Building information digital operation and maintenance system based on BIM technology

Abstract

The invention relates to the field of building operation and maintenance, in particular to a building information digital operation and maintenance system based on a BIM (Building Information Modeling) technology, which comprises the following steps: acquiring related data information of a building system by deploying an Internet of Things sensor, associating Internet of Things equipment with a BIM model, establishing a building abnormality diagnosis neural network model, and outputting a building abnormality diagnosis result. Whether the building operation and maintenance process is optimized and regulated or not is judged according to the building abnormity diagnosis result; a building regular maintenance frequency correction coefficient is calculated according to different influence factors related to the building system, and the building regular maintenance frequency is adjusted; obtaining a building use state according to the obtained building workload and environmental conditions, and calculating building preventive maintenance correction parameters so as to adjust building preventive maintenance frequency; and according to the adjusted regular maintenance frequency and preventive maintenance frequency of the building, introducing a building maintenance quality adjustment factor, and calculating a building operation and maintenance stability evaluation reference.

Description

Technical Field

[0001] The present invention relates to the field of building operation and maintenance, and in particular to a building information digital operation and maintenance system based on BIM technology. Background Art

[0002] With the digital transformation of the construction industry, BIM technology is increasingly used in building lifecycle management, and the operation and maintenance management of building facility systems is becoming more and more complex. Traditional operation and maintenance management methods rely on paper documents and manual operations, which have problems such as untimely information updates, inaccurate data, and low management efficiency.

[0003] Existing intelligent technology can be used as an advanced building information management tool to achieve information integration and management throughout the life cycle of a building. However, its application in the operation and maintenance stage is still insufficient: there is no diagnosis and judgment of the actual situation of the building facility system itself, and it is impossible to accurately determine whether there are any abnormal conditions in the building or the degree of abnormality, and the operation and maintenance methods lack pertinence; there is a lack of consideration of multiple influencing factors, resulting in inaccurate adjustment of building operation and maintenance parameters. At the same time, there is no distinction between the frequency of regular maintenance inspections of buildings and the frequency of preventive maintenance inspections of buildings, which greatly reduces the effectiveness of operation and maintenance, and cannot accurately prevent potential problems and reduce operation and maintenance costs, resulting in a certain degree of resource waste; there is a lack of hierarchical early warning settings for building facility systems, and it is impossible to further ensure the stability and reliability of the building system.

[0004] Therefore, a digital operation and maintenance system of building information based on BIM technology is needed. Summary of the invention

[0005] The digital operation and maintenance system for building information based on BIM technology provided by the present invention aims to diagnose and judge the actual situation of the building facility system itself, accurately capture the abnormal situation or degree of abnormality of the building system, and provide more accurate information for operation and maintenance personnel; the building operation and maintenance system based on BIM technology can comprehensively consider multiple influencing factors to more accurately adjust the building operation and maintenance parameters; by distinguishing the frequency of regular maintenance inspections of buildings and the frequency of preventive maintenance inspections of buildings, the digital operation and maintenance system for building information improves the effectiveness of operation and maintenance effects, realizes accurate prevention of potential problems and reduces operation and maintenance costs, and avoids waste of resources; a hierarchical early warning mechanism is established to further ensure the stability and reliability of the building system, and help operation and maintenance personnel make timely processing and decisions based on prior early warning to reduce potential risks.

[0006] The technical solution of the present invention is specifically as follows: The digital operation and maintenance system of building information based on BIM technology includes the following contents: Building related information collection module, building abnormality diagnosis module, first-level early warning module, building regular maintenance frequency adjustment module, building preventive maintenance frequency adjustment module, building operation and maintenance stability early warning module, and second-level early warning module; Building related information collection module, which collects various building related data information by deploying IoT sensors; The building anomaly diagnosis module uses the collected data to establish a building anomaly diagnosis neural network model to analyze the operation of the building system and detect and diagnose the abnormal state of the building system; The first-level early warning module compares the output of the building anomaly diagnosis neural network model with the building safety standards and immediately issues an alarm if the building safety standards are not met; The building regular maintenance frequency adjustment module calculates the building regular maintenance frequency correction coefficient according to various influencing factors related to the building system, and adjusts the building regular maintenance frequency; The building preventive maintenance frequency adjustment module obtains the building usage status according to the acquired building workload and environmental conditions, calculates the building preventive maintenance correction parameters, and adjusts the building preventive maintenance frequency; The building operation and maintenance stability evaluation module calculates the building operation and maintenance stability evaluation benchmark based on the adjustment results of the regular maintenance and preventive maintenance frequencies; The secondary early warning module provides early warning information based on the building operation and maintenance stability evaluation benchmark to indicate possible problems; The building periodic maintenance frequency adjustment module includes a first-layer adjustment unit and a second-layer adjustment unit; The first-level adjustment unit compares the building periodic maintenance frequency correction coefficient with the preset building periodic maintenance frequency correction coefficient, and performs the first-level adjustment on the building periodic maintenance frequency according to the comparison result; The second-level adjustment unit determines whether to make a second-level adjustment to the adjusted building regular maintenance frequency by calculating the building workload and the expected building workload.

[0007] The digital operation and maintenance method of building information based on BIM technology includes the following steps: Step S1. Obtain relevant data information of the building system by deploying IoT sensors, associate IoT devices with BIM models, establish a building abnormality diagnosis neural network model, output building abnormality diagnosis results, and determine whether to optimize and regulate the building operation and maintenance process based on the building abnormality diagnosis results; Step S2. Calculate the building regular maintenance frequency correction coefficient according to different influencing factors related to the building system, and adjust the building regular maintenance frequency; Step S3. Obtain the building usage status according to the acquired building workload and environmental conditions, calculate the building preventive maintenance correction parameters, and adjust the building preventive maintenance frequency; Step S4. According to the adjusted building regular maintenance inspection frequency and building preventive maintenance inspection frequency, introduce the building maintenance quality adjustment factor and calculate the building operation and maintenance stability evaluation benchmark.

[0008] Further, step S1 specifically includes: The building anomaly diagnosis neural network model is established based on deep learning, including an input gate, a forget gate, a reset gate, and an output gate; the output result of the building anomaly diagnosis neural network model is converted into Compared with the building safety standards, if the output results of the building abnormality diagnosis neural network model do not meet the building safety standards, an alarm will be issued immediately; if it is still within the building safety standard range, it is necessary to optimize and control the building operation and maintenance process.

[0009] Further, step S1 specifically includes: Combined with the output of the neural network model for building anomaly diagnosis , set the building operation emergency level, which includes level one emergency, level two emergency, and level three emergency; define the regular maintenance frequency of the building as level one according to the emergency category and level three emergency according to the emergency category. .

[0010] Further, step S2 calculates the building regular maintenance frequency correction coefficient At the same time, a nonlinear function is introduced. The specific process is as follows: ; in, Indicates the number of influencing factors; Indicates The weight of the influencing factors; Indicates The standardized value of each influencing factor; Indicates The influencing factors and The weight of the interaction terms between the factors; represents the weight coefficient of the exponential function; Indicates the exponential function The weight coefficient of each influencing factor; Represents the weight coefficient of the logarithmic function; Represents the logarithmic function The weight coefficient of the influencing factors.

[0011] Further, step S2 specifically includes: Correction factor for building regular maintenance frequency The frequency of regular building maintenance is compared with the preset correction coefficient, and the first adjustment of the regular building maintenance frequency is made according to the comparison result; then the building workload is calculated. and expected building workload The calculation result determines whether to make a second-level adjustment to the adjusted building regular maintenance frequency.

[0012] Further, step S2 specifically includes: According to the building workload and expected building workload , and the building workload deviation rate is , At the same time, set is the judgment threshold; if , then the second level adjustment is performed, and the regular maintenance frequency of the second building is defined as , ,in, represents the adjustment factor; if , then there is no need to make the second-level adjustment, that is, define the regular maintenance frequency of the second building as , .

[0013] Further, in step S3, the building usage status parameter is calculated , the specific process is as follows: ; in, It indicates the recommended safe service life of a building given by experts and engineers based on the current condition of the building; represents the building operation and maintenance cost; represents the average cost of building operation and maintenance; represents the building workload weight factor; Indicates that the building is Time workload; Indicates the maximum workload that a building can withstand; Indicated in The ambient humidity obtained at the time Indicates the maximum ambient humidity during the monitoring period; represents the weight coefficient of environmental conditions; Indicates average humidity; represents the state stability factor; represents the constant adjustment coefficient; Represents the environmental adaptability index.

[0014] Further, in step S3, the parameters are corrected according to the building preventive maintenance , adjust the frequency of building preventive maintenance inspection, set the first preset building preventive maintenance inspection correction parameter comparison parameter Compare the parameters with the second preset building preventive maintenance correction parameters ,and ; Correction parameters for preventive maintenance of buildings Respectively and In comparison, the frequency of preventive maintenance of basic buildings is , the specific process is as follows: if , then adjust the frequency of preventive maintenance of buildings to , that is, the frequency of preventive maintenance of the first building ; if , then adjust the frequency of preventive maintenance of buildings to , that is, the frequency of preventive maintenance of the first building ; if , then adjust the frequency of preventive maintenance of buildings to , that is, the frequency of preventive maintenance of the first building .

[0015] Further, step S4 calculates the building operation and maintenance stability evaluation benchmark , the specific process is as follows: ; in, , They represent the mapping values ​​of the regular building maintenance frequency and the preventive building maintenance frequency respectively; , They represent the dynamic weight coefficients of the frequency of regular building maintenance and the frequency of preventive building maintenance respectively; Represents the building maintenance quality adjustment factor.

[0016] Beneficial effects: 1. The present invention establishes a neural network model for building abnormality diagnosis, analyzes and processes a large amount of building data, and determines whether to formulate a corresponding maintenance plan based on the abnormality diagnosis results, and provides support for the subsequent optimization of the building operation and maintenance process, thereby realizing intelligent operation and maintenance decision-making; timely adjusts the building system parameters based on the building abnormality diagnosis results, optimizes energy utilization and the working status of the building system. The building operation urgency level is associated with the building maintenance inspection frequency, so as to realize precise operation and maintenance scheduling according to the actual situation of the building; at the same time, combined with real-time monitoring and abnormality diagnosis, it helps managers to discover problems in advance and deal with them effectively, thereby reducing maintenance costs and improving the safety of buildings.

[0017] 2. The present invention calculates the correction coefficient of the regular maintenance frequency of buildings based on comprehensive consideration of multiple factors and different characteristics and needs of different buildings, so that the maintenance plan is more in line with the actual situation, the building maintenance needs are evaluated more accurately, blind maintenance or ignoring potential problems is avoided, and the accuracy of operation and maintenance is improved; the optimized regular maintenance frequency of buildings can help to timely discover building system problems and perform maintenance, thereby extending the service life; considering environmental factors to calculate the correction coefficient of the regular maintenance frequency of buildings can promote the improvement of building energy efficiency, reduce energy waste, and reduce energy consumption, thereby achieving the purpose of energy conservation and environmental protection. By comparing with the preset correction coefficient of the regular maintenance frequency of buildings, the accuracy of the current regular maintenance frequency of buildings can be evaluated, and the first level of adjustment can be made to ensure that the regular maintenance frequency of buildings is consistent with the actual situation, thereby improving the accuracy and effectiveness of maintenance; according to the comparison results of the building workload and the expected building workload, it is judged whether the current maintenance frequency is suitable for the actual operating status of the building, and the second level of adjustment is made to optimize the maintenance plan and improve the efficiency of building operation and maintenance.

[0018] 3. The present invention calculates the building preventive maintenance correction parameters according to the actual use status of the building, can detect potential problems in advance and perform preventive maintenance, reduce the risk of equipment failure, and ensure the stability and reliability of the building system; optimizes the building preventive maintenance frequency according to the building preventive maintenance correction parameters, and can maintain the good state of the building system. Comparison is made based on the preset comparison parameters and the actual building preventive maintenance correction parameters, which improves the accuracy of building operation and maintenance, ensures the accuracy of the building preventive maintenance frequency, and accurately adjusts the building preventive maintenance frequency to improve the performance and operation efficiency of the building.

[0019] 4. The present invention can comprehensively evaluate the performance of building operation and maintenance by calculating the building operation and maintenance stability early warning benchmark, taking into account the adjustment results of the building's regular maintenance and building preventive maintenance frequencies; the building operation and maintenance stability early warning benchmark, as a monitoring standard, can warn of potential problems and risks, and take necessary maintenance measures in a timely manner to ensure the stability and reliability of the building system; the building operation and maintenance stability early warning benchmark provides an important reference for operation and maintenance decisions, helping managers or relevant departments to more accurately formulate maintenance plans and adjust operation and maintenance strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a structural diagram of the building information digital operation and maintenance system based on BIM technology of the present invention; Figure 2 It is a flow chart of the digital operation and maintenance method of building information based on BIM technology of the present invention; Figure 3 It is a schematic diagram of a building regular maintenance frequency adjustment module of the present invention. DETAILED DESCRIPTION

[0021] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. It should also be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0022] Refer to the attached Figure 1 This embodiment provides a building information digital operation and maintenance system based on BIM technology, including the following contents: Building related information collection module, building abnormality diagnosis module, first-level early warning module, building regular maintenance frequency adjustment module, building preventive maintenance frequency adjustment module, building operation and maintenance stability early warning module, and second-level early warning module; The building-related information collection module collects various building data, including building structure, equipment information, energy consumption data, environmental conditions, etc., to provide a basis for subsequent operation and maintenance decisions; The building abnormality diagnosis module uses the collected data to establish a building abnormality diagnosis neural network model to analyze the operation of the building system, detect and diagnose the abnormal state of the building system, and help operation and maintenance personnel to find problems in time and make corresponding treatments; The first-level warning module converts the output of the building anomaly diagnosis neural network model into Compare with building safety standards and issue an immediate alarm if they do not meet building safety standards; The building regular maintenance inspection frequency adjustment module calculates the building regular maintenance inspection frequency correction coefficient according to various influencing factors related to the building system, adjusts the building regular maintenance inspection frequency, and ensures that the maintenance plan is more in line with the actual situation; The building preventive maintenance frequency adjustment module obtains the building usage status according to the acquired building workload and environmental conditions, calculates the building preventive maintenance correction parameters, and adjusts the building preventive maintenance frequency; The building operation and maintenance stability evaluation module calculates the building operation and maintenance stability evaluation benchmark based on the adjustment results of regular maintenance and preventive maintenance frequencies, evaluates the effectiveness of building operation and maintenance, and promptly identifies potential problems and takes measures; The secondary warning module, based on the building operation and maintenance stability evaluation benchmark, provides further warning information, indicates possible problems, and helps operation and maintenance personnel respond quickly to avoid accidents.

[0023] Refer to the attached Figure 3 ,The building regular maintenance inspection frequency adjustment module includes a first layer adjustment unit and a second layer adjustment unit; The first-level adjustment unit compares the building periodic maintenance frequency correction coefficient with the preset building periodic maintenance frequency correction coefficient, and performs the first-level adjustment on the building periodic maintenance frequency according to the comparison result; The second-level adjustment unit determines whether to make a second-level adjustment to the adjusted building regular maintenance frequency by calculating the building workload and the expected building workload.

[0024] Refer to the attached Figure 2 This embodiment provides a digital operation and maintenance method for building information based on BIM technology, including the following steps: S1. Deploy IoT sensors to obtain building system-related data information, associate IoT devices with BIM models, establish a building anomaly diagnosis neural network model, output building anomaly diagnosis results, and combine the building anomaly diagnosis results to determine whether to optimize and regulate the building operation and maintenance process.

[0025] Deploy IoT sensors at key locations of buildings to collect data and information related to the building system, including but not limited to temperature, humidity, energy consumption, equipment operating status, etc., and monitor the operating status of the building facility system in real time; associate IoT devices with BIM models to achieve real-time data collection and transmission.

[0026] A building anomaly diagnosis neural network model is established based on deep learning. The training data is divided into a training set and a test set. The training set is the acquired historical data related to building use and abnormal labeling information. The building anomaly diagnosis neural network model is fitted through the training set. The predicted value of the model is calculated for each training sample data, and the loss between the predicted value and the true label is calculated. The gradient calculation is performed through the existing algorithm, and the weight parameters of the building anomaly diagnosis neural network model are updated according to the gradient.

[0027] For any training sample , , Indicates the historical data information related to building use obtained. Represents the number of elements in the historical data information related to building use. The building anomaly diagnosis neural network model includes input gate, forget gate, reset gate, and output gate. The specific training process of the building anomaly diagnosis neural network model is as follows: Will Input into the building anomaly diagnosis neural network model, its initial state is expressed as , ,in, Represents the weight value at the initial state; Indicates the bias at the initial state.

[0028] The input gate determines which new sensor data needs to be included in the building anomaly diagnosis neural network model analysis, and uses the sigmoid function to determine which new data is important and control the impact of new information on the cell state. The specific process is as follows:

[0029] in, Indicated in The output of the input gate; Represents the weight value of the update gate; represents the learning factor; Indicated in Status at the time; Indicates the current state; represents the time response interval; Represents the bias vector corresponding to the input gate; Represents cell control parameters.

[0030] The forget gate determines which historical information is no longer relevant and can be forgotten. The sigmoid function is used to determine which historical data needs to be forgotten, thereby freeing up space for the cell state. The specific process is as follows:

[0031] in, Indicated in The output of the forget gate; Represents the weight value of the forget gate; represents the cell selection judgment coefficient; express Status at the time; Represents the bias of the forget gate; Indicated in The weight value of the forget gate at the moment; Represents the adjustment coefficient.

[0032] The reset gate uses the sigmoid function to determine whether the cell state needs to be reset, so as to better capture the current changes and reflect the latest abnormal conditions. The specific process is as follows:

[0033] in, Indicated in Reset the gate output when represents the enhancement coefficient; express When the forget gate output; Indicates resetting the weight value of the gate; represents the bias of the reset gate; Indicated in Reset gate weight value at time.

[0034] The output gate passes key information to the next layer or is used for final decision-making, determining which information needs to be output for building anomaly diagnosis. The specific process is as follows: ; in, Represents the output result of the output gate; Indicates a time gap; Represents the weight value of the output result, Indicates the bias of the output result. In the embodiment of the present invention, Represents the output of the building anomaly diagnosis neural network model.

[0035] In the first-level warning module, the output of the building anomaly diagnosis neural network model is Compared with the building safety standards (wherein the building safety standards include national building standards, international building safety standards, industry standards, etc., which are matched with the building safety standards of the embodiment of the present invention according to the actual situation and combined with expert opinions; hereinafter collectively referred to as "building safety standards"). Specifically, if the output result of the building anomaly diagnosis neural network model If it does not meet the building safety standards, an alarm will be issued immediately and the relevant management department will come forward to solve the problem; if it is still within the building safety standard range, it is necessary to further optimize and control the building operation and maintenance process, as shown below: In the embodiment of the present invention, combined with the output results of the building abnormality diagnosis neural network model , set the building operation emergency level, which includes level 1 emergency, level 2 emergency, and level 3 emergency; define the frequency of regular building maintenance and frequency of preventive building maintenance The frequency of regular maintenance of basic buildings is The frequency of regular building maintenance refers to the operation and maintenance activities carried out according to a fixed plan, focusing on maintaining and ensuring the normal operation of the building system; the frequency of preventive maintenance of basic buildings is , building preventive maintenance frequency refers to the maintenance work carried out to prevent building system failures. The purpose is to discover and repair potential problems in advance through appropriate preventive maintenance frequency; building regular maintenance frequency and building preventive maintenance frequency have different roles and purposes in building digital operation and maintenance management; specifically: When the building operation emergency level is level one, it indicates that the building has a high safety risk or important equipment failure, which needs to be solved in time to avoid further deterioration; the first building regular maintenance frequency is set to , , Indicates the adjustment factor of the regular maintenance frequency of the first-level emergency building; When the building operation emergency level is level 2, it indicates that there are some problems or abnormalities in the building, and more attention needs to be paid to avoid the expansion of the problem; the regular maintenance frequency of the first building is set to , , Indicates the adjustment factor of the regular maintenance frequency of secondary emergency buildings; When the building operation emergency level is level 3 emergency, it indicates that there are some minor problems or abnormalities in the building, which will not have a significant impact on the safety and normal operation of the building; the regular maintenance frequency of the first building is set to , , It indicates the adjustment factor of the regular maintenance frequency of the third-level emergency building; in, , , ; and for actual operation and maintenance work, if the building operation and maintenance resources are limited, it can be appropriately reduced to balance resource allocation.

[0036] The present invention establishes a neural network model for building abnormality diagnosis, analyzes and processes a large amount of building data, and determines whether to formulate a corresponding maintenance plan based on the abnormality diagnosis results, and provides support for the subsequent optimization of the building operation and maintenance process, thereby realizing intelligent operation and maintenance decision-making; timely adjusts the building system parameters based on the building abnormality diagnosis results, optimizes energy utilization and the working status of the building system. The building operation urgency level is associated with the building maintenance inspection frequency, and accurate operation and maintenance scheduling is realized according to the actual situation of the building; at the same time, combined with real-time monitoring and abnormality diagnosis, it helps managers to discover problems in advance and deal with them effectively, thereby reducing maintenance costs and improving the safety of buildings.

[0037] S2. Based on various influencing factors related to the building system, including the importance of the building equipment system, the historical failure rate of the building, environmental conditions, the frequency of use of the building equipment system, operation and maintenance resources, etc., calculate the correction coefficient for the regular maintenance frequency of the building and adjust the regular maintenance frequency of the building.

[0038] Correction factor for building regular maintenance frequency The frequency of regular building maintenance is compared with the preset correction coefficient, and the first adjustment of the regular building maintenance frequency is made according to the comparison result; then the building workload is calculated. and expected building workload The calculation result determines whether to make a second-level adjustment to the adjusted building regular maintenance frequency.

[0039] Calculate the correction factor of the building's regular maintenance frequency based on the building's relevant data information In the embodiment of the present invention, the correction coefficient of the building regular maintenance frequency is calculated according to factors such as the importance of the building equipment system, the historical failure rate of the building, environmental conditions, the frequency of use of the building equipment system, and operation and maintenance resources, and a nonlinear function is introduced to enhance the complexity and flexibility of the calculation process; the specific process is as follows: ; in, Indicates the number of influencing factors; Indicates The weight of the influencing factors; Indicates The standardized value of each influencing factor; Indicates The influencing factors and The weight of the interaction terms between the factors; represents the weight coefficient of the exponential function; Indicates the exponential function The weight coefficient of each influencing factor; Represents the weight coefficient of the logarithmic function; Represents the logarithmic function The weight coefficient of each influencing factor; Used to enhance the significant impact of certain factors; Used to smooth the effects of extreme values; It represents the interaction between various influencing factors and is used to capture the nonlinear relationship between factors.

[0040] Specifically, when the influencing factors are the importance of the building equipment system, the historical failure rate of the building, environmental conditions, the frequency of use of the building equipment system (involving the frequency of use of various equipment, systems or facilities inside the building, including but not limited to elevators, air conditioning systems, lighting systems, water supply systems, fire protection systems, etc. within a certain period of time), and operation and maintenance resources, the process is as follows: The standardized importance value of building equipment system is , It represents the importance score of the building equipment system during the historical record period, usually set based on expert opinions or experience. indicates full marks; the standardized value of the building's historical failure rate is , Indicates the number of faults within the historical record time. Indicates the highest number of historical faults; the standardized value under environmental conditions is , Indicates the environmental condition score, Indicates the worst environmental rating, which is based on the evaluation of the responsible department; the standard value of the frequency of use of building equipment systems is , Indicates the frequency of use of building equipment systems during the historical record period. Indicates the maximum frequency of building equipment system use; the normalized value of operation and maintenance resources is , Indicates available operation and maintenance resources. Indicates the total operation and maintenance resources.

[0041] Preset the correction coefficient of the building's regular maintenance frequency. Compare it with the preset building regular maintenance frequency correction coefficient, and then make the first adjustment to the building regular maintenance frequency according to the comparison result. Define the first preset building regular maintenance frequency comparison amount as ; then through the building workload and expected building workload The calculation result determines whether to make a second-level adjustment to the adjusted building regular maintenance frequency. The specific process is as follows: The building workload deviation rate is , At the same time, set To determine the threshold, it is based on historical data and experience; if , then the second level adjustment is performed, and the regular maintenance frequency of the second building is defined as , ,in, Represents the adjustment coefficient, which is used to control the influence of the deviation rate on the maintenance inspection frequency; if , then there is no need to make the second-level adjustment, that is, define the regular maintenance frequency of the second building as , .

[0042] The present invention calculates the correction coefficient of the regular maintenance frequency of buildings based on comprehensive consideration of multiple factors and different characteristics and needs of different buildings, so that the maintenance plan is more in line with the actual situation, the building maintenance needs are evaluated more accurately, blind maintenance or ignoring potential problems is avoided, and the accuracy of operation and maintenance is improved; the optimized regular maintenance frequency of buildings can help to timely discover building system problems and perform maintenance, thereby extending the service life; considering environmental factors to calculate the correction coefficient of the regular maintenance frequency of buildings can promote the improvement of building energy efficiency, reduce energy waste, and reduce energy consumption, thereby achieving the purpose of energy conservation and environmental protection. By comparing with the preset regular maintenance frequency correction coefficient of buildings, the accuracy of the current regular maintenance frequency of buildings can be evaluated, and the first level of adjustment can be made to ensure that the regular maintenance frequency of buildings is consistent with the actual situation, thereby improving the accuracy and effectiveness of maintenance; according to the comparison results of the building workload and the expected building workload, it is judged whether the current maintenance frequency is suitable for the actual operating status of the building, and the second level of adjustment is made to optimize the maintenance plan and improve the efficiency of building operation and maintenance.

[0043] S3. Obtain the building usage status based on the acquired building workload and environmental conditions, and calculate the building preventive maintenance correction parameters , thus increasing the frequency of preventive maintenance of buildings Make adjustments.

[0044] In the embodiment of the present invention, the building usage status parameters are calculated based on the acquired building workload and environmental conditions. , the specific process is as follows: ; in, It indicates the recommended safe service life of a building given by experts and engineers based on the current condition of the building; represents the building operation and maintenance cost; represents the average cost of building operation and maintenance; represents the building workload weight factor; Indicates that the building is Time workload; Indicates the maximum workload that a building can withstand; Indicated in The ambient humidity obtained at the time Indicates the maximum ambient humidity during the monitoring period; represents the weight coefficient of environmental conditions; Indicates average humidity; represents the state stability factor; represents the constant adjustment coefficient; Indicates the environmental adaptability index and calculates the environmental adaptability index , the process is as follows: ;in, Indicates temperature, Relative humidity, , , represents empirical parameters. In the embodiment of the present invention, the corresponding values ​​are usually 0.8, 14.4, and 46.4 respectively.

[0045] Calculate building preventive maintenance correction parameters , the process is as follows: ; in, It represents the correction parameter of the frequency of building preventive maintenance; represents the output of the neural network model for building anomaly diagnosis; Indicates the total recording time; Indicates the normalized value of the building usage status parameter; represents the correction scale factor; represents a constant term, and represents other influencing factors.

[0046] The frequency of preventive maintenance of the basic building has been predefined as , now according to the building preventive maintenance calibration parameters , adjust the frequency of building preventive maintenance inspection, set the first preset building preventive maintenance inspection correction parameter comparison parameter Compare the parameters with the second preset building preventive maintenance correction parameters ,and ; Correction parameters for preventive maintenance of buildings Respectively and For comparison, the specific process is as follows: if , then adjust the frequency of preventive maintenance of buildings to , that is, the frequency of preventive maintenance of the first building ; if , then adjust the frequency of preventive maintenance of buildings to , that is, the frequency of preventive maintenance of the first building ; if , then adjust the frequency of preventive maintenance of buildings to , that is, the frequency of preventive maintenance of the first building .

[0047] The present invention calculates the building preventive maintenance correction parameters according to the actual use status of the building, can detect potential problems in advance and perform preventive maintenance, reduce the risk of equipment failure, and ensure the stability and reliability of the building system; optimizes the building preventive maintenance frequency according to the building preventive maintenance correction parameters, and can maintain the good state of the building system. Comparison is made based on the preset comparison parameters and the actual building preventive maintenance correction parameters, which improves the accuracy of building operation and maintenance, ensures the accuracy of the building preventive maintenance frequency, and accurately adjusts the building preventive maintenance frequency to improve the performance and operation efficiency of the building.

[0048] S4. Based on the adjusted frequency of regular building maintenance Frequency of preventive building maintenance , calculate the building operation and maintenance stability evaluation benchmark , and evaluate the effectiveness of building operation and maintenance.

[0049] In the secondary warning module, calculate the building operation and maintenance stability evaluation benchmark , introduce building maintenance quality adjustment factor , to evaluate the effectiveness of building operation and maintenance, the specific process is as follows: ;

[0050] in, , They represent the mapping values ​​of the regular building maintenance frequency and the preventive building maintenance frequency, respectively. Specifically, , , , They represent the slope parameters, which control the steepness of the function. , They represent the corresponding center point parameters and control function offset respectively; , They represent the dynamic weight coefficients of the regular building maintenance frequency and the preventive building maintenance frequency, , ; It indicates the safe service life of a building recommended by experts and engineers based on the current condition of the building.

[0051] When building operation and maintenance stability evaluation benchmark When the value falls below the set standard threshold, an early warning is triggered and the relevant departments and managers take timely measures.

[0052] The present invention can comprehensively evaluate the performance of building operation and maintenance by calculating the building operation and maintenance stability evaluation benchmark, taking into account the adjustment results of the frequency of regular building maintenance and building preventive maintenance, and comprehensively evaluating the performance of building operation and maintenance; the building operation and maintenance stability evaluation benchmark, as a monitoring standard, can warn of potential problems and risks, and take necessary maintenance measures in time to ensure the stability and reliability of the building system; the building operation and maintenance stability evaluation benchmark provides an important reference for operation and maintenance decision-making, helping managers or relevant departments to formulate maintenance plans and adjust operation and maintenance strategies more accurately.

[0053] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0054] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0055] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0056] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0057] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. The digital operation and maintenance system of building information based on BIM technology is characterized by: Includes the following: Building related information collection module, building abnormality diagnosis module, first-level early warning module, building regular maintenance frequency adjustment module, building preventive maintenance frequency adjustment module, building operation and maintenance stability early warning module, and second-level early warning module; The building related information collection module collects various building related data information by deploying IoT sensors; The building anomaly diagnosis module uses the collected data to establish a building anomaly diagnosis neural network model to analyze the operation of the building system and detect and diagnose the abnormal state of the building system; The first-level warning module compares the output of the building anomaly diagnosis neural network model with the building safety standards, and immediately issues an alarm if the building safety standards are not met; The building regular maintenance inspection frequency adjustment module calculates the building regular maintenance inspection frequency correction coefficient according to various influencing factors related to the building system, and adjusts the building regular maintenance inspection frequency; The building preventive maintenance frequency adjustment module obtains the building usage status according to the acquired building workload and environmental conditions, calculates the building preventive maintenance correction parameters, and adjusts the building preventive maintenance frequency; The building operation and maintenance stability evaluation module calculates the building operation and maintenance stability evaluation benchmark in combination with the adjustment results of the regular maintenance inspection and preventive maintenance inspection frequency; The secondary warning module provides warning information based on the building operation and maintenance stability evaluation benchmark to indicate possible problems; The building periodic maintenance frequency adjustment module includes a first-layer adjustment unit and a second-layer adjustment unit; The first-level adjustment unit compares the building periodic maintenance frequency correction coefficient with a preset building periodic maintenance frequency correction coefficient, and performs a first-level adjustment on the building periodic maintenance frequency according to the comparison result; The second-level adjustment unit determines whether to make a second-level adjustment to the adjusted building periodic maintenance frequency by calculating the building workload and the expected building workload.

2. A digital operation and maintenance method for building information based on BIM technology, applied to a digital operation and maintenance system for building information based on BIM technology as claimed in claim 1, characterized in that: The following steps are involved: Step S1. Obtain relevant data information of the building system by deploying IoT sensors, associate IoT devices with BIM models, establish a building abnormality diagnosis neural network model, output building abnormality diagnosis results, and determine whether to optimize and regulate the building operation and maintenance process based on the building abnormality diagnosis results; Step S2. Calculate the building regular maintenance frequency correction coefficient according to different influencing factors related to the building system, and adjust the building regular maintenance frequency; Step S3. Obtain the building usage status according to the acquired building workload and environmental conditions, calculate the building preventive maintenance correction parameters, and adjust the building preventive maintenance frequency; Step S4. According to the adjusted building regular maintenance inspection frequency and building preventive maintenance inspection frequency, introduce the building maintenance quality adjustment factor and calculate the building operation and maintenance stability evaluation benchmark.

3. The digital operation and maintenance method of building information based on BIM technology according to claim 2 is characterized in that: The step S1 specifically includes: The building anomaly diagnosis neural network model is established based on deep learning, including an input gate, a forget gate, a reset gate, and an output gate; the output result of the building anomaly diagnosis neural network model is converted into Compared with the building safety standards, if the output results of the building abnormality diagnosis neural network model do not meet the building safety standards, an alarm will be issued immediately; if it is still within the building safety standard range, it is necessary to optimize and control the building operation and maintenance process.

4. The digital operation and maintenance method of building information based on BIM technology according to claim 3 is characterized in that: The step S1 specifically includes: Combined with the output of the neural network model for building anomaly diagnosis , set the building operation emergency level, which includes level one emergency, level two emergency, and level three emergency; define the regular maintenance frequency of the building as level one according to the emergency category and level three emergency according to the emergency category. .

5. The digital operation and maintenance method of building information based on BIM technology according to claim 2 is characterized in that: The step S2 calculates the building regular maintenance frequency correction coefficient At the same time, a nonlinear function is introduced. The specific process is as follows: ; in, Indicates the number of influencing factors; Indicates The weight of the influencing factors; Indicates The standardized value of each influencing factor; Indicates The influencing factors and The weight of the interaction terms between the factors; represents the weight coefficient of the exponential function; Indicates the exponential function The weight coefficient of each influencing factor; Represents the weight coefficient of the logarithmic function; Represents the logarithmic function The weight coefficient of the influencing factors.

6. The digital operation and maintenance method of building information based on BIM technology according to claim 5 is characterized in that: The step S2 specifically includes: Correction factor for building regular maintenance frequency The frequency of regular building maintenance is compared with the preset correction coefficient, and the first adjustment of the regular building maintenance frequency is made according to the comparison result; then the building workload is calculated. and expected building workload The calculation result determines whether to make a second-level adjustment to the adjusted building regular maintenance frequency.

7. The digital operation and maintenance method of building information based on BIM technology according to any one of claims 4 or 6, characterized in that: The step S2 specifically includes: According to the building workload and expected building workload , and the building workload deviation rate is , At the same time, set is the judgment threshold; if , then the second level adjustment is performed, and the regular maintenance frequency of the second building is defined as , ,in, represents the adjustment factor; if , then there is no need to make the second-level adjustment, that is, define the regular maintenance frequency of the second building as , .

8. The digital operation and maintenance method of building information based on BIM technology according to claim 2 is characterized in that: In step S3, the building usage status parameters are calculated. , the specific process is as follows: ； in, It indicates the recommended safe service life of a building given by experts and engineers based on the current condition of the building; represents the building operation and maintenance cost; represents the average cost of building operation and maintenance; represents the building workload weight factor; Indicates that the building is Time workload; Indicates the maximum workload that a building can withstand; Indicated in The ambient humidity obtained at the time Indicates the maximum ambient humidity during the monitoring period; represents the weight coefficient of environmental conditions; Indicates average humidity; represents the state stability factor; represents the constant adjustment coefficient; Represents the environmental adaptability index.

9. The digital operation and maintenance method of building information based on BIM technology according to claim 2 is characterized in that: The building preventive maintenance correction parameters calculated in step S3 are , according to the building preventive maintenance calibration parameters , adjust the frequency of building preventive maintenance inspection, set the first preset building preventive maintenance inspection correction parameter comparison parameter Compare the parameters with the second preset building preventive maintenance correction parameters ,and ; Correction parameters for preventive building maintenance Respectively and In comparison, the frequency of preventive maintenance of basic buildings is , the specific process is as follows: if , then adjust the frequency of preventive maintenance of buildings to , that is, the frequency of preventive maintenance of the first building ; if , then adjust the frequency of preventive maintenance of buildings to , that is, the frequency of preventive maintenance of the first building ; if , then adjust the frequency of preventive maintenance of buildings to , that is, the frequency of preventive maintenance of the first building .

10. The digital operation and maintenance method of building information based on BIM technology according to claim 2 is characterized in that: The step S4 calculates the building operation and maintenance stability evaluation benchmark , the specific process is as follows: ; in, , They represent the mapping values ​​of the regular building maintenance frequency and the preventive building maintenance frequency respectively; , They represent the dynamic weight coefficients of the frequency of regular building maintenance and the frequency of preventive building maintenance respectively; Represents the building maintenance quality adjustment factor.

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