Underground large space positioning signal quantitative characterization method and system

Abstract

The invention provides an underground large space positioning signal quantitative characterization method and system, a sliding window is introduced, a spatial non-uniformity index is defined from the perspective of spatial heterogeneity, and the spatial non-uniformity index is obtained by calculating the voxel signal distribution non-uniformity in the sliding window and quantizing the signal distribution heterogeneity. The distribution characteristics of the spatial unevenness index are analyzed, the optimal threshold value is determined according to the sensitivity degree of the connected domain to the threshold value, signal blind area detection is achieved under the optimal threshold value, and anomaly detection is more scientific and accurate due to introduction of the spatial unevenness index. According to the method, the non-uniform characteristic of signal distribution of the whole and local areas in the underground large space can be comprehensively captured from a three-dimensional angle, blind area detection is realized, and the application scene of the signal space distribution characterization method is obviously expanded.

Description

Technical Field

[0001] The present invention belongs to the technical field related to signal quantization, and particularly relates to a method and system for quantifying and characterizing underground large-space positioning signals. Background Technique

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

[0003] The spatial distribution of signals determines the coverage range and signal quality of the network. Quantifying the spatial distribution characteristics of signals can help identify signal blind spots, and then optimize the positions of routers or access points to ensure stable signal coverage throughout the area. This is of great significance for research such as wireless network planning and optimization, indoor positioning accuracy, and wireless spectrum management.

[0004] Underground spaces usually include underground pedestrian streets, underground sidewalks, subways, urban roads and tunnel traffic facilities, underground commercial facilities, as well as underground pipe networks and civil air defense facilities. Underground large spaces are the main spaces for human activities. However, due to the airtightness of underground large spaces, traditional GNSS is difficult to provide positioning information.

[0005] Quantifying the three-dimensional spatio-temporal differentiation characteristics of signal strength in large spaces, especially underground spaces, and detecting signal blind spots have become difficult problems that need to be solved urgently. It is also the forefront position for building resilient cities, reducing disaster losses, and rationally allocating emergency rescue resources. Currently, the solutions for signal quantization include: a solution for quantifying the spatial distribution characteristics in the signal field through a series of statistical characteristic indicators of RSSI eigenvalues. Some technologies attempt to simulate the signal propagation characteristics in space according to the signal transmission characteristics and the indoor environment to obtain the signal strength at different positions in the space. In recent years, deep learning technologies have been introduced into signal distribution characterization. For example, convolutional neural networks (CNNs) and fusion capsule networks (Caps Nets) are used to capture the spatio-temporal distribution characteristics of signals to achieve signal strength characterization in indoor spaces.

[0006] The above-mentioned existing technologies focus on the exploration of the overall signal distribution pattern in space, relying on various factors such as base station location parameters and spatial environment. They lack the ability to quantify the spatial inhomogeneity in local areas and have not quantified the signal distribution pattern for large underground spaces to achieve precise detection of blind areas. In addition, in the large underground space environment, due to the unique physical structure and complex propagation environment, such as the bends and branch structures in subways and tunnels, as well as a large number of partitions and obstacles in underground commercial facilities, signal propagation will be severely affected, resulting in more complex and diverse signal anomalies. Compared with traditional ground or simple indoor environments, the signal reflection, scattering, and attenuation laws in large underground spaces are special. Most existing methods are for simple scenarios, and the simulation conditions are too ideal to be applicable to the real large space characteristics and cannot fully display the real signal spatial distribution characteristics.

[0007] In summary, there are problems such as limited characterization ability, poor adaptability to complex environments, high cost, and low efficiency in the existing signal quantization. Summary of the Invention

[0008] To overcome the deficiencies of the above-mentioned existing technologies, the present invention provides a method and system for quantifying and characterizing positioning signals in large underground spaces, which can comprehensively capture the inhomogeneous characteristics of signal distribution in the overall and local areas of large underground spaces from a three-dimensional perspective, achieve blind area detection, and significantly expand the application scenarios of signal spatial distribution characterization methods.

[0009] To achieve the above object, the present invention adopts the following technical solutions:

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

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

[0012] Based on spatial heterogeneity, perform operations on the standard signal voxel data through a sliding window to obtain a connected domain matrix, and calculate the spatial inhomogeneity within the range of the sliding window by using the generated connected domain matrix;

[0013] Based on the spatial inhomogeneity corresponding to different thresholds, determine the optimal threshold, and perform blind area detection according to the spatial inhomogeneity corresponding to the optimal threshold.

[0014] In the second aspect, the present invention provides a system for quantifying and characterizing positioning signals in large underground spaces, including:

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

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

[0017] A blind area detection module, which is configured to: determine an optimal threshold based on the spatial non-uniformity corresponding to different thresholds, and perform blind area detection according to the spatial non-uniformity corresponding to the optimal threshold.

[0018] In a third aspect, the present invention provides an electronic device, including a memory, a processor, and computer instructions stored on the memory and running on the processor. When the computer instructions are run by the processor, the method described in the first aspect is completed.

[0019] In a fourth aspect, the present invention provides a computer-readable storage medium for storing computer instructions. When the computer instructions are executed by a processor, the method described in the first aspect is completed.

[0020] In a fifth aspect, the present invention provides a computer program product, including a computer program. When the computer program is executed by a processor, the method described in the first aspect is implemented to be completed.

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

[0022] The present 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 range of the sliding window, the signal distribution heterogeneity is quantified. By analyzing the distribution characteristics of the spatial non-uniformity index, the optimal threshold is determined according to the sensitivity of the connected component to the threshold. Signal blind area detection is realized under the optimal threshold. The introduction of the spatial non-uniformity index makes the anomaly detection more scientific and accurate. The solution of the present invention breaks through the limitation of the traditional two-dimensional perspective signal overall characterization, can comprehensively capture the non-uniform characteristics of the signal distribution in the overall and local regions in the underground large space from a three-dimensional perspective, realizes blind area detection, and significantly expands the application scenario of the signal spatial distribution characterization method.

[0023] The advantages of the additional aspects of the present invention will be partially given in the following description, partially will become obvious from the following description, or will be understood through the practice of the present invention. Description of the Drawings

[0024] The accompanying drawings forming a part of this invention are used to provide a further understanding of the invention. The schematic 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 It is a block diagram of the method for quantifying and characterizing the positioning signal in a large underground space in the first embodiment of the present invention;

[0026] Figure 2 It is a flowchart of the method for quantifying and characterizing the positioning signal in a large underground space in the first embodiment of the present invention;

[0027] Figure 3 It is a schematic diagram of a sliding window in the first embodiment of the present invention;

[0028] Figure 4 It is a three-dimensional model of the test area in the first embodiment of the present invention;

[0029] Figure 5 It is a schematic diagram of the variance result of the spatial inhomogeneity algorithm of the global signal field under different thresholds in the first embodiment of the present invention;

[0030] Figure 6 It is the operation result of the algorithm under the threshold of 1.5 dBm in the first embodiment of the present invention;

[0031] Figure 7 It is the detection result of the algorithm blind area in the first embodiment of the present invention. Detailed implementation mode

[0032] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0033] It should be noted that the terms used herein are only for describing the specific implementation mode and are not intended to limit the exemplary implementation mode according to the present invention.

[0034] Without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0035] Term explanation:

[0036] Voxel: It is a combined word of "volume" (volume) and "pixel" (pixel), which is the basic unit in three-dimensional space and is similar to the pixel in a two-dimensional image. Voxels are used to represent discrete volume elements in three-dimensional space.

[0037] Spatial Inhomogeneity Index: Abbreviated as SI.

[0038] The first embodiment

[0039] This embodiment discloses a method for quantitatively characterizing 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 respectively to obtain standard signal voxel data corresponding to different thresholds;

[0041] Based on spatial heterogeneity, perform operations on the standard signal voxel data through a sliding window to obtain a connected domain matrix, and calculate the spatial non-uniformity within the sliding window range using the generated connected domain matrix;

[0042] Based on the spatial non-uniformity corresponding to different thresholds, determine the optimal threshold, and perform blind area detection 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 realize the characterization of the spatial distribution of the signal field and accurately detect signal blind areas. This embodiment is illustrated by taking the WiFi signal as an example. A spatial non-uniformity calculation method based on a sliding window is adopted, and the real-time distribution characterization and blind area detection of the WiFi signal field are realized by combining on-site measurement data.

[0044] As Figure 1 shown, it is mainly divided into 4 parts: signal voxel construction, spatial non-uniformity calculation, optimal threshold determination, and blind area identification.

[0045] S1: Set the threshold selection range. First, try to select a larger value to calculate the global spatial non-uniformity index for all data. If the results are all less than 0.05, then determine this value as the maximum value w max of the threshold selection. The threshold selection range is determined as (0, w max ), and the threshold selection interval The threshold selection interval is determined according to the sampling data volume and the threshold range, and the default setting is 0.1.

[0046] 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, that is, the value of the voxel data after discretization processing is the standard value.

[0047] First, it is necessary to preprocess the signal sampling data, screen and remove abnormal data, and then perform descriptive statistical analysis to initially obtain the statistical characteristics of the WiFi signal field. After preprocessing, determine the threshold selection range WS according to the statistical characteristics of the signal field. Traverse the threshold selection range WS, and select a value as the threshold each time. Use the threshold to perform voxel discretization processing on the sampling data to generate a set of standard signal voxel data sd.

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

[0049]

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

[0051] S2: Calculate the index of the sliding window. Set a window with dimensions (d, h, w) to determine the calculation range of the local signal field heterogeneity, and calculate the spatial non-uniformity within the window each time the sliding window slides.

[0052] Suppose the WiFi signal field is a three-dimensional data X with dimensions (D, H, W), and the sliding window K is a three-dimensional filter with dimensions (d, h, w). K(i, j, k) represents that the center point of the window has a spatial index of (i, j, k) in the three-dimensional data X. Each sliding operation of the method is expressed as Figure 3 the process shown. Calculate the output calculation result SI(i, j, k) within the range of K(i, j, k), and then change (i, j, k) to perform the next sliding operation until the signal field is traversed.

[0053] In three-dimensional space, spatial heterogeneity can be manifested as the difference in the spatial distribution of aggregated cluster regions with spatial autocorrelation. The overall scale and distribution of such regions in space reflect the difference in spatial heterogeneity, which is reflected as continuous regions with the same standard value and spatial proximity within the standard voxel model. In this embodiment, such regions are defined as connected domains. The connected domain can be used as an estimate of the aggregated cluster region of the WiFi signal field within the standard value field, and the scale of the connected domain is the number of voxels included in a connected domain.

[0054] Based on the concept of the connected domain, this embodiment proposes an index of spatial non-uniformity SI that can quantify the spatial heterogeneity of the three-dimensional WiFi signal field. The calculation process of this index is as follows: First, set the sliding window size and set a mask for the area outside each sliding window; count the connected domain information for the area within the sliding window, and store the number of connected domains with the same scale for each standard value within the sliding window area into the connected domain matrix CM. The value of the connected domain matrix CM(c n , a m ) is equal to the scale size of c n and the standard value is a mThe number of connected components; considering the negative contribution of large-scale connected components to heterogeneity, use the matrix CM to assign a connected component scale weight to large-scale connected components in all spaces, then divide the sum result by the sum of the scales of all connected components without weights, and map the result value range to [0,1] to calculate SI. The calculation formula is as follows:

[0055]

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

[0057] SI quantifies the uniformity of the distribution of connected components of different scales in space, can be used as a measure of spatial heterogeneity, and has a value range of [0,1]. A decrease in SI indicates that the space is more inclined to exist in the form of large-scale connected components. Based on the above steps, a sliding window operation is performed, and a global spatial non-uniformity calculation result is formed for each threshold width.

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

[0059] The setting of the threshold is the key to mining the distribution characteristics of WiFi signals and blind area detection. It is necessary to determine the optimal threshold width_best according to the algorithm results under different thresholds width.

[0060] This embodiment gives a calculation method for selecting the threshold. Calculate the variance of the algorithm results for each threshold to determine the sensitivity difference of the connected components of the WiFi signal field to different threshold selections. The sensitivity increases with the increase of the threshold and then tends to be stable. The optimal threshold width_best is the minimum threshold in the stable state.

[0061] S4: Identify blind areas according to the algorithm results under width_best.

[0062] WiFi blind areas are caused by reasons such as being far from the signal base station or interfering physical obstacles, resulting in ineffective coverage of WiFi signals and having a small signal strength. According to the distance attenuation characteristics of WiFi signals, blind areas usually have extremely small non-uniformity. In general, areas with the output result of the spatial non-uniformity algorithm less than 0.2 are identified as blind areas.

[0063] To verify the universality and reliability of the technical solution, this embodiment selects the underground space covered by the signal as the test area. This signal coverage area is located in the underground space and belongs to an unopened area. As Figure 2As shown in the figure, the test area is an underground narrow corridor with three WiFi signal base stations and multiple signal-blocking walls. Signal acquisition was carried out at the sampling point positions in the test area. Some sampling data of the test area are shown in Table 1. Three base stations are arranged in sequence along the length direction of the corridor in the test area. The horizontal resolution of the measurement data is 1m * 1m, the vertical resolution is 0.4m, and the data dimension is (4, 75, 9), representing a total of 2,700 signal strength sample points in the width, length, and height directions. Considering the characteristic that the width of the experimental space in the horizontal dimension is much smaller than the length, in this embodiment, the research focus is determined as the spatial distribution characteristics along the length and height directions. Therefore, the sliding window size is set to (4, 6, 3), so that the window width covers the width of the experimental space and slides along the length and height directions.

[0064] Table 1 Data of Some WiFi Sampling Points in the Test Area

[0065]

[0066] After statistical analysis of the experimental data, the threshold selection range is determined to be [0.1, 4.0] according to the value range and distribution characteristics of the sample points. The interval of each threshold width is 0.1. Therefore, there are 40 thresholds to be selected in Widths. Traverse Widths, and run the convolution-based global signal field spatial non-uniformity algorithm on the signal strength data set for each threshold respectively. First, calculate the variance of the algorithm results for each threshold to preliminarily determine the sensitivity difference of the connected regions of the narrow WiFi signal field to different threshold selections. The calculation results are as Figure 3 shown.

[0067] It can be found that when running the algorithm with different thresholds, when the threshold is less than or equal to 1.5dBm, the variance of the convolution result of the WiFi signal field non-uniformity increases rapidly from the minimum value, and the scale of the connected region formed with the increase of the threshold changes greatly. At this time, the selected threshold cannot be used for characterization. After the selected threshold is greater than 1.5dBm, the variance shows a relatively stable fluctuation state along 0.02. Until the threshold increases to 4.0dBm, the variance still remains around 0.02. This shows that after the threshold is greater than 1.5dBm, a connected region with a relatively large scale has been formed in the signal field, and the distribution of the large-scale connected region is relatively stable and does not change violently with the increase of the threshold. When the threshold is selected as 1.5dBm, a connected region with a relatively stable scale and a large scale can be detected in the algorithm running results, and at the same time, the area range with a large local non-uniformity can be detected.

[0068] Figure 4 Shows the algorithm running results with the selected threshold of 1.5dBm for the subsequent characterization analysis of the distribution characteristics in different regions and dimensions in space. The local spatial non-uniformity results restored to the spatial position according to the spatial index after the algorithm runs are shown in the figure. The red dots represent the positions of the known WiFi base stations.

[0069] It can be seen from Figure 6 that there are significant spatial inhomogeneities at different positions within the experimental space. The lowest inhomogeneity within the region is 0.2, and the highest can reach 0.7. There are three regions with high inhomogeneity and one region with low inhomogeneity within the region. The regions with high inhomogeneity coincide exactly with the positions of the Wi-Fi base stations, and the closer to the signal source, the greater the inhomogeneity. This is because the signal intensity differences are relatively large. In the regions close to the signal source, the 1.5 dBm threshold will subdivide the regions into more small-scale connected domains, reflecting the rapid attenuation characteristics of the Wi-Fi signal. The region with low inhomogeneity appears in the range of 22 - 28 meters, indicating that the signal distribution here is relatively uniform. During convolution, the signal differences are small, forming a relatively large-scale connected domain. This region is far from the two Wi-Fi signal sources, indicating that this is the overlapping region of the two signal sources and the signal distribution is uniform. A similar region with low inhomogeneity also appears at 55 meters, but due to being closer to the signal source, the inhomogeneity is 0.4, and the overall level is relatively high. Therefore, the algorithm can effectively reflect the rapid attenuation characteristics of the Wi-Fi signal and accurately identify the distribution characteristics of the signal sources and the overlapping regions.

[0070] Meanwhile, the distribution heterogeneity of the signals within the experimental space shows different performances in the length and height directions. Along the length direction, the Wi-Fi signals show obvious spatial heterogeneity, with significant signal attenuation, and the inhomogeneity within the region fluctuates. Along the height direction, the change in inhomogeneity is relatively small, only slightly decreasing in the Wi-Fi signal source region, but overall remaining at a relatively high level.

[0071] WiFi blind spots are caused by reasons such as being far from the signal base station or interference from physical obstacles, resulting in ineffective coverage of the WiFi signal and having a relatively small signal intensity. Considering the distance attenuation characteristics of the WiFi signal, within the signal blind spots, the signal intensity values are small, the changes are small, and the distribution is uniform. Therefore, the blind spots usually have extremely small inhomogeneity. In this experiment, the regions with the output results of the spatial inhomogeneity algorithm less than 0.2 are identified as blind spots. The detected positions of the blind spots are as Figure 5 shown, where the red dots represent the positions of the WiFi base stations.

[0072] The blind area detection results show that there is a large - area blind area in the 22 - 28m area in the length direction of the underground experimental space. The blind area detected by the algorithm is far from the two nearest base stations. This makes the signal strength in the blind area generally small, resulting in the device being unable to stably connect to the network and affecting the stability of the communication positioning function. The distance between the two nearest WiFi base stations along the length direction to the blind area reaches 33m. The too - large distance makes the WiFi signals transmitted from the two base stations to the blind area have small signal strength, resulting in the appearance of the blind area. While the distance between the other adjacent base stations is 21m, and the blind area detection results show that only a local small - area blind area appears between these two base stations. It can be seen that reducing the signal strength distance can avoid the appearance of the blind area. Therefore, in the underground experimental space, to avoid the appearance of signal blind areas, the distance between adjacent WiFi base stations should be reduced to less than 20m in the length direction to avoid the appearance of blind areas.

[0073] Embodiment 2

[0074] The purpose of this embodiment is to provide an underground large - space positioning signal quantization and characterization system, including:

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

[0076] A calculation module, which is configured to: based on spatial heterogeneity, perform operations on the standard signal voxel data through a sliding window to obtain a connectivity domain matrix, and calculate the spatial non - uniformity within the window range by using the generated connectivity domain matrix;

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

[0078] In more embodiments, there is also provided:

[0079] An electronic device, including a memory, a processor, and computer instructions stored on the memory and running on the processor. When the computer instructions are run by the processor, the method described in Embodiment 1 is completed. For the sake of brevity, it will not be elaborated here.

[0080] It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general - purpose processors, digital signal processors DSP, application - specific integrated circuits ASIC, off - the - shelf programmable gate arrays FPGA, or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general - purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0081] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A part of the memory may also include a non-volatile random access memory. For example, the 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, implement the method described in Embodiment 1.

[0083] The method in Embodiment 1 can be directly implemented by a hardware processor, or by a combination of hardware and software modules in the processor. The software module can be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the steps of the above method. To avoid repetition, it will not be described in detail here.

[0084] A computer program product, including a computer program, which, 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 are executed in a device on a target real or virtual processor to perform the process / method as described above. Generally, 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 functions of program modules can be combined or divided as needed. The machine-executable instructions for program modules can be executed locally or within a distributed device. In a distributed device, program modules can be located in local and remote storage media.

[0086] The computer program code for implementing the method of the present invention can be written in one or more programming languages. These computer program codes can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing devices, such that when the program code is executed by the computer or other programmable data processing devices, the functions / operations specified in the flowchart and / or block diagram are implemented. The program code can be executed entirely on the computer, partially on the computer, as an independent software package, partially on the computer and partially on a remote computer, or entirely on a remote computer or server.

[0087] In the context of the present invention, the computer program code or related data can be carried by any suitable carrier so that the device, apparatus or processor can perform the various processes and operations described above. Examples of the carrier include signals, computer-readable media, and the like. Examples of signals can include electrical, optical, radio, acoustic or other forms of propagated signals, such as carrier waves, infrared signals, etc.

[0088] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in conjunction with this embodiment can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of this application.

[0089] Although the specific implementation manners of the present invention have been described above in conjunction with the accompanying drawings, it is not a limitation on the protection scope of the present invention. Those skilled in the art should understand that, based on the technical solution of the present invention, various modifications or deformations that can be made by those skilled in the art without creative efforts are still within the protection scope of the present invention.

Claims

1. A method for quantifying and characterizing underground large-space positioning signals, characterized in that, Comprising: Set a threshold selection range, and based on different thresholds within the threshold selection range, perform voxel discretization processing on the signal sampling data respectively to obtain standard signal voxel data corresponding to different thresholds; Based on spatial heterogeneity, perform operations on the standard signal voxel data through a sliding window to obtain a connected component matrix, and calculate the spatial non-uniformity within the sliding window range using the generated connected component matrix; Based on the spatial non-uniformity corresponding to different thresholds, determine the optimal threshold, and perform blind area detection according to the spatial non-uniformity corresponding to the optimal threshold.

2. The method for quantifying and characterizing the positioning signal of the large underground space according to claim 1, wherein, Based on different thresholds within the threshold selection range, perform voxel discretization processing on the signal sampling data respectively to obtain standard signal voxel data corresponding to different thresholds, specifically: Determine the selected threshold; Subtract the minimum value in the signal sampling data from the signal sampling data respectively, divide the difference result by the selected threshold and then take the integer to obtain the standard signal voxel data corresponding to the selected threshold.

3. The method for quantifying and characterizing the positioning signal in a large underground space according to claim 1, wherein Calculate the spatial non-uniformity within the sliding window range using the generated connected component matrix, specifically: Among them, i is the standard value; j is the size of the connected component; N s is the size of the largest connected component; CM(i, j) is the number of connected components with a standard value of i and a size of j, and t m is the maximum standard value of the standard signal voxel data.

4. The method for quantitatively characterizing the positioning signal of the underground large space according to claim 1, characterized in that Calculate the variance of the spatial non-uniformity corresponding to each threshold respectively to determine the sensitivity difference of the signal field connected component to different threshold selections, and determine the optimal threshold according to the variance corresponding to different thresholds.

5. The method for quantifying and characterizing the positioning signal in a large underground space according to claim 1, wherein Based on spatial heterogeneity, perform operations on the standard signal voxel data through a sliding window to obtain a connected component matrix, specifically: Set the size of the sliding window; Set a mask for the area outside the sliding window each time, and count the connected component information for the area within the sliding window; Store the connected component data with the same scale of each standard value in the area within the sliding window into the connected component matrix.

6. A method for quantifying and characterizing underground large-space positioning signals as described in claim 1 or 5, characterized in that It also includes preprocessing 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 quantization characterization system, characterized in that Comprising: A discretization module configured to: set a threshold selection range, and based on different thresholds within the threshold selection range, perform voxel discretization processing on the signal sampling data respectively to obtain standard signal voxel data corresponding to different thresholds; A calculation module configured to: based on spatial heterogeneity, perform operations on the standard signal voxel data through a sliding window to obtain a connected component matrix, and calculate the spatial non-uniformity within the sliding window range using the generated connected component matrix; A blind area detection module configured to: based on the spatial non-uniformity corresponding to different thresholds, determine the optimal threshold, and perform blind area detection according to the spatial non-uniformity corresponding to the optimal threshold.

8. An electronic device, characterized in that, Including a memory, a processor, and computer instructions stored on the memory and running on the processor. When the computer instructions are run by the processor, the method according to any one of claims 1-6 is completed.

9. A computer-readable storage medium, characterized in that, For storing computer instructions, when the computer instructions are executed by the processor, the method according to any one of claims 1-6 is completed.

10. A computer program product, characterized in that, Including a computer program, when the computer program is executed by the processor, the method according to any one of claims 1-6 is implemented.

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