioning signals in large underground spaces, including:

[0075] The discretization module is configured to: set a threshold selection range, and perform voxel discretization processing on the signal sampling data based on different thresholds within the threshold selection range to obtain standard signal voxel data corresponding to different thresholds;

[0076] The calculation module is configured to: perform calculations on the standard signal voxel data through a sliding window based on spatial heterogeneity to obtain a connected component matrix, and use the generated connected component matrix to calculate the spatial non-uniformity within the window range;

[0077] The blind spot detection module is configured to: determine the optimal threshold based on the spatial non-uniformity corresponding to different thresholds, and perform blind spot detection based on the spatial non-uniformity corresponding to the optimal threshold.

[0078] In further embodiments, the following is also provided:

[0079] An electronic device includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor. When executed by the processor, the computer instructions perform the method described in Embodiment 1. For brevity, further details are omitted here.

[0080] It should be understood that in this embodiment, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0081] Memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of memory may also include non-volatile random access memory. For example, memory may also store information about the device type.

[0082] A computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the method described in Embodiment 1.

[0083] The method in Embodiment 1 can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor. The software modules can reside in readily available storage media in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, a detailed description is not provided here.

[0084] A computer program product includes a computer program that, when executed by a processor, implements the method described in Embodiment 1.

[0085] The present invention also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions included in program modules, which execute in a device on a target real or virtual processor to perform the processes / methods described above. Typically, program modules include routines, programs, libraries, objects, classes, components, data structures, etc., that perform specific tasks or implement specific abstract data types. In various embodiments, the functionality of program modules can be combined or divided among program modules as needed. The machine-executable instructions for the program modules can execute within a local or distributed device. In a distributed device, the program modules can reside in both local and remote storage media.

[0086] The computer program code used to implement the methods of the present invention may be written in one or more programming languages. This computer program code may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the computer or other programmable data processing device, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a computer, partially on a computer, as a stand-alone software package, partially on a computer and partially on a remote computer, or entirely on a remote computer or server.

[0087] In the context of this invention, computer program code or related data may be carried by any suitable carrier to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, and the like. Examples of signals may include electrical, optical, radio, sound, or other forms of propagation signals, such as carrier waves, infrared signals, etc.

[0088] Those skilled in the art will recognize that the units and algorithm steps described in conjunction with the embodiments herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0089] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for positioning signal quantification characterization of underground large spaces, characterized by, The method comprises the following steps: a threshold selection range is set, and voxel discretization processing is performed on signal sampling data based on different threshold values in the threshold selection range to obtain standard signal voxel data corresponding to different threshold values; based on spatial heterogeneity, the standard signal voxel data is operated through a sliding window to obtain a connected domain matrix, and spatial non-uniformity within the sliding window range is calculated using the generated connected domain matrix; the connected domain matrix is a continuous region with the same standard value and spatial proximity in a standard voxel model; based on the spatial non-uniformity corresponding to different threshold values, an optimal threshold value is determined, and blind area detection is performed according to the spatial non-uniformity corresponding to the optimal threshold value.

2. The method of claim 1, wherein the positioning signal quantification of a large underground space is characterized by, The method comprises the following steps: a threshold selection range is set, and voxel discretization processing is performed on signal sampling data based on different threshold values in the threshold selection range to obtain standard signal voxel data corresponding to different threshold values, specifically as follows: a selected threshold value is determined; 3. The method of claim 1, wherein the positioning signal quantification of a large underground space is characterized by, the signal sampling data is subtracted from the minimum value in the signal sampling data, and the result is divided by the selected threshold value to obtain the standard signal voxel data corresponding to the selected threshold value. wherein, i is a standard value; j is a connected component size; N s is a maximum connected component size; The method comprises the following steps: ( CM ) is a standard value is i , size is j the number of connected components, is a maximum standard value of standard signal voxel data.

4. The method of claim 1, wherein the underground large space positioning signal quantification characterization method is characterized by, the variance of the spatial non-uniformity corresponding to each threshold value is calculated respectively, so as to determine the difference in sensitivity of the signal field connected domain to different threshold values, and the optimal threshold value is determined according to the variance of the spatial non-uniformity corresponding to different threshold values.

5. The method of claim 1, wherein the positioning signal quantification of a large underground space is characterized by, Based on spatial heterogeneity, the standard signal voxel data is operated through a sliding window to obtain a connected domain matrix, specifically as follows: the size of the sliding window is set; a mask is set for the outer region of each sliding window, and connected domain information is counted for the inner region of the sliding window; connected domain data with the same size of each standard value in the inner region of the sliding window is stored in the connected domain matrix.

6. The method of claim 1 or 5, wherein, The method further comprises pre-processing the signal sampling data, screening and removing abnormal data, and performing descriptive statistical analysis to determine the threshold selection range.

7. An underground large space positioning signal quantification characterization system, characterized by, The method comprises the following steps: a discretization module configured to set a threshold selection range, and perform voxel discretization processing on signal sampling data based on different threshold values in the threshold selection range to obtain standard signal voxel data corresponding to different threshold values; a calculation module configured to operate the standard signal voxel data through a sliding window based on spatial heterogeneity to obtain a connected domain matrix, and calculate spatial non-uniformity within the sliding window range using the generated connected domain matrix; the connected domain matrix is a continuous region with the same standard value and spatial proximity in a standard voxel model; a blind area detection module configured to determine an optimal threshold value based on the spatial non-uniformity corresponding to different threshold values, and perform blind area detection according to the spatial non-uniformity corresponding to the optimal threshold value.

8. An electronic device, comprising: The computer instructions are executed by the processor to complete the method of any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer instructions are executed by the processor to complete the method of any one of claims 1-6.

10. A computer program product, characterised in that, A computer program comprising computer program elements which, when executed by a processor, perform the method according to any one of claims 1-6. A computer program comprising computer program elements which, when executed by a processor, perform the method according to any one of claims 1-6.

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