Intelligent operation and maintenance management system and method based on BIM and digital twin technology

Abstract

The present invention discloses an intelligent operation and maintenance management system and method based on BIM and digital twin technology, relates to the field of BIM and digital twin technology, comprises a monitoring center, wherein the monitoring center is communicatively connected with a data acquisition module, a BIM model construction module, a digital twin dynamic simulation module, a fault prediction module and an intelligent management module; the building information data, equipment information data and environmental data of the building are collected; a three-dimensional model of the building and a three-dimensional model of the equipment are constructed according to the building information data and the equipment information data; digital twin simulation is performed on the three-dimensional model of the building and the three-dimensional model of the equipment to construct a digital twin model of the building; historical equipment operation data is obtained according to the digital twin model of the building and the environmental data, a fault prediction model is constructed, and a predicted fault feature index is obtained; according to the predicted fault feature index, the priority of the predicted fault of the equipment operation is evaluated, and operation and maintenance management is performed on the building; and the operation and maintenance efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of BIM and digital twin technologies, and specifically to an intelligent operation and maintenance management system and method based on BIM and digital twin technologies. Background Art

[0002] Traditional operation and maintenance management systems usually use different tools and platforms to handle various aspects of operation and maintenance, resulting in data and information being scattered across multiple systems, forming information islands. Data cannot be effectively shared and integrated, making it difficult for operation and maintenance personnel to obtain a comprehensive view, reducing the efficiency of problem diagnosis and resolution. Many traditional systems do not handle data and events in real time, and usually rely on periodic data collection and reporting. This may lead to delayed responses and handling of potential problems, increasing the risk of system failures. At the same time, the automation functions of traditional operation and maintenance management systems may be relatively limited, relying on manual operations and intervention, increasing the possibility of human errors and reducing operation and maintenance efficiency.

[0003] With the rapid development of building information modeling (BIM) technology, the concept of building life cycle management has gradually been widely accepted and applied. BIM technology integrates the management of buildings from design, construction to operation and maintenance through digital three-dimensional models. BIM is not just a three-dimensional model, it also contains a lot of building-related information, such as structure, materials, equipment, progress, etc., making all aspects of the building more coordinated and efficient. However, traditional BIM technology is mainly concentrated in the design and construction stages of buildings, while in the operation and maintenance stages of buildings, there is still a lot of room for development in the application of BIM.

[0004] On the other hand, with the rise of emerging technologies such as the Internet of Things, big data, and artificial intelligence (AI), digital twin technology has gradually become a hot spot in building operation and maintenance management. Digital twin technology dynamically combines the real-time data of physical buildings with BIM models to achieve interaction and linkage between virtual models and real buildings. This technology can reflect the status of buildings in real time and provide support for building operation and maintenance management through data analysis and prediction.

[0005] The intelligent operation and maintenance management system based on BIM and digital twin technology came into being. This intelligent operation and maintenance management system combines the static data of BIM technology and the dynamic data of digital twin technology to form an all-round and multi-dimensional building operation and maintenance management platform. This system can not only improve the efficiency of operation and maintenance management, but also reduce maintenance costs and extend the service life of buildings. By collecting and analyzing real-time monitoring data of buildings, the intelligent operation and maintenance management system can promptly discover and prevent potential problems, optimize resource allocation, and improve the operation and maintenance efficiency of buildings. However, the existing intelligent operation and maintenance management system based on BIM and digital twin technology still has the following problems: (1) Data and information are usually distributed in different systems and platforms, making it difficult for operation and maintenance personnel to obtain a comprehensive system view; (2) Many traditional systems still require manual intervention for maintenance and troubleshooting, and the degree of automation is low; (3) Data collection and processing are often not real-time enough, which may lead to delayed response to problems; (4) System expansion and adjustment often require major modifications and cannot quickly adapt to business changes; (5) Integration with other enterprise systems (such as ERP, CRM) often requires complex custom development; (6) Maintaining traditional systems usually involves complex hardware and software management, resulting in high costs and resource consumption. The existence of the above problems leads to low operation and maintenance efficiency of the intelligent operation and maintenance management system.

[0006] Therefore, an intelligent operation and maintenance management system and method based on BIM and digital twin technology is now provided. Summary of the invention

[0007] In order to solve the above technical problems, the purpose of the present invention is to provide an intelligent operation and maintenance management system and method based on BIM and digital twin technology.

[0008] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: an intelligent operation and maintenance management system based on BIM and digital twin technology, comprising a monitoring center, wherein the monitoring center is communicatively connected with a data acquisition module, a BIM model construction module, a digital twin dynamic simulation module, a fault prediction module and an intelligent management module;

[0009] The data acquisition module is used to collect building information data, equipment information data and environmental data of the building;

[0010] The model building module is used to build a three-dimensional model of the building according to the building information data of the building, and to build a three-dimensional model of the equipment according to the equipment information data;

[0011] The digital twin dynamic simulation module is used to perform digital twin simulation on the building three-dimensional model and the equipment three-dimensional model, construct a building digital twin model, obtain equipment operation data based on the building digital twin model and environmental data, and then obtain historical equipment operation data;

[0012] The fault prediction module is used to construct a fault prediction model based on historical equipment operation data, and obtain a predicted fault feature index based on the fault prediction model;

[0013] The intelligent management module is used to evaluate the priority of equipment operation prediction failures according to the prediction failure feature index, and perform operation and maintenance management on the building according to the priority.

[0014] Furthermore, the process of the data acquisition module acquiring building information data, equipment information data and environmental data of a building includes:

[0015] The data acquisition module is provided with a building information acquisition unit, a device information acquisition unit and an environment information acquisition unit, and a collection cycle is set;

[0016] The building information acquisition unit is used to acquire building information data of a building; the equipment information acquisition unit is used to acquire equipment information data; and the environment information acquisition unit is used to acquire environment data.

[0017] Furthermore, the process of constructing the three-dimensional model of the building by the model construction module includes:

[0018] Design the floor plan, elevation and section of the building based on the building information data;

[0019] The plan view, elevation view and section view are imported into BIM modeling software to construct a three-dimensional model of the building.

[0020] Furthermore, the specific process of the model building module building the three-dimensional model of the device includes:

[0021] Based on the equipment information data, a distribution map of each device is designed; and the distribution map is imported into the BIM modeling software to construct a three-dimensional model of the equipment.

[0022] Furthermore, the process of constructing a digital twin model of a building by the digital twin dynamic simulation module includes:

[0023] Unify the scale of the 3D model of the building and the 3D model of the equipment according to the actual size of the building and the equipment;

[0024] The three-dimensional model of the building and the three-dimensional model of the equipment after unified scale are digitized to obtain the coordinate position data of the building and the equipment, and a digital twin model of the building is constructed based on the coordinate position data of the building and the equipment.

[0025] Furthermore, the process of the digital twin dynamic simulation module obtaining historical equipment operation data includes:

[0026] Obtain building information data, equipment information data and environmental data of the next collection cycle, construct new building three-dimensional model and equipment three-dimensional model according to the building information data and equipment information data of the next collection cycle, and then update the building digital twin model according to the new building three-dimensional model and equipment three-dimensional model, and digitize the environmental data of the next collection cycle to obtain new digital environmental data, and import the new digital environmental data as boundary conditions into the updated building digital twin model, obtain the equipment operation data of the next collection cycle, and record the equipment operation data obtained in the previous collection cycle as historical equipment operation data.

[0027] Furthermore, the process of the fault prediction module constructing a fault prediction model and obtaining a predicted fault feature index includes:

[0028] Based on convolutional neural network technology, a standard fault prediction model is constructed;

[0029] Acquire several groups of historical equipment operation data, and acquire the historical error code times and historical error code types corresponding to the historical equipment operation data, and use the several groups of historical equipment operation data, the corresponding historical error code times and historical error code types as training sample sets, import the obtained training sample sets into a standard fault prediction model, train the standard fault prediction model, and record the trained standard fault prediction model as a fault prediction model;

[0030] Get the equipment operation data of the current collection cycle;

[0031] The equipment operation data of the current acquisition cycle is imported into the fault prediction model. The fault prediction model obtains the predicted error code times and predicted error code types according to the equipment operation data of the current acquisition cycle. According to the predicted error code times, the predicted fault feature index of different equipment is obtained as follows:

[0032] ,in, To predict the fault characteristic index, To predict the number of error codes, Indicates that the collection cycle is from 1 to The sum of the error code times corresponding to the historical collection cycle, is a natural number.

[0033] Furthermore, the process of the intelligent management module performing operation and maintenance management on the building includes:

[0034] Divide the building digital twin model into equipment areas according to equipment types to obtain area blocks;

[0035] Based on the predicted error code type of each different device, feature recognition is performed to identify the potential fault type of each device:

[0036] Preset error code types for different fault types;

[0037] When the obtained predicted error code type matches the error code type of the corresponding fault type, the device is of the corresponding fault type, and the calculation formula for generating the corresponding fault severity factor is:

[0038] ;in, is the fault severity factor, is the fault severity error coefficient;

[0039] The fault severity factor of each different device is obtained and converted visually, that is, a corresponding pixel density number is assigned according to the size of the fault severity factor, and the pixel density number is mapped to the corresponding area block;

[0040] Defining the pixel density: setting different colors according to the density of the pixel density, so as to further distinguish the pixel density of similar number, wherein the colors include red, yellow and green;

[0041] If the number of pixel points in the area block is denser, the priority of the device operation prediction fault defining the area block is higher, and vice versa, the priority of the device operation prediction fault defining the area block is lower;

[0042] The red color represents that the equipment operation prediction failure of the block in the area has the highest priority, followed by yellow and finally green;

[0043] A real-time monitoring unit is provided in the intelligent management module;

[0044] The real-time monitoring unit is used to monitor the color distribution state of the area block in real time, and generate a warning alarm corresponding to the fault type and with a high priority level according to the color distribution state and the fault type.

[0045] A second aspect of the present invention provides an intelligent operation and maintenance management method based on BIM and digital twin technology, comprising the following steps:

[0046] Step S1: Collect building information data, equipment information data, and environmental data of a building; construct a three-dimensional model of the building based on the building information data, and construct a three-dimensional model of the equipment based on the equipment information data;

[0047] Step S2: Perform digital twin simulation on the building 3D model and the equipment 3D model to construct a building digital twin model, and obtain equipment operation data based on the building digital twin model and environmental data, and then obtain historical equipment operation data;

[0048] Step S3: Based on the historical equipment operation data, a fault prediction model is constructed, and a predicted fault feature index is obtained; based on the predicted fault feature index, the priority of the equipment operation predicted fault is evaluated, and based on the priority, operation and maintenance management of the building is performed.

[0049] Compared with the prior art, the beneficial effects of the present invention are: collecting building information data, equipment information data and environmental data of a building; constructing a three-dimensional model of the building according to the building information data, and constructing a three-dimensional model of the equipment according to the equipment information data; performing digital twin simulation on the three-dimensional model of the building and the three-dimensional model of the equipment to construct a digital twin model of the building, and obtaining historical equipment operation data based on the digital twin model of the building and the environmental data, thereby constructing a fault prediction model and obtaining a predicted fault feature index; evaluating the priority of the predicted equipment operation fault based on the predicted fault feature index, and performing operation and maintenance management on the building; and improving operation and maintenance efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0051] Figure 1 This is the schematic diagram of the intelligent operation and maintenance management system based on BIM and digital twin technology.

[0052] Figure 2 This is a schematic diagram of the intelligent operation and maintenance management method based on BIM and digital twin technology. DETAILED DESCRIPTION

[0053] To make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in detail below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other implementation methods obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0054] like Figure 1As shown, an intelligent operation and maintenance management system based on BIM and digital twin technology includes a monitoring center, which is communicatively connected to a data acquisition module, a BIM model building module, a digital twin dynamic simulation module, a fault prediction module, and an intelligent management module;

[0055] The data acquisition module is used to collect building information data, equipment information data and environmental data of the building;

[0056] The model building module is used to build a three-dimensional model of the building according to the building information data of the building, and to build a three-dimensional model of the equipment according to the equipment information data;

[0057] The digital twin dynamic simulation module is used to perform digital twin simulation on the building three-dimensional model and the equipment three-dimensional model, construct a building digital twin model, obtain equipment operation data based on the building digital twin model and environmental data, and then obtain historical equipment operation data;

[0058] The fault prediction module is used to construct a fault prediction model based on historical equipment operation data and obtain a predicted fault feature index;

[0059] The intelligent management module is used to evaluate the priority of equipment operation prediction failures according to the prediction failure feature index, and perform operation and maintenance management on the building according to the priority.

[0060] It should be further explained that, in the specific implementation process, the specific process of the data acquisition module collecting the building information data, equipment information data and environmental data of the building includes:

[0061] The data acquisition module is provided with a building information acquisition unit, a device information acquisition unit and an environment information acquisition unit, and a collection cycle is set;

[0062] The building information collection unit is used to collect the specific geometric shapes, sizes and layouts of the walls, columns, beams, floors, roofs, doors and windows, stairs, railings, ground, passages and public spaces of the building;

[0063] The equipment information collection unit is used to collect the equipment type and location of the heating, ventilation or air-conditioning system in the building, the model, power, wiring diagram of the transformer, distribution box, lighting equipment, etc. in the electrical system, the layout, diameter, material of the drainage, fire protection, gas and other pipeline systems, and the wiring diagram and cable type of the weak current system communication line;

[0064] The environmental information collection unit is used to collect the climate conditions, wind direction, temperature, humidity and pressure of the environment in which the building is located;

[0065] It should be further explained that the data collected by the building information collection unit is recorded as building information data; the data collected by the equipment information collection unit is recorded as equipment information data; and the data collected by the environment information collection unit is recorded as environment data.

[0066] The data acquisition module uploads the collected building information data and equipment information data to the model construction module; and uploads the environmental data to the digital twin dynamic simulation module.

[0067] It should be further explained that, in the specific implementation process, the specific process of the model building module building the three-dimensional model of the building according to the building information data of the building includes:

[0068] Obtain building information data;

[0069] Designing a plan view, elevation view, and section view of a building according to the building information data;

[0070] The plan view, elevation view and section view are imported into BIM modeling software to construct a three-dimensional model of the building.

[0071] It should be further explained that, in the specific implementation process, the specific process of the model building module building the three-dimensional model of the device according to the device information data includes:

[0072] Get device information data;

[0073] Design a distribution map of the equipment based on the equipment information data; and import the distribution map into the BIM modeling software to construct a three-dimensional model of the equipment;

[0074] The model building module uploads the three-dimensional model of the building and the three-dimensional model of the equipment to the digital twin dynamic simulation module.

[0075] It should be further explained that, in the specific implementation process, the digital twin dynamic simulation module performs digital twin simulation on the three-dimensional model of the building and the three-dimensional model of the equipment. The specific process of constructing the digital twin model of the building includes:

[0076] Obtaining a 3D model of a building and a 3D model of equipment;

[0077] And according to the actual size of the building and the equipment, unify the scale of the 3D model of the building and the 3D model of the equipment;

[0078] The three-dimensional model of the building and the three-dimensional model of the equipment after unified scale are digitized to obtain the coordinate position data of the building and the equipment, and a digital twin model of the building is constructed based on the coordinate position data of the building and the equipment.

[0079] It should be further explained that, in the specific implementation process, the digital twin dynamic simulation module obtains the equipment operation data according to the building digital twin model and environmental data, and the specific process of obtaining the historical equipment operation data includes:

[0080] Obtain environmental data;

[0081] The environmental data is digitized to obtain digital environmental data, and the digital environmental data is imported into the digital twin model of the building as a boundary condition to obtain equipment operation data of the current acquisition cycle;

[0082] Acquire building information data, equipment information data, and environmental data of the next collection cycle, respectively construct a new building 3D model and equipment 3D model according to the building information data and equipment information data of the next collection cycle, and then update the building digital twin model according to the new building 3D model and equipment 3D model, and digitize the environmental data of the next collection cycle to obtain new digitized environmental data, and import the new digitized environmental data as boundary conditions into the updated building digital twin model, obtain the equipment operation data of the next collection cycle, and record the equipment operation data obtained in the previous collection cycle as historical equipment operation data;

[0083] It should be further explained that the purpose of acquiring building information data, equipment information data and environmental data in each collection cycle is to update the digital twin model of the building in real time.

[0084] The digital twin dynamic simulation module uploads historical equipment operation data to the fault prediction module.

[0085] It should be further explained that, in the specific implementation process, the fault prediction module constructs a fault prediction model based on historical equipment operation data, and the specific process of obtaining the predicted fault feature index includes:

[0086] Based on convolutional neural network technology, a standard fault prediction model is constructed;

[0087] It should be further explained that the standard fault prediction model is an original model that has not been trained with data;

[0088] Acquire several groups of historical equipment operation data, and acquire the historical error code times and historical error code types corresponding to the historical equipment operation data, and use the several groups of historical equipment operation data, the corresponding historical error code times and historical error code types as training sample sets, import the obtained training sample sets into a standard fault prediction model, train the standard fault prediction model, and record the trained standard fault prediction model as a fault prediction model;

[0089] Get the equipment operation data of the current collection cycle;

[0090] The equipment operation data of the current acquisition cycle is imported into the fault prediction model. The fault prediction model obtains the predicted error code times and predicted error code types according to the equipment operation data of the current acquisition cycle. According to the predicted error code times, the predicted fault feature index of different equipment is obtained as follows:

[0091] ,in, To predict the fault characteristic index, The number of error codes for the device operation data in the current collection cycle. Indicates that the collection cycle is from 1 to The sum of the error code times corresponding to the historical collection cycle, is a natural number.

[0092] It should be further explained that, in the specific implementation process, the intelligent management module evaluates the priority of the equipment operation prediction fault according to the prediction fault characteristic index, and performs operation and maintenance management on the building according to the priority. The specific process includes:

[0093] The digital twin model of the building is divided into equipment areas according to the equipment type to obtain Area blocks, where n represents the number of levels and m represents the number of devices in each level;

[0094] Based on the predicted error code type of each different device, feature recognition is performed to identify the potential fault type of each device:

[0095] It should be further explained that the potential failure types of each device include mechanical failure, electrical failure, control system failure and thermal management failure;

[0096] Preset error code types for different fault types;

[0097] When the obtained predicted error code type matches the error code type of the corresponding fault type, the device is of the corresponding fault type, and the calculation formula for generating the corresponding fault severity factor is:

[0098] ;in, is the fault severity factor, is the fault severity error coefficient;

[0099] The fault severity factor of each different device is obtained and converted visually, that is, a corresponding pixel density number is assigned according to the size of the fault severity factor, and the pixel density number is mapped to the corresponding area block;

[0100] Defining the pixel density: setting different colors according to the density of the pixel density, so as to further distinguish the pixel density of similar number, the colors include but are not limited to red, yellow and green;

[0101] If the number of pixel points in the area block is denser, the priority of the device operation prediction fault defining the area block is higher, and vice versa, the priority of the device operation prediction fault defining the area block is lower;

[0102] It should be further explained that the red color represents that the equipment operation prediction failure of the area block has the highest priority, followed by yellow and finally green;

[0103] A real-time monitoring unit is provided in the intelligent management module;

[0104] The real-time monitoring unit is used to monitor the color distribution state of the area block in real time, and generate a warning alarm corresponding to the fault type and with a high priority level according to the color distribution state and the fault type.

[0105] like Figure 2 As shown in the figure, the intelligent operation and maintenance management method based on BIM and digital twin technology includes the following steps:

[0106] Step S1: Collect building information data, equipment information data, and environmental data of a building; construct a three-dimensional model of the building based on the building information data, and construct a three-dimensional model of the equipment based on the equipment information data;

[0107] Step S2: Perform digital twin simulation on the building 3D model and the equipment 3D model to construct a building digital twin model, and obtain equipment operation data based on the building digital twin model and environmental data, and then obtain historical equipment operation data;

[0108] Step S3: construct a fault prediction model based on historical equipment operation data, and obtain a predicted fault feature index based on the fault prediction model; evaluate the priority of equipment operation predicted faults based on the predicted fault feature index, and perform operation and maintenance management on the building based on the priority.

[0109] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. Intelligent operation and maintenance management system based on BIM and digital twin technology, including a monitoring center, characterized by: The monitoring center is communicatively connected with a data acquisition module, a BIM model construction module, a digital twin dynamic simulation module, a fault prediction module and an intelligent management module; The data acquisition module is used to collect building information data, equipment information data and environmental data of the building; The model building module is used to build a three-dimensional model of the building according to the building information data of the building, and to build a three-dimensional model of the equipment according to the equipment information data; The digital twin dynamic simulation module is used to perform digital twin simulation on the building three-dimensional model and the equipment three-dimensional model, construct a building digital twin model, obtain equipment operation data based on the building digital twin model and environmental data, and then obtain historical equipment operation data; The fault prediction module is used to construct a fault prediction model based on historical equipment operation data, and obtain a predicted fault feature index based on the fault prediction model; The intelligent management module is used to evaluate the priority of the equipment operation prediction fault according to the prediction fault characteristic index, and perform operation and maintenance management on the building according to the priority; The process of the data acquisition module acquiring building information data, equipment information data and environmental data of a building includes: The data acquisition module is provided with a building information acquisition unit, a device information acquisition unit and an environment information acquisition unit, and a collection cycle is set; The building information acquisition unit is used to acquire building information data of the building; the equipment information acquisition unit is used to acquire equipment information data; the environment information acquisition unit is used to acquire environment data; The process of constructing a three-dimensional model of a building by the model construction module includes: Design the floor plan, elevation and section of the building based on the building information data; Importing the plan, elevation and section drawings into BIM modeling software to construct a three-dimensional model of the building; The specific process of constructing the three-dimensional model of the equipment by the model construction module includes: According to the equipment information data, a distribution map of each equipment is designed; and the distribution map is imported into the BIM modeling software to construct a three-dimensional model of the equipment; The process of constructing a digital twin model of a building by the digital twin dynamic simulation module includes: Unify the scale of the 3D model of the building and the 3D model of the equipment according to the actual size of the building and the equipment; The three-dimensional model of the building and the three-dimensional model of the equipment after the unified scale are digitized to obtain the coordinate position data of the building and the equipment, and a digital twin model of the building is constructed according to the coordinate position data of the building and the equipment; The process of obtaining historical equipment operation data by the digital twin dynamic simulation module includes: Acquire building information data, equipment information data, and environmental data of the next collection cycle, respectively construct a new building 3D model and equipment 3D model according to the building information data and equipment information data of the next collection cycle, and then update the building digital twin model according to the new building 3D model and equipment 3D model, and digitize the environmental data of the next collection cycle to obtain new digitized environmental data, and import the new digitized environmental data as boundary conditions into the updated building digital twin model, obtain the equipment operation data of the next collection cycle, and record the equipment operation data obtained in the previous collection cycle as historical equipment operation data; The process of the fault prediction module constructing a fault prediction model and obtaining a predicted fault feature index includes: Based on convolutional neural network technology, a standard fault prediction model is constructed; Acquire several groups of historical equipment operation data, and acquire the historical error code times and historical error code types corresponding to the historical equipment operation data, and use the several groups of historical equipment operation data, the corresponding historical error code times and historical error code types as training sample sets, import the obtained training sample sets into a standard fault prediction model, train the standard fault prediction model, and record the trained standard fault prediction model as a fault prediction model; Get the equipment operation data of the current collection cycle; The equipment operation data of the current acquisition cycle is imported into the fault prediction model. The fault prediction model obtains the predicted error code times and predicted error code types according to the equipment operation data of the current acquisition cycle. According to the predicted error code times, the predicted fault feature index of different equipment is obtained as follows: ,in, To predict the fault characteristic index, To predict the number of error codes, Indicates that the collection cycle is from 1 to The sum of the error code times corresponding to the historical collection cycle, is a natural number; The process of the intelligent management module performing operation and maintenance management on the building includes: Divide the building digital twin model into equipment areas according to equipment types to obtain area blocks; Based on the predicted error code type of each different device, feature recognition is performed to identify the potential fault type of each device: Preset error code types for different fault types; When the obtained predicted error code type matches the error code type of the corresponding fault type, the device is of the corresponding fault type, and the calculation formula for generating the corresponding fault severity factor is: ;in, is the fault severity factor, is the fault severity error coefficient; The fault severity factor of each different device is obtained and converted visually, that is, a corresponding pixel density number is assigned according to the size of the fault severity factor, and the pixel density number is mapped to the corresponding area block; Defining the pixel density: setting different colors according to the density of the pixel density, so as to further distinguish the pixel density of similar number, wherein the colors include red, yellow and green; If the number of pixel points in the area block is denser, the priority of the device operation prediction fault defining the area block is higher, and vice versa, the priority of the device operation prediction fault defining the area block is lower; The red color represents that the equipment operation prediction failure of the block in the area has the highest priority, followed by yellow and finally green; A real-time monitoring unit is provided in the intelligent management module; The real-time monitoring unit is used to monitor the color distribution state of the area block in real time, and generate a warning alarm corresponding to the fault type and with a high priority level according to the color distribution state and the fault type.

2. The intelligent operation and maintenance management method based on BIM and digital twin technology is specifically applied to the intelligent operation and maintenance management system based on BIM and digital twin technology according to claim 1, characterized in that: The following steps are involved: Step S1: Collect building information data, equipment information data, and environmental data of a building; construct a three-dimensional model of the building based on the building information data, and construct a three-dimensional model of the equipment based on the equipment information data; Step S2: Perform digital twin simulation on the building 3D model and the equipment 3D model to construct a building digital twin model, and obtain equipment operation data based on the building digital twin model and environmental data, and then obtain historical equipment operation data; Step S3: construct a fault prediction model based on historical equipment operation data, and obtain a predicted fault feature index based on the fault prediction model; evaluate the priority of equipment operation predicted faults based on the predicted fault feature index, and perform operation and maintenance management on the building based on the priority.

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