Intelligent slope health monitoring system and standardized information management equipment

Abstract

The invention provides an intelligent slope health monitoring system and standardized information management equipment, the equipment is applied to the system, and the system comprises a data processing module, a digital twin module, a network module and a prediction module. Wherein the network module adopts a multi-protocol transmission strategy to carry out data transmission among a plurality of modules, so that the transmission efficiency and reliability of data in the system are ensured; the data processing module automatically acquires and processes slope data of the transmission line tower in real time, so that the digital twinborn module performs data mapping and fusion on the acquired and processed data to generate a slope data twinborn model, and the accuracy and timeliness of data monitoring can be ensured through a digital twinborn technology; and finally, a prediction module carries out data visualization on the side slope data twinborn model and predicts the side slope deformation of the power transmission line tower after an influence factor is extracted based on the side slope data twinborn model, so that real-time and comprehensive automatic monitoring and early warning on the health condition of the side slope are realized.

Description

Technical Field

[0001] The present application relates to the field of power monitoring technology, and in particular to an intelligent slope health monitoring system and standardized information management equipment. Background Art

[0002] Currently, transmission line tower slope monitoring technology widely incorporates advanced positioning and IoT technologies, such as the Beidou satellite positioning system, to enable real-time monitoring and assessment of slope stability, capturing slope displacement and deformation in real time. Furthermore, to provide a more comprehensive understanding of slope stability, these systems can be combined with traditional monitoring technologies, such as total stations and levels, for regular measurements. Therefore, under certain conditions, these systems play an irreplaceable role.

[0003] However, satellite positioning technology can be susceptible to signal interference in adverse weather conditions, reducing monitoring accuracy. The purchase, installation, and maintenance costs of high-precision monitoring equipment are relatively high, increasing project costs. Furthermore, the interpretation and analysis of monitoring data is complex, requiring the involvement of specialized data analysts and taking a long time. In short, with the development of smart grids and the continued expansion of power infrastructure, current slope health monitoring systems cannot meet the accuracy and intelligence requirements for health monitoring of transmission line tower slopes. Summary of the Invention

[0004] The purpose of this application is to solve at least one of the above-mentioned technical defects, especially the technical defect that the slope health monitoring system in the prior art cannot meet the accuracy and intelligence requirements of health monitoring of the slope of the transmission line tower.

[0005] The present application provides an intelligent slope health monitoring system based on digital twins, the system comprising a data processing module, a digital twin module, a network module and a prediction module;

[0006] The data processing module is directly connected to the digital twin module; the data processing module and the digital twin module are respectively connected to the prediction module through the network module; the network module adopts a multi-protocol transmission strategy to transmit data between multiple modules;

[0007] The data processing module is used to collect and process the slope data of the transmission line tower in real time to obtain real-time data and historical data of the current time period;

[0008] The digital twin module is used to perform data mapping and fusion on the real-time data and the historical data to generate a slope data twin model;

[0009] The prediction module is used to visualize the slope data twin model, and to extract the influencing factor of the transmission line tower based on the slope data twin model, and to predict the slope deformation of the transmission line tower based on the influencing factor.

[0010] Optionally, the data processing module includes a data acquisition submodule, a data processing submodule and a data storage submodule;

[0011] The data acquisition submodule is used to collect slope data of the transmission line tower in real time using a SAR sensor to obtain collected data;

[0012] The data processing submodule is used to determine the current time period according to a preset time length, and process the collected data based on the current time period to obtain real-time data and historical data of the current time period;

[0013] The data storage submodule is used to update the real-time data and historical data of the current time period to the big data center.

[0014] Optionally, the data processing submodule includes a real-time data processing unit and a historical data processing unit;

[0015] The real-time data processing unit is used to optimize the collected data of the current time period to obtain the real-time data of the current time period;

[0016] The historical data processing unit is used to extract the real-time data and historical data of the previous time period in the big data center, and integrate the real-time data and historical data of the previous time period to obtain the historical data corresponding to the current time period.

[0017] Optionally, the digital twin module includes application services, a twin data center, and a digital twin model;

[0018] The application service is used to perform data mapping on the real-time data and the historical data, to perform multi-source integration according to the mapping results to obtain integrated data, and to perform multi-field monitoring on the transmission line tower based on the integrated data;

[0019] The twin data center is used to perform data fusion and mining on the integrated data to obtain slope digital twin data, and perform data management on the slope digital twin data;

[0020] The digital twin model is used to convert the slope digital twin data into a slope data twin model in a modeling manner.

[0021] Optionally, the application service includes a physical scene visualization unit, an information integration unit, a business management and control unit, a slope deformation monitoring unit, and a power grid security unit;

[0022] The entity scene visualization unit is used to support data interaction between the user and each unit in the application service;

[0023] The information integration unit is used to perform data mapping on the real-time data and the historical data, and to perform multi-source integration according to the mapping result to obtain integrated data;

[0024] The business control unit is used to perform task scheduling and business process control on each unit in the application service;

[0025] The slope deformation monitoring unit is used to monitor the slope health of the transmission line tower based on the integrated data, and issue an early warning when there is an abnormality in the monitoring result;

[0026] The grid safety unit is used to monitor the grid status of the transmission line tower and perform fault detection and early warning when an abnormal state is detected.

[0027] Optionally, the twin data center includes a database, a data processing unit, a data fusion unit, a data computing unit and a data mining unit;

[0028] The database is used to manage the data of each unit in the twin data center;

[0029] The data processing unit is used to perform data preprocessing on the integrated data to obtain preprocessed data;

[0030] The data fusion is used to perform multi-mode fusion on the pre-processed data to form fused data;

[0031] The data calculation unit is used to perform model calculation on the fusion data using a model calculation algorithm to form initial model data;

[0032] The data mining unit is used to perform in-depth mining on the initial model data to optimize the performance of the initial model data according to the mining results to obtain slope digital twin data.

[0033] Optionally, the prediction module is used to extract and obtain the influencing factor of the transmission line tower based on the slope data twin model, including:

[0034] The prediction module uses a statistical analysis method to identify the relationship between various factors in the slope data twin model and the slope deformation, and determines the influencing factor of the transmission line tower from various factors based on the identification results;

[0035] Among them, the various factors in the slope data twin model include meteorological factors, geological factors, vegetation coverage and human activities.

[0036] Optionally, the prediction module is used to predict the slope deformation of the transmission line tower according to the influencing factor, including:

[0037] After determining the slope deformation prediction model, the prediction module extracts the slope influence data corresponding to the influencing factor in the slope data twin model, and uses the slope deformation prediction model to predict the slope influence data to obtain the slope deformation of the transmission line tower.

[0038] Optionally, the process of the network module adopting a multi-protocol transmission strategy to transmit data between multiple modules includes:

[0039] The network module uses the Ethernet of the acquisition and editing group as a data transmission trust bus, and sets a multi-protocol strategy to use a transmission protocol corresponding to the data transmission demand for real-time data transmission according to the multi-protocol strategy;

[0040] The transmission protocols include Internet Protocol, Transmission Control Protocol, User Datagram Protocol and Communication Protocol.

[0041] The present application also provides a standardized information management device, which is applied to the intelligent slope health monitoring system of any one of the above embodiments, and the device includes a front-end mobile application, a back-end database and an intermediate service layer;

[0042] The front-end mobile application is used to receive user operation instructions and perform data interaction in the intelligent slope health monitoring system according to the operation instructions;

[0043] The back-end database is used to store and manage data in the intelligent slope health monitoring system;

[0044] The intermediate service layer is used to extract data corresponding to the data request from the back-end database after receiving the data request sent by the front-end mobile application, analyze and process the data, obtain a response result, and return the response result to the front-end mobile application.

[0045] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0046] This application provides an intelligent slope health monitoring system and standardized information management device. The device is applied to the system, which includes a data processing module, a digital twin module, a network module, and a prediction module. The data processing module is directly connected to the digital twin module, which are each connected to the prediction module via the network module. The network module uses a multi-protocol transmission strategy to transmit data between multiple modules, thereby ensuring the efficiency and reliability of data transmission in the system. In addition, the data processing module is mainly responsible for real-time collection and processing of slope data of transmission line towers, obtaining real-time data and historical data for the current time period, and realizing automatic data collection and analysis; the digital twin module is mainly responsible for data mapping and integration of real-time data and historical data, generating a slope data twin model, so that data-driven decision support can be achieved through digital twin technology, while ensuring the accuracy and timeliness of data monitoring; and the prediction module is mainly responsible for data visualization of the slope data twin model, as well as extracting the influencing factors of the transmission line towers based on the slope data twin model, and predicting the slope deformation of the transmission line towers based on the influencing factors, thereby realizing real-time, comprehensive and automated monitoring and early warning of the slope health status, providing strong technical support for the power industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0048] Figure 1 A schematic diagram of the structure of an intelligent slope health monitoring system provided in an embodiment of the present application;

[0049] Figure 2 A schematic diagram of the structure of a data fusion process provided in an embodiment of the present application;

[0050] Figure 3 A schematic structural diagram of a digital twin module provided in an embodiment of the present application;

[0051] Figure 4 A schematic diagram of the structure of a network module provided in an embodiment of the present application;

[0052] Figure 5 This is a schematic diagram of the architecture of the standardized information management device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0054] Satellite positioning technology can be susceptible to signal interference in adverse weather conditions, reducing monitoring accuracy. The purchase, installation, and maintenance costs of high-precision monitoring equipment are relatively high, increasing project costs. Furthermore, the interpretation and analysis of monitoring data is complex, requiring the involvement of specialized data analysts and taking a long time. In short, with the development of smart grids and the continued expansion of power infrastructure, current slope health monitoring systems cannot meet the accuracy and intelligence requirements for health monitoring of transmission line tower slopes.

[0055] Based on this, this application proposes the following technical solutions, please refer to the following for details:

[0056] In one embodiment, Figure 1 As shown, Figure 1 A structural schematic diagram of an intelligent slope health monitoring system provided in an embodiment of the present application; the present application provides an intelligent slope health monitoring system based on digital twins, which may include a data processing module, a digital twin module, a network module and a prediction module.

[0057] The data processing module is directly connected to the digital twin module; the data processing module and the digital twin module are respectively connected to the prediction module through the network module; the network module adopts a multi-protocol transmission strategy to transmit data between multiple modules.

[0058] The data processing module is used to collect and process the slope data of the transmission line tower in real time to obtain the real-time data and historical data of the current time period.

[0059] The digital twin module is used to map and fuse real-time data and historical data to generate a slope data twin model.

[0060] The prediction module is used to visualize the slope data twin model, extract the influencing factors of the transmission line tower based on the slope data twin model, and predict the slope deformation of the transmission line tower based on the influencing factors.

[0061] In this embodiment, the intelligent slope health monitoring system consists of four main modules: a data processing module, a digital twin module, a network module, and a prediction module. These four modules are interconnected via a wireless network to ensure system operation. However, it should be noted that the data processing module is directly connected to the digital twin module; the data processing module and the digital twin module are each connected to the prediction module via the network module, thus forming a complete system.

[0062] It is understandable that the main function of the network module is to ensure data transmission between different modules within the system. It is equipped with a variety of transmission protocols, so a multi-protocol transmission strategy can be used to transmit data between multiple modules. In the system architecture of the present application, data can be transferred between the data processing module and the digital twin module through an internal interface or shared data storage. Based on this, the connection between the two modules can be achieved through local data exchange or intranet protocol; and the digital twin module of the present application needs to update the virtual three-dimensional model in real time, while the data processing module can process data in batches. Therefore, the two can directly share data through a local interface or a dedicated data bus, thereby reducing dependence on the network module, thereby simplifying the system architecture and improving overall efficiency.

[0063] In addition, the data processing module is mainly responsible for real-time collection of slope data of transmission line towers, and uses the collection results as various data to be processed. Then, time-series InSAR technology can be used for data processing and analysis to obtain real-time data and historical data for the current time period. All this data can be stored in the big data center for subsequent data acquisition and source tracing.

[0064] The digital twin module is primarily responsible for mapping and fusing real-time and historical data to generate a slope data twin model. This module can construct a digital twin model of the entity based on the relevant data from the entity module. Therefore, in this application, after receiving relevant environmental data, such as historical and real-time data, from the data processing module via a data interface, the digital twin module can map this environmental data to a virtual 3D model, forming a slope data twin model. Furthermore, the digital twin module can also update the data in the slope data twin model based on relevant environmental data received in subsequent time periods to ensure consistency.

[0065] It should be noted that the construction of the slope data twin model is based on the physical entity and virtual mapping of the physical track network and data fusion. In this process, data fusion is particularly important. It is necessary to aggregate the historical data called and the real-time data collected in the current time period to complete the data fusion required for the construction of the slope data twin model. Figure 2 As shown, Figure 2A structural diagram of a data fusion process provided in an embodiment of the present application; Figure 2 It can be seen that when constructing the slope data twin model, data fusion can enable closed-loop connectivity between data, thereby completing the visualization of transmission line tower slope data and the integration of control services.

[0066] The prediction module is primarily responsible for visualizing the slope data twin model, extracting the influencing factors of transmission line towers based on the slope data twin model, and predicting the slope deformation of the transmission line towers based on the influencing factors. This module can present the data processing results of the data processing module and the digital twin module, namely the slope data twin model, through the server. In addition, it can also perform multi-source data fusion based on the relevant environmental data mapped in the slope data twin model, extract the influencing factors that affect the slope stability of the transmission line tower, predict the slope deformation, and provide data support for slope stability protection and disaster prevention.

[0067] Among them, the relevant environmental data in the slope data twin model may come from multiple data sources, such as sensors, meteorological monitoring systems, and geological monitoring systems. These data include real-time monitored slope displacement data, rainfall, wind speed, temperature and other environmental data. Therefore, this application can perform multi-source data fusion on the relevant environmental data from multiple data sources to generate a unified data set, thereby improving the prediction accuracy of influencing factors and slope deformation.

[0068] Specifically, the data collection time of different data sources may be different. Therefore, during the data fusion process, the prediction module can first synchronize the data in time to ensure that the data of different time periods can be accurately aligned, and then clean the aligned data to remove noise data, fill in missing values, etc., to ensure the consistency and accuracy of the data; finally, the prediction module can use appropriate data fusion algorithms, such as weighted averaging, Kalman filtering, Bayesian inference and other algorithms, to synthesize data from different sources and generate a unified data set to provide support for subsequent analysis and prediction.

[0069] In the above embodiment, the system may include a data processing module, a digital twin module, a network module, and a prediction module. The data processing module is directly connected to the digital twin module, which is then connected to the prediction module via the network module. The network module uses a multi-protocol transmission strategy to transmit data between multiple modules, thereby ensuring data transmission efficiency and reliability in the system. Furthermore, the data processing module is primarily responsible for real-time acquisition and processing of slope data from transmission line towers, obtaining real-time data and historical data for the current time period, and enabling automated data collection and analysis. The digital twin module is primarily responsible for mapping and fusing real-time and historical data to generate a slope data twin model, enabling data-driven decision support through digital twin technology while ensuring the accuracy and timeliness of data monitoring. The prediction module is primarily responsible for visualizing the slope data twin model, extracting influencing factors of transmission line towers based on the slope data twin model, and predicting the slope deformation of transmission line towers based on the influencing factors, thereby enabling real-time, comprehensive, automated monitoring and early warning of slope health, providing strong technical support for the power industry.

[0070] In one embodiment, the data processing module may include a data acquisition submodule, a data processing submodule, and a data storage submodule.

[0071] The data acquisition submodule is used to collect slope data of transmission line towers in real time using SAR sensors to obtain collected data.

[0072] The data processing submodule is used to determine the current time period according to the preset duration, and process the collected data based on the current time period to obtain real-time data and historical data of the current time period.

[0073] The data storage submodule is used to update the real-time data and historical data of the current time period to the big data center.

[0074] In this embodiment, the data processing module can be further divided into three submodules: a data acquisition submodule, a data processing submodule, and a data storage submodule. Each submodule performs a corresponding function. For example, the data acquisition submodule is primarily responsible for collecting data, the data processing submodule is primarily responsible for processing the collected data, and the data storage module is primarily responsible for storing the processed data.

[0075] Specifically, if Figure 1 As shown, Figure 1In this system, the data acquisition submodule uses a SAR (Synthetic Aperture Radar) sensor to collect slope data from transmission line towers in real time, generating collected data. The SAR sensor is a high-resolution, all-weather, and penetrating radar system, and the data it collects is comprehensive and highly accurate. After the SAR sensor collects high-resolution SAR data from the transmission line towers, the data processing submodule further processes the SAR data collected during the current time period, including data identification, cleaning, and format conversion, according to a preset duration, to generate real-time and historical data for the current time period. Finally, the data storage module updates the real-time and historical data for the current time period to the big data center. For example, the real-time data can be stored in the big data center's real-time database, while the historical data can be stored in the big data center's historical database, thereby improving the data management efficiency of the big data center.

[0076] In one embodiment, the data processing submodule may include a real-time data processing unit and a historical data processing unit;

[0077] The real-time data processing unit is used to optimize the collected data of the current time period to obtain the real-time data of the current time period.

[0078] The historical data processing unit is used to extract the real-time data and historical data of the previous time period in the big data center, and integrate the real-time data and historical data of the previous time period to obtain the historical data corresponding to the current time period.

[0079] In this embodiment, the data processing submodule can be further divided into two units: a real-time data processing unit and a historical data processing unit. The real-time data processing unit is primarily responsible for optimizing the collected data for the current time period to obtain real-time data for the current time period, while the historical data processing unit is primarily responsible for extracting the real-time data and historical data for the previous time period from the big data center, integrating and processing the real-time data and historical data for the previous time period to obtain historical data corresponding to the current time period, thereby achieving standardization of the data processing process.

[0080] In one embodiment, Figure 3 As shown, Figure 3 A schematic structural diagram of a digital twin module provided in an embodiment of the present application; Figure 3 In the digital twin module, the digital twin module can include application services, twin data centers and digital twin models.

[0081] The application service is used to perform data mapping between real-time data and historical data, to integrate multiple sources based on the mapping results, to obtain integrated data, and to perform multi-field monitoring of transmission line towers based on the integrated data.

[0082] The twin data center is used to fuse and mine the integrated data to obtain slope digital twin data and manage the slope digital twin data.

[0083] The digital twin model is used to convert the slope digital twin data into a slope data twin model in a modeling manner.

[0084] In this embodiment, the digital twin module can be further divided into three submodules: application services, a twin data center, and a digital twin model. The application service is primarily responsible for mapping real-time and historical data, integrating multiple sources based on the mapping results to generate integrated data, and using this integrated data to perform multi-domain monitoring of transmission line towers. The twin data center is primarily responsible for fusing and mining the integrated data to generate slope digital twin data and manage this data. The digital twin model is primarily responsible for converting the slope digital twin data into a modeled slope data twin model.

[0085] Schematically, as Figure 3 As shown, Figure 3 In the Entity Module, the primary function of the digital twin module is to construct a digital twin model of the entity based on the relevant data of the entity module. Specifically, application services can provide data to drive the twin data center, which can then perform multi-level processing on the data provided by the application services and feed it back to the application services, providing data for the application services. Furthermore, the twin data center can also data-driven digital twin models to present the processed data, and the digital twin model's presentation results can be fed back to the twin data center for data perception. Furthermore, data exchange can also occur between application services and digital twin models.

[0086] In one embodiment, Figure 3 As shown, the application services may include a physical scene visualization unit, an information integration unit, a business management and control unit, a slope deformation monitoring unit, and a power grid security unit.

[0087] The entity scene visualization unit is used to support data interaction between users and various units in the application service.

[0088] The information integration unit is used to perform data mapping on real-time data and historical data, so as to perform multi-source integration according to the mapping results to obtain integrated data.

[0089] The business control unit is used to schedule and allocate tasks and control business processes for each unit in the application service;

[0090] The slope deformation monitoring unit is used to monitor the slope health of transmission line towers based on integrated data and issue early warnings when there are abnormalities in the monitoring results.

[0091] The grid safety unit is used to monitor the grid status of transmission line towers and perform fault detection and early warning when abnormal conditions are detected.

[0092] In this embodiment, multiple units can be set in the application service according to actual business needs, such as a physical scene visualization unit, an information integration unit, a business management unit, a slope deformation monitoring unit, and a power grid safety unit, which are not limited here.

[0093] Specifically, if Figure 3 As shown, Figure 3 The physical scene visualization unit is responsible for real-time display, interactive operations, and enhanced decision support. The information integration unit is responsible for multi-source data aggregation, data standardization and processing, and data synchronization. The business control unit is responsible for business process management, task allocation and scheduling, and process supervision and management. The slope deformation monitoring unit is responsible for slope health monitoring, real-time early warning, and trend prediction. The power grid security unit is responsible for power grid status monitoring, fault detection and early warning, and emergency response. Therefore, through the interaction of these units, the application service can achieve a visual display of slope health status, making it highly real-time and interactive.

[0094] In one embodiment, Figure 3 As shown, the twin data center may include a database, a data processing unit, a data fusion unit, a data computing unit, and a data mining unit.

[0095] The database is used to manage the data of each unit in the twin data center.

[0096] The data processing unit is used to perform data preprocessing on the integrated data to obtain preprocessed data.

[0097] Data fusion is used to perform multi-mode fusion on pre-processed data to form fused data.

[0098] The data calculation unit is used to perform model calculation on the fusion data using the model calculation algorithm to form initial model data.

[0099] The data mining unit is used to perform in-depth mining on the initial model data to optimize the performance of the initial model data according to the mining results and obtain the slope digital twin data.

[0100] In this embodiment, the twin data center can set up multiple units according to actual business needs, such as database, data processing unit, data fusion unit, data calculation unit and data mining unit, without any restriction here.

[0101] Specifically, if Figure 3 As shown, Figure 3In the twin data center, the database is primarily responsible for data storage, data retrieval, backup and recovery, and data management; the data processing unit is primarily responsible for data cleaning and preprocessing, data format conversion, time series data processing, and data preprocessing; the data fusion unit is primarily responsible for multi-source data fusion, fusion of different data formats, data consistency processing, and cross-domain data fusion; the data computing unit is primarily responsible for real-time computing, model calculation, algorithm application, and performance optimization; and the data mining unit is primarily responsible for pattern recognition, anomaly detection, predictive analysis, association rule mining, and data-driven decision support. Through the interaction of these various units, the twin data center can achieve precise processing and analysis of slope data, resulting in high-precision, high-efficiency, and high-reliability output data, providing strong data support for slope deformation prediction.

[0102] In one embodiment, the process of extracting the influencing factors of transmission line towers based on the slope data twin model by the prediction module may include:

[0103] The prediction module uses statistical analysis methods to identify the relationship between various factors and slope deformation in the slope data twin model, and determines the influencing factors of transmission line towers from various factors based on the identification results.

[0104] Among them, the various factors in the slope data twin model include meteorological factors, geological factors, vegetation cover and human activities.

[0105] In this embodiment, the prediction module can determine the various factors in the slope data twin model based on existing domain knowledge and relevant data. These factors may include meteorological factors, geological factors, vegetation cover and human activities, etc., which are not limited here; then the prediction module can use statistical analysis methods, such as correlation analysis methods, regression analysis methods, etc. to identify the relationship between different factors and slope deformation, and determine which factors have a significant impact on deformation, and use them as influencing factors of transmission line towers.

[0106] Specifically, meteorological factors may include precipitation, temperature changes, and wind speed; among them, precipitation will increase the humidity and moisture of the soil, resulting in an increase in the weight of the soil, which may cause landslides or subsidence, temperature changes will cause the soil to expand and contract, thereby affecting the stability of the slope, and wind speed will cause the loose materials on the upper part of the slope to be blown by the wind, causing soil loss or causing objects to slide. Geological factors may include soil type, rock structure, and groundwater level; among them, the characteristics of soil type and rock structure are important geological factors affecting slope stability. For example, clay is prone to landslides under the action of saturated water, and the rise in groundwater level will weaken the soil's supporting capacity, which is prone to landslides. Vegetation cover may include vegetation roots and vegetation types; among them, vegetation roots help stabilize the soil, and different vegetation types have different soil fixation capabilities. Human activities can include excavation and engineering construction, overgrazing or land development; among them, excavation and engineering construction will destroy the stability of the slope, leading to geological disasters such as landslides and collapses, overgrazing or land development will lead to the destruction of vegetation, thereby increasing soil erosion and affecting the stability of the slope.

[0107] In one embodiment, Figure 3 As shown, the prediction module is used to predict the slope deformation of the transmission line tower according to the influencing factors, which may include:

[0108] After determining the slope deformation prediction model, the prediction module extracts the slope influence data corresponding to the influencing factors in the slope data twin model, and uses the slope deformation prediction model to predict the slope influence data to obtain the slope deformation of the transmission line tower.

[0109] In this embodiment, after determining the influencing factors of the transmission line towers, the prediction model can first determine the slope deformation prediction model, and then extract the slope influence data corresponding to the influencing factors in the slope data twin model, and use the slope deformation prediction model to predict the slope influence data to obtain the slope deformation of the transmission line towers, so as to improve the prediction efficiency and prediction accuracy of the slope deformation.

[0110] It is understood that the slope data twin model here refers to a model that predicts slope deformation based on input influencing factors and their corresponding slope impact data and obtains prediction results. This model is generated by training a pre-trained model selected based on actual conditions using multiple different influencing factors and their corresponding historical slope impact data. This pre-trained model can use statistical models such as linear regression and time series analysis, machine learning models such as support vector machines, random forests, neural networks, etc., and deep learning models such as convolutional neural networks and recursive neural networks, etc., without limitation here.

[0111] Specifically, when training the pre-trained model, different influencing factors and their corresponding historical slope impact data and slope deformation can be collected and input into the pre-trained model. This allows the model to learn the patterns and regularities in the data and establish the relationship between the influencing factors and their slope impact data (input) and slope deformation (output). Once the model is trained and verified, it can use real-time data to predict slope deformation. This allows the system to warn of potential risks, such as landslides and subsidence, based on future trends in slope deformation predictions based on current influencing factors, allowing for timely prevention and intervention.

[0112] In one embodiment, the process of the network module using a multi-protocol transmission strategy to perform data transmission between multiple modules may include:

[0113] The network module uses the Ethernet as the data transmission trust bus and sets a multi-protocol strategy to adopt the transmission protocol corresponding to the data transmission requirements for real-time data transmission according to the multi-protocol strategy.

[0114] Among them, transmission protocols include Internet Protocol, Transmission Control Protocol, User Datagram Protocol and Communication Protocol.

[0115] In this embodiment, the Internet Protocol, as the basic protocol for network transmission, will be used in all network communications. When it is used as a carrier to transmit data, it has a faster rate; the Transmission Control Protocol has higher reliability and sequentiality during data transmission; the User Datagram Protocol can provide lower latency and higher transmission rate; the communication protocol is mainly used for data transmission protocols in specific application scenarios, and usually includes some industry standards or customized protocols to achieve specific communications between applications, such as Modbus, MQTT, CoAP, etc.

[0116] For example, when the digital twin module needs to update the health status of the slope data twin model in real time to ensure the integrity and order of data transmission, the transmission control protocol can be used to ensure data reliability; in some real-time monitoring or high-frequency update scenarios, the user data packet protocol can be used; when the system needs to transmit specific types of application data, or work together between different devices, a dedicated communication protocol can be used.

[0117] It is understandable that if Figure 4 As shown, Figure 4This is a schematic diagram of the structure of a network module provided in an embodiment of the present application; the network module is an important support for the system to build a rail transit network and is the basis for achieving real-time data transmission in rail transit. It can improve the efficiency and reliability of data transmission and directly determine the generation effect of digital twins. Therefore, in order to ensure the effect of real-time data transmission, the present application can use the Ethernet of the editing group as a data transmission trust bus and set a data transmission protocol to achieve real-time data transmission in rail transit. The network module structure is as follows Figure 4 As shown; in this network module, the transmission between terminal devices must be completed according to the transmission protocol and a security layer can be designed in the terminal to ensure the security of data transmission.

[0118] In one embodiment, Figure 5 As shown, Figure 5 Schematic diagram of the architecture of the standardized information management device provided for the embodiments of the present application; the present application also provides a standardized information management device, which is applied to the intelligent slope health monitoring system of any of the above embodiments, and the device may include a front-end mobile application, a back-end database and an intermediate service layer.

[0119] The front-end mobile application is used to receive user operation instructions and perform data interaction in the intelligent slope health monitoring system according to the operation instructions.

[0120] The back-end database is used to store and manage data in the intelligent slope health monitoring system.

[0121] The middle service layer is used to extract the data corresponding to the data request from the back-end database after receiving the data request sent by the front-end mobile application, analyze and process it, obtain the response result, and return the response result to the front-end mobile application.

[0122] In this embodiment, the standardized information management device primarily consists of three modules: a front-end mobile application, a back-end database, and a middle service layer. The front-end mobile application provides an interactive interface between users and the system, allowing them to view real-time slope deformation data, historical data, prediction results, and other information. It is closely connected to the physical scene visualization unit in the intelligent slope health monitoring system. The back-end database is primarily responsible for storing and managing all monitoring data, such as real-time data, historical data, and slope impact data, and provides support for data query and analysis. It is closely related to the data storage submodule in the system. The middle service layer is primarily responsible for coordinating and interacting with data between the front-end mobile application and the back-end database, playing a key role in data interaction and business logic processing in the system. Furthermore, the middle service layer is responsible for mapping the front-end mobile application's operations and requests to the system's business logic, such as activating alarm functions and intelligent prediction functions. Through the middle service layer, business processes can be automatically executed, thereby ensuring the accuracy and timeliness of slope deformation prediction and power grid security monitoring.

[0123] Specifically, front-end mobile applications are the primary interface for users to interact with the system, featuring user-friendly interfaces and convenient operations. React Native can be used as a cross-platform development technology, combined with modern front-end development frameworks and libraries to implement application interface design and data management, including functional interfaces such as login, registration, information entry, information retrieval, and data analysis. Flexbox layout is also used to achieve flexible interface layout, ensuring that the application can be well adapted to mobile devices of different sizes.

[0124] When using Flexbox layout, first, on the main axis (horizontally), the flex-direction property determines the direction of the main axis, and the space on the main axis is determined by the width of the parent container. On the cross axis (vertically), the size of the cross axis is determined by the element's height and the align-items property. Second, the distribution of space on the main axis is determined by the flex and flex-grow properties of the child elements. If the flex-grow property is 0, the child element will not be enlarged. If the flex-grow property is a positive number, the child element's size will be enlarged proportionally based on the flex property. The layout of the child elements on the main axis is shown below:

[0125]

[0126] Where Subproperties is the size of the child element; flex is the flex property; Sub_flex is the flex property of all child elements; and R_space is the remaining space on the main axis. Finally, on the cross axis, the alignment of the child elements is determined by the align-items and align-self properties. If the align-items property is set to stretch, the child element will stretch to the same size as the cross axis. Otherwise, the alignitems and align-self properties will determine the child element's position on the cross axis.

[0127] Furthermore, when designing user authentication features in front-end mobile applications, JWT can be used as an authentication mechanism. JWT is a secure transmission method for cross-domain authentication. It encrypts the user's identity information and generates a token. This token is transmitted to the front-end mobile application via the network after the user logs in. Each subsequent request carries the token to the back-end service for permission verification. The implementation process of user authentication is as follows:

[0128] 1. User login process:

[0129] (1) The user enters the username and password on the front-end mobile application interface;

[0130] (2) The front-end mobile application sends the username and password entered by the user to the back-end service for authentication;

[0131] (3) The backend service verifies the user's identity. If the authentication is successful, a JWT token is generated and sent to the frontend mobile application.

[0132] (4) After the front-end mobile application receives the token, it stores it locally, and all subsequent requests will carry the token.

[0133] 2. JWT generation process:

[0134] (1) The backend service uses the private key to encrypt the user's identity information and generate a JWT token;

[0135] (2) The JWT token contains the user's identity information and other necessary information, such as expiration time;

[0136] (3) The backend service sends the generated JWT token to the frontend mobile application.

[0137] 3. JWT verification process:

[0138] (1) The front-end mobile application carries the JWT token to the back-end service on each request;

[0139] (2) After receiving the request, the backend service parses the JWT token and verifies its authenticity using the public key;

[0140] (3) If the JWT token is valid and has not expired, the authentication is successful; otherwise, the authentication fails and you need to log in again.

[0141] When designing the information entry function in the front-end mobile application, you can use the form component provided by React Native to design an intuitive and easy-to-use interface. At the same time, combine it with the Yup library to perform data validation to ensure the legitimacy and accuracy of the information entered by the user. The implementation process of information entry is as follows:

[0142] (1) Use React Native’s TextInput component to collect text information entered by the user, and use the Picker component to provide a drop-down selection function;

[0143] (2) Use the Yup library to define a form validation schema, including the data type and format requirements of each field;

[0144] (3) When the form is submitted, the verification method provided by Yup is called to verify the data entered by the user. If the verification fails, the corresponding error message is displayed on the interface; if the verification passes, the data entered by the user is sent to the back-end service for processing.

[0145] When designing information retrieval functionality in front-end mobile applications, you can use the TF-IDF algorithm to retrieve relevant information by entering keywords. The TF-IDF algorithm is a commonly used information retrieval technique that measures the importance of a term in a document, calculating its weight based on the term's frequency (TF) in the document and its inverse document frequency (IDF) across the entire document collection. The information retrieval process is as follows:

[0146] 1. Create an inverted index:

[0147] Traverse the document collection and create an inverted index for each term, recording the list of documents containing the term. The structure of the inverted index can be a dictionary, where the term is the key and the corresponding value is the list of documents containing the term.

[0148] 2. Calculate TF-IDF weight:

[0149] For each term in the query, we first calculate its TF (term frequency) in the query document and its IDF (inverse document frequency) in the entire document collection. We then multiply the TF and IDF to get the TF-IDF weight. The calculation process is as follows:

[0150] (1) TF (word frequency) is calculated as follows:

[0151]

[0152] Where TF(t,d) is the word frequency of term t in document d; is the number of times term t appears in document d; is the sum of the occurrences of all terms in document d.

[0153] (2) IDF (Inverse Document Frequency) is calculated as follows:

[0154]

[0155] Where IDF(t) is the inverse document frequency of term t; N is the total number of documents in the document set; df(t) is the number of documents containing term t.

[0156] (3) TF-IDF weight calculation is as follows:

[0157]

[0158] Where TF-IDF(t,d) is the TF-IDF weight of term t in document d.

[0159] 3. Match and sort documents:

[0160] For each query term, the inverted index is used to find a list of documents containing the term and the TF-IDF weight of each document is calculated. The documents are sorted by TF-IDF weight, with documents with higher weights at the top to provide more relevant search results.

[0161] 4. Display search results:

[0162] The sorted document list is displayed on the interface for users to view. Each search result can display relevant information such as the document title, abstract, and a link or button for users to view detailed information.

[0163] The back-end database is responsible for data storage and management, as well as providing key functions such as data backup and recovery. For database design, MySQL can be chosen as the database for the standardized information management system to implement the system's data storage, data management, and data backup and recovery functions.

[0164] (1) Data storage:

[0165] In the MySQL database management system, the data storage process is to insert the information entered by the user into the corresponding database table according to the pre-designed data model.

[0166] (2) Data management:

[0167] In the MySQL database management system, data management primarily involves deleting, querying, and modifying data. To delete data, use the DELETE FROM statement to remove matching records from a database table. To modify data, use the UPDATE statement to update matching records in a database table. To query data, use the SELECT statement to retrieve matching records from a database table.

[0168] (3) Data backup and recovery:

[0169] During the data backup and recovery process, we use the MySQL backup tool mysqldump to regularly back up the database and store the backed-up data on a dedicated backup server. In the event of data loss or corruption, we use the backup data to restore the database to normal working condition.

[0170] Middle-tier services are primarily responsible for data interaction and business logic processing between the front-end and back-end, and are implemented using a back-end development framework, such as Spring Boot or Django. This study explores the use of Django to implement interface design and data transmission for middle-tier services.

[0171] Furthermore, when designing the interface of a standardized information management device, you can use URL routing and view functions in Django to achieve this. Use Django's URLconf mechanism to map URLs to corresponding view functions, thereby achieving a clear and flexible interface design. Here, the mapping relationship between URL routing and view functions is as follows:

[0172]

[0173] In this formula, URL_route is a function that maps a URL to a corresponding view function; URL is the URL path of the front-end request; and View function is the view function that handles the request. After mapping the URL to the corresponding view function, by defining different URL paths and HTTP request methods, different functional interfaces are implemented, including operations such as querying, adding, modifying, and deleting data.

[0174] The logical relationship between the view function and the request processing is as follows:

[0175]

[0176] In the formula, Request is the HTTP request sent by the frontend; Response is the HTTP response returned by the view function after processing. The view function receives the request from the frontend mobile application and executes the corresponding business logic based on the request parameters; for example, it performs user authentication and data processing. The view function encapsulates the processing results into an HTTP response and returns it to the frontend mobile application.

[0177] Furthermore, standardized information management devices can utilize the object-relational mapping (ORM) framework provided by Django to handle data transfer between front-end mobile applications and back-end databases. The ORM framework maps database tables to Python objects, simplifying database operations and improving development efficiency. Model classes are defined to describe the structure of data tables, and query sets are used to perform database operations, including adding, deleting, modifying, and querying data. Data transfer can be implemented through code in three steps: defining model classes, executing database migrations, and performing database operations.

[0178] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0179] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referenced to each other.

[0180] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An intelligent slope health monitoring system based on digital twins, characterized by: The system includes a data processing module, a digital twin module, a network module and a prediction module; The data processing module is directly connected to the digital twin module; the data processing module and the digital twin module are respectively connected to the prediction module through the network module; the network module adopts a multi-protocol transmission strategy to transmit data between multiple modules; The data processing module is used to collect and process the slope data of the transmission line tower in real time to obtain real-time data and historical data of the current time period; The digital twin module is used to perform data mapping and fusion on the real-time data and the historical data to generate a slope data twin model; The prediction module is used to visualize the slope data twin model, and to extract the influencing factor of the transmission line tower based on the slope data twin model, and to predict the slope deformation of the transmission line tower based on the influencing factor.

2. The intelligent slope health monitoring system according to claim 1, characterized in that: The data processing module includes a data acquisition submodule, a data processing submodule and a data storage submodule; The data acquisition submodule is used to collect slope data of the transmission line tower in real time using a SAR sensor to obtain collected data; The data processing submodule is used to determine the current time period according to a preset time length, and process the collected data based on the current time period to obtain real-time data and historical data of the current time period; The data storage submodule is used to update the real-time data and historical data of the current time period to the big data center.

3. The intelligent slope health monitoring system according to claim 2, characterized in that: The data processing submodule includes a real-time data processing unit and a historical data processing unit; The real-time data processing unit is used to optimize the collected data of the current time period to obtain the real-time data of the current time period; The historical data processing unit is used to extract the real-time data and historical data of the previous time period in the big data center, and integrate the real-time data and historical data of the previous time period to obtain the historical data corresponding to the current time period.

4. The intelligent slope health monitoring system according to claim 1, characterized in that: The digital twin module includes application services, twin data centers and digital twin models; The application service is used to perform data mapping on the real-time data and the historical data, to perform multi-source integration according to the mapping results to obtain integrated data, and to perform multi-field monitoring on the transmission line tower based on the integrated data; The twin data center is used to perform data fusion and mining on the integrated data to obtain slope digital twin data, and perform data management on the slope digital twin data; The digital twin model is used to convert the slope digital twin data into a slope data twin model in a modeling manner.

5. The intelligent slope health monitoring system according to claim 4, characterized in that: The application services include a physical scene visualization unit, an information integration unit, a business management and control unit, a slope deformation monitoring unit, and a power grid security unit; The entity scene visualization unit is used to support data interaction between the user and each unit in the application service; The information integration unit is used to perform data mapping on the real-time data and the historical data, and to perform multi-source integration according to the mapping result to obtain integrated data; The business control unit is used to perform task scheduling and business process control on each unit in the application service; The slope deformation monitoring unit is used to monitor the slope health of the transmission line tower based on the integrated data, and issue an early warning when there is an abnormality in the monitoring result; The grid safety unit is used to monitor the grid status of the transmission line tower and perform fault detection and early warning when an abnormal state is detected.

6. The intelligent slope health monitoring system according to claim 4, characterized in that: The twin data center includes a database, a data processing unit, a data fusion unit, a data computing unit and a data mining unit; The database is used to manage the data of each unit in the twin data center; The data processing unit is used to perform data preprocessing on the integrated data to obtain preprocessed data; The data fusion is used to perform multi-mode fusion on the pre-processed data to form fused data; The data calculation unit is used to perform model calculation on the fusion data using a model calculation algorithm to form initial model data; The data mining unit is used to perform in-depth mining on the initial model data to optimize the performance of the initial model data according to the mining results to obtain slope digital twin data.

7. The intelligent slope health monitoring system according to claim 1, characterized in that: The prediction module is used to extract the influencing factor of the transmission line tower based on the slope data twin model, including: The prediction module uses a statistical analysis method to identify the relationship between various factors in the slope data twin model and the slope deformation, and determines the influencing factor of the transmission line tower from various factors based on the identification results; Among them, the various factors in the slope data twin model include meteorological factors, geological factors, vegetation coverage and human activities.

8. The intelligent slope health monitoring system according to claim 1, characterized in that: The prediction module is used to predict the slope deformation of the transmission line tower according to the influencing factors, including: After determining the slope deformation prediction model, the prediction module extracts the slope influence data corresponding to the influencing factor in the slope data twin model, and uses the slope deformation prediction model to predict the slope influence data to obtain the slope deformation of the transmission line tower.

9. The intelligent slope health monitoring system according to claim 1, characterized in that: The process of the network module using a multi-protocol transmission strategy to transmit data between multiple modules includes: The network module uses the Ethernet of the acquisition and editing group as a data transmission trust bus, and sets a multi-protocol strategy to use a transmission protocol corresponding to the data transmission demand for real-time data transmission according to the multi-protocol strategy; The transmission protocols include Internet Protocol, Transmission Control Protocol, User Datagram Protocol and Communication Protocol.

10. A standardized information management device, applied to the intelligent slope health monitoring system according to any one of claims 1 to 9, characterized in that: The device includes a front-end mobile application, a back-end database and an intermediate service layer; The front-end mobile application is used to receive user operation instructions and perform data interaction in the intelligent slope health monitoring system according to the operation instructions; The back-end database is used to store and manage data in the intelligent slope health monitoring system; The intermediate service layer is used to extract data corresponding to the data request from the back-end database after receiving the data request sent by the front-end mobile application, analyze and process the data, obtain a response result, and return the response result to the front-end mobile application.

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