Underground cable system risk perception operation and maintenance method, device, equipment, medium and program product

Abstract

The application discloses a kind of underground cable system risk perception operation and maintenance method, device, equipment, medium and program product, the method includes: the acquisition of underground cable system is the data of multiple-source heterogeneous, pre-processes multiple-source heterogeneous data, generates fault mapping matrix, inputs fault mapping matrix into space-time coupling fault analysis model, obtains the risk contribution evaluation result of each characteristic factor, determines the risk level of each region in underground cable system based on risk contribution evaluation result, and generates the operation and maintenance strategy corresponding to each region based on risk level;Since the application is by collecting and processing multiple-source heterogeneous data, so as to fuse heterogeneous perception information to carry out fault characteristic perception on underground cable system, realize dynamic fault analysis on system in space-time dimension by combining space-time coupling fault analysis model, improve risk identification perception accuracy, accurately and timely identify underground cable fault hidden danger, realize continuous tracking risk factor, improve system risk early warning capability.

Description

Technical Field

[0001] This invention relates to the field of underground cables, and in particular to a risk perception and maintenance method, apparatus, equipment, medium, and program product for underground cable systems. Background Technology

[0002] Underground cables, as a crucial carrier of electrical energy transmission in urban power grids, are widely used in densely urbanized areas, and their operational reliability directly impacts the safety and stability of the power system. With the increasing complexity of urban underground space resources, the diversity and uncertainty of the operating environment for underground cables have significantly increased, leading to a continuous rise in the frequency of various faults. Particularly under complex environmental conditions such as humidity, strong corrosiveness, and frequent geological activity, cable insulation materials are susceptible to long-term erosion and stress accumulation, inducing a series of fault problems.

[0003] Common faults in underground cables are mainly caused by insulation aging and joint failure. These faults are often closely related to the high-temperature soil environment, drastic temperature fluctuations, low resistivity soil conditions, and frequent load impacts experienced during long-term operation. Against this backdrop, existing research primarily focuses on fault location and diagnosis technologies after a fault occurs, such as those based on traveling waves, spectrum analysis, intelligent algorithms, and inspection robots, to quickly identify and locate fault points, which has improved the timeliness and accuracy of cable maintenance to some extent. These methods are relatively mature in engineering practice, mainly serving emergency response and planned maintenance after a fault occurs. However, existing technologies still have certain limitations in the field of underground cable maintenance. Most methods rely on trigger signals of fault symptoms to carry out the diagnostic process, making it difficult to achieve continuous perception and early identification of the evolution of potential faults. These studies mainly focus on fault location and repair after a fault occurs, remaining at the level of planned maintenance in power systems, lacking the ability to predict faults before they occur, and failing to achieve proactive identification of high-risk areas and trend extrapolation of fault modes. Summary of the Invention

[0004] The main objective of this invention is to provide a risk perception and maintenance method, device, equipment, medium, and program product for underground cable systems. This invention aims to solve the technical problems of existing technologies, such as the lack of spatiotemporal dynamic fault detection methods, the difficulty in accurately identifying potential faults in underground cables, the difficulty in continuously tracking the evolution of risk factors, and the limited early warning effect.

[0005] To achieve the above objectives, the present invention provides a risk perception and maintenance method for underground cable systems, the method comprising the following steps:

[0006] Collect multi-source heterogeneous data of the underground cable system, including sensor data, auxiliary detection terminal data, historical operation and maintenance data, and historical fault data;

[0007] The multi-source heterogeneous data is preprocessed to generate a fault mapping matrix. The preprocessing includes feature extraction and structured processing. The fault mapping matrix includes fault records of the underground cable system and the feature factors and fault results corresponding to each fault record. The feature factors are composed of multiple observation values.

[0008] The fault mapping matrix is ​​input into a pre-built spatiotemporal coupled fault analysis model to obtain the risk contribution assessment results of each characteristic factor. The spatiotemporal coupled fault analysis model is used to measure and analyze the risk contribution of the characteristic factor under different times and spaces. The risk contribution assessment results include the time dimension risk assessment results and the spatial dimension risk assessment results of the characteristic factor.

[0009] Based on the risk contribution assessment results, the risk level of each area in the underground cable system is determined, and the corresponding operation and maintenance strategy for each area is generated based on the risk level.

[0010] Optionally, before inputting the fault mapping matrix into the pre-built spatiotemporal coupled fault analysis model, the method further includes:

[0011] Based on the fault mapping matrix, time dependency analysis is performed on each component to obtain the time dependency risk characteristics of the underground cable system components. The components are the binary characteristics of each observation value in the characteristic factors.

[0012] A target time-dimensional risk assessment model is constructed based on the time-dependent risk characteristics of the components.

[0013] The system structure function of the underground cable system is constructed based on the matrix structure information of the fault mapping matrix;

[0014] A spatial dimension risk assessment model is constructed based on the system structure function.

[0015] A spatiotemporal coupled fault analysis model is constructed based on the time-dimensional risk assessment model and the spatial-dimensional risk assessment model.

[0016] Optionally, constructing a target time-dimensional risk assessment model based on the component's time-dependent risk characteristics includes:

[0017] An initial time-dimensional risk assessment model is constructed based on the time-dependent risk characteristics of the aforementioned components:

[0018]

[0019]

[0020]

[0021] in, This represents the component risk assessment results over a time dimension. Represents a time variable. and These respectively represent the components of the underground cable system. The probability of not failing under working and non-working conditions. and These respectively represent the components of the underground cable system. The probability of not failing under working and non-working conditions. and Representing components respectively Working status and not working status and Representing components respectively Working status and not working status Representation Component In time The failure increment, Representation Component In time The failure increment, A reliability function representing an underground cable system. Representation Component A reliable function, Representation Component The cumulative distribution function of the first failure;

[0022] The minimum cut set of the underground cable system is generated based on the fault mapping matrix. The minimum cut set is the minimum set of components that cause the failure of the underground cable system.

[0023] The initial time-dimensional risk assessment model is adjusted based on the minimum cut set to generate a target time-dimensional risk assessment model, which includes:

[0024]

[0025] in, This represents the set of indices for all components of an underground cable system. This represents the minimal cut set that indicates a system failure. The minimal cut set is the set of the smallest components that cause the underground cable system to fail. Indicates the minimum cut set In addition to components Other component indexes, This indicates a flag that forces the state of the corresponding component in the set to available / invalid. This represents the set of indexes for all components in an underground cable system, excluding the minimum cut set. Indicates in component The state of an underground cable system when it is functioning normally and other components have failed. This indicates that the remaining components within the same minimal cut set are preceding each other. Expired joint probabilities Indicates the system in time The probability of failure before the event.

[0026] Optionally, constructing a spatial dimension risk assessment model based on the system structure function includes:

[0027] Based on the system structure function analysis, the failure logic relationships between each component and the underground cable system are analyzed, and characteristic fault cause structure functions are constructed based on the failure logic relationships. The system structure function includes:

[0028]

[0029]

[0030] in, Indicates the reliability of the system structure. Indicates the number of system features. Indicates the first One characteristic, Indicates the first The reliability of each feature Indicates the first The number of components in each feature Indicates the first The first feature The failure probability of each component. Indicates the first One fault record, Indicates the first Validity screening of fault records Indicates sample Through screening, Indicates the first The fault record is in the first The values ​​that can be taken on each feature Indicates the first The first feature The specific tag values ​​for each component;

[0031] The characteristic fault cause structure function includes:

[0032]

[0033] in, Indicates the cause of the failure Reliability of features Indicates the first Substructure reliability, Index variables representing the three types of substructures;

[0034] A spatial dimension risk assessment model is constructed based on the system structure function and the characteristic fault cause structure function. The spatial dimension risk assessment model includes:

[0035]

[0036] in, This represents a structural importance metric based on path constraints. Represents a collection of components. Indicates the first The first of the features One component's state is set to 1, while the states of other components remain unchanged. Indicates the first The first of the features The state of one component is set to 0, while the states of other components remain unchanged. and Represents component state operators.

[0037] Optionally, the spatiotemporal coupled fault analysis model includes:

[0038]

[0039]

[0040] in, Indicates the first The importance of structural perturbations to each component over the entire period of time.

[0041] Optionally, the preprocessing of the multi-source heterogeneous data to generate a fault mapping matrix includes:

[0042] The multi-source heterogeneous data is processed to obtain data analysis samples. The data cleaning process includes outlier removal, missing value imputation, normalization, time series alignment, and feature extraction. The data analysis samples include:

[0043]

[0044]

[0045]

[0046]

[0047] in, Represents a set of fault events. Indicates the first One failure event, This represents the set of feature factors extracted from a fault event. Indicates the first Each feature factor consists of multiple observations. Indicates the first The characteristic factors in the first Observations at each observation location Represents the set of fault results. Indicates the first The failure result of each failure event;

[0048] A feature space is constructed, and the data analysis samples are mapped to the feature space to generate a fault mapping matrix, the fault mapping matrix including:

[0049]

[0050] in, This represents the fault mapping matrix.

[0051] Optionally, determining the risk level of each area in the underground cable system based on the risk contribution assessment results, and generating corresponding operation and maintenance strategies for each area based on the risk levels, includes:

[0052] Based on the risk contribution assessment results, risk time-series characteristics of each line in the underground cable system are generated, and the risk time-series characteristics represent the time variation characteristics of the failure probability of the line.

[0053] The risk level of each area in the underground cable system is determined based on the risk time sequence characteristics.

[0054] Based on the cable path continuity information of the underground cable system and the risk level of each area, the spatial risk distribution variation characteristics of the underground cable system along the line are determined;

[0055] A risk distribution heatmap is constructed based on the spatial risk distribution change characteristics, and operation and maintenance strategies corresponding to each region are generated based on the risk distribution heatmap.

[0056] Furthermore, to achieve the above objectives, the present invention also proposes a risk perception and maintenance device for underground cable systems, the underground cable system risk perception and maintenance device comprising:

[0057] The data acquisition module is used to collect multi-source heterogeneous data of the underground cable system, including sensor data, auxiliary detection terminal data, historical operation and maintenance data, and historical fault data.

[0058] The data processing module is used to preprocess the multi-source heterogeneous data to generate a fault mapping matrix. The preprocessing includes feature extraction and structured processing. The fault mapping matrix includes fault records of the underground cable system and the feature factors and fault results corresponding to each fault record. The feature factors are composed of multiple observation values.

[0059] The risk assessment module is used to input the fault mapping matrix into a pre-built spatiotemporal coupled fault analysis model to obtain the risk contribution assessment results of each characteristic factor. The spatiotemporal coupled fault analysis model is used to measure and analyze the risk contribution of the characteristic factor under different times and spaces. The risk contribution assessment results include the time dimension risk assessment results and the spatial dimension risk assessment results of the characteristic factor.

[0060] The risk operation and maintenance module is used to determine the risk level of each area in the underground cable system based on the risk contribution assessment results, and to generate the corresponding operation and maintenance strategy for each area based on the risk level.

[0061] In addition, to achieve the above objectives, this application also proposes an underground cable system risk perception and maintenance device, the device comprising: a memory, a processor, and an underground cable system risk perception and maintenance program stored in the memory, the processor being used to run the underground cable system risk perception and maintenance program, the computer program being configured to implement the steps of the underground cable system risk perception and maintenance method as described above.

[0062] In addition, to achieve the above objectives, this application also proposes a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the risk perception and maintenance method for underground cable systems as described above.

[0063] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the underground cable system risk perception and maintenance method described above.

[0064] This invention collects multi-source heterogeneous data from underground cable systems, including sensor data, auxiliary detection terminal data, historical operation and maintenance data, and historical fault data. The multi-source heterogeneous data is preprocessed to generate a fault mapping matrix. This preprocessing includes feature extraction and structured processing. The fault mapping matrix includes fault records of the underground cable system, as well as corresponding feature factors and fault results for each fault record. Each feature factor consists of multiple observations. The fault mapping matrix is ​​then input into a pre-constructed spatiotemporal coupled fault analysis model to obtain risk contribution assessment results for each feature factor. The spatiotemporal coupled fault analysis model is used to assess the risk contribution of each feature factor at different times and under different conditions. The risk contribution in the spatial dimension is measured and analyzed. The risk contribution assessment results include the time dimension risk assessment results and the spatial dimension risk assessment results of the characteristic factors. Based on the risk contribution assessment results, the risk level of each area in the underground cable system is determined, and the corresponding operation and maintenance strategy for each area is generated based on the risk level. Because this invention collects and processes multi-source heterogeneous data, it integrates heterogeneous sensing information to perceive fault characteristics of the underground cable system. Combined with a spatiotemporal coupled fault analysis model, it realizes dynamic fault analysis of the system in the spatiotemporal dimension, improves the accuracy of risk identification and perception, accurately and timely identifies potential underground cable faults, realizes continuous tracking of risk factors, and improves the system's risk early warning capability. Attached Figure Description

[0065] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0066] Figure 1 This is a schematic diagram of the structure of the underground cable system risk perception and maintenance equipment in the hardware operating environment involved in the embodiments of the present invention;

[0067] Figure 2 This is a flowchart illustrating the first embodiment of the risk perception and maintenance method for underground cable systems of the present invention.

[0068] Figure 3(a) is a risk heat map of insulation faults in one embodiment of the risk perception and maintenance method for underground cable systems of the present invention;

[0069] Figure 3(b) is a heat map of thermal overload fault risk in one embodiment of the risk perception and maintenance method for underground cable systems of the present invention;

[0070] Figure 4 This is a flowchart illustrating the second embodiment of the risk perception and maintenance method for underground cable systems of the present invention.

[0071] Figure 5 This is a system reliability block diagram in one embodiment of the risk perception and maintenance method for underground cable systems of the present invention;

[0072] Figure 6 This is a reliability block diagram of fault causes in one embodiment of the risk perception and maintenance method for underground cable systems of the present invention;

[0073] Figure 7 This is a structural block diagram of the first embodiment of the underground cable system risk perception and maintenance device of the present invention.

[0074] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0075] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0076] Reference Figure 1 , Figure 1 This is a schematic diagram of the structure of the underground cable system risk perception and maintenance equipment in the hardware operating environment involved in the embodiments of the present invention.

[0077] like Figure 1 As shown, the risk perception and maintenance equipment for the underground cable system may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wireless-Fidelity (Wi-Fi) interface). The memory 1005 may be high-speed random access memory (RAM) or stable non-volatile memory (NVM), such as a disk drive. The memory 1005 may also optionally be a storage device independent of the aforementioned processor 1001.

[0078] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the risk perception and maintenance equipment for underground cable systems, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0079] like Figure 1As shown, the memory 1005, which is a computer-readable storage medium, may include an operating system, a network communication module, a user interface module, and a risk perception and maintenance program for underground cable systems.

[0080] exist Figure 1 In the underground cable system risk perception and maintenance equipment shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and memory 1005 in the underground cable system risk perception and maintenance equipment of the present invention can be set in the underground cable system risk perception and maintenance equipment. The underground cable system risk perception and maintenance equipment calls the underground cable system risk perception and maintenance program stored in the memory 1005 through the processor 1001 and executes the underground cable system risk perception and maintenance method provided in the embodiment of the present invention.

[0081] This invention provides a risk perception and maintenance method for underground cable systems, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of the risk perception and maintenance method for underground cable systems according to the present invention.

[0082] In this embodiment, the risk perception and maintenance method for underground cable systems includes the following steps:

[0083] Step S10: Collect multi-source heterogeneous data of the underground cable system.

[0084] It should be noted that this embodiment is applied to predict potential faults in underground cables during operation and maintenance. Existing underground cable operation and maintenance technologies mostly focus on post-fault location and diagnosis, relying on obvious fault signals as triggering conditions. They identify fault points using methods such as traveling wave analysis, spectrum analysis, and intelligent inspection, but these methods are insufficient for early detection of potential faults. In actual operation, underground cables are subject to long-term coupling effects from various external factors, and their operating state continuously evolves over time. Furthermore, different regions exhibit significant differences in geological structure, temperature and humidity conditions, and electrical loads, forming typical spatiotemporal coupled data characteristics. Existing technologies generally lack the ability to model these spatiotemporal dynamic characteristics, making it difficult to continuously track the evolution of risk factors and limiting the effectiveness of early warning systems.

[0085] To address the aforementioned shortcomings, this embodiment proposes a spatiotemporal coupling-based method for predicting underground cable fault risks, taking into account the complexity and dynamism of the underground cable operating environment. The aim is to identify potentially high-risk areas and improve the early warning capability of underground cable systems through joint analysis of key environmental and state factors.

[0086] It should be understood that the executing entity of this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or a terminal electronic device capable of performing the above functions. The following description uses an underground cable system risk perception and maintenance device (hereinafter referred to as the perception device) as an example to illustrate this embodiment and the following embodiments.

[0087] It should be noted that the multi-source heterogeneous data includes sensor data, auxiliary detection terminal data, historical operation and maintenance data, and historical fault data.

[0088] In practice, the sensing device collects multi-source heterogeneous data related to the operating status of the underground cable system, including but not limited to ambient humidity, historical fault tags, current carrying capacity, and laying depth.

[0089] In some embodiments, the sensing device can deploy various types of sensors and auxiliary detection terminals based on the operating structure and laying environment of the underground cable system to achieve real-time acquisition of typical operating state quantities. The acquired data covers electrical quantities, environmental parameters, and spatial information, and integrates historical operation and maintenance information and fault records to form a complete multi-source observation data system.

[0090] Step S20: Preprocess the multi-source heterogeneous data to generate a fault mapping matrix.

[0091] It should be noted that the preprocessing includes feature extraction and structured processing. The fault mapping matrix includes fault records of the underground cable system and the feature factors and fault results corresponding to each fault record. The feature factors are composed of multiple observations.

[0092] In practice, the sensing device constructs a unified data analysis sample by processing missing values, normalizing, aligning time series, and extracting features from the original multi-source heterogeneous data.

[0093] Furthermore, in order to accurately map each feature to the same dimensional space and improve the accuracy of risk analysis, step S20 above may include:

[0094] Step S201: Perform data processing on the multi-source heterogeneous data to obtain data analysis samples.

[0095] It should be noted that the data cleaning process includes outlier removal, missing value imputation, normalization, time series alignment, and feature extraction. The data analysis samples include:

[0096]

[0097]

[0098]

[0099]

[0100] in, Represents a set of fault events. Indicates the first One failure event, This represents the set of feature factors extracted from a fault event. Indicates the first Each feature factor consists of multiple observations. Indicates the first The characteristic factors in the first Observations at each observation location Represents the set of fault results. Indicates the first The failure result of a fault event.

[0101] In some embodiments, the sensing device can remove outliers and fill in missing values ​​in the collected data to address issues such as abnormal fluctuations and missing segments, thereby improving data integrity and reliability. Considering the differences in dimensions, distribution, and sampling frequency among different types of monitoring data, a unified approach is adopted for feature scale alignment using strategies such as normalization and standard deviation scaling.

[0102] Step S202: Construct a feature space and map the data analysis samples to the feature space to generate a fault mapping matrix.

[0103] In practical implementation, sensing devices perform structured processing on raw data by establishing a unified feature space. Assume a set of fault events. Each fault event This represents a historical record. For each event... Feature extraction This represents a type of feature collected by a monitoring device. Each feature is composed of observations from multiple measurement locations, denoted as . ,in Indicates a certain feature in the first place The observed values ​​at each monitoring point. The consequences of the fault event are represented as the target variable. , forming a set ,in For a fault event The corresponding fault results. Finally, an input fault mapping matrix is ​​constructed. It can be represented as a fault mapping matrix in the following form:

[0104]

[0105] in, This represents a fault mapping matrix, where each row represents a fault record. Number the fault record. express All characteristic factors in the fault log, This is the result of the fault handling.

[0106] Step S30: Input the fault mapping matrix into the pre-built spatiotemporal coupled fault analysis model to obtain the risk contribution assessment results of each characteristic factor.

[0107] It should be noted that the spatiotemporal coupled fault analysis model can be based on the Spatiotemporal Coupled Importance Model (SCIM) model, used to quantitatively model the potential fault risks of underground cables. This model is used to measure and analyze the risk contribution of the characteristic factors at different times and in different spaces. The risk contribution assessment results include both the time-dimensional and spatial-dimensional risk assessment results of the characteristic factors.

[0108] In some embodiments, the sensing device constructs a spatiotemporal coupled fault analysis model by combining the Birnbaum importance metric theory to introduce a feature importance assessment method in the time dimension and combining a structure function to introduce a feature importance assessment method in the spatial dimension.

[0109] Step S40: Determine the risk level of each area in the underground cable system based on the risk contribution assessment results, and generate the corresponding operation and maintenance strategy for each area based on the risk level.

[0110] In practical implementation, the sensing equipment can establish an underground cable fault risk prediction platform, and formulate differentiated inspection and maintenance strategies based on risk score calculation and heat map visualization.

[0111] Furthermore, to enhance risk perception and continuous tracking capabilities, and to ensure the timeliness of risk warnings, step S40 above may include:

[0112] Step S401: Generate the risk time sequence characteristics of each line in the underground cable system based on the risk contribution assessment results.

[0113] It should be noted that the risk timing feature represents the time variation feature of the probability of failure of the line.

[0114] Understandably, sensing devices can use spatiotemporal coupled fault analysis models to calculate the importance of various parts of underground cable lines and obtain risk values ​​for the lines or components. This indicates a continuous change from a state where failure is impossible to a state where failure is inevitable.

[0115] Step S402: Determine the risk level of each area in the underground cable system based on the risk time sequence characteristics.

[0116] It should be understood that sensing devices can distinguish between high-risk, medium-risk, and low-risk levels based on risk level values.

[0117] Step S403: Based on the cable path continuity information of the underground cable system and the risk level of each area, determine the spatial risk distribution variation characteristics of the underground cable system along the line.

[0118] It should be noted that, for cable lines, the sensing equipment can construct a heat map representation based on path continuity, generating different risk heat maps according to different fault types, and visually presenting the comprehensive risk level of each fault type. In the heat map, the color gradually transitions from green (low risk) to red (high risk), intuitively reflecting the spatial distribution change of risk along the line. Taking a section of urban underground cable in a certain area as an example, two types of fault heat maps are obtained, as shown in Figure 3(a) and Figure 3(b). Figure 3(a) is a risk heat map of insulation fault in one embodiment, and Figure 3(b) is a risk heat map of thermal overload fault in another embodiment.

[0119] Step S404: Construct a risk distribution heatmap based on the spatial risk distribution change characteristics, and generate operation and maintenance strategies corresponding to each region based on the risk distribution heatmap.

[0120] In some embodiments, the sensing device distinguishes the risk level of each area based on a risk distribution heatmap of different fault types, thereby executing operation and maintenance strategies. For example, for high-risk areas, manual on-site inspections should be organized in an orderly manner, and local replacement and other disposal operations should be carried out in a timely manner; for medium-risk areas, key operating parameters should be collected and analyzed regularly, and potential anomalies should be monitored periodically; for low-risk areas, standard maintenance procedures should be implemented, and status tracking and file updates should be carried out in combination with historical operating data.

[0121] This embodiment collects multi-source heterogeneous data from an underground cable system, including sensor data, auxiliary detection terminal data, historical operation and maintenance data, and historical fault data. The multi-source heterogeneous data is preprocessed to generate a fault mapping matrix. This preprocessing includes feature extraction and structured processing. The fault mapping matrix includes fault records of the underground cable system, as well as the corresponding feature factors and fault results for each fault record. Each feature factor consists of multiple observations. The fault mapping matrix is ​​then input into a pre-constructed spatiotemporal coupled fault analysis model to obtain the risk contribution assessment results of each feature factor. The spatiotemporal coupled fault analysis model is used to assess the risk contribution of each feature factor at different times and under different conditions. The risk contribution under the same space is measured and analyzed. The risk contribution assessment results include the time dimension risk assessment results and the spatial dimension risk assessment results of the characteristic factors. Based on the risk contribution assessment results, the risk level of each area in the underground cable system is determined, and the corresponding operation and maintenance strategy for each area is generated based on the risk level. Since this invention collects and processes multi-source heterogeneous data, it integrates heterogeneous sensing information to perceive fault characteristics of the underground cable system. Combined with the spatiotemporal coupled fault analysis model, it realizes dynamic fault analysis of the system in the spatiotemporal dimension, improves the accuracy of risk identification and perception, accurately and timely identifies potential underground cable faults, realizes continuous tracking of risk factors, and improves the system's risk early warning capability.

[0122] refer to Figure 4 , Figure 4 This is a flowchart illustrating the second embodiment of the risk perception and maintenance method for underground cable systems of the present invention.

[0123] Based on the first embodiment described above, in this embodiment, before step S30, the method further includes:

[0124] Step S31: Perform time dependency analysis on each component based on the fault mapping matrix to obtain the component time dependency risk characteristics of the underground cable system.

[0125] It should be noted that the component is a binary feature of each observation value in the characteristic factors. The component is equivalent to discretizing the characteristic factors into 0 or 1, representing an on / off state bit. The aforementioned characteristic factors can be electrical operating parameters such as the cable's service life, fault current history, and peak load rate, or state quantities such as insulation resistance and sheath test leakage current.

[0126] It is understandable that this embodiment can incorporate Birnbaum's importance measurement theory, introduce time variables, and perform importance analysis on components based on time dependencies.

[0127] It should be noted that the Birnbaum importance metric is a structural importance measurement method used to quantitatively assess the impact of a component on the reliability of a system. Its core idea is to measure the probability that the system's functional state will change when the state of that component changes (from failure to normal or vice versa).

[0128] Step S32: Construct a target time dimension risk assessment model based on the time-dependent risk characteristics of the components.

[0129] It is understood that this embodiment can analyze the failure probability of the underground cable system under the conditions of each component working and not working based on the component time-dependent risk characteristics, thereby constructing a target time dimension risk assessment model.

[0130] Furthermore, to improve the accuracy of component risk analysis over time, step S32 above may include:

[0131] Step S321: Construct an initial time-dimensional risk assessment model based on the time-dependent risk characteristics of the components;

[0132] Step S322: Generate the minimum cut set of the underground cable system based on the fault mapping matrix. The minimum cut set is the minimum set of components that cause the failure of the underground cable system.

[0133] Step S323: Adjust the initial time dimension risk assessment model according to the minimum cut set to generate the target time dimension risk assessment model.

[0134] It should be noted that the sensing device first incorporates the Birnbaum importance metric theory, introducing time... As a variable, the following time-dependent component importance (CIM) formula is defined:

[0135]

[0136]

[0137]

[0138] in, This represents the component risk assessment results over a time dimension. Represents a time variable. and These respectively represent the components of the underground cable system. The probability of not failing under working and non-working conditions. and These respectively represent the components of the underground cable system. The probability of not failing under working and non-working conditions. and Representing components respectively Working status and not working status and Representing components respectively Working status and not working status Representation Component In time The failure increment, Representation Component In time The failure increment, A reliability function representing an underground cable system. Representation Component A reliable function, Representation Component The cumulative distribution function of the first failure.

[0139] Component Importance Measure (CIM) is used in complex engineering systems such as power systems to quantify the sensitivity of system performance to the failure of individual components, helping to identify critical components. Its core idea is based on structure functions or reliability models, calculating the importance value of a component by comparing the changes in system reliability under normal operating and failure states.

[0140] Assume the components have a continuous lifetime distribution. and its density function Then its time The reliability function can be determined by its hazard rate function:

[0141]

[0142] Furthermore, assuming the hazard rate of component lifetime is proportional and the component lifetime follows a Weibull distribution, the formula can be further written as:

[0143]

[0144] in, For proportional parameters, It is a positive shape parameter. The scale parameter and shape parameter will be estimated by combining the maximum likelihood estimation method with historical statistical data.

[0145] The minimal cut set is the smallest set of components that causes system failure. In a series system, each component itself constitutes a minimal cut set; while in a parallel system, the combination of all components constitutes a minimal cut set. Let the minimal cut set of the system be... ,in Component importance can also be defined as the system's importance over time. Under the premise of prior failure, by This causes the system to be in time The probability of pre-failure, the component importance formula can be reformulated as follows, i.e., the target time dimension risk assessment model is:

[0146]

[0147] in, This represents the set of indices for all components of an underground cable system. This represents the minimal cut set that indicates a system failure. The minimal cut set is the set of the smallest components that cause the underground cable system to fail. Indicates the minimum cut set In addition to components Other component indexes, This indicates a flag that forces the state of the corresponding component in the set to available / invalid. This represents the set of indexes for all components in an underground cable system, excluding the minimum cut set. Indicates in component The state of an underground cable system when it is functioning normally and other components have failed. This indicates that the remaining components within the same minimal cut set are preceding each other. Expired joint probabilities Indicates the system in time The probability of failure before the event.

[0148] Step S33: Construct the system structure function of the underground cable system based on the matrix structure information of the fault mapping matrix.

[0149] It should be understood that this embodiment can perform risk analysis on components from a spatial dimension by introducing system structure functions.

[0150] Step S34: Construct a spatial dimension risk assessment model based on the system structure function.

[0151] It should be noted that the spatial dimension risk assessment model can be a structural importance (SIM) metric model based on path constraints. Unlike CIM, SIM focuses on the role of a component in the system topology in achieving system functionality. Its core idea is to analyze the combination relationships among components in the structural function to measure the frequency or degree of influence of a component on the critical path during changes in system functional state.

[0152] It should be noted that structure functions are a fundamental tool in system reliability modeling, used to characterize the logical relationship between the functional state of a system and the states of its components. Essentially, it is a Boolean mapping function that takes the state of each component (operating or failing) as input and outputs the overall state of the system (operating or failing).

[0153] Furthermore, to improve the accuracy of component risk analysis in the spatial dimension, step S36 above:

[0154] Step S361: Analyze the failure logic relationship between each component and the underground cable system based on the system structure function, and construct a characteristic fault cause structure function based on the failure logic relationship;

[0155] Step S362: Construct a spatial dimension risk assessment model based on the system structure function and the characteristic fault cause structure function.

[0156] It should be noted that this embodiment introduces a structure function. It can be represented by a reliability block diagram, such as Figure 5 As shown, Figure 5 Here is a system reliability block diagram in one embodiment, wherein, Indicates the characteristics of the causes of the failure. Indicates the "first" in the first feature "One component". In this case, the structure function of the underground cable system can be expressed as:

[0157]

[0158]

[0159] in, Indicates the reliability of the system structure. Indicates the number of system features. Indicates the first One characteristic, Indicates the first The reliability of each feature Indicates the first The number of components in each feature Indicates the first The first feature The failure probability of each component. Indicates the first One fault record, Indicates the first Validity screening of fault records Indicates sample Through screening, Indicates the first The fault record is in the first The values ​​that can be taken on each feature Indicates the first The first feature The specific tag value of each component The ">" symbol indicates an event that satisfies both of these conditions simultaneously.

[0160] Unlike other characteristics, characteristic failure causes ( The reliability structure of a system includes both series and parallel paths, such as... Figure 6 As shown, Figure 6 As a reliability block diagram of fault causes in one embodiment, it needs to be modeled independently. In the empirical case study, each fault record includes the main fault type and its corresponding fault cause. In one embodiment, the database identifies 14 different types of faults and their causes, represented by the following symbols: A (insulation fault), B (joint fault), C (thermal overload), D (service life), E (dielectric loss factor), F (soil moisture), G (soil temperature), H (load fluctuation), I (current carrying capacity), J (contact resistance), K (daily load rate), L (thermal resistivity), and M (laying depth).

[0161] Therefore, based on the characteristics of "fault causes", the following characteristic fault cause structure function is constructed:

[0162]

[0163] In the formula, It can be calculated using the following formula:

[0164]

[0165]

[0166]

[0167] in, Indicates the cause of the failure Reliability of features Indicates the first Substructure reliability, Index variables representing three types of substructures, This indicates the probability of each fault cause occurring within the recorded time.

[0168] by For example, the binary composite event that triggers the failure is... . express" "The probability of not occurring. The product of four parentheses represents the probability that none of the four binary events will occur. This formula represents the probability of 'at least one of the binary events occurring'."

[0169] Combined with known structure functions This embodiment designs a structural importance (SIM) measurement method based on path constraints, defined as a spatial dimension risk assessment model, with the following expression:

[0170]

[0171] in, This represents a structural importance metric based on path constraints. Represents a collection of components. Indicates the first The first of the features One component's state is set to 1, while the states of other components remain unchanged. Indicates the first The first of the features The state of one component is set to 0, while the states of other components remain unchanged. and Represents component state operators.

[0172] Step S35: Construct a spatiotemporal coupled fault analysis model based on the time-dimensional risk assessment model and the spatial-dimensional risk assessment model.

[0173] Understandably, based on improvements to structural and component importance metrics, a spatiotemporally coupled importance metric method is proposed. Considering the dynamic failure characteristics of the system during operation, the reliability function... Indicates time Previously, components The probability of being in a normal state. Then define the system state combination. In time The probability weight formulas that appear below are used to characterize the probability evolution characteristics of different state combinations at each moment within the time period.

[0174]

[0175] in, Indicates the first Each component at time Reliability,

[0176] Indicates at time The system is in a specific state combination The probability,

[0177] Indicates the first Each component at time The status is 1, indicating normal operation, and 0, indicating failure.

[0178] Furthermore, the dynamic failure behavior of the component throughout the entire time period is introduced, and a weighted integral model is constructed based on its failure rate function to define the component. Measuring the importance of structural perturbations over a time period:

[0179]

[0180] This indicates that during the entire period of time, when the first... The failure of a single component increases the probability of system failure, and its impact accumulates over time.

[0181] Furthermore, in order to accurately account for the impact of paths on disturbances, and given that components exist within the set of structural paths... In this section, the current system reliability is introduced. After normalization, a final spatiotemporal fusion importance expression is formed. The spatiotemporal coupling fault analysis model includes:

[0182]

[0183]

[0184] in, Indicates the first Each component during the entire time window The importance of structural perturbations within the entire time period, i.e., when the first... The failure of a single component increases the probability of system failure, and its impact accumulates over time.

[0185] This embodiment performs time-dependency analysis on each component based on the fault mapping matrix to obtain the time-dependent risk characteristics of the underground cable system components. Each component represents the binary characteristics of each observation in the characteristic factors. A target time-dimensional risk assessment model is constructed based on these component time-dependent risk characteristics. A system structure function for the underground cable system is constructed based on the matrix structure information of the fault mapping matrix. A spatial-dimensional risk assessment model is constructed based on the system structure function. Finally, a spatiotemporal coupled fault analysis model is constructed based on the time-dimensional and spatial-dimensional risk assessment models. Because this embodiment introduces a time-dimensional risk assessment model through time-dependency analysis and performs structural analysis through the structure function to construct a spatial-dimensional risk assessment model, it achieves a spatiotemporal coupled risk analysis method for the underground cable system. This allows for spatiotemporal risk perception in response to the complexity and dynamism of the underground cable system's operating environment, improving the accuracy and efficiency of risk identification.

[0186] Furthermore, this embodiment of the invention also proposes a computer-readable storage medium storing a risk perception and maintenance program for an underground cable system. When the underground cable system risk perception and maintenance program is executed by a processor, it implements the steps of the underground cable system risk perception and maintenance method described above.

[0187] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0188] The aforementioned computer-readable storage medium may be included in the risk perception and maintenance equipment for underground cable systems; or it may exist independently and not be assembled into the risk perception and maintenance equipment for underground cable systems.

[0189] Furthermore, this invention also proposes a computer program product, including an underground cable system risk perception and maintenance program, which, when executed by a processor, implements the steps of the underground cable system risk perception and maintenance method as described above.

[0190] The specific implementation of the computer program product of this invention is basically the same as the various embodiments of the risk perception and operation and maintenance method for underground cable systems described above, and will not be repeated here.

[0191] Reference Figure 7 , Figure 7 This is a structural block diagram of the first embodiment of the underground cable system risk perception and maintenance device of the present invention.

[0192] like Figure 7As shown, the underground cable system risk perception and maintenance device proposed in this embodiment of the invention includes:

[0193] Data acquisition module 10 is used to acquire multi-source heterogeneous data of the underground cable system, including sensor data, auxiliary detection terminal data, historical operation and maintenance data and historical fault data.

[0194] Data processing module 20 is used to preprocess the multi-source heterogeneous data to generate a fault mapping matrix. The preprocessing includes feature extraction and structured processing. The fault mapping matrix includes fault records of the underground cable system and the feature factors and fault results corresponding to each fault record. The feature factors are composed of multiple observation values.

[0195] The risk assessment module 30 is used to input the fault mapping matrix into a pre-built spatiotemporal coupled fault analysis model to obtain the risk contribution assessment results of each characteristic factor. The spatiotemporal coupled fault analysis model is used to measure and analyze the risk contribution of the characteristic factor under different times and spaces. The risk contribution assessment results include the time dimension risk assessment results and the spatial dimension risk assessment results of the characteristic factor.

[0196] The risk operation and maintenance module 40 is used to determine the risk level of each area in the underground cable system based on the risk contribution assessment results, and to generate the corresponding operation and maintenance strategy for each area based on the risk level.

[0197] This embodiment collects multi-source heterogeneous data from an underground cable system, including sensor data, auxiliary detection terminal data, historical operation and maintenance data, and historical fault data. The multi-source heterogeneous data is preprocessed to generate a fault mapping matrix. This preprocessing includes feature extraction and structured processing. The fault mapping matrix includes fault records of the underground cable system, as well as the corresponding feature factors and fault results for each fault record. Each feature factor consists of multiple observations. The fault mapping matrix is ​​then input into a pre-constructed spatiotemporal coupled fault analysis model to obtain the risk contribution assessment results of each feature factor. The spatiotemporal coupled fault analysis model is used to assess the risk contribution of each feature factor at different times and under different conditions. The risk contribution under the same space is measured and analyzed. The risk contribution assessment results include the time dimension risk assessment results and the spatial dimension risk assessment results of the characteristic factors. Based on the risk contribution assessment results, the risk level of each area in the underground cable system is determined, and the corresponding operation and maintenance strategy for each area is generated based on the risk level. Since this invention collects and processes multi-source heterogeneous data, it integrates heterogeneous sensing information to perceive fault characteristics of the underground cable system. Combined with the spatiotemporal coupled fault analysis model, it realizes dynamic fault analysis of the system in the spatiotemporal dimension, improves the accuracy of risk identification and perception, accurately and timely identifies potential underground cable faults, realizes continuous tracking of risk factors, and improves the system's risk early warning capability.

[0198] The underground cable system risk perception and maintenance device provided in this application, employing the underground cable system risk perception and maintenance method described in the above embodiments, can solve the technical problems of underground cable system risk perception and maintenance. Compared with the prior art, the beneficial effects of the underground cable system risk perception and maintenance device provided in this application are the same as those of the underground cable system risk perception and maintenance method provided in the above embodiments, and other technical features in the underground cable system risk perception and maintenance device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0199] It should be understood that the above are merely illustrative examples and do not constitute any limitation on the technical solutions of the present invention. In specific applications, those skilled in the art can make settings as needed, and the present invention does not impose any restrictions on this.

[0200] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of this invention. In practical applications, those skilled in the art can select some or all of the workflow to achieve the purpose of this embodiment according to actual needs, and no restrictions are imposed here.

[0201] In addition, for technical details not described in detail in this embodiment, please refer to the risk perception and maintenance method for underground cable systems provided in any embodiment of the present invention, which will not be repeated here.

[0202] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0203] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0204] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory / random access memory, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0205] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A risk perception and maintenance method for underground cable systems, characterized in that, The risk perception and maintenance methods for the underground cable system include: Collect multi-source heterogeneous data of the underground cable system, including sensor data, auxiliary detection terminal data, historical operation and maintenance data, and historical fault data; The multi-source heterogeneous data is preprocessed to generate a fault mapping matrix. The preprocessing includes feature extraction and structured processing. The fault mapping matrix includes fault records of the underground cable system and the feature factors and fault results corresponding to each fault record. The feature factors are composed of multiple observation values. The fault mapping matrix is ​​input into a pre-built spatiotemporal coupled fault analysis model to obtain the risk contribution assessment results of each characteristic factor. The spatiotemporal coupled fault analysis model is used to measure and analyze the risk contribution of the characteristic factor under different times and spaces. The risk contribution assessment results include the time dimension risk assessment results and the spatial dimension risk assessment results of the characteristic factor. Based on the risk contribution assessment results, the risk level of each area in the underground cable system is determined, and the corresponding operation and maintenance strategy for each area is generated based on the risk level. Before inputting the fault mapping matrix into the pre-built spatiotemporal coupled fault analysis model, the method further includes: Based on the fault mapping matrix, time dependency analysis is performed on each component to obtain the time dependency risk characteristics of the underground cable system components. The component is the constituent unit of each characteristic factor in the fault mapping matrix on the corresponding observation unit. The state of the component is characterized by a binary method to represent its working state or failure state. A target time-dimensional risk assessment model is constructed based on the time-dependent risk characteristics of the components. The system structure function of the underground cable system is constructed based on the matrix structure information of the fault mapping matrix; A spatial dimension risk assessment model is constructed based on the system structure function. A spatiotemporal coupled fault analysis model is constructed based on the time-dimensional risk assessment model and the spatial-dimensional risk assessment model. The construction of the spatial dimension risk assessment model based on the system structure function includes: Based on the system structure function analysis, the failure logic relationships between each component and the underground cable system are analyzed, and characteristic fault cause structure functions are constructed based on the failure logic relationships. The system structure function includes: in, Indicates the reliability of the system structure. Indicates the number of system features. Indicates the first One characteristic, Indicates the first The reliability of each feature Indicates the first The number of components in each feature Indicates the first The first feature The failure probability of each component. Indicates the first One fault record, Indicates the first Validity screening of fault records Indicates sample Through screening, Indicates the first The fault record is in the first The values ​​that can be taken on each feature Indicates the first The first feature The specific tag values ​​for each component; The characteristic fault cause structure function includes: in, Indicates the cause of the failure Reliability of features Indicates the first Substructure reliability, Index variables representing three types of substructures, The values ​​of 1, 2, and 3 correspond to the structures of three different sub-features associated with the fault causes. All three types of sub-structures belong to the feature structures related to the fault causes of underground cable systems. A spatial dimension risk assessment model is constructed based on the system structure function and the characteristic fault cause structure function. The spatial dimension risk assessment model includes: in, This represents a structural importance metric based on path constraints. Represents a collection of components. Indicates the first The first of the features One component's state is set to 1, while the states of other components remain unchanged. Indicates the first The first of the features The state of one component is set to 0, while the states of other components remain unchanged. and Represents component state operators.

2. The risk perception and maintenance method for underground cable systems as described in claim 1, characterized in that, The construction of the target time-dimensional risk assessment model based on the component time-dependent risk characteristics includes: An initial time-dimensional risk assessment model is constructed based on the time-dependent risk characteristics of the aforementioned components: in, This represents the component risk assessment results over a time dimension. Represents a time variable. and These respectively represent the components of the underground cable system. The probability of not failing under working and non-working conditions. and These respectively represent the components of the underground cable system. The probability of not failing under working and non-working conditions. and Representing components respectively Working status and not working status and Representing components respectively Working status and not working status Representation Component In time The failure increment, Representation Component In time The failure increment, A reliability function representing an underground cable system. Representation Component A reliable function, Representation Component The cumulative distribution function of the first failure; The minimum cut set of the underground cable system is generated based on the fault mapping matrix. The minimum cut set is the minimum set of components that cause the failure of the underground cable system. The initial time-dimensional risk assessment model is adjusted based on the minimum cut set to generate a target time-dimensional risk assessment model, which includes: in, This represents the set of indices for all components of an underground cable system. This represents the minimal cut set that indicates a system failure. The minimal cut set is the set of the smallest components that cause the underground cable system to fail. Indicates the minimum cut set In addition to components Other component indexes, This indicates a flag that forces the state of the corresponding component in the set to available / invalid. This represents the set of indexes for all components in an underground cable system, excluding the minimum cut set. Indicates the minimum cut set Components External components fail, component The probability that the system is in normal working condition under normal working conditions. This indicates that the remaining components within the same minimal cut set are preceding each other. Expired joint probabilities Indicates the system in time The probability of failure before the event.

3. The risk perception and maintenance method for underground cable systems as described in claim 1, characterized in that, The spatiotemporal coupled fault analysis model includes: in, Indicates the entire time window The first The probability of a system failure due to the failure of a component.

4. The risk perception and maintenance method for underground cable systems as described in any one of claims 1 to 3, characterized in that, The preprocessing of the multi-source heterogeneous data to generate a fault mapping matrix includes: The multi-source heterogeneous data is processed to obtain data analysis samples. The data cleaning process includes outlier removal, missing value imputation, normalization, time series alignment, and feature extraction. The data analysis samples include: in, Represents a set of fault events. Indicates the first One failure event, This represents the set of feature factors extracted from a fault event. Indicates the first Each feature factor consists of multiple observations. Indicates the first The characteristic factors in the first Observations at each observation location Represents the set of fault results. Indicates the first The failure result of each failure event; A feature space is constructed, and the data analysis samples are mapped to the feature space to generate a fault mapping matrix, the fault mapping matrix including: in, This represents the fault mapping matrix.

5. The risk perception and maintenance method for underground cable systems as described in any one of claims 1 to 3, characterized in that, The process of determining the risk level of each area in the underground cable system based on the risk contribution assessment results, and generating corresponding operation and maintenance strategies for each area based on the risk levels, includes: Based on the risk contribution assessment results, risk time-series characteristics of each line in the underground cable system are generated, and the risk time-series characteristics represent the time variation characteristics of the failure probability of the line. The risk level of each area in the underground cable system is determined based on the risk time sequence characteristics. Based on the cable path continuity information of the underground cable system and the risk level of each area, the spatial risk distribution variation characteristics of the underground cable system along the line are determined; A risk distribution heatmap is constructed based on the spatial risk distribution change characteristics, and operation and maintenance strategies corresponding to each region are generated based on the risk distribution heatmap.

6. An underground cable system risk perception and maintenance device that applies the risk perception and maintenance method for underground cable systems according to any one of claims 1 to 5, characterized in that, The underground cable system risk perception and maintenance device includes: The data acquisition module is used to collect multi-source heterogeneous data of the underground cable system, including sensor data, auxiliary detection terminal data, historical operation and maintenance data, and historical fault data. The data processing module is used to preprocess the multi-source heterogeneous data to generate a fault mapping matrix. The preprocessing includes feature extraction and structured processing. The fault mapping matrix includes fault records of the underground cable system and the feature factors and fault results corresponding to each fault record. The feature factors are composed of multiple observation values. The risk assessment module is used to input the fault mapping matrix into a pre-built spatiotemporal coupled fault analysis model to obtain the risk contribution assessment results of each characteristic factor. The spatiotemporal coupled fault analysis model is used to measure and analyze the risk contribution of the characteristic factor under different times and spaces. The risk contribution assessment results include the time dimension risk assessment results and the spatial dimension risk assessment results of the characteristic factor. The risk operation and maintenance module is used to determine the risk level of each area in the underground cable system based on the risk contribution assessment results, and to generate the corresponding operation and maintenance strategy for each area based on the risk level.

7. A risk perception and maintenance device for underground cable systems, characterized in that, The underground cable system risk perception and maintenance equipment includes: a memory, a processor, and an underground cable system risk perception and maintenance program stored in the memory. The processor is used to run the underground cable system risk perception and maintenance program, which is configured to implement the underground cable system risk perception and maintenance method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a risk perception and maintenance program for an underground cable system, which, when executed by a processor, implements the risk perception and maintenance method for an underground cable system as described in any one of claims 1 to 5.

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