Property facility digital twin modeling and intelligent operation and maintenance management platform

Abstract

The invention provides a digital twin modeling and intelligent operation and maintenance management platform for property facilities, belongs to the technical field of data processing, and is used for accurate evaluation, predictive maintenance and closed-loop intelligent management of the health state of the facilities. According to the method, a dynamic multi-scale visualization mechanism is combined with an edge-cloud cooperative processing architecture, so that the calculation load of the system and the information presentation precision are intelligently balanced, and the method is suitable for large-scale popularization and application. A lightweight model is adopted to ensure the smoothness of the system when the facility runs normally, a high-precision model is automatically switched to provide decision support in an abnormal or key scene, meanwhile, preprocessing is performed at a data source, and the collaborative mechanism optimizes the allocation of system resources and ensures the high performance and high availability of the platform in different application scenes.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a property facility digital twin modeling and intelligent operation and maintenance management platform. BACKGROUND

[0002] In the field of property management, informatization and digitization are the key directions to improve management efficiency and service quality. Among them, using computer technology to build a management platform to integrate and analyze various types of information of building facilities has become an industry consensus. Digital twin, as an advanced digital technology, creates a dynamic mapping of physical entities in a virtual space, providing a new way of thinking for the management of the whole life cycle of facilities. Its core lies in data processing, model building, and intelligent analysis based on the model, all of which belong to the category of computer data processing and application.

[0003] Currently, some property management platforms have begun to try to apply building information modeling technology for three-dimensional visualization display, and collect facility operation data through Internet of Things technology. In specific implementation, a static three-dimensional building model display system is usually established, and an independent data monitoring dashboard is attached beside it to list real-time parameters collected by various sensors. When equipment maintenance is needed, another independent work order management software is relied on for manual distribution and process tracking. These systems run within their respective functional modules, and there is a lack of effective integration and linkage between data.

[0004] The existing technical solutions have obvious defects. First, there is a lack of deep integration mechanism between static building information model and dynamic Internet of Things operation data. The two are usually displayed in parallel rather than organically combined, resulting in the platform's inability to form a unified and dynamic understanding of the facility status. Second, existing three-dimensional visualization usually loads the entire building model at once. The large amount of data causes the system to respond slowly, and the model precision is fixed and cannot be dynamically adjusted according to actual operation and maintenance needs. In addition, the judgment of facility failures relies on simple threshold alarms, lacks deep analysis and future state prediction capabilities based on multi-dimensional data and internal mechanisms, and cannot dynamically optimize its analysis model based on feedback from operation and maintenance practices. SUMMARY

[0005] The property facility digital twin modeling and intelligent operation and maintenance management platform provided by the embodiments of the present application is used for accurate assessment, predictive maintenance, and closed-loop intelligent management of facility health status.

[0006] To achieve the above-mentioned purpose, the technical solutions adopted by the present application are as follows: In a first aspect, a property facility digital twin modeling and intelligent operation and maintenance management platform is provided, which is applied to a management terminal. The management terminal comprises: The management terminal acquires M building information model data representing static structures of property facility and Internet of Things sensor real-time monitoring data representing dynamic operation of the facility; The management terminal constructs a multi-level digital twin of the property facility containing a hierarchical structure of equipment level, functional area level and building level based on the building information model data and the Internet of Things sensor real-time monitoring data through a hierarchical fusion modeling method; The management terminal renders and displays the multi-level digital twin of the property facility according to the operation state of the facility in the multi-level digital twin by using a dynamic multi-scale visualization mechanism, evaluates and predicts the health state of the facility by a hybrid driven prediction model coupled by a physical mechanism model and a machine learning model based on the real-time monitoring data of the Internet of Things sensor and historical data in the multi-level digital twin, and generates a facility health state prediction result; The management terminal dynamically adjusts the hybrid driven prediction model through an adaptive health management mechanism according to the facility health state prediction result, and outputs an operation and maintenance decision suggestion.

[0007] Optionally, the multi-level digital twin of the property facility is constructed through a hierarchical fusion modeling method, specifically including: The building information model data is structurally analyzed to extract equipment level components, functional area level components and building level components; The real-time monitoring data of the Internet of Things sensor is associated with the equipment level components, functional area level components and building level components according to spatial position and functional attribute to generate a mapping relationship between components and data; According to the mapping relationship, equipment level digital twin sub-models, functional area level digital twin sub-models and building level digital twin sub-models are constructed layer by layer to fuse real-time data; The equipment level, functional area level and building level digital twin sub-models are integrated to generate the multi-level digital twin.

[0008] Optionally, the multi-level digital twin of the property facility is rendered and displayed by using a dynamic multi-scale visualization mechanism, specifically including: The operation state data in the multi-level digital twin is monitored in real time and compared with a rule set defining a normal working condition range to generate a model rendering mode instruction; If the model rendering mode instruction is a normal working condition, a lightweight simplified model is called for visualization rendering; If the model rendering mode instruction is an abnormal working condition or a key maintenance scene, a high-precision detailed model is automatically switched to for visualization rendering, and the operation state data is superimposed and displayed by the high-precision detailed model.

[0009] Optionally, the facility health state evaluation and prediction by the hybrid driving prediction model specifically comprises: constructing a physical mechanism model based on facility physical degradation rules and environmental impact factors; training a machine learning prediction model for compensating for the prediction deviation of the physical mechanism model using historical data in the multi-level digital twin; weighting and coupling the physical mechanism model and the machine learning prediction model to form the hybrid driving prediction model; driving the hybrid driving prediction model to operate using real-time monitoring data of the Internet of Things sensors, and outputting the facility health state prediction result.

[0010] Optionally, before the facility health state evaluation and prediction by the hybrid driving prediction model, further comprising: performing edge computing preprocessing on the collected original monitoring data at the Internet of Things sensor end, and extracting key feature data capable of representing the core operation state of the facility; uploading the key feature data to the cloud end; updating the real-time state in the multi-level digital twin using the key feature data to provide input for the hybrid driving prediction model.

[0011] Optionally, the dynamic adjustment of the hybrid driving prediction model by the adaptive health management mechanism specifically comprises: comparing the facility health state prediction result with subsequent real-time monitoring data of the Internet of Things sensors to generate a prediction error vector; dynamically adjusting the weight coefficients of the physical mechanism model and the machine learning model in the hybrid driving prediction model according to the size and trend of the prediction error vector to generate a weight adjustment result; applying the weight adjustment result to subsequent prediction cycles to optimize the accuracy of the facility health state prediction result.

[0012] Optionally, the output of the operation and maintenance decision suggestion specifically comprises: comparing the facility health state prediction result with the warning threshold and the critical range to generate a state level judgment; generating a warning signal and a maintenance suggestion according to the state level judgment if it is below the warning threshold; generating a monitoring frequency adjustment scheme if it is in the critical range, or a continuous operation suggestion if it is above the critical range according to the state level judgment.

[0013] Optionally, after the output of the operation and maintenance decision suggestion, further comprising: automatically generating a maintenance work order containing fault location and repair guidance according to the maintenance suggestion. receiving and recording a maintenance execution result corresponding to the maintenance work order; feeding back the maintenance execution result to the multi-level digital twin for expanding its historical data set and calibrating parameters of the hybrid-driven prediction model.

[0014] Optionally, the multi-level digital twin also supports on-demand local loading and visualization, specifically including: receiving a user interaction instruction specifying a target range or target device; extracting corresponding sub-model data from the multi-level digital twin according to the user interaction instruction; only loading and rendering the sub-model data to achieve a quick response to the user interaction instruction.

[0015] In a second aspect, a property facility digital twin modeling and intelligent operation and maintenance management device is provided. a data acquisition module configured to acquire building information model data and Internet of Things sensor real-time monitoring data; a modeling module configured to construct a multi-level digital twin based on the data acquired by the data acquisition module; a visualization module configured to dynamically switch the rendering model's precision according to the multi-level digital twin's running state; a prediction module configured to generate a facility health state prediction result by combining real-time data and historical data through a hybrid-driven prediction model; an adjustment and decision module configured to adaptively adjust the prediction module and output operation and maintenance decision suggestions according to the facility health state prediction result.

[0016] In a third aspect, an electronic device is provided, including a processor and a memory; the memory is configured to store a computer program, when the processor executes the computer program, so that the electronic device executes the property facility digital twin modeling and intelligent operation and maintenance management platform of the first aspect.

[0017] In a possible design scheme, the electronic device of the third aspect can further include a transceiver. The transceiver can be a transceiver circuit or an interface circuit. The transceiver can be used for the electronic device of the third aspect to communicate with other electronic devices.

[0018] In the embodiments of the present application, the electronic device of the third aspect can be a terminal, or a chip (system) or other components or assemblies provided in the terminal, or a system containing the terminal.

[0019] In a third aspect, a computer-readable storage medium is provided, comprising: a computer program or instructions; when the computer program or instructions are run on a computer, the computer is caused to execute the property facility digital twin modeling and intelligent operation management platform of the first aspect.

[0020] To sum up, the above method and system have the following technical effects: The present application realizes unified and fine digital expression of complex property facilities from a macroscopic whole to a microscopic part by constructing a multi-level digital twin body including device level, function area level and building level hierarchical structure and supporting on-demand local loading, provides an intuitive visual interface, improves the efficiency of data retrieval and interactive operation, enables management personnel to quickly locate problems and master the overall situation, thereby improving the accuracy and timeliness of decision-making, adopts a dynamic multi-scale visualization mechanism combined with an edge-cloud collaborative processing architecture, intelligently balances the computing load of the system and the accuracy of information presentation, adopts a lightweight model during normal operation of the facility to ensure smoothness of the system, and automatically switches to a high-precision model in abnormal or critical scenarios to provide decision support, and at the same time, pre-processes at the data source, the collaborative mechanism optimizes the allocation of system resources, and ensures high performance and high availability of the platform in different application scenarios. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 A schematic diagram of the control system provided by the embodiment of the present application is shown in the figure. Figure 2 A flowchart of the property facility digital twin modeling and intelligent operation management platform provided by the embodiment of the present application is shown in the figure. Figure 3 A structural schematic diagram of the electronic device provided by the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0022] The technical solutions in the present application will be described below with reference to the accompanying drawings.

[0023] In the embodiments of the present application, the indication can include direct indication and indirect indication, and can also include explicit indication and implicit indication. The information indicated by certain information is referred to as to-be-indicated information. In the implementation process, there are many ways to indicate the to-be-indicated information, for example, but not limited to, the to-be-indicated information can be directly indicated, such as the to-be-indicated information itself or an index of the to-be-indicated information. The to-be-indicated information can also be indirectly indicated by indicating other information, where the other information and the to-be-indicated information have an association relationship. The to-be-indicated information can also be indicated only by a part of the to-be-indicated information, and the other part of the to-be-indicated information is known or agreed in advance. For example, the indication of a specific information can also be achieved by means of the arrangement order of each information agreed in advance (for example, specified by a protocol), thereby reducing the indication overhead to a certain extent. Meanwhile, a common part of each information can be identified and uniformly indicated, so as to reduce the indication overhead caused by separately indicating the same information.

[0024] In addition, the specific indication manner can also be various existing indication manners, for example, but not limited to, the above-mentioned indication manners and various combinations thereof. The specific details of various indication manners can refer to the prior art, and will not be described herein. As known from the above, for example, when multiple information of the same type needs to be indicated, the indication manners of different information can be different. In the implementation process, the required indication manner can be selected according to the specific needs, and the selected indication manner is not limited in the embodiments of the present application. In this way, the indication manner involved in the embodiments of the present application should be understood as covering various methods that can enable the to-be-indicated party to know the to-be-indicated information.

[0025] It should be understood that the to-be-indicated information can be sent as a whole, or can be sent separately into multiple sub-information, and the sending period and / or sending occasion of the sub-information can be the same or different. The specific sending method is not limited in the embodiments of the present application. The sending period and / or sending occasion of the sub-information can be pre-defined, for example, pre-defined according to a protocol, or configured by the sending end device by sending configuration information to the receiving end device.

[0026] The "pre-defined" or "pre-configured" can be implemented by pre-saving corresponding codes, tables or other methods that can be used to indicate related information in the device, and the specific implementation manner is not limited in the embodiments of the present application. The "saving" can mean saving in one or more memories. The one or more memories can be separately set, or integrated in the encoder or decoder, processor, or electronic device. The one or more memories can be partially separately set and partially integrated in the decoder, processor, or electronic device. The type of the memory can be any form of storage medium, and the embodiments of the present application do not limit this.

[0027] The protocol involved in the embodiments of the present application can refer to a protocol family in the communication field, a standard protocol similar to the protocol family frame structure, or a related protocol applied to a future property setting digital twin modeling and intelligent operation and maintenance platform system, and the embodiments of the present application do not make specific limitations.

[0028] In the embodiments of the present application, the descriptions such as “when”, “in the case of”, “if” and “whether” all refer to that the device will make corresponding processing under certain objective conditions, and are not limited by time, and also do not require the device to have a judgment action when implemented, and also do not mean that there are other limitations.

[0029] In the description of the embodiments of the present application, unless otherwise specified, “ / ” represents that the objects before and after the “ / ” are in an “or” relationship, for example, A / B can represent A or B; “and / or” in the embodiments of the present application is only a description of the association relationship of the associated objects, and represents that there can be three kinds of relationships, for example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone, wherein A and B can be singular or plural. In addition, in the description of the embodiments of the present application, unless otherwise specified, “multiple” refers to two or more than two. “At least one of the following” or the like refers to any combination of these items, including any combination of single item or multiple items. For example, at least one of a, b or c can represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, and c can be single or multiple. In addition, in order to clearly describe the technical solutions of the embodiments of the present application, in the embodiments of the present application, the same items or similar items with basically the same functions and effects are distinguished by using “first”, “second” and the like. Those skilled in the art can understand that “first”, “second” and the like do not limit the quantity and execution order, and “first”, “second” and the like do not necessarily mean different. At the same time, in the embodiments of the present application, “exemplary” or “for example” is used to represent as an example, illustration or description. Any embodiment or design scheme described as “exemplary” or “for example” in the embodiments of the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the use of “exemplary” or “for example” is intended to present the relevant concept in a specific manner, for understanding.

[0030] The network architecture and business scenarios described in the embodiments of the present application are used to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art can know that, with the evolution of network architecture and the appearance of new business scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0031] For the convenience of understanding the embodiments of the present application, first, the control system shown in Figure 1 The communication system suitable for the embodiments of the present application is described in detail taking the control system shown in the

[0032] Figure 1 The architecture diagram of a control system suitable for the property digital twin modeling and intelligent operation and maintenance management platform provided by the embodiments of the present application is shown in Figure 1 As shown in the figure, the control system comprises a management terminal and M Internet of Things sensors, and M is an integer greater than 2.

[0033] The management terminal can be a terminal device with transceiving and processing functions, or a chip or chip system that can be provided in the terminal device. The terminal device can also be referred to as user equipment (UE), access terminal device, subscriber unit, user station, mobile station (MS), mobile station, remote station, remote terminal device, mobile device, user terminal device, terminal device, wireless communication device, user agent, or user apparatus. The terminal device in the embodiments of the present application can be a mobile phone, a cellular phone, a smartphone, a tablet computer (Pad), a wireless data card, a personal digital assistant (PDA), a wireless modem, a handset, a laptop computer, a machine type communication (MTC) terminal device, a computer with wireless transceiving function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device in industrial control, a wireless terminal device in self-driving, a wireless terminal device in remote medical treatment, a wireless terminal device in smart grid, a wireless terminal device in transportation safety, a wireless terminal device in smart city, a wireless terminal device in smart home, a vehicle-mounted terminal device, a roadside unit (RSU) with terminal device function, etc. The terminal device in the present application can also be a vehicle-mounted module, a vehicle-mounted module, a vehicle-mounted component, a vehicle-mounted chip, or a vehicle-mounted unit built into a vehicle as one or more components or units. Or the terminal device can also be customer-premises equipment (CPE).

[0034] The Internet of Things sensor can be a smart sensor in communication connection with the management terminal, and the Internet of Things sensor represents building information model data representing static structures of the property facility and represents dynamic operation of the facility.

[0035] Figure 2 The flowchart of the method provided by the embodiments of the present application is shown. The property facility digital twin modeling and intelligent operation and maintenance management platform can be applied to the above-mentioned management terminal. The specific process is as follows: The management terminal acquires M building information model data representing static structures of property facility and Internet of Things sensor real-time monitoring data representing dynamic operation of the facility; The management terminal constructs a multi-level digital twin of the property facility containing a hierarchical structure of equipment level, functional area level and building level based on the building information model data and the Internet of Things sensor real-time monitoring data through a hierarchical fusion modeling method; The management terminal renders and displays the multi-level digital twin of the property facility according to the operation state of the facility in the multi-level digital twin by using a dynamic multi-scale visualization mechanism, evaluates and predicts the health state of the facility by a hybrid driven prediction model coupled by a physical mechanism model and a machine learning model based on the real-time monitoring data of the Internet of Things sensor and historical data in the multi-level digital twin, and generates a facility health state prediction result; The management terminal dynamically adjusts the hybrid driven prediction model through an adaptive health management mechanism according to the facility health state prediction result, and outputs an operation and maintenance decision suggestion.

[0036] Further, the multi-level digital twin of the property facility constructed by the hierarchical fusion modeling method specifically includes: The building information model data is structurally analyzed to extract equipment level components, functional area level components and building level components; The real-time monitoring data of the Internet of Things sensor is associated with the equipment level components, functional area level components and building level components according to spatial position and functional attribute to generate a mapping relationship between components and data; According to the mapping relationship, equipment level digital twin sub-models, functional area level digital twin sub-models and building level digital twin sub-models fused with real-time data are constructed layer by layer; The equipment level, functional area level and building level digital twin sub-models are integrated to generate the multi-level digital twin.

[0037] Further, the multi-level digital twin of the property facility constructed by the hierarchical fusion modeling method specifically includes: The operation state data in the multi-level digital twin is monitored in real time and compared with a rule set defining a normal working condition range to generate a model rendering mode instruction; If the model rendering mode instruction is a normal working condition, a lightweight simplified model is called for visualization rendering; If the model rendering mode instruction is an abnormal working condition or a key maintenance scene, a high-precision detailed model is automatically switched to for visualization rendering, and the operation state data is superimposed and displayed by the high-precision detailed model.

[0038] Further, the evaluation and prediction of the facility health state through the hybrid driving prediction model specifically includes: constructing a physical mechanism model based on the physical degradation law of the facility and environmental impact factors; training a machine learning prediction model for compensating the prediction deviation of the physical mechanism model using historical data in the multi-level digital twin; weighting and coupling the physical mechanism model and the machine learning prediction model to form the hybrid driving prediction model; driving the hybrid driving prediction model to perform operation using real-time monitoring data of the Internet of Things sensors, and outputting the facility health state prediction result.

[0039] Further, before the evaluation and prediction of the facility health state through the hybrid driving prediction model, it further includes: performing edge computing preprocessing on the collected original monitoring data at the Internet of Things sensor end, and extracting key feature data capable of representing the core operation state of the facility; uploading the key feature data to the cloud end; updating the real-time state in the multi-level digital twin using the key feature data, and providing input for the hybrid driving prediction model.

[0040] Further, the dynamic adjustment of the hybrid driving prediction model through the adaptive health management mechanism specifically includes: comparing the facility health state prediction result with subsequent real-time monitoring data of the Internet of Things sensors to generate a prediction error vector; dynamically adjusting the weight coefficients of the physical mechanism model and the machine learning model in the hybrid driving prediction model according to the size and trend of the prediction error vector, and generating a weight adjustment result; applying the weight adjustment result to subsequent prediction cycles to optimize the accuracy of the facility health state prediction result.

[0041] Further, the output of the operation and maintenance decision suggestion specifically includes: comparing the facility health state prediction result with the warning threshold and the critical range to generate a state level judgment; generating a warning signal and a maintenance suggestion according to the state level judgment if it is lower than the warning threshold; generating a monitoring frequency adjustment scheme if it is in the critical range according to the state level judgment, and generating a continuous operation suggestion if it is higher than the critical range.

[0042] Further, after the output of the operation and maintenance decision suggestion, it further includes: automatically generating a maintenance work order including fault location and maintenance guidance according to the maintenance suggestion; receiving and recording maintenance execution results corresponding to the maintenance work order; feeding back the maintenance execution results to the multi-level digital twin for expanding its historical data set and calibrating parameters of the hybrid-driven prediction model.

[0043] Further, the multi-level digital twin also supports on-demand local loading and visualization, specifically including: receiving user interaction instructions specifying target ranges or target devices; extracting corresponding sub-model data from the multi-level digital twin according to the user interaction instructions; only loading and rendering the sub-model data to achieve fast response to user interaction instructions.

[0044] The above Figure 2 The method provided by the embodiment of the application is described in detail. The device for executing the method provided by the embodiment of the application is described in detail below, and the device includes a management terminal, and the device is configured to: a data acquisition module configured to acquire building information model data and Internet of Things sensor real-time monitoring data; a modeling module configured to construct a multi-level digital twin based on the data acquired by the data acquisition module; a visualization module configured to dynamically switch the fineness of a rendered model according to the running state of the multi-level digital twin; a prediction module configured to generate a facility health state prediction result by combining real-time data and historical data through a hybrid-driven prediction model; an adjustment and decision module configured to adaptively adjust the prediction module according to the facility health state prediction result and output an operation and maintenance decision suggestion.

[0045] Figure 3 A structural schematic diagram of an electronic device provided by the embodiment of the application is shown. Exemplarily, the electronic device can be a network device, or a chip (system) or other components or assemblies that can be arranged in the network device. As shown in the figure, Figure 3 The electronic device 400 can include a processor 401. Optionally, the electronic device 400 can also include a memory 402 and / or a transceiver 403. The processor 401 is coupled with the memory 402 and the transceiver 403, for example, through a communication bus.

[0046] The above Figure 3 The various constituent components of the electronic device 400 are described in detail as follows: The processor 401 is the control center of the electronic device 400, which can be a processor or a combination of multiple processing elements. For example, the processor 401 is one or more central processing units (CPUs), which can also be application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement embodiments of the present application, such as one or more microprocessors (digital signal processors (DSPs)), or field programmable gate arrays (FPGAs).

[0047] Optionally, the processor 401 can execute various functions of the electronic device 400 by running or executing software programs stored in the memory 402 and calling data stored in the memory 402, such as executing the above-mentioned Figure 2 The property digital twin modeling and intelligent operation and maintenance management platform shown.

[0048] In a specific implementation, as an embodiment, the processor 401 can include one or more CPUs, such as Figure 3 The CPU0 and CPU1 shown in the above.

[0049] In a specific implementation, as an embodiment, the electronic device 400 can also include multiple processors. Each of these processors can be a single-CPU or a multi-CPU. Here, the processor can refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0050] The memory 402 is used to store software programs for executing the schemes of the present application, and is controlled by the processor 401 to execute, and the specific implementation can refer to the above-mentioned method embodiments, which will not be repeated here.

[0051] Optionally, the memory 402 can be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disk storage, a magnetic disk storage or other magnetic storage devices, or any other medium capable of storing desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to this. The memory 402 can be integrated with the processor 401 or exist independently and be coupled to the processor 401 through the interface circuit (not shown in the figure) of the electronic device 400, and the embodiments of the present application do not make specific limitations hereon. Figure 3

[0052] The transceiver 403 is configured to communicate with other electronic devices. For example, when the electronic device 400 is a terminal, the transceiver 403 can be configured to communicate with a network device or another terminal device. For another example, when the electronic device 400 is a network device, the transceiver 403 can be configured to communicate with a terminal or another network device.

[0053] Optionally, the transceiver 403 can include a receiver and a transmitter (not shown in the figure separately). The receiver is configured to implement the receiving function, and the transmitter is configured to implement the transmitting function. Figure 3

[0054] Optionally, the transceiver 403 can be integrated with the processor 401 or exist independently and be coupled to the processor 401 through the interface circuit (not shown in the figure) of the electronic device 400, and the embodiments of the present application do not make specific limitations hereon. Figure 3

[0055] It can be understood that the structure of the electronic device 400 shown in the figure does not constitute a limitation on the electronic device, and the actual electronic device can include more or fewer components than those shown in the figure, or combine certain components, or different component arrangements. Figure 3

[0056] In addition, the technical effects of the electronic device 400 can refer to the technical effects of the property digital twin modeling and intelligent operation and maintenance management platform described in the above method embodiments, which will not be described here again. ​​​​

[0057] It should be appreciated that a processor in the embodiments of the present application can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) or other programmable logic devices, discrete gates or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can be any conventional processor.

[0058] It should also be understood that the memory in the embodiments of the present application can be a volatile memory or a nonvolatile memory, or can include both volatile and nonvolatile memory. The nonvolatile memory can be a read-only memory (ROM), programmable ROM (PROM), erasable PROM (EPROM), electrically EPROM (EEPROM) or flash memory. The volatile memory can be a random access memory (RAM) used as external cache memory. By way of example, and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM) and direct Rambus RAM (DRRAM).

[0059] The above-described embodiments can be implemented in whole or in part by software, hardware (e.g., circuitry), firmware, or any combination thereof. When implemented in software, the above-described embodiments can be implemented in the form of a computer program product. The computer program product includes or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the above-described processes or functions are generated in whole or in part according to the embodiments of the present application. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from a website, a computer, a server, or a data center to another website, computer, server, or data center through a wired (e.g., infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server, data center, etc. containing or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0060] It should be understood that the term "and / or" herein merely describes an association relationship of associated objects, which means that there can be three relationships, for example, A and / or B can mean that A exists alone, A and B exist together, and B exists alone, where A and B can be singular or plural. In addition, the character " / " herein generally represents an "or" relationship between the front and rear associated objects, but can also represent an "and / or" relationship, which can be understood in the context before and after.

[0061] In the present application, "at least" means one or more, and "more" means two or more. "At least one of the following" or the like means any combination of the items, including any combination of single item or multiple items. For example, at least one of a, b, or c can mean a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, and c can be single or multiple.

[0062] It should be understood that in various embodiments of the present application, the size of the sequence number of the above-described processes does not mean the order of execution, and the execution order of the processes should be determined by their functions and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0063] Those skilled in the art can clearly understand that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0064] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

[0065] In several embodiments provided by the present application, it should be understood that the disclosed system, device and method can be implemented by other ways. For example, the above-described device embodiments are merely schematic, for example, the division of the units is merely a logical function division, and actual implementation can have another division manner, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0066] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place or distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0067] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can be physically present alone, or two or more units can be integrated into a unit.

[0068] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the present application that essentially contribute to the prior art or the parts of the technical solutions can be embodied in the form of software products. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The aforementioned storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0069] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A property facility digital twin modeling and intelligent operation and maintenance management platform, characterized in that, The management platform is applied to a management terminal, and the management terminal modeling and intelligent operation and maintenance management of the property facilities specifically include: The management terminal acquires M building information model data representing static structures of the property facilities and Internet of Things sensor real-time monitoring data representing dynamic operation of the facilities; The management terminal constructs, based on the building information model data and the Internet of Things sensor real-time monitoring data, a multi-level digital twin of the property facilities through a hierarchical fusion modeling method, and the multi-level digital twin includes a device level, a functional area level, and a building level hierarchical structure; The management terminal renders and displays the multi-level digital twin according to an operation state of the facilities in the multi-level digital twin by using a dynamic multi-scale visualization mechanism, and the management terminal combines the Internet of Things sensor real-time monitoring data and historical data in the multi-level digital twin, evaluates and predicts a health state of the facilities by using a hybrid driving prediction model coupled by a physical mechanism model and a machine learning model, and generates a facility health state prediction result; The management terminal dynamically adjusts the hybrid driving prediction model by using an adaptive health management mechanism according to the facility health state prediction result, and outputs an operation and maintenance decision suggestion. 2.The property facility digital twin modeling and intelligent operation and maintenance management platform of claim 1, wherein, The multi-level digital twin of the property facilities constructed through the hierarchical fusion modeling method specifically includes: Structurally analyzing the building information model data, and extracting device level components, functional area level components, and building level components; Associating the Internet of Things sensor real-time monitoring data according to spatial positions and functional attributes with the device level components, the functional area level components, and the building level components, and generating a mapping relationship between components and data; Constructing, layer by layer, device level digital twin sub-models, functional area level digital twin sub-models, and building level digital twin sub-models fused with real-time data according to the mapping relationship; Integrating the device level digital twin sub-models, the functional area level digital twin sub-models, and the building level digital twin sub-models, and generating the multi-level digital twin. 3.The real property digital twin modeling and intelligent operation management platform according to claim 2, characterized in that, The rendering and display of the multi-level digital twin by using the dynamic multi-scale visualization mechanism specifically includes: Real-time monitoring operation state data in the multi-level digital twin, and comparing the operation state data with a rule set defining a normal working condition range to generate a model rendering mode instruction; If the model rendering mode instruction is a normal working condition, a lightweight simplified model is called to perform visual rendering; If the model rendering mode instruction is an abnormal working condition or a key maintenance scene, a high-precision detailed model is automatically switched to perform visual rendering, and the operation state data is superimposed and displayed by using the high-precision detailed model. 4.The property facility digital twin modeling and intelligent operation and maintenance management platform of claim 1, wherein, The evaluation and prediction of the health state of the facilities by using the hybrid driving prediction model specifically include: Constructing a physical mechanism model based on a facility physical degradation law and environmental impact factors; Training a machine learning prediction model for compensating for a prediction deviation of the physical mechanism model by using historical data in the multi-level digital twin; Coupling the physical mechanism model and the machine learning prediction model by weighting to form the hybrid driving prediction model; and Real-time monitoring data of the Internet of Things sensor drives the operation of the hybrid driving prediction model, and the facility health state prediction result is output. 5.The property facility digital twin modeling and intelligent operation and maintenance management platform according to claim 4, characterized in that, Before the evaluation and prediction of the facility health state by the hybrid driving prediction model, the method further comprises: Edge computing preprocessing of the collected original monitoring data at the Internet of Things sensor end extracts key feature data capable of representing the core operation state of the facility; The key feature data is uploaded to the cloud end; The real-time state in the multi-level digital twin is updated using the key feature data to provide input for the hybrid driving prediction model. 6.The real property digital twin modeling and intelligent operation management platform according to claim 1, wherein, The dynamic adjustment of the hybrid driving prediction model by the adaptive health management mechanism comprises: Comparing the facility health state prediction result with subsequent real-time monitoring data of the Internet of Things sensor to generate a prediction error vector; According to the size and trend of the prediction error vector, the weight coefficients of the physical mechanism model and the machine learning model in the hybrid driving prediction model are dynamically adjusted to generate a weight adjustment result; The weight adjustment result is applied to subsequent prediction cycles to optimize the accuracy of the facility health state prediction result.

7. The property facility digital twin modeling and intelligent operation and maintenance management platform of claim 1, wherein, The output of the operation and maintenance decision suggestion comprises: Comparing the facility health state prediction result with the warning threshold and the critical range to generate a state level judgment; According to the state level judgment, if it is lower than the warning threshold, a warning signal and a maintenance suggestion are generated; According to the state level judgment, if it is in the critical range, a monitoring frequency adjustment scheme is generated, and if it is higher than the critical range, a continuous operation suggestion is generated. 8.The property facility digital twin modeling and intelligent operation and maintenance management platform of claim 7, wherein, After the output of the operation and maintenance decision suggestion, the method further comprises: Automatically generating a maintenance work order containing fault location and repair instructions according to the maintenance suggestion; Receiving and recording the maintenance execution result corresponding to the maintenance work order; The maintenance execution result is fed back to the multi-level digital twin for expanding its historical data set and calibrating the parameters of the hybrid driving prediction model. 9.The property facility digital twin modeling and intelligent operation and maintenance management platform of claim 7, wherein, The multi-level digital twin also supports on-demand local loading and visualization, comprising: Receiving a user interaction instruction specifying a target range or target device; According to the user interaction instruction, the corresponding sub-model data is extracted from the multi-level digital twin; Only the sub-model data is loaded and rendered to achieve fast response to the user interaction instruction.

10. The property facility digital twin modeling and intelligent operation and maintenance management platform of any one of claims 1-9, wherein, The property facility digital twin modeling and intelligent operation and maintenance management device comprises: A data acquisition module for acquiring building information model data and real-time monitoring data of Internet of Things sensors; A modeling module for constructing a multi-level digital twin based on the data acquired by the data acquisition module; A visualization module for dynamically switching the rendering model precision according to the operation state of the multi-level digital twin; A prediction module for generating a facility health state prediction result by combining real-time data and historical data through a hybrid driving prediction model; An adjustment and decision module for adaptively adjusting the prediction module according to the facility health state prediction result and outputting operation and maintenance decision suggestions.

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