Method and device for simulating and early warning of underground cable fault based on sensing device

Abstract

Embodiments of the present disclosure disclose a method and device for underground cable fault simulation early warning based on a sensing device. A specific embodiment of the method comprises: obtaining a real-time data set of an underground cable detected by a plurality of environment sensors distributedly deployed; inputting the real-time data set of the underground cable into a pre-constructed three-dimensional model of urban underground cables to obtain a current three-dimensional model of the cables; performing anomaly prediction on the real-time data of the underground cable in the current three-dimensional model of the cables to generate an anomaly prediction information set; and performing the following simulation early warning steps: extracting historical anomaly data matching a predicted fault type in the anomaly prediction information from a pre-set historical anomaly database as to-be-simulated anomaly data; performing cable fault simulation on the underground cable at a location of the predicted fault coordinates; performing simulation emergency handling on the cable fault simulation data to generate simulation emergency strategy information; and issuing a cable emergency early warning. The embodiment can improve the early warning efficiency of cable faults.

Description

Technical Field

[0001] The embodiments disclosed herein relate to the fields of computer technology and cable fault simulation and early warning, specifically to a method and apparatus for underground cable fault simulation and early warning based on sensing devices. Background Technology

[0002] Underground cables are widely used in power, communications, and other fields, but their failures can severely impact production and daily life. Underground cable fault simulation is an important tool for studying fault mechanisms, detection methods, and protective measures. Currently, the common approach to underground cable fault simulation is to build a physical model or set up artificial faults in a real cable system to reproduce the fault scenario and collect data, then use the collected data for simulated early warning.

[0003] However, when using the above method for underground cable fault simulation and early warning, it is difficult to conduct timely and targeted fault simulations for different areas and different fault types, resulting in insufficient early warning efficiency for cable faults.

[0004] The information disclosed in this background section is only intended to enhance the understanding of the background of the inventive concept, and therefore may contain information that does not form prior art known to those skilled in the art. Summary of the Invention

[0005] The summary portion of this disclosure is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description portion. This summary portion is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0006] Some embodiments of this disclosure propose a method and apparatus for simulating and warning of underground cable faults based on sensing devices, in order to solve the technical problems mentioned in the background section above.

[0007] In a first aspect, some embodiments of this disclosure provide a method for simulating and warning of underground cable faults based on sensing devices. The method includes: acquiring a real-time dataset of underground cables detected by multiple distributed environmental sensors, wherein each real-time cable dataset includes an environmental sensor identifier and environmental sensor coordinates; inputting the aforementioned real-time underground cable dataset into a pre-constructed three-dimensional model of urban underground cables to obtain a current three-dimensional cable model; performing anomaly prediction on the real-time underground cable data in the current three-dimensional cable model to generate anomaly prediction information set, wherein the anomaly prediction information includes a predicted fault type and predicted fault coordinates; for each anomaly prediction information in the aforementioned anomaly prediction information set, performing the following simulation and warning steps: extracting historical anomaly data from a preset historical anomaly database that matches the predicted fault type in the aforementioned anomaly prediction information, as anomaly data to be simulated; based on the anomaly data to be simulated, simulating cable faults in the underground cables at the locations of the predicted fault coordinates in the aforementioned anomaly prediction information within the current three-dimensional cable model, and recording the cable fault simulation data; based on the aforementioned current three-dimensional cable model, performing simulated emergency processing on the aforementioned cable fault simulation data to generate simulated emergency strategy information; and issuing a cable emergency warning in response to the simulated emergency strategy information not meeting preset emergency processing conditions.

[0008] Secondly, some embodiments of this disclosure provide an underground cable fault simulation and early warning device based on sensing devices. The device includes: an acquisition unit configured to acquire real-time datasets of underground cables detected by multiple distributed environmental sensors, wherein each real-time cable dataset includes an environmental sensor identifier and environmental sensor coordinates; an input unit configured to input the aforementioned real-time underground cable datasets into a pre-constructed three-dimensional model of urban underground cables to obtain a current three-dimensional cable model; an anomaly prediction unit configured to perform anomaly prediction on the real-time underground cable data in the current three-dimensional cable model to generate an anomaly prediction information set, wherein the anomaly prediction information includes predicted fault type and predicted fault coordinates; and a simulation early warning unit. The unit is configured to perform the following simulation warning steps for each anomaly prediction in the above anomaly prediction information set: extract historical anomaly data that matches the predicted fault type in the above anomaly prediction information from a preset historical anomaly database, as the anomaly data to be simulated; based on the anomaly data to be simulated, simulate cable faults in the underground cable at the location of the predicted fault coordinates in the above anomaly prediction information in the above current cable 3D model, and record the cable fault simulation data; based on the above current cable 3D model, perform simulated emergency processing on the above cable fault simulation data to generate simulated emergency strategy information; and issue a cable emergency warning in response to the simulated emergency strategy information not meeting the preset emergency processing conditions.

[0009] Thirdly, some embodiments of this disclosure provide an electronic device, including: one or more processors; and a storage device having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0010] Fourthly, some embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.

[0011] The above-described embodiments of this disclosure have the following beneficial effects: the underground cable fault simulation and early warning method based on sensing devices according to some embodiments of this disclosure can improve the early warning efficiency of cable faults. Based on this, the underground cable fault simulation and early warning method based on sensing devices according to some embodiments of this disclosure first acquires a real-time dataset of underground cables detected by multiple distributed environmental sensors, wherein each real-time cable data includes an environmental sensor identifier and environmental sensor coordinates. Here, by introducing environmental sensors, actual cable data of the underground power grid can be acquired in a timely manner. Therefore, fault simulation can be performed using actual cable data, improving the reliability of the simulation results. Then, the above-mentioned real-time dataset of underground cables is input into a pre-constructed three-dimensional model of urban underground cables to obtain the current three-dimensional cable model. Here, by introducing the three-dimensional model of urban underground cables, the structure of the actual underground cables can be fully replaced. Therefore, there is no need to build a physical model for cable faults; simulation tests can be directly performed at any point in the three-dimensional model of urban underground cables. This improves the efficiency of cable fault simulation. Next, anomaly prediction is performed on the real-time data of underground cables in the current three-dimensional cable model to generate an anomaly prediction information set. The anomaly prediction information includes predicted fault type and predicted fault coordinates. Here, anomaly prediction can be used to detect abnormal fluctuations in data from underground cables in a timely manner. This allows for early prediction of potential cable faults. Subsequently, for each anomaly prediction in the aforementioned anomaly prediction information set, the following simulation and early warning steps are executed: First, historical anomaly data matching the predicted fault type in the aforementioned anomaly prediction information is extracted from a pre-set historical anomaly database and used as the anomaly data to be simulated. Introducing the historical anomaly database helps in determining detected cable anomalies and provides data references for actual fault scenarios in cable fault simulation. Second, based on the anomaly data to be simulated, cable fault simulation is performed on the underground cable at the predicted fault coordinates in the aforementioned anomaly prediction information within the current cable 3D model, and the cable fault simulation data is recorded. Cable fault simulation allows for early fault prediction of locations where data fluctuations occur. This facilitates the early collection of fault data. Third, based on the current cable 3D model, simulated emergency processing is performed on the cable fault simulation data to generate simulated emergency strategy information. The inclusion of the current cable 3D model facilitates the simulation of handling measures for cable faults in actual scenarios. Therefore, not only can cable faults be simulated, but emergency response measures can also be simulated and tested. This allows for further assessment of the adequacy of emergency response measures. Fourth, in response to simulated emergency strategy information failing to meet preset emergency response conditions, a cable emergency warning is issued.Here, by issuing cable emergency warnings, we can not only conduct advance simulations of cable faults and provide data references for actual cable faults, but also improve emergency strategies in response to cable faults. This enhances the efficiency and practicality of cable fault warnings. Attached Figure Description

[0012] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and elements are not necessarily drawn to scale.

[0013] Figure 1 This is a flowchart of some embodiments of the underground cable fault simulation and early warning method based on sensing devices according to the present disclosure;

[0014] Figure 2 This is a schematic diagram of the cross-section of the fiber optic cable laying corridor;

[0015] Figure 3 This is a schematic diagram of the structure of some embodiments of the underground cable fault simulation and early warning device based on sensing devices according to the present disclosure;

[0016] Figure 4 This is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. Detailed Implementation

[0017] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0018] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0019] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0020] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0021] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0022] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0023] Figure 1 A flowchart 100 of some embodiments of the underground cable fault simulation and early warning method based on sensing devices according to this disclosure is shown. The underground cable fault simulation and early warning method based on sensing devices includes the following steps:

[0024] Step 101: Obtain the real-time dataset of underground cables detected by multiple environmental sensors deployed in a distributed manner.

[0025] In some embodiments, the implementing entity (e.g., an electronic device) of the underground cable fault simulation and early warning method based on sensing devices can acquire real-time datasets of underground cables detected by multiple distributed environmental sensors via wired or wireless means. Each real-time cable dataset may include environmental sensor identifiers and coordinates. Here, the environmental sensors may be distributed power distribution equipment within the cable, used to acquire data at various locations along the cable via temperature-measuring optical fibers, vibration-measuring optical fibers, etc., laid within the cable. Each real-time underground cable dataset may correspond to one cable, characterizing the state of each location along that cable.

[0026] As an example, real-time data for underground cables can also include a sequence of current fiber optic temperature values.

[0027] It should be noted that the aforementioned computing devices can be either hardware or software. When the computing device is hardware, it can be implemented as a distributed cluster consisting of multiple servers or terminal devices, or as a single server or a single terminal device. When the computing device is software, it can be installed on the hardware devices listed above. It can be implemented as, for example, multiple software programs or software modules used to provide distributed services, or as a single software program or software module. No specific limitations are made here.

[0028] Step 102: Input the real-time dataset of underground cables into the pre-built 3D model of urban underground cables to obtain the current 3D model of cables.

[0029] In some embodiments, the aforementioned executing entity can input the aforementioned real-time dataset of underground cables into a pre-built three-dimensional model of urban underground cables to obtain the current three-dimensional model of the cables.

[0030] In some optional implementations of certain embodiments, the real-time underground cable data in the aforementioned real-time underground cable dataset further includes cable location identifiers. The aforementioned three-dimensional model of the urban underground cable includes multiple distributed simulation sensors. Each simulation sensor corresponds to a location distributed with each environmental sensor. The executing entity inputs the aforementioned real-time underground cable dataset into the pre-constructed three-dimensional model of the urban underground cable to obtain the current three-dimensional cable model, including the following steps:

[0031] S1. Based on the cable location identifiers and environmental sensor identifiers included in the real-time underground cable data set, determine the simulation sensor corresponding to each real-time underground cable data. The aforementioned three-dimensional model of the urban underground cable also includes: cable node groups representing power distribution equipment, cable simulation line groups representing the underground cable, and cable coverage areas corresponding to each cable node. Simulation sensors are set at the cable node locations in the three-dimensional model of the urban underground cable and associated with at least one cable simulation line.

[0032] In practice, the 3D model of urban underground cables is established based on the actual cable laying structure. Therefore, the environmental sensors installed in the power distribution equipment correspond to the simulated sensors in the 3D model. Thus, the correspondence between each environmental sensor and the simulated sensor can be established using the coordinates of the environmental sensors or the cable nodes of the power distribution equipment they are located at. Simultaneously, the communication relationship between the environmental sensors and the simulated sensors can be established.

[0033] S2, using various simulation sensors, real-time data from the underground cables is filled into the corresponding simulated cable lines and cable nodes to obtain the current 3D cable model. Specifically, through the communication relationship between environmental sensors and simulation sensors, the actual data of each cable can be directly transmitted to the simulation sensors in the 3D urban underground cable model. Secondly, the data collected by the simulation sensors for each cable can be used to fill in the corresponding real-time underground cable data for each simulated cable line. In this way, the current 3D cable model can be obtained.

[0034] In practice, by establishing communication relationships between environmental sensors and simulation sensors, data from underground cables can be mapped to the current 3D cable model in real time. Additionally, environmental sensors measure the state data of optical fibers (e.g., temperature, vibration signals). Power transmission data within the cable can be transmitted to the current 3D cable model through the correspondence between power distribution equipment and cable nodes. This allows the current 3D cable model to synchronously simulate the operation of the actual underground power grid. Therefore, by establishing communication relationships between environmental sensors and simulation sensors, and between cable equipment and cable nodes, data transmission between actual cable data and the cable in the 3D cable model can be achieved. This eliminates the need to establish a more complex correspondence between the simulated cable lines and the actual cables, thus saving computational resources.

[0035] Step 103: Perform anomaly prediction on the real-time data of underground cables in the current three-dimensional cable model to generate anomaly prediction information set.

[0036] In some embodiments, the aforementioned executing entity can perform anomaly prediction on the real-time data of the underground cable in the current three-dimensional cable model to generate an anomaly prediction information set. The anomaly prediction information includes predicted fault type and predicted fault coordinates. Here, the fault type may include, but is not limited to, at least one of the following: short-circuit fault, high-resistance fault, open-circuit fault, cable overload fault, low-resistance fault, etc. The predicted fault coordinates may be the center coordinates of a cable segment where data fluctuations exist.

[0037] In some optional implementations of certain embodiments, the execution entity performs anomaly prediction on the real-time data of underground cables in the current three-dimensional cable model to generate anomaly prediction information set, including:

[0038] For each cable node in the current 3D cable model described above, perform the following steps:

[0039] S1, perform node pressure detection on the aforementioned cable nodes to generate the node pressure change. This involves obtaining the voltage values ​​of each power distribution interface within the cable node. Furthermore, the ratio of this voltage value to the corresponding standard voltage can be determined as the node pressure change.

[0040] S2, perform line anomaly detection on each simulated cable line under the aforementioned cable node to generate line anomaly information. This line anomaly information includes line anomaly values, predicted fault types, and predicted fault coordinates. Secondly, a preset line anomaly detection algorithm can be used to perform line anomaly detection on each simulated cable line under the aforementioned cable node to generate line anomaly information. Additionally, voltage or current values ​​exceeding corresponding thresholds in the simulated cable lines can be extracted as line anomaly values. Here, line anomaly values ​​can characterize the degree of anomaly in a particular abnormal line.

[0041] As an example, the above-mentioned line anomaly detection algorithms may include, but are not limited to, at least one of the following: low-voltage pulse method, high-voltage bridge method, pulse current method, traveling wave method, etc.

[0042] S3, in response to determining that the aforementioned node pressure change or the aforementioned line anomaly value meets a preset anomaly condition, the aforementioned cable node is marked as an anomaly to obtain a target cable node group, and the aforementioned target cable node group, the aforementioned node pressure change, the aforementioned line anomaly information, and the corresponding cable simulation line are determined as anomaly prediction information. The preset anomaly condition can be that the node pressure change is greater than a preset change threshold, or the line anomaly value is greater than a preset circuit threshold. For example, the current value is greater than a preset current threshold, and the voltage value is greater than a corresponding preset voltage threshold. Secondly, the cable node corresponding to the cable simulation line where the predicted fault coordinates are located can be marked as an anomaly, and used as the target cable node.

[0043] In practice, by performing cable anomaly detection on the current 3D cable model, the location of anomalies can be pinpointed promptly based on cable data fluctuations. This facilitates direct subsequent cable fault simulation. Compared to detecting anomalies in the actual underground cable and then transmitting the anomaly location to the current 3D cable model, this reduces transmission steps and improves the efficiency of cable fault simulation.

[0044] Step 104: For each anomaly prediction information in the anomaly prediction information set, perform the following simulated early warning steps:

[0045] Step 1041: Extract historical anomaly data from the preset historical anomaly database that matches the predicted fault type in the anomaly prediction information, and use it as the anomaly data to be simulated.

[0046] In some embodiments, the execution entity can extract historical anomaly data that matches the predicted fault type in the anomaly prediction information from a preset historical anomaly database, and use this as the anomaly data to be simulated. Here, "matching" can mean that the fault type in the historical anomaly data is the same as the predicted fault type. Additionally, to improve the accuracy of the simulation, "matching" can also mean that the historical anomaly data and the predicted fault type correspond to the same cable model.

[0047] In some optional implementations of certain embodiments, the aforementioned historical anomaly database is constructed through the following steps:

[0048] S1. Obtain at least one historical cable fault data set. This historical cable fault data may include, but is not limited to, at least one of the following: a historical fault interval data sequence set, fault type, historical fault impact range, and historical fault standard handling strategy information. Each historical fault interval data set in the aforementioned historical fault interval data sequence set includes, but is not limited to, at least one of the following: historical cable current value, historical cable voltage value, historical cable temperature value, historical corridor humidity value, and historical cable node pressure value. The historical fault interval data sequence set can be cable data within a continuous time period during the historical fault. Here, fault types can be categorized into internal factors (e.g., voltage overload, equipment aging, line aging) and external factors (e.g., fire, flood, cable damage). The historical fault impact range can be the area where power supply is disrupted due to the cable fault. The historical fault standard handling strategy information may include a fault handling record table. For example, the fault handling record table may include logs such as: fault detection at a certain time point, fault warning issued at a certain time point, and fault handling completed at a certain time point.

[0049] Here, historical cable fault data can be categorized before storage to facilitate storage. Specifically, categorization can be based on fault type and faulty cable model, among other things, within the historical cable fault data.

[0050] S2, store each data item from at least one historical cable fault data point according to preset field attributes into the initial anomaly database to obtain the aforementioned historical anomaly database. Here, by introducing the historical anomaly database, matching historical fault data can be selected for different fault types in the same area, thereby improving the realism and practicality of fault simulation.

[0051] Step 1042: Based on the anomaly data to be simulated, perform cable fault simulation on the underground cable at the location of the predicted fault coordinates in the anomaly prediction information in the current three-dimensional cable model, and record the cable fault simulation data.

[0052] In some embodiments, the execution entity can, based on the anomaly data to be simulated, perform cable fault simulation on the underground cable at the predicted fault coordinates location in the anomaly prediction information within the current three-dimensional cable model, and record the cable fault simulation data. Specifically, a preset three-dimensional engine particle system can be used to perform cable fault simulation on the underground cable at the predicted fault coordinates location and record the cable fault simulation data.

[0053] In some optional implementations of certain embodiments, the real-time data for underground cables includes corridor humidity values ​​and cable temperature values. Specifically, the executing entity, based on the anomaly data to be simulated, performs cable fault simulation on the underground cable at the predicted fault coordinates location in the anomaly prediction information within the current three-dimensional cable model, including:

[0054] S1, in response to determining the predicted fault type as the first fault type, the underground cable at the location of the predicted fault coordinates is identified as the target simulated cable. Here, the first fault type characterizes a fault in the simulated cable line.

[0055] As an example, the first fault type may include, but is not limited to, at least one of the following: short circuit fault, high resistance fault, open circuit fault, cable overload fault, etc.

[0056] S2, extract the coordinates of the pipe gallery space structure at the location of the predicted fault coordinates in the current 3D model of the cable, and obtain the pipe gallery space structure coordinate set. Specifically, the fault influence space can be selected from both sides of the predicted fault coordinate location as the center point, according to a preset fault influence size. Then, the vertex coordinates of the fault influence space are determined as the pipe gallery space structure coordinates.

[0057] As an example, such as Figure 2 The schematic diagram shows a cross-sectional view of the fiber optic cable laying corridor. The cable corridor is a square passageway with cable supports installed on the walls on both sides. For example, Figure 2 The first layer of support on the left side is equipped with temperature-sensing and vibration-sensing optical fibers wrapped in flame-retardant tubing. These are used to measure the temperature and vibration data of the tunnel. Secondly... Figure 2 Two power distribution cables are laid on the bottom layer on the left, and another temperature-measuring optical fiber is laid between the two power distribution cables to measure their temperature data. Therefore, the fault influence space can be a three-dimensional rectangular space centered on the predicted fault coordinates, i.e., the fault influence space. Thus, the coordinates of the eight vertices of the fault influence space can be determined as the structural coordinates of the utility tunnel, resulting in the utility tunnel structural coordinate set.

[0058] Here, different cable types correspond to different fault impact dimensions. For example, ordinary cables are non-flame-retardant cables, and in the event of an arc particle breakdown and ignition, they can cause fire to spread. Therefore, the corresponding fault impact dimension is set to be relatively large (e.g., 10 meters). If the cable is a flame-retardant cable, it has strong flame-retardant properties, so a smaller fault impact dimension can be set (e.g., 3 meters). Thus, by appropriately selecting the range of the simulation space, the computational resource consumption during the fault simulation process can be reduced.

[0059] Optionally, the historical fault impact range in the abnormal data to be simulated can be used as the fault impact space, and then the corresponding coordinate set of the utility tunnel space structure can be extracted.

[0060] S3. Using the aforementioned anomaly data to be simulated and the corridor humidity value included in the real-time underground cable data of the target simulated cable, the temperature change of the cable temperature value included in the real-time underground cable data is simulated to generate a core temperature change curve. The core temperature change curve characterizes the temperature change of the cable at the predicted fault coordinate location within a preset time period. Here, a Gaussian kernel function can be used to calculate the temperature value of the cable at the predicted fault coordinate location within the preset time period based on the aforementioned cable temperature value, thus obtaining the core temperature change curve.

[0061] Specifically, the Gaussian kernel function can also include the corridor humidity heat dissipation rate as a constant term to simulate the effect of corridor humidity on cable temperature changes. Here, the corridor humidity heat dissipation rate can be the product of the corridor humidity value (for example, the corridor humidity value is 50%, i.e., humidity percentage) and the corresponding preset humidity coupling coefficient (for example, 0.1℃·m3 / kg).

[0062] S4. Based on the aforementioned temperature change curves, temperature change simulations are performed on the adjacent regions of the predicted fault coordinates on the target simulated cable to generate a sequence of associated temperature change curves. Each associated temperature change curve in the sequence corresponds to a point in time within the preset time period. Here, the temperature change simulations for each cable coordinate location within the adjacent regions can be performed using the aforementioned Gaussian kernel function to obtain the associated temperature change curves.

[0063] S5, based on the aforementioned sequence of associated temperature change curves, the tunnel space enclosed by the coordinates of each tunnel space structure in the aforementioned tunnel space structure coordinate group is dynamically divided into simulated regions to generate a first simulated region coordinate group sequence and a second simulated region coordinate group sequence. The first simulated region corresponding to the first simulated region coordinate group sequence gradually expands following the changes in the associated temperature change curves. Here, the cable coordinates corresponding to the temperature values ​​of each associated temperature change curve in the associated temperature change curve sequence that are greater than a preset temperature threshold can be determined as the region segmentation coordinate group. Then, the tunnel space is divided into intervals according to the lines connecting the region segmentation coordinates in each region segmentation coordinate group as the region division boundaries, resulting in the first simulated region coordinate group and the second simulated region coordinate group. Here, the temperature value within the region corresponding to the first simulated region coordinate group is greater than the temperature value within the region corresponding to the second simulated region coordinate group.

[0064] In practice, the degree of temperature influence varies across different areas in a fault simulation. For example, the closer to the ignition point, the higher the temperature and the greater the influence of temperature; therefore, this area can be used for detailed simulation, forming the first simulation region. In contrast, the influence of flame temperature is relatively low in the second simulation region, allowing for a more coarse simulation. Furthermore, as the fire intensifies, the temperature influence range gradually expands outwards, thus the first simulation region gradually enlarges. Here, the boundary between the first and second simulation regions can be an irregular interface.

[0065] S6, establish a fault simulation dense particle field for each region in the first simulation region coordinate group sequence, resulting in a fault simulation dense particle field set. The fault simulation dense particles in this field are used to simulate temperature radiation. This fault simulation dense particle field can be established using a 3D engine particle system. The range of each particle in the fault simulation dense particle field lies within the region of the first simulation region coordinate group. The fault simulation dense particle field is used to refine the simulation of the diffusion of faults (e.g., flames or smoke) within the first simulation region. Here, the 3D engine particle system can establish attributes such as the generation rate, lifespan, emission speed, color, and size of each fault simulation dense particle, thereby simulating fault diffusion through particle motion.

[0066] S7. Establish a fault simulation sparse vector field for the region where the second simulated coordinate group is located in the above-mentioned second simulated region coordinate group sequence, and obtain a fault simulation sparse vector field set. The fault simulation coefficient vector field is generated through the following steps: First, obtain the cable structure features of the target simulated cable within the space corresponding to the pipe gallery spatial structure coordinate group. Here, the cable structure features may include the cable central axis equation and cable dimensions (e.g., cable radius). Then, determine the simulated coordinates of the cable outer wall through the cable structure features, and obtain a set of simulated coordinates of the cable outer wall. Here, the simulated coordinates of the cable outer wall can be coordinates located on the cable outer wall, serving as the vector starting point. Next, within the coordinate range corresponding to each second simulated region coordinate group, select simulated coordinates of the cable outer wall from the above-mentioned simulated coordinate set of the cable outer wall according to a second coordinate interval (e.g., 10 mm), serving as the second vector starting point coordinates, and obtain a vector starting point coordinate group. Then, using each vector starting point coordinate in the above-mentioned vector starting point coordinate group as the starting point, determine the corresponding vector direction. Here, the vector direction can be a direction perpendicular to the cable central axis equation and passing through the location of the vector starting point coordinate. Then, based on the directions of each vector, initial vectors can be obtained by taking the coordinates of each vector's starting point as the starting point and the boundary of the region containing the second simulation region's coordinate set as the ending point. Finally, the coordinates of each initial vector on the boundary between the first and second simulation regions can be used as the starting point to extract the fault simulation sparse vectors. Here, the fault simulation sparse vector is a vector segment from the boundary between the first and second simulation regions to the boundary of the region containing the second simulation region's coordinate set. Thus, the fault simulation sparse vectors corresponding to the starting coordinates of each vector can be used as a vector field to obtain the fault simulation sparse vector field. Each fault simulation sparse vector field can correspond to a time point.

[0067] S8. Topological connections are performed on the fault simulation sparse vector fields corresponding to the same time moment in the fault simulation sparse vector field set to generate a fault simulation topology network sequence. Specifically, for fault simulation sparse vector fields corresponding to the same time moment, the endpoint coordinates of adjacent fault simulation sparse vectors can be connected to obtain a fault simulation topology network with a triangle-based structure. For example, each fault simulation topology network can represent the (outermost) expansion boundary of a flame (or smoke, etc.) at a certain time.

[0068] S9. Cable fault simulation is performed using the aforementioned core temperature change curve, the aforementioned associated temperature change curve sequence, the aforementioned first simulation region coordinate group sequence, the aforementioned second simulation region coordinate group sequence, the fault simulation dense particle field set, the aforementioned fault simulation sparse vector field set, and the aforementioned fault simulation topology sequence. During the cable fault simulation, cable data is adjusted using a historical fault interval data sequence set. Here, the aforementioned core temperature change curve, the aforementioned associated temperature change curve sequence, the aforementioned first simulation region coordinate group sequence, the aforementioned second simulation region coordinate group sequence, the fault simulation dense particle field set, the aforementioned fault simulation sparse vector field set, and the aforementioned fault simulation topology sequence can be set on the same time axis according to time points, and then the data is displayed sequentially using the corresponding rendering methods, thereby achieving cable fault simulation. Here, the rendering colors corresponding to the boundaries of the first and second simulation regions can be different. The fault simulation sparse vectors can map different temperature values ​​through different values ​​in the vectors, thereby achieving the effect of simulating flames and temperature.

[0069] Here, considering the irregular shape of flame combustion, the minimum value in the fault simulation sparse vector can be 0. Therefore, when mapping the flame and temperature based on the values ​​in the vector, the characteristic of the flame's irregular shape can be achieved.

[0070] In addition, the data corresponding to the target simulated cable (or target cable node) and its corridor in the current 3D cable model can be adjusted synchronously based on the rate of change of historical cable current, voltage, temperature, corridor humidity, and pressure values ​​from the anomaly data to be simulated. Here, by adjusting the data corresponding to the corridor where the target simulated cable (or target cable node) is located, the impact of the fault simulation on the cable operation data can be determined. This facilitates further reflection of the cable fault situation based on the cable operation data.

[0071] S10, in response to determining that the predicted fault type is the second fault type, the equipment space structure coordinates corresponding to the target cable node group in the current cable 3D model are extracted to obtain the equipment space structure coordinate set. Cable fault simulation is then performed based on the equipment space structure coordinate set. The second fault type indicates that a circuit fault exists in the power distribution equipment corresponding to the cable node. Here, the fault space corresponding to the power distribution equipment fault is relatively fixed. That is, the equipment space structure coordinate set can represent the vertex coordinates of the equipment box. Therefore, circuit fault simulation can be performed within the equipment box through the above steps.

[0072] As an example, the second fault type may include, but is not limited to, at least one of the following: equipment short circuit fault, equipment overload fault, etc.

[0073] In practice, cable fault simulation typically employs a 3D engine particle system for full-scale fault simulation. This involves defining attributes such as particle quantity, lifecycle of each particle, initial velocity, color changes, and size changes to simulate cable faults. To improve simulation accuracy, a large number of particles are often required across the entire simulated fault area. However, rendering all particles simultaneously demands significant computational resources, leading to a surge in GPU (Graphics Processing Unit) load and impacting simulation smoothness. Furthermore, the lack of an intelligent culling mechanism results in particles outside the field of view continuing to update, further consuming computational resources. This can cause simulation stuttering, crashes, and failures. Therefore, step 1042 and related content in this application firstly divide the fault area into different regions, setting a dense particle field for the core region (first simulation region) and a sparse vector field for the edge region (second simulation region), and dynamically dividing the core and edge regions. This not only ensures the effectiveness of the fault simulation but also reduces computational resource consumption. Specifically, three-dimensional vectors replace three-dimensional particles, forming vector fields at different time points. The characteristics of the three-dimensional particles are mapped through vector values ​​(e.g., particle size, color, and dimensions). This achieves the replacement of three-dimensional particles. Because of the introduction of vector fields, not only can the changes in continuous fields (e.g., flames, smoke) be simulated by varying the length and value of the vectors, but batch processing can also be performed through matrix operations, avoiding the need for cyclic processing of individual particles. This significantly reduces the number of particles required to generate during the simulation. Correspondingly, it also reduces the number of repetitions of the dynamic generation and destruction of three-dimensional particles, and greatly reduces logical judgments. This further reduces the consumption of computational resources, thereby improving the efficiency of fault simulation and avoiding lag and simulation failures.

[0074] Step 1043: Based on the current three-dimensional cable model, perform simulated emergency processing on the cable fault simulation data to generate simulated emergency strategy information.

[0075] In some embodiments, the aforementioned execution entity may perform simulated emergency processing on the aforementioned cable fault simulation data based on the aforementioned current three-dimensional cable model to generate simulated emergency strategy information. The cable fault simulation data may include the aforementioned core temperature change curve, the aforementioned sequence of associated temperature change curves, the aforementioned sequence of coordinate sets for the first simulation region, the aforementioned sequence of coordinate sets for the second simulation region, the fault simulation dense particle field set, the aforementioned fault simulation sparse vector field set, and the aforementioned fault simulation topology sequence.

[0076] In some optional implementations of certain embodiments, the execution entity performs simulated emergency processing on the cable fault simulation data based on the current three-dimensional cable model to generate simulated emergency strategy information, including the following steps:

[0077] S1. Based on the aforementioned cable fault simulation data and the corresponding cable coverage area, locate the fault-affected area in the current three-dimensional cable model. Each cable node or simulated cable in the current three-dimensional cable model can correspond to a preset power supply range according to the actual cable location and power consumption.

[0078] Here, during the fault simulation process, for the first fault type, power monitoring can be performed on at least one distribution node connected to the target simulated cable to determine if there is any data exceeding a preset threshold. If not, the power supply range (i.e., the cable coverage area) corresponding to the target simulated cable is determined as the fault-affected area. If there is a situation where the threshold is exceeded, the power supply range corresponding to the distribution node is determined as the fault-affected area. For the second fault type, the power supply range corresponding to the distribution node can be directly determined as the fault-affected area.

[0079] S2 controls the automatic adjustment of power supply equipment at each cable node within the aforementioned fault-affected area, and sends the location of the fault-affected area and the simulated cable fault data to the corresponding fault handling terminal. Specifically, the cable nodes can be controlled to cut off power to the simulated cable line (or power supply equipment) with the fault. Furthermore, the location of the fault-affected area and the simulated cable fault data can be sent to the fault handling terminal corresponding to each cable node via a preset address book, allowing maintenance personnel to be notified for cable maintenance. Here, the fault handling terminal can also be associated with the current 3D cable model for timely communication and to ensure smooth communication.

[0080] In practice, after receiving the aforementioned location of the affected area and the simulated cable fault data, the fault handling terminal can return information on whether the fault handling device corresponding to the fault type is complete. Here, the fault handling device includes, but is not limited to, fire extinguishers, replacement fiber optic cables, and other equipment.

[0081] S3: Receive the fault handling result returned by the fault handling terminal, and generate simulated emergency strategy information based on the fault handling result and historical fault standard handling strategy information. Specifically, if the fault handling result indicates that the fault handling device is intact, then multiple preset strategy steps corresponding to the fault type are determined as simulated emergency strategy information. If the fault handling result indicates that the faulty device is missing, then the corresponding preset strategy steps are marked as erroneous steps. Here, the preset strategy step information can be processing steps from historical fault standard handling strategy information.

[0082] Step 1044: In response to the simulated emergency strategy information not meeting the preset emergency handling conditions, a cable emergency warning is issued.

[0083] In some embodiments, the aforementioned implementing entity may issue a cable emergency warning in response to the simulated emergency strategy information not meeting preset emergency handling conditions.

[0084] In some optional implementations of certain embodiments, the aforementioned emergency handling conditions include a group of emergency handling sub-conditions. Specifically, the executing entity, in response to the simulated emergency strategy information not meeting preset emergency handling conditions, issues a cable emergency warning, including:

[0085] In response to the determination that data in the simulated emergency strategy information does not meet any of the emergency handling sub-conditions in the aforementioned emergency handling sub-condition group, corresponding emergency handling requirement information is generated, and this emergency handling requirement information is sent to the corresponding emergency handling terminal for cable emergency warning. The emergency handling sub-condition can be whether a fault handling device is missing. Each emergency handling sub-condition can correspond to one fault handling device. Furthermore, if there is an error step in the simulated emergency strategy information, i.e., feedback that a fault handling device is missing, the indication information and device identifier of the missing fault handling device are identified as cable warning information. Therefore, cable emergency warnings can be used to notify the responsible party to replenish equipment.

[0086] The above-described embodiments of this disclosure have the following beneficial effects: the underground cable fault simulation and early warning method based on sensing devices according to some embodiments of this disclosure can improve the early warning efficiency of cable faults. Based on this, the underground cable fault simulation and early warning method based on sensing devices according to some embodiments of this disclosure first acquires a real-time dataset of underground cables detected by multiple distributed environmental sensors, wherein each real-time cable data includes an environmental sensor identifier and environmental sensor coordinates. Here, by introducing environmental sensors, actual cable data of the underground power grid can be acquired in a timely manner. Therefore, fault simulation can be performed using actual cable data, improving the reliability of the simulation results. Then, the above-mentioned real-time dataset of underground cables is input into a pre-constructed three-dimensional model of urban underground cables to obtain the current three-dimensional cable model. Here, by introducing the three-dimensional model of urban underground cables, the structure of the actual underground cables can be fully replaced. Therefore, there is no need to build a physical model for cable faults; simulation tests can be directly performed at any point in the three-dimensional model of urban underground cables. This improves the efficiency of cable fault simulation. Next, anomaly prediction is performed on the real-time data of underground cables in the current three-dimensional cable model to generate an anomaly prediction information set. The anomaly prediction information includes predicted fault type and predicted fault coordinates. Here, anomaly prediction can be used to detect abnormal fluctuations in data from underground cables in a timely manner. This allows for early prediction of potential cable faults. Subsequently, for each anomaly prediction in the aforementioned anomaly prediction information set, the following simulation and early warning steps are executed: First, historical anomaly data matching the predicted fault type in the aforementioned anomaly prediction information is extracted from a pre-set historical anomaly database and used as the anomaly data to be simulated. Introducing the historical anomaly database helps in determining detected cable anomalies and provides data references for actual fault scenarios in cable fault simulation. Second, based on the anomaly data to be simulated, cable fault simulation is performed on the underground cable at the predicted fault coordinates in the aforementioned anomaly prediction information within the current cable 3D model, and the cable fault simulation data is recorded. Cable fault simulation allows for early fault prediction of locations where data fluctuations occur. This facilitates the early collection of fault data. Third, based on the current cable 3D model, simulated emergency processing is performed on the cable fault simulation data to generate simulated emergency strategy information. The inclusion of the current cable 3D model facilitates the simulation of handling measures for cable faults in actual scenarios. Therefore, not only can cable faults be simulated, but emergency response measures can also be simulated and tested. This allows for further assessment of the adequacy of emergency response measures. Fourth, in response to simulated emergency strategy information failing to meet preset emergency response conditions, a cable emergency warning is issued.Here, by issuing cable emergency warnings, we can not only conduct advance simulations of cable faults and provide data references for actual cable faults, but also improve emergency strategies in response to cable faults. This enhances the efficiency and practicality of cable fault warnings.

[0087] Further reference Figure 3 As an implementation of the methods shown in the above figures, this disclosure provides some embodiments of an underground cable fault simulation and early warning device based on sensing equipment. These device embodiments are similar to... Figure 1 Corresponding to the method embodiments shown, this sensing device-based underground cable fault simulation and early warning device can be specifically applied to various electronic devices.

[0088] like Figure 3 As shown, an underground cable fault simulation and early warning device 300 based on sensing devices in some embodiments includes: an acquisition unit 301, an input unit 302, an anomaly prediction unit 303, and a simulation and early warning unit 304. The acquisition unit 301 is configured to acquire real-time datasets of underground cables detected by multiple distributed environmental sensors, wherein each real-time data set includes an environmental sensor identifier and environmental sensor coordinates. The input unit 302 is configured to input the aforementioned real-time underground cable datasets into a pre-constructed three-dimensional model of urban underground cables to obtain a current three-dimensional model of the cables. The anomaly prediction unit 303 is configured to perform anomaly prediction on the real-time data of the underground cables in the current three-dimensional model of the cables to generate an anomaly prediction information set, wherein the anomaly prediction information includes predicted fault type and predicted fault coordinates. The simulation and early warning unit 304 is configured to... For each anomaly prediction information in the information set, the following simulation and early warning steps are performed: historical anomaly data matching the predicted fault type in the above anomaly prediction information is extracted from a preset historical anomaly database and used as the anomaly data to be simulated; based on the anomaly data to be simulated, cable fault simulation is performed on the underground cable at the location of the predicted fault coordinates in the above anomaly prediction information in the above current cable 3D model, and the cable fault simulation data is recorded; based on the above current cable 3D model, the above cable fault simulation data is subjected to simulated emergency processing to generate simulated emergency strategy information; in response to the simulated emergency strategy information not meeting the preset emergency processing conditions, a cable emergency early warning is issued.

[0089] It is understandable that the units described in the underground cable fault simulation and early warning device 300 based on sensing devices are similar to those in the reference device. Figure 1 The steps in the described method correspond accordingly. Therefore, the operations, features, and beneficial effects described above for the method are also applicable to the underground cable fault simulation and early warning device 300 based on sensing devices and the units contained therein, and will not be repeated here.

[0090] The following is for reference. Figure 4 It illustrates a schematic diagram of the structure of an electronic device (such as a computing device) suitable for implementing some embodiments of the present disclosure. Figure 4 The electronic device shown is merely an example and should not be construed as limiting the functionality or scope of the embodiments of this disclosure. Figure 4 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The memory may include a non-volatile storage medium and internal memory. The non-volatile storage medium may store an operating system and a computer program. The computer program includes program instructions that, when executed, cause the processor to perform any of the methods described above. The processor provides computational and control capabilities to support the operation of the entire computer device. The internal memory provides an environment for the execution of the computer program in the non-volatile storage medium; when executed by the processor, the computer program causes the processor to perform any of the methods described above. The network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present disclosure and does not constitute a limitation on the computer device to which the present disclosure is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0091] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0092] In one embodiment, the processor is configured to run a computer program stored in a memory to perform the following steps: acquiring a real-time dataset of underground cables detected by multiple distributed environmental sensors, wherein each real-time cable dataset includes an environmental sensor identifier and environmental sensor coordinates; inputting the real-time underground cable dataset into a pre-constructed three-dimensional model of urban underground cables to obtain a current three-dimensional model of the cables; performing anomaly prediction on the real-time underground cable data in the current three-dimensional model of the cables to generate an anomaly prediction information set, wherein the anomaly prediction information includes a predicted fault type and a predicted fault coordinate; for each anomaly prediction information in the anomaly prediction information set, performing the following simulation warning steps: extracting historical anomaly data matching the predicted fault type in the anomaly prediction information from a preset historical anomaly database as anomaly data to be simulated; performing cable fault simulation on the underground cable at the location of the predicted fault coordinates in the current three-dimensional model of the cables based on the anomaly data to be simulated, and recording the cable fault simulation data; performing simulated emergency processing on the cable fault simulation data based on the current three-dimensional model of the cables to generate simulated emergency strategy information; and issuing a cable emergency warning in response to the simulated emergency strategy information not meeting preset emergency processing conditions.

[0093] This disclosure also provides a computer-readable storage medium storing a computer program, the computer program including program instructions, and the method implemented when the program instructions are executed can be referred to the various embodiments of the methods described above.

[0094] The aforementioned computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiments, such as the hard disk or memory of the computer device. Alternatively, the aforementioned computer-readable storage medium may be an external storage device of the computer device, such as a plug-in hard disk, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the computer device.

[0095] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0096] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A method for simulating and early warning of underground cable faults based on sensing devices, characterized in that, include: Obtain real-time datasets of underground cables detected by multiple distributed environmental sensors. Each cable's real-time data includes environmental sensor identifiers and coordinates, and the underground cable's real-time data includes corridor humidity values ​​and cable temperature values. The real-time dataset of underground cables is input into a pre-built 3D model of urban underground cables to obtain the current 3D model of the cables; Anomaly prediction is performed on the real-time data of underground cables in the current three-dimensional cable model to generate anomaly prediction information set, wherein the anomaly prediction information includes predicted fault type and predicted fault coordinates. For each anomaly prediction information in the anomaly prediction information set, perform the following simulated early warning steps: Historical anomaly data that matches the predicted fault type in the anomaly prediction information is extracted from a preset historical anomaly database and used as anomaly data to be simulated. Based on the anomaly data to be simulated, in the current three-dimensional cable model, a cable fault simulation is performed on the underground cable at the location of the predicted fault coordinates in the anomaly prediction information, and the cable fault simulation data is recorded. Based on the current three-dimensional model of the cable, the cable fault simulation data is subjected to simulated emergency processing to generate simulated emergency strategy information; In response to the simulated emergency strategy information not meeting the preset emergency handling conditions, a cable emergency warning is issued; The process of simulating cable faults in underground cables at the predicted fault coordinates in the anomaly prediction information includes: In response to determining that the predicted fault type is a first fault type, the underground cable at the location of the predicted fault coordinates is identified as the target simulated cable, wherein the first fault type characterizes a fault in the simulated cable line. Extract the spatial structure coordinates of the pipe gallery at the location of the predicted fault coordinates in the current three-dimensional model of the cable to obtain the spatial structure coordinate set of the pipe gallery. Using the corridor humidity value included in the underground cable real-time data of the target simulated cable and the anomaly data to be simulated, the temperature change of the cable temperature value included in the underground cable real-time data is simulated to generate a core temperature change curve, wherein the core temperature change curve represents the temperature change of the cable at the location of the predicted fault coordinate within a preset time period. Based on the temperature change curve, temperature change simulation is performed on the adjacent area of ​​the predicted fault coordinate on the target simulated cable to generate a sequence of associated temperature change curves, wherein each associated temperature change curve in the sequence of associated temperature change curves corresponds to a time point within the preset time period. Based on the associated temperature change curve sequence, the space enclosed by each space structure coordinate of the utility tunnel in the utility tunnel space structure coordinate group is dynamically divided into simulated regions to generate a first simulated region coordinate group sequence and a second simulated region coordinate group sequence. The first simulated region corresponding to the first simulated region coordinate group sequence gradually expands following the changes in the associated temperature change curve. A fault simulation dense particle field is established for each region in the first simulation region coordinate group sequence to obtain a fault simulation dense particle field set. A fault simulation sparse vector field is established for the region where the second region simulation coordinate group is located in the second simulation region coordinate group sequence, and a fault simulation sparse vector field set is obtained. Topological connections are made between the fault simulation sparse vector fields at the same time in the fault simulation sparse vector field set to generate a fault simulation topology network sequence. Cable fault simulation is performed using the core temperature change curve, the associated temperature change curve sequence, the first simulation region coordinate group sequence, the second simulation region coordinate group sequence, the fault simulation dense particle field set, the fault simulation sparse vector field set, and the fault simulation topology sequence. During the cable fault simulation process, cable data is adjusted using the historical fault interval data sequence set. In response to determining that the predicted fault type is the second fault type, the equipment space structure coordinates corresponding to the target cable node group in the current cable 3D model are extracted to obtain the equipment space structure coordinate group, and cable fault simulation is performed based on the equipment space structure coordinate group, wherein the second fault type indicates that there is a circuit fault in the power distribution equipment corresponding to the cable node.

2. The method according to claim 1, characterized in that, The real-time underground cable dataset also includes cable location identifiers. The urban underground cable 3D model includes multiple distributed simulation sensors, where each simulation sensor corresponds to a distributed location of each environmental sensor. The step of inputting the real-time underground cable dataset into the pre-constructed urban underground cable 3D model to obtain the current cable 3D model includes: Based on the cable location identifier and environmental sensor identifier included in the real-time data of underground cables in the real-time data of underground cables, the simulation sensor corresponding to each real-time data of underground cables is determined. The three-dimensional model of urban underground cables also includes: a group of cable nodes representing power distribution equipment, a group of cable simulation lines representing underground cables, and a cable coverage area corresponding to each cable node. The simulation sensor is set at the cable node position of the three-dimensional model of urban underground cables and associated with at least one cable simulation line. By using various simulation sensors, real-time data of the underground cable is filled into the corresponding cable simulation lines and cable nodes to obtain the current three-dimensional model of the cable.

3. The method according to claim 2, characterized in that, The step of performing anomaly prediction on real-time data of underground cables in the current 3D cable model to generate anomaly prediction information set includes: For each cable node in the current 3D cable model, perform the following steps: The cable node is subjected to node pressure detection to generate the node pressure change. Line anomaly detection is performed on each simulated cable line under the cable node to generate line anomaly information, which includes line anomaly value, predicted fault type and predicted fault coordinates; In response to determining that the node pressure change or the line anomaly value meets the preset anomaly conditions, the cable node is marked as an anomaly to obtain the target cable node group, and the target cable node group, the node pressure change, the line anomaly information and the corresponding cable simulation line are determined as anomaly prediction information.

4. The method according to claim 3, characterized in that, The historical anomaly database is constructed through the following steps: Acquire at least one historical cable fault data, wherein the historical cable fault data includes at least one of the following: a historical fault interval data sequence set, fault type, historical fault impact range, and historical fault standard processing strategy information, and each historical fault interval data in the historical fault interval data sequence set includes at least one of the following: historical cable current value, historical cable voltage value, historical cable temperature value, historical corridor humidity value, and historical cable node pressure value. The data from the at least one historical cable fault data are stored in the initial anomaly database according to the preset field attributes to obtain the historical anomaly database.

5. The method according to claim 4, characterized in that, The process of performing simulated emergency processing on the cable fault simulation data based on the current three-dimensional cable model to generate simulated emergency strategy information includes: Based on the cable fault simulation data and the corresponding cable coverage area, locate the fault-affected area in the current three-dimensional cable model; The system automatically adjusts the power supply equipment at each cable node within the affected area of ​​the fault, and sends the location of the affected area and the simulated cable fault data to the corresponding fault processing terminal. Receive the fault handling result returned by the fault handling terminal, and generate simulated emergency strategy information based on the fault handling result and historical fault standard handling strategy information.

6. The method according to claim 5, characterized in that, The emergency handling conditions include a group of emergency handling sub-conditions, wherein the issuance of a cable emergency warning in response to the simulated emergency strategy information not meeting the preset emergency handling conditions includes: In response to determining that there is data in the simulated emergency strategy information that does not meet any of the emergency handling sub-conditions in the emergency handling sub-condition group, corresponding emergency handling requirement information is generated, and the emergency handling requirement information is sent to the corresponding emergency handling terminal for cable emergency early warning.

7. A fault simulation and early warning device for underground cables based on sensing equipment, characterized in that, include: The acquisition unit is configured to acquire real-time datasets of underground cables detected by multiple distributed environmental sensors, wherein each real-time data set of the cable includes an environmental sensor identifier and environmental sensor coordinates, and the real-time data of the underground cable includes corridor humidity values ​​and cable temperature values. The input unit is configured to input the real-time dataset of the underground cable into a pre-built three-dimensional model of the urban underground cable to obtain the current three-dimensional model of the cable. An anomaly prediction unit is configured to perform anomaly prediction on real-time data of underground cables in the current three-dimensional cable model to generate an anomaly prediction information set, wherein the anomaly prediction information includes predicted fault type and predicted fault coordinates. The simulated early warning unit is configured to perform the following simulated early warning steps for each anomaly prediction in the anomaly prediction information set: Historical anomaly data that matches the predicted fault type in the anomaly prediction information is extracted from a preset historical anomaly database and used as anomaly data to be simulated. Based on the anomaly data to be simulated, in the current three-dimensional cable model, a cable fault simulation is performed on the underground cable at the location of the predicted fault coordinates in the anomaly prediction information, and the cable fault simulation data is recorded; wherein, performing a cable fault simulation on the underground cable at the location of the predicted fault coordinates in the anomaly prediction information includes: In response to determining that the predicted fault type is a first fault type, the underground cable at the location of the predicted fault coordinates is identified as the target simulated cable, wherein the first fault type characterizes a fault in the simulated cable line. Extract the spatial structure coordinates of the pipe gallery at the location of the predicted fault coordinates in the current three-dimensional model of the cable to obtain the spatial structure coordinate set of the pipe gallery. Using the corridor humidity value included in the underground cable real-time data of the target simulated cable and the anomaly data to be simulated, the temperature change of the cable temperature value included in the underground cable real-time data is simulated to generate a core temperature change curve, wherein the core temperature change curve represents the temperature change of the cable at the location of the predicted fault coordinate within a preset time period. Based on the temperature change curve, temperature change simulation is performed on the adjacent area of ​​the predicted fault coordinate on the target simulated cable to generate a sequence of associated temperature change curves, wherein each associated temperature change curve in the sequence of associated temperature change curves corresponds to a time point within the preset time period. Based on the associated temperature change curve sequence, the space enclosed by each space structure coordinate of the utility tunnel in the utility tunnel space structure coordinate group is dynamically divided into simulated regions to generate a first simulated region coordinate group sequence and a second simulated region coordinate group sequence. The first simulated region corresponding to the first simulated region coordinate group sequence gradually expands following the changes in the associated temperature change curve. A fault simulation dense particle field is established for each region in the first simulation region coordinate group sequence to obtain a fault simulation dense particle field set. A fault simulation sparse vector field is established for the region where the second region simulation coordinate group is located in the second simulation region coordinate group sequence, and a fault simulation sparse vector field set is obtained. Topological connections are made between the fault simulation sparse vector fields at the same time in the fault simulation sparse vector field set to generate a fault simulation topology network sequence. Cable fault simulation is performed using the core temperature change curve, the associated temperature change curve sequence, the first simulation region coordinate group sequence, the second simulation region coordinate group sequence, the fault simulation dense particle field set, the fault simulation sparse vector field set, and the fault simulation topology sequence. During the cable fault simulation process, cable data is adjusted using the historical fault interval data sequence set. In response to determining that the predicted fault type is the second fault type, the equipment space structure coordinates corresponding to the target cable node group in the current cable three-dimensional model are extracted to obtain the equipment space structure coordinate group, and cable fault simulation is performed based on the equipment space structure coordinate group, wherein the second fault type indicates that there is a circuit fault in the power distribution equipment corresponding to the cable node; Based on the current three-dimensional model of the cable, the cable fault simulation data is subjected to simulated emergency processing to generate simulated emergency strategy information; In response to the simulated emergency strategy information not meeting the preset emergency handling conditions, a cable emergency warning is issued.

8. An electronic device, characterized in that, include: One or more processors; Storage device, on which one or more programs are stored, When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-6.

9. A computer-readable medium, characterized in that, It stores a computer program thereon, wherein the program, when executed by a processor, implements the method as described in any one of claims 1-6.

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