Comprehensive house monitoring system based on digital twinborn technology and early warning model

Abstract

The invention discloses a comprehensive house monitoring system based on a digital twinborn technology and an early warning model, belongs to the technical field of comprehensive house monitoring, and solves the problems of low comprehensive house monitoring data processing efficiency, low early warning precision and poor equipment compatibility in the prior art. The on-site sensing layer is used for collecting monitoring data and uploading the detected data to the system; the early warning model is used for carrying out evaluation and early warning on the health state of the building according to the data obtained by the field sensing layer; and the digital twin platform is used for simulating the monitored comprehensive house in a virtual three-dimensional space and giving a building health state evaluation result. According to the invention, the digital twin technology and the self-developed early warning model are fused, real-time monitoring of the operation state of the building and fault early warning based on data analysis are realized, and the safety risk is effectively reduced.

Description

Technical Field

[0001] The present invention relates to the field of comprehensive room monitoring technology, and in particular, to a comprehensive room monitoring system based on digital twin technology and early warning model. Background Art

[0002] With the acceleration of urbanization, there are more and more high-rise buildings and large public facilities, which puts higher requirements on the health monitoring of building structures. Traditional building health monitoring mainly relies on manual inspections and regular inspections. This method is not only time-consuming and labor-intensive, but also difficult to achieve continuous and real-time monitoring. With the increasing number of comprehensive rooms, the safe operation of these facilities is crucial to the stability of the power grid. However, traditional inspection methods have problems such as low efficiency, strong blindness, and inability to detect potential faults in a timely manner. In the prior art, the application with publication number CN110417837A discloses a comprehensive room monitoring system, but the system has the following disadvantages:

[0003] 1. Low data processing efficiency: The data collected by sensor networks is huge and complex. Traditional data processing methods often require a lot of time and resources, resulting in low real-time performance.

[0004] 2. Low warning accuracy: Existing systems often issue warnings based on simple threshold settings or rule judgments, and have limited ability to predict complex equipment status changes and faults;

[0005] 3. Poor device compatibility: The types and specifications of sensors in the sensor network are diverse, and the compatibility between different devices is poor, which makes the deployment and maintenance of the monitoring system difficult. Therefore, an efficient monitoring system is urgently needed to improve the safety and reliability of the integrated room.

[0006] An efficient monitoring system is currently needed to improve the safety and reliability of comprehensive buildings. Summary of the invention

[0007] The purpose of the present invention is to provide a comprehensive room monitoring system based on digital twin technology and early warning model to solve the problems of low processing efficiency of comprehensive room monitoring data, low early warning accuracy and poor equipment compatibility in the prior art.

[0008] The present invention is achieved through the following technical solutions:

[0009] A comprehensive room monitoring system based on digital twin technology and early warning model, including:

[0010] On-site perception layer: used to collect monitoring data and upload the detected data to the system;

[0011] Early warning model: evaluates and warns the health status of buildings based on data obtained from the on-site perception layer;

[0012] Digital twin platform: simulates the monitored comprehensive rooms in a virtual three-dimensional space and provides building health status assessment results.

[0013] Furthermore, the on-site perception layer includes a micro-meteorological monitoring station and a fiber optic Bragg grating monitoring sensor: the micro-meteorological monitoring station is used to evaluate the climate conditions of the surrounding environment of the comprehensive building; the fiber optic Bragg grating monitoring sensor is used to monitor the deformation, settlement, tilt and cracks of the building structure of the comprehensive building.

[0014] Furthermore, the fiber grating monitoring sensor includes a fiber grating static level, a fiber grating strain gauge, a fiber grating inclinometer and a fiber grating wire displacement meter; the fiber grating static level monitors the settlement of comprehensive building, cable trench and power tower; the fiber grating strain gauge measures the strain distribution of the structure of the comprehensive building; the fiber grating inclinometer monitors the surface level and tilt change of the comprehensive building; the fiber grating wire displacement meter monitors the displacement of the structure of the comprehensive building.

[0015] Furthermore, the steps of the early warning model include:

[0016] S1, data collection and preprocessing: collect raw monitoring data from various sensors in the field perception layer;

[0017] S2. Feature engineering: Extract features useful for early warning models through feature engineering;

[0018] S3. Weight calculation: Use the analytic hierarchy process to determine the relative importance of each influencing factor, calculate the weight of each factor on the building health status, and use the weight as the initial condition for neural network training;

[0019] S4, Model training: Use BP neural network learning technology, combined with the calculated weights, to train the early warning model;

[0020] S5. Health status assessment: The trained model will analyze the input monitoring data and assess the health status of the building;

[0021] S6. Early warning signal generation: When the model evaluation results show that the building health status is lower than the preset threshold, the system will automatically issue an early warning signal;

[0022] S7. Model optimization and update: Regularly optimize and update the early warning model based on real-time feedback and new monitoring data.

[0023] Furthermore, the BP neural network learning technology includes an input layer, a hidden layer and an output layer. During the simulation training, the input layer uses preprocessed data, the hidden layer is initialized according to weights, and the network weights and thresholds are continuously adjusted through a back propagation algorithm.

[0024] Furthermore, the digital twin platform analyzes the structure of the building through digital modeling and simulation technology, predicts possible deformation and crack problems, and provides corresponding warnings; by comparing the actual monitoring data with the warning model, the digital twin platform can evaluate the health status of the building and issue warning signals in a timely manner.

[0025] Furthermore, the digital twin platform simulates the structure and status of the actual building by establishing a virtual model of the comprehensive building; the virtual model focuses on marking the location of each sensor in the on-site perception layer, displays real-time monitoring data, and displays the building health status information calculated in the background in real time in the virtual model.

[0026] Furthermore, the digital twin platform adopts an open hierarchical distributed system, including a station control layer, an interval layer and a process layer; the station control layer is responsible for data processing, centralized monitoring and data communication, the interval layer includes relay protection devices and measurement and control devices, and the process layer includes merging units and intelligent terminals.

[0027] Furthermore, the station control layer and the bay layer are directly interconnected via a network to achieve transmission of data and control commands, and the communication protocol should adopt the DL / T 860 standard.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] 1. The present invention integrates digital twin technology with a self-developed early warning model to achieve real-time monitoring of the building's operating status and fault early warning based on data analysis, effectively reducing safety risks.

[0030] 2. The present invention utilizes a variety of sensors to carry out high-precision monitoring of a number of key parameters of a building (such as temperature, strain, and displacement) to fully grasp the state of the building and its minute changes.

[0031] 3. The present invention realizes remote monitoring through a wireless sensor network and combines it with a three-dimensional visualization platform to intuitively display monitoring data and building status, thereby improving management efficiency and response speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings are part of the present invention and are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but do not constitute an improper limitation of the present invention. Obviously, the drawings described below are only some embodiments. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the accompanying drawings:

[0033] Figure 1 It is a structural block diagram of the present invention;

[0034] Figure 2 It is a flow chart of the early warning model of the present invention. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The following embodiments are used to illustrate the present invention but are not used to limit the scope of the present invention.

[0036] In the description of the present invention, it should be noted that the directions or positional relationships indicated by terms such as “upper”, “lower”, “front”, “back”, “left”, “right”, “vertical”, “inside” and “outside” are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as a limitation on the present invention.

[0037] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0038] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0039] The present invention will be further described below in conjunction with the accompanying drawings.

[0040] Embodiment 1: A comprehensive room monitoring system based on digital twin technology and early warning model, such as Figure 1As shown, including:

[0041] On-site perception layer: used to collect monitoring data and upload the detected data to the system;

[0042] Early warning model: Use AHP, BP neural network, and Markov prediction model to build an early warning model, and evaluate and warn the health status of the building based on the data obtained from the on-site perception layer;

[0043] Digital twin platform: simulates the monitored comprehensive rooms in a virtual three-dimensional space, and provides the sensor layout location of the on-site perception layer and the building health status assessment results.

[0044] Embodiment 2: A comprehensive room monitoring system based on digital twin technology and early warning model, wherein the on-site perception layer includes a micro-meteorological monitoring station and a fiber grating monitoring sensor;

[0045] The micro-meteorological monitoring station is used to evaluate the climate conditions of the environment around the complex building and provide a reference for the safety assessment of the system; its monitoring parameters include temperature, humidity, wind speed, wind direction, rainfall, groundwater level, etc. The monitoring station is installed at a high place in the building to monitor the changes in environmental parameters in real time. For example, extreme weather conditions such as high temperature, high humidity and strong wind may have an impact on the building, so the micro-meteorological monitoring station can be used to timely understand environmental changes and take corresponding preventive measures.

[0046] High temperature prevention measures: When the temperature is too high, control the cooling capacity of the air conditioning system, turn on ventilation equipment, open sunshade facilities, etc. to reduce the indoor temperature and prevent overheating from affecting the building structure and equipment. Increase the green area around the building to reduce the ambient temperature through the transpiration of plants.

[0047] High humidity prevention measures: Use a dehumidifier or the dehumidification function of the air conditioner to keep the indoor humidity within an appropriate range to prevent mold growth and material corrosion caused by moisture.

[0048] Take rainfall prevention measures to ensure that the building's drainage system is unobstructed, regularly inspect and clean rainwater pipes, gutters, etc., to prevent structural damage caused by water accumulation.

[0049] Fiber Bragg grating monitoring sensor: used to monitor the deformation, settlement, tilt and crack of the building structure of the comprehensive building. Through the fiber Bragg grating technology, it can realize the all-round and real-time monitoring of the building structure. The fiber Bragg grating monitoring sensor includes the fiber Bragg grating static level, fiber Bragg grating strain gauge, fiber Bragg grating inclinometer and fiber Bragg wire displacement meter; the fiber Bragg grating static level monitors the settlement of the comprehensive building, cable trench and power tower; the connecting pipe principle is adopted. When any static level moves in the vertical direction relative to other water tanks, the liquid level remains unchanged, the buoyancy (pressure) of the float changes, and the FBG at the float connecting rod senses the change of buoyancy (pressure), so as to monitor the change of settlement of the point, measure the relative elevation change between two points or multiple points, and send out an alarm when the monitoring value approaches or exceeds the control value to ensure the safety of the structure during operation. The instrument adopts the fiber Bragg grating static level and multiple groups of liquid level sensors, which is suitable for occasions of high-precision monitoring of vertical displacement.

[0050] The fiber grating strain gauge is fixed on the surface of the object to be measured to measure the strain distribution of the structure of the comprehensive building. The fiber grating inclinometer is fixed on the surface of the comprehensive building to monitor the surface level and tilt changes of the comprehensive building. When the optical fiber is subjected to strain, the grating period will change, causing the wavelength of the reflected light to change. By measuring the change in the wavelength of the reflected light, the strain value of the optical fiber can be obtained.

[0051] The fiber Bragg grating inclinometer reflects or transmits light of a specific wavelength through the grating structure. When the optical fiber is bent or tilted by external force, the grating period changes, causing the change of the reflected wavelength. By measuring the change of the reflected wavelength, the inclination angle information of the comprehensive building can be obtained. The inclinometer adopts a dual grating packaging structure, and uses the different response coefficients of two fiber Bragg gratings with different grating pitches to temperature and strain to achieve temperature self-compensation, thereby eliminating the influence of temperature on wavelength changes. The fiber Bragg grating inclinometer is not affected by electromagnetic interference and can be used in harsh electromagnetic environments. In addition, due to the wavelength modulation characteristics of the fiber Bragg grating sensor, the measurement signal is not affected by factors such as light source instability, fiber bending or connection loss.

[0052] The fiber Bragg grating wire displacement meter monitors the displacement of the structure of the comprehensive building. When the measured object, the comprehensive building, is displaced, the wire stretches or contracts, driving the rotating measuring element to rotate, and then converting the displacement change into an electrical signal output. As a sensitive element, when the fiber Bragg grating is stretched or compressed, its periodic refractive index changes will cause the Bragg reflection wavelength to move. The displacement can be obtained by demodulating the change in the reflection wavelength.

[0053] like Figure 2 As shown, the steps of the early warning model include:

[0054] S1, data collection and preprocessing: collect raw monitoring data from various sensors in the field perception layer;

[0055] S2. Feature engineering: Extract features useful for early warning models through feature engineering;

[0056] S3. Weight calculation: Use the analytic hierarchy process to determine the relative importance of each influencing factor, calculate the weight of each factor on the building health status, and use the weight as the initial condition for neural network training;

[0057] S4, Model training: Use BP neural network learning technology, combined with the calculated weights, to train the early warning model;

[0058] S5. Health status assessment: The trained model will analyze the input monitoring data and assess the health status of the building;

[0059] S6. Early warning signal generation: When the model evaluation results show that the building health status is lower than the preset threshold, the system will automatically issue an early warning signal;

[0060] S7. Model optimization and update: Regularly optimize and update the early warning model based on real-time feedback and new monitoring data.

[0061] The early warning model uses the AHP (Analytical Hierarchy Process) and neural network training techniques to conduct a comprehensive assessment of the building's health status based on multiple indicators and factors. First, the AHP method clearly defines the goal of maintaining the health of the building and analyzes the various indicators that affect the building's health status, such as settlement, tilt, displacement, cracks, etc. Then, the factors that affect these indicators, such as groundwater level, temperature, soil properties, etc., are further analyzed.

[0062] By solving the eigenvector of the judgment matrix and the weighted sum method, the early warning model obtains the final weight of each factor on the building health status and determines the factor with the greatest influence. These weights are used as the initial conditions of the hidden layer to train the neural network.

[0063] In the early warning model, the input signal is the rainfall, temperature, wind speed, groundwater level, vertical displacement, stress value and other data collected from the field perception layer; the BP neural network learning technology includes an input layer, a hidden layer and an output layer. During the simulation training process, the input layer uses preprocessed data, and the hidden layer is initialized according to the weight. These data are nonlinearly transformed through the calculation conditions of the hidden layer and transmitted to the output node to generate an evaluation result. If the actual output does not match the expected output, that is, there is an error between the predicted result and the actual situation, the model will perform back propagation of the error and adjust the weights and thresholds of the neural units in each layer.

[0064] Cloud computing and edge computing technologies are used to achieve distributed processing and storage of data. Cloud computing can provide powerful computing power and storage resources, while edge computing can achieve real-time data processing and rapid response, reducing data transmission delays.

[0065] After training with a large number of input signals and correct output signals, the early warning model can accurately predict the health status of the building. In the monitoring system, the model provides users with a visual interface to display the real-time assessment results of the building's health status, helping users to understand the safety status of the building in a timely manner and take corresponding measures to protect the stability of the building.

[0066] Through real-time data analysis and visualization technology, monitoring data can be displayed intuitively in the form of charts, maps, etc., to help managers quickly understand and make decisions. For example, using GIS (Geographic Information System) technology, the building's geographical location, surrounding environment and other information can be combined with monitoring data to provide a comprehensive view.

[0067] In terms of the correspondence between data processing and evaluation results, comparative analysis methods are applied to compare the monitoring data of the same building at different time points or conditions to reveal its development trend, changes or differences. For example, through year-on-year and month-on-month analysis, the changes in the health status of the building over time can be evaluated; through horizontal comparison, the differences in the health status of different buildings or similar building groups can be compared. In addition, difference comparison and target comparison can be used to evaluate the consistency of early warning results with expected goals, as well as the difference between monitoring data and early warning thresholds.

[0068] The digital twin platform analyzes the structure of the building through digital modeling and simulation technology, predicts possible deformation and crack problems, and provides corresponding early warnings; by comparing actual monitoring data with the early warning model, the digital twin platform can evaluate the health status of the building and issue early warning signals in a timely manner.

[0069] The digital twin platform simulates the structure and status of the actual building by building a virtual model of the comprehensive room; the virtual model focuses on marking the location of each sensor in the on-site perception layer, displays real-time monitoring data, and displays the building health status information calculated by the background in real time in the virtual model. The digital twin platform can simulate the structure, material properties, environmental conditions, etc. of the building, and evaluate the health status of the building by comparing it with the actual monitoring data.

[0070] The digital twin platform adopts an open hierarchical distributed system, including a station control layer, an interval layer and a process layer; the station control layer is responsible for data processing, centralized monitoring and data communication, the interval layer includes relay protection devices and measurement and control devices, and the process layer includes merging units and intelligent terminals; the station control layer and the interval layer are directly interconnected through the network to realize the transmission of data and control commands, and the communication protocol should adopt the DL / T 860 standard.

[0071] Digital twin technology is based on the modeling and simulation of physical systems. By inputting sensor data, real-time monitoring data and other key parameters into digital models, it can achieve accurate simulation and prediction of real-world systems.

[0072] In this system, digital twin technology is applied to the health status monitoring of comprehensive buildings. The digital twin platform simulates the structure and status of the actual building by building a virtual model of the comprehensive building. The model focuses on marking the location of each sensor in the on-site perception layer, displays real-time monitoring data, and displays the building health status information calculated by the background in real time in the virtual model. With the help of the highly simulated virtual model of digital twin technology, the status of the actual building is displayed in a graphical form, giving users a more intuitive data presentation.

[0073] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0074] The above description is only a preferred embodiment of the present invention, and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technician familiar with this patent can make some changes or modifications to equivalent embodiments of equivalent changes by using the technical content suggested above without departing from the scope of the technical solution of the present invention. The implementation scheme in the above embodiment can also be further combined or replaced. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention still falls within the scope of the solution of the present invention.

Claims

1. A comprehensive room monitoring system based on digital twin technology and early warning model, characterized in that: include: On-site perception layer: used to collect monitoring data and upload the detected data to the system; Early warning model: evaluates and warns the health status of buildings based on data obtained from the on-site perception layer; Digital twin platform: simulates the monitored comprehensive rooms in a virtual three-dimensional space and provides building health status assessment results.

2. The comprehensive room monitoring system based on digital twin technology and early warning model according to claim 1 is characterized by: The on-site perception layer includes a micro-meteorological monitoring station and a fiber optic Bragg grating monitoring sensor: the micro-meteorological monitoring station is used to evaluate the climate conditions of the surrounding environment of the comprehensive building; the fiber optic Bragg grating monitoring sensor is used to monitor the deformation, settlement, tilt and cracks of the building structure of the comprehensive building.

3. The comprehensive room monitoring system based on digital twin technology and early warning model according to claim 2 is characterized by: The fiber grating monitoring sensor includes a fiber grating static level, a fiber grating strain gauge, a fiber grating inclinometer and a fiber grating wire displacement meter; the fiber grating static level monitors the settlement of comprehensive building, cable trench and power tower; the fiber grating strain gauge measures the strain distribution of the structure of the comprehensive building; the fiber grating inclinometer monitors the surface level and tilt change of the comprehensive building; the fiber grating wire displacement meter monitors the displacement of the structure of the comprehensive building.

4. The comprehensive room monitoring system based on digital twin technology and early warning model according to claim 1 is characterized in that: The steps of the early warning model include: S1, data collection and preprocessing: collect raw monitoring data from various sensors in the field perception layer; S2. Feature engineering: Extract features useful for early warning models through feature engineering; S3. Weight calculation: Use the analytic hierarchy process to determine the relative importance of each influencing factor, calculate the weight of each factor on the building health status, and use the weight as the initial condition for neural network training; S4, Model training: Use BP neural network learning technology, combined with the calculated weights, to train the early warning model; S5. Health status assessment: The trained model will analyze the input monitoring data and assess the health status of the building; S6. Early warning signal generation: When the model evaluation results show that the building health status is lower than the preset threshold, the system will automatically issue an early warning signal; S7. Model optimization and update: Regularly optimize and update the early warning model based on real-time feedback and new monitoring data.

5. The comprehensive room monitoring system based on digital twin technology and early warning model according to claim 4 is characterized in that: The BP neural network learning technology includes an input layer, a hidden layer and an output layer. During the simulation training process, the input layer uses preprocessed data, the hidden layer is initialized according to weights, and the network weights and thresholds are continuously adjusted through the back propagation algorithm.

6. The comprehensive room monitoring system based on digital twin technology and early warning model according to claim 1 is characterized by: The digital twin platform analyzes the structure of the building through digital modeling and simulation technology, predicts possible deformation and crack problems, and provides corresponding early warnings; By comparing actual monitoring data with the early warning model, the digital twin platform can assess the health status of the building and issue early warning signals in a timely manner.

7. The comprehensive room monitoring system based on digital twin technology and early warning model according to claim 6 is characterized by: The digital twin platform simulates the structure and status of the actual building by establishing a virtual model of the comprehensive building. The virtual model focuses on marking the location of each sensor in the on-site perception layer, displays real-time monitoring data, and displays the building health status information calculated in the background in real time in the virtual model.

8. The comprehensive room monitoring system based on digital twin technology and early warning model according to claim 7 is characterized by: The digital twin platform adopts an open hierarchical distributed system, including a station control layer, an interval layer and a process layer; the station control layer is responsible for data processing, centralized monitoring and data communication, the interval layer includes relay protection devices and measurement and control devices, and the process layer includes merging units and intelligent terminals.

9. The comprehensive room monitoring system based on digital twin technology and early warning model according to claim 8 is characterized by: The station control layer and the spacing layer are directly interconnected via a network to achieve the transmission of data and control commands.

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