Digital twin-driven intelligent building weak current system virtual-real interaction method
By building a digital twin model and a virtual and real interactive decision-making mechanism, the problem of inefficient operation and maintenance in traditional weak current systems in intelligent buildings is solved, and the system's intelligent decision-making and fault diagnosis is realized, which improves operation and maintenance efficiency and reduces costs.
Patent Information
- Application Number
- CN202510430650.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-08
- Publication Date
- 2025-07-25
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Traditional weak current systems are difficult to achieve real-time and accurate virtual and real interactions in smart buildings, resulting in low operation and maintenance efficiency and difficulty in troubleshooting and prediction.
Build a digital twin model of weak current system, collect and transmit virtual and real interactive data, and establish a virtual and real interactive decision-making mechanism, and use physical modeling, behavioral modeling, neural networks, decision trees and genetic algorithms to achieve intelligent decision-making and optimization of the system.
It improves the intelligence level of the system, improves operation and maintenance efficiency, enhances fault diagnosis and processing capabilities, and reduces costs.
Smart Images
Figure CN120372498A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of intelligent buildings, and particularly relates to a method for virtual-real interaction of a weak current system in an intelligent building driven by digital twin. Background Art
[0002] With the development of intelligent buildings, the complexity of weak current systems has been increasing continuously. During the operation of traditional weak current systems, it is difficult to reflect the state of the actual physical system in real time and accurately, and there is a lack of effective virtual-real interaction means, resulting in low operation and maintenance efficiency of the system and difficulties in fault diagnosis and prediction. The emergence of digital twin technology provides a new way to solve these problems, but currently, the method of deeply applying digital twin technology to the virtual-real interaction of intelligent building weak current systems still needs to be improved. Summary of the Invention
[0003] In view of this, the present invention aims to provide a method for virtual-real interaction of a weak current system in an intelligent building driven by digital twin, realizing real-time and accurate interaction between the physical entity and the virtual model of the weak current system, and improving the intelligent level and operation and maintenance efficiency of the system.
[0004] The technical solution of the embodiment of the present invention is implemented as follows:
[0005] A method for virtual-real interaction of a weak current system in an intelligent building driven by digital twin includes the following steps:
[0006] S1. Construct a digital twin model of the weak current system;
[0007] S2. Collect and transmit virtual-real interaction data;
[0008] S3. Establish a virtual-real interaction decision-making mechanism;
[0009] Among them: the S1. constructing a digital twin model of the weak current system includes physical modeling and behavior modeling and fuses the two; the S2. collecting and transmitting virtual-real interaction data is to collect data from the physical entity and transmit it to the digital twin model, and at the same time transmit the control instructions of the digital twin model to the physical entity; the S3. establishing a virtual-real interaction decision-making mechanism is to analyze the collected data based on the digital twin model, and then make a decision and optimize the system operation parameters.
[0010] Preferably, in the process of physical modeling, for signal transmission in the communication system, the formula is used to describe the attenuation of signal strength during transmission, where P out is the received signal strength, P in is the transmitted signal strength, α is the attenuation coefficient, and d is the transmission distance.
[0011] Preferably, in the process of behavior modeling, a prediction model for abnormal behaviors of cameras in the security monitoring system is constructed using a neural network algorithm, and the output result where x i is the input feature, w i is the weight, b is the bias, and f is the activation function.
[0012] Preferably, in the process of S2, collecting and transmitting virtual-real interaction data, the data transmission delay T = T processing + T transmission where T processing is the data processing delay and T transmission is the output transmission delay.
[0013] Preferably, in S3, establishing a virtual-real interaction decision-making mechanism, a decision tree algorithm is used to evaluate and make decisions on the operating state of the system.
[0014] Preferably, in S3, establishing a virtual-real interaction decision-making mechanism, a genetic algorithm is used to optimize the cable layout of the integrated wiring system, and the objective function where c i is the cable cost coefficient, x i is the cable length, p j is the signal interference penalty coefficient, and y j is the degree of signal interference.
[0015] Preferably, in the process of S1, constructing a digital twin model of the low-voltage system, each subsystem in the low-voltage system of the intelligent building, such as the integrated wiring system, the security monitoring system, and the communication system, is modeled separately.
[0016] Preferably, in S2, the virtual-real interaction data collection in the process of collecting and transmitting virtual-real interaction data is realized by deploying sensors in the physical entity of the low-voltage system.
[0017] Preferably, in S2, the virtual-real interaction data transmission in the process of collecting and transmitting virtual-real interaction data uses wireless communication technology.
[0018] Preferably, the control instruction is generated by the digital twin model according to the analysis result and sent to the physical entity.
[0019] Due to the adoption of the above technical solutions in the embodiments of the present invention, the following advantages are achieved:
[0020] First, the intelligent level of the system is improved: By constructing a digital twin model, the operating state of the low-voltage system can be simulated and analyzed in real time. Based on intelligent decision-making mechanisms such as the decision tree algorithm, the system can automatically make optimization decisions according to the actual situation, realizing intelligent self-regulation and greatly improving the intelligent level of the system.
[0021] II. Improved operation and maintenance efficiency: The virtual-real interaction data collection and transmission mechanism enables operation and maintenance personnel to obtain real-time device operation status data, eliminating the need for manual on-site inspections and saving a large amount of labor and time costs. The digital twin model can predict and analyze potential faults, schedule maintenance in advance, and avoid system downtime caused by sudden failures.
[0022] III. Enhanced fault diagnosis and handling capabilities: The integration of the physical model and the behavior model provides a more comprehensive and accurate basis for fault diagnosis. When the system shows abnormalities, the digital twin model can quickly locate the fault point and give the best fault handling solution through optimization algorithms.
[0023] IV. Reduced costs: Optimizing system operation parameters and layouts through optimization algorithms such as genetic algorithms can reduce system energy consumption and equipment wear. For example, optimizing the cable layout of the comprehensive wiring system can reduce signal interference, lower the additional energy consumption of equipment caused by signal problems, and at the same time reduce equipment failure rates, lowering equipment replacement and maintenance costs. In the long run, it saves a large amount of funds for the operation of intelligent buildings.
[0024] The above summary is only for the purpose of the specification and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the present invention will become readily apparent by reference to the drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS
[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.
[0026] Figure 1 is the overall architecture flowchart of the present invention;
[0027] Figure 2 is the sub-flowchart for constructing the digital twin model of the present invention;
[0028] Figure 3 is the sub-flowchart of the virtual-real interaction decision-making mechanism of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0029] In the following, only some exemplary embodiments are briefly described. As those skilled in the art can recognize, the described embodiments can be modified in various different ways without departing from the spirit or scope of the present invention. Therefore, the drawings and the description are considered to be exemplary in nature rather than restrictive.
[0030] It should be noted that terms such as "first", "second", "symmetric", "array", etc. are only used for the purpose of distinguishing descriptions and position descriptions, and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, those defined with features such as "first", "symmetric", etc. may explicitly or implicitly include one or more such features; similarly, when there is no numerical limitation on certain features in the form of words such as "two", "three", etc., it should be noted that such features also belong to explicitly or implicitly including one or more feature quantities;
[0031] In the present invention, unless otherwise clearly stipulated and defined, terms such as "installation", "connection", "fixation", etc. should be understood in a broad sense; for example, it can be a fixed connection, a detachable connection, or an integral molding; it can be a mechanical connection, a direct connection, a welding connection, or an indirect connection through an intermediate medium, and it can be the communication inside two components or the interaction relationship between two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood in combination with the drawings of the specification and specific circumstances.
[0032] The embodiments of the present invention will be described in detail below with reference to the drawings.
[0033] As Figures 1-3 shown, the present invention provides a method for virtual-real interaction of a digital-twin-driven intelligent building weak-current system, which includes the following steps:
[0034] S1. Construct a digital-twin model of the weak-current system;
[0035] S2. Collect and transmit virtual-real interaction data;
[0036] S3. Establish a virtual-real interaction decision-making mechanism;
[0037] Among them: S1. Constructing a digital-twin model of the weak-current system includes physical modeling and behavior modeling and integrating the two; S2. Collecting and transmitting virtual-real interaction data means collecting data from physical entities and transmitting it to the digital-twin model, and at the same time transmitting the control instructions of the digital-twin model to the physical entities; S3. Establishing a virtual-real interaction decision-making mechanism analyzes the collected data based on the digital-twin model, and then makes decisions and optimizes the system operation parameters. In the process of physical modeling, for signal transmission in the communication system, the formula is used to describe the attenuation of signal strength during transmission, where P out is the received signal strength, P in is the transmitted signal strength, α is the attenuation coefficient, d is the transmission distance. In the process of behavior modeling, a prediction model for abnormal behaviors of cameras in the security monitoring system is constructed using a neural network algorithm, and the output result where xi is the input feature, w i is the weight, b is the bias, and f is the activation function.
[0038] As Figures 1-3 shown, during the process of S2 for virtual-real interaction data collection and transmission, the data transmission delay T = T processing + T transmission , where T processing is the data processing delay, and T transmission is the output transmission delay.
[0039] As Figures 1-3 shown, in S3 for establishing the virtual-real interaction decision-making mechanism, the decision tree algorithm is used to evaluate and make decisions on the operating state of the system. In S3 for establishing the virtual-real interaction decision-making mechanism, the genetic algorithm is used to optimize the cable layout of the integrated wiring system, and the objective function where c i is the cable cost coefficient, x i is the cable length, p j is the signal interference penalty coefficient, and y j is the degree of signal interference.
[0040] As Figures 1-3 shown, in the process of S1 for constructing the digital twin model of the weak current system, each subsystem in the weak current system of the intelligent building, such as the integrated wiring system, the security monitoring system, and the communication system, is modeled separately. In S2 for virtual-real interaction data collection and transmission, the virtual-real interaction data collection is realized by deploying sensors in the physical entities of the weak current system. In S2 for virtual-real interaction data collection and transmission, the virtual-real interaction data transmission uses wireless communication technology, and the control instructions are generated by the digital twin model according to the analysis results and sent to the physical entities.
[0041] In this embodiment, specifically, when the present invention works: First, detailed physical modeling and behavior modeling are performed on the integrated wiring system, the security monitoring system, etc. to construct a digital twin model. Subsequently, sensors are deployed to collect the operation data of the physical system, and the data is transmitted to the server where the digital twin model is located through a wireless communication module. Finally, the digital twin model is used to perform real-time analysis on the data, and the decision tree algorithm is used to determine whether there is an abnormality in the system. If there is an abnormality, the optimal regulation strategy is calculated through an optimization algorithm, and the control instructions are sent to the physical system for execution.
[0042] The following are several specific embodiments of applying the present invention:
[0043] Embodiment 1
[0044] Optimization of the Weak Current System in an Office Building: First, in the weak current system of a certain office building, for the comprehensive cabling system, by detailed mapping of cable routes, connection device ports and other information, a physical model is constructed based on relevant electrical parameters. At the same time, network traffic data from different past time periods is collected, and a behavior model is constructed using machine learning algorithms to simulate network data transmission behaviors during different business peak hours. Then, a digital twin model of the comprehensive cabling system is formed by fusion. Subsequently, temperature and flow sensors are deployed on key devices such as network switches and servers, and the device operation data is transmitted at high speed to the server where the digital twin model is located through 5G wireless communication technology. Finally, the digital twin model analyzes the data in real time. If it detects that the network traffic in a certain area continuously exceeds the threshold from 10 am to 12 pm on weekdays, the decision tree algorithm is used to judge that the transmission efficiency may be reduced due to the aging of some cables. Immediately, the genetic algorithm is started to optimize the cable layout plan, calculate the optimal strategy for replacing specific aging cables and adjusting the connection directions of some lines, and issue control instructions to the physical system to arrange maintenance personnel to execute, effectively alleviating network congestion.
[0045] Example 2
[0046] Upgrade of the Hotel Security Monitoring System: For the hotel security monitoring system, first, the installation positions, shooting angles, resolutions and other parameters of the cameras are accurately measured and recorded to construct a physical model. Based on the hotel's monitoring video data in the past few months, a neural network algorithm is used to train a prediction model for abnormal camera behaviors, such as identifying abnormal situations such as camera image jitter and image occlusion. Finally, a digital twin model of the security monitoring system is formed by fusion. Subsequently, status monitoring sensors are installed on each camera and related power supply devices, and data such as device temperature, power, and video transmission quality are transmitted to the digital twin model through Wi-Fi communication. Finally, when the digital twin model detects that the image of a certain camera appears abnormally blurred for a long time, it is judged by the decision tree algorithm as lens dirt or device failure. Immediately, the optimization algorithm is started to preferentially dispatch nearby movable cameras to temporarily cover the monitoring of this area, and at the same time, cleaning personnel or maintenance personnel are arranged to go to deal with it to ensure that there are no dead corners in the hotel security monitoring.
[0047] Example 3
[0048] Hospital communication system guarantee: In terms of the communication system in the hospital's weak current system, first, comprehensive parameter collection is carried out on communication base stations, signal amplifiers, internal communication lines, etc. to construct a physical model. Combining historical data on the fluctuation law of the hospital's daily communication traffic, such as frequent calls during the outpatient hours during the day and high communication requirements in specific departments during the emergency hours at night, a behavior model is constructed using machine learning, and then a digital twin model of the communication system is formed by fusion. Subsequently, signal strength and interference detection sensors are deployed at communication base stations and signal transmission line nodes, and LoRa wireless communication technology is used to stably transmit data to the digital twin model. Finally, when the digital twin model analyzes that the communication signal strength in the emergency area at night decreases, it is judged by the decision tree algorithm that it may be interference caused by temporary construction in the vicinity. An optimization algorithm is used to calculate a plan to adjust the base station transmission frequency and enable standby signal amplifiers, and an instruction is sent to the physical system to ensure smooth emergency communication and avoid the risk of medical accidents.
[0049] As described above, it is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of various changes or substitutions, and these should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims
1. A virtual-real interaction method for the weak current system of a digital twin-driven intelligent building, characterized in that It includes the following steps: S1. Construct a digital twin model of the low-voltage system; S2. Collect and transmit virtual-real interaction data; S3. Establish a virtual-real interaction decision-making mechanism; Among them: the S1. Constructing a digital twin model of the low-voltage system includes physical modeling and behavior modeling and integrates the two; The S2. Collecting and transmitting virtual-real interaction data is to collect data from physical entities and transmit it to the digital twin model, and at the same time transmit the control instructions of the digital twin model to the physical entities; The S3. Establishing a virtual-real interaction decision-making mechanism is to analyze the collected data based on the digital twin model, and then make decisions and optimize the system operation parameters.
2. A method for virtual-real interaction of a digital twin-driven intelligent building weak current system according to claim 1, characterized in that: In the process of the physical modeling, for the signal transmission in the communication system, the formula is used to describe the attenuation of the signal strength during the transmission process, where P out is the signal strength at the receiving end, P in is the signal strength at the sending end, α is the attenuation coefficient, and d is the transmission distance.
3. A virtual-real interaction method for a digital twin-driven intelligent building weak current system according to claim 1, characterized in that: During the process of behavior modeling, a prediction model for abnormal behaviors of cameras in a security monitoring system is constructed using a neural network algorithm, and the output result where x i is the input feature, w i is the weight, b is the bias, and f is the activation function.
4. A method for virtual-real interaction of a digital twin-driven intelligent building weak current system according to claim 1, characterized in that: During the process of S2 for virtual-real interaction data collection and transmission, the data transmission delay T = T processing + T transmission , where T processing is the data processing delay, and T transmission is the output transmission delay.
5. A virtual-real interaction method for a weak current system of an intelligent building driven by digital twins according to claim 1, characterized in that: In the S3. Establishing a virtual-real interaction decision-making mechanism, the decision tree algorithm is used to evaluate and make decisions on the operation status of the system.
6. A virtual-real interaction method for a weak current system of an intelligent building driven by digital twin according to claim 1, characterized in that: In S3, when establishing the virtual-real interaction decision-making mechanism, the genetic algorithm is used to optimize the cable layout of the integrated wiring system, and the objective function where c i is the cable cost coefficient, x i is the cable length, p j is the signal interference penalty coefficient, and y j is the degree of signal interference.
7. A method for virtual-real interaction of a digital twin-driven intelligent building weak current system according to claim 1, characterized in that: In the process of the S1. Constructing a digital twin model of the low-voltage system, each subsystem in the intelligent building low-voltage system, such as the comprehensive wiring system, security monitoring system, communication system, etc., is modeled separately.
8. A method for virtual-real interaction of a digital twin-driven intelligent building weak current system according to claim 1, characterized in that: The virtual-real interaction data collection in the S2. Collecting and transmitting virtual-real interaction data is realized by deploying sensors in the physical entities of the low-voltage system.
9. A method for virtual-real interaction of a digital twin-driven intelligent building weak current system according to claim 1, characterized in that: The virtual-real interaction data transmission in the S2. Collecting and transmitting virtual-real interaction data uses wireless communication technology.
10. A method for virtual-real interaction of a digital twin-driven intelligent building weak current system according to claim 1, characterized in that: The control instructions are generated by the digital twin model according to the analysis results and sent to the physical entities.