Method and device for detecting high-altitude anti-falling cable

Abstract

The invention relates to the technical field of cable detection, in particular to a high-altitude anti-falling cable detection method and device, and the method comprises the steps: obtaining a coding waveform of a cable based on a radar array, and constructing an observation matrix; constructing a high-dimensional feature space based on the observation matrix and calculating embedded data; mapping the embedded data into the curved surface topological structure, calculating to obtain a critical threshold value, and obtaining a characteristic value of the critical threshold value; acquiring a change index of the characteristic value to establish a defect characteristic spectrum, and inputting the defect characteristic spectrum into a pulse neural network for time sequence coding to generate a defect probability cloud picture; extracting spatial features and time features in the defect probability cloud picture to form a defect type tensor; evolution processing is conducted on the defect type tensor, an evolution process is calculated based on a numerical solution to obtain a repair strategy matrix, and the repair strategy matrix comprises adjustment schemes of repair measures on different space points and time. Cable faults can be effectively prevented and handled in time, and potential safety hazards are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of cable detection, and in particular to a detection method and device for a high-altitude anti-fall cable. Background Art

[0002] High-altitude anti-fall cables are widely used in modern urban construction in the fields of power transmission, communication network construction, and safety protection of large buildings. These cables face complex environmental conditions during operation, including wind, rain, temperature changes, ultraviolet radiation and other external factors. They are prone to aging, breakage or loosening. When the cables fail or fail, it may cause serious economic losses and may even endanger people's lives. Therefore, how to achieve real-time and accurate detection of high-altitude cables and timely discover and warn potential defects has always been a technical problem that needs to be solved in the engineering field.

[0003] At present, most of the detection technologies for high-altitude anti-fall cables are through physical detection methods, such as visual inspection, vibration analysis, temperature monitoring or mechanical stress monitoring. Although they can reflect the status of the cables to a certain extent, they have low detection accuracy and cannot fully cover the entire life cycle of the cables. For example, vibration-based detection methods are difficult to capture tiny defects or damage, especially in complex environments. Signals are easily interfered by noise, resulting in insufficient reliability of detection results, making it difficult to accurately identify different types of defects, and unable to meet the high requirements of modern engineering for cable safety detection.

[0004] Therefore, building an efficient, accurate and dynamically updated high-altitude fall protection cable detection and repair system is still a key issue that needs to be urgently solved in current technology. Summary of the invention

[0005] The main purpose of the present invention is to provide a detection method for high-altitude anti-fall cables, aiming to overcome the technical problem of low detection accuracy of existing cable detection methods.

[0006] In order to solve the above technical problems, the present invention proposes a detection method for a high-altitude anti-fall cable, comprising: Constructing a radar array with a preset working frequency, acquiring a coding waveform of the cable based on the radar array, and constructing an observation matrix according to the coding waveform; Constructing a high-dimensional feature space based on the measurement matrix, and calculating embedded data of the high-dimensional phase space; Mapping the embedded data into a surface topological structure, calculating a critical threshold of the surface topological structure, and obtaining a characteristic value of the critical threshold in the surface topological structure; Obtaining a variation index of the characteristic value, establishing a defect characteristic spectrum according to the variation index, inputting the defect characteristic spectrum into a pulse neural network for time series encoding, and generating a defect probability cloud map; Mapping the probability value of each spatial point in the defect probability cloud map to a specific position in the three-dimensional space, extracting the spatial features and time features in the defect probability cloud map, and forming a defect type tensor; The defect type tensor is subjected to evolution processing, and the evolution process is calculated based on a numerical solution to obtain a repair strategy matrix, wherein the repair strategy matrix includes adjustment schemes of repair measures at different spatial points and times.

[0007] Furthermore, the step of constructing a radar array with a preset working frequency, acquiring a coded waveform of the cable based on the radar array, and constructing an observation matrix according to the coded waveform includes: Modulating and configuring a radar array of a preset operating frequency, generating a radar signal based on a set frequency range, and generating a radar signal with a specific periodicity in frequency through a modulation process; Based on multiple receiving channels, the reflected signals of the radar signal are collected in parallel, and the multiple reflected signals are subjected to time synchronization processing to obtain the coded waveform; Extracting signal strength information at each moment in the coded waveform, and converting the reflected signal into a discrete signal sequence according to the signal strength information; Setting a time window of preset bits, controlling the time window to slide on the discrete signal sequence, and generating a plurality of discrete signals; The discrete signal is subjected to time-frequency transformation to obtain a plurality of frequency features, and an observation matrix is ​​constructed according to the signal amplitude and phase information of each time-frequency point in the frequency features.

[0008] Furthermore, the step of constructing a high-dimensional feature space based on the measurement matrix and calculating the embedded data of the high-dimensional phase space includes: Performing matrix decomposition on the observation matrix to obtain a set of basis vectors, wherein each basis vector represents a potential frequency feature; Sorting basis vectors in the basis vector set, and selecting basis vectors whose order exceeds a preset value as main features; Linearly combining a plurality of the main features to form a feature space, mapping the data in the feature space through an inner product operation to obtain a preliminary representation of a high-dimensional data space; The obtained high-dimensional data is reconstructed in phase space, and the embedded data is calculated based on the dynamic relationship between the dimension of the reconstructed high-dimensional phase space and the original data.

[0009] Furthermore, the step of mapping the embedded data into a surface topological structure, calculating a critical threshold of the surface topological structure, and obtaining a characteristic value of the critical threshold in the surface topological structure includes: Extracting dimensional features of the embedded data, and determining initial parameters of the surface topology structure based on the dimensional features; Based on a topological transformation algorithm, each embedded data point in the high-dimensional phase space is associated with a corresponding point on the initial parameter to obtain a mapping data point set; Calculate the local features of the surface topology structure according to the mapping data point set to obtain the position features of each mapping data point on the surface topology structure; Calculate the overall curvature of the surface topological structure according to the position features, and obtain high curvature areas and low curvature areas on the surface; Identifying distribution information of the high curvature region and the low curvature region, obtaining a critical point based on the distribution information, and calculating a critical threshold of a surface topological structure based on the critical point; Segmenting the surface topology structure according to the critical threshold to obtain a plurality of sub-regions; The density feature of each sub-region mapping data point set is calculated, and the feature value in each sub-region is obtained according to the density feature.

[0010] Furthermore, the step of obtaining a variation index of the characteristic value, establishing a defect characteristic spectrum according to the variation index, inputting the defect characteristic spectrum into a pulse neural network for time series encoding, and generating a defect probability cloud map includes: Performing time series analysis on the eigenvalues, calculating the variation between adjacent eigenvalues, and obtaining a variation index of the eigenvalues; Constructing a time series model according to the variation index, converting the time series model into a frequency domain representation, and obtaining a defect characteristic spectrum; Constructing a spiking neural network structure, taking the defect characteristic spectrum as an input signal of the spiking neural network, and performing temporal encoding through the pulse emission and transmission process of neurons; According to the encoding result, the defect characteristics at each time point are mapped onto a two-dimensional plane to generate a defect probability cloud map, wherein the density of the defect probability cloud map represents the probability of the defect occurring.

[0011] Furthermore, the step of mapping the probability value of each spatial point in the defect probability cloud map to a specific position in the three-dimensional space, extracting the spatial features and time features in the defect probability cloud map, and forming a defect type tensor includes: Performing spatial analysis on the defect probability cloud map, taking the probability value of each spatial point in the cloud map as coordinate information, mapping it to a three-dimensional space coordinate system, and obtaining a specific position point set in the three-dimensional space; Extracting spatial features based on the specific location point set, the spatial features including distribution density, spatial distance and spatial direction of spatial points; Extracting time features from the defect probability cloud map, wherein the time features include a change trend and periodicity of a time point; The spatial features and the temporal features are fused to construct a defect type tensor, wherein the dimensions of the tensor include a spatial dimension and a temporal dimension, and the value of each dimension represents corresponding feature information.

[0012] Furthermore, the defect type tensor is subjected to evolution processing, and the evolution process is calculated based on a numerical solution to obtain a repair strategy matrix, wherein the repair strategy matrix includes the steps of adjusting the repair measures at different spatial points and times, including: Acquire an initial value based on cable material characteristics, and set a repair parameter of the defect type tensor based on the initial value; An evolution model is constructed according to the defect type tensor, the repair parameters are input into the evolution model, the distribution state of the repair parameters at each time step is calculated, and the intermediate evolution results are obtained frame by frame; Optimizing the intermediate results of the evolution based on a particle swarm optimization algorithm to obtain an optimal combination of repair measure parameters at each spatial point; An initial repair matrix is ​​constructed based on the combination of repair measure parameters, the initial repair matrix is ​​iteratively updated, and the repair measure parameters are adjusted according to the repair effect at each time step to obtain a repair strategy matrix.

[0013] The present invention also provides a detection device for a high altitude anti-fall cable, comprising: An acquisition module, used to construct a radar array of a preset working frequency, acquire a coding waveform of the cable based on the radar array, and construct an observation matrix according to the coding waveform; A calculation module, used for constructing a high-dimensional feature space based on the observation matrix, and calculating the embedded data of the high-dimensional phase space; A mapping module, used for mapping the embedded data into a surface topological structure, calculating a critical threshold of the surface topological structure, and obtaining a characteristic value of the critical threshold in the surface topological structure; A generation module, used for obtaining a variation index of the characteristic value, establishing a defect characteristic spectrum according to the variation index, inputting the defect characteristic spectrum into a pulse neural network for time series encoding, and generating a defect probability cloud map; An extraction module is used to map the probability value of each spatial point in the defect probability cloud map to a specific position in the three-dimensional space, extract the spatial features and time features in the defect probability cloud map, and form a defect type tensor; The evolution module is used to perform evolution processing on the defect type tensor and obtain a repair strategy matrix based on the numerical solution to calculate the evolution process. The repair strategy matrix includes adjustment schemes of repair measures at different spatial points and times.

[0014] The present invention further provides a computer device, which includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above method when executing the computer program.

[0015] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above method is implemented.

[0016] Beneficial effects: The present application proposes a detection method for high-altitude anti-fall cables. By constructing a radar array with a preset working frequency and combining the construction of a coded waveform and an observation matrix, the cable status information can be acquired with high precision, thereby improving the accuracy and reliability of the detection. By constructing a high-dimensional feature space based on the observation matrix and calculating the embedded data of the high-dimensional phase space, a multi-dimensional and in-depth analysis of the cable status is realized. The cable can accurately capture the tiny defects or damages that may occur during operation, and map the embedded data to the surface topological structure, calculate the critical threshold and its eigenvalue, and by analyzing the change index of the eigenvalue, a defect characteristic spectrum can be established and a defect probability cloud map can be generated, thereby realizing a visual expression of the defect type and distribution. By extracting the spatial and temporal features in the defect probability cloud map as a defect type tensor, and subjecting it to evolutionary processing and numerical solution calculation, a repair strategy matrix is ​​finally generated, which can adaptively formulate spatial and temporal adjustment plans for repair measures, and can also realize dynamic optimization of repair strategies to ensure the efficiency and intelligence of the repair process.

[0017] In summary, the present invention constructs a set of efficient, accurate and dynamically updated high-altitude fall prevention cable detection and repair system, which can effectively prevent and promptly respond to cable failures and reduce safety hazards. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the steps of a detection method for a high altitude anti-fall cable in one embodiment of the present invention; Figure 2 It is a structural schematic block diagram of a detection device for a high altitude anti-fall cable according to an embodiment of the present invention; Figure 3It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention. The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0020] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "one", "above", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of features, integers, steps, operations, elements, modules, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, modules, components, and / or groups thereof. It should be understood that when an element is said to be "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be an intermediate element. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The term "and / or" used herein includes all or any module and all combinations of one or more associated listed items.

[0021] Those skilled in the art will understand that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the field to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined as here.

[0022] Reference Figure 1 The embodiment of the present invention provides a method for detecting a high altitude anti-fall cable, the method comprising: S1: constructing a radar array with a preset working frequency, acquiring a coding waveform of the cable based on the radar array, and constructing an observation matrix according to the coding waveform; In step S1, the radar array is an array system composed of multiple radar transmitting and receiving units, which can simultaneously detect the target object at multiple angles and multiple frequencies. A frequency band that can penetrate or bypass external environmental interference while detecting the cable is set as the operating frequency of the radar array. The radar array obtains reflected echoes by sending and receiving signals. These echo signals contain the reflection characteristics of the cable, such as reflection intensity, reflection time, and waveform changes, which directly reflect the state of the cable and the defects that may exist on its surface and inside. The waveform signal received by the radar array is encoded, the original waveform is mathematically transformed, and the reflected signal is modulated by a specific algorithm, so that different defect types can present different coding features in the waveform, and the key features in the signal are extracted. Based on these coded waveforms, an observation matrix is ​​constructed. By organizing the multidimensional waveform data obtained from the radar array into a matrix form, it aims to comprehensively describe the overall state of the cable, including the detection signal of each radar unit, and covering the correlation between each signal, timing changes and other information. In actual operation, the construction of the observation matrix can be based on multiple parameters, such as the layout of the radar array, the receiving range of each unit, the allocation of signals of different frequencies, etc., and thus build a data foundation.

[0023] S2: constructing a high-dimensional feature space based on the measurement matrix, and calculating the embedded data of the high-dimensional phase space; In step S2, the data information contained in the observation matrix is ​​often multidimensional, and these multidimensional data are mapped to a high-dimensional feature space. The high-dimensional feature space is a mathematical construction that maps the original data to a higher dimension through a high-dimensional coordinate system, thereby retaining more information and structural features. By constructing a high-dimensional feature space, the complex patterns and laws hidden in the observation data can be captured. Specifically, the construction of a high-dimensional feature space based on phase space reconstruction is a mathematical method for recovering the system dynamic characteristics from time series data. In this embodiment, the time series data obtained from the radar array is used as input, and the phase space is constructed using the delay embedding technology. By constructing the waveform data of the observation matrix into a high-dimensional space under different time delays, the dynamic characteristics of the original signal can be retained, indicating the time dependence and possible nonlinear behavior of the signal. For example, if cracks or other defects appear on the surface of the cable, the time variation characteristics of the reflected signal will show different laws in the embedded space, thereby identifying the specific location and type of the defect. The calculation process of the embedded data can be based on some mathematical tools, such as Takens' theorem, which provides a theoretical basis for reconstructing the phase space from the observation data. By selecting appropriate embedding dimensions and delay times, more cable status information can be captured in a high-dimensional space. The mathematical formula of this process can be expressed as converting a one-dimensional time series into a multi-dimensional phase space vector through delay mapping. The position of each data point in the phase space represents a specific state of the cable. By analyzing the distribution of these positions, the health status of the cable and whether there are potential defects can be inferred.

[0024] S3: Mapping the embedded data to a surface topological structure, calculating a critical threshold of the surface topological structure, and obtaining a characteristic value of the critical threshold in the surface topological structure; In step S3, the embedded data of the high-dimensional phase space is calculated in step S2, and the embedded data contains characteristic information of the cable in different states. These high-dimensional embedded data are mapped to a surface topological structure of a mathematical structure, and the change law of these data can be analyzed by mathematical means. Specifically, the embedded data is mapped to the surface topological structure, and the high-dimensional data is converted into a structure that can be expressed in two-dimensional or three-dimensional space. The topological structure is a mathematical concept that describes the shape, connectivity and structure of objects in space. The embedded data is constructed into a surface by a topological method, and the surface can reflect the different states or characteristics of the cable. The critical threshold refers to certain changes or conversion points in the surface topological structure under specific conditions. These points represent the transition of the health state of the cable from normal to abnormal. By calculating the critical threshold, it is possible to determine which areas or parts of the cable have potential defects. When the embedded data is mapped to the surface topological structure, these critical points can be found on the surface, and these points correspond to different degrees of cable damage. For example, these thresholds can be set using information such as the distance between data points and density changes. The critical threshold reflects the boundaries where mutations occur under specific circumstances, and areas where problems may exist can be identified. Specifically, when a data point exceeds a critical threshold, it means that there are potential defects in a part of the cable, which may affect the normal operation of the cable, and the eigenvalues ​​corresponding to the critical point are extracted. The eigenvalues ​​reflect the morphological characteristics of the surface topology near these critical points. The eigenvalues ​​may represent that a part of the cable has been deformed or worn, while other eigenvalues ​​may correspond to excessive strain or vibration at certain points of the cable.

[0025] S4: obtaining a variation index of the characteristic value, establishing a defect characteristic spectrum according to the variation index, inputting the defect characteristic spectrum into a pulse neural network for time series encoding, and generating a defect probability cloud map; In step S4, by further analyzing the eigenvalues, the changing trend of these eigenvalues ​​can be observed, thereby obtaining a change index, which reflects the rate and amplitude of the change of the eigenvalues ​​over time or spatial position, indicating the structural changes that may occur in the cable during use, and using the index to construct a defect characteristic spectrum. The defect characteristic spectrum is a spectrum that reflects the health status of the cable. The change of the eigenvalues ​​indicates the types of defects that may occur in the cable at different times or spatial positions. By constructing the defect characteristic spectrum, the potential problems of the cable can be visualized, and the defect characteristic spectrum can be input into the pulse neural network for time series coding. The pulse neural network is a neural network model based on biological principles. It uses discrete pulse signals for information processing. The pulse neural network converts the defect characteristic spectrum into time series information through time series coding, thereby capturing the evolution of defects in the time dimension and generating a defect probability cloud map. The defect probability cloud map is an image of dual dimensions of space and time, indicating the probability of defects at each spatial point. The probability value can be calculated through the data obtained by the radar array and the output of the pulse neural network. Each point in the cloud map represents the health status of a certain position of the cable, and the probability value indicates the probability of defects at that position.

[0026] S5: Mapping the probability value of each spatial point in the defect probability cloud map to a specific position in the three-dimensional space, extracting the spatial features and time features in the defect probability cloud map, and forming a defect type tensor; In step S5, the probability value of each spatial point in the cloud map is mapped to a specific position in the three-dimensional space, and the spatial distribution of each defect is introduced into a three-dimensional coordinate system. In this coordinate system, a multidimensional space reflecting the cable state is constructed by combining the spatial position and time characteristics, and spatial features and time features are extracted from it. The spatial features reflect the spatial distribution of defects that may exist in various parts of the cable, such as stress concentration areas in certain specific parts of the cable, or structural unevenness that may be caused by construction technology or material problems. The time characteristics reflect the dynamic characteristics of these defects over time, such as the impact of cable aging, wear or changes in the external environment on the cable. By extracting these two aspects of features, the damage state of the cable can be fully understood, the location of the damage and the evolution process of the damage can be identified, as well as the severity of the damage at different time points, and the defect type tensor can be formed by combining the spatial features and time features. A tensor is a multidimensional data structure that can integrate spatial and temporal characteristics and become the basis for analysis and processing.

[0027] S6: performing evolution processing on the defect type tensor, and calculating the evolution process based on a numerical solution to obtain a repair strategy matrix, wherein the repair strategy matrix includes adjustment schemes of repair measures at different spatial points and times.

[0028] In step S6, the temporal variation of defects can be captured by performing evolutionary processing on the defect type tensor. Evolutionary processing refers to analyzing the state changes of the system within a certain period of time and predicting the future behavior of the system through certain mathematical methods or calculation models. In an embodiment, the goal of evolutionary processing is to identify the evolution trend of cable defects by calculating the evolution path of the defect type tensor, such as whether certain defects will gradually expand or worsen over time, whether new damage points will appear, etc. Numerical solutions (such as finite element analysis, dynamic models or other numerical simulation methods) can be used to predict the evolution of defects. In a specific implementation, the defect type tensor can be regarded as a high-dimensional data set and deduced step by step using numerical solutions. Numerical solutions can be performed based on different calculation models, such as differential methods, integral methods, etc. Through these numerical methods, the possible distribution of defects at future time nodes can be inferred, thereby making reasonable predictions on future maintenance and repair needs. For example, if the defects at a certain location have gradually intensified over the past period of time, the numerical solution can help infer the defect development trend of the location in the next few months, formulate corresponding repair measures for this purpose, and convert these evolutionary data into a specific repair strategy matrix. The repair strategy matrix is ​​a multidimensional matrix, in which each element represents what kind of repair measures should be taken at a specific spatial location and time point. Repair measures may include replacing cables, reinforcing certain stress-bearing parts, adjusting the installation angle of cables, and performing local repair treatments. According to the different dimensions of the repair strategy matrix, different adjustment plans can be proposed at different times and spaces to ensure the safety and service life of the entire cable. For example, suppose that in a certain inspection cycle, the probability of detecting defects at the cable joint position gradually increases, and this defect is extensible and may cause more serious damage. Through evolution processing, the defect evolution trend of this joint position in the next few months can be obtained, such as the expansion speed and impact range of the defect. Based on this information, the repair strategy matrix will propose repair measures for this position, such as reinforcing the joint position in the short term, or replacing it over a longer period of time.

[0029] In one embodiment, the step of constructing a radar array with a preset working frequency, acquiring a coded waveform of the cable based on the radar array, and constructing an observation matrix according to the coded waveform includes: Modulating and configuring a radar array of a preset operating frequency, generating a radar signal based on a set frequency range, and generating a radar signal with a specific periodicity in frequency through a modulation process; Based on multiple receiving channels, the reflected signals of the radar signal are collected in parallel, and the multiple reflected signals are subjected to time synchronization processing to obtain the coded waveform; Extracting signal strength information at each moment in the coded waveform, and converting the reflected signal into a discrete signal sequence according to the signal strength information; Setting a time window of preset bits, controlling the time window to slide on the discrete signal sequence, and generating a plurality of discrete signals; The discrete signal is subjected to time-frequency transformation to obtain a plurality of frequency features, and an observation matrix is ​​constructed according to the signal amplitude and phase information of each time-frequency point in the frequency features.

[0030] In the above embodiment, the working frequency of the radar array is modulated and configured during detection. Through the modulation process, the frequency of the radar signal will present a specific periodicity, which enables the radar signal to effectively interact with the cable when penetrating the cable, thereby capturing the reflected signal on its surface and inside. Specifically, the frequency range of the radar signal is set according to the physical characteristics of the detected cable and the required detection accuracy. The multiple receiving channels of the radar array collect the reflected signal of the radar signal in parallel to improve the efficiency of data acquisition. The reflected signal reflects the information reflected by the radar signal from the cable surface and its internal structure. The reflected signal is processed in time synchronization, and the signals collected by multiple receiving channels are aligned in time. They can be analyzed in the same time window to ensure the consistency and reliability of the data, and obtain a coded waveform containing the time information of different reflected signals. The signal strength information at each moment is extracted. The signal strength is an important indicator to measure the signal reflection strength, which reflects the strength of the interaction between the radar signal and the object. By extracting this strength information, the energy distribution of the reflected signal can be obtained, and the synchronous reflected signal can be converted into a discrete signal sequence. By sampling the signal in the time domain, the continuous signal can be converted into a form suitable for digital processing. For example, the signal strength value of the synchronous reflection signal at a certain moment is a real number, such as 1.23. After sampling, 1000 samples are taken at this moment, and a set of signal strength values ​​(such as 1.23, 1.25, 1.20, etc.) are obtained. Then these signal strength values ​​are quantized into discrete levels (such as 1, 2, 0, etc.) to improve the efficiency of data processing. A time window with a preset number of bits is set, and the time window is controlled to slide on the discrete signal sequence to generate multiple discrete signals. The entire signal sequence is divided into multiple short time segments. The signal in each segment can be analyzed independently. These discrete signals are transformed in time and frequency to obtain multiple frequency features, thereby obtaining the characteristic information of the signal at different time points and frequencies. Time-frequency transformation can use methods such as wavelet transform and short-time Fourier transform to identify the frequency components in the signal and their laws of change over time. According to the signal amplitude and phase information of the time-frequency points in these frequency features, an observation matrix can be constructed. The observation matrix is ​​a multidimensional data structure that organizes time-frequency information in a certain way in the matrix. Through this matrix, the characteristics of the signal can be captured from multiple dimensions, such as the amplitude, phase, and frequency components of the signal.

[0031] In one embodiment, the step of constructing a high-dimensional feature space based on the measurement matrix and calculating embedded data of the high-dimensional phase space includes: Performing matrix decomposition on the observation matrix to obtain a set of basis vectors, wherein each basis vector represents a potential frequency feature; Sorting basis vectors in the basis vector set, and selecting basis vectors whose order exceeds a preset value as main features; Linearly combining a plurality of the main features to form a feature space, mapping the data in the feature space through an inner product operation to obtain a preliminary representation of a high-dimensional data space; The obtained high-dimensional data is reconstructed in phase space, and the embedded data is calculated based on the dynamic relationship between the dimension of the reconstructed high-dimensional phase space and the original data.

[0032] In the above embodiment, the observation matrix is ​​matrix decomposed, and a large matrix is ​​decomposed into the product of multiple small matrices to obtain a set of basis vectors, each of which represents a potential frequency feature in the observation matrix. The basis vector can be understood as an abstract representation of the original data matrix, and the main structural features in the matrix are extracted. Through these basis vectors, the frequency characteristics and change trends in the signal can be more clearly understood. After matrix decomposition, these basis vectors are sorted to screen out the most representative and relevant basis vectors. The sorting can be based on the contribution of the basis vector, or the importance of the frequency feature represented by the basis vector in the entire data set. For example, if the frequency feature corresponding to a certain basis vector shows a strong signal strength at multiple times and frequency ranges, then this basis vector will be considered as an important feature. After sorting, the basis vector whose sorting exceeds the preset value is selected as the main feature. This preset value can be a set threshold, indicating that the basis vector with a larger contribution is selected to enter the final feature set, so that the information most relevant to the cable status is retained. Multiple main features are linearly combined to form a new feature space, by combining different basis vectors according to certain weights, and these weights are usually determined according to the importance of the basis vector. The result of the linear combination is a new vector space that integrates all the main features, providing a more comprehensive perspective to describe the state of the cable, and mapping the data through the inner product operation to obtain a preliminary representation of the high-dimensional data space. Mapping the data from one space to another can better capture the potential structure and pattern in the data and reconstruct the phase space of the high-dimensional data. Phase space reconstruction is used to analyze the time series data of dynamic systems. Through phase space reconstruction, the dynamic relationship between the high-dimensional data space and the original data can be understood, so as to calculate the embedded data. Embedded data refers to the data obtained through phase space reconstruction, which can effectively reflect the dynamic evolution process of the system and represent the dynamic characteristics of the cable state.

[0033] In one embodiment, the steps of mapping the embedded data into a surface topological structure, calculating a critical threshold of the surface topological structure, and obtaining a characteristic value of the critical threshold in the surface topological structure include: Extracting dimensional features of the embedded data, and determining initial parameters of the surface topology structure based on the dimensional features; Based on a topological transformation algorithm, each embedded data point in the high-dimensional phase space is associated with a corresponding point on the initial parameter to obtain a mapping data point set; Calculate the local features of the surface topology structure according to the mapping data point set to obtain the position features of each mapping data point on the surface topology structure; Calculate the overall curvature of the surface topological structure according to the position features, and obtain high curvature areas and low curvature areas on the surface; Identifying distribution information of the high curvature region and the low curvature region, obtaining a critical point based on the distribution information, and calculating a critical threshold of a surface topological structure based on the critical point; Segmenting the surface topology structure according to the critical threshold to obtain a plurality of sub-regions; The density feature of each sub-region mapping data point set is calculated, and the feature value in each sub-region is obtained according to the density feature.

[0034] In the above embodiment, the embedded data reflects the state change of the high-altitude anti-fall cable, including signal features such as vibration and bending, and extracts the dimensional features of these data. The extraction of dimensional features can be understood as obtaining the complexity and structure of the embedded data in the multidimensional space. For example, the main dimensions of the data can be effectively extracted by methods such as principal component analysis (PCA) to understand its distribution in the high-dimensional space. Based on these dimensional features, the initial parameters of the surface topology structure can be determined, that is, the values ​​closely related to the distribution characteristics and change trends of the data, such as the starting point, preliminary curvature information, etc. According to the topological transformation algorithm, each embedded data point in the high-dimensional phase space is associated with a corresponding point on the initial parameter to form a mapping data point set. The topological transformation can map the data from the original high-dimensional space to the surface topology structure. In this process, the mapping relationship of each data point depends on its position in the original space and its relative relationship with the initial parameters. Through this mapping, a new set of data points can be obtained, which show the distribution characteristics and change laws of the data in the surface topology structure. The mapped data point set can be used to calculate the local features of the surface topology structure. Local features refer to the features of the location of each data point after mapping, which reflects the local geometric properties of the point on the surface. For example, the area where the data point is located may have different densities or geometric forms, which can reflect a certain state or change of the cable. Based on these local features, the positional features of each mapped data point in the surface topology can be obtained, and the overall curvature can be calculated. The overall curvature refers to the degree of curvature of the surface at a certain point or a small area. In this embodiment, calculating the overall curvature of the surface can identify high curvature areas and low curvature areas in the surface. The high curvature area indicates the part of the surface where the bending or deformation is more obvious, corresponding to the damage or abnormality of the cable; the low curvature area indicates the part of the surface where the change is smaller, corresponding to the normal or stable state. Based on the distribution information of high curvature areas and low curvature areas, critical points are identified. Critical points refer to the junction of high curvature and low curvature areas in the surface topology structure, and the critical threshold of the surface topology structure is calculated based on the critical points. The calculation method can be to take the weighted average of the curvature values ​​at the critical points or take the maximum value, minimum value and other statistics as the critical threshold, and an effective indicator can be obtained to determine whether the cable is in a safe state or whether maintenance is required. The surface topology structure is segmented based on the critical threshold, and the surface is divided into multiple sub-areas. The features inside each sub-area are relatively consistent, and each sub-area corresponds to different physical or geometric characteristics. Through this segmentation, the density characteristics and other information of different areas on the surface can be separated, so as to conduct targeted analysis on each sub-area.Calculate the density feature of each sub-region. The density feature refers to the concentration of data points in each sub-region, reflecting the distribution of data points in the local area. A region with higher density may indicate that the characteristics of the region are more obvious, while a region with lower density may indicate that the characteristics of the region are relatively stable or have less changes. By calculating the density feature of each sub-region, the characteristic value of each sub-region can be further obtained. The calculation of the characteristic value is a quantitative indicator obtained by comprehensive evaluation based on the density feature and other attributes of the sub-region, such as average curvature, geometric shape, etc.

[0035] In another embodiment, the calculation expression of the above embodiment is: Among them, represents the final characteristic value, reflecting the state change of the cable; is the weighting coefficient, which indicates the weight of each data point’s contribution to the eigenvalue after mapping, depending on the relative importance or sensitivity of the data; n is the size of the mapped data point set, indicating the number of data points after mapping; is the density feature vector of the ith data point, which contains the local density information of the data point in the sub-region (i.e., the degree of distribution in the region), and can be expressed as: ,in, is the neighborhood set of the i-th point, is the square of the Euclidean distance between data point i and its neighboring point j; is the topological transformation feature of the i-th data point. Based on the transformation relationship after high-dimensional data is embedded in the surface topological structure, it can be expressed as: ,in, is the coordinate of data point i in the original high-dimensional space, are initial parameters (such as starting point), are the parameters obtained after topological transformation (such as preliminary curvature information); is the curvature feature of the i-th data point, indicating the overall curvature change after being mapped to the surface topology. It can be expressed as: ,in, is the Laplace operator at the data point i, reflecting the local curvature change of the surface; is the density region feature of the i-th data point, which can be expressed as: , where m is the number of sample points in the area, C i,k Represents the curvature value of each point in the area. Through the above comprehensive formula, we can combine the local and global features of high-dimensional data, map them to the surface topology, and finally calculate the state characteristic value of the cable.

[0036] In one embodiment, the steps of obtaining a variation index of the characteristic value, establishing a defect characteristic spectrum according to the variation index, inputting the defect characteristic spectrum into a pulse neural network for time series encoding, and generating a defect probability cloud map include: Performing time series analysis on the eigenvalues, calculating the variation between adjacent eigenvalues, and obtaining a variation index of the eigenvalues; Constructing a time series model according to the variation index, converting the time series model into a frequency domain representation, and obtaining a defect characteristic spectrum; Constructing a spiking neural network structure, taking the defect characteristic spectrum as the input signal of the spiking neural network, and performing temporal encoding through the pulse emission and transmission process of neurons; According to the encoding result, the defect characteristics at each time point are mapped onto a two-dimensional plane to generate a defect probability cloud map, wherein the density of the defect probability cloud map represents the probability of the defect occurring.

[0037] In the above embodiment, the characteristic value is subjected to time series analysis to obtain the dynamic change over time, and the change amount between adjacent characteristic values ​​is calculated. This change amount reflects the difference or change between each two consecutive data points, and the change index is obtained. The index reflects the change amplitude and change rate of the characteristic value. A time series model is constructed based on these change indexes and converted into a frequency domain representation. Through mathematical modeling, the regularity in the change of characteristic values ​​is extracted to understand the behavior pattern of the cable in the time series. By performing frequency domain conversion on these time series models, the characteristic value change can be analyzed from another perspective to obtain the defect characteristic spectrum, which reflects the change characteristics of the cable in different frequency ranges. The spectrum is input into the spiking neural network (SNN) for time series encoding, simulating the working mode of the biological nervous system, and processing information through the emission and transmission of pulses. In this embodiment, the input signal of the pulse neural network is a defect characteristic spectrum, which represents the changing characteristics of the cable at different frequencies. After receiving these input signals, the neurons in the neural network process this information by emitting pulses. These pulses are transmitted between neurons, and after a series of neuron activation and inhibition processes, a timing coding result is finally generated. The timing characteristics of the input signal are reflected by the pulse emission of neurons, and the time structure characteristics of the signal can be retained. The pulse neural network can simulate the dynamic evolution process of defects in the model by encoding the timing of the signal. According to the encoding results of the pulse neural network, the defect characteristics of each time point are mapped to a two-dimensional plane to generate a defect probability cloud map. In this process, the characteristics of each time point occupy a position in the two-dimensional plane, and the area with higher density represents the area with a higher probability of defect occurrence.

[0038] In one embodiment, the step of mapping the probability value of each spatial point in the defect probability cloud map to a specific position in three-dimensional space, extracting the spatial features and time features of the defect probability cloud map, and forming a defect type tensor includes: Performing spatial analysis on the defect probability cloud map, taking the probability value of each spatial point in the cloud map as coordinate information, mapping it to a three-dimensional space coordinate system, and obtaining a specific position point set in the three-dimensional space; Extracting spatial features based on the specific location point set, the spatial features including distribution density, spatial distance and spatial direction of spatial points; Extracting time features from the defect probability cloud map, wherein the time features include a change trend and periodicity of a time point; The spatial features and the temporal features are fused to construct a defect type tensor, wherein the dimensions of the tensor include a spatial dimension and a temporal dimension, and the value of each dimension represents corresponding feature information.

[0039] In the above embodiment, the probability value of each spatial point in the cloud map is mapped to a specific position in the three-dimensional space. Through spatial analysis, the probability value of each spatial point is mapped to a three-dimensional coordinate system according to its position in the cloud map to form a point set in the three-dimensional space. Based on these specific position point sets mapped to the three-dimensional space, spatial features are extracted, mainly including information such as the distribution density, spatial distance and spatial direction of spatial points. Spatial distance refers to the distance between different defect points, which can help analyze the extension degree of defects in space. If the distance between points is small, it may indicate that these defects are related to each other and form a certain pattern or trend; spatial direction involves the directionality of defect point distribution. If the number of defect points in a certain direction is significantly more than that in other directions, it may reflect a specific damage or stress condition in a certain direction, and extract time features. Time features are mainly reflected in the change trend and periodicity of time points. The change law of defects in time can be obtained, so as to predict the possible failure or risk of cables in a certain period of time in the future. The spatial features and time features are fused to construct a defect type tensor, and the dimensions include spatial dimension and time dimension. The spatial dimension corresponds to the spatial distribution information of the defect, and the time dimension indicates the change of the defect at different time points. In this tensor, the value of each dimension represents the corresponding feature information. For example, a value in the spatial dimension represents the distribution density of a certain location point, and a value in the time dimension represents the changing trend of defects at that point in time. By combining these two types of features to form a defect type tensor, the spatial and temporal characteristics of cable defects can be fully described, and then through further analysis of the tensor, the defect type, development trend and possibility of occurrence of the cable can be accurately predicted.

[0040] In one embodiment, the defect type tensor is subjected to evolution processing, and the evolution process is calculated based on a numerical solution to obtain a repair strategy matrix, wherein the repair strategy matrix includes the steps of adjusting the repair measures at different spatial points and times, including: Acquire an initial value based on cable material characteristics, and set a repair parameter of the defect type tensor based on the initial value; An evolution model is constructed according to the defect type tensor, the repair parameters are input into the evolution model, the distribution state of the repair parameters at each time step is calculated, and the intermediate evolution results are obtained frame by frame; Optimizing the intermediate results of the evolution based on a particle swarm optimization algorithm to obtain an optimal combination of repair measure parameters at each spatial point; An initial repair matrix is ​​constructed based on the combination of repair measure parameters, the initial repair matrix is ​​iteratively updated, and the repair measure parameters are adjusted according to the repair effect at each time step to obtain a repair strategy matrix.

[0041] In the above embodiment, the initial value is obtained based on the characteristics of the cable material. The material of each cable has different physical and chemical properties, which determine the damage of the cable and its repair method. For example, some materials will have microcracks when subjected to pressure or tension, and some will have brittle fractures due to long-term exposure to high temperature or cold environment. These factors will affect the formulation of the repair strategy, so the initial repair parameters are determined from the material properties of the cable. The parameters may include preliminary evaluations of material strength, fatigue life, damage tolerance, etc. An evolution model is constructed based on these initial repair parameters. The goal of this evolution model is to simulate the development process of cable defects over time, and how to take different repair measures at different times to prevent defects from deteriorating, covering the dynamic characteristics of defect expansion. Considering the passage of time, defects may expand at different speeds at different spatial points. By inputting the repair parameters into the evolution model, the distribution state of these repair parameters at each time step can be calculated, and then the intermediate results of the evolution frame by frame can be obtained, reflecting the changing trend of cable defects and the corresponding repair needs at different time points. As the evolution process proceeds, the intermediate results obtained need to be further optimized to find the optimal repair measures. The particle swarm optimization algorithm is an optimization technology that simulates group behavior and can quickly find the optimal solution in a larger solution space. In this embodiment, the particle swarm represents different combinations of repair measures, and each particle represents a possible repair solution. The particles move in the search space and constantly adjust their positions to find the repair measures that make the evolution model result optimal. At each time step, the particle swarm optimization algorithm dynamically adjusts the parameter combination of the repair measures by evaluating the error between the intermediate results of the evolution and the repair target, and finally determines the most suitable repair solution at each spatial point, and constructs the initial repair matrix based on these parameters, and records and adjusts the specific content of the repair measures at each time step in the entire repair process. The construction of the matrix is ​​an iterative process. When the initial repair matrix is ​​constructed, the content is based on the preliminary plan of the optimal repair measure. As time goes by and the feedback of the repair effect, the parameters in the repair matrix will be continuously updated and adjusted. Each update will recalculate the effectiveness of the repair measures based on the feedback of the actual repair effect during the evolution process, and adjust the parameters accordingly to ensure that the most appropriate repair strategy can be provided according to the defect status of the cable at each time step, so that the repair strategy can be gradually optimized to cope with dynamically changing defect conditions. Ultimately, through iterative updates, a repair strategy that is more suitable for the defects in the area is obtained.

[0042] Reference Figure 2 , a detection device for a high altitude anti-fall cable, comprising: An acquisition module 100 is used to construct a radar array of a preset working frequency, obtain a coding waveform of the cable based on the radar array, and construct an observation matrix according to the coding waveform; A calculation module 200 is used to construct a high-dimensional feature space based on the measurement matrix and calculate the embedded data of the high-dimensional phase space; A mapping module 300, configured to map the embedded data into a surface topological structure, calculate a critical threshold of the surface topological structure, and obtain a characteristic value of the critical threshold in the surface topological structure; A generation module 400 is used to obtain a variation index of the characteristic value, establish a defect characteristic spectrum according to the variation index, input the defect characteristic spectrum into a pulse neural network for time series encoding, and generate a defect probability cloud map; An extraction module 500 is used to map the probability value of each spatial point in the defect probability cloud map to a specific position in the three-dimensional space, extract the spatial features and time features in the defect probability cloud map, and form a defect type tensor; The evolution module 600 is used to perform evolution processing on the defect type tensor and obtain a repair strategy matrix based on the numerical solution to calculate the evolution process. The repair strategy matrix includes adjustment schemes of repair measures at different spatial points and times.

[0043] In this embodiment, a radar array is used to obtain the coded waveform of the cable at a preset working frequency, and an observation matrix is ​​constructed based on these waveforms to provide preliminary data on the cable status. Then, by analyzing the observation matrix, a high-dimensional feature space is constructed, and embedded data is calculated in this space, and these embedded data are mapped to the surface topology structure, and critical thresholds and eigenvalues ​​are calculated to identify potential defect areas. Further, according to the change index of the eigenvalue, a defect feature spectrum is established, and it is input into the pulse neural network for time series encoding, and finally a defect probability cloud map is generated to show the defect probability at different spatial points. By mapping these probability values ​​to three-dimensional space and extracting spatial and temporal features, a defect type tensor is formed to describe the spatial distribution and temporal changes of the defects. Finally, the defect type tensor is evolved using a numerical solution to deduce the evolution process of the defect, and a repair strategy matrix is ​​generated based on this to provide corresponding repair solutions for different time and space points.

[0044] Reference Figure 3 In an embodiment of the present application, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 3As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data and other information related to this application. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for detecting a high-altitude anti-fall cable is implemented.

[0045] An embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, a detection method for a high-altitude anti-fall cable is implemented, including the steps of: constructing a radar array with a preset working frequency, obtaining a coded waveform of the cable based on the radar array, and constructing an observation matrix based on the coded waveform; constructing a high-dimensional feature space based on the observation matrix, and calculating embedded data of the high-dimensional phase space; mapping the embedded data to a surface topological structure, calculating a critical threshold of the surface topological structure, and obtaining a characteristic value of the critical threshold in the surface topological structure; obtaining a change index of the characteristic value, establishing a defect characteristic spectrum according to the change index, inputting the defect characteristic spectrum into a pulse neural network for time series encoding, and generating a defect probability cloud map; mapping the probability value of each spatial point in the defect probability cloud map to a specific position in three-dimensional space, extracting spatial features and time features in the defect probability cloud map, and forming a defect type tensor; performing evolution processing on the defect type tensor, and calculating the evolution process based on a numerical solution to obtain a repair strategy matrix, wherein the repair strategy matrix includes adjustment schemes for repair measures at different spatial points and times.

[0046] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided in this application and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0047] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for detecting a high altitude anti-fall cable, characterized in that: The method comprises: Constructing a radar array with a preset working frequency, acquiring a coding waveform of the cable based on the radar array, and constructing an observation matrix according to the coding waveform; Constructing a high-dimensional feature space based on the measurement matrix, and calculating embedded data of the high-dimensional phase space; Mapping the embedded data into a surface topological structure, calculating a critical threshold of the surface topological structure, and obtaining a characteristic value of the critical threshold in the surface topological structure; Obtaining a variation index of the characteristic value, establishing a defect characteristic spectrum according to the variation index, inputting the defect characteristic spectrum into a pulse neural network for time series encoding, and generating a defect probability cloud map; Mapping the probability value of each spatial point in the defect probability cloud map to a specific position in the three-dimensional space, extracting the spatial features and time features in the defect probability cloud map, and forming a defect type tensor; The defect type tensor is subjected to evolution processing, and the evolution process is calculated based on a numerical solution to obtain a repair strategy matrix, wherein the repair strategy matrix includes adjustment schemes of repair measures at different spatial points and times.

2. The detection method of the high altitude anti-fall cable according to claim 1 is characterized in that: The step of constructing a radar array with a preset working frequency, acquiring a coded waveform of the cable based on the radar array, and constructing an observation matrix according to the coded waveform includes: Modulating and configuring a radar array of a preset operating frequency, generating a radar signal based on a set frequency range, and generating a radar signal with a specific periodicity in frequency through a modulation process; Based on multiple receiving channels, the reflected signals of the radar signal are collected in parallel, and the multiple reflected signals are subjected to time synchronization processing to obtain the coded waveform; Extracting signal strength information at each moment in the coded waveform, and converting the reflected signal into a discrete signal sequence according to the signal strength information; Setting a time window of preset bits, controlling the time window to slide on the discrete signal sequence, and generating a plurality of discrete signals; The discrete signal is subjected to time-frequency transformation to obtain a plurality of frequency features, and an observation matrix is ​​constructed according to the signal amplitude and phase information of each time-frequency point in the frequency features.

3. The detection method of the high altitude anti-fall cable according to claim 1 is characterized in that: The step of constructing a high-dimensional feature space based on the measurement matrix and calculating the embedded data of the high-dimensional phase space comprises: Performing matrix decomposition on the observation matrix to obtain a set of basis vectors, wherein each basis vector represents a potential frequency feature; Sorting basis vectors in the basis vector set, and selecting basis vectors whose order exceeds a preset value as main features; Linearly combining a plurality of the main features to form a feature space, mapping the data in the feature space through an inner product operation to obtain a preliminary representation of a high-dimensional data space; The obtained high-dimensional data is reconstructed in phase space, and the embedded data is calculated based on the dynamic relationship between the dimension of the reconstructed high-dimensional phase space and the original data.

4. The detection method of the high altitude anti-fall cable according to claim 1 is characterized in that: The step of mapping the embedded data into a surface topological structure, calculating a critical threshold of the surface topological structure, and obtaining a characteristic value of the critical threshold in the surface topological structure comprises: Extracting dimensional features of the embedded data, and determining initial parameters of the surface topology structure based on the dimensional features; Based on a topological transformation algorithm, each embedded data point in the high-dimensional phase space is associated with a corresponding point on the initial parameter to obtain a mapping data point set; Calculate the local features of the surface topology structure according to the mapping data point set to obtain the position features of each mapping data point on the surface topology structure; Calculate the overall curvature of the surface topological structure according to the position features, and obtain high curvature areas and low curvature areas on the surface; Identifying distribution information of the high curvature region and the low curvature region, obtaining a critical point based on the distribution information, and calculating a critical threshold of a surface topological structure based on the critical point; Segmenting the surface topology structure according to the critical threshold to obtain a plurality of sub-regions; The density feature of each sub-region mapping data point set is calculated, and the feature value in each sub-region is obtained according to the density feature.

5. The detection method of the high altitude anti-fall cable according to claim 1 is characterized in that: The step of obtaining a variation index of the characteristic value, establishing a defect characteristic spectrum according to the variation index, inputting the defect characteristic spectrum into a pulse neural network for time series encoding, and generating a defect probability cloud map comprises: Performing time series analysis on the eigenvalues, calculating the variation between adjacent eigenvalues, and obtaining a variation index of the eigenvalues; Constructing a time series model according to the variation index, converting the time series model into a frequency domain representation, and obtaining a defect characteristic spectrum; Constructing a spiking neural network structure, taking the defect characteristic spectrum as an input signal of the spiking neural network, and performing temporal encoding through the pulse emission and transmission process of neurons; According to the encoding result, the defect characteristics at each time point are mapped onto a two-dimensional plane to generate a defect probability cloud map, wherein the density of the defect probability cloud map represents the probability of the defect occurring.

6. The detection method of the high altitude anti-fall cable according to claim 1 is characterized in that: The step of mapping the probability value of each spatial point in the defect probability cloud map to a specific position in the three-dimensional space, extracting the spatial features and time features in the defect probability cloud map, and forming a defect type tensor comprises: Performing spatial analysis on the defect probability cloud map, taking the probability value of each spatial point in the cloud map as coordinate information, mapping it to a three-dimensional space coordinate system, and obtaining a specific position point set in the three-dimensional space; Extracting spatial features based on the specific location point set, the spatial features including distribution density, spatial distance and spatial direction of spatial points; Extracting time features from the defect probability cloud map, wherein the time features include a change trend and periodicity of a time point; The spatial features and the temporal features are fused to construct a defect type tensor, wherein the dimensions of the tensor include a spatial dimension and a temporal dimension, and the value of each dimension represents corresponding feature information.

7. The detection method of the high altitude anti-fall cable according to claim 1 is characterized in that: The step of performing evolution processing on the defect type tensor and calculating the evolution process based on a numerical solution to obtain a repair strategy matrix, wherein the repair strategy matrix includes adjustment schemes of repair measures at different spatial points and times, includes: Acquire an initial value based on cable material characteristics, and set a repair parameter of the defect type tensor based on the initial value; An evolution model is constructed according to the defect type tensor, the repair parameters are input into the evolution model, the distribution state of the repair parameters at each time step is calculated, and an intermediate result of the evolution is obtained frame by frame; Optimizing the intermediate results of the evolution based on a particle swarm optimization algorithm to obtain an optimal combination of repair measure parameters at each spatial point; An initial repair matrix is ​​constructed based on the combination of repair measure parameters, the initial repair matrix is ​​iteratively updated, and the repair measure parameters are adjusted according to the repair effect at each time step to obtain a repair strategy matrix.

8. A detection device for a high altitude anti-fall cable, applied to the method according to any one of claims 1 to 7, characterized in that: include: An acquisition module, used to construct a radar array of a preset working frequency, acquire a coding waveform of the cable based on the radar array, and construct an observation matrix according to the coding waveform; A calculation module, used for constructing a high-dimensional feature space based on the observation matrix, and calculating the embedded data of the high-dimensional phase space; A mapping module, used for mapping the embedded data into a surface topological structure, calculating a critical threshold of the surface topological structure, and obtaining a characteristic value of the critical threshold in the surface topological structure; A generation module, used for obtaining a variation index of the characteristic value, establishing a defect characteristic spectrum according to the variation index, inputting the defect characteristic spectrum into a pulse neural network for time series encoding, and generating a defect probability cloud map; An extraction module is used to map the probability value of each spatial point in the defect probability cloud map to a specific position in the three-dimensional space, extract the spatial features and time features in the defect probability cloud map, and form a defect type tensor; The evolution module is used to perform evolution processing on the defect type tensor and obtain a repair strategy matrix based on the numerical solution to calculate the evolution process. The repair strategy matrix includes adjustment schemes of repair measures at different spatial points and times.

9. A computer device, characterized in that: The method comprises a processor, a memory and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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