Real-time digital twin modeling method and system for building facilities

Abstract

The invention provides a real-time digital twinning modeling method and system for building facilities, relates to the technical field of building parameter modeling and monitoring, and aims to perform accurate extraction and more accurate modeling mode design in twinning digital modeling of a building. The plurality of detection devices are respectively used for collecting different actual real-time data and associated data; collecting actual real-time data and associated data; establishing a digital twinborn model based on the actual real-time data and the associated data of one acquisition time period; synchronously and periodically acquiring twinborn real-time data of the digital twinborn model and actual real-time data of the building; if the difference between the twinborn real-time data and the actual real-time data is greater than a preset threshold value, updating the digital twinborn model; and if the updating duration of the last digital twin model reaches a certain duration, updating the digital twin model. The method has the advantages that digital twin modeling is accurate, and the building facility state is reliably simulated.

Description

Technical Field

[0001] The present invention relates to the technical field of building parameter modeling and monitoring, and in particular to a real-time digital twin modeling method and system for building facilities. Background Art

[0002] Digital twins make full use of physical models, sensor updates, operation history and other data, integrate multi-disciplinary, multi-physical quantity, multi-scale, and multi-probability simulation processes, complete mapping in virtual space, and thus reflect the continuous cycle of the corresponding physical facilities.

[0003] In terms of building monitoring, conventional sensors have limited real-time monitoring fields, making it difficult to achieve comprehensive and predictive monitoring. The digital twin model can reflect the multi-faceted status of the building in real time, enabling timely discovery and prediction of potential problems, reducing downtime and maintenance costs. By analyzing the digital twin model of the building, the use of resources can be optimized, such as energy management and water resource management, to reduce operating costs. In the digital twin modeling of buildings, there are many factors that affect its observation data, so the modeling faces certain challenges, and then there are problems of being too redundant or too incomplete.

[0004] Therefore, it is necessary to accurately refine and design more precise modeling methods in the twin digital modeling of buildings to achieve more accurate predictive observation of buildings by digital twin modeling. Summary of the invention

[0005] The purpose of the present invention is to provide a real-time digital twin modeling method and system for building facilities, which can accurately refine and design more precise modeling methods in the twin digital modeling of buildings.

[0006] The embodiments of the present invention are implemented by the following technical solutions:

[0007] A real-time digital twin modeling method for building facilities includes the following steps:

[0008] Establishing a variety of detection devices for building settings, the multiple detection devices are used to collect different actual real-time data and associated data, and the detection devices include image acquisition devices and multiple sensors;

[0009] The actual real-time data and the associated data are collected with T1 as a first unit time length;

[0010] Establishing a digital twin model based on the actual real-time data and the associated data in a collection time period;

[0011] Every second time unit T2, synchronously and periodically collect the twin real-time data of the digital twin model and the actual real-time data of the building within the third unit time T3;

[0012] If the difference between the twin real-time data and the actual real-time data is greater than a preset threshold, the digital twin model is updated;

[0013] If the time since the last update of the digital twin model reaches M of the first unit time periods, the digital twin model is updated.

[0014] Preferably, the real-time data includes power consumption per unit time, operating power of each power facility in the building, vibration frequency of multiple points in the building, and static image pixel data.

[0015] Preferably, the associated data includes temperature, humidity, month, time and rainfall.

[0016] Preferably, the method for establishing a digital twin model based on the actual real-time data and the associated data in a collection time period is:

[0017] Determine the type of fitting relationship between the actual real-time data and the associated data through a trained model recognition network;

[0018] A fitting parameter is obtained based on the fitting relationship type, and a functional relationship between the actual real-time data and the associated data is established.

[0019] Preferably, the method for determining the type of fitting relationship between the actual real-time data and the associated data through the trained model recognition network is:

[0020] Inputting the actual real-time data and the associated data into the model recognition network;

[0021] Perform feature extraction;

[0022] Output the fitted relationship type through the activation function;

[0023] Determine the fitted relationship.

[0024] Preferably, the method for performing feature extraction is:

[0025] For each of the actual real-time data, fitting relationship characteristic parameters are established based on the associated data:

[0026]

[0027] in, and are respectively the first fitting relationship characteristic parameter, the second fitting relationship characteristic parameter, the third fitting relationship characteristic parameter and the fourth fitting relationship characteristic parameter based on the a-th associated data for the b-th actual real-time data, b,i and x a,iare the ith actual real-time data and the ith associated data collected in the first unit time, respectively; N is the total number of the actual real-time data collected in the first unit time, and d1, d2 and d3 are coefficients respectively;

[0028] For each of the actual real-time data, a fitting relationship feature matrix REL is established b :

[0029]

[0030] Among them, REL b (a,q) represents the element in the ath row and qth column.

[0031] Preferably, the method of outputting the fitting relationship type through the activation function is:

[0032] Based on the REL b Outputting the functional relationship type of the b-th actual real-time data to each of the associated data respectively through a Softmax function, wherein the functional relationship type includes a linear function, a quadratic function, a ln function and an exp function;

[0033] Obtain the fitting relationship type of the bth actual real-time data to the associated data:

[0034] y b =∑ a γ ab *f ab (x a );

[0035] Among them, y b represents the fitting output of the b-th actual real-time data, i.e., the twin real-time data, x a is the collection value of the associated data described in type a, f ab (.) represents the functional relationship type of the actual real-time data of type b to the associated data of type a, γ ab It is the coefficient to be determined of the function of the actual real-time data of the bth type to the associated data of the ath type.

[0036] Preferably, the method for determining the fitting relationship is to solve the γ by the least square method or the gradient descent method. ab The value of y b and x a The function between .

[0037] Preferably, a method for determining whether the difference between the twin real-time data and the actual real-time data is greater than a preset threshold is:

[0038] Get the comprehensive difference parameter ε:

[0039]

[0040] Among them, y b,t and They are respectively the tth twin real-time data and the actual real-time data of the bth actual real-time data within the third unit time length T3, J is the total number of the twin real-time data collected within the third unit time length T3, and B is the number of types of the actual real-time data.

[0041] The present invention also provides a real-time digital twin modeling system for building facilities, which is applied to any one of the above-mentioned real-time digital twin modeling methods for building facilities, including:

[0042] The perception module collects different actual real-time data and related data through various detection devices. These real-time data include monitoring images of the corresponding area and equipment posture data corresponding to the images. The related data include comprehensive working status S1 of the equipment, such as the current main electrical parameters, network connection status, etc. The related data are transmitted back through the unified external network interface of general industrial control systems such as PLC or DCS;

[0043] The data acquisition module is used to collect the actual real-time data and the associated data with T1 as the first unit time. The actual real-time data is aggregated to the database through the network within the building, and the image data is identified and recorded through image recognition algorithms such as Faster-RCNN for the image position information P1 of the device within the T1 time range and the status information E1 of whether there is human intrusion. The position information and status information are both static associated data of the current device image, which are stored in the database through the network transmission value for subsequent module calls;

[0044] A model building module is used to build a digital twin model based on the actual real-time data and the associated data of a collection time period, wherein the model parameters and appearance status are mainly determined through the image position information P1, the equipment working status S1, and the personnel intrusion status E1, and the model status is rendered in real time;

[0045] A model checking module, used for synchronously and periodically collecting the twin real-time data of the digital twin model and the actual real-time data of the building within a third unit time T3 at every second time unit T2;

[0046] A model update module is used to update the digital twin model if the difference between the twin real-time data and the actual real-time data is greater than a preset threshold; and if the time since the last update of the digital twin model reaches M of the first unit time lengths, the digital twin model is updated.

[0047] The technical solution of the embodiment of the present invention has at least the following advantages and beneficial effects:

[0048] The present invention uses digital twin technology to remotely simulate the real-time status of a building in a virtual environment, and can quickly and timely obtain the building status without relying on remote communication to receive the building status data;

[0049] The present invention can create a virtual model that is highly consistent with the state of the actual building, which is convenient for accurately simulating the performance, environmental impact, etc. of the building through analysis of the model;

[0050] The present invention achieves real-time monitoring and prediction through remote simulation to help timely discover and deal with potential building problems, reducing the risk of building and equipment failure and operational interruption;

[0051] The present invention helps to realize the visual observation of the building status, and extracts the model effectively, accurately and comprehensively based on the environmental parameters that affect the status, and obtains the prediction data by extending the model itself;

[0052] The present invention is reasonably designed and helps to realize forward-looking observation of the state of a building and achieve comprehensive control of the state of the building. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 A schematic flow chart of a real-time digital twin modeling method for building facilities provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0055] Example 1

[0056] This embodiment provides a real-time digital twin modeling method for building facilities. Figure 1 , including the following steps:

[0057] Testing device construction:

[0058] Establishing a variety of detection devices for building settings, the multiple detection devices are used to collect different actual real-time data and associated data, and the detection devices include image acquisition devices and multiple sensors;

[0059] Collect actual real-time data and relevant data:

[0060] The actual real-time data and the associated data are collected with T1 as a first unit time length;

[0061] Building a digital twin model:

[0062] Establishing a digital twin model based on the actual real-time data and the associated data in a collection time period;

[0063] Perform model update operations:

[0064] Every second time unit T2, synchronously and periodically collect the twin real-time data of the digital twin model, the actual real-time data of the building, and the static image pixel data within the third unit time T3;

[0065] If the difference between the twin real-time data and the actual real-time data is greater than a preset threshold, the digital twin model is updated;

[0066] If the time since the last update of the digital twin model reaches M of the first unit time periods, the digital twin model is updated.

[0067] This embodiment mainly relies on the relationship between associated data and actual real-time data to establish a digital twin model. The actual real-time data is the state data that needs to be observed for the building. Based on the digital twin model, the building state can be effectively simulated remotely, and the state can be observed based on the simulation results. At the same time, in order to ensure the effectiveness of the model, certain conditions need to be set to errata and update the model. This embodiment can extract data based on sensors, images, and video acquisition devices to conduct all-round observations of the building, realize all-round digital twin modeling, and perform data prediction based on this.

[0068] Example 2

[0069] This embodiment is based on the technical solution of Embodiment 1 and further explains the specific implementation method of each step.

[0070] In this embodiment, the real-time data includes power consumption per unit time, operating power of each power facility in the building, vibration frequency of multiple points in the building, and static image pixel data.

[0071] It should be noted that the static image pixel data can be obtained through an image acquisition device, or through frame extraction through a video acquisition device. The static image pixel data finally obtained can be used to extract the standard deviation and / or average value of the pixel values, or the image can be cut into regions to extract the standard deviation and / or average value as the final parameters to be added to the data processing in subsequent steps.

[0072] Furthermore, the associated data includes temperature, humidity, month, time and rainfall.

[0073] This embodiment builds a relationship between the data to be observed based on environmental factors and time factors and establishes a model based on this.

[0074] As a preferred solution, the method for establishing a digital twin model based on the actual real-time data and the associated data in a collection time period is:

[0075] Determine the type of fitting relationship between the actual real-time data and the associated data through a trained model recognition network;

[0076] A fitting parameter is obtained based on the fitting relationship type, and a functional relationship between the actual real-time data and the associated data is established.

[0077] The method of determining the type of fitting relationship between the actual real-time data and the associated data through the trained model recognition network is preferably:

[0078] Inputting the actual real-time data and the associated data into the model recognition network;

[0079] Perform feature extraction;

[0080] Output the fitted relationship type through the activation function;

[0081] Determine the fitted relationship.

[0082] Specifically, the method for extracting features is preferably:

[0083] For each of the actual real-time data, fitting relationship characteristic parameters are established based on the associated data:

[0084]

[0085]

[0086] in, and are respectively the first fitting relationship characteristic parameter, the second fitting relationship characteristic parameter, the third fitting relationship characteristic parameter and the fourth fitting relationship characteristic parameter based on the a-th associated data for the b-th actual real-time data, b,i and x a,i are the ith actual real-time data and the ith associated data collected in the first unit time, respectively; N is the total number of the actual real-time data collected in the first unit time, and d1, d2 and d3 are coefficients respectively;

[0087] For each of the actual real-time data, a fitting relationship feature matrix REL is established b :

[0088]

[0089] Among them, REL b (a,q) represents the element in the ath row and qth column.

[0090] On this basis, the method of outputting the fitting relationship type through the activation function can be:

[0091] Based on the REL b Outputting the functional relationship type of the b-th actual real-time data to each of the associated data respectively through a Softmax function, wherein the functional relationship type includes a linear function, a quadratic function, a ln function and an exp function;

[0092] Obtain the fitting relationship type of the bth actual real-time data to the associated data:

[0093] y b =∑ a γ ab *f ab (x a );

[0094] Among them, y b represents the fitting output of the b-th actual real-time data, i.e., the twin real-time data, x a is the collection value of the associated data described in type a, f ab (.) represents the functional relationship type of the actual real-time data of type b to the associated data of type a, γ ab It is the coefficient to be determined of the function of the actual real-time data of the bth type to the associated data of the ath type.

[0095] As a preferred solution of this embodiment, the method for determining the fitting relationship is to solve the γ by the least square method or the gradient descent method. ab The value of y b and x a The function between .

[0096] This embodiment assists the neural network in capturing the influence of coefficients of different types of functions in the fitting relationship between different types of associated data and actual real-time data by adjusting the coefficients in front of different types of functions. After the associated data and actual real-time data are closest to the fitting type, the relationship between all independent variables and the dependent variable is integrated to establish the functional relationship of each dependent variable to the comprehensive multi-independent variables, and the most suitable function coefficient is fitted by the least squares method or gradient descent method, so as to realize the establishment of a real-time digital twin model of various state data of the building. This embodiment trains the neural network by using the widest possible range of function type labels to capture more accurately the type of function relationship under the influence of different independent variables for each dependent variable. The parameters such as the function coefficients finally found help to further reduce the fitting error and ensure the reliability of the fitting.

[0097] Finally, the method for judging whether the difference between the twin real-time data and the actual real-time data is greater than the preset threshold is:

[0098] Get the comprehensive difference parameter ε:

[0099]

[0100] Among them, y b,t and They are respectively the tth twin real-time data and the actual real-time data of the bth actual real-time data within the third unit time length T3, J is the total number of the twin real-time data collected within the third unit time length T3, and B is the number of types of the actual real-time data.

[0101] In this embodiment, in order to ensure the reliability of the digital twin model, the model built in this embodiment is updated periodically and verified.

[0102] Example 3

[0103] This embodiment provides a real-time digital twin modeling system for building facilities, which is applied to any one of the above-mentioned real-time digital twin modeling methods for building facilities, including:

[0104] The perception module collects different actual real-time data and related data through various detection devices;

[0105] A data collection module, used for collecting the actual real-time data and the associated data with T1 as a first unit time length;

[0106] A model building module, used to build a digital twin model based on the actual real-time data and the associated data in a collection time period;

[0107] A model checking module, used for synchronously and periodically collecting the twin real-time data of the digital twin model and the actual real-time data of the building within a third unit time T3 at every second time unit T2;

[0108] A model update module is used to update the digital twin model if the difference between the twin real-time data and the actual real-time data is greater than a preset threshold; and if the time since the last update of the digital twin model reaches M of the first unit time lengths, the digital twin model is updated.

[0109] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A real-time digital twin modeling method for building facilities, characterized in that: The following steps are involved: Establishing a variety of detection devices for building settings, the multiple detection devices are used to collect different actual real-time data and associated data, and the detection devices include image acquisition devices and multiple sensors; The actual real-time data and the associated data are collected with T1 as a first unit time length; Establishing a digital twin model based on the actual real-time data and the associated data in a collection time period; Every second time unit T2, synchronously and periodically collect the twin real-time data of the digital twin model and the actual real-time data of the building within the third unit time T3; If the difference between the twin real-time data and the actual real-time data is greater than a preset threshold, the digital twin model is updated; If the time since the last update of the digital twin model reaches M of the first unit time periods, the digital twin model is updated.

2. A real-time digital twin modeling method for building facilities according to claim 1, characterized in that: The real-time data includes power consumption per unit time, operating power of each power facility in the building, vibration frequency of multiple points in the building, and static image pixel data.

3. A real-time digital twin modeling method for building facilities according to claim 2, characterized in that: The associated data include temperature, humidity, month, time and rainfall.

4. The real-time digital twin modeling method for building facilities according to claim 1, characterized in that: The method for establishing a digital twin model based on the actual real-time data and the associated data in a collection time period is: Determine the type of fitting relationship between the actual real-time data and the associated data through a trained model recognition network; A fitting parameter is obtained based on the fitting relationship type, and a functional relationship between the actual real-time data and the associated data is established.

5. A real-time digital twin modeling method for building facilities according to claim 4, characterized in that: The method for determining the fitting relationship type between the actual real-time data and the associated data through the trained model recognition network is: Inputting the actual real-time data and the associated data into the model recognition network; Perform feature extraction; Output the fitted relationship type through the activation function; Determine the fitted relationship.

6. A real-time digital twin modeling method for building facilities according to claim 5, characterized in that: The method for feature extraction is: For each of the actual real-time data, fitting relationship characteristic parameters are established based on the associated data: in, and are respectively the first fitting relationship characteristic parameter, the second fitting relationship characteristic parameter, the third fitting relationship characteristic parameter and the fourth fitting relationship characteristic parameter based on the a-th associated data for the b-th actual real-time data, b,i and x a,i are the ith actual real-time data and the ith associated data collected in the first unit time, respectively; N is the total number of the actual real-time data collected in the first unit time, and d1, d2 and d3 are coefficients respectively; For each of the actual real-time data, a fitting relationship feature matrix REL is established b : Among them, REL b (a,q) represents the element in the ath row and qth column.

7. A real-time digital twin modeling method for building facilities according to claim 6, characterized in that: The method of outputting the fitting relationship type through the activation function is: Based on the REL b Outputting the functional relationship type of the b-th actual real-time data to each of the associated data respectively through a Softmax function, wherein the functional relationship type includes a linear function, a quadratic function, a ln function and an exp function; Obtain the fitting relationship type of the bth actual real-time data to the associated data: y b =∑ a c ab *f ab (x a ); Among them, y b represents the fitting output of the b-th actual real-time data, i.e., the twin real-time data, x a is the collection value of the associated data described in type a, f ab (.) represents the functional relationship type of the actual real-time data of type b to the associated data of type a, γ ab It is the coefficient to be determined of the function of the actual real-time data of the bth type to the associated data of the ath type.

8. The real-time digital twin modeling method for building facilities according to claim 6, characterized in that: The method for determining the fitting relationship is to solve the γ by the least square method or the gradient descent method. ab The value of y b and x a The function between .

9. The real-time digital twin modeling method for building facilities according to claim 1, characterized in that: The method for judging whether the difference between the twin real-time data and the actual real-time data is greater than a preset threshold is as follows: Get the comprehensive difference parameter ε: Among them, y b,t and They are respectively the tth twin real-time data and the actual real-time data of the bth actual real-time data within the third unit time length T3, J is the total number of the twin real-time data collected within the third unit time length T3, and B is the number of types of the actual real-time data.

10. A real-time digital twin modeling system for building facilities, applied to a real-time digital twin modeling method for building facilities as claimed in any one of claims 1 to 9, characterized in that: include: The perception module collects different actual real-time data and related data through various detection devices; A data collection module, used for collecting the actual real-time data and the associated data with T1 as a first unit time length; A model building module, used to build a digital twin model based on the actual real-time data and the associated data in a collection time period; A model checking module, used for synchronously and periodically collecting the twin real-time data of the digital twin model and the actual real-time data of the building within a third unit time T3 at every second time unit T2; A model update module is used to update the digital twin model if the difference between the twin real-time data and the actual real-time data is greater than a preset threshold; and if the time since the last update of the digital twin model reaches M of the first unit time lengths, the digital twin model is updated.

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