Urban heat supply pipe network emergency knowledge graph construction method and system

Abstract

The invention discloses an urban heat supply pipe network emergency knowledge graph construction method and system, and relates to the technical field of emergency knowledge graph data processing. The method comprises the following steps: collecting emergency knowledge original data of the urban heat supply pipe network; constructing an emergency knowledge graph basic model of the urban heat supply pipe network; and real-time earthquake disaster information is obtained, if the emergency earthquake information is not updated, adjustment is not carried out, and if the emergency earthquake information is updated, the urban heat supply pipe network emergency knowledge map basic model is reconstructed according to the updated emergency earthquake information, and a real-time urban heat supply pipe network earthquake emergency knowledge map is obtained. According to the method, the city heat supply pipe network emergency knowledge graph construction method is optimized according to the disaster model construction contrastive analysis result, so that the effect of improving the adaptability of city heat supply pipe network emergency knowledge graph construction to external disaster changes is achieved; the problem that in the prior art, the adaptability of urban heat supply pipe network emergency knowledge graph construction to external disaster changes is insufficient is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of emergency knowledge graph data processing, and particularly to a method and system for constructing an emergency knowledge graph of urban heating pipe networks. Background Art

[0002] Urban heating pipe networks are an important part of urban infrastructure, directly related to the living quality of residents and the normal operation of cities. With the acceleration of urbanization and the increase in extreme weather events, the challenges faced by heating pipe networks are becoming increasingly severe. Especially during natural disasters and major accidents, the emergency response ability of the heating system is particularly important. The construction of an emergency knowledge graph of urban heating pipe networks is an important part of smart cities and digital social governance. Through knowledge graph technology, the intelligent and efficient management of heating pipe network emergencies can be realized, the response time can be shortened, and disaster losses can be reduced. Improve the reliability and stability of the heating system to ensure the basic living needs of residents in extreme weather or disaster situations. Promote the construction of smart cities: The construction of an emergency knowledge graph of heating pipe networks will promote the digital transformation of urban infrastructure, facilitate the collaborative cooperation of multiple departments, and improve the overall emergency management level.

[0003] The existing methods for constructing an emergency knowledge graph of urban heating pipe networks are realized through the following technologies, including the following aspects: real-time monitoring of the operation status of heating pipe networks through sensors and Internet of Things devices; obtaining external environment data using earthquake early warning systems, meteorological monitoring systems, etc.; constructing a knowledge graph of heating pipe networks based on a graph database; using natural language processing technology to extract knowledge from text data.

[0004] For example, the patent for invention with the publication number CN118093732A discloses a method, query method, and construction system for constructing a knowledge graph of a water supply and drainage pipe network, including: obtaining knowledge data of different data sources related to the water supply and drainage pipe network and establishing a knowledge database; extracting entity information and attribute information of the entity information from the knowledge database; performing knowledge fusion on all the extracted entity information and attribute information to generate an information library; establishing a triple semantic model based on the information library; constructing an initial knowledge graph of the water supply and drainage pipe network based on the triple semantic model; and using the parallel algorithm used during actual query to perform distributed consistency verification on the initial knowledge graph of the water supply and drainage pipe network to obtain a target knowledge graph of the water supply and drainage pipe network, and the target knowledge graph of the water supply and drainage pipe network is used for visual query by users.

[0005] For example, the method and processor for constructing a knowledge graph based on an oil and gas pipeline disclosed in the invention patent with the publication number of CN114428862A include: obtaining text data in the field of oil and gas pipelines; preprocessing the text data and annotating the preprocessed text data to construct an annotated data corpus of oil and gas pipelines; inputting the sentences included in the annotated data corpus into an entity recognition learning model to extract the entities included in the sentences through the entity recognition learning model; inputting the entities into an entity relationship extraction model to determine the entity relationships between the entities through the entity relationship extraction model; and constructing a knowledge graph based on the oil and gas pipeline according to the entities and entity relationships. According to the above technical solution, by obtaining the text data in the field of oil and gas pipelines, extracting the entities and entity relationships, and constructing a knowledge graph of oil and gas pipelines, it can better support intelligent pipeline network applications such as knowledge retrieval and decision support.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, it is found that the above technology has at least the following technical problems:

[0007] In the prior art, since the emergency knowledge graph data of urban heating pipe networks is limited by less original emergency data for construction, and at the same time, for continuously changing external disaster situations, the knowledge graph cannot be dynamically updated, and the referenceability for the actual pipe management department application is insufficient. Furthermore, the reliability of the urban heating pipe network emergency knowledge graph updated according to the real-time external disaster change situation is insufficient, and there is a problem of insufficient adaptability of the construction of the urban heating pipe network emergency knowledge graph to external disaster changes. Summary of the Invention

[0008] The embodiments of the present application provide a method and system for constructing an emergency knowledge graph of urban heating pipe networks, which solve the problem of insufficient adaptability of the construction of the emergency knowledge graph of urban heating pipe networks to external disaster changes in the prior art, and achieve the effect of improving the adaptability of the construction of the emergency knowledge graph of urban heating pipe networks to external disaster changes.

[0009] The embodiments of the present application provide a method for constructing an emergency knowledge graph of urban heating pipe networks, including the following steps: collecting the original data of the emergency knowledge of urban heating pipe networks; constructing a basic model of the emergency knowledge graph of urban heating pipe networks; obtaining real-time earthquake disaster information. If the emergency earthquake information does not update, no adjustment is made. If the emergency earthquake information updates, the basic model of the emergency knowledge graph of urban heating pipe networks is reconstructed according to the updated emergency earthquake information to obtain a real-time earthquake emergency knowledge graph of urban heating pipe networks.

[0010] The embodiment of the present application provides a system for constructing an emergency knowledge graph of urban heating pipe networks, including a module for collecting emergency data of urban heating pipe networks, a module for constructing a basic model of the knowledge graph of urban heating pipe networks, and a module for dynamically updating the basic model of the knowledge graph of urban heating pipe networks; the module for collecting emergency data of urban heating pipe networks: used to collect the original data of emergency knowledge of urban heating pipe networks; the module for constructing a basic model of the knowledge graph of urban heating pipe networks: used to construct a basic model of the emergency knowledge graph of urban heating pipe networks; the module for dynamically updating the basic model of the knowledge graph of urban heating pipe networks: used to obtain real-time earthquake disaster information. If the emergency earthquake information does not update, it will not be adjusted. If the emergency earthquake information updates, the basic model of the emergency knowledge graph of urban heating pipe networks will be reconstructed according to the updated emergency earthquake information to obtain a real-time earthquake emergency knowledge graph of urban heating pipe networks.

[0011] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0012] 1. By optimizing the construction method of the emergency knowledge graph of urban heating pipe networks according to the comparative analysis results of the disaster model, the present invention achieves the effect of improving the adaptability of the construction of the emergency knowledge graph of urban heating pipe networks to external disaster changes, and solves the problem of insufficient adaptability of the construction of the emergency knowledge graph of urban heating pipe networks to external disaster changes in the prior art.

[0013] 2. By correcting the corresponding nodes and relationships in several groups of small models of the emergency knowledge graph of urban heating pipe networks, a first-level model of the emergency knowledge graph of urban heating pipe networks in the earthquake heating pipe network area is obtained. Through data collection, model construction, knowledge graph representation, and knowledge graph association, a differentiated emergency knowledge graph of urban heating pipe networks is constructed, thereby providing support for earthquake emergency response and refined pipe network management.

[0014] 3. By adding the corresponding nodes and relationships in the first-level model of the emergency knowledge graph of urban heating pipe networks in the earthquake heating pipe network area, a real-time earthquake emergency knowledge graph of urban heating pipe networks in the earthquake heating pipe network area is obtained, thereby realizing risk quantification, risk visualization, and preventive response, and further achieving multi-dimensional potential risk assessment, providing full-cycle emergency support from early warning to recovery for urban heating pipe networks. Description of the Drawings

[0015] Figure 1 It is a flowchart of the method for constructing an emergency knowledge graph of urban heating pipe networks provided by the embodiment of the present application;

[0016] Figure 2 It is a structural diagram of the system for constructing an emergency knowledge graph of urban heating pipe networks provided by the embodiment of the present application. Detailed Embodiments

[0017] By providing a method and system for constructing an emergency knowledge graph of urban heating pipe networks in the embodiments of the present application, the problem in the prior art that the construction of the emergency knowledge graph of urban heating pipe networks is insufficient in adapting to external disaster changes is solved.

[0018] To better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0019] As Figure 1 shown, it is a flowchart of the method for constructing an emergency knowledge graph of urban heating pipe networks provided by the embodiments of the present application. This method is applied to the system for constructing an emergency knowledge graph of urban heating pipe networks. The method includes the following steps: collecting the original data of the emergency knowledge of urban heating pipe networks; constructing the basic model of the emergency knowledge graph of urban heating pipe networks; obtaining real-time earthquake disaster information. If the emergency earthquake information does not update, it is not adjusted. If the emergency earthquake information updates, the basic model of the emergency knowledge graph of urban heating pipe networks is reconstructed according to the updated emergency earthquake information to obtain the real-time earthquake emergency knowledge graph of urban heating pipe networks.

[0020] Further, collecting the original data of the emergency knowledge of urban heating pipe networks specifically includes: obtaining the predefined data types and predefined database types according to the predefined data collection scheme of the knowledge graph of urban heating pipe networks; calling data from the databases in the predefined database types through database query languages, and collecting the original data of the emergency knowledge of urban heating pipe networks through data collection tools.

[0021] In this embodiment, the data types included in the predefined data collection scheme of the knowledge graph of urban heating pipe networks include disaster type data, affected object data, emergency problem data, emergency measure data, and emergency resource data.

[0022] The predefined data collection scheme of the knowledge graph of urban heating pipe networks includes databases and systems available for data collection, such as urban heating pipe network databases, meteorological databases, geographic information systems, emergency management systems, etc. Data is called from relevant databases through database query languages. Databases to be called: urban heating pipe network databases, used to obtain detailed information about equipment such as pipes, valves, pump stations, and heat exchangers. Meteorological databases, used to obtain real-time data and historical records of disasters such as earthquakes, floods, and extreme low temperatures. Geographic information systems, used to obtain the geographical distribution and spatial relationship data of heating pipe networks. Emergency management systems, used to obtain emergency resource information such as emergency plans, repair teams, and spare equipment.

[0023] Use data collection tools to collect data, such as crawler software, data extraction tools, ETL (Extract, Transform, Load) tools, etc. Configure data collection parameters: Automatically or manually set the tool parameters for data collection, including data source address, authentication information, collection frequency, data format, etc. Monitor the progress and status of data collection to ensure the stability and integrity of data collection. Further, construct a basic model of the emergency knowledge graph of the urban heating pipeline network; specifically, according to the predefined urban heating pipeline network emergency knowledge graph nodes, capture natural disaster type data, affected object data, emergency problem data, emergency measures data and emergency resource data through natural language algorithms; according to the predefined urban heating pipeline network emergency knowledge graph relationship, natural disaster type data, affected object data, emergency problem data, emergency measures data and emergency resource data are constructed into a group of three, and several groups of urban heating pipeline network emergency knowledge graph small models are obtained; several groups of urban heating pipeline network emergency knowledge graph small models are associated through natural language processing models to construct and obtain the basic model of the urban heating pipeline network emergency knowledge graph.

[0024] In this embodiment, when constructing the basic model of the emergency knowledge graph of the urban heating network, the core idea of ​​"each three is built into a group" is to locally associate entities and relationships of different data types in the form of triples to form the preliminary structure of the knowledge graph. The specific steps are as follows:

[0025] Among the predefined five types of data (disaster type, affected objects, emergency issues, emergency measures, and emergency resources), three types are selected for combination each time. The combination logic is based on the actual relevance of the emergency scenario, for example: 1. Disaster type + affected objects + emergency measures (such as earthquake → pipeline rupture → activation of backup pipelines) 2. Emergency issues + affected objects + emergency resources (such as valve leakage → pump station shutdown → dispatch of repair teams) 3. Disaster type + emergency issues + emergency resources (such as extreme low temperature → heat exchanger freezing → calling heating equipment) 4. Selection basis: Based on domain knowledge or historical cases, determine which data types have direct causal relationships or operational dependencies.

[0026] Each set of data is constructed into a small model through triples (subject-relation-object) or attribute associations.

[0027] Extract triples from text using natural language processing (NLP) techniques such as entity extraction and relation extraction. Define logical relationships through a rule engine or knowledge graph construction tools such as Neo4j and Protégé. Map heterogeneous data from different databases (such as pipeline parameters, disaster records, and emergency response plans) to a unified format. Establish association rules among the three types of data through semantic matching (such as ontology alignment) or manual annotation. Determine the priority or manually correct conflicting data (such as inconsistent descriptions of emergency measures for the same disaster in different systems). Associate multiple small models into a complete knowledge graph through a natural language processing model or a graph neural network (GNN): Use word vectors (Word2Vec, BERT) to discover implicit semantic associations between different small models. For example, "pipe rupture" and "valve leakage" may share similar emergency resources (repair teams). Graph structure fusion: Use the common nodes of multiple subgraphs (such as "repair teams") as hubs and merge the overlapping parts. Use graph embedding technology (such as Node2Vec) to vectorize the global graph representation.

[0028] An example of building a basic model for the emergency knowledge graph of urban heating pipe networks is as follows:

[0029] 1. Node definition: Disaster / accident type: such as earthquake, flood, extreme low temperature, third-party damage, equipment failure, etc. Affected objects: such as heating pipes, valves, pump stations, heat exchangers, electrical equipment, etc. Emergency problems: such as pipe rupture, equipment damage, pipe flooding, pipe freeze rupture, heating interruption, etc. Emergency measures: such as isolating the damaged part, starting standby equipment, organizing repairs, draining water, increasing insulation, etc. Emergency resources: such as repair teams, standby equipment, earthquake early warning systems, pumping equipment, insulation materials, etc.

[0030] 2. Relationship definition: Disaster / accident → Causes → Possible problems: such as an earthquake causing a pipe rupture and a flood causing pipe flooding. Possible problems → Require → Emergency measures: such as a pipe rupture requiring isolation measures and pipe flooding requiring drainage measures. Emergency measures → Depend on → Resources: such as isolation measures requiring repair teams and drainage measures requiring pumping equipment. Early warning system → Provides → Early warning information: such as an earthquake early warning system providing early warning information. Early warning information → Triggers → Activation of emergency response plan: such as early warning information triggering the activation of an emergency response plan.

[0031] 3. Internal principle: Relevance: Information such as disasters, accidents, problems, measures, and resources is associated through nodes and relationships to form a complete emergency knowledge network. Quick response: Utilize the graph structure to quickly locate problems and find corresponding emergency measures, shortening the response time. Dynamic update: Dynamically update the knowledge graph based on real-time data (such as earthquake early warnings, temperature monitoring), triggering corresponding emergency plans. Resource optimization: Through the resource nodes in the graph, quickly allocate human, material, and technical resources to improve emergency efficiency.

[0032] 4. Example of specific construction method:

[0033] (1) Earthquake emergency knowledge graph: Nodes: Earthquake, pipeline rupture, isolation measures, repair team, earthquake early warning system. Relationships: Earthquake → causes → pipeline rupture, pipeline rupture → requires → isolation measures, isolation measures → rely on → repair team.

[0034] (2) Flood emergency knowledge graph: Nodes: Flood, pipeline inundation, drainage measures, pumping equipment, flood control plan. Relationships: Flood → causes → pipeline inundation, pipeline inundation → requires → drainage measures, drainage measures → rely on → pumping equipment.

[0035] (3) Extreme low temperature emergency knowledge graph: Nodes: Extreme low temperature, pipeline freeze rupture, heat preservation measures, backup heat source, temperature monitoring system. Relationships: Extreme low temperature → causes → pipeline freeze rupture, pipeline freeze rupture → requires → heat preservation measures, heat preservation measures → rely on → backup heat source.

[0036] (4) Third-party damage emergency knowledge graph: Nodes: Third-party damage, pipeline rupture, isolation measures, repair team, legal support. Relationships: Third-party damage → causes → pipeline rupture, pipeline rupture → requires → isolation measures, isolation measures → rely on → repair team.

[0037] (5) Equipment failure emergency knowledge graph: Nodes: Equipment failure, heating interruption, troubleshooting, technical personnel, backup equipment. Relationships: Equipment failure → causes → heating interruption, heating interruption → requires → troubleshooting, troubleshooting → rely on → technical personnel.

[0038] The above are examples of several small models of the emergency knowledge graph for urban heating pipe networks. By constructing knowledge graphs for different disasters and accidents, urban heating pipe networks can achieve quick response and efficient recovery in emergencies, minimizing losses and negative impacts to the greatest extent and ensuring that corresponding measures can be taken quickly and accurately in emergency situations.

[0039] Connect several small models of the emergency knowledge graph for urban heating pipe networks through a natural language processing model to construct and obtain the basic model of the emergency knowledge graph for urban heating pipe networks. The example steps are as follows:

[0040] Preprocessing for small model docking: Use GraphSAGE or other graph neural networks to generate low-dimensional embedding representations for each node, which can capture the local structural information and attribute features of the node. Through the aggregation function of GraphSAGE, the neighbor information of each node is aggregated to update the node representation. Low-dimensional embedding representation is a technique that maps high-dimensional data (such as text, images, graph structures) to a low-dimensional space, aiming to reduce the complexity and computational cost of the data while retaining the key features of the data. In graph neural networks, low-dimensional embedding representations are usually used to represent nodes, edges, or subgraphs in a graph to better capture the structural information and semantic relationships of the graph.

[0041] Based on the predefined relationships between nodes, use natural language processing models to learn the semantic representations of these relationships so as to correctly connect different nodes in the knowledge graph. Use edge attention mechanisms (such as GAT) or relation-aware convolutions (such as RGCN) in graph neural networks to model and strengthen the relationships between nodes.

[0042] Extract the deep features of nodes through multi-layer graph neural networks, which helps to more accurately associate different small models. Stack multiple GraphSAGE layers to gradually extract and abstract the features of nodes.

[0043] Small model docking: Identify the common nodes or relationships between small models as docking points, and connect different small models through the common nodes or relationships to form a larger connected component.

[0044] It should be noted that the common nodes or relationships between small models are generally the same or similar text / graphical data;

[0045] Global structure optimization: During the fusion process, optimize the global structure, and use graph optimization algorithms (such as PageRank, spectral clustering) to adjust the node positions and relationship strengths to form a more compact and organized structure.

[0046] According to the actual situation of the knowledge graph and expert knowledge, adjust the relationships between nodes to improve the accuracy and practicality of the knowledge graph. Through manual review or semi-automated methods, identify and correct incorrect or inappropriate relationships. For example, use graph simplification algorithms (such as minimum spanning tree, core decomposition) to identify and eliminate redundant nodes and relationships.

[0047] Through these refinement steps, it becomes clearer how to integrate several groups of small models of the urban heating pipe network emergency knowledge graph into a basic model and optimize it to support more efficient and accurate emergency response and decision-making.

[0048] Furthermore, the basic model of the urban heating pipe network emergency knowledge graph is reconstructed according to the updated emergency earthquake information, which specifically includes: if the emergency earthquake information is updated, the epicenter location and the earthquake influence range are obtained from the earthquake early warning database; taking the epicenter location as the center point of the coordinate system, the earthquake influence range is divided into circular ring ranges at predefined intervals, and each circular ring range is evenly divided into several regions to obtain several earthquake heating pipe network regions; the urban heating pipe state data is divided according to the earthquake heating pipe network regions through the sensor network database to obtain the urban heating pipe state data of different earthquake heating pipe network regions; the urban heating pipe state of different earthquake heating pipe network regions is directly extracted from the urban heating pipe state data of different earthquake heating pipe network regions; if the urban heating pipe under the earthquake heating pipe network region is in a leakage state or a rupture state, the corresponding earthquake heating pipe network region and the earthquake heating pipe network region state data are sent to relevant personnel and a first-level alarm is issued; if the urban heating pipe under the earthquake heating pipe network region is not in a leakage state or a rupture state, the corresponding earthquake heating pipe network region and the earthquake heating pipe network region state data are analyzed for the first time to obtain the earthquake disaster value of the urban heating pipe under the earthquake heating pipe network region, and a new basic model of the urban heating pipe network emergency knowledge graph is constructed according to the comparative analysis result of the earthquake disaster value of the urban heating pipe under the earthquake heating pipe network region to obtain the first-level model of the urban heating pipe network emergency knowledge graph under the earthquake heating pipe network region, and the corresponding earthquake heating pipe network region and the earthquake heating pipe network region state data are analyzed for the second time to obtain the comprehensive value of the earthquake influence degree of the urban heating pipe under the earthquake heating pipe network region, and the first-level model of the urban heating pipe network emergency knowledge graph is adjusted according to the comparative analysis result of the earthquake potential hazard value of the urban heating pipe under the earthquake heating pipe network region to obtain the real-time urban heating pipe network earthquake emergency knowledge graph under the earthquake heating pipe network region, which is sent to relevant personnel and a second-level alarm is issued.

[0049] In this embodiment, for the multi-source fusion of regional state data, 1. not only the pipe state data is obtained through the sensor network, but also information such as historical data, pipe material, and service life is combined to comprehensively evaluate the pipe state.

[0050] If the urban heating pipe under the earthquake heating pipe network region is in a leakage state or a rupture state, the corresponding earthquake heating pipe network region and the earthquake heating pipe network region state data are sent to relevant personnel and an alarm is issued. In this case, it is not necessary to construct a new basic model of the urban heating pipe network emergency knowledge graph because the basic model of the urban heating pipe network emergency knowledge graph obtained from the historical data in the past can cover obvious disaster situations; the first-level alarm is more serious than the second-level alarm.

[0051] The example steps are as follows: 1. Dynamically divide the earthquake influence range according to the magnitude and geological conditions, and divide the epicenter area into high-risk areas. 2. Through sensor data and historical data, detect abnormal pressure in a certain main pipeline and judge it to be in a ruptured state. 3. Mark this area as high-risk and give priority to dispatching maintenance resources. 4. Calculate the disaster-affected values of other areas and issue warnings in advance. 5. Update the knowledge graph and display the disaster situation and emergency suggestions through a visualization interface. Through the above refined steps, risks can be identified more accurately, responses can be optimized, and the impact of earthquakes on the urban heating pipe network can be minimized to the greatest extent.

[0052] Furthermore, the specific process of obtaining the earthquake disaster-affected value of urban heating pipes in the earthquake heating pipe network area is as follows: Number the earthquake heating pipe network areas in sequence to obtain different earthquake heating pipe network areas; number the earthquake heating pipe network monitoring time periods in sequence to obtain different earthquake heating pipe network monitoring time periods; number the number of segments of the heating pipes in sequence to obtain different segments of heating pipes; directly extract the service life of the urban heating pipes and the corresponding pipe service years from the urban heating pipe network database, divide the service life of the urban heating pipes by the corresponding pipe service years to obtain the service fatigue value of the urban heating pipes; measure and upload to the urban heating pipe network database in real time through a differential transformer displacement sensor, directly extract the maximum vibration displacement of the urban heating pipes, the corresponding maximum allowable vibration displacement of the urban heating pipes, and the corresponding pipe material correction factor from the urban heating pipe network database, compare and analyze the maximum vibration displacement of the urban heating pipes with the corresponding maximum allowable vibration displacement of the urban heating pipes, and correct through the corresponding pipe material correction factor to obtain the displacement risk value of the urban heating pipes; measure and upload to the urban heating pipe network database in real time through a predefined pressure sensor, directly extract the stress of the supporting soil under the pipe, the corresponding standard stress of the supporting soil under the pipe, the corresponding correction factor for the influence of the supporting soil type under the pipe, and the correction factor for the influence of the groundwater level around the pipe from the urban heating pipe network database, and jointly correct the comparison analysis result of the stress of the supporting soil under the pipe and the corresponding standard stress of the supporting soil under the pipe by the correction factor for the influence of the supporting soil type under the pipe and the correction factor for the influence of the groundwater level around the pipe to obtain the stress level value of the urban heating pipes in different segments of different earthquake heating pipe network areas; the earthquake disaster-affected value of the urban heating pipes represents the analytical magnitude of the relative disaster negative distortion degree of the service fatigue value of the urban heating pipes, the displacement risk value of the urban heating pipes, and the stress level value of the urban heating pipes comprehensively for the heating pipes.

[0053] In this embodiment, the earthquake heating network areas are numbered in sequence, GR0 represents the number of the earthquake heating network areas, GR0=1, 2, ..., GR, GR represents the total number of earthquake heating network areas. The earthquake heating network monitoring time periods are numbered in sequence, DQ0 represents the number of earthquake heating network monitoring time periods, DQ0=1, 2, ..., DQ, DQ represents the total number of earthquake heating network monitoring time periods. The sections of the heating pipeline are numbered in sequence, DS0 represents the number of the sections of the heating pipeline, DS0=1, 2, ..., DS, DS represents the total number of sections of the heating pipeline.

[0054] It represents the earthquake damage value of the urban heating pipeline of the DS0th heating pipeline in the DQ0th earthquake heating network monitoring time period in the GR0th earthquake heating network area. The earthquake damage value of the urban heating pipeline is used to quantify the relative damage negative distortion degree values ​​of the heating pipelines in different earthquake heating network monitoring time periods in different earthquake heating network areas. If the earthquake damage value of the urban heating pipeline is larger, it means that the actual urban heating pipeline is relatively affected by the negative sudden impact of external factors brought by the earthquake.

[0055]

[0056]

[0057] e represents a natural constant.

[0058] It represents the usage time of the urban heating pipeline of the DS0th heating pipeline in the DQ0th earthquake heating pipeline network monitoring time period in the GR0th earthquake heating pipeline network area.

[0059] Indicates the service life of the heating pipeline of the DS0 section in the heating pipeline network area of ​​the GR0 earthquake. The service life of the pipeline refers to the preset maximum service life of the heating pipeline.

[0060] It indicates the maximum vibration displacement of the urban heating pipeline of the DS0 section of the heating pipeline network area in the GR0 earthquake. The maximum vibration displacement of the urban heating pipeline is the relative maximum displacement relative to the standard position when it was pre-buried. Earthquakes may cause the pipeline to move, and excessive displacement may cause the connection to rupture.

[0061] It represents the allowable maximum vibration displacement value of the urban heating pipeline for the DS0th section of the heating pipeline in the GR0th seismic heating pipeline network area. The allowable maximum vibration displacement value of the urban heating pipeline is directly extracted from the urban heating pipeline network database. Since the status data of the urban heating pipeline has been judged above, the allowable maximum vibration displacement value of the urban heating pipeline is greater than the maximum vibration displacement amount of the urban heating pipeline at this time.

[0062] It represents the pipeline material correction factor for the DS0th section of the heating pipeline in the GR0th seismic heating pipeline network area. Since the seismic resistance performances of different materials (such as steel, cast iron, and PE pipes) are different, for the heating pipelines with different materials in a certain seismic heating pipeline network area, different pipeline material correction factors are required for correction, which are used to quantify that under the same vibration displacement, the heating pipelines with different materials have different levels of negative influence. The pipeline material correction factor is directly extracted from the urban heating pipeline network database.

[0063] The specific steps for directly extracting the pipeline material correction factor from the urban heating pipeline network database are as follows: Collect the material types used for the heating pipelines, such as steel, cast iron, PE pipes, etc. Collect the physical and mechanical properties of each material, including elastic modulus, yield strength, elongation, density, etc. Use seismic engineering software: such as SAP2000, ETABS, which are specifically used for the response analysis of structures under seismic loads. Establish a vibration displacement model describing the vibration displacement of the heating pipeline in the earthquake situation. According to the vibration displacement model, calculate the stress and strain distributions of the pipelines with different materials during the vibration process. Select a common and representative material as the reference material, such as steel. Compare the seismic resistance performances of other materials with the reference material, and analyze the differences in stress, strain, damage degree, etc. under the same vibration displacement. According to the comparison and analysis results, quantify the differences in seismic resistance performances of different materials relative to the reference material, and determine the correction factor for each material. Calculate the seismic resistance performance indicators of the reference material: such as the stress, strain, damage degree, etc. of the reference material under a specific vibration displacement. Calculate the seismic resistance performance indicators of other materials: Under the same conditions, calculate the seismic resistance performance indicators of other materials. Calculate the ratio: Calculate the ratio of the seismic resistance performance indicators of other materials to those of the reference material. Determine the correction factor: According to the ratio results, determine the correction factor for each material. The correction factor represents the degree of enhancement or weakening of the seismic resistance performance of this material relative to the reference material. The value range of the pipeline material correction factor for the heating pipelines in the seismic heating pipeline network area is (0, 2).

[0064] It represents the soil stress under the pipeline of the DS0th section of the heating pipeline in the GR0th seismic heating pipeline network area. Predefined pressure sensors are buried at key positions around the pipeline (such as the top, bottom, and sides of the pipeline). The predefined pressure sensors can include earth pressure gauges, pore water pressure gauges, and strain gauges, and transmit the sensor data to the urban heating pipeline network database in real time wirelessly.

[0065] It represents the standard soil stress under the pipeline of the DS0th section of the heating pipeline in the GR0th seismic heating pipeline network area, and the standard soil stress under the pipeline is directly extracted from the urban heating pipeline network database.

[0066] Soil type indirectly affects the vibration, displacement, and stress of the pipeline by influencing the propagation speed, amplitude, and soil strength of seismic waves. The groundwater level directly affects the support condition and stress state of the pipeline by changing the pore water pressure and buoyancy effect of the soil. Soil type and groundwater level jointly determine the stress mode, displacement amount, and failure risk of the pipeline during an earthquake.

[0067] The soil type refers to the soil type under the pipeline when the actual heating pipeline is buried.

[0068] Soft soil has a slow propagation speed for seismic waves, but it will amplify the amplitude of seismic waves, resulting in greater ground vibration. The shear modulus of soft soil is relatively low, and the energy loss of seismic waves during propagation is small, and the vibration energy is more likely to be transmitted to the pipeline. The pipeline may bear greater vibration and displacement in the soft soil area, increasing the risk of rupture.

[0069] Sand has a fast propagation speed for seismic waves, but liquefaction may occur. During an earthquake, the pore water pressure between sand particles increases, causing the soil to lose strength and liquefy. Liquefaction may cause the pipeline to sink or float, increasing the stress concentration at the joints.

[0070] Rock and soil have the fastest propagation speed for seismic waves and less vibration attenuation. The rigidity and high shear modulus of rock and soil make seismic waves propagate rapidly with a small amplitude. The vibration and displacement of the pipeline in the rock and soil area are small, but high-frequency vibration may cause fatigue damage.

[0071] It represents the influence correction factor of the soil type under the pipeline of the DS0th section of the heating pipeline in the GR0th seismic heating pipeline network area. Due to the different seismic resistance performances of soil types, different pipeline material correction factors are required for different sections of the heating pipeline in a certain seismic heating pipeline network area for correction, which is used to quantify the different negative influence levels of different soil types under the pipeline on the heating pipeline under the same soil stress under the pipeline. The value range of the influence correction factor of the soil type under the pipeline is (0, 1).

[0072] The correction factor for the influence of the soil type under the pipeline support is directly extracted from the urban heating pipeline network database.

[0073] The influence of the groundwater level on soil strength: at a high groundwater level, the soil strength is reduced, which may lead to soil liquefaction or softening. Groundwater fills the soil pores, increases the pore water pressure, and reduces the effective stress of the soil. Soil liquefaction or softening may cause the pipeline to lose support and result in displacement or rupture.

[0074] At a low groundwater level, the forces acting on the pipeline mainly come from seismic waves. The soil pore water pressure is relatively low, and the soil maintains high strength and stability. The pipeline displacement and stress concentration are relatively small.

[0075] Denote the correction factor for the influence of the groundwater level around the pipeline of the DS0th section of the heating pipeline in the GR0th seismic heating pipeline network area. Since the seismic performance of the heating pipeline network is different due to the high or low groundwater level around the pipeline, for different sections of the heating pipeline in a certain seismic heating pipeline network area, different correction factors for the influence of the groundwater level around the pipeline are required for correction, which is used to quantify the different negative influence levels of different groundwater levels around the pipeline on the heating pipeline under the same stress of the soil under the pipeline support. The value range of the correction factor for the influence of the groundwater level around the pipeline is (0, 1). The correction factor for the influence of the groundwater level around the pipeline is directly extracted from the urban heating pipeline network database.

[0076] Further, obtain the first-level model of the urban heating pipe network emergency knowledge graph under the earthquake heating pipe network area, which specifically includes: comparing and analyzing the earthquake disaster values of urban heating pipes in different sections of heating pipes in different earthquake heating pipe network monitoring time periods in different earthquake heating pipe network areas with the corresponding earthquake disaster thresholds of urban heating pipes: if the earthquake disaster values of urban heating pipes in different sections of heating pipes in different earthquake heating pipe network monitoring time periods in different earthquake heating pipe network areas are equal to or less than the corresponding earthquake disaster thresholds of urban heating pipes, there is no need to construct a new basic model of the urban heating pipe network emergency knowledge graph, and the first-level model of the urban heating pipe network emergency knowledge graph is the same as the basic model of the urban heating pipe network emergency knowledge graph; if the earthquake disaster values of urban heating pipes in different sections of heating pipes in different earthquake heating pipe network monitoring time periods in different earthquake heating pipe network areas are greater than the corresponding earthquake disaster thresholds of urban heating pipes, subtract the corresponding earthquake disaster thresholds of urban heating pipes from the earthquake disaster values of urban heating pipes in different sections of heating pipes in different earthquake heating pipe network monitoring time periods in different earthquake heating pipe network areas to obtain the earthquake disaster differences of urban heating pipes in different sections of heating pipes in different earthquake heating pipe network monitoring time periods in different earthquake heating pipe network areas; according to the earthquake disaster differences of urban heating pipes, the corresponding fatigue values of urban heating pipes in use, the corresponding displacement risk values of urban heating pipes, and the corresponding stress level values of urban heating pipes, correct the corresponding nodes and relationships in several groups of small models of the urban heating pipe network emergency knowledge graph to obtain the first-level model of the urban heating pipe network emergency knowledge graph under the earthquake heating pipe network area.

[0077] In this embodiment, for the earthquake disaster differences of urban heating pipes, the corresponding fatigue values of urban heating pipes in use, the corresponding displacement risk values of urban heating pipes, and the corresponding stress level values of urban heating pipes, for the correction that simultaneously meets the above multiple factor conditions, the correction methods are superimposed. Specifically, for a specific earthquake heating pipe network area, there is a corresponding first-level model of the urban heating pipe network emergency knowledge graph, and the correction methods of the corresponding factor conditions are superimposed on the corresponding nodes in this first-level model of the urban heating pipe network emergency knowledge graph.

[0078] Use a graph database (such as Neo4j) to represent the visualization of the urban heating pipe network emergency knowledge graph.

[0079] The construction of the urban heating pipe network emergency knowledge graph needs to be designed differently according to factors such as different regions, disaster levels, usage duration, and soil types. The following are the correction methods for the differences in the construction of the knowledge graph for different factors and the specific predefined first-level rules for matching the emergency knowledge graph:

[0080] Differences in the construction of the knowledge graph for different regions. Due to different factors such as earthquake risks, soil types, and groundwater levels in different regions, the construction of the knowledge graph should reflect these differences.

[0081] (1) Earthquake risk construction differential correction method:

[0082] If the fatigue value of the urban heating pipeline is equal to or greater than the preset earthquake risk threshold, it is judged as a high earthquake risk area, and the number of relevant nodes, the number of relationships, and the corresponding correlation degree associated with earthquake historical data, earthquake wave propagation models, and emergency plans in the small model of the urban heating pipe network emergency knowledge graph are increased. According to the requirements of the emergency scenario, new entity types are introduced. For example: Disaster-related entities: earthquake wave propagation models, soil liquefaction monitoring points, aftershock records. Pipeline-related entities: pipeline material, service life, displacement sensors, stress sensors. Emergency resource entities: repair teams, spare equipment, emergency plans. In high earthquake risk areas, 5 - 10 nodes related to earthquake historical data, earthquake wave propagation models, and emergency plans are added. In low earthquake risk areas, 3 - 5 nodes related to daily maintenance and routine fault handling are added. Refine entity attributes and further subdivide the attributes of existing entities to generate new nodes. For example: Subdivide the "pipeline" node into "steel pipe", "PE pipe", "cast iron pipe", etc. Subdivide the "earthquake" node into "main shock", "aftershock", "focal depth", etc.

[0083] If the fatigue value of the urban heating pipeline is less than the preset earthquake risk threshold, it is judged as a low earthquake risk area: reduce the number of relevant nodes and relationships associated with earthquake historical data, earthquake wave propagation models, and emergency plans in the small model of the urban heating pipe network emergency knowledge graph, and increase the number of relevant nodes, the number of relationships, and the corresponding correlation degree associated with the daily maintenance and routine fault handling of urban heating pipelines in the corresponding small model of the urban heating pipe network emergency knowledge graph.

[0084] (2) Regional soil type construction differential correction method:

[0085] If the displacement risk value of the urban heating pipeline is equal to or greater than the first preset displacement threshold of the urban heating pipeline, it is judged as a high soil risk area, and the number of relevant nodes, the number of relationships, and the corresponding correlation degree associated with soil liquefaction monitoring, pipeline displacement monitoring, and reinforcement measures in the small model of the urban heating pipe network emergency knowledge graph are increased. 3 - 5 nodes related to soil liquefaction monitoring, pipeline displacement monitoring, and reinforcement measures are added in high soil risk areas.

[0086] If the displacement risk value of the urban heating pipeline is less than the first preset displacement threshold of the urban heating pipeline and greater than the second preset displacement threshold of the urban heating pipeline, it is judged as a medium soil risk area, and the number of relevant nodes, the number of relationships, and the corresponding correlation degree associated with liquefaction warning and pipeline support schemes in the small model of the urban heating pipe network emergency knowledge graph are increased. 2 - 4 nodes related to regional liquefaction warning and pipeline support schemes are added in medium soil risk areas.

[0087] If the displacement risk value of the urban heating pipeline is less than the second preset displacement threshold of the urban heating pipeline, it is judged as a medium soil risk area, and the number of relevant nodes, the number of relationships and the corresponding correlation degree related to high-frequency vibration monitoring and fatigue damage assessment in the corresponding small model of the urban heating pipe network emergency knowledge graph are increased. In the low soil risk area, 1-3 nodes related to high-frequency vibration monitoring and fatigue damage assessment are added. New entities are introduced from external data sources (such as geographic information systems, meteorological databases). For example: the "soil type" and "groundwater level" nodes are introduced from the geographic information system. The "seismic intensity" and "peak ground velocity" nodes are introduced from the meteorological database.

[0088] (3) Regional groundwater level construction difference correction method:

[0089] If the stress level value of the urban heating pipeline is equal to or greater than the preset stress threshold of the urban heating pipeline, it is judged as a high groundwater level risk area, and the number of relevant nodes, the number of relationships and the corresponding correlation degree related to pore water pressure monitoring, soil strength assessment and pipeline support in the corresponding small model of the urban heating pipe network emergency knowledge graph are increased. In the low groundwater level risk area: 2-4 nodes related to seismic wave propagation and pipeline vibration monitoring are added.

[0090] If the stress level value of the urban heating pipeline is less than the preset stress threshold of the urban heating pipeline, it is judged as a low groundwater level risk area, and the number of relevant nodes, the number of relationships and the corresponding correlation degree related to pore water pressure monitoring, soil strength assessment and pipeline support in the corresponding small model of the urban heating pipe network emergency knowledge graph are reduced, and the number of relevant nodes, the number of relationships and the corresponding correlation degree related to seismic wave propagation and pipeline vibration monitoring in the corresponding small model of the urban heating pipe network emergency knowledge graph are increased. In the high groundwater level risk area: 5-7 nodes related to pore water pressure monitoring, soil strength assessment and pipeline support are reduced. 5-7 relevant nodes of seismic wave propagation and pipeline vibration monitoring are increased.

[0091] (4) Regional urban heating pipeline earthquake disaster difference correction method:

[0092] If the earthquake disaster difference of the urban heating pipeline is less than the second preset earthquake disaster difference threshold of the urban heating pipeline, it is judged as a lightly affected area, and the number of relevant nodes, the number of relationships and the corresponding correlation degree related to vibration monitoring, displacement risk assessment, rapid repair and daily maintenance in the corresponding small model of the urban heating pipe network emergency knowledge graph are increased. In the low earthquake risk area: 3-5 relationships related to daily maintenance and routine fault handling are added.

[0093] If the earthquake disaster difference of urban heating pipelines is equal to or greater than the second threshold of the preset earthquake disaster difference of urban heating pipelines and less than the first threshold of the preset earthquake disaster difference of urban heating pipelines, it is judged as a moderately affected area, and the number of relevant nodes and relationships related to pipeline stress distribution, soil stress assessment, and local reinforcement plans in the corresponding small model of the urban heating pipe network emergency knowledge graph is increased, and the corresponding correlation degree between the above nodes and relationships and local repair and emergency plans is greatly improved.

[0094] If the earthquake disaster difference of urban heating pipelines is equal to or greater than the first threshold of the preset earthquake disaster difference of urban heating pipelines, it is judged as a severely affected area, and the number of relevant nodes and relationships related to pipeline rupture risk assessment, soil liquefaction warning, and comprehensive repair plans in the corresponding small model of the urban heating pipe network emergency knowledge graph is increased, and the corresponding correlation degree between the above nodes and relationships and comprehensive repair and disaster recovery is greatly improved. High earthquake risk area: Increase 5 - 10 relationships related to earthquake historical data, seismic wave propagation models, and emergency plans.

[0095] The corresponding nodes and relationships in several groups of small models of the urban heating pipe network emergency knowledge graph are corrected as follows:

[0096] (1) Entity: Area A: {Seismic risk level = high, soil type = soft soil, groundwater level = high}.

[0097] The seismic risk level of Area A is high, the soil type is soft soil, and the groundwater level is high, which is associated with the need to strengthen pipeline displacement monitoring and soil liquefaction risk assessment.

[0098] (2) Pipeline 1: {Service life = 10 years, material = steel, displacement risk value = 0.8, stress level value = 0.9}.

[0099] Sensor S1: {Type = displacement sensor, location = top of Pipeline 1}.

[0100] Relationships: Area A - contains - Pipeline 1. Pipeline 1 - is monitored by - Sensor S1. Pipeline 1 - is located in - soft soil. Pipeline 1 - is affected by - high groundwater level. Pipeline 1 - disaster degree - moderate.

[0101] The displacement risk value of Pipeline 1 is 0.8 and the stress level value is 0.9, which is associated with the need for local reinforcement.

[0102] Further, a comprehensive value for constructing the seismic influence degree of urban heating pipelines in the seismic heating pipeline network area is obtained, which specifically includes: measuring and uploading to the urban heating pipeline network database in real time through a velocimeter, directly extracting from the urban heating pipeline network database the maximum peak ground velocity, the minimum peak ground velocity, the historical average value of the corresponding historical peak ground velocity, and the correction factor for the influence of the corresponding aftershock times, analyzing the difference between the maximum peak ground velocity and the minimum peak ground velocity, then conducting a ratio analysis with the historical average value of the historical peak ground velocity, and jointly processing with the correction factor for the influence of the corresponding aftershock times to obtain the risk value of fatigue damage mutation of urban heating pipelines; measuring and uploading to the urban heating pipeline network database in real time through a pore water pressure sensor, directly extracting from the urban heating pipeline network database the maximum value of the pore water pressure of the surrounding soil, the minimum value of the pore water pressure of the surrounding soil, and the standard value of the pore water pressure of the corresponding surrounding soil; analyzing the difference between the maximum value of the pore water pressure of the surrounding soil and the minimum value of the pore water pressure of the surrounding soil, then conducting a ratio analysis with the standard value of the pore water pressure of the corresponding surrounding soil, and combining with the weight factor of the calibration level of the pore water pressure mutation of the surrounding soil to obtain the calibration level value of the pore water pressure mutation of the surrounding soil; measuring and uploading to the urban heating pipeline network database in real time through an earth pressure gauge, directly extracting from the urban heating pipeline network database the maximum value of the stress of the surrounding soil, the minimum value of the stress of the surrounding soil, and the standard value of the stress of the corresponding surrounding soil; analyzing the mutation degree between the minimum value of the stress of the surrounding soil and the maximum value of the stress of the surrounding soil, conducting a ratio analysis with the standard value of the stress of the corresponding surrounding soil, and jointly analyzing with the weight factor of the calibration level of the stress mutation of the surrounding soil to obtain the calibration level value of the stress mutation of the surrounding soil; the relative negative potential risk pre-estimation value represents the quantitative analysis data for pre-estimating the relative negative potential risk through the comprehensive analysis of the risk value of fatigue damage mutation of urban heating pipelines, the calibration level value of the pore water pressure mutation of the surrounding soil, and the calibration level value of the stress mutation of the surrounding soil; furthermore, a comprehensive value for constructing the seismic influence degree of urban heating pipelines in the seismic heating pipeline network area is obtained through analysis.

[0103] In this embodiment, the velocimeter has the function of directly measuring the ground motion speed. The internal principle is: based on the principle of electromagnetic induction, the coil inside the sensor generates an induced current under the action of an earthquake, and the speed is obtained by measuring the current change. The sensor is installed near the key nodes (such as valves, elbows, joints) of the heating pipeline network to monitor the direct influence of the earthquake on the pipeline in real time. The seismic waveform data is recorded by the velocimeter. The waveform data is processed to extract the maximum peak ground velocity and the minimum peak ground velocity for each monitoring time period.

[0104] Represents the potential earthquake hazard value of the DS0th section of the heating pipeline in the DQ0th earthquake heating pipeline monitoring time period in the GR0th earthquake heating pipeline area. The potential earthquake hazard value of the urban heating pipeline is used to quantify the relative negative potential risk prediction value of the heating pipeline in different earthquake heating pipeline monitoring time periods under different earthquake heating pipeline areas. If the potential earthquake hazard value of the urban heating pipeline is larger, it indicates that the relative degree of the negative sudden impact of the actual urban heating pipeline caused by external factors during the earthquake is higher.

[0105]

[0106]

[0107] Peak ground velocity, the maximum velocity of ground motion during an earthquake, is also closely related to the degree of structural damage.

[0108] Represents the maximum value of the peak ground velocity of the DS0th section of the heating pipeline in the DQ0th earthquake heating pipeline monitoring time period in the GR0th earthquake heating pipeline area;

[0109] Represents the minimum value of the peak ground velocity of the DS0th section of the heating pipeline in the DQ0th earthquake heating pipeline monitoring time period in the GR0th earthquake heating pipeline area; There will be multiple seismic waves in a seismic heating pipeline monitoring time period, and the minimum value of the peak ground velocity is extracted from them.

[0110] Represents the historical average value of the peak ground velocity corresponding to the earthquake magnitude of the DS0th section of the heating pipeline in the GR0th earthquake heating pipeline area, which is directly extracted from the urban heating pipeline database;

[0111] Represents the aftershock number impact correction factor of the DS0th section of the heating pipeline in the DQ0th earthquake heating pipeline monitoring time period in the GR0th earthquake heating pipeline area. Since the different numbers of aftershocks in the seismic heating pipeline monitoring time period have different negative impacts on the urban heating pipeline, for the heating pipelines in different seismic heating pipeline monitoring time periods in a certain earthquake heating pipeline area, different aftershock number impact correction factors are required for correction, which is used to quantify the different negative impact levels of different aftershock numbers on the heating pipeline. The frequency of aftershocks, high-frequency aftershocks may exacerbate the fatigue damage of the pipeline.

[0112] The pore water pressure in the soil is monitored in real time using a pore water pressure sensor. The pore water pressure sensor is installed in the soil around the heating pipeline, usually buried below or on the side of the pipeline. Based on the pressure sensing technology, the pressure-sensitive element (such as piezoresistive or piezoelectric) inside the sensor converts the pore water pressure into an electrical signal, which is uploaded to the urban heating pipe network database through a signal transmission device.

[0113] The stress state in the soil is monitored in real time using an earth pressure cell. The earth pressure cell is installed in the soil around the heating pipeline, usually buried below or on the side of the pipeline. Based on the strain measurement technology, the strain gauge inside the sensor deforms under the action of soil stress, and the stress is calculated by measuring the strain change.

[0114] Represents the maximum value of the pore water pressure in the soil around the DS0th section of the heating pipeline in the DQ0th seismic heating pipe network monitoring time period in the GR0th seismic heating pipe network area;

[0115] Represents the maximum value of the soil stress in the soil around the DS0th section of the heating pipeline in the DQ0th seismic heating pipe network monitoring time period in the GR0th seismic heating pipe network area;

[0116] Represents the minimum value of the pore water pressure in the soil around the DS0th section of the heating pipeline in the DQ0th seismic heating pipe network monitoring time period in the GR0th seismic heating pipe network area;

[0117] Represents the minimum value of the soil stress in the soil around the DS0th section of the heating pipeline in the DQ0th seismic heating pipe network monitoring time period in the GR0th seismic heating pipe network area;

[0118] Represents the standard value of the pore water pressure in the soil around the DS0th section of the heating pipeline in the GR0th seismic heating pipe network area;

[0119] Represents the standard value of the soil stress in the soil around the DS0th section of the heating pipeline in the GR0th seismic heating pipe network area;

[0120] Represents the weight factor for calibrating the sudden change level of the pore water pressure in the soil around the DS0th section of the heating pipeline in the GR0th seismic heating pipe network area.

[0121] Represents the weight factor for calibrating the sudden change level of the soil stress in the soil around the DS0th section of the heating pipeline in the GR0th seismic heating pipe network area.

[0122] The possibility of soil liquefaction, which may cause the pipeline to sink or float. The sudden change in pore water pressure of the surrounding soil is the main inducement for soil liquefaction. After liquefaction, the sudden change in the stress of the surrounding soil of the soil drops, which may cause pipeline displacement. Different soil types have different effects on the sudden change in pore water pressure of the surrounding soil and the sudden change in the stress of the surrounding soil;

[0123] Examples of methods for determining specific weights are as follows:

[0124] There are the following several soil types: loose sand, dense sand, and saturated silt.

[0125] Loose sand has weak connections between particles and is prone to liquefaction; dense sand has tight connections between particles and has strong anti-liquefaction ability; saturated silt has fine particles and is prone to liquefaction in the saturated state;

[0126] The probability of liquefaction of loose sand in multiple earthquakes is 70%; the probability of liquefaction of dense sand is 20%; the probability of liquefaction of saturated silt is 50%;

[0127] It is found that for every 10 kPa increase in pore water pressure, the liquefaction probability of loose sand increases by 15%, that of dense sand increases by 5%, and that of saturated silt increases by 10%.

[0128] For every 5 kPa increase in sudden stress change, the pipeline displacement in loose sand increases by 2 cm, that in dense sand increases by 0.5 cm, and that in saturated silt increases by 1 cm.

[0129] Compare the importance of sudden changes in pore water pressure and sudden stress changes under different soil types. The following weights are obtained:

[0130] For loose sand, the calibration level weight factor for sudden change in pore water pressure of the surrounding soil: 0.6, and the calibration level weight factor for sudden change in stress of the surrounding soil: 0.4.

[0131] For dense sand, the calibration level weight factor for sudden change in pore water pressure of the surrounding soil: 0.4, and the calibration level weight factor for sudden change in stress of the surrounding soil: 0.6.

[0132] For saturated silt, the calibration level weight factor for sudden change in pore water pressure of the surrounding soil: 0.5, and the calibration level weight factor for sudden change in stress of the surrounding soil: 0.5.

[0133] Through the above steps, we can reasonably determine the weight factors for sudden changes in pore water pressure and sudden stress changes of the supporting soil under the heating pipeline according to different soil types. This process combines soil mechanics principles, liquefaction mechanisms, pipeline-soil interactions, and statistical analysis methods, ensuring the scientificity and practicality of the weight factors.

[0134] Further, the comprehensive value of the seismic impact degree of urban heating pipelines in the seismic heating pipe network area is obtained through further analysis. The specific process is as follows: The seismic disaster value of urban heating pipelines is processed with the corresponding weight factor of the seismic disaster value of urban heating pipelines to obtain the first component of the seismic disaster of urban heating pipelines; The potential hidden danger value of urban heating pipelines in the earthquake is processed with the corresponding weight factor of the potential hidden danger value of urban heating pipelines in the earthquake to obtain the second component of the seismic disaster of urban heating pipelines; By comprehensively analyzing the first component and the second component of the seismic disaster of urban heating pipelines, the comprehensive value of the seismic impact degree of urban heating pipelines is obtained through processing.

[0135] In this embodiment, represents the comprehensive value of the seismic impact degree of urban heating pipelines for the DS0th section of heating pipelines in the DQ0th seismic heating pipe network monitoring time period in the GR0th seismic heating pipe network area. The comprehensive value of the seismic impact degree of urban heating pipelines is used to quantify the comprehensive pre-estimated value of the relative disaster-affected negative risk of heating pipelines in different seismic heating pipe network areas and different seismic heating pipe network monitoring time periods. If the comprehensive value of the seismic impact degree of urban heating pipelines is larger, it indicates that the relative degree of the negative sudden impact of the actual urban heating pipelines caused by external factors such as earthquakes is higher.

[0136]

[0137] represents the seismic disaster value of urban heating pipelines for the DS0th section of heating pipelines in the GR0th seismic heating pipe network area.

[0138] represents the potential hidden danger value of urban heating pipelines in the earthquake for the DS0th section of heating pipelines in the GR0th seismic heating pipe network area.

[0139] represents the weight factor of the seismic disaster value of urban heating pipelines for the DS0th section of heating pipelines in the GR0th seismic heating pipe network area. represents the weight factor of the potential hidden danger value of urban heating pipelines in the earthquake for the DS0th section of heating pipelines in the GR0th seismic heating pipe network area.

[0140] Seismic intensity is an intensity index that describes the impact of an earthquake on the earth's surface, usually expressed by the Modified Mercalli Intensity or the Chinese Seismic Intensity Scale. The higher the seismic intensity, the greater the direct damage (affected value) of the earthquake to the pipeline. High-intensity earthquakes may also trigger secondary disasters such as soil liquefaction and landslides, increasing the potential hidden dangers of the pipeline. In high-intensity areas, the weight of the affected value is increased, and in medium- and low-intensity areas, the weight of the potential hidden danger value is increased because the direct damage may be small, but potential risks (such as aftershocks and soil liquefaction) still need to be concerned. Therefore, different seismic intensities correspond to different weight factors of the seismic affected value of urban heating pipelines and different weight factors of the seismic potential hidden danger value of urban heating pipelines. A mapping relationship between seismic intensity and the corresponding weight factors of the seismic affected value of urban heating pipelines and the weight factors of the seismic potential hidden danger value of urban heating pipelines is constructed. In the urban heating pipe network database, the seismic intensity of the heating pipelines in the real-time earthquake heating pipe network area is input to obtain the corresponding weight factors of the seismic affected value of urban heating pipelines and the weight factors of the seismic potential hidden danger value of urban heating pipelines.

[0141] Furthermore, a real-time urban heating pipe network earthquake emergency knowledge graph for the earthquake heating pipe network area is obtained. The specific process is as follows: The comprehensive value of the seismic impact degree of different heating pipelines in different monitoring time periods of different earthquake heating pipe networks in different earthquake heating pipe network areas is constructed and compared with the corresponding comprehensive threshold of the seismic impact degree of urban heating pipelines: If the comprehensive value of the seismic impact degree of different heating pipelines in different monitoring time periods of different earthquake heating pipe networks in different earthquake heating pipe network areas is equal to or less than the corresponding comprehensive threshold of the seismic impact degree of urban heating pipelines, there is no need to construct a new basic model of the urban heating pipe network emergency knowledge graph, and the real-time urban heating pipe network earthquake emergency knowledge graph is the same as the first-level model of the urban heating pipe network emergency knowledge graph; If the comprehensive value of the seismic impact degree of different heating pipelines in different monitoring time periods of different earthquake heating pipe networks in different earthquake heating pipe network areas is greater than the corresponding comprehensive threshold of the seismic impact degree of urban heating pipelines, then the comprehensive value of the seismic impact degree of different heating pipelines in different monitoring time periods of different earthquake heating pipe networks in different earthquake heating pipe network areas is subtracted from the corresponding comprehensive threshold of the seismic impact degree of urban heating pipelines to obtain the comprehensive difference in the seismic impact degree of different heating pipelines in different monitoring time periods of different earthquake heating pipe networks in different earthquake heating pipe network areas; According to the comprehensive difference in the seismic impact degree of urban heating pipelines, the corresponding nodes and relationships in the first-level model of the urban heating pipe network emergency knowledge graph in the earthquake heating pipe network area are added to obtain the real-time urban heating pipe network earthquake emergency knowledge graph in the earthquake heating pipe network area.

[0142] In this embodiment, after obtaining the comprehensive value of the seismic impact degree of urban heating pipelines, it is necessary to dynamically adjust the node relationships and emergency strategies in the knowledge graph based on the in-depth assessment of potential risks. The following are the optimized steps, highlighting the potential risk assessment of urban heating pipelines during earthquakes and its impact on the construction of the knowledge graph:

[0143] 1. Comparative analysis of the comprehensive difference and threshold of the seismic impact degree of urban heating pipelines

[0144] Low-risk area (when the comprehensive difference in the seismic impact degree of urban heating pipelines is less than the first-level comprehensive threshold): The current potential damage risk is low, but the long-term fatigue damage and aftershock cumulative effect need to be evaluated.

[0145] Medium-risk area (when the comprehensive difference in the seismic impact degree of urban heating pipelines is equal to or greater than the first-level comprehensive threshold and less than the second-level comprehensive threshold): There are risks of local pipeline displacement and soil liquefaction.

[0146] High-risk area (when the comprehensive difference in the seismic impact degree of urban heating pipelines is equal to or greater than the second-level comprehensive threshold): The direct damage is significant, and the risk of secondary disasters (such as soil liquefaction, pipeline fatigue fracture) is extremely high.

[0147] The comprehensive threshold, first-level comprehensive threshold, and second-level comprehensive threshold of the seismic impact degree of urban heating pipelines are directly extracted from the urban heating pipe network database.

[0148] 2. Adjustment of knowledge graph nodes and relationships (embedding potential risk nodes)

[0149] Low-risk area: New nodes are added, namely pipeline fatigue accumulation and aftershock impact prediction; new relationships are added, pipeline fatigue accumulation → trigger → preventive maintenance, aftershock impact prediction → associated → long-term monitoring strategy.

[0150] Medium-risk area: New nodes are added, namely sudden change of soil pore water pressure and local stress concentration; new relationships are added, sudden change of soil pore water pressure → trigger → liquefaction risk, local stress concentration → require → reinforcement plan.

[0151] High-risk area: New nodes are added, namely pipeline rupture probability and secondary disaster chain (such as liquefaction → pipeline floating → joint fracture); new relationships are added, pipeline rupture probability → depend on → material fatigue coefficient, secondary disaster chain → trigger → comprehensive emergency response.

[0152] 3. Update of associated text (highlighting risk prediction and prevention and control)

[0153] Low-risk area: The text is updated to add "Remaining life prediction based on the fatigue accumulation model" and "Analysis of the long-term impact of aftershock frequency on pipelines".

[0154] Medium-risk area: Text updated to provide "Soil liquefaction probability calculation model" and "Local stress concentration mitigation plan".

[0155] High-risk area: Text updated to embed "Pipeline rupture probability formula" and "Secondary disaster chain blocking strategy".

[0156] 4. Visualization and Interaction Design (Dynamic Risk Display)

[0157] Implement risk heatmap overlay in Neo4j: Red highlight: High-risk nodes (e.g., pipeline rupture probability > 80%). Yellow annotation: Potential risk conduction paths. Click on nodes to view real-time sensor data (e.g., pore water pressure curve). Drag relationship lines to adjust emergency resource allocation weights. Visual enhancement: Intuitively display risk distribution through heatmap and dynamic annotations. Decision support: Interaction functions allow emergency personnel to simulate the effects of different resource allocation scenarios.

[0158] 5. Hierarchical Alarms and Preventive Responses

[0159] Low-risk area: Trigger a level-three alarm: Prompt "Initiate preventive monitoring" (e.g., increase the sampling frequency of vibration sensors).

[0160] Medium-risk area: Trigger a level-two alarm: Prompt "Deploy local reinforcement resources" and "Initiate real-time liquefaction monitoring".

[0161] High-risk area: Trigger a level-one alarm: Prompt "Cut off the energy supply to high-risk pipelines" and "Evacuate surrounding personnel".

[0162] Preventive alarms: Alarms in low-risk areas focus on long-term risk prevention rather than immediate response. Secondary disaster blocking: High-risk areas need to prioritize blocking the risk chain (e.g., closing valves to prevent leakage from spreading).

[0163] Construct a comprehensive difference based on the seismic impact degree of urban heating pipelines, and add corresponding nodes and relationships in the first-level model of the urban heating pipeline emergency knowledge graph in the seismic heating pipeline network area to obtain an example of the real-time urban heating pipeline seismic emergency knowledge graph in the seismic heating pipeline network area as follows:

[0164] Scenario: Area C: Comprehensive value = 1.5, soil type = loose sandy soil, groundwater level = 2m, aftershock frequency = 5 times per hour. The pore water pressure is detected to have suddenly increased to 120% of the critical value.

[0165] Knowledge Graph Adjustment: 1. New Nodes: High Risk of Soil Liquefaction, Probability of Pipeline Floating, Exceeding Stress Limit at Joints. 2. New Relationships: High Risk of Soil Liquefaction → Causes → Increase in Probability of Pipeline Floating. Increase in Probability of Pipeline Floating → Triggers → Exceeding Stress Limit at Joints. 3. Associated Texts: Text Update, Embedding "Pipeline Failure Probability Formula" and "Secondary Disaster Chain Blocking Strategy". 4. Alarm Response: Automatic Push: "Immediately close the valves in Area C and initiate soil grouting reinforcement."

[0166] Through the above improvements, the process of constructing the knowledge graph deeply embeds potential risk assessment into each link: 1. Risk Quantification: Realize risk grading through dynamic formulas and threshold models. 2. Risk Visualization: Use graph algorithms and heat maps to display the risk conduction path. 3. Preventive Response: Trigger preventive measures based on fatigue accumulation and liquefaction probability.

[0167] This framework not only covers the immediate impact of earthquakes, but also provides full-cycle emergency support from early warning to recovery for urban heating pipe networks through multi-dimensional potential risk assessment.

[0168] As Figure 2 shown, it is the structural diagram of the urban heating pipe network emergency knowledge graph construction system provided by the embodiment of the present application, including a module for collecting urban heating pipe network emergency data, a module for constructing the basic model of the urban heating pipe network knowledge graph, and a module for dynamically updating the basic model of the urban heating pipe network knowledge graph; the module for collecting urban heating pipe network emergency data: used to collect the original data of urban heating pipe network emergency knowledge; the module for constructing the basic model of the urban heating pipe network knowledge graph: used to construct the basic model of the urban heating pipe network emergency knowledge graph; the module for dynamically updating the basic model of the urban heating pipe network knowledge graph: used to obtain real-time earthquake disaster information. If the emergency earthquake information does not update, there is no adjustment. If the emergency earthquake information updates, the basic model of the urban heating pipe network emergency knowledge graph is reconstructed according to the updated emergency earthquake information to obtain the real-time urban heating pipe network earthquake emergency knowledge graph.

[0169] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0170] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general purpose computers, special purpose computers, embedded processors, or other programmable data processing devices to produce a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in the Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0171] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means that implement the functions specified in the Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0172] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are performed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in the Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0173] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made to these embodiments by those skilled in the art once they learn the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0174] Obviously, those skilled in the art can make various changes and deformations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and deformations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these changes and deformations.

Claims

1. A method for constructing an emergency knowledge graph for a city heating network, characterized in that: The following steps are involved: Collect original data of emergency knowledge of urban heating network; Construct a basic model of emergency knowledge graph for urban heating network; Obtain real-time earthquake disaster information. If the emergency earthquake information is not updated, do not make adjustments. If the emergency earthquake information is updated, rebuild the basic model of the urban heating pipeline network emergency knowledge graph according to the updated emergency earthquake information to obtain the real-time urban heating pipeline network earthquake emergency knowledge graph.

2. The method for constructing an emergency knowledge graph for a city heating network according to claim 1, characterized in that: The collection of urban heating network emergency knowledge original data specifically includes: Acquire predefined data types and predefined database types according to a predefined urban heating network knowledge graph data collection solution; Data is called from a database in a predefined database type through a database query language, and original data on emergency knowledge of the urban heating network is collected through a data collection tool.

3. The method for constructing an emergency knowledge graph for a city heating network according to claim 1, characterized in that: The construction of the basic model of the urban heating network emergency knowledge graph specifically includes: According to the predefined urban heating network emergency knowledge graph nodes, natural disaster type data, affected object data, emergency problem data, emergency measures data and emergency resource data are captured through natural language algorithms; According to the predefined urban heating network emergency knowledge graph relationship, natural disaster type data, affected object data, emergency problem data, emergency measures data and emergency resource data are grouped into three groups to obtain several groups of urban heating network emergency knowledge graph models; Through the natural language processing model, several groups of small models of urban heating pipeline network emergency knowledge graph are linked together to construct and obtain the basic model of urban heating pipeline network emergency knowledge graph.

4. The method for constructing an emergency knowledge graph for a city heating network according to claim 1, characterized in that: The above method reconstructs the basic model of the urban heating network emergency knowledge graph based on the updated emergency earthquake information, specifically including: If the emergency earthquake information is updated, the epicenter location and earthquake impact range are obtained according to the earthquake early warning database; Taking the epicenter as the center point of the coordinate system, the earthquake impact range is divided into circular ranges at predefined distances, and the circular range is evenly divided into several areas to obtain several earthquake heating network areas; The sensor network database is used to divide the urban heating pipeline status data according to the earthquake heating pipeline network area, and the urban heating pipeline status data of different earthquake heating pipeline network areas are obtained; According to the direct data extraction of the urban heating pipeline status data in different earthquake heating pipeline network areas, the urban heating pipeline status in different earthquake heating pipeline network areas is obtained; If the urban heating pipeline under the earthquake heating network area is leaking or ruptured, the corresponding earthquake heating network area and earthquake heating network area status data will be sent to relevant personnel and a first-level alarm will be issued; If the urban heating pipelines in the earthquake heating network area do not leak or rupture, the corresponding earthquake heating network area and the earthquake heating network area status data are subjected to a first analysis to obtain the earthquake damage value of the urban heating pipeline in the earthquake heating network area, and a new basic model of the urban heating network emergency knowledge graph is constructed based on the comparative analysis results of the earthquake damage value of the urban heating pipeline in the earthquake heating network area to obtain a first-level model of the urban heating network emergency knowledge graph in the earthquake heating network area, and the corresponding earthquake heating network area and the earthquake heating network area status data are subjected to a second analysis to obtain a comprehensive value of the earthquake impact degree of the urban heating pipeline in the earthquake heating network area, and the first-level model of the urban heating network emergency knowledge graph is adjusted based on the comparative analysis results of the earthquake potential hidden danger values ​​of the urban heating pipeline in the earthquake heating network area to obtain a real-time urban heating network earthquake emergency knowledge graph in the earthquake heating network area, which is sent to relevant personnel and a second-level alarm is issued.

5. The method for constructing an emergency knowledge graph for a city heating network as claimed in claim 4, characterized in that: The specific process of obtaining the earthquake damage value of the urban heating pipeline in the earthquake heating pipeline network area is as follows: Sequentially number the earthquake heating pipe network areas to obtain different earthquake heating pipe network areas; Sequentially number the earthquake heating network monitoring time periods to obtain different earthquake heating network monitoring time periods; The sections of the heating pipeline are numbered in sequence to obtain different sections of the heating pipeline; Directly extract the use time of the urban heating pipeline and the corresponding service life of the pipeline through the urban heating pipeline network database, divide the use time of the urban heating pipeline by the corresponding service life of the pipeline and process it to obtain the use fatigue value of the urban heating pipeline; The differential transformer displacement sensor is used to measure in real time and upload to the urban heating pipe network database. The maximum vibration displacement of the urban heating pipeline, the corresponding maximum vibration displacement allowable value of the urban heating pipeline and the corresponding pipe material correction factor are directly extracted through the urban heating pipe network database. The maximum vibration displacement of the urban heating pipeline and the corresponding maximum vibration displacement allowable value of the urban heating pipeline are compared and analyzed, and the corresponding pipe material correction factor is used to correct them to obtain the displacement risk value of the urban heating pipeline. The predefined pressure sensor is used to measure in real time and upload to the urban heating network database. The stress of the supporting soil under the pipeline, the corresponding standard stress of the supporting soil under the pipeline, the corresponding correction factor of the supporting soil type under the pipeline and the correction factor of the groundwater level around the pipeline are directly extracted through the urban heating network database. The correction factor of the supporting soil type under the pipeline and the correction factor of the groundwater level around the pipeline are used together to correct the comparative analysis results of the stress of the supporting soil under the pipeline and the corresponding standard stress of the supporting soil under the pipeline, so as to obtain the stress level values ​​of the urban heating pipelines of different sections of the heating pipelines in different earthquake heating network areas. The stress level values ​​of the urban heating pipelines are used to describe the quantitative level of the negative sudden impact of external factors brought by the earthquake on the urban heating pipelines after the correction of the stress level of the supporting soil under the pipeline by the supporting soil type under the pipeline and the groundwater level around the pipeline; The earthquake damage value of urban heating pipelines represents the analytical value of the relative negative distortion degree of heating pipelines, which is a combination of the fatigue value of urban heating pipelines, the displacement risk value of urban heating pipelines and the stress level value of urban heating pipelines.

6. The method for constructing an emergency knowledge graph for a city heating network according to claim 4, characterized in that: The first-level model of the urban heating network emergency knowledge graph under the earthquake heating network area is obtained, specifically including: Compare and analyze the earthquake damage values ​​of urban heating pipelines in different sections of heating pipelines in different earthquake heating pipeline network areas and different earthquake heating pipeline network monitoring time periods with the corresponding urban heating pipeline earthquake damage thresholds: If the earthquake damage value of the urban heating pipelines in different sections of the heating pipelines in different earthquake heating pipeline network monitoring time periods in different earthquake heating pipeline network areas is equal to or less than the corresponding earthquake damage threshold of the urban heating pipelines, there is no need to construct a new basic model of the urban heating pipeline network emergency knowledge graph, and the first-level model of the urban heating pipeline network emergency knowledge graph is consistent with the basic model of the urban heating pipeline network emergency knowledge graph; If the earthquake damage values ​​of the urban heating pipelines of different sections of the heating pipelines in different earthquake heating pipeline network monitoring time periods in different earthquake heating pipeline network areas are greater than the corresponding earthquake damage threshold values ​​of the urban heating pipelines, then the earthquake damage values ​​of the urban heating pipelines of different sections of the heating pipelines in different earthquake heating pipeline network monitoring time periods in different earthquake heating pipeline network areas are subtracted from the corresponding earthquake damage threshold values ​​of the urban heating pipelines, to obtain the earthquake damage differences of the urban heating pipelines of different sections of the heating pipelines in different earthquake heating pipeline network monitoring time periods in different earthquake heating pipeline network areas; According to the difference in earthquake damage to urban heating pipelines, the corresponding fatigue value of urban heating pipelines, the corresponding displacement risk value of urban heating pipelines and the corresponding stress level value of urban heating pipelines, the corresponding nodes and relationships in several groups of small models of urban heating pipeline emergency knowledge graphs are modified to obtain the first-level model of the urban heating pipeline emergency knowledge graph in the earthquake heating pipeline area.

7. The method for constructing an emergency knowledge graph for a city heating network according to claim 4, characterized in that: The comprehensive value of the earthquake impact degree of the urban heating pipeline in the earthquake heating pipeline network area is obtained, specifically including: The speedometer is used to measure in real time and upload to the urban heating network database. The maximum peak ground velocity, the minimum peak ground velocity, the corresponding historical peak ground velocity historical average value and the corresponding aftershock number correction factor are directly extracted from the urban heating network database. The difference between the maximum peak ground velocity and the minimum peak ground velocity is analyzed, and then the proportion analysis is performed with the historical peak ground velocity historical average value, and the corresponding aftershock number correction factor is processed together to obtain the fatigue damage mutation risk value of the urban heating pipeline; The pore water pressure sensor is used to measure in real time and upload to the urban heating network database, and the maximum value of the surrounding soil pore water pressure, the minimum value of the surrounding soil pore water pressure and the corresponding standard value of the surrounding soil pore water pressure are directly extracted through the urban heating network database; the difference between the maximum value of the surrounding soil pore water pressure and the minimum value of the surrounding soil pore water pressure is analyzed, and then the ratio of the corresponding standard value of the surrounding soil pore water pressure is analyzed, and the surrounding soil pore water pressure mutation calibration level value is obtained by combining the surrounding soil pore water pressure mutation calibration level weight factor processing; Through the soil pressure gauge, real-time measurement is performed and uploaded to the urban heating network database, and the maximum value of surrounding soil stress, the minimum value of surrounding soil stress and the corresponding standard value of surrounding soil stress are directly extracted through the urban heating network database; the mutation degree of the minimum value of surrounding soil stress and the maximum value of surrounding soil stress are analyzed, and the proportion of the corresponding standard value of surrounding soil stress is analyzed, and the surrounding soil stress mutation calibration level value is obtained by joint analysis with the surrounding soil stress mutation calibration level weight factor; The relative negative potential risk estimation value of disaster damage represents the quantitative analysis data of the relative negative potential risk estimation through comprehensive analysis of the risk value of fatigue damage mutation of urban heating pipelines, the calibration level value of the mutation of surrounding soil pore water pressure, and the calibration level value of the mutation of surrounding soil stress; Then, a comprehensive value of the earthquake impact on urban heating pipelines in the earthquake heating network area is constructed through analysis.

8. The method for constructing an emergency knowledge graph for a city heating network according to claim 7, characterized in that: The above analysis further obtains the comprehensive value of the earthquake impact degree of the urban heating pipeline in the earthquake heating pipeline network area, and the specific process is as follows: The earthquake damage value of the urban heating pipeline and the corresponding weight factor of the earthquake damage value of the urban heating pipeline are processed to obtain the first component of the earthquake damage of the urban heating pipeline; The potential hazard value of earthquake in urban heating pipelines and the corresponding weight factor of potential hazard value of earthquake in urban heating pipelines are processed to obtain the second component of earthquake damage to urban heating pipelines; The first component of earthquake damage to urban heating pipelines and the second component of earthquake damage to urban heating pipelines are analyzed comprehensively, and the comprehensive value of the earthquake impact on urban heating pipelines is constructed.

9. The method for constructing an emergency knowledge graph for a city heating network according to claim 4, characterized in that: The specific process of obtaining the real-time earthquake emergency knowledge graph of the urban heating network in the earthquake heating network area is as follows: Compare and analyze the comprehensive values ​​of the earthquake impact degree of urban heating pipelines in different earthquake heating pipeline network areas and different earthquake heating pipeline network monitoring time periods with the corresponding comprehensive thresholds of the earthquake impact degree of urban heating pipelines: If the comprehensive value of the degree of earthquake impact on urban heating pipelines for different heating pipelines in different earthquake heating pipeline network monitoring time periods in different earthquake heating pipeline network areas is equal to or less than the corresponding comprehensive threshold value for the degree of earthquake impact on urban heating pipelines, there is no need to construct a new basic model of the urban heating pipeline network emergency knowledge graph, and the real-time urban heating pipeline network earthquake emergency knowledge graph is consistent with the first-level model of the urban heating pipeline network emergency knowledge graph; If the comprehensive value of the degree of earthquake impact on urban heating pipelines for different heating pipelines in different earthquake heating pipeline network monitoring time periods in different earthquake heating pipeline network areas is greater than the corresponding comprehensive threshold value for the degree of earthquake impact on urban heating pipelines, then the comprehensive value of the degree of earthquake impact on urban heating pipelines for different heating pipelines in different earthquake heating pipeline network monitoring time periods in different earthquake heating pipeline network areas is subtracted from the corresponding comprehensive threshold value for the degree of earthquake impact on urban heating pipelines to obtain the comprehensive difference value for the degree of earthquake impact on urban heating pipelines for different heating pipelines in different earthquake heating pipeline network monitoring time periods in different earthquake heating pipeline network areas; According to the degree of earthquake impact on urban heating pipelines, a comprehensive difference is constructed to add corresponding nodes and relationships in the first-level model of the urban heating pipeline emergency knowledge graph under the earthquake heating pipeline network area, so as to obtain a real-time urban heating pipeline earthquake emergency knowledge graph under the earthquake heating pipeline network area.

10. The urban heating network emergency knowledge graph construction system is characterized by: It includes a module for collecting emergency data of urban heating pipe network, a module for building a basic model of urban heating pipe network knowledge graph, and a module for dynamically updating the basic model of urban heating pipe network knowledge graph; Module for collecting emergency data of urban heating network: used to collect original data of emergency knowledge of urban heating network; Urban heating network knowledge graph basic model construction module: used to construct the urban heating network emergency knowledge graph basic model; Dynamic update module of the basic model of the urban heating pipeline network knowledge graph: used to obtain real-time earthquake disaster information. If the emergency earthquake information is not updated, no adjustment will be made. If the emergency earthquake information is updated, the basic model of the urban heating pipeline network emergency knowledge graph will be rebuilt according to the updated emergency earthquake information to obtain the real-time urban heating pipeline network earthquake emergency knowledge graph.

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