A cable thermal risk monitoring method and system based on multi-source data fusion

Abstract

The application relates to the technical field of cable safety monitoring, and discloses a cable thermal risk monitoring method and system based on multi-source data fusion, which comprises the following steps: collecting, standardizing and processing multi-source data, extracting thermal risk features and identifying abnormal points, constructing a regional network topology to analyze a risk propagation path, and dynamically adjusting monitoring resource configuration. A hierarchical early warning signal is generated in real time, the influence of risk propagation is simulated, cable operation parameters are adjusted, and control effects are tracked. The application realizes intelligent monitoring and early warning of cable thermal risks, and improves cable operation safety and reliability.

Description

Technical Field

[0001] The present invention relates to the technical field of cable safety monitoring, and in particular to a cable thermal risk monitoring method and system based on multi-source data fusion. Background Art

[0002] Currently, cable failures caused by overheating during operation may lead to serious consequences such as power outages or even fires. Therefore, accurate monitoring and timely intervention of cable thermal risks are extremely critical.

[0003] One existing technology monitors cable thermal risks by integrating data from various sources. While this technology can analyze cable operating conditions in a single area to a certain extent, it often struggles to fully integrate multi-source data in complex environments and variable operating conditions, lacking the dynamic interaction and collaborative analysis of risk information across regions.

[0004] Due to the diversity and heterogeneity of data sources, data analysis of a single region often fails to reveal overall risk trends. The lack of an information-sharing mechanism between regions further exacerbates this problem, making it difficult to capture and leverage the correlations between risks across different regions. Consequently, existing technologies have resulted in ineffective risk prevention and control for medical device cables. Summary of the Invention

[0005] The present invention provides a cable thermal risk monitoring method and system based on multi-source data fusion to solve the problem that the existing technology leads to poor risk prevention and control effects of medical equipment cables.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides a cable thermal risk monitoring method based on multi-source data fusion, comprising:

[0007] Acquire multi-source data related to cable operation and pre-process it to obtain the initial cable data set;

[0008] Dividing the data in the cable initial data set into regions according to location, extracting thermal risk characteristic indicators of each region, and obtaining a thermal risk characteristic set;

[0009] Comparing the thermal risk characteristic indicators in the thermal risk characteristic set with historical data, determining and marking potential abnormal points, and generating a regional characteristic data set;

[0010] Performing cross-regional correlation analysis on the potential abnormal points in the regional feature data set to determine the possibility of thermal risk propagation and obtain a cable thermal risk propagation path map;

[0011] Based on the cable thermal risk propagation path diagram, the potential impact range of thermal risks between adjacent areas is analyzed, target areas requiring increased monitoring frequency are identified, and a dynamic monitoring adjustment plan is generated;

[0012] reallocate monitoring resources in the affected area according to the dynamic monitoring adjustment plan and obtain a monitoring resource allocation list;

[0013] According to the monitoring resource configuration list, the operating status of the monitoring equipment in each area is updated, and the operating data is collected and analyzed in real time. When thermal risk anomalies are detected, thermal risk warning signals are generated and graded.

[0014] Analyze the impact path of the warning area according to the heat risk warning signal and generate a specific control instruction set;

[0015] According to the specific control instruction set, the cable operating parameters of the affected area are updated, the adjusted operating status data is obtained and analyzed, and a thermal risk control status report is generated.

[0016] As an optional implementation, the acquisition of multi-source data related to cable operation and preprocessing to obtain an initial cable data set includes:

[0017] Acquiring multi-source data related to cable operation; the multi-source data includes temperature, current and ambient humidity;

[0018] Partition and store the multi-source data according to preset classification rules to obtain a classified data set;

[0019] Performing format conversion on the heterogeneous data in the classification data set to obtain a standard data set;

[0020] Eliminate outliers in the standard data set and fill in missing values ​​to obtain a complete data set;

[0021] Field mapping and structure adjustment are performed on the complete data set to generate the cable initial data set.

[0022] As an optional implementation, comparing the thermal risk characteristic indicators in the thermal risk characteristic set with historical data, determining and marking potential abnormal points, and generating a regional characteristic data set includes:

[0023] Obtaining the thermal risk feature indicator from the thermal risk feature set;

[0024] Comparing the heat risk characteristic indicators with the heat risk values ​​in the historical data one by one to obtain an abnormal point set;

[0025] Determining the thermal risk value exceeding a preset thermal risk threshold as a potential abnormal point;

[0026] The geographic information and time information of the potential abnormal points are marked and fused with the thermal risk feature set to generate the regional feature data set.

[0027] As an optional implementation, performing cross-regional correlation analysis on the potential abnormal points in the regional feature dataset to determine the possibility of thermal risk propagation and obtain a cable thermal risk propagation path map includes:

[0028] Comparing point markers and spatial distances in the regional feature dataset to construct a regional network topology based on geographic proximity;

[0029] Conduct cross-regional correlation analysis on abnormal points in the regional network topology and the heat risk values ​​of adjacent areas to determine the potential connections between the abnormal points;

[0030] If the heat risk value in the potential connection exceeds the preset transmission determination threshold, the possibility of heat risk transmission is determined according to the preset transmission rules, and a transmission possibility list is generated;

[0031] According to the propagation possibility list, the propagation paths between abnormal points are simulated to generate the cable heat risk propagation path map.

[0032] As an optional implementation, the cable thermal risk propagation path map is used to analyze the potential impact range of thermal risks between adjacent areas, determine target areas where monitoring frequency needs to be increased, and generate a dynamic monitoring adjustment plan, including:

[0033] Analyze the thermal risk characteristic indicators and thermal risk values ​​of adjacent areas according to the cable thermal risk propagation path map to obtain a potential impact range;

[0034] Determine the area in the potential impact range where the heat risk value exceeds a preset heat risk threshold as a target area where the monitoring frequency needs to be increased;

[0035] The dynamic monitoring adjustment plan is generated according to the monitoring requirements of the target area.

[0036] As an optional implementation manner, reallocating monitoring resources in the affected area according to the dynamic monitoring adjustment plan and obtaining a monitoring resource configuration list includes:

[0037] According to the dynamic monitoring adjustment plan, the monitoring resource demand of the affected area is compared with the current resource distribution, and the resource gap value of each area is calculated;

[0038] Marking the area where the resource gap value exceeds the preset resource gap threshold as a high priority area; marking the area where the resource gap value is less than or equal to the preset resource gap threshold as a low priority area, and determining the resource allocation priority;

[0039] updating the resource allocation status of each area according to the resource allocation priority to obtain an updated resource allocation status;

[0040] The monitoring resource configuration list is generated according to the updated resource allocation situation.

[0041] As an optional implementation, the operation status of the monitoring equipment in each area is updated according to the monitoring resource configuration list, the operation data is collected and analyzed in real time, and when a thermal risk anomaly is detected, a thermal risk warning signal is generated and graded, including:

[0042] According to the monitoring resource configuration list, the operating status of the equipment is periodically scanned to obtain updated operating status records of the monitoring equipment in each area;

[0043] Marking data in the updated operating status record where the operating indicator deviates from a preset indicator threshold, determining potential thermal risk abnormality records and generating corresponding early warning signals;

[0044] According to the preset regional monitoring requirements, an early warning signal is generated for the affected areas not covered by the thermal risk anomaly records, and all the early warning signals whose signal strength exceeds the preset strength threshold are graded to obtain the final thermal risk early warning signal.

[0045] As an optional implementation manner, analyzing the impact path of the warning area according to the heat risk warning signal and generating a specific control instruction set includes:

[0046] Acquiring connection relationship data of a target area in the regional network topology;

[0047] Analyzing the impact path of the warning area on adjacent areas based on the heat risk warning signal to obtain a distribution map of the impact path;

[0048] According to the distribution map, combined with preset linkage response rules and response time constraints, areas whose impact values ​​exceed preset impact thresholds are determined as target areas to be adjusted;

[0049] According to the target area, combined with a preset parameter adjustment strategy, the operating data is compared and adjusted to determine an adjusted parameter value set;

[0050] generating the specific control instruction set according to the adjusted parameter value set;

[0051] As an optional implementation, updating the cable operating parameters of the affected area according to the specific control instruction set, obtaining and analyzing the adjusted operating status data, and generating a thermal risk control status report include:

[0052] Transmitting the specific control instruction set to the cable device node in the target area through the instruction issuing channel, and receiving instruction reception confirmation information returned by the cable device node;

[0053] Receiving confirmation information according to the instruction, obtaining adjusted cable operating parameter data from an operating database, and generating a secondary adjustment instruction for data of the cable operating parameter data that does not meet a preset parameter threshold;

[0054] Adjusting the cable operating parameter data according to the secondary adjustment instruction, obtaining operating status information corresponding to the secondary adjusted cable operating parameter data, and tracking the change trend;

[0055] When the change trend tends to be stable, the thermal risk control status report is generated.

[0056] In a second aspect, the present invention provides a cable thermal risk monitoring system based on multi-source data fusion, comprising:

[0057] The data acquisition and processing module is used to obtain multi-source data related to cable operation and perform preprocessing to obtain the initial cable data set;

[0058] A regional feature extraction module is used to divide the data in the cable initial data set into regions according to location, extract thermal risk feature indicators of each region, and obtain a thermal risk feature set;

[0059] An anomaly detection and marking module, used to compare the thermal risk characteristic indicators in the thermal risk characteristic set with historical data, determine potential anomaly points and mark them, and generate a regional feature data set;

[0060] a risk propagation analysis module, configured to perform cross-regional correlation analysis on the potential abnormal points in the regional feature data set, determine the possibility of thermal risk propagation, and obtain a cable thermal risk propagation path map;

[0061] A dynamic monitoring adjustment module is used to analyze the potential impact range of thermal risks between adjacent areas based on the cable thermal risk propagation path map, determine the target areas where the monitoring frequency needs to be increased, and generate a dynamic monitoring adjustment plan;

[0062] A resource allocation optimization module is used to reallocate monitoring resources in the affected area according to the dynamic monitoring adjustment plan and obtain a monitoring resource configuration list;

[0063] A real-time warning generation module is used to update the operating status of monitoring equipment in each area according to the monitoring resource configuration list, collect and analyze operating data in real time, and generate a thermal risk warning signal and mark it in a graded manner when a thermal risk anomaly is detected;

[0064] A control instruction generation module is used to analyze the impact path of the warning area according to the heat risk warning signal and generate a specific control instruction set;

[0065] The risk status assessment module is used to update the cable operating parameters of the affected area according to the specific control instruction set, obtain and analyze the adjusted operating status data, and generate a thermal risk control status report.

[0066] In a third aspect, the present invention also provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements any one of the above-mentioned cable thermal risk monitoring methods based on multi-source data fusion.

[0067] In a fourth aspect, the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned cable thermal risk monitoring methods based on multi-source data fusion.

[0068] Compared with the prior art, the present invention has the following beneficial effects:

[0069] (1) The present invention obtains multi-source data related to cable operation and performs preprocessing, and performs standardized processing such as classification and partitioning storage, format conversion, outlier elimination and missing value filling on the data, thereby ensuring the integrity, consistency and accuracy of the initial cable data set, providing a reliable data basis for subsequent thermal risk analysis, and enabling the system to carry out subsequent monitoring and analysis work more efficiently and accurately.

[0070] (2) Extract thermal risk characteristic indicators, compare them with historical data one by one to determine potential abnormal points and mark them, so as to achieve accurate identification of cable thermal risks in various regions, timely discover potential risk points, provide a basis for taking prevention and control measures in advance, and improve the pertinence and effectiveness of cable thermal risk monitoring.

[0071] (3) Conduct cross-regional correlation analysis on potential abnormal points, build a regional network topology based on geographical proximity, determine the possibility of thermal risk transmission and generate a transmission path map, analyze the potential impact range of thermal risks between adjacent regions, determine the target areas where monitoring frequency needs to be increased and generate a dynamic monitoring adjustment plan, grasp the dynamics of thermal risks from an overall level, effectively capture the correlation between risks in different regions, and realize dynamic and accurate monitoring of cable thermal risks.

[0072] (4) The operating status of the monitoring equipment is updated according to the detection resource configuration list, data is collected and analyzed in real time, and a graded warning signal is generated when a thermal risk anomaly is detected, thereby realizing real-time monitoring of the cable operating status. The graded warning signal helps to quickly understand the severity of the risk and take corresponding measures in a timely manner, thereby improving the timeliness and effectiveness of the thermal risk warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 1 is a flow chart of a cable thermal risk monitoring system based on multi-source data fusion provided by the first embodiment of the present invention;

[0074] Figure 2 It is a structural diagram of a cable thermal risk monitoring system based on multi-source data fusion provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0075] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0076] Reference Figure 1 The first embodiment of the present invention provides a cable thermal risk monitoring method based on multi-source data fusion, comprising the following steps:

[0077] S11, acquiring multi-source data related to cable operation and performing preprocessing to obtain an initial cable data set;

[0078] S12, dividing the data in the initial cable data set into regions according to location, extracting thermal risk characteristic indicators of each region, and obtaining a thermal risk characteristic set;

[0079] S13, comparing the thermal risk characteristic indicators in the thermal risk characteristic set with historical data, determining and marking potential abnormal points, and generating a regional characteristic data set;

[0080] S14, performing cross-regional correlation analysis on the potential abnormal points in the regional feature data set to determine the possibility of thermal risk propagation and obtain a cable thermal risk propagation path map;

[0081] S15, analyzing the potential impact range of thermal risks between adjacent areas based on the cable thermal risk propagation path map, determining target areas where monitoring frequency needs to be increased, and generating a dynamic monitoring adjustment plan;

[0082] S16, reallocating monitoring resources in the affected area according to the dynamic monitoring adjustment plan, and obtaining a monitoring resource allocation list;

[0083] S17, based on the monitoring resource configuration list, updating the operating status of the monitoring equipment in each area, collecting and analyzing the operating data in real time, and generating a thermal risk warning signal and marking it in a graded manner when a thermal risk anomaly is detected;

[0084] S18, analyzing the impact path of the warning area according to the heat risk warning signal, and generating a specific control instruction set;

[0085] S19: updating the cable operating parameters in the affected area according to the specific control instruction set, obtaining and analyzing the adjusted operating status data, and generating a thermal risk control status report.

[0086] In step S11, the multi-source data related to cable operation is acquired and pre-processed to obtain an initial cable data set, including:

[0087] Obtain multi-source data related to cable operation;

[0088] Partition and store the multi-source data according to preset classification rules to obtain a classified data set;

[0089] Performing format conversion on the heterogeneous data in the classification data set to obtain a standard data set;

[0090] Eliminate outliers in the standard data set and fill in missing values ​​to obtain a complete data set;

[0091] Field mapping and structure adjustment are performed on the complete data set to generate the cable initial data set.

[0092] Multi-source data related to cable operation refers to information on cable operation status collected from different sources and dimensions, including temperature, current, and ambient humidity. While these data may vary in acquisition frequency, format, and accuracy, they collectively provide a comprehensive description of the cable's operating status, avoiding the limitations of a single data source.

[0093] The classified data set is a data set formed by partitioning and storing multi-source data according to preset classification rules, providing a structured basis for subsequent data processing. The classification rule is to classify according to data type.

[0094] The standard dataset is a unified format of heterogeneous data after format conversion; the complete dataset is data after outliers are removed and missing values ​​are filled in. Both improve data usability from different dimensions. Field mapping maps the names of data of the same type in multi-source data to the same field. For example, the temperature fields in multi-source data are named "temp" and "T," and both are mapped to the "temperature" field. Structuring is the process of standardizing heterogeneous data. For example, temperature data in multi-source data is originally formatted as a text file, current data is in table format, and humidity data is in database records. All data is then uniformly processed into JSON format. Field mapping and restructuring integrate standardized data, eliminating format redundancy or structural inconsistencies between different data sources.

[0095] It should be noted that in complex cable operating environments, a single data source can be affected by factors such as sensor failure, communication interruptions, and environmental interference, making it difficult to obtain complete and accurate cable status information. Multi-source data captures cable characteristics from different dimensions, time intervals, and accuracy levels, significantly improving data integrity and reliability.

[0096] In one implementation, the system synchronously collects data from multiple sensors, including temperature, current, and humidity sensors, and establishes a unified data coordinate system through timestamp alignment and numerical unit standardization. The raw data is preprocessed, including outlier detection and missing value filling, to produce a clear dataset. Field mapping and structural adjustments are performed on the data based on the multidimensional characteristics of cable operating status. Finally, the standardized data is fused with the status information to produce the initial cable dataset.

[0097] It should be noted that the multi-source data reflects the operating characteristics of the cable, such as temperature, current, humidity, etc. at different time points, and can determine the state distribution of the cable at different time nodes.

[0098] In step S12, the data in the cable initial data set is divided into regions according to location, and the thermal risk characteristic index of each region is extracted to obtain a thermal risk characteristic set, including:

[0099] According to the geographical distribution information of the cable network, the cable initial data set is divided into regions according to a preset spatial division rule;

[0100] Aggregate the cable operation data in each divided area to obtain a regional data set;

[0101] Extract key parameters such as temperature characteristics, current characteristics and environmental characteristics from the regional data set to obtain basic characteristic data of each region;

[0102] Thermal risk correlation analysis and feature engineering processing are performed on the basic feature data to generate the thermal risk feature set.

[0103] The spatial division rules are based on the physical layout and management requirements of the cable network, and include criteria such as geographic boundaries, equipment density, and load distribution. These rules ensure that each area is relatively independent and manageable.

[0104] Among them, the regional data set is a data combination formed by aggregating all cable operation data in the same divided area, providing a data basis for regional-level feature extraction.

[0105] The basic characteristic data is parameter information extracted from regional data that reflects the basic operating status of cables. The thermal risk feature set is a combination of characteristic indicators closely related to thermal risk, generated through thermal risk correlation analysis and feature engineering. Together, they fully describe the regional thermal risk status from the perspectives of basic data and risk characteristics. This process integrates basic characteristics with risk associations, eliminating the lack of direct correspondence between raw data and risk assessments.

[0106] It should be noted that in cable thermal risk monitoring systems, cable operating environments and risk characteristics vary significantly across regions, necessitating the establishment of a feature extraction mechanism based on regional divisions. Through reasonable spatial division and targeted feature extraction, the thermal risk distribution characteristics of each region can be more accurately reflected, providing precise data support for subsequent risk analysis and early warning.

[0107] For example, the thermal characteristics of 10kV cables in Area A (the high-voltage transmission area) of a distribution network show a daily peak temperature between 13:00 and 15:00, and a current characteristic with a load fluctuation of ±12A during this period. When recording these characteristics in layers, the first layer is labeled Area A - 10kV cables, the second layer records the temperature peak period between 13:00 and 15:00, and the third layer records the current characteristic with a load fluctuation of ±12A. After classification, the regional basic dataset contains an average temperature of 65°C for 10kV cables in Area A between 13:00 and 15:00, and a preliminary thermal risk characteristic range of 53°C to 77°C (a temperature range corresponding to 65°C - 12°C to 65°C + 12°C).

[0108] In step S13, the thermal risk characteristic indicators in the thermal risk characteristic set are compared with historical data to determine and mark potential abnormal points, thereby generating a regional characteristic data set, including:

[0109] Obtaining the thermal risk feature indicator from the thermal risk feature set;

[0110] Comparing the heat risk characteristic indicators with the heat risk values ​​in the historical data one by one to obtain an abnormal point set;

[0111] Determining the thermal risk value exceeding a preset thermal risk threshold as a potential abnormal point;

[0112] The geographic information and time information of the potential abnormal points are marked and fused with the thermal risk feature set to generate the regional feature data set.

[0113] Thermal risk characteristic indicators are key parameter information extracted from the thermal risk characteristic set that can reflect the thermal risk status of the cable, including key indicators such as cable temperature and heating rate. These indicators can quantitatively characterize the degree of thermal risk of the cable in different areas and time periods.

[0114] Among them, the abnormal point set is a summary of risk points that are beyond the normal range after comparison with historical data.

[0115] Potential anomaly points are specific risk locations that exceed preset thermal risk thresholds. Geographic and temporal information precisely describe the spatial coordinates and time of occurrence of these anomaly points, providing a comprehensive picture of the anomaly's occurrence from both spatial and temporal dimensions. Fusion integrates location and temporal information with risk signatures to address the ambiguity in spatial and temporal relationships between data.

[0116] It's important to note that in complex cable network environments, the distribution of thermal risks is spatially heterogeneous and temporally dynamic, making it difficult to accurately determine the severity and development trends of risks solely based on real-time data. Comparative analysis with historical data can identify abnormal patterns that deviate from normal operating conditions, significantly improving the accuracy and reliability of risk identification.

[0117] In one implementation, the system establishes a multidimensional historical database containing temperature history curves, current load history records, environmental condition history data, etc., and establishes normal operating ranges and abnormality discrimination criteria through statistical analysis. For each thermal risk characteristic indicator, the system uses a method based on threshold comparison and trend analysis to perform abnormality detection, etc. The thermal risk characteristic indicator set is preprocessed, including using data smoothing, noise filtering, trend extraction and other technologies to obtain stable risk indicator data. Then, based on the nonlinear change pattern of cable thermal risk, statistical comparison, deviation analysis, trend prediction and other data analysis technologies are used to identify abnormal points that are significantly different from the historical normal pattern. By comparing the abnormal point with a preset thermal risk threshold, when the risk value exceeds the preset threshold, the point is determined as a potential abnormal point, and its precise geographic coordinates and timestamp information are obtained. Finally, the geographic information and the time information are annotated and fused to obtain the regional feature data set.

[0118] It should be noted that the thermal risk characteristic indicators reflect the risk characteristics of cables in different regions and time points, such as the thermal risk level, rate of change, and duration, and can determine the risk distribution pattern of cables in different spatial locations. By annotating and integrating the geographic information with the temporal information, and by spatially and temporally correlating data from multiple time periods and regions, the continuous change process from normal to abnormal state is obtained. Combined with historical patterns, this process is used to determine whether it conforms to the characteristics of cable thermal risk evolution laws, such as whether the temperature rise follows the physical laws of heat transfer and whether the abnormal pattern is reproducible.

[0119] For example, a historical database stores thermal risk baseline values ​​for cables under different time periods and load conditions. By comparing the current thermal risk index with historical data from the same period, if the deviation from the historical normal operating mode exceeds a preset threshold of 30%, the point is marked as a potential anomaly. By comparing the numerical values ​​and changing trends of each risk index, the geographic coordinates and occurrence time of the anomaly point in the cable network are accurately determined, forming a regional feature dataset containing spatial and temporal attributes.

[0120] In step S14, cross-regional correlation analysis is performed on the potential abnormal points in the regional feature data set to determine the possibility of thermal risk propagation and obtain a cable thermal risk propagation path map, including:

[0121] Comparing point markers and spatial distances in the regional feature dataset to construct a regional network topology based on geographic proximity;

[0122] Conduct cross-regional correlation analysis on abnormal points in the regional network topology and the heat risk values ​​of adjacent areas to determine the potential connections between the abnormal points;

[0123] If the heat risk value in the potential connection exceeds the preset transmission determination threshold, the possibility of heat risk transmission is determined according to the preset transmission rules, and a transmission possibility list is generated;

[0124] According to the propagation possibility list, the propagation paths between abnormal points are simulated to generate the cable heat risk propagation path map.

[0125] The comparison of point markers and spatial distances involves extracting the geographic coordinates of each potential anomaly point in the regional feature dataset, calculating the straight-line distance between any two anomaly points using the Euclidean distance formula, calculating the network distance based on the actual connection paths of the cable network, and weightedly fusing the straight-line distance and network distance to obtain a comprehensive distance metric. A distance threshold for determining proximity is set, and when the comprehensive distance metric is less than the threshold, it is determined that the two points are geographically adjacent.

[0126] Among them, the construction of the regional network topology includes: using potential abnormal points as network nodes, using point pairs with geographical proximity as network edges, using the data structure of the adjacency matrix to represent the topological relationship, and assigning weight values ​​to the network edges according to the inverse of the comprehensive distance measurement value. The larger the weight value, the stronger the spatial correlation between the two regions, forming a regional network topology structure that reflects the spatial distribution characteristics of the cable network.

[0127] The regional network topology is a spatially connected network constructed based on the geographic proximity of potential anomaly points, reflecting the spatial correlation between different areas in the cable network. This topological structure provides a basic framework for analyzing the spatial spread of thermal risks.

[0128] Among them, cross-regional correlation analysis is the calculation of the correlation between thermal risk values ​​between adjacent abnormal points in the regional network topology, which is used to identify the spatial propagation characteristics and impact range of risks.

[0129] The preset propagation rules include the proximity-based rule, the distance-attenuation rule, and the risk gradient rule. The proximity-based rule states that thermal risk propagation is only possible when two abnormal points meet a "geographic proximity relationship," such as when the combined distance is less than or equal to a preset threshold. The distance-attenuation rule states that the closer the combined distance, the less resistance there is to thermal risk transmission through the air, the cable itself, or the surrounding environment, and the higher the probability of propagation. The risk gradient rule states that thermal risk is more likely to propagate from points with high risk intensity to points with low risk intensity, rather than vice versa.

[0130] Among them, the transmission possibility list is a summary of the probability of heat risk transmission between different regions determined according to preset transmission rules; the transmission path map is a visual representation of the risk diffusion path generated based on the transmission possibility simulation. Both fully describe the transmission characteristics of heat risk from the probability and path dimensions.

[0131] It's important to note that in cable network systems, thermal risks exhibit distinct spatial propagation characteristics. Physical transmission mechanisms such as heat conduction and convection exist between adjacent areas, causing local anomalies to trigger chain reactions. Cross-regional correlation analysis can identify the potential paths and impact areas of risk propagation, providing important support for risk prevention and emergency response. By simulating and integrating these propagation possibilities with the propagation paths, and spatially modeling propagation data across multiple regions and time periods, we can derive a complete propagation chain from the anomaly's source to the affected endpoint.

[0132] For example, the coordinates of abnormal point A are (100, 200), and the coordinates of abnormal point B are (150, 250). The calculated straight-line distance is 70.71 meters, the network distance is 85 meters, and the distance weight ratio is set to 0.6:0.4. The comprehensive distance metric is 70.71×0.6+85×0.4=76.43 meters. If the proximity determination threshold is set to 100 meters, it is determined that A and B have a geographical proximity relationship and a connecting edge is established in the topology with an edge weight of 1 / 76.43=0.0131. By calculating the thermal risk coefficient between adjacent abnormal points, when the ratio of the correlation coefficient to the preset propagation determination threshold exceeds 0.75, it is determined that the path has a high probability of propagation. Combining the geographical distance and comprehensive distance of the abnormal points, the risk of thermal risk propagation between points A and B is analyzed to obtain a list of propagation possibilities. By analyzing the spatial location and risk intensity of abnormal points, the propagation path of thermal risk from high-risk areas to low-risk areas is accurately simulated, forming a cable thermal risk propagation path map that includes propagation direction, propagation probability and propagation time.

[0133] In step S15, based on the cable thermal risk propagation path map, the potential impact range of thermal risks between adjacent areas is analyzed, the target areas where the monitoring frequency needs to be increased are determined, and a dynamic monitoring adjustment plan is generated, including:

[0134] Analyze the thermal risk characteristic indicators and thermal risk values ​​of adjacent areas according to the cable thermal risk propagation path map to obtain a potential impact range;

[0135] Determine the area in the potential impact range where the heat risk value exceeds a preset heat risk threshold as a target area where the monitoring frequency needs to be increased;

[0136] The dynamic monitoring adjustment plan is generated according to the monitoring requirements of the target area.

[0137] The potential impact range is the spatial area affected by the thermal risk, including both direct and indirect impact areas, derived from the cable thermal risk propagation path analysis. This range reflects the spatial diffusion boundary and impact distribution of thermal risk in the cable network.

[0138] Among them, the target areas are key areas identified in the potential impact range that require special attention and strengthened monitoring. The heat risk values ​​in these areas have exceeded or are close to the preset heat risk thresholds.

[0139] Among them, the dynamic monitoring adjustment plan is a monitoring resource allocation plan formulated according to the risk level and monitoring needs of the target area; the monitoring needs are the monitoring frequency, monitoring parameters and monitoring method requirements determined based on the risk level and transmission characteristics. Both fully describe the adjustment plan of the monitoring strategy from the dimensions of resource allocation and technical requirements.

[0140] It's important to note that as cable thermal risk dynamically evolves, risk levels and trends vary across regions, making fixed monitoring models difficult to adapt to the dynamic changes in risk distribution. By analyzing the transmission paths and impact areas of thermal risk, we can identify key areas where risk is concentrated and actively spreading, enabling the optimal allocation and precise delivery of monitoring resources.

[0141] It should be noted that the potential impact range analysis reflects the spatial distribution characteristics of thermal risk in different spatial regions, such as the impact intensity, impact radius, and attenuation pattern. It can determine the risk level and monitoring priority of different areas in the cable network. The monitoring needs and resource allocation are planned and integrated. By coordinating the monitoring requirements of multiple regions and multiple levels, a complete adjustment plan from the current monitoring state to the optimized monitoring state is obtained. In combination with resource constraints, it is determined whether it meets the actual operational requirements, such as whether the number of monitoring equipment is sufficient and whether the increase in monitoring frequency is within the technical capabilities.

[0142] For example, by analyzing the risk value and spatial location of each node in the propagation path diagram, if the ratio of a region's heat risk value to a preset heat risk threshold exceeds 1.2, that region is identified as a high-priority target area. By assessing the risk level and monitoring status of each target area, a dynamic monitoring adjustment plan is precisely formulated, including adjustments to monitoring frequency, additions to monitoring parameters, and deployment of monitoring equipment, to precisely allocate monitoring resources to high-risk areas.

[0143] In step S16, the monitoring resources of the affected area are reallocated according to the dynamic monitoring adjustment plan, and a monitoring resource configuration list is obtained, including:

[0144] According to the dynamic monitoring adjustment plan, the monitoring resource demand of the affected area is compared with the current resource distribution, and the resource gap value of each area is calculated;

[0145] Marking the area where the resource gap value exceeds the preset resource gap threshold as a high priority area; marking the area where the resource gap value is less than or equal to the preset resource gap threshold as a low priority area, and determining the resource allocation priority;

[0146] updating the resource allocation status of each area according to the resource allocation priority to obtain an updated resource allocation status;

[0147] The monitoring resource configuration list is generated according to the updated resource allocation situation.

[0148] The monitoring resource demand is the total resource requirement for each region, including the number of monitoring devices, monitoring personnel, and monitoring frequency requirements, as determined by the dynamic monitoring adjustment plan. The resource gap is the difference between the monitoring resource demand and the current resource distribution, reflecting the balance between supply and demand for monitoring resources in each region.

[0149] Among them, the resource allocation priority is the importance ranking of resource allocation in each region determined based on the size of the resource gap value, which provides a decision-making basis for the rational allocation of monitoring resources.

[0150] Among them, the updated resource allocation situation is the monitoring resource configuration status of each region after readjustment according to the resource allocation priority; the monitoring resource configuration list is a detailed record and summary of the updated resource allocation situation. Both fully describe the results of resource reallocation from the configuration status and list management dimensions.

[0151] It's important to note that in cable thermal risk monitoring systems, the effective allocation of monitoring resources directly impacts the timeliness and accuracy of risk identification. Due to the dynamic nature of risk distribution and the limited availability of resources, a risk-priority-based resource allocation mechanism is necessary to ensure adequate monitoring coverage in key areas while avoiding resource waste and imbalances.

[0152] In one implementation, for each affected area, the system uses a method based on resource demand calculation and gap analysis to perform resource allocation, etc. The dynamic monitoring adjustment plan is pre-processed, including demand quantification, resource inventory, gap calculation, etc., to obtain accurate resource demand data. Then, based on the limited monitoring resources and configuration constraints, the optimal solution and adjustment strategy for resource allocation are obtained through resource optimization, priority sorting, and configuration adjustment. By comparing the resource gap value with the preset resource gap threshold, when the gap value exceeds the preset threshold, the area is marked as high priority, its resource demand is prioritized, and the urgency and configuration order of its resource allocation are determined. Finally, the resource configuration information is sorted and summarized to obtain the monitoring resource configuration list.

[0153] It should be noted that the resource allocation list analysis reflects resource characteristics such as the supply and demand status, scarcity, and allocation urgency of monitoring equipment, technicians, and monitoring frequency in each region. This can determine resource allocation priorities and adjustment ranges for different regions in the cable network. By comprehensively integrating these resource allocation priorities and allocation adjustments, the resource requirements of multiple regions and types are coordinated to obtain an optimized resource allocation plan.

[0154] In step S17, according to the monitoring resource configuration list, the operating status of the monitoring equipment in each area is updated, and the operating data is collected and analyzed in real time. When a thermal risk anomaly is detected, a thermal risk warning signal is generated and graded, including:

[0155] According to the monitoring resource configuration list, the operating status of the equipment is periodically scanned to obtain updated operating status records of the monitoring equipment in each area;

[0156] Marking data in the updated operating status record where the operating indicator deviates from a preset indicator threshold, determining potential thermal risk abnormality records and generating corresponding early warning signals;

[0157] According to the preset regional monitoring requirements, an early warning signal is generated for the affected areas not covered by the thermal risk anomaly records, and all the early warning signals whose signal strength exceeds the preset strength threshold are graded to obtain the final thermal risk early warning signal.

[0158] Among them, the updated operating status record is a real-time record of equipment operating parameters and performance indicators obtained after periodic scanning of monitoring equipment in each area based on the monitoring resource configuration list, including key operating information such as equipment online status, data collection quality, and sensor accuracy.

[0159] Among them, potential thermal risk anomaly records are data records that are identified as having thermal risks by comparing operating indicators with preset thresholds, providing a data basis for the generation of early warning signals.

[0160] Among them, the final heat risk warning signal is a complete warning information formed after anomaly identification, regional supplementation and hierarchical processing; hierarchical processing is the process of classifying warning signals according to signal strength and risk level.

[0161] It's important to note that in the actual operation of a cable thermal risk monitoring system, the operating status of the monitoring equipment directly impacts the accuracy of data collection and the reliability of early warnings. Real-time monitoring of equipment status and operating data enables timely detection of equipment failures and data anomalies, ensuring the continued effective operation of the monitoring system. Furthermore, a tiered early warning mechanism enables precise and differentiated risk response management.

[0162] It should be noted that the operating status reflects the operating characteristics of monitoring equipment at different points in time, including its working status, data quality, and abnormal conditions. This can determine the reliability and effectiveness of the monitoring system in different regions. By processing and integrating the warning signals with the classification standards and uniformly grading multi-regional and multi-type warning information, the targeted nature of regional monitoring can be effectively improved, ensuring the rationality of resource allocation while providing data support for subsequent dynamic adjustments.

[0163] In step S18, based on the thermal risk warning signal, the impact path of the warning area is analyzed to generate a specific control instruction set, including:

[0164] Acquiring connection relationship data of a target area in the regional network topology;

[0165] Analyzing the impact path of the warning area on adjacent areas based on the heat risk warning signal to obtain a distribution map of the impact path;

[0166] According to the distribution map, combined with preset linkage response rules and response time constraints, areas whose impact values ​​exceed preset impact thresholds are determined as target areas to be adjusted;

[0167] According to the target area, combined with a preset parameter adjustment strategy, the operating data is compared and adjusted to determine an adjusted parameter value set;

[0168] The specific control instruction set is generated according to the adjusted parameter value set.

[0169] The target area is the area where the thermal risk warning signal is located. The connection relationship data is the relationship information such as physical connection, electrical connection, and thermal conduction connection between each target area recorded in the regional network topology, providing a topological basis for impact path analysis.

[0170] Among them, the distribution map of the impact path is a visual representation of the spatial distribution and propagation path of the impact of the warning area on the adjacent areas based on the analysis of the heat risk warning signal, including key information such as impact intensity, propagation direction and attenuation law.

[0171] The preset parameter adjustment strategies include: a temperature adjustment strategy that reduces the operating temperature by 5-10°C when the impact value of the target area is within the range of 0.3-0.5, by 10-15°C when the impact value is within the range of 0.5-0.8, and by 15-20°C when the impact value exceeds 0.8; and a load adjustment strategy that proportionally reduces the load current according to the impact value level: an impact value of 0.3-0.5 corresponds to a load reduction of 10%-20%, an impact value of 0.5-0.8 corresponds to a load reduction of 20%-40%, and an impact value exceeding 0.8 corresponds to a load reduction of 40%-60%. This parameter adjustment strategy is based on the analysis and summary of historical thermal risk events. By statistically analyzing effective control measures under different impact value levels, a mapping relationship between parameter adjustment and risk control effect is established. After simulation verification and field test optimization, a standardized adjustment strategy library is formed.

[0172] Among them, the preset linkage response rules include: when the risk level of the warning area is high risk, the response radius is set to all adjacent areas within 500 meters; when the risk level of the warning area is medium risk, the response radius is set to adjacent areas within 300 meters; when the risk level of the warning area is low risk, the response radius is set to adjacent areas within 150 meters. The response time constraint is set as follows: high-risk areas must complete parameter adjustment within 10 minutes, medium-risk areas must complete adjustment within 30 minutes, and low-risk areas must complete adjustment within 60 minutes. The preset impact threshold is determined according to the regional importance level: the impact threshold for key areas is 0.3, the impact threshold for important areas is 0.5, and the impact threshold for general areas is 0.7.

[0173] For example, when the warning area C001 is identified as a high-risk level, the system automatically scans areas C002, C003, C004, and C005 within a range of 500 meters, where C002 is a critical area (impact value 0.45>0.3), C003 is an important area (impact value 0.55>0.5), and C004 is a general area (impact value 0.65<0.7). Therefore, C002 and C003 are determined as target areas that need to be adjusted, and parameter adjustment is required to be completed within 10 minutes.

[0174] The adjusted parameter value set is the optimized operating parameter combination determined based on the target area's risk profile and parameter adjustment strategy. The specific control instruction set is a set of executable control commands generated based on the adjusted parameter value set. For example, for target area D001 with an impact value of 0.62, the strategy library determines that the temperature needs to be reduced by 13°C (within a range of 0.5-0.8%) and the load needs to be reduced by 35% (within a range of 0.5-0.8%).

[0175] It should be noted that by analyzing the impact mechanisms and propagation characteristics of warning areas on surrounding areas, it is possible to identify key areas requiring coordinated control, achieving a shift from passive warning to active control, and effectively blocking the spread and diffusion of risks. Generating the specific control instruction set integrates the parameter value set. By unifying the adjustment requirements of multiple areas and multiple parameters into unified instructions, a complete instruction sequence from risk identification to control execution is obtained.

[0176] In step S19, according to the specific control instruction set, the cable operating parameters of the affected area are updated, the adjusted operating status data is obtained and analyzed, and a thermal risk control status report is generated, including:

[0177] Transmitting the specific control instruction set to the cable device node in the target area through the instruction issuing channel, and receiving instruction reception confirmation information returned by the cable device node;

[0178] Receiving confirmation information according to the instruction, obtaining adjusted cable operating parameter data from an operating database, and generating a secondary adjustment instruction for data of the cable operating parameter data that does not meet a preset parameter threshold;

[0179] Adjusting the cable operating parameter data according to the secondary adjustment instruction, obtaining operating status information corresponding to the secondary adjusted cable operating parameter data, and tracking the change trend;

[0180] When the change trend tends to be stable, the thermal risk control status report is generated.

[0181] The command delivery channel is the communication link used to transmit specific control command sets to the cable equipment nodes in the target area. It can use a variety of transmission methods, including wired communication, wireless communication, and fieldbus. The command reception confirmation information is the execution status feedback returned by the cable equipment node after receiving the control command, ensuring the reliability of command transmission and the effectiveness of execution.

[0182] For example, TCP / IP transmission is used for device nodes that support Ethernet protocol, and RS485 transmission is used for device nodes that support serial communication; instructions are sent to the target device node through the specified communication port, and the instruction sending timeout is set to 30 seconds. If no confirmation information is received within the timeout, the instruction is resent.

[0183] Among them, the generating of the secondary adjustment instruction includes: identifying abnormal parameter items that do not meet the preset parameter threshold, calculating the deviation amount and deviation direction of the abnormal parameters, and determining the amplitude of the secondary adjustment according to the size of the deviation amount. For example, when the deviation amount is within the range of 1-2 times of the threshold, the adjustment amplitude is 80% of the deviation amount; when the deviation amount is within the range of 2-3 times of the threshold, the adjustment amplitude is 100% of the deviation amount; when the deviation amount exceeds 3 times the threshold, the adjustment amplitude is 120% of the deviation amount; generating the secondary adjustment instruction according to the format of the primary adjustment instruction, adding the "SEC_" prefix to the instruction identifier to distinguish the secondary adjustment instruction, and setting the execution priority of the secondary adjustment instruction to high.

[0184] Among them, the adjusted cable operation parameter data is the actual operation data after the cable equipment node performs parameter adjustment according to the control instructions, reflecting the direct effect of the control measures and the parameter changes.

[0185] The secondary adjusted cable operating parameter data is the operating parameter optimized by the secondary adjustment instruction; and the operating status information is the system status description of the corresponding operating parameter.

[0186] Among them, the tracking of change trends includes: establishing a parameter change trend monitoring mechanism, collecting operating parameter data every 5 minutes, calculating the change rate of two adjacent collected data, and the change rate is obtained by calculating the difference between the current value and the previous value, and then converting the difference into a percentage after comparing it with the previous value. The change rate data of 5 consecutive monitoring cycles are trend analyzed, and the slope of the change trend is calculated using a linear regression algorithm. When the absolute value of the slope is less than 0.05 and maintains this state for 3 consecutive cycles, it is determined that the change trend is stable.

[0187] It should be noted that the thermal risk control status report is generated after the cable thermal risk control measures are implemented, when the changing trend of the cable operating parameters tends to be stable. It is a comprehensive report used to fully reflect the thermal risk control process and the final effect.

[0188] For example, the system transmits control commands to each cable device node through a command distribution channel. When the device node returns a command reception confirmation rate exceeding 95%, the command transmission is confirmed to be successful. By monitoring changes in the adjusted cable operating parameters, secondary adjustment commands are generated for parameters that deviate from the preset parameter threshold by more than 15%. If the parameter change rate is less than 5% over three consecutive monitoring cycles, the change trend is determined to be stable, and a thermal risk control status report is generated, including control effect evaluation, parameter stability analysis, and risk control effectiveness.

[0189] In summary, the present invention discloses a cable thermal risk monitoring method based on multi-source data fusion, comprising:

[0190] Acquire multi-source data related to cable operation and pre-process it to obtain an initial cable data set; divide the data in the initial cable data set into regions according to location, extract thermal risk characteristic indicators of each region, and obtain a thermal risk feature set; compare the thermal risk characteristic indicators in the thermal risk feature set with historical data, determine potential abnormal points and mark them, and generate a regional feature data set; perform cross-regional correlation analysis on the potential abnormal points in the regional feature data set to determine the possibility of thermal risk propagation and obtain a cable thermal risk propagation path map; analyze the potential impact range of thermal risks between adjacent regions based on the cable thermal risk propagation path map, and determine For target areas where the monitoring frequency needs to be increased, a dynamic monitoring adjustment plan is generated; based on the dynamic monitoring adjustment plan, the monitoring resources of the affected areas are reallocated, and a monitoring resource configuration list is obtained; based on the monitoring resource configuration list, the operating status of the monitoring equipment in each area is updated, and the operating data is collected and analyzed in real time. When thermal risk anomalies are detected, a thermal risk warning signal is generated and graded; based on the thermal risk warning signal, the impact path of the warning area is analyzed and a specific control instruction set is generated; based on the specific control instruction set, the cable operating parameters of the affected area are updated, the adjusted operating status data is obtained and analyzed, and a thermal risk control status report is generated.

[0191] Reference Figure 2 The second embodiment of the present invention provides a cable thermal risk monitoring system based on multi-source data fusion, including:

[0192] The data acquisition and processing module is used to obtain multi-source data related to cable operation and perform preprocessing to obtain the initial cable data set;

[0193] A regional feature extraction module is used to divide the data in the cable initial data set into regions according to location, extract thermal risk feature indicators of each region, and obtain a thermal risk feature set;

[0194] An anomaly detection and marking module, used to compare the thermal risk characteristic indicators in the thermal risk characteristic set with historical data, determine potential anomaly points and mark them, and generate a regional feature data set;

[0195] a risk propagation analysis module, configured to perform cross-regional correlation analysis on the potential abnormal points in the regional feature data set, determine the possibility of thermal risk propagation, and obtain a cable thermal risk propagation path map;

[0196] A dynamic monitoring adjustment module is used to analyze the potential impact range of thermal risks between adjacent areas based on the cable thermal risk propagation path map, determine the target areas where the monitoring frequency needs to be increased, and generate a dynamic monitoring adjustment plan;

[0197] A resource allocation optimization module is used to reallocate monitoring resources in the affected area according to the dynamic monitoring adjustment plan and obtain a monitoring resource configuration list;

[0198] A real-time warning generation module is used to update the operating status of monitoring equipment in each area according to the monitoring resource configuration list, collect and analyze operating data in real time, and generate a thermal risk warning signal and mark it in a graded manner when a thermal risk anomaly is detected;

[0199] A control instruction generation module is used to analyze the impact path of the warning area according to the heat risk warning signal and generate a specific control instruction set;

[0200] The risk status assessment module is used to update the cable operating parameters of the affected area according to the specific control instruction set, obtain and analyze the adjusted operating status data, and generate a thermal risk control status report.

[0201] It should be noted that the cable thermal risk monitoring system based on multi-source data fusion provided in an embodiment of the present invention is used to execute all the process steps of the cable thermal risk monitoring method based on multi-source data fusion in the above embodiment. The working principles and beneficial effects of the two correspond one to one, so they will not be repeated here.

[0202] An embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a cable hot air detection program. When the processor executes the computer program, the steps in each of the above-mentioned cable hot air detection method embodiments are implemented, such as Figure 1 Alternatively, the processor implements the functions of the modules / units in the above-mentioned device embodiments when executing the computer program.

[0203] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device.

[0204] The electronic device may be a computing device such as a desktop computer, notebook, PDA, or smart tablet. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will appreciate that the aforementioned components are merely examples of electronic devices and do not constitute a limitation of the electronic device. The electronic device may include more or fewer components than those described above, or a combination of certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, and the like.

[0205] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor. The processor is the control center of the electronic device and connects various parts of the entire electronic device using various interfaces and lines.

[0206] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and accessing the data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function, such as a sound playback function or an image playback function; the data storage area may store data generated based on the use of the mobile phone, such as audio data and a phone book. In addition, the memory may include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0207] If the module / unit integrated into the electronic device is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention can also implement all or part of the process steps in the above-mentioned embodiment method by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium. It should be noted that the content of the computer-readable medium can be appropriately increased or decreased based on the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media does not include electric carrier signals and telecommunication signals.

[0208] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without inventive effort.

[0209] The specific embodiments described above further illustrate the objectives, technical solutions, and beneficial effects of the present invention. The above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A cable thermal risk monitoring method based on multi-source data fusion, characterized in that: Executed by a computer, including: Acquire multi-source data related to cable operation and pre-process it to obtain the initial cable data set; Dividing the data in the cable initial data set into regions according to location, extracting thermal risk characteristic indicators of each region, and obtaining a thermal risk characteristic set; Comparing the thermal risk characteristic indicators in the thermal risk characteristic set with historical data, determining and marking potential abnormal points, and generating a regional characteristic data set; Performing cross-regional correlation analysis on the potential abnormal points in the regional feature data set to determine the possibility of thermal risk propagation and obtain a cable thermal risk propagation path map; Based on the cable thermal risk propagation path diagram, the potential impact range of thermal risks between adjacent areas is analyzed, target areas requiring increased monitoring frequency are identified, and a dynamic monitoring adjustment plan is generated; reallocate monitoring resources in the affected area according to the dynamic monitoring adjustment plan and obtain a monitoring resource allocation list; According to the monitoring resource configuration list, the operating status of the monitoring equipment in each area is updated, and the operating data is collected and analyzed in real time. When thermal risk anomalies are detected, thermal risk warning signals are generated and graded. Analyze the impact path of the warning area according to the heat risk warning signal and generate a specific control instruction set; According to the specific control instruction set, the cable operating parameters of the affected area are updated, the adjusted operating status data is obtained and analyzed, and a thermal risk control status report is generated; The step of performing cross-regional correlation analysis on the potential abnormal points in the regional feature dataset to determine the possibility of thermal risk propagation and obtain a cable thermal risk propagation path map includes: Comparing point markers and spatial distances in the regional feature dataset to construct a regional network topology based on geographic proximity; Conduct cross-regional correlation analysis on abnormal points in the regional network topology and the heat risk values ​​of adjacent areas to determine the potential connections between the abnormal points; If the heat risk value in the potential connection exceeds the preset transmission determination threshold, the possibility of heat risk transmission is determined according to the preset transmission rules, and a transmission possibility list is generated; According to the propagation possibility list, simulating the propagation path between abnormal points to generate the cable heat risk propagation path map; The step of analyzing the impact path of the warning area according to the heat risk warning signal and generating a specific control instruction set includes: Acquiring connection relationship data of a target area in the regional network topology; Analyzing the impact path of the warning area on adjacent areas based on the heat risk warning signal to obtain a distribution map of the impact path; According to the distribution map, combined with preset linkage response rules and response time constraints, areas whose impact values ​​exceed preset impact thresholds are determined as target areas to be adjusted; According to the target area, combined with a preset parameter adjustment strategy, the operating data is compared and adjusted to determine an adjusted parameter value set; The specific control instruction set is generated according to the adjusted parameter value set.

2. The cable thermal risk monitoring method according to claim 1, characterized in that: The method of obtaining multi-source data related to cable operation and preprocessing it to obtain an initial cable data set includes: Acquiring multi-source data related to cable operation; the multi-source data includes temperature, current and ambient humidity; Partition and store the multi-source data according to preset classification rules to obtain a classified data set; Performing format conversion on the heterogeneous data in the classification data set to obtain a standard data set; Eliminate outliers in the standard data set and fill in missing values ​​to obtain a complete data set; Field mapping and structure adjustment are performed on the complete data set to generate the cable initial data set.

3. The cable thermal risk monitoring method according to claim 1, characterized in that: The step of comparing the thermal risk characteristic indicators in the thermal risk characteristic set with historical data, determining and marking potential abnormal points, and generating a regional characteristic data set includes: Obtaining the thermal risk feature indicator from the thermal risk feature set; Comparing the heat risk characteristic indicators with the heat risk values ​​in the historical data one by one to obtain an abnormal point set; Determining the thermal risk value exceeding a preset thermal risk threshold as a potential abnormal point; The geographic information and time information of the potential abnormal points are marked and fused with the thermal risk feature set to generate the regional feature data set.

4. The cable thermal risk monitoring method according to claim 1, characterized in that: According to the cable thermal risk propagation path map, the potential impact range of thermal risks between adjacent areas is analyzed, target areas requiring increased monitoring frequency are determined, and a dynamic monitoring adjustment plan is generated, including: Analyze the thermal risk characteristic indicators and thermal risk values ​​of adjacent areas according to the cable thermal risk propagation path map to obtain a potential impact range; Determine the area in the potential impact range where the heat risk value exceeds a preset heat risk threshold as a target area where the monitoring frequency needs to be increased; The dynamic monitoring adjustment plan is generated according to the monitoring requirements of the target area.

5. The cable thermal risk monitoring method according to claim 1, characterized in that: The reallocation of monitoring resources in the affected area according to the dynamic monitoring adjustment plan and obtaining a monitoring resource configuration list include: According to the dynamic monitoring adjustment plan, the monitoring resource demand of the affected area is compared with the current resource distribution, and the resource gap value of each area is calculated; Marking the area where the resource gap value exceeds the preset resource gap threshold as a high priority area; marking the area where the resource gap value is less than or equal to the preset resource gap threshold as a low priority area, and determining the resource allocation priority; updating the resource allocation status of each area according to the resource allocation priority to obtain an updated resource allocation status; The monitoring resource configuration list is generated according to the updated resource allocation situation.

6. The cable thermal risk monitoring method according to claim 1, characterized in that: The monitoring resource configuration list is used to update the operating status of monitoring equipment in each area, collect and analyze operating data in real time, and generate a thermal risk warning signal and grade it when a thermal risk anomaly is detected, including: According to the monitoring resource configuration list, the operating status of the equipment is periodically scanned to obtain updated operating status records of the monitoring equipment in each area; Marking data in the updated operating status record where the operating indicator deviates from a preset indicator threshold, determining potential thermal risk abnormality records and generating corresponding early warning signals; According to the preset regional monitoring requirements, an early warning signal is generated for the affected areas not covered by the thermal risk anomaly records, and all the early warning signals whose signal strength exceeds the preset strength threshold are graded to obtain the final thermal risk early warning signal.

7. The cable thermal risk monitoring method according to claim 1, characterized in that: The method of updating the cable operating parameters in the affected area according to the specific control instruction set, obtaining and analyzing the adjusted operating status data, and generating a thermal risk control status report includes: Transmitting the specific control instruction set to the cable device node in the target area through the instruction issuing channel, and receiving instruction reception confirmation information returned by the cable device node; Receiving confirmation information according to the instruction, obtaining adjusted cable operating parameter data from an operating database, and generating a secondary adjustment instruction for data of the cable operating parameter data that does not meet a preset parameter threshold; Adjusting the cable operating parameter data according to the secondary adjustment instruction, obtaining operating status information corresponding to the secondary adjusted cable operating parameter data, and tracking the change trend; When the change trend tends to be stable, the thermal risk control status report is generated.

8. A cable thermal risk monitoring system based on multi-source data fusion, used to implement the method described in any one of claims 1 to 7, characterized in that: include: The data acquisition and processing module is used to obtain multi-source data related to cable operation and perform preprocessing to obtain the initial cable data set; A regional feature extraction module is used to divide the data in the cable initial data set into regions according to location, extract thermal risk feature indicators of each region, and obtain a thermal risk feature set; An anomaly detection and marking module, used to compare the thermal risk characteristic indicators in the thermal risk characteristic set with historical data, determine potential anomaly points and mark them, and generate a regional feature data set; a risk propagation analysis module, configured to perform cross-regional correlation analysis on the potential abnormal points in the regional feature data set, determine the possibility of thermal risk propagation, and obtain a cable thermal risk propagation path map; A dynamic monitoring adjustment module is used to analyze the potential impact range of thermal risks between adjacent areas based on the cable thermal risk propagation path map, determine the target areas where the monitoring frequency needs to be increased, and generate a dynamic monitoring adjustment plan; A resource allocation optimization module is used to reallocate monitoring resources in the affected area according to the dynamic monitoring adjustment plan and obtain a monitoring resource configuration list; A real-time warning generation module is used to update the operating status of monitoring equipment in each area according to the monitoring resource configuration list, collect and analyze operating data in real time, and generate a thermal risk warning signal and mark it in a graded manner when a thermal risk anomaly is detected; A control instruction generation module is used to analyze the impact path of the warning area according to the heat risk warning signal and generate a specific control instruction set; The risk status assessment module is used to update the cable operating parameters of the affected area according to the specific control instruction set, obtain and analyze the adjusted operating status data, and generate a thermal risk control status report.

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