Power transmission tunnel fault early warning method and device and electronic equipment

Abstract

The invention discloses a power transmission tunnel fault early warning method, a power transmission tunnel fault early warning device and electronic equipment. The method comprises the following steps: carrying out digital twinborn modeling on a power transmission tunnel to obtain a target digital twinborn model; acquiring the current operation data and the current water level depth of the power transmission tunnel in the current time period; based on the current operation data and the current water level depth, the target digital twinborn model is adopted, early warning information of the power transmission tunnel is obtained, and the early warning information is at least used for indicating the abnormal state and the abnormal type of the power transmission tunnel. According to the invention, the technical problem of low fault identification efficiency and accuracy of a cable tunnel fault early warning method in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent power monitoring, and in particular to a method, device and electronic equipment for early warning of faults in a power transmission tunnel. Background Art

[0002] In the field of power industry operation and maintenance, transmission tunnels are key nodes, and their stable operation is of vital importance to the safety and reliability of the entire power system. However, the current operation and maintenance of transmission tunnels still faces many challenges, especially during on-site operations, where operation and maintenance personnel mainly rely on offline and limited information data resources such as limited observation data, operating procedures, and operating instructions. This model not only limits the ability to make judgments, but also has low operating efficiency, and the quality of work is highly dependent on the skills and equipment level of personnel.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present invention provide a transmission tunnel fault warning method, device and electronic equipment to at least solve the technical problem of low fault identification efficiency and accuracy in the cable tunnel fault warning method in the related art.

[0005] According to one aspect of an embodiment of the present invention, a power transmission tunnel fault warning method is provided, comprising: performing digital twin modeling on the power transmission tunnel to obtain a target digital twin model; obtaining current operating data and a current water level depth of the power transmission tunnel in a current period; based on the current operating data and the current water level depth, using the target digital twin model to obtain warning information of the power transmission tunnel, wherein the warning information is at least used to indicate an abnormal state and an abnormal type of the power transmission tunnel.

[0006] Optionally, performing digital twin modeling on the transmission tunnel to obtain a target digital twin model includes: performing digital twin modeling on the transmission tunnel to obtain an initial digital twin model; obtaining operation data sets corresponding to a plurality of fault types, wherein the operation data sets include operation data corresponding to the corresponding fault types at a plurality of water level depths; and obtaining the target digital twin model based on the operation data sets corresponding to the plurality of fault types and the initial digital twin model.

[0007] Optionally, the digital twin modeling of the transmission tunnel to obtain the initial digital twin model includes: obtaining physical structure information of the transmission tunnel and historical water level change information of the transmission tunnel in historical periods; obtaining geological condition information of the target area where the transmission tunnel is located; and constructing the initial digital twin model based on the physical structure information, the historical water level change information and the geological condition information.

[0008] Optionally, obtaining operation data sets corresponding to multiple fault types includes: performing modeling and simulation on any fault type among the multiple fault types at multiple water level depths to obtain operation data corresponding to any fault type at the multiple water level depths; constructing an operation data set corresponding to any fault type based on the operation data corresponding to any fault type at the multiple water level depths; obtaining operation data sets corresponding to the multiple fault types by adopting the method of obtaining the operation data set corresponding to any fault type.

[0009] Optionally, in the case that the warning information includes fault warning information, the target digital twin model is used based on the current operating data and the current water level depth to obtain the warning information of the transmission tunnel, including: determining a plurality of operating data corresponding to the current water level depth from a set of operating data corresponding to a plurality of fault types stored in the target digital twin model; determining the degree of matching between the plurality of operating data and the current operating data respectively; determining target operating data having a matching degree greater than a preset matching degree threshold from the plurality of operating data; and generating the fault warning information based on the fault type corresponding to the target operating data.

[0010] Optionally, when the warning information includes water level warning information, the warning information of the transmission tunnel is obtained based on the current operating data and the current water level depth using the target digital twin model, including: detecting whether the current water level depth is greater than a preset water level depth; and generating the water level warning information based on the current water level depth when the current water level depth is greater than the preset water level depth.

[0011] Optionally, the method also includes: obtaining external environmental parameters of the transmission tunnel, wherein the external environmental parameters include at least: expected precipitation and groundwater level information in the target area where the transmission tunnel is located in a prediction period, and the prediction period is a period after the current period; based on the current water level depth and the external environmental parameters, obtaining the predicted water level depth of the transmission tunnel in the prediction period; when the predicted water level depth is greater than the preset water level depth, generating the water level warning information based on the predicted water level depth.

[0012] Optionally, the method also includes: iteratively optimizing the target digital twin model based on the current operating data, the current water level depth and the warning information to obtain an optimized target digital twin model.

[0013] According to another aspect of an embodiment of the present invention, a power transmission tunnel fault warning device is also provided, including: a modeling module, used to perform digital twin modeling of the power transmission tunnel to obtain a target digital twin model; a data acquisition module, used to obtain current operating data and current water level depth of the power transmission tunnel in a current period; and a warning module, used to obtain warning information of the power transmission tunnel based on the current operating data and the current water level depth using the target digital twin model, wherein the warning information is at least used to indicate an abnormal state and abnormal type of the power transmission tunnel.

[0014] According to another aspect of an embodiment of the present invention, there is also provided an electronic device, comprising one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement any one of the power transmission tunnel fault warning methods.

[0015] In an embodiment of the present invention, a target digital twin model is obtained by digital twin modeling of a transmission tunnel; the current operating data and the current water level depth of the transmission tunnel in the current period are obtained; based on the current operating data and the current water level depth, the target digital twin model is used to obtain early warning information of the transmission tunnel, wherein the early warning information is at least used to indicate the abnormal state and abnormal type of the transmission tunnel, thereby achieving the purpose of efficiently and accurately generating early warning information for the transmission tunnel by using the pre-built target digital twin model with the current operating data and the current water level as input, thereby achieving the technical effect of improving the accuracy of transmission tunnel fault identification and the efficiency of fault warning, and further solving the technical problem of low fault identification efficiency and accuracy in the cable tunnel fault warning method in the related art. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0017] Figure 1 is a flow chart of a power transmission tunnel fault early warning method according to an embodiment of the present invention;

[0018] Figure 2 is a detailed schematic diagram of an optional digital twin platform for a power transmission tunnel according to an embodiment of the present invention;

[0019] Figure 3 is a flow chart of an optional power transmission tunnel fault early warning method according to an embodiment of the present invention;

[0020] Figure 4is a schematic diagram of a power transmission tunnel fault warning device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0022] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0023] First, to facilitate understanding of the embodiments of the present invention, some terms or nouns involved in the present invention are explained below:

[0024] A digital twin model is a highly detailed virtual model that corresponds to an actual physical entity or system and can reflect and simulate the behavior, performance, and status of the entity or system. Digital twin technology combines the Internet of Things (IoT), big data analysis, machine learning, and advanced visualization technology. Through the integration of real-time and historical data, the virtual model can simulate and predict the operating status of the physical entity in real time, thereby providing in-depth insights into the entity's performance, health status, and optimization potential.

[0025] According to an embodiment of the present invention, a method embodiment of a power transmission tunnel fault warning is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0026] Figure 1 is a flow chart of a power transmission tunnel fault early warning method according to an embodiment of the present invention. Figure 1As shown, the method comprises the following steps:

[0027] Step S102, performing digital twin modeling on the transmission tunnel to obtain a target digital twin model;

[0028] Optionally, the target digital twin model can reflect the actual operating status and environmental conditions of the transmission tunnel in real time, including water level depth, temperature, humidity, cable status, etc., so that operation and maintenance personnel can intuitively understand the real-time situation inside the tunnel in a virtual environment.

[0029] In an optional embodiment, digital twin modeling is performed on a transmission tunnel to obtain a target digital twin model, including: performing digital twin modeling on the transmission tunnel to obtain an initial digital twin model; obtaining operation data sets corresponding to a plurality of fault types, wherein the operation data sets include operation data corresponding to the corresponding fault types at a plurality of water level depths; obtaining a target digital twin model based on the operation data sets corresponding to the plurality of fault types and the initial digital twin model.

[0030] Optionally, by integrating the operating data of multiple fault types at different water depths, the target digital twin model can more comprehensively understand the behavior patterns of transmission tunnels under various conditions. This enables the model to take into account a wider range of possibilities during early warning analysis, thereby improving the accuracy and comprehensiveness of abnormal situation prediction. The target digital twin model constructed in the above manner can not only issue early warnings, but also accurately identify specific fault types based on real-time data and preset fault type operating data sets. This is crucial for quickly locating problems and taking targeted measures, which can significantly shorten fault handling time and reduce economic losses and safety risks. By considering multiple fault conditions at the construction stage of the target digital twin model, the target digital twin model can automatically identify and warn of potential problems at any stage of tunnel operation, reduce the frequency and complexity of manual inspections, and effectively improve operation and maintenance efficiency and reduce costs.

[0031] Optionally, the multiple fault types may include but are not limited to poor contact, cable wear, increased dielectric loss due to cable moisture, cable material defects, etc.

[0032] It should be noted that according to the requirements of digital twin technology, full use is made of data such as physical models and sensor update operation history, and multi-disciplinary, multi-physical quantity, multi-scale, and multi-probability simulation processes are integrated to complete the mapping in the virtual space, thereby reflecting the full life cycle of the corresponding physical equipment. The full life cycle runs through the entire cycle of design and development, manufacturing, service, maintenance, and even scrapping and recycling of physical equipment. Figure 2 is a detailed schematic diagram of an optional digital twin platform for a power transmission tunnel according to an embodiment of the present invention, such as Figure 2As shown in the figure, based on the digital three-dimensional model of the transmission tunnel and combined with a variety of real-scene rendering methods, a real-scene three-dimensional model of the transmission tunnel is established as the initial digital twin model.

[0033] Specifically, the modeling is carried out according to the real scene of the power transmission tunnel, which is divided into external scene and internal scene. For example, the internal scene includes power transmission lines, water level depth monitoring sensor equipment, grounding boxes and other equipment.

[0034] Exemplarily, a corresponding simulation digital twin modeling platform can be used to perform refined, visualized, and digital three-dimensional twin modeling of the transmission tunnel according to Computer-Aided Design (CAD) drawings or object (obj) file format models. For example, a one-to-one ratio digital twin modeling restoration can be used to form a digital twin model.

[0035] In an optional embodiment, digital twin modeling is performed on the transmission tunnel to obtain an initial digital twin model, including: obtaining physical structure information of the transmission tunnel, historical water level change information of the transmission tunnel in historical periods; obtaining geological condition information of the target area where the transmission tunnel is located; and constructing an initial digital twin model based on the physical structure information, historical water level change information and geological condition information.

[0036] Optionally, by obtaining the physical structure information of the transmission tunnel, such as tunnel size, material properties, cable layout, drainage system, etc., the constructed initial digital twin model can accurately replicate the physical characteristics of the tunnel, making the simulation results closer to the real situation. The addition of historical water level change information and geological condition information allows the initial digital twin model to take into account the dynamic environmental factors of tunnel operation, such as seasonal water level fluctuations, the impact of geological activities on tunnel stability, etc., thereby improving the accuracy of the model's prediction of water level changes and structural responses. The consideration of geological condition information enables the constructed initial digital twin model to adapt to the specific environment of the tunnel, such as different rock types, soil properties, groundwater levels, etc., which is crucial for evaluating the permeability and stability of the tunnel, and can help the model more accurately predict the risk of abnormal water levels under specific geological conditions. The integration of historical water level change information enables the constructed model to learn and analyze the laws of past water level changes in the tunnel, including seasonal changes, long-term trends, and abnormal fluctuations, which helps the model identify the change pattern of abnormal water levels and improve the sensitivity and timeliness of early warning. The initial digital twin model, which is built based on physical structure information, historical water level change information, and geological condition information, can perform dynamic simulation, that is, according to real-time data input, the model can update and reflect the current status of the tunnel in real time, providing a basis for real-time monitoring and early warning. The physical structure, historical water level changes, and geological conditions of the tunnel were fully considered during the construction of the initial digital twin model, which contributes to the long-term stability and reliability of the model and ensures the continued effective operation of the early warning system.

[0037] In an optional embodiment, operation data sets corresponding to multiple fault types are obtained, including: modeling and simulating any fault type among the multiple fault types at multiple water level depths to obtain operation data corresponding to any fault type at multiple water level depths; constructing an operation data set corresponding to any fault type based on the operation data corresponding to any fault type at multiple water level depths; and obtaining operation data sets corresponding to multiple fault types by obtaining an operation data set corresponding to any fault type.

[0038] Optionally, by modeling and simulating each fault type at different water level depths, the performance and impact of each fault under different environmental conditions can be carefully understood, the model's simulation capability for complex fault situations can be improved, and fault diagnosis can be made more accurate. Since each fault type has a targeted set of operating data, the digital twin model can more accurately compare real-time monitoring data with simulation data when conducting early warning analysis, thereby determining whether the tunnel is in an abnormal state, significantly improving the accuracy and reliability of early warning. The simulation calculation data obtained by modeling and simulation (i.e., the operating data sets corresponding to various fault types) cover a variety of water level depths and fault types, which means that the target digital twin model obtained by combining the simulation calculation data can adapt to various possible operating scenarios, and can identify potential fault risks in a timely manner regardless of how the water level in the tunnel changes, thereby enhancing the environmental adaptability of the early warning. By comparing real-time data with the constructed operating data set, the target digital twin model can not only detect faults, but also identify specific fault types, which is crucial for rapid response and taking corresponding measures, and improves the efficiency and pertinence of transmission tunnel maintenance.

[0039] Optionally, the transmission tunnel can be modeled and simulated using the algorithm analysis calculation results. Specifically, simulation technology is used to simulate and calculate different types of faults that may occur in the transmission line cables in the transmission line, such as poor contact, cable wear, increased dielectric loss caused by cable moisture, cable material defects, etc., to obtain the impact record data caused by the transmission line cables and water level depth data under different defect types as operating data (such as rated current carrying capacity, load rate, highest temperature position, highest temperature, maximum temperature difference between phases, cable length, etc.), and compare with the simulation results under normal working conditions.

[0040] Specifically, the data of the transmission line cables under normal operation and typical fault types and the line data under the acquired water level data can be compared and calculated. For the transmission line cables, different load conditions and environmental water level conditions of the transmission lines can be considered in the modeling and calculation, and relevant boundary conditions and fault conditions can be set according to the common fault types that may affect the surface water level depth of the two cables. The simulation calculation data is integrated with the digital twin model as a database.

[0041] Specifically, by calculating the surface water level depth warning of the transmission line cables under different status conditions, such as different environmental water levels, different fault types, etc., simulation calculation data is obtained. The real-time monitoring results can be reflected in the digital twin model. Regardless of whether it is normal or abnormal, there are corresponding simulation results, and the simulation results can be reflected in the digital twin model, that is, the simulation results can be directly reflected in the digital model.

[0042] The purpose of fusion is to give full play to the functions of digital twin models. At present, the application of digital twin technology in relevant technologies in actual power engineering is only at the level of "effect display", that is, only the three-dimensional model of the physical entity can be seen on the terminal computer screen. But in fact, the connotation of "digital twin" requires that the working status of the physical entity can be reflected on the digital variant, that is, the 3D model, that is, through the observation or operation of the digital twin model, the real-time working parameters of the physical entity can be read, without the traditional method of obtaining the operating status of the transmission tunnel and investing manpower to patrol or detect the physical entity.

[0043] To realize the connotation of "digital twin", it is actually necessary to carry out real-time simulation of digital models, but current technology cannot achieve this. Therefore, this embodiment, based on historical experience, pre-models and simulates various faults that have occurred in the transmission tunnel. Of course, it is also necessary to simulate and calculate the equipment when there is no fault as the control group data. Then, the simulation calculation data is stored as the data basis in the computer on which the digital twin model is established. The so-called "fusion" means that the above database can be called in real time on the digital twin model interface, and the results of a certain set of data retrieved are displayed on the device. When to call depends on the judgment of the real-time monitoring results of the physical entity of the equipment, that is, when the water level depth on the surface of the physical entity is found to be abnormal, it can be judged that a certain fault has occurred in the equipment, and then the set of simulation calculation data corresponding to the fault is retrieved from the database, and the results (i.e., warning information) are displayed on the target digital twin model.

[0044] Step S104, obtaining current operation data and current water level depth of the transmission tunnel in the current period;

[0045] Optionally, the current operation data may be collected in real time based on monitoring equipment installed in the cable tunnel. The current operation data may include, but is not limited to, the rated current carrying capacity, load rate, highest temperature position, highest temperature, maximum temperature difference between phases, and other information collected in the transmission tunnel during the current period.

[0046] Optionally, since the transmission tunnel includes transmission lines, water level depth monitoring sensors, and grounding boxes, etc., a universal immersion level transmitter is used to obtain real-time water level depth data of the transmission tunnel for these devices. Specifically, a water level monitoring module is arranged at the transmission tunnel project site to detect the water level data of the transmission tunnel in real time, and the real-time water level depth data of the transmission tunnel obtained is shared with the digital twin model in real time.

[0047] Step S106: Based on the current operating data and the current water level depth, the target digital twin model is used to obtain early warning information of the transmission tunnel, wherein the early warning information is at least used to indicate the abnormal state and abnormal type of the transmission tunnel.

[0048] Optionally, the abnormal state is used to indicate whether there is an abnormality in the transmission tunnel. Using the target digital twin model to conduct real-time analysis of the operating data and water level depth of the current period can instantly evaluate the health status and operating environment of the transmission tunnel and determine whether there is an abnormal state, such as abnormally high water level, equipment overheating, cable damage, etc. The early warning information not only indicates whether there is an abnormality in the tunnel, but also further identifies the specific type or cause of the abnormality, for example, whether the water level change is caused by equipment failure, structural damage, external environmental influences (such as rainfall, groundwater level changes), or internal system problems, which is crucial for taking targeted response measures.

[0049] In an optional embodiment, when the warning information includes fault warning information, based on the current operating data and the current water level depth, a target digital twin model is used to obtain warning information of the transmission tunnel, including: determining multiple operating data corresponding to the current water level depth from the operating data sets corresponding to the multiple fault types stored in the target digital twin model; determining the matching degree between the multiple operating data and the current operating data respectively; determining the target operating data whose matching degree is greater than a preset matching degree threshold from the multiple operating data; and generating fault warning information based on the fault type corresponding to the target operating data.

[0050] Optionally, by searching for operating data that matches the current water level depth from the target digital twin model, it is possible to more accurately determine whether the current operating state is consistent with the known fault mode, thereby improving the accuracy and reliability of the fault warning information. Determining the degree of match between the operating data and the current operating data is an intelligent process based on data analysis. The target digital twin model can intelligently identify which fault modes are more likely to occur through comparison. Setting a matching threshold allows the most relevant fault types to be quickly screened out, avoiding detailed analysis of all possible faults, thereby improving the efficiency of warning generation and ensuring the timeliness of warnings. Matching analysis based on the target digital twin model and the preset operating data set can reduce false positives and false negatives caused by imperfect models or insufficient data, and ensure the accuracy and effectiveness of warning information. The above method enables the target digital twin model to maintain a strong fault warning capability under complex and changeable water level depths and operating conditions. Through accurate comparison with the current operating data, the target digital twin model can timely warn of potential fault risks and prevent faults from expanding.

[0051] Optionally, it is also possible to detect whether there is abnormal data in the current operating data and the current water level depth; when abnormal data is detected, multiple operating data corresponding to the current water level depth are determined from the operating data sets corresponding to the various fault types stored in the target digital twin model, and the fault type is matched, thereby avoiding unnecessary matching degree calculation procedures and reducing computing pressure.

[0052] In an optional embodiment, when the warning information includes water level warning information, based on the current operating data and the current water level depth, a target digital twin model is used to obtain warning information of the transmission tunnel, including: detecting whether the current water level depth is greater than the preset water level depth; when the current water level depth is greater than the preset water level depth, generating water level warning information based on the current water level depth.

[0053] Optionally, by detecting whether the current water level depth exceeds the preset water level depth, water level changes can be monitored in real time and abnormal water levels can be identified in a timely manner, which is crucial to preventing electrical equipment failures caused by water accumulation in the tunnel. The preset water level depth serves as a threshold, providing a clear trigger point for water level warnings. When the actual water level exceeds this threshold, an early warning is automatically issued to avoid delays and omissions in manual monitoring and ensure the timeliness and effectiveness of the early warning. In the case of abnormal water levels, water level early warning information can be automatically generated based on the current water level depth without manual intervention, which can significantly improve the automation of early warnings and reduce the workload of operation and maintenance personnel.

[0054] Optionally, the water level warning information may include not only the water level warning prompt and the current water level depth, but also the water level warning level of the current water level depth, so as to better remind the staff of the current warning status of the transmission tunnel so as to take corresponding countermeasures more timely.

[0055] In an optional embodiment, the method also includes: obtaining external environmental parameters of the transmission tunnel, wherein the external environmental parameters include at least: expected precipitation and groundwater level information in the target area where the transmission tunnel is located in a prediction period, and the prediction period is a period after the current period; based on the current water level depth and the external environmental parameters, obtaining the predicted water level depth of the transmission tunnel in the prediction period; when the predicted water level depth is greater than the preset water level depth, generating water level warning information based on the predicted water level depth.

[0056] Optionally, in order to ensure the comprehensiveness of the water level warning, in addition to considering whether the water level depth is safe in the current period, it is also possible to further consider whether there will be a sudden increase in the water level in the transmission tunnel in the future. Specifically, by obtaining and analyzing the expected precipitation and groundwater level information in the area where the transmission tunnel is located, it is possible to predict the future trend of water level changes, so that an early warning can be issued before the water level actually reaches the warning threshold, providing a more forward-looking safety guarantee. Predicting the rise in water levels in advance gives the operation and maintenance team sufficient time to respond to possible water level crises, avoid equipment damage and power outages, and thus reduce potential economic losses and impacts on power supply. Taking into account the impact of external environmental parameters on water levels makes the early warning system more adaptable to changing environmental conditions and enhances its robustness and accuracy under different climatic and geological conditions.

[0057] In an optional embodiment, the method also includes: iteratively optimizing the target digital twin model based on current operating data, current water level depth and warning information to obtain an optimized target digital twin model.

[0058] Optionally, through continuous iterative optimization, the parameters and algorithms in the model can be gradually corrected and improved, so that the optimized digital twin model can more accurately reflect the actual operating status and water level change rules of the transmission tunnel, and improve the accuracy and effectiveness of the early warning. Iterative optimization can enable the digital twin model to better adapt to factors such as environmental changes and equipment aging, ensuring that the digital twin model still maintains high-precision simulation and prediction capabilities under different conditions. The optimized target digital twin model can more accurately predict abnormal conditions that may occur in the future, reduce the occurrence of false alarms and missed alarms, and improve the reliability and stability of the entire early warning system. The large amount of operating data and water level depth information continuously collected and analyzed during the iterative optimization process provides the possibility for in-depth mining and analysis of data, helps to discover new failure modes and trends, and further enhances the prediction ability of the digital twin model.

[0059] Through the above steps S102 to S106, the purpose of efficiently and accurately generating transmission tunnel warning information by using the pre-built target digital twin model with the current operating data and the current water level depth as input can be achieved, thereby achieving the technical effect of improving the accuracy of transmission tunnel fault identification and the efficiency of fault warning, and further solving the technical problem of low fault identification efficiency and accuracy in the cable tunnel fault warning method in the related technology.

[0060] Based on the above embodiments and optional embodiments, the present invention proposes an optional implementation mode: Figure 3 is a flow chart of an optional power transmission tunnel fault early warning method according to an embodiment of the present invention, such as Figure 3 As shown, the method includes:

[0061] S1, conduct digital twin modeling of the transmission tunnel to form an initial digital twin model.

[0062] S2, obtain the real-time water level depth data of the transmission tunnel and share the real-time water level depth data with the initial digital twin model.

[0063] S3, modeling and simulating the transmission tunnel, calculating the water level depth data of the transmission tunnel under different water level depth operating states (such as different fault types), and forming simulation calculation data. The simulation calculation data may include the operating data of various fault types at different water level depths (such as rated current carrying capacity, load rate, maximum temperature position, maximum temperature, maximum temperature difference between phases, etc.), and the simulation calculation data is integrated with the digital twin model as a database to obtain a target digital twin model for water level monitoring of the transmission tunnel.

[0064] S4 monitors the current operating status, environmental parameters, and tunnel structure status data of the transmission tunnel, and uses the target digital twin model to monitor and warn of faults in the transmission tunnel based on real-time monitoring data and simulation calculation data. Early warning information is released and sent to relevant managers and emergency response teams in real time through preset communication channels. At the same time, the emergency plan is automatically activated to ensure timely and effective response measures.

[0065] S5, based on real-time monitoring data and simulation calculation data, realizes the operation status monitoring of the transmission tunnel on the digital twin model. According to the early warning effect and actual water level changes, the digital twin model is iteratively optimized to improve the prediction accuracy and ensure the long-term stability and reliability of the early warning system.

[0066] The real-time monitoring data and the simulation calculation data of the transmission tunnel under abnormal conditions are matched and compared to determine the operating status of the transmission tunnel, and abnormal temperature rise faults in the transmission tunnel are warned based on the matching and comparison results.

[0067] It should be noted that, in this embodiment, digital twin modeling of the transmission tunnel is first performed to form an initial digital twin model; the real-time water level data of the transmission tunnel is obtained, and the real-time water level data is shared with the initial digital twin model; the transmission tunnel is modeled and simulated, and the distribution of the water level field and electric field of the transmission tunnel under different water level depths is calculated to form simulation calculation data, and the simulation calculation data is stored as a database in the initial digital twin model to obtain the target digital twin model; the operation status of the transmission tunnel is monitored, and based on the real-time monitoring data and simulation calculation data, the target digital twin model is used to perform fault monitoring and early warning of the transmission tunnel. This embodiment uses digitization as a means to achieve efficient and intelligent monitoring of the transmission tunnel and high-accuracy fault diagnosis and early warning, effectively improving the operation and maintenance level and efficiency of the transmission tunnel. In the above method, the digital twin technology is innovatively applied to the field of water level monitoring of the transmission tunnel, which can realize real-time monitoring and accurate early warning of water level changes in the tunnel. By building a digital twin model of the transmission tunnel, it is possible to comprehensively consider various influencing factors inside and outside the tunnel, improve the accuracy and timeliness of water level prediction, and provide strong guarantees for the safe and stable operation of the transmission tunnel. At the same time, this embodiment is highly practical and scalable, and can be widely used in the field of water level monitoring and early warning of various underground projects, promoting technological progress and development in the industry.

[0068] The transmission tunnel fault warning method provided in this embodiment is equivalent to a transmission tunnel monitoring method based on digital twins. The method uses digital twin technology to construct a digital twin model of the physical entity of the transmission tunnel through mapping and mirroring, simulates and models some key equipment, and obtains a large amount of data under different water level depths through calculation. The calculated data is integrated and compared with the physical entity measurement data. Compared with the existing transmission tunnel detection technology, this method can save manpower, increase the amount of data, and realize intelligent detection of equipment.

[0069] In addition, this embodiment creates a digital twin model of the transmission tunnel with the goal of equipment health evaluation and operation and maintenance strategy mining by refining, visualizing, and digitalizing the transmission tunnel in three dimensions, integrating multidimensional data such as transmission tunnel operation and equipment status with historical operation and maintenance data, and realizes its three-dimensional display. Secondly, by applying digital twin technology and combining the existing online monitoring function of the transmission tunnel to organically integrate data with the digital twin kernel, it is possible to realize the status evaluation and fault prediction of the transmission tunnel, and construct typical scenarios such as operation display, historical analysis, and fault inversion of the transmission tunnel, which is conducive to the monitoring of the equipment throughout its life cycle, meet the operation and maintenance needs of the transmission tunnel, promote the value of data, and provide solutions for exploring useful experience for the digital transformation of the power grid. Finally, through the practice and application of digital twin technology on specific transmission tunnel equipment, the asset evaluation of the transmission tunnel is effectively carried out, the digitalization of the power grid is realized in the planning stage, and the digitalization of the entire life cycle of Shengong equipment is promoted, effectively reducing the digitalization costs of various professions.

[0070] In this embodiment, a transmission tunnel fault warning device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the terms "module" and "device" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0071] According to an embodiment of the present invention, there is also provided an embodiment of a device for implementing the above-mentioned power transmission tunnel fault early warning method. Figure 4 is a schematic diagram of the structure of a transmission tunnel fault warning device according to an embodiment of the present invention. Figure 4 As shown, the above-mentioned transmission tunnel fault warning device includes: a modeling module 200, a data acquisition module 202, and an early warning module 204, wherein:

[0072] A modeling module 200 is used to perform digital twin modeling on the transmission tunnel to obtain a target digital twin model;

[0073] The data acquisition module 202 is connected to the modeling module 200 and is used to obtain the current operation data and current water level depth of the transmission tunnel in the current period;

[0074] The early warning module 204 is connected to the data acquisition module 202, and is used to obtain early warning information of the transmission tunnel based on the current operation data and the current water level depth using the target digital twin model, wherein the early warning information is at least used to indicate the abnormal state and abnormal type of the transmission tunnel.

[0075] In an embodiment of the present invention, a modeling module 200 is set up to perform digital twin modeling on a transmission tunnel to obtain a target digital twin model; a data acquisition module 202 is connected to the modeling module 200 to obtain the current operating data and the current water level depth of the transmission tunnel in the current period; an early warning module 204 is connected to the data acquisition module 202 to obtain early warning information of the transmission tunnel based on the current operating data and the current water level depth, using the target digital twin model, wherein the early warning information is at least used to indicate the abnormal state and abnormal type of the transmission tunnel, thereby achieving the purpose of efficiently and accurately generating early warning information for the transmission tunnel using the current operating data and the current water level as input and using a pre-built target digital twin model, thereby achieving the technical effect of improving the accuracy of transmission tunnel fault identification and the efficiency of fault warning, thereby solving the technical problem of low fault identification efficiency and accuracy in the cable tunnel fault early warning method in the related art.

[0076] It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0077] It should be noted that the above-mentioned modeling module 200, data acquisition module 202, and early warning module 204 correspond to steps S102 to S106 in the embodiment, and the examples and application scenarios implemented by the above-mentioned modules and corresponding steps are the same, but are not limited to the contents disclosed in the above-mentioned embodiment. It should be noted that the above-mentioned modules can be run in a computer terminal as part of the device.

[0078] It should be noted that the optional or preferred implementation of this embodiment can refer to the relevant description in the embodiment, which will not be repeated here.

[0079] The above-mentioned transmission tunnel fault warning device may also include a processor and a memory. The above-mentioned modeling module 200, data acquisition module 202, warning module 204, etc. are all stored in the memory as program modules, and the processor executes the above-mentioned program modules stored in the memory to realize corresponding functions.

[0080] The processor includes a kernel, which retrieves the corresponding program module from the memory. The kernel may be one or more. The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. The memory includes at least one memory chip.

[0081] According to an embodiment of the present application, an embodiment of a non-volatile storage medium is also provided. Optionally, in this embodiment, the non-volatile storage medium includes a stored program, wherein when the program is executed, the device where the non-volatile storage medium is located is controlled to execute any of the above-mentioned power transmission tunnel fault warning methods.

[0082] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group, and the non-volatile storage medium includes a stored program.

[0083] Optionally, when the program is running, the device where the non-volatile storage medium is located is controlled to perform the following functions: perform digital twin modeling on the transmission tunnel to obtain a target digital twin model; obtain current operating data and current water level depth of the transmission tunnel in the current period; based on the current operating data and the current water level depth, use the target digital twin model to obtain early warning information of the transmission tunnel, wherein the early warning information is at least used to indicate the abnormal state and abnormal type of the transmission tunnel.

[0084] According to an embodiment of the present application, an embodiment of a processor is also provided. Optionally, in this embodiment, the processor is used to run a program, wherein when the program is run, any one of the above-mentioned power transmission tunnel fault early warning methods is executed.

[0085] According to an embodiment of the present application, an embodiment of a computer program product is also provided. When executed on a data processing device, it is suitable for executing a program that initializes any of the steps of the above-mentioned power transmission tunnel fault early warning method.

[0086] Optionally, the above-mentioned computer program product, when executed on a data processing device, is suitable for executing a program that initializes the following method steps: performing digital twin modeling on the transmission tunnel to obtain a target digital twin model; obtaining current operating data and current water level depth of the transmission tunnel in the current period; based on the current operating data and the current water level depth, using the target digital twin model to obtain early warning information of the transmission tunnel, wherein the early warning information is at least used to indicate the abnormal state and abnormal type of the transmission tunnel.

[0087] An embodiment of the present invention provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, the following steps are implemented: performing digital twin modeling on a power transmission tunnel to obtain a target digital twin model; obtaining current operating data and a current water level depth of the power transmission tunnel in a current period; based on the current operating data and the current water level depth, using the target digital twin model, obtaining early warning information of the power transmission tunnel, wherein the early warning information is at least used to indicate an abnormal state and an abnormal type of the power transmission tunnel.

[0088] The above sequence of the embodiments of the present invention is for description only and does not represent the superiority or inferiority of the embodiments.

[0089] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0090] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the above modules can be a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, modules or indirect coupling or communication connection of modules, which can be electrical or other forms.

[0091] The modules described above as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0092] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of software functional modules.

[0093] If the above-mentioned integrated module is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable non-volatile storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a non-volatile storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the various embodiments of the present invention. The aforementioned non-volatile storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0094] The above are only preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A power transmission tunnel fault early warning method, characterized in that: include: Conduct digital twin modeling of the transmission tunnel to obtain the target digital twin model; Obtaining current operation data and current water level depth of the transmission tunnel in the current period; Based on the current operating data and the current water level depth, the target digital twin model is used to obtain early warning information of the power transmission tunnel, wherein the early warning information is at least used to indicate an abnormal state and an abnormal type of the power transmission tunnel.

2. The method according to claim 1, characterized in that The digital twin modeling of the transmission tunnel is performed to obtain a target digital twin model, including: Performing digital twin modeling on the power transmission tunnel to obtain an initial digital twin model; Acquire operation data sets corresponding to a plurality of fault types, wherein the operation data sets include operation data corresponding to the corresponding fault types at a plurality of water level depths; Based on the operation data sets corresponding to the multiple fault types respectively and the initial digital twin model, the target digital twin model is obtained.

3. The method according to claim 2, characterized in that The digital twin modeling of the transmission tunnel is performed to obtain an initial digital twin model, including: Acquire physical structure information of the power transmission tunnel and historical water level change information of the power transmission tunnel in historical periods; Obtaining geological condition information of the target area where the power transmission tunnel is located; The initial digital twin model is constructed based on the physical structure information, the historical water level change information and the geological condition information.

4. The method according to claim 2, characterized in that: The obtaining of the operation data sets corresponding to the various fault types includes: Modeling and simulating any fault type among the multiple fault types at multiple water level depths to obtain operating data corresponding to any fault type at the multiple water level depths; Based on the operation data corresponding to each of the fault types at the multiple water level depths, construct an operation data set corresponding to each of the fault types; The operating data sets corresponding to the plurality of fault types are obtained by obtaining the operating data set corresponding to any one of the fault types.

5. The method according to claim 2, characterized in that: In the case where the warning information includes fault warning information, the obtaining of the warning information of the transmission tunnel by using the target digital twin model based on the current operation data and the current water level depth includes: Determine, from the operation data sets corresponding to the multiple fault types respectively stored in the target digital twin model, a plurality of operation data corresponding to the current water level depth; Determining the matching degree between the plurality of operation data and the current operation data respectively; Determining target operating data whose matching degree is greater than a preset matching degree threshold from the plurality of operating data; The fault warning information is generated based on the fault type corresponding to the target operation data.

6. The method according to claim 1, characterized in that In the case where the warning information includes water level warning information, the obtaining of the warning information of the transmission tunnel by using the target digital twin model based on the current operation data and the current water level depth includes: Detecting whether the current water level depth is greater than a preset water level depth; When the current water level depth is greater than the preset water level depth, the water level warning information is generated based on the current water level depth.

7. The method according to claim 6, characterized in that The method further comprises: Acquire external environmental parameters of the power transmission tunnel, wherein the external environmental parameters include at least: expected precipitation and groundwater level information in the target area where the power transmission tunnel is located in a forecast period, and the forecast period is a period after the current period; Based on the current water level depth and the external environmental parameters, obtaining a predicted water level depth of the power transmission tunnel in the predicted time period; When the predicted water level depth is greater than the preset water level depth, the water level warning information is generated based on the predicted water level depth.

8. The method according to any one of claims 1 to 7, characterized in that The method further comprises: Based on the current operating data, the current water level depth and the warning information, the target digital twin model is iteratively optimized to obtain an optimized target digital twin model.

9. A transmission tunnel fault warning device, characterized in that: include: A modeling module is used to perform digital twin modeling on the transmission tunnel and obtain a target digital twin model; A data acquisition module, used to acquire current operation data and current water level depth of the transmission tunnel in a current period; An early warning module is used to obtain early warning information of the power transmission tunnel based on the current operating data and the current water level depth using the target digital twin model, wherein the early warning information is at least used to indicate an abnormal state and abnormal type of the power transmission tunnel.

10. An electronic device, characterized in that: It includes one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the power transmission tunnel fault warning method according to any one of claims 1 to 8.

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