Urban underground cable pipe network management method and system based on digital twinning

Abstract

The application provides a kind of urban underground cable pipe network management method and system based on digital twinning, first, the basic attributes of urban underground cable pipe network and real-time sensing data are integrated, a pipe network digital twin mapping model containing physical entity digital replication and dynamic correlation between entities is constructed, the model is updated by continuously inputting real-time running state data of physical pipe network through a preset data interaction protocol, correlation coupling analysis is carried out based on the updated pipe network digital twin mapping model, abnormal pipe network key nodes and abnormal conduction links are identified, the state diffusion process of abnormal conduction links in different environments is simulated using the model and risk evolution simulation results are generated, pipe network maintenance instructions containing priority ranking and resource allocation scheme are generated according to the risk evolution simulation results and key node location information, and are issued to the operation and maintenance execution system to trigger maintenance response, to realize intelligent management.

Description

Technical Field

[0001] The present invention relates to the field of digital twin technology, and in particular to a method and system for managing an urban underground cable pipeline network based on digital twins. Background Art

[0002] In urban infrastructure construction, urban underground cable networks serve as key channels for power transmission, and their safe and stable operation is crucial to the normal operation of the city. However, traditional urban underground cable network management methods have many limitations.

[0003] On the one hand, existing management relies primarily on manual inspections and periodic local testing. This approach not only consumes significant manpower, material resources, and time, but also makes it difficult to fully and comprehensively understand the overall operational status of the pipeline network in real time. Without timely access to real-time pipeline data, potential problems are difficult to detect in advance. Often, problems are not addressed until they have become serious or even caused failures, leading to wider power outages and increased repair costs.

[0004] On the other hand, existing management methods lack in-depth analysis of the interconnectedness between various components of the network. Urban underground cable networks are complex systems, with interconnected and mutually influential components. A failure at one node can propagate through specific links to other nodes, triggering wider problems. However, traditional management methods struggle to accurately identify these abnormal transmission links, making it impossible to effectively predict and mitigate risks. Summary of the Invention

[0005] In view of the above-mentioned problems, in combination with the first aspect of the present invention, the present invention provides a method for managing an urban underground cable network based on digital twins, the method comprising:

[0006] Integrate basic attribute data and real-time perception data of urban underground cable pipeline networks to build a digital twin mapping model of the pipeline network. The digital twin mapping model includes the digital reproduction of the physical entities of the pipeline network and the dynamic association relationships between entities;

[0007] The real-time operating status data of the physical pipe network is continuously input into the pipe network digital twin mapping model through a preset data interaction protocol, driving the pipe network digital twin mapping model to update state parameters to maintain the state correspondence between the pipe network digital twin mapping model and the physical pipe network;

[0008] Based on the updated digital twin mapping model of the pipeline network, a correlation coupling analysis is performed on the operating status data of each component of the urban underground cable pipeline network to identify key pipeline nodes with abnormal status and abnormal transmission links between nodes;

[0009] The pipeline network digital twin mapping model is used to simulate the state diffusion process of abnormal transmission links under different environmental conditions to generate risk evolution simulation results;

[0010] Based on the risk evolution simulation results and the location information of key nodes in the pipeline network, a pipeline network maintenance instruction including priority sorting and resource allocation plan is generated, and the pipeline network maintenance instruction is sent to the operation and maintenance execution system of the physical pipeline network to trigger a maintenance response.

[0011] On the other hand, the present invention also provides an urban underground cable pipeline management system based on digital twins, including a processor and a machine-readable storage medium, the machine-readable storage medium is connected to the processor, the machine-readable storage medium is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the machine-readable storage medium to implement the above method.

[0012] Based on the above aspects, a digital twin mapping model of the urban underground cable network is constructed by integrating basic attribute data with real-time perception data. This achieves a comprehensive digital reproduction of the physical entities of the pipeline network and an accurate presentation of the dynamic relationships between entities. Using a preset data interaction protocol, the real-time operating status data of the physical pipeline network is continuously input to drive model updates, ensuring real-time correspondence between the digital twin mapping model and the physical pipeline network status. This enables managers to understand the actual operation of the pipeline network in real time. Correlation coupling analysis based on the updated model can accurately identify key nodes and abnormal transmission links in the pipeline network with abnormal status, breaking through the limitation of traditional management methods that make it difficult to detect potential problems. The model simulates the state diffusion process of abnormal transmission links under different environmental conditions and generates risk evolution simulation results. Finally, based on the risk evolution simulation results and key node location information, pipeline maintenance instructions with priority sorting and resource allocation plans are generated and issued to the operation and maintenance execution system. This realizes intelligent management of the entire process from risk identification to maintenance response, effectively improving the efficiency, accuracy, and reliability of urban underground cable network management, and reducing the risk of failure and repair costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a schematic diagram of the execution flow of the urban underground cable network management method based on digital twins provided in an embodiment of the present invention.

[0014] Figure 2 It is a schematic diagram of exemplary hardware and software components of the urban underground cable network management system based on digital twin provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0015] The present invention will be described in detail below with reference to the accompanying drawings. Figure 1This is a flow chart of a method for managing an urban underground cable network based on digital twins provided by an embodiment of the present invention. The method for managing an urban underground cable network based on digital twins is introduced in detail below.

[0016] Step S110: Integrate the basic attribute data and real-time perception data of the urban underground cable pipeline network to construct a pipeline network digital twin mapping model, which includes the digital reproduction of the pipeline network physical entities and the dynamic association relationship between entities.

[0017] In the daily operation of cities, underground cable networks play a vital role in transmitting electricity. However, due to their widespread distribution and deep burial depth, traditional management methods struggle to provide a comprehensive, real-time understanding of their operational status. To improve management efficiency and reliability, a digital twin mapping model that accurately reflects the actual network conditions is needed.

[0018] Take a commercial center, for example. Its underground cable network is vast, connecting numerous shops, office buildings, and public facilities. Effective management requires integrating two types of data: basic attribute data and real-time perception data. Basic attribute data describes the network's fundamental characteristics, while real-time perception data reflects the network's current operating status and surrounding environment. By integrating these two types of data, a model can be constructed that digitally represents the network's physical entities and dynamically connects them.

[0019] Step S111: collecting basic attribute data of the urban underground cable network, which includes the laying path data of the network, cable specification data, connector configuration data and ancillary facilities distribution data.

[0020] Laying path data clearly defines the underground route of cables. Cable specification data includes parameters such as cable material and cross-sectional area, which determine the cable's load-carrying capacity and transmission performance. Connector configuration data records the connection method and location between cables. The quality and configuration of connectors directly affect the stability of power transmission. Ancillary facility distribution data covers the location information of facilities such as transformers and distribution boxes.

[0021] In the case of the large commercial center mentioned above, basic attribute data can be collected from design drawings, construction records, and other materials, including pipeline routing data. Cable specifications can be obtained from cable product specifications and relevant test reports. Connector configuration data can be collected through site surveys and construction records. Ancillary facility distribution data can be determined through field measurements and review of relevant archives.

[0022] Step S112: Collect real-time sensing data of the urban underground cable pipe network, which includes operation state data and surrounding environment related data obtained by deploying sensing devices along the pipe network.

[0023] In this embodiment, the operation state data can be obtained by sensors installed on the cable, such as current sensors that can measure the current size in the cable, and temperature sensors that can monitor the temperature of the cable surface. The surrounding environment related data can be collected by environmental monitoring devices, such as underground water level sensors that can monitor changes in underground water level, and soil moisture sensors that can measure soil moisture.

[0024] In the underground cable pipe network of a large commercial center, various sensing devices can be deployed at key locations. For example, temperature sensors are installed at the joints of the cable to monitor the temperature changes of the joints in real time, as the joints are prone to heat, and high temperature may cause failure. Underground water level sensors are installed in the cable trench to monitor whether the underground water level is too high, and high underground water level may cause immersion damage to the cable. These sensing devices continuously collect data and transmit it to the data processing center.

[0025] Step S113: Structured processing of basic attribute data, converting laying path data into spatial coordinate sequence, establishing correspondence between cable specification data and joint configuration data, forming structured basic data set.

[0026] In order to facilitate subsequent data processing and analysis, the basic attribute data needs to be structured. Converting laying path data into spatial coordinate sequence can represent the laying path of the cable in a unified spatial coordinate system, thus the trend and position relationship of the cable can be more intuitively displayed. Establishing the correspondence between cable specification data and joint configuration data can clearly understand the cable specification connected by each joint, which helps to quickly locate the problem during troubleshooting and maintenance.

[0027] When processing the basic attribute data of a large commercial center, for laying path data, geographic information system (GIS) technology can be used to convert it into spatial coordinate sequence. For cable specification data and joint configuration data, a database table can be created to associate the two. For example, a cable specification table and a joint configuration table are created in the database, and by adding a cable specification association field in the joint configuration table, the correspondence between the two is established.

[0028] Step S114: Spatio-temporal alignment processing of real-time sensing data, mapping the data collected by different sensing devices at the same time with the corresponding spatial coordinates in the structured basic data set, forming a spatio-temporal alignment sensing data set.

[0029] Due to different sensing devices may have different sampling frequencies and timestamps, in order to make real-time sensing data can be effectively combined with the basic attribute data, need to carry out space-time alignment processing. The purpose of space-time alignment processing is to ensure that at the same time point, the data collected by different sensing devices can establish accurate mapping relationship with the corresponding spatial coordinates in the structured basic data set.

[0030] In the underground cable network of large commercial center, the time of data collection by sensing devices in different positions may be different. For example, a current sensor located on the east side of the commercial center and a temperature sensor located on the west side, the time of data collection may not be completely consistent. Through space-time alignment processing, these data can be unified to a time scale, and associated with the corresponding spatial coordinates in the structured basic data set. The specific processing method can be to sort and interpolate the data according to the timestamps of the sensing devices, and then match the processed data with the spatial coordinates in the structured basic data set. After space-time alignment processing, the formed space-time alignment sensing data set can more accurately reflect the running state of the pipe network at a specific time and space.

[0031] Step S115: associate and fuse the structured basic data set with the space-time alignment sensing data set, and construct a digital reproduction model of the physical entities of the pipe network based on the fused data, which includes the geometric parameters, material property parameters and initial state parameters of each entity.

[0032] Associating and fusing the structured basic data set and the space-time alignment sensing data set can make full use of the advantages of the two types of data, and construct a more accurate and detailed digital reproduction model of the physical entities of the pipe network. The geometric parameters can be determined according to the laying path data and spatial coordinates, such as the length of the cable, the degree of bending, etc. The material property parameters can be obtained from the cable specification data, such as the material of the cable, the insulation performance, etc. The initial state parameters can be determined in combination with real-time sensing data, such as the initial current, voltage, etc. of the cable.

[0033] In the case of a large commercial center, when associating and fusing the structured basic data set and the space-time alignment sensing data set, the relevant records in the two types of data can be matched and merged according to the spatial coordinates and time information. For example, the basic attribute data of a cable node (such as cable specification, joint configuration) is associated with the real-time sensing data of the node at the same time (such as current, temperature). Based on the fused data, three-dimensional modeling software or database technology can be used to construct a digital reproduction model of the physical entities of the pipe network. In this digital reproduction model, each cable, joint and auxiliary facility has clear geometric shape, material property and initial state.

[0034] Step S116: Analyze the connection relationship, spatial position relationship and functional dependency relationship between the physical entities of the pipe network, and construct a dynamic association relationship model between the entities. The dynamic association relationship model is used to characterize the influence of the state change of one entity on other related entities.

[0035] In a pipeline network, physical entities do not exist in isolation; complex connections, spatial relationships, and functional dependencies exist between them. Connections describe the direct connections between entities, such as between cables and connectors, or between cables and auxiliary facilities. Spatial relationships reflect the relative positions of entities in space. These relationships may affect the interactions between entities; for example, adjacent cables may be affected by each other's heat generation. Functional dependencies indicate that the normal operation of one entity depends on the support of other entities. For example, the normal operation of a transformer is crucial to ensuring the power supply of a cable.

[0036] In the underground cable network of a large commercial center, the relationship between entities can be analyzed from multiple perspectives. For connection relationships, a connection topology diagram of cables and connectors can be drawn to clearly identify which connectors each cable is connected to, and which other cables or ancillary facilities these connectors are connected to. For spatial position relationships, 3D modeling technology can be used to display the spatial distribution of entities underground and analyze the distance and position relationships between adjacent entities. For functional dependencies, the role of each entity in the power transmission and distribution process can be shared to determine which entities are interdependent. Based on these analysis results, a dynamic association relationship model between entities can be constructed. This dynamic association relationship model can be represented by a mathematical formula, rule, or algorithm to predict how the state change of an entity affects other related entities.

[0037] Step S117: The digital reproduction model and the dynamic association relationship model are integrated to generate a digital twin mapping model of the pipeline network that includes the digital reproduction of the physical entities of the pipeline network and the dynamic association relationship between the entities.

[0038] By integrating the digital reproduction model with the dynamic association model, we can obtain the final pipeline network digital twin mapping model. This pipeline network digital twin mapping model not only includes the precise digital representation of the pipeline network's physical entities, but also reflects the dynamic associations between entities, and can comprehensively and accurately reflect the actual situation of the urban underground cable pipeline network.

[0039] In the case of a large commercial center, when these two models are integrated, the rules and algorithms in the dynamic relationship model can be embedded in the digital reproduction model. For example, when the status of a cable node in the digital reproduction model changes, the dynamic relationship model can predict how the change will affect other associated cable nodes, connectors, and ancillary facilities based on pre-set rules and algorithms.

[0040] Step S120: The real-time operating status data of the physical pipeline network is continuously input into the pipeline network digital twin mapping model through a preset data interaction protocol, driving the pipeline network digital twin mapping model to update the status parameters to maintain the status correspondence between the pipeline network digital twin mapping model and the physical pipeline network.

[0041] Step S121: Generate a preset data interaction protocol, which specifies the transmission format, data field definition, transmission frequency and verification rules of real-time operation status data.

[0042] The data exchange protocol is the guiding principle for data transmission. The transmission format should be chosen based on both data accuracy and efficiency. Different types of data may require different transmission formats. Data field definitions should be clear and unambiguous, ensuring that the meaning and purpose of each data field are accurately understood. The transmission frequency should be determined based on the importance and frequency of data changes. For rapidly changing data, a higher transmission frequency is required. Verification rules should be developed to effectively detect and correct errors during data transmission to ensure data reliability.

[0043] In the case of a large commercial center, when generating a preset data interaction protocol, the real-time operating status data that needs to be transmitted will first be classified and analyzed. For numerical data such as current and voltage, binary format may be used for transmission to improve transmission efficiency. For text data such as equipment identification and fault type, text format may be used for transmission. The data field definition will specify the name, meaning and value range of each data field in detail. For example, the current data field will clearly indicate its unit and measurement range. The transmission frequency will be set according to the importance and change frequency of the data. For critical current and temperature data, it may be set to transmit once every minute. The verification rule will use the CRC algorithm to verify the data to ensure that there are no errors in the data transmission process.

[0044] Step S122: deploying state sensing equipment at each key monitoring point of the physical pipe network, wherein the state sensing equipment is used to collect real-time operation status data of the corresponding location, wherein the real-time operation status data includes cable operation parameters and environmental impact parameters.

[0045] To obtain real-time operational status data for the physical pipeline network, state-aware devices must be deployed at key monitoring points. These typically include cable joints, branch points, and locations near ancillary facilities, which are crucial for monitoring the network's operational status. These devices can collect real-time cable operating parameters such as current, voltage, and temperature, as well as environmental parameters such as groundwater level, soil moisture, and temperature.

[0046] In underground cable networks in large commercial centers, appropriate status sensing devices can be deployed at key monitoring points. For example, temperature and current sensors can be installed at cable joints. Temperature sensors monitor temperature changes in real time, as joints easily heat up and excessive temperatures can cause malfunctions. Current sensors measure the current flowing in the cable to determine whether it is overloaded. Groundwater level sensors and soil moisture sensors can be installed in cable trenches. Groundwater level sensors monitor groundwater level fluctuations, as excessively high groundwater levels can cause damage to cables. Soil moisture sensors measure soil moisture, as excessively high soil moisture can affect cable insulation. These status sensing devices continuously collect data and transmit it to a data processing center.

[0047] Step S123: Assign a unique device identifier to each state-sensing device, and establish a binding relationship between the device identifier and the corresponding physical entity digital reproduction unit in the pipeline network digital twin mapping model.

[0048] In order to ensure that the collected data can accurately correspond to the corresponding entities in the pipeline network digital twin mapping model, it is necessary to assign a unique device identifier to each state-sensing device.

[0049] In the case of a large commercial center, a unified coding rule can be used to assign a device ID to each state-sensing device. For example, a combination of the device type code, installation location code, and serial number can be used to generate the device ID. The device ID is then bound to the corresponding physical entity digital reproduction unit in the pipeline network digital twin mapping model. The specific binding method can be to establish an association table in the model's database to associate the device ID with the unique identifier of the corresponding physical entity. In this way, when the state-sensing device collects data, the data can be accurately updated to the state parameters of the corresponding physical entity digital reproduction unit in the model through the device ID.

[0050] Step S124: The real-time operating status data collected by the status perception device is continuously uploaded to the data processing center through the data transmission network in accordance with the preset data interaction protocol, so that the data processing center can perform format verification and outlier filtering on the uploaded data, and search for the corresponding physical entity digital reproduction unit according to the device identification, and update the filtered real-time operating status data to the state parameter set of the physical entity digital reproduction unit to achieve a preliminary update of the model state parameters.

[0051] The data transmission network is the channel that transmits data collected by status-aware devices to the data processing center. Following the pre-defined data exchange protocol, status-aware devices upload the collected real-time operating status data to the data processing center via the data transmission network. Upon receiving the data, the data processing center first performs a format check to verify that the data conforms to the transmission format specified by the pre-defined data exchange protocol. If the data format does not meet the requirements, appropriate processing may be performed, such as discarding it or requesting retransmission.

[0052] Next, the data processing center filters the uploaded data for outliers. These outliers can be caused by sensor failures, data transmission errors, and other factors, and they can affect model accuracy, necessitating filtering. This filtering approach involves setting reasonable thresholds and removing data that falls outside these thresholds as outliers.

[0053] The data processing center then searches for the corresponding physical entity digital reproduction unit based on the device identifier. Using the previously established binding relationship between the device identifier and the physical entity digital reproduction unit, the corresponding physical entity is found in the model database. Finally, the filtered real-time operating status data is updated to the state parameter set of the physical entity digital reproduction unit, completing the initial update of the model state parameters.

[0054] In the underground cable network of a large commercial center, data transmission can be done via either a wired or wireless network. Data collected by state-sensing devices is uploaded to a data processing center via the network. The data processing center processes and updates the data according to the aforementioned steps, ensuring that the digital twin mapping model of the network reflects the real-time status of the physical network.

[0055] Step S125: Compare the deviation value between the updated model state parameters and the actual operating state of the physical pipeline network. When the deviation value exceeds the preset allowable range, adjust the transmission frequency or verification rules in the data interaction protocol, and execute the data upload and parameter update steps again until the model state parameters correspond to the actual operating state of the physical pipeline network.

[0056] To ensure the accuracy of the digital twin model, it's necessary to regularly compare updated model state parameters with the actual operating status of the physical network. Model accuracy can be assessed by calculating the deviation between the two. If the deviation exceeds the preset tolerance, it indicates a significant discrepancy between the model and actual conditions, necessitating adjustments to the data exchange protocol.

[0057] Adjustments to the data exchange protocol can be made by focusing on transmission frequency and validation rules. If large deviations are caused by untimely data updates, the transmission frequency can be increased, increasing the number of data uploads to enable the model to more promptly reflect changes in the physical pipe network. If large deviations are caused by data transmission errors, validation rules can be adjusted, using stricter validation algorithms or increasing the length of the check digit to improve data transmission accuracy.

[0058] In the case of a large commercial center, the updated model state parameters can be regularly compared with the actual operating status of the physical pipe network. For example, every evening, a comparative analysis of the model state parameters and the actual operating status for the day is performed. If the deviation between the model temperature and the actual temperature of a cable node is found to exceed the preset allowable range, the first step is to check whether the data transmission is normal. If the data transmission is normal, the data transmission frequency of the node status sensing device may be increased from once per minute to once every half minute. If errors are found in the data transmission, the verification rules may be adjusted and a more complex CRC algorithm may be used to verify the data. After adjusting the data exchange protocol, the data upload and parameter update steps can be performed again until the model state parameters correspond to the actual operating status of the physical pipe network.

[0059] Step S130: Based on the updated digital twin mapping model of the pipeline network, an association coupling analysis is performed on the operating status data of each component of the urban underground cable pipeline network to identify key pipeline nodes with abnormal status and abnormal transmission links between nodes.

[0060] After updating the state parameters of the pipeline network digital twin mapping model, it is necessary to conduct in-depth analysis of the operational status data within the model. The urban underground cable network is a complex system, with its various components interconnected and influencing each other. Through correlation and coupling analysis, it is possible to identify key pipeline nodes with abnormal conditions and abnormal transmission links between these nodes.

[0061] In underground cable networks in large commercial centers, correlation and coupling analysis comprehensively considers multiple factors. For example, it can analyze the relationship between cable operating parameters such as current, voltage, and temperature, as well as the relationship between these parameters and surrounding environmental factors. If a sudden increase in current and an abnormally high temperature are detected at a certain cable node, further analysis is needed to determine the relationship between that node and other nodes to identify possible abnormal transmission links.

[0062] Step S131: extract the current operating status parameters of each physical entity digital reproduction unit and the dynamic association relationship model between entities from the updated pipeline network digital twin mapping model.

[0063] To conduct a correlation and coupling analysis, the required data must first be extracted from the updated digital twin mapping model of the pipeline network. The current operating parameters of each physical entity's digital replica, such as current, voltage, and temperature, reflect the real-time operation of each component of the pipeline network. The dynamic correlation model between entities describes the interactions and influence patterns between them.

[0064] In the case of a large commercial center, when extracting data from the digital twin mapping model of the pipeline network, the current operating status parameters of the digital reproduction unit of each physical entity can be obtained through the database interface of the model. At the same time, the dynamic association relationship model between entities can be extracted. This dynamic association relationship model may exist in the form of a rule set, algorithm, or graph structure. For example, by querying the current, voltage, temperature and other fields in the database, the current operating status parameters of each cable node can be obtained. For the dynamic association relationship model between entities, a graph structure representing the connection relationship and influence degree between cable nodes may be obtained, where each node represents a cable node, the edge represents the association relationship between nodes, and the edge weight represents the strength of the association.

[0065] Step S132: Determine the normal threshold range of each operating status parameter based on the pipeline network design standard and historical normal operation data.

[0066] To determine whether each operating parameter is abnormal, it's necessary to determine its normal threshold range. Pipeline network design standards specify the rated parameters and safe operating ranges for equipment like cables and connectors. Historical normal operation data records the values ​​of each operating parameter under normal conditions. Combining these two pieces of information allows us to determine the normal threshold range for each operating parameter.

[0067] In underground cable networks in large commercial centers, the normal threshold range can be determined by referring to the cable's product manual and design specifications. For example, for the current parameters of a cable, its rated current value can be determined based on the cable's cross-sectional area and material, and the normal threshold range can be determined based on this. At the same time, historical normal operation data can be analyzed to calculate statistical quantities such as the average value and standard deviation of each operating status parameter to further refine the normal threshold range. For example, by analyzing historical current data from the past year, it was found that the normal current value of a certain cable fluctuated within a specific range. This range was used as the normal threshold range for the current parameter of the cable.

[0068] Step S133: traverse all physical entity digital reproduction units, mark the physical entity digital reproduction units whose operating status parameters exceed the normal threshold range as initial abnormal nodes, and record the abnormal parameter type and deviation degree.

[0069] After determining the normal threshold range for each operating status parameter, the operating status parameters of all physical entity digital reproduction units need to be checked. Each physical entity digital reproduction unit is traversed and any units with operating status parameters exceeding the normal threshold range are marked as initial abnormal nodes. The abnormal parameter type and degree of deviation of these abnormal nodes are also recorded for subsequent analysis and processing.

[0070] In the case of a large commercial center, when traversing all the digital reproduction units of the physical entity, a program can be written to traverse the data in the model one by one. For example, for each cable node, its operating status parameters such as current, voltage, and temperature can be checked to see if they exceed the normal threshold range. If the temperature of a cable node exceeds the normal threshold range, the node can be marked as an initial abnormal node, and its abnormal parameter type can be recorded as a temperature anomaly. At the same time, the difference between the temperature value and the upper limit of the normal threshold is calculated as a measure of the degree of deviation.

[0071] Step S134: determining directly associated entity units of the initial abnormal node based on the dynamic association relationship model, wherein the directly associated entity units include entity units that have a connection relationship, a spatial position relationship, or a functional dependency relationship with the initial abnormal node.

[0072] After identifying the initial abnormal nodes, we need to find their directly associated entity units based on the dynamic association model. The directly associated entity units have a connection relationship, spatial position relationship, or functional dependency relationship with the initial abnormal node, which may cause the abnormal situation to be transmitted between nodes.

[0073] In the underground cable network of a large commercial center, when determining directly associated entity units based on the dynamic association relationship model, a previously constructed graph structure or rule set can be referenced. For example, if a cable node is marked as an initial abnormal node, the graph structure can be used to find other cable nodes and connectors directly connected to the node. These nodes and connectors are the directly associated entity units of the initial abnormal node. At the same time, spatial position relationships and functional dependencies can be considered. For example, if an ancillary facility is spatially adjacent to the cable node and its normal operation depends on the power supply of the cable node, then the ancillary facility will also be considered a directly associated entity unit.

[0074] Step S135: extracting the operating status parameters of the directly associated entity units, and calculating the status influence coefficient of the initial abnormal node on each directly associated entity unit according to the association relationship type and the deviation degree of the initial abnormal node.

[0075] After determining the direct associated entity units of the initial abnormal node, it is necessary to further calculate the state influence coefficient of the initial abnormal node to each direct associated entity unit. The state influence coefficient reflects the influence degree of the abnormal situation of the initial abnormal node on the direct associated entity unit, and its calculation needs to consider the association relationship type and the deviation degree of the initial abnormal node.

[0076] The influence degree on the direct associated entity unit is different for different association relationship types. For example, the connection relationship and the electrical conduction association usually have greater influence than the spatial proximity association and the functional synergy association. The greater the deviation degree of the initial abnormal node, the greater the influence on the direct associated entity unit.

[0077] In the case of a large commercial center, after extracting the running state parameters of the direct associated entity units, a basic influence weight can be set for each association relationship according to the association relationship type. For example, a higher basic influence weight is set for the connection relationship and the electrical conduction association, and a lower basic influence weight is set for the spatial proximity association and the functional synergy association. Then, the deviation degree of the abnormal parameter of the initial abnormal node is calculated, which is fused with the basic influence weight to obtain the preliminary influence coefficient of the initial abnormal node on the direct associated entity unit.

[0078] Step S1351: Obtain the association relationship type between the initial abnormal node and the direct associated entity unit from the dynamic association relationship model, which includes mechanical connection association, electrical conduction association, spatial proximity association, and functional synergy association.

[0079] To calculate the state influence coefficient of the initial abnormal node on the direct associated entity unit, it is necessary to first determine the association relationship type between them. The dynamic association relationship model records the association relationship information between entities, and the association relationship type between the initial abnormal node and the direct associated entity unit can be obtained by querying the dynamic association relationship model.

[0080] In the underground cable network of a large commercial center, different association relationship types have different characteristics. Mechanical connection association means that entities are associated with each other through physical connection, such as the connection between cables and connectors. Electrical conduction association means that there is an electrical signal transmission relationship between entities, such as power transmission between cables. Spatial proximity association means that entities are close in space and may be affected by each other's heat radiation, electromagnetic interference, etc. Functional synergy association means that entities cooperate with each other in function to complete a task, such as the cooperative work between transformers and distribution boxes.

[0081] Step S1352: Set a basic influence weight for each association relationship type, wherein the basic influence weight of the mechanical connection association and the electrical conduction association is higher than that of the spatial proximity association and the functional synergy association.

[0082] Different association relationship types have different degrees of influence on direct association entity units, so it is necessary to set a basic influence weight for each association relationship type. Mechanical connection association and electrical conduction association usually have more direct and stronger influence on direct association entity units, so their basic influence weights should be higher than those of spatial proximity association and functional synergy association.

[0083] In the case of a large commercial center, when setting the basic influence weight, it can be adjusted according to experience and actual situation. For example, for mechanical connection association and electrical conduction association, the basic influence weight can be set to a higher value, such as 0.8; for spatial proximity association and functional synergy association, the basic influence weight can be set to a lower value, such as 0.2. These weight values can be optimized and adjusted according to actual situation to more accurately reflect the influence degree of different association relationship types.

[0084] Step S1353: Calculate the abnormal parameter deviation degree of the initial abnormal node, which is the ratio of the absolute value of the difference between the abnormal parameter value and the median of the normal threshold range to the half width of the normal threshold range.

[0085] The abnormal parameter deviation degree can measure the severity of the abnormal situation of the initial abnormal node. By calculating the absolute value of the difference between the abnormal parameter value and the median of the normal threshold range, and comparing it with the half width of the normal threshold range, the abnormal parameter deviation degree can be obtained.

[0086] In the underground cable network of a large commercial center, if the current parameter of a certain cable node is abnormal, first determine the normal threshold range of the current parameter, for example, the normal threshold range is 10-20A, the median is 15A, and the half width is 5A. If the actual current value of the node is 25A, then the abnormal parameter deviation degree is (|25-15|) / 5=2. The larger the abnormal parameter deviation degree, the more serious the abnormal situation.

[0087] Step S1354: Fuse the basic influence weight and the abnormal parameter deviation degree to calculate the preliminary influence coefficient of the initial abnormal node on the direct association entity unit.

[0088] Fusing the basic influence weight and the abnormal parameter deviation degree can consider the influence of association relationship type and abnormal severity on direct association entity units. The method of fusion calculation can be selected according to the specific situation, for example, the basic influence weight and the abnormal parameter deviation degree can be multiplied to obtain the preliminary influence coefficient.

[0089] In the case of a large commercial center, if the association type between an initial abnormal node and a directly associated entity unit is an electrical conduction association, the basic impact weight is 0.8, and the abnormal parameter deviation degree of the initial abnormal node is 2, then the preliminary impact coefficient is 0.8*2=1.6.

[0090] Step S1355: Extract the current operating status parameters of the directly associated entity unit and determine whether it is close to the boundary of the normal threshold range. If it is close to the boundary, the preliminary influence coefficient is amplified and weighted to obtain a weighted processing result. The proportion of the amplified weight is related to the degree of proximity to the boundary.

[0091] In addition to considering the type of association and the degree of deviation of abnormal parameters, the operating status of the directly associated entity unit itself must also be considered. If the current operating status parameters of the directly associated entity unit are close to the boundary of the normal threshold range, it indicates that the unit itself is already in a relatively fragile state, and the abnormality of the initial abnormal node may have a greater impact on it.

[0092] In the underground cable network of a large commercial center, the current operating status parameters of the directly associated entity units are extracted and compared with the normal threshold range. If the current parameter of a directly associated entity unit is close to the upper limit of the normal threshold range, it means that the unit may be close to an overload state. At this time, the preliminary influence coefficient can be amplified and weighted. The proportion of the amplification weight can be determined according to the degree of proximity to the boundary. For example, if the difference between the current parameter of the directly associated entity unit and the upper limit of the normal threshold range is less than 10% of the half-width of the normal threshold range, the preliminary influence coefficient can be amplified by 1.5 times.

[0093] Step S1356: The initial influence coefficient and the weighted processing result are combined to obtain the final state influence coefficient of the initial abnormal node on each directly associated entity unit.

[0094] By combining the preliminary impact coefficient and the weighted processing results, we can obtain the final state impact coefficient of the initial abnormal node on each directly associated entity. The final state impact coefficient more comprehensively considers the impact of various factors on directly associated entity elements.

[0095] In the case of a large commercial center, if the initial impact coefficient of an initial abnormal node on a directly associated entity is 1.6, and the weighted result is 1.5 times the initial impact coefficient (1.6 * 1.5 = 2.4), then the final impact coefficient is 2.4. This final impact coefficient can be used to determine whether the directly associated entity will be affected by the initial abnormal node and become abnormal.

[0096] Step S136: Mark the directly associated entity unit whose state influence coefficient exceeds the preset influence threshold as a secondary abnormal node, and mark the association relationship between the initial abnormal node and the secondary abnormal node as a potential conduction link.

[0097] After calculating the final state impact coefficient of the initial abnormal node on each directly associated entity, a preset impact threshold is set. Directly associated entities whose state impact coefficients exceed the preset impact threshold are marked as secondary abnormal nodes, indicating that these nodes may become abnormal due to the abnormality of the initial abnormal node. Furthermore, the associations between the initial abnormal node and the secondary abnormal nodes are marked as potential transmission links, which may be the paths for the abnormality to propagate within the pipeline network.

[0098] In the underground cable network of a large commercial center, the preset impact threshold can be set based on actual conditions. For example, the preset impact threshold can be set to 2. If the final state impact coefficient of a directly associated entity element is 2.4, exceeding the preset impact threshold, the element is marked as a secondary abnormal node. At the same time, the association between the initial abnormal node and the secondary abnormal node, such as the electrical conduction association or mechanical connection association, is marked as a potential conduction link.

[0099] Step S137: using the secondary abnormal node as a new initial node, repeating the steps of determining directly associated entity units, calculating state influence coefficients, and marking secondary abnormal nodes until no new secondary abnormal nodes are generated.

[0100] Abnormal conditions can continue to propagate within the network. A secondary abnormal node can affect other directly connected nodes, leading to the generation of new secondary abnormal nodes. Therefore, the steps of determining directly connected entity elements, calculating state influence coefficients, and marking secondary abnormal nodes need to be repeated, starting with the secondary abnormal node as the new initial node, until no new secondary abnormal nodes are generated.

[0101] In the case of a large commercial center, once a secondary abnormal node is marked, it can be used as a new initial node to re-determine its directly associated physical units. Then, the state influence coefficient of this new initial node on each directly associated physical unit is calculated according to the previous steps. Any directly associated physical units whose state influence coefficient exceeds the preset influence threshold are marked as new secondary abnormal nodes. This process is repeated until no new secondary abnormal nodes are generated.

[0102] Step S138: All initial abnormal nodes and secondary abnormal nodes are integrated into a set of key pipe network nodes with abnormal status, and all potential transmission links are integrated into a set of abnormal transmission links between nodes.

[0103] After the previous steps, all initial and secondary abnormal nodes, as well as the potential transmission links between them, have been identified. These initial and secondary abnormal nodes are integrated into a set of key pipeline network nodes with abnormal status. All potential transmission links are integrated into a set of abnormal transmission links between nodes.

[0104] In an underground cable network in a large commercial center, all identified initial and secondary abnormal nodes are added to a list to form a set of key network nodes with abnormal status. All marked potential transmission links are added to another list to form a set of abnormal transmission links between nodes.

[0105] Step S140: Utilize the pipeline network digital twin mapping model to simulate the state diffusion process of abnormal conduction links under different environmental conditions to generate risk evolution simulation results.

[0106] After identifying key pipeline nodes with abnormal conditions and the abnormal transmission links between them, it is necessary to further understand the spread and impact of these abnormalities under different environmental conditions. Using the pipeline network digital twin mapping model, we can simulate the spread of abnormal transmission links under different environmental conditions, thereby generating risk evolution simulation results. These results can help managers predict the development trend of abnormal conditions and formulate appropriate response measures.

[0107] In underground cable networks in large commercial centers, different environmental conditions can have varying impacts on the spread of abnormalities. For example, high temperatures can accelerate cable aging and fault propagation, while high humidity can degrade cable insulation and increase the probability of failure. By simulating the state diffusion process under different environmental conditions, we can more comprehensively assess the risk of abnormalities.

[0108] Step S141: Acquire a plurality of set environmental condition combinations, which include different soil environmental conditions, groundwater activity conditions, external load conditions, and temperature change conditions.

[0109] To simulate the state diffusion of abnormal conduction links, you first need to obtain a combination of multiple environmental conditions. Soil environmental conditions include parameters such as soil moisture and pH, which affect the insulation performance and heat dissipation of the cable. Groundwater activity conditions include parameters such as the height of the groundwater level and the water flow rate. Excessively high groundwater levels may cause immersion damage to cables. External load conditions include the pressure exerted on underground cable networks by buildings and vehicles on the ground. Excessive external loads may cause cable deformation or damage. Temperature change conditions include the amplitude and rate of change of the ambient temperature. Sharp temperature changes may cause cable materials to expand and contract, thereby affecting cable performance.

[0110] In the case of a large commercial center, various environmental condition combinations can be set based on actual conditions. For example, soil moisture can be set to low, medium, and high, and pH can be set to acidic, neutral, and alkaline. The groundwater level can be set to normal, high, and super-high, and the water flow rate can be set to slow, medium, and fast. External load conditions can be categorized based on the type of buildings on the ground and vehicle traffic. Temperature change conditions can be set to various conditions, such as slow temperature rise, rapid temperature rise, slow temperature drop, and rapid temperature drop. By combining these different parameters, a variety of environmental condition combinations can be obtained.

[0111] Step S142: extracting the topological structure information of the abnormal conduction link from the pipeline network digital twin mapping model. The topological structure information includes the number of key nodes in the abnormal conduction link, the connection mode between the nodes, and the link length.

[0112] The topological structure of abnormal transmission links is crucial for simulating the state diffusion process. The number of key nodes determines the scope of the abnormality's potential impact, the connection method between nodes determines the propagation path of the abnormality, and the link length affects the propagation speed of the abnormality.

[0113] In the underground cable network of a large commercial center, when extracting the topological structure information of abnormal transmission links from the digital twin mapping model of the network, the relevant information can be queried through the database interface of the model. For example, the number of key nodes included in the abnormal transmission link can be queried to understand how many nodes may be affected by the abnormal situation. Check the connection method between nodes, whether it is a series connection, parallel connection, or a mixed connection. Different connection methods will cause the abnormal situation to propagate in different ways. Calculate the link length. The link length can be determined based on the actual laying length of the cable. The longer the link, the longer it may take for the abnormal situation to propagate to the end.

[0114] Step S143: Input each combination of environmental conditions into the pipeline network digital twin mapping model in sequence, set the simulation time step, and start the state diffusion simulation module of the pipeline network digital twin mapping model to simulate the state diffusion process of the abnormal conduction link under different environmental conditions.

[0115] After obtaining information about various environmental condition combinations and the topological structure of abnormal transmission links, each combination is sequentially input into the pipeline network digital twin mapping model. Simultaneously, a simulation time step is required, which determines the time interval during the simulation. The state diffusion simulation module of the pipeline network digital twin mapping model is then activated. This module simulates the state diffusion process of abnormal transmission links under different environmental conditions based on the input environmental condition combinations and topological structure information.

[0116] In the case of a large commercial center, each combination of environmental conditions can be input into the pipe network digital twin mapping model one by one. For example, the environmental conditions of low soil moisture, normal groundwater level, low external load, and slowly rising temperature variation can be input first. The simulation time step is set to 1 hour, simulating the state diffusion every hour. After starting the state diffusion simulation module, the model calculates the state change of each key node at each time step based on this combination of environmental conditions and the topological structure of the abnormal transmission link, simulating the diffusion of the abnormal condition in the pipe network.

[0117] Step S144: During the simulation process, the change curve of the state parameters of each key node in the abnormal conduction link over time is recorded in real time, and the transmission time interval of the state parameters between adjacent nodes is calculated as the diffusion speed.

[0118] Step S1441: Before the simulation starts, a state parameter monitoring point is set for each key node in the abnormal conduction link, and the initial state parameter value of each monitoring point is recorded.

[0119] Before simulating the state diffusion of an abnormal transmission link, it's necessary to set up state parameter monitoring points for each key node. These monitoring points can record the state parameter values ​​of key nodes in real time. The initial state parameter values ​​for each monitoring point are recorded. These initial values ​​serve as the starting point for the simulation, and subsequent state changes are calculated based on these initial values.

[0120] In the underground cable network of a large commercial center, for each key node in an abnormal transmission link, a location can be designated in the model as a state parameter monitoring point. For example, for a cable node, a monitoring point can be set in the middle of the cable. Before the simulation begins, the initial state parameter values ​​of each monitoring point, such as current, voltage, and temperature, are recorded. These initial values ​​can be obtained from the latest state parameters of the pipeline network digital twin mapping model.

[0121] Step S1442: Drive the pipeline network digital twin mapping model to run according to the set simulation time step, and collect the current state parameter value of each monitoring point at the end of each time step.

[0122] The pipeline network digital twin model runs according to the set simulation time step. At the end of each time step, the current state parameter values ​​of each monitoring point are collected. These values ​​reflect the status of key nodes at that point in time.

[0123] In the case of a large commercial center, the simulation time step was set to one hour. Every hour, the model calculated the state changes of each key node based on the current environmental conditions and the topological structure of the abnormal transmission link. At the end of each time step, the current state parameters such as current, voltage, and temperature at each monitoring point were collected. The collected values ​​were saved in the model database for subsequent analysis.

[0124] Step S1443: Arrange the state parameter values ​​of the same monitoring point at different time steps in chronological order to form a curve of the change of the state parameter of the key node over time.

[0125] Arranging the state parameter values ​​of the same monitoring point at different time steps in chronological order can produce a time-varying curve of the state parameter of the key node. This curve can intuitively show the changing trend of the key node's state during the simulation process.

[0126] In an underground cable network in a large commercial center, the state parameter values ​​at each key node monitoring point at different time steps can be organized in chronological order. For example, the current values ​​at a cable node monitoring point in the first hour, the second hour, the third hour, and so on, can be arranged in sequence to form a curve showing the node's current change over time. By analyzing this curve, we can understand how the node's current changes during the simulation and whether any abnormal fluctuations occur.

[0127] Step S1444: Comparing and analyzing the state parameter change curves of two adjacent key nodes, identifying the starting time point when the state parameter of the preceding node exceeds the normal threshold and the starting time point when the state parameter of the succeeding node exceeds the normal threshold.

[0128] By comparing and analyzing the state parameter change curves of two adjacent key nodes, we can determine the propagation time of the abnormality between adjacent nodes. We identify the starting time when the state parameter of the preceding node exceeds the normal threshold and the starting time when the state parameter of the subsequent node exceeds the normal threshold. The difference between these two time points is the propagation time of the abnormality between adjacent nodes.

[0129] In the case of a large commercial center, compare the current curves of two adjacent cable nodes. Assume that the current at the preceding node exceeds the normal threshold in the third hour, and the current at the succeeding node exceeds the normal threshold in the fifth hour. The difference of two hours between these two time points is the propagation time of the abnormality between the two adjacent nodes.

[0130] Step S1445: Calculate the difference between the two starting time points as the time interval for transmitting the state parameters between adjacent nodes.

[0131] The time interval between the state parameters of adjacent nodes is obtained by subtracting the time when the state parameter of the preceding node exceeds the normal threshold from the time when the state parameter of the succeeding node exceeds the normal threshold. This time interval reflects the speed at which the abnormality propagates between adjacent nodes.

[0132] In an underground cable network in a large commercial center, if the starting time point when the status parameter of the preceding node exceeds the normal threshold is the third hour, and the starting time point when the status parameter of the subsequent node exceeds the normal threshold is the fifth hour, then the time interval for transmitting the status parameters between adjacent nodes is 5-3=2 hours.

[0133] Step S1446: Divide the total length of the abnormal transmission link by the transmission time interval to obtain the diffusion speed of the state parameter between adjacent nodes.

[0134] Dividing the total length of the abnormal transmission link by the time interval between the transmission of the state parameters between adjacent nodes gives the diffusion speed of the state parameters between adjacent nodes. This diffusion speed can be used to assess how quickly the abnormality spreads between adjacent nodes.

[0135] In the case of a large commercial center, if the total length of the abnormal transmission link is 100 meters and the time interval for transmitting the status parameters between adjacent nodes is 2 hours, then the diffusion speed of the status parameters between adjacent nodes is 100 / 2=50 meters / hour.

[0136] Step S1447: Perform statistical analysis on the diffusion speeds of all adjacent node pairs in the abnormal conduction link, and calculate the average diffusion speed and maximum diffusion speed of the link as the diffusion speed characteristic values ​​under the environmental condition combination.

[0137] By statistically analyzing the diffusion velocity of all pairs of adjacent nodes in an abnormally conductive link, we can obtain the characteristic value of the diffusion velocity under this combination of environmental conditions. The average and maximum diffusion velocities of the link are calculated. The average diffusion velocity reflects the average propagation speed of the anomaly throughout the entire link, while the maximum diffusion velocity reflects the fastest propagation of the anomaly within the link.

[0138] In an underground cable network in a large commercial center, the diffusion velocity is calculated for each pair of adjacent nodes in an abnormally conductive link. Then, the diffusion velocities of all pairs of adjacent nodes are statistically analyzed. The average of these diffusion velocities is calculated to obtain the average diffusion velocity of the link. The maximum value is found to obtain the maximum diffusion velocity of the link. These two values ​​can be used as the characteristic diffusion velocity values ​​for this combination of environmental conditions.

[0139] Step S145: Determine the time point when each key node reaches the abnormal threshold value based on the state parameter change curve, and draw a dynamic map of the diffusion range of the abnormal state in space in combination with the spatial coordinate information of the node.

[0140] In addition to recording the change curve of the state parameter over time and calculating the diffusion speed, it is also necessary to understand the diffusion range of the abnormal state in space. According to the change curve of the state parameter of each key node to determine the time point when they reach the abnormal threshold, combined with the spatial coordinate information of the node, the diffusion range dynamic atlas of the abnormal state in space can be drawn. The diffusion range dynamic atlas can intuitively show the diffusion process of the abnormal situation in space over time.

[0141] In the underground cable pipe network of a large commercial center, the continuous change sequence of the state parameter over time is extracted from the change curve of the state parameter of each key node. The abnormal threshold of each key node is compared with the corresponding state parameter change sequence step by step, the time step when the first state parameter value in the state parameter change sequence exceeds the abnormal threshold is located, and the simulation time value corresponding to the time step is recorded as the time point when the key node reaches the abnormal threshold. The spatial coordinate information of each key node in the pipe network digital twin mapping model is obtained, and a correspondence table of key nodes and time points reaching the abnormal threshold is established. The time points reaching the abnormal threshold in the correspondence table are sorted in ascending order of simulation time value to obtain a time sorting result. For each time point in the time sorting result, all key nodes corresponding to the time point are determined according to the correspondence table, and the spatial coordinate information of these key nodes is marked in a preset three-dimensional coordinate system to form an abnormal node spatial distribution graph of the time point. The abnormal node spatial distribution graphs of all time points are arranged in sequence according to the time sorting result to form a continuous visual sequence of the diffusion of the abnormal state in space over time, which is the diffusion range dynamic atlas of the abnormal state in space.

[0142] Step S1451: Extract the continuous change sequence of the state parameter over time from the change curve of the state parameter of each key node in the abnormal conduction link, which is the curve of the state parameter value of the key node changing with the simulation time step.

[0143] To determine the time point when each key node reaches the abnormal threshold, it is necessary to first extract the continuous change sequence of the state parameter over time from the state parameter change curve. The state parameter change curve records the change of the state parameter value of the key node with the time step in the simulation process.

[0144] In the underground cable pipe network of a large commercial center, for each key node in the abnormal conduction link, the continuous change sequence of the state parameter over time is extracted from its state parameter change curve. For example, for the current change curve of a cable node, the current values of the node at 1 hour, 2 hours, 3 hours, … are extracted to form a continuous change sequence of the current over time.

[0145] Step S1452: Compare the abnormal threshold of each key node with the corresponding state parameter change sequence step by step, locate the first time step in the state parameter change sequence where the state parameter value exceeds the abnormal threshold, and record the simulation time value corresponding to the time step as the time point when the key node reaches the abnormal threshold.

[0146] Compare the abnormal threshold of each key node with the corresponding state parameter change sequence step by step, find the time step where the state parameter value first exceeds the abnormal threshold. Record the simulation time value corresponding to the time step, which is the time point when the key node reaches the abnormal threshold.

[0147] In the case of a large commercial center, for a certain cable node, the abnormal threshold is 50A. Compare the continuous change sequence of the current of the node with 50A step by step. If the current value of the node first exceeds 50A at 4 hours, record 4 hours as the time point when the node reaches the abnormal threshold.

[0148] Step S1453: Obtain the spatial coordinate information of each key node in the digital twin mapping model of the pipe network, and establish a correspondence table between the key nodes and the time points when the abnormal threshold is reached. The spatial coordinate information is the coordinate value of the key node in the preset three-dimensional coordinate system.

[0149] In order to draw the dynamic atlas of the diffusion range of abnormal state in space, we need to obtain the spatial coordinate information of each key node in the digital twin mapping model of the pipe network. These coordinate information can represent the position of the key node in the preset three-dimensional coordinate system. Establish a correspondence table between the key nodes and the time points when the abnormal threshold is reached, which is convenient for subsequent analysis and drawing.

[0150] In the underground cable pipe network of a large commercial center, obtain the spatial coordinate information of each key node from the database of the digital twin mapping model of the pipe network. For example, the spatial coordinate information of a certain cable node is (x1, y1, z1). Associate the spatial coordinate information of the node with the time point when the abnormal threshold is reached, and establish a correspondence table. The table records the name, spatial coordinate information and time point when the abnormal threshold is reached of each key node.

[0151] Step S1454: Sort the time points when the abnormal threshold is reached in the correspondence table in ascending order of simulation time value to obtain a time sorting result, and each time point in the time sorting result corresponds to at least one key node.

[0152] Sort the time points when the abnormal threshold is reached in the correspondence table in ascending order of simulation time value. The sorted result can clearly show the development order of abnormal conditions in time.

[0153] In the case of a large commercial center, sort the time points in the corresponding relationship table that reach the anomaly threshold. For example, there are three key nodes, A, B, and C, whose anomaly thresholds are reached at the third hour, the fifth hour, and the second hour, respectively. After sorting, the resulting time ranking is the second hour (corresponding to node C), the third hour (corresponding to node A), and the fifth hour (corresponding to node B).

[0154] Step S1455: For each time point in the time sorting result, all key nodes corresponding to the time point are determined according to the correspondence table, and the spatial coordinate information of these key nodes is marked in a preset three-dimensional coordinate system to form a spatial distribution graph of the abnormal nodes at the time point.

[0155] For each time point in the time-sorted results, all key nodes corresponding to that time point are found using the correspondence table. The spatial coordinates of these key nodes are marked in a pre-set three-dimensional coordinate system to form a spatial distribution graph of abnormal nodes at that time point. This abnormal node spatial distribution graph can intuitively show which key nodes experienced abnormalities at that time point.

[0156] In the underground cable network of a large commercial center, for the second hour in the time-sorted results, the corresponding key node is determined to be C according to the correspondence table. The spatial coordinate information (x1, y1, z1) of node C is marked in a preset three-dimensional coordinate system to form a spatial distribution graph of abnormal nodes in the second hour. For the third hour, the corresponding key node is A. The spatial coordinate information (x2, y2, z2) of node A is marked in a preset three-dimensional coordinate system to form a spatial distribution graph of abnormal nodes in the third hour.

[0157] Step S1456: Arrange the abnormal node spatial distribution graphs at all time points in sequence according to the time sorting results to form a continuous visual sequence of the abnormal state spreading in space over time. This continuous visual sequence is a dynamic map of the diffusion range of the abnormal state in space.

[0158] The spatial distribution graphs of abnormal nodes at all time points are arranged in chronological order to form a continuous visual sequence. This sequence can intuitively demonstrate the diffusion of abnormal states in space over time, i.e., a dynamic map of the diffusion range of abnormal states in space.

[0159] In the case of a large commercial center, the spatial distribution graphs of abnormal nodes at the second, third, fifth, and so on hours are arranged in chronological order. By observing this continuous visual sequence, we can clearly see how the abnormal state starts from one node and gradually spreads to other nodes, as well as the spatial distribution range of the abnormal state at different time points.

[0160] Step S146: Analyze the dynamic maps of diffusion speed and diffusion range under different environmental condition combinations to determine the critical environmental parameter values ​​for the diffusion of abnormal conditions under each environmental condition.

[0161] For example, step S1461: associate the diffusion rate data with the various environmental parameters in the corresponding environmental condition combination to establish a corresponding relationship between the diffusion rate and the environmental parameters, which include soil moisture parameters and soil pH parameters in soil environmental conditions, water level parameters and water flow velocity parameters in groundwater activity conditions, load size parameters and load action frequency parameters in external load conditions, and temperature change amplitude parameters and temperature change rate parameters in temperature change conditions.

[0162] To analyze the impact of different environmental conditions on the diffusion rate of abnormal conditions, it is necessary to correlate the diffusion rate data with the various environmental parameters in the corresponding environmental condition combinations. Establishing a corresponding relationship between diffusion rate and environmental parameters can help identify the inherent connection between environmental parameters and diffusion rate.

[0163] In the underground cable network of a large commercial center, corresponding diffusion velocity data is recorded for each combination of environmental conditions. This diffusion velocity data is correlated with environmental parameters such as soil moisture, soil pH, water level, water flow velocity, load magnitude, load frequency, temperature variation, and temperature change rate. For example, the diffusion velocity under a specific combination of environmental conditions is recorded in correspondence with the soil moisture, groundwater level, and temperature variation within that combination. By analyzing large amounts of data, a corresponding relationship table or function model between diffusion velocity and environmental parameters can be established.

[0164] Step S1462: Based on the correspondence between the diffusion speed and the environmental parameter, identifying the environmental parameter interval where the diffusion speed changes, wherein the diffusion speed change means that the difference in diffusion speeds corresponding to adjacent environmental parameter values ​​exceeds a preset speed difference standard.

[0165] After establishing the corresponding relationship between diffusion rate and environmental parameters, it is necessary to identify the environmental parameter range where the diffusion rate changes. A change in diffusion rate means that the change in environmental parameters has a significant impact on the diffusion rate of the abnormal state.

[0166] In the case of a large commercial center, a speed difference standard is preset. For example, when the diffusion speed difference corresponding to adjacent environmental parameter values ​​exceeds 10%, it is considered that the diffusion speed has changed. Based on the correspondence between the diffusion speed and the environmental parameters, the environmental parameters are analyzed one by one. For example, for the soil moisture parameter, when the soil moisture increases from low to medium, the diffusion speed increases from 50 m / h to 60 m / h, and the speed difference is (60-50) / 50=20%, which exceeds the preset speed difference standard. Then it can be determined that the interval from low to medium soil moisture is the environmental parameter interval where the diffusion speed changes.

[0167] Step S1463: extracting spatial boundary features of abnormal state diffusion under different environmental condition combinations from the diffusion range dynamic map, the spatial boundary features including the maximum horizontal span, maximum vertical depth and spatial distribution density of boundary nodes of the diffusion area.

[0168] In addition to analyzing the diffusion speed, it is also necessary to extract the spatial boundary features of the abnormal state diffusion from the diffusion range dynamic map. These features can reflect the diffusion range and distribution of the abnormal state in space.

[0169] In the underground cable network of a large commercial center, extracting the spatial boundary characteristics of the spread of abnormal conditions under different environmental conditions from the diffusion range dynamic map allows for detailed analysis of multiple aspects of the diffusion area. The maximum lateral span of the diffusion area is determined by determining the locations of the leftmost and rightmost nodes affected by the abnormal condition in the plan view of the diffusion range dynamic map. The lateral distance between these two nodes is then calculated as the maximum lateral span. This process requires precise identification of the spatial coordinates of the nodes and takes into account the potential tortuosity and irregularities in the layout of the underground cable network.

[0170] For the maximum vertical depth, we can focus on the vertical extent of the abnormal condition. By examining the vertical projection of the dynamic diffusion range map, we can find the vertical distance between the deepest node of the abnormal condition and the ground or a reference plane, thereby determining the maximum vertical depth. This requires an understanding of the different hierarchical structures of the underground cable network and how to accurately measure vertical distances.

[0171] The spatial distribution density of boundary nodes is also an important spatial boundary feature. To calculate this density, we can first determine the set of boundary nodes in the diffusion area. This can be achieved by analyzing the nodes on the edge of abnormal conditions in the dynamic map of the diffusion range. Then, we calculate the number of these boundary nodes within a certain spatial range and compare it with the size of the spatial range. For example, we can select a specific area or volume, count the number of boundary nodes within it, and then divide the number of nodes by the area or volume of the area to obtain the spatial distribution density of the boundary nodes.

[0172] Step S1464: Match the spatial boundary features with the environmental parameters in the corresponding environmental condition combination to determine the environmental parameter values ​​where the spatial boundary features have changed.

[0173] After determining the spatial boundary characteristics of the abnormal state diffusion under different environmental condition combinations, it is necessary to match these characteristics with the environmental parameters in the corresponding environmental condition combinations. By comparing the spatial boundary characteristics under different environmental conditions, the environmental parameter values ​​that cause these characteristics to change can be found.

[0174] In the case of a large commercial center, each combination of environmental conditions has a corresponding dynamic map of the diffusion range and spatial boundary characteristics. These spatial boundary characteristics are mapped one-to-one with environmental parameters such as soil moisture, soil pH, water level, water velocity, load magnitude, load frequency, temperature variation, and temperature change rate. For example, if the maximum lateral spread of an abnormal state under a certain set of environmental conditions is found to have increased significantly, the environmental parameters in that combination are examined and compared with the values ​​of the same parameters under other combinations to identify the environmental parameters that may have caused the change in the maximum lateral spread.

[0175] A boundary difference criterion is preset. When the difference in spatial boundary characteristics corresponding to adjacent environmental parameter values ​​exceeds this criterion, the environmental parameter value is considered to be the parameter value that causes the spatial boundary characteristics to change. For example, if the boundary difference criterion is set to a change rate of more than 15% in spatial boundary characteristics, and when the soil moisture changes from one value to another, the spatial distribution density of the boundary nodes where the abnormal state spreads reaches a change rate of 20%, then the change in soil moisture value is determined to be the environmental parameter value that causes the spatial boundary characteristics to change.

[0176] Step S1465: Integrate the environmental parameter intervals where the diffusion speed changes and the environmental parameter values ​​where the spatial boundary characteristics change, to obtain an environmental parameter change association set.

[0177] By integrating the environmental parameter intervals where the diffusion rate changes and the environmental parameter values ​​where the spatial boundary features change, a more comprehensive set of environmental parameter change associations can be obtained. This set contains all environmental parameter information that may affect the spread of abnormal states.

[0178] In the management of underground cable networks in large commercial centers, previously identified environmental parameter intervals for diffusion rate changes, such as soil moisture ranging from a certain value to another or temperature fluctuations within a certain range, are aggregated with environmental parameter values ​​for changes in spatial boundary characteristics, such as specific water level parameters and load frequency parameters. During the integration process, these parameters can be checked for duplication or conflict. If so, further analysis and processing can be performed to ensure the accuracy and completeness of the associated set of environmental parameter changes.

[0179] Step S1466: Filter out environmental parameter values ​​corresponding to both the diffusion velocity change and the spatial boundary feature change from the environmental parameter change association set as critical environmental parameter values ​​for the diffusion of the abnormal state.

[0180] From the set of environmental parameter changes, we select the environmental parameter values ​​that correspond to both changes in diffusion rate and changes in spatial boundary characteristics. These values ​​are the critical environmental parameter values ​​for the spread of abnormal conditions. These parameter values ​​are important for predicting and controlling the spread of abnormal conditions.

[0181] In the case of a large commercial center, each environmental parameter in the set of environmental parameter changes is examined one by one. Each parameter is checked to see if it falls within the range of environmental parameters where the diffusion rate changes and also corresponds to a change in spatial boundary characteristics. For example, if a specific soil moisture value is found to cause a significant change in the diffusion rate of the abnormal state, as well as a significant change in the maximum horizontal span of the diffusion area and the spatial distribution density of boundary nodes, then that soil moisture value is selected as the critical environmental parameter value for the diffusion of the abnormal state.

[0182] Step S147: Integrate the diffusion rate data, diffusion range dynamic map and critical environmental parameter values ​​under different environmental condition combinations to generate risk evolution simulation results.

[0183] By integrating the diffusion rate data, diffusion range dynamic maps and critical environmental parameter values ​​under different environmental condition combinations, comprehensive risk evolution simulation results can be generated.

[0184] In the underground cable network of a large commercial center, for each combination of environmental conditions, corresponding diffusion velocity data, a dynamic diffusion range map, and critical environmental parameter values ​​are available. These data and maps are organized and summarized to form a unified risk evolution simulation result. Diffusion velocity data can be presented in tables or charts to intuitively illustrate the changes in the diffusion velocity of abnormal conditions under different environmental conditions. The dynamic diffusion range map is arranged in chronological order to form a continuous video or animation, clearly demonstrating the spatial diffusion process of the abnormal condition. Critical environmental parameter values ​​are also listed separately, noting the corresponding environmental conditions and their impact on the spread of the abnormal condition.

[0185] Step S150: Generate a pipeline maintenance instruction including priority sorting and resource allocation plan based on the risk evolution simulation results and the location information of key nodes in the pipeline network, and send the pipeline maintenance instruction to the operation and maintenance execution system of the physical pipeline network to trigger a maintenance response.

[0186] After obtaining the risk evolution simulation results, combined with the location information of key nodes in the pipeline network, reasonable pipeline network maintenance instructions can be formulated. These pipeline network maintenance instructions include priority sorting and resource allocation plans, which can effectively guide the physical pipeline network operation and maintenance execution system to carry out maintenance work.

[0187] In the underground cable network of a large commercial center, risk evolution simulation results provide information on the spread of abnormal conditions and critical environmental parameter values ​​under different environmental conditions. The location of key nodes in the network clearly identifies those requiring attention. By comprehensively analyzing this information, scientific maintenance instructions can be generated.

[0188] Step S151: extracting diffusion-related information, impact range-related information, and critical condition-related information of each key node in the pipeline network from the risk evolution simulation results, and extracting spatial correlation information of each key node and surrounding facility correlation information from the location information of the key node in the pipeline network.

[0189] When extracting diffusion-related information for key nodes in the pipeline network from the risk evolution simulation results, we can focus on information such as the diffusion speed and direction of abnormal conditions at each key node under different environmental conditions. Information related to the impact range includes the size of the area that the abnormal condition at each key node may affect and the number of other nodes involved. Information related to critical conditions records the environmental parameter values ​​that cause significant changes in the diffusion of abnormal conditions at that key node.

[0190] The system also extracts spatial correlation information for each key node from its location information, including its distance to adjacent nodes and its connection method. Surrounding facility correlation information considers the location and importance of nearby shops, office buildings, and public facilities. For example, if a key node is located near a large shopping mall, an abnormal condition at that node could significantly impact its normal operations.

[0191] Step S152: Correlate the diffusion-related information, the impact range-related information, the critical condition-related information with the spatial correlation information and the surrounding facility correlation information to form correlation analysis results for each key node of the pipe network.

[0192] By correlating various types of information extracted from the risk evolution simulation results with the location information of key nodes in the pipeline network, we can generate correlation analysis results for each key node in the pipeline network. This result can comprehensively reflect the importance and potential impact of each key node.

[0193] In the case of a large commercial center, for each key node in the pipeline network, information related to its diffusion, impact range, and critical conditions is matched one by one with spatial association information and surrounding facility association information. For example, if a key node has a fast diffusion rate, a large impact range, and critical conditions that are easily reached in the current environment, and is also surrounded by important commercial facilities, then this node will be considered highly important in the association analysis results.

[0194] Step S153: Arrange all key nodes of the pipeline network in sequence according to the association analysis result to obtain a priority ranking result of the pipeline network maintenance.

[0195] By arranging all key nodes in the pipeline network according to the results of the correlation analysis, the priority of pipeline network maintenance can be determined. The principle of sorting is to give priority to those nodes that may have a greater impact on the operation of the pipeline network and surrounding facilities.

[0196] In the underground cable network of a large commercial center, multiple factors can be comprehensively considered in the correlation analysis results. For example, key nodes with rapid abnormality spread, large impact range, nearby important facilities, and easily accessible critical conditions can be prioritized. Nodes with slower spread, smaller impact range, and less important surrounding facilities will be prioritized. By performing this ranking on all key nodes, a prioritized pipeline network maintenance result can be obtained.

[0197] Step S154: Collect the current resource status information of the physical pipe network operation and maintenance execution system, which includes the availability of maintenance personnel, the availability of maintenance equipment, and the maintenance material reserves.

[0198] Collecting information about the current resource status of the physical pipeline network operation and maintenance execution system is the foundation for developing resource allocation plans. Understanding available maintenance personnel reveals the number of professionals available for pipeline network maintenance, as well as their skill levels and experience. Maintenance equipment availability determines whether sufficient tools and instruments are available for troubleshooting and repairs. Maintenance material reserves include sufficient cables, connectors, accessories, and other materials to replace damaged components.

[0199] In the case of a large commercial center, resource status information can be collected through the management platform of the operation and maintenance execution system. For maintenance personnel availability, information such as shift schedules, skill certificates, and work records can be viewed. For maintenance equipment availability, inventory lists, usage status, and maintenance records can be checked. For maintenance material reserves, the quantity and quality of materials in the warehouse can be counted.

[0200] Step S155: According to the priority sorting results and resource status information, the corresponding maintenance personnel, maintenance equipment and maintenance materials are matched to each key node of the pipeline network to form a resource allocation plan.

[0201] According to the priority ranking results of pipeline network maintenance and the resource status information of the physical pipeline network operation and maintenance execution system, corresponding maintenance personnel, maintenance equipment and maintenance materials are matched for each key node of the pipeline network to form a resource allocation plan.

[0202] In the underground cable network of a large commercial center, priority nodes can be prioritized for experienced maintenance personnel, high-performance maintenance equipment, and sufficient maintenance materials. For example, if an abnormal condition at a critical node could affect the normal operation of the mall, multiple skilled maintenance personnel will be dispatched with advanced testing equipment and sufficient cables, connectors, and other materials to perform maintenance at that node. For lower-priority nodes, resources will be allocated appropriately based on available resources.

[0203] Step S156: Integrate the priority sorting result with the resource allocation plan to generate a pipeline network maintenance instruction including the maintenance sequence of key nodes of the pipeline network, corresponding maintenance personnel information, required maintenance equipment information and maintenance material information.

[0204] The pipeline network maintenance priority ranking results and resource allocation plan are integrated to generate a complete pipeline network maintenance order. The pipeline network maintenance order clearly defines the maintenance order of each key node in the pipeline network, as well as the maintenance personnel, maintenance equipment and maintenance materials assigned to each node.

[0205] In the case of a large commercial center, pipeline maintenance instructions are presented in a detailed document. This document lists the name and location of each key pipeline node, sorted by priority. For each node, the corresponding maintenance personnel's name, skills, and contact information are listed, along with a list of required maintenance equipment and models, and the type and quantity of required maintenance materials. This allows personnel in the operations and maintenance execution system to quickly and accurately carry out maintenance work based on these instructions.

[0206] Step S157: Send the pipe network maintenance instruction to the operation and maintenance execution system of the physical pipe network to trigger a maintenance response.

[0207] In the underground cable network of a large commercial center, maintenance instructions can be sent via network communication to the management platform of the operation and maintenance execution system. Upon receiving the instructions, the operation and maintenance execution system staff can immediately organize the appropriate maintenance personnel, prepare maintenance equipment and materials, and dispatch them to designated key nodes in the network for maintenance. During the maintenance process, staff will provide real-time feedback on maintenance progress and encountered problems, allowing for timely adjustments to the maintenance plan. This approach effectively ensures the normal operation of the underground cable network and reduces the impact of abnormal conditions on the commercial center.

[0208] Figure 2 A schematic diagram illustrating exemplary hardware and software components of a digital twin-based urban underground cable pipe network management system 100 that can implement the concepts of the present application, as provided in some embodiments of the present application. For example, the processor 120 can be used in the digital twin-based urban underground cable pipe network management system 100 to perform the functions of the present application.

[0209] For example, the urban underground cable pipe network management system 100 based on digital twins may include a network port 110 connected to the network, one or more processors 120 for executing program instructions, a communication bus 130, and different forms of storage media 140, such as a disk, ROM, or RAM, or any combination thereof. Exemplarily, the urban underground cable pipe network management system 100 based on digital twins may also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The method of the present application can be implemented according to these program instructions. The urban underground cable pipe network management system 100 based on digital twins also includes an I / O interface 150 between the computer and other input and output devices.

[0210] In addition, an embodiment of the present invention also provides a readable storage medium, in which computer-executable instructions are preset. When the processor executes the computer-executable instructions, the above-mentioned urban underground cable pipeline management method based on digital twins is implemented.

[0211] It should be noted that in order to simplify the description of the present invention and thus help understand one or more embodiments of the invention, in the foregoing description of the embodiments of the present invention, multiple features are sometimes combined into one embodiment, figure or description thereof.

Claims

1. A method for managing urban underground cable networks based on digital twins, characterized in that: The method comprises: Integrate basic attribute data and real-time perception data of urban underground cable pipeline networks to build a digital twin mapping model of the pipeline network. The digital twin mapping model includes the digital reproduction of the physical entities of the pipeline network and the dynamic association relationships between entities; The real-time operating status data of the physical pipe network is continuously input into the pipe network digital twin mapping model through a preset data interaction protocol, driving the pipe network digital twin mapping model to update state parameters to maintain the correspondence between the pipe network digital twin mapping model and the physical pipe network state; Based on the updated digital twin mapping model of the pipe network, correlation and coupling analysis is performed on the operating status data of each component of the urban underground cable pipe network to identify key pipe network nodes with abnormal status and abnormal transmission links between nodes. Specifically, the analysis includes: extracting the current operating status parameters of each physical entity digital reproduction unit and the dynamic correlation relationship model between entities from the updated digital twin mapping model of the pipe network; Determine the normal threshold range of each operating status parameter based on the pipeline network design standards and historical normal operation data; Traverse all physical entity digital reproduction units, mark the physical entity digital reproduction units whose operating status parameters exceed the normal threshold range as initial abnormal nodes, and record their abnormal parameter types and deviation levels; Determine directly associated entity units of the initial abnormal node based on the dynamic association relationship model, wherein the directly associated entity units include entity units that have a connection relationship, a spatial position relationship, or a functional dependency relationship with the initial abnormal node; Extract the operating status parameters of directly associated entity units, and calculate the state influence coefficient of the initial abnormal node on each directly associated entity unit based on the type of association relationship and the deviation degree of the initial abnormal node; The directly associated entity units whose state influence coefficient exceeds the preset influence threshold are marked as secondary abnormal nodes, and the association relationship between the initial abnormal node and the secondary abnormal node is marked as a potential transmission link; Taking the secondary abnormal node as the new initial node, repeat the steps of determining the directly associated entity unit, calculating the state influence coefficient, and marking the secondary abnormal node until no new secondary abnormal node is generated; All initial abnormal nodes and secondary abnormal nodes are integrated into a set of key nodes in the pipeline network with abnormal status, and all potential transmission links are integrated into a set of abnormal transmission links between nodes; The pipeline network digital twin mapping model is used to simulate the state diffusion process of abnormal transmission links under different environmental conditions to generate risk evolution simulation results; Based on the risk evolution simulation results and the location information of key nodes in the pipeline network, a pipeline network maintenance instruction including priority sorting and resource allocation plan is generated, and the pipeline network maintenance instruction is sent to the operation and maintenance execution system of the physical pipeline network to trigger a maintenance response.

2. The urban underground cable network management method based on digital twin according to claim 1 is characterized in that: The integration of basic attribute data and real-time perception data of urban underground cable pipeline networks to build a pipeline network digital twin mapping model includes: Collect basic attribute data of urban underground cable pipeline networks, including pipeline network laying path data, cable specification data, connector configuration data, and ancillary facility distribution data; Collecting real-time perception data of the city's underground cable network, including operating status data and surrounding environment related data obtained by sensing devices deployed along the network; Performing structured processing on the basic attribute data, converting the laying path data into a spatial coordinate sequence, establishing a correspondence between the cable specification data and the connector configuration data, and forming a structured basic data set; Performing spatiotemporal alignment processing on the real-time perception data so that data collected by different perception devices at the same time are mapped to corresponding spatial coordinates in the structured basic data set to form a spatiotemporal aligned perception data set; The structured basic data set is associated and fused with the spatiotemporal alignment perception data set, and a digital reproduction model of the physical entities of the pipeline network is constructed based on the fused data. The digital reproduction model includes the geometric parameters, material property parameters, and initial state parameters of each entity. Analyze the connection relationship, spatial location relationship and functional dependency between physical entities in the pipeline network, and build a dynamic association relationship model between entities. The dynamic association relationship model is used to characterize the influence of the state change of one entity on other related entities. The digital reproduction model and the dynamic association relationship model are integrated to generate a pipeline network digital twin mapping model that includes the digital reproduction of the pipeline network's physical entities and the dynamic association relationships between entities.

3. The urban underground cable network management method based on digital twin according to claim 1 is characterized in that: The real-time operating status data of the physical pipe network is continuously input into the pipe network digital twin mapping model through a preset data interaction protocol, and the pipe network digital twin mapping model is driven to update the state parameters to maintain the correspondence between the pipe network digital twin mapping model and the physical pipe network state, including: Generate a preset data interaction protocol, wherein the data interaction protocol specifies the transmission format, data field definition, transmission frequency and verification rules of the real-time operation status data; Deploy status sensing devices at key monitoring points in the physical pipeline network to collect real-time operating status data at the corresponding locations. The real-time operating status data includes cable operating parameters and environmental impact parameters. Assign a unique device identifier to each state-sensing device, and establish a binding relationship between the device identifier and the corresponding physical entity digital reproduction unit in the pipeline network digital twin mapping model; The real-time operating status data collected by the state-sensing device is continuously uploaded to the data processing center through the data transmission network according to the preset data interaction protocol, so that the data processing center can perform format verification and outlier filtering on the uploaded data, and find the corresponding physical entity digital reproduction unit according to the device identifier, and update the filtered real-time operating status data to the state parameter set of the physical entity digital reproduction unit to achieve the preliminary update of the model state parameters; Compare the deviation between the updated model state parameters and the actual operating state of the physical pipeline network. When the deviation exceeds the preset allowable range, adjust the transmission frequency or verification rules in the data interaction protocol, and execute the data upload and parameter update steps again until the model state parameters correspond to the actual operating state of the physical pipeline network.

4. The urban underground cable network management method based on digital twin according to claim 1 is characterized in that: The step of extracting the operating status parameters of the directly associated entity units and calculating the status influence coefficient of the initial abnormal node on each directly associated entity unit according to the association relationship type and the deviation degree of the initial abnormal node includes: Obtaining the type of association relationship between the initial abnormal node and the directly associated entity unit from the dynamic association relationship model, wherein the association relationship type includes mechanical connection association, electrical conduction association, spatial proximity association, and functional coordination association; A basic impact weight is set for each type of association relationship, wherein the basic impact weights of mechanical connection association and electrical conduction association are higher than the basic impact weights of spatial proximity association and functional synergy association; Calculate the degree of deviation of the abnormal parameter of the initial abnormal node, where the degree of deviation of the abnormal parameter is the ratio of the absolute value of the difference between the abnormal parameter value and the median of the normal threshold range to the half-width of the normal threshold range; The basic influence weight and the deviation degree of abnormal parameters are integrated and calculated to obtain the preliminary influence coefficient of the initial abnormal node on the directly associated entity unit; Extract the current operating status parameters of the directly related entity units to determine whether they are close to the boundary of the normal threshold range. If they are close to the boundary, the preliminary influence coefficients are amplified and weighted to obtain a weighted processing result. The amplification weight ratio is related to the degree of proximity to the boundary. The initial impact coefficient and weighted processing results are combined to obtain the final state impact coefficient of the initial abnormal node on each directly associated entity unit.

5. The urban underground cable network management method based on digital twin according to claim 1 is characterized in that: The pipeline network digital twin mapping model is used to simulate the state diffusion process of abnormal transmission links under different environmental conditions to generate risk evolution simulation results, including: Acquire a plurality of preset environmental condition combinations, wherein the environmental condition combinations include different soil environmental conditions, groundwater activity conditions, external load conditions, and temperature change conditions; Extracting topological structure information of abnormal transmission links from the pipeline network digital twin mapping model, wherein the topological structure information includes the number of key nodes in the abnormal transmission links, the connection mode between nodes, and the link length; Each combination of environmental conditions is input into the pipeline network digital twin mapping model in turn, the simulation time step is set, and the state diffusion simulation module of the pipeline network digital twin mapping model is started to simulate the state diffusion process of the abnormal conduction link under different environmental conditions; During the simulation, the state parameter variation curves of each key node in the abnormal transmission link are recorded in real time, and the time interval for the transmission of state parameters between adjacent nodes is calculated as the diffusion speed. Determine the time point when each key node reaches the abnormal threshold based on the state parameter change curve, and draw a dynamic map of the spread range of the abnormal state in space by combining the spatial coordinate information of the node; Analyze the dynamic maps of diffusion speed and diffusion range under different environmental conditions, and determine the critical environmental parameter value for the diffusion of abnormal conditions under each environmental condition, wherein the critical environmental parameter value is the environmental parameter threshold that causes a change in diffusion speed or diffusion range; Integrate diffusion rate data, diffusion range dynamic maps and critical environmental parameter values ​​under different combinations of environmental conditions to generate risk evolution simulation results.

6. The urban underground cable network management method based on digital twin according to claim 5 is characterized in that: During the simulation, the state parameter change curve of each key node in the abnormal conduction link is recorded in real time over time, and the transmission time interval of the state parameters between adjacent nodes is calculated as the diffusion speed, including: Before the simulation begins, a state parameter monitoring point is set for each key node in the abnormal conduction link, and the initial state parameter value of each monitoring point is recorded; The pipeline network digital twin mapping model is driven to run according to the set simulation time step. At the end of each time step, the current state parameter value of each monitoring point is collected; Arrange the state parameter values ​​of the same monitoring point at different time steps in chronological order to form a curve of the state parameter change of the key node over time; Compare and analyze the state parameter change curves of two adjacent key nodes to identify the starting time point when the state parameter of the preceding node exceeds the normal threshold and the starting time point when the state parameter of the succeeding node exceeds the normal threshold; Calculate the difference between the two starting time points as the time interval for transmitting state parameters between adjacent nodes; Divide the total length of the abnormal transmission link by the transmission time interval to obtain the diffusion speed of the state parameters between adjacent nodes; The diffusion speed of all adjacent node pairs in the abnormal conduction link is statistically analyzed, and the average diffusion speed and maximum diffusion speed of the link are calculated as the diffusion speed characteristic values ​​under this combination of environmental conditions.

7. The urban underground cable network management method based on digital twin according to claim 5 is characterized in that: The method of determining the time point at which each key node reaches the abnormal threshold value based on the state parameter change curve and drawing a dynamic map of the diffusion range of the abnormal state in space in combination with the spatial coordinate information of the node includes: Extracting a continuous change sequence of state parameters over time from a state parameter change curve of each key node in the abnormal conduction link, wherein the state parameter change curve is a curve of the state parameter value of the key node changing with the simulation time step; Compare the abnormal threshold of each key node with the corresponding state parameter change sequence time step by time step, locate the time step in the state parameter change sequence where the first state parameter value exceeds the abnormal threshold, and record the simulation time value corresponding to the time step as the time point when the key node reaches the abnormal threshold; Obtain the spatial coordinate information of each key node in the digital twin mapping model of the pipeline network, and establish a correspondence table between the key nodes and the time points when the abnormality threshold is reached; the spatial coordinate information is the coordinate value of the key node in the preset three-dimensional coordinate system; Sort the time points reaching the abnormal threshold in the correspondence table in ascending order of the simulation time value to obtain a time sorting result, wherein each time point in the time sorting result corresponds to at least one key node; For each time point in the time sorting results, all key nodes corresponding to the time point are determined according to the correspondence table, and the spatial coordinate information of these key nodes is marked in a preset three-dimensional coordinate system to form a spatial distribution graph of abnormal nodes at that time point; The spatial distribution graphs of abnormal nodes at all time points are arranged in sequence according to the time sorting results to form a continuous visual sequence of the abnormal state spreading in space over time. This continuous visual sequence is the dynamic map of the diffusion range of the abnormal state in space.

8. The urban underground cable network management method based on digital twin according to claim 1 is characterized in that: The generating of a pipeline network maintenance instruction including a priority ranking and a resource allocation plan based on the risk evolution simulation results and the location information of key pipeline network nodes, and sending the pipeline network maintenance instruction to the operation and maintenance execution system of the physical pipeline network to trigger a maintenance response, includes: Extract diffusion-related information, impact range-related information, and critical condition-related information of each key node in the pipeline network from the risk evolution simulation results, and extract spatial correlation information of each key node and surrounding facility correlation information from the location information of the key nodes in the pipeline network; Correlate the diffusion-related information, impact range-related information, critical condition-related information with the spatial correlation information and surrounding facility correlation information to form correlation analysis results for key nodes of each pipe network; Arrange all key nodes of the pipeline network in sequence according to the results of the correlation analysis to obtain the priority ranking results of the pipeline network maintenance; Collecting the current resource status information of the physical pipe network operation and maintenance execution system, including the availability of maintenance personnel, the availability of maintenance equipment, and the maintenance material reserves; Based on the priority sorting results and resource status information, the corresponding maintenance personnel, maintenance equipment and maintenance materials are matched to each key node of the pipeline network to form a resource allocation plan; The priority sorting results are integrated with the resource allocation plan to generate pipeline maintenance instructions that include the maintenance sequence of key nodes in the pipeline network, corresponding maintenance personnel information, required maintenance equipment information, and maintenance material information.

9. An urban underground cable network management system based on digital twin, characterized in that: It includes a processor and a memory, the memory is connected to the processor, the memory is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the memory to implement the urban underground cable network management method based on digital twins as described in any one of claims 1 to 8.

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