Health Monitoring Method for Building Electromechanical Systems Based on Digital Twin Technology

Abstract

The present invention provides a method for health monitoring of building mechanical and electrical systems based on digital twin technology, which relates to the technical field of mechanical and electrical system monitoring. The present invention obtains the replacement time and working data of each vulnerable component, obtains the equivalent life loss rate of each vulnerable component for each task and forms a life loss digital twin model, constructs an overall life score according to the current working data, forms a fatigue coefficient according to system parameters and historical data, obtains a predicted life loss rate and a predicted wear score according to the next operation, and constructs a health status score, and judges the health status in combination with a threshold, reflecting the improvement effect on the health status of the air-conditioning system due to the replacement of vulnerable components, taking into account the different life states of different vulnerable components when performing tasks, and more accurately judging the health status of the air-conditioning system.

Description

Technical Field

[0001] The present invention relates to the technical field of electromechanical system monitoring, and specifically to a method for health monitoring of building electromechanical systems based on digital twin technology. Background Art

[0002] The building electromechanical system is an indispensable part of modern buildings, including multiple subsystems such as power supply, water supply, heating, ventilation and air conditioning (HVAC), and fire protection. The health monitoring methods for different subsystems are also different. Especially for the air conditioning system, it has a complex structure, high working intensity, and the working conditions it faces are ever-changing. During long-term use, the service life of some components will be reduced due to long-term use. For example, the air filter will affect the normal working effect of the air conditioner due to dust and pollutants as the operation of the air conditioning system accumulates, so it needs to be replaced regularly. For example, the fan bearings and belts have limited service lives themselves, and need to be replaced when their service lives reach the requirements. Moreover, different components are replaced at different times, and different new and old components have different impacts on the working ability of the air conditioning system. Therefore, it is necessary to analyze the components replaced at different times in the air conditioner separately to form a more accurate health monitoring method for the air conditioning system.

[0003] In the prior art, the published document with the publication number CN119397887A discloses an online simulation method and system for wear testing of electromechanical products, which collects historical multi-dimensional monitoring data during the operation of electromechanical products from the historical database; establishes an initial wear prediction model for electromechanical products based on Bayesian network and convolutional neural network; trains and tests the initial wear prediction model for electromechanical products through historical multi-dimensional monitoring data to obtain an online wear prediction model for electromechanical products; and collects the multi-dimensional monitoring data of the operation of electromechanical products during the operation of electromechanical products in real time, inputs the multi-dimensional monitoring data of the operation of electromechanical products into the online wear prediction model for electromechanical products, and outputs the wear prediction result of electromechanical products.

[0004] Although the disclosed document realizes the wear monitoring and evaluation of the electromechanical system, it does not consider the change in the health degree caused by replacing vulnerable components in the system, cannot reflect the improvement effect of system overhaul and maintenance on the system, and does not consider the specific impact of actual operation on the system.

[0005] The above information disclosed in the background art section is only used to strengthen the understanding of the background of the present disclosure, so it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0006] The purpose of the present invention is to provide a method for health monitoring of building electromechanical systems based on digital twin technology to solve the problems raised in the above background art.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] The building electromechanical system health monitoring method based on digital twin technology includes the following steps:

[0009] Step 1: Obtain the replacement time of the air conditioning system's vulnerable parts, obtain the air conditioning system's operating data and operating data timestamps. The operating data includes the operating power and operating time of each task. Compare the replacement time of the vulnerable parts with the timestamp of the operating data to obtain a sample set of tasks that each vulnerable part has experienced after each replacement.

[0010] Step 2: Based on the sample set of tasks each vulnerable part undergoes after each replacement, obtain the unit life depreciation rate of each vulnerable part for each replacement. The unit life depreciation rate of each vulnerable part during all replacement processes is taken as the equivalent life depreciation rate of the vulnerable part. The equivalent life depreciation rates of all vulnerable parts are summarized to construct a life depreciation digital twin model.

[0011] Step 3: Use the maintenance log to obtain the last replacement time of each vulnerable component and the work data since the replacement. Input this work data into the life loss digital twin model to obtain the life loss rate of each vulnerable component for each task. This generates a life loss sequence for each vulnerable component. Combining the life loss sequence of each vulnerable component and the number of vulnerable components with high loss, an overall life score is constructed.

[0012] Step 4: Based on the air conditioning system parameters and historical data, filter out tasks with operating power exceeding the rated power and their running duration, and tasks with operating power below 70% of the rated power, obtain the average power of these tasks, and construct the system fatigue coefficient.

[0013] Step 5: Based on the next temperature control task to be performed by the air-conditioning system, the next operating power and next operating time are obtained, and the life depreciation digital twin model is input to obtain the next life depreciation rate of each vulnerable component respectively. The corrected next life depreciation rate is obtained by correcting the system fatigue coefficient. The corrected next life depreciation rate is added to the life depreciation sequence of each vulnerable component to form a predicted life depreciation sequence. The predicted wear score is obtained again based on the overall life score to generate a health score. The status of the air-conditioning system is judged based on the relationship between the health score and thresholds I and II.

[0014] Furthermore, the replacement time of vulnerable parts of the air conditioning system is obtained. The vulnerable parts include air filters, fan bearings, fan belts, compressor valves, and condenser tubes. The replacement time is obtained from the maintenance log. The working data and working data timestamp of the air conditioning system are obtained from the working log. The working data and timestamp are obtained from the working log. The working data includes the operating power and operating time of each task. The operating power is the operating power of the entire air conditioning system. The working data is mapped to the replacement time. The mapping logic is as follows:

[0015] Compare the replacement time and the timestamp of the working data to obtain the total number of tasks each vulnerable component has experienced after each replacement, as well as the operating power and operating time of each task. The operating power of each vulnerable component is the operating power of the entire system during this task, and the operating time of each vulnerable component is the total operating time of the entire system during this task.

[0016] Each vulnerable part is numbered, and a sample set of tasks that each vulnerable part has experienced after each replacement is obtained. The formula is as follows:

[0017]

[0018] in, Indicates the Consumable parts After the replacement of the sample set, Indicates the The operating power of the task, Indicates the The running time of the task, Retrieve variables for consumable part numbers, , , Retrieve variables for task times, , , For this vulnerable parts The total number of tasks after replacement, Retrieve the variable for the number of replacements, , , For the The total number of replacements of wearing parts.

[0019] Furthermore, based on the sample set after each replacement of each vulnerable component, the equivalent life depreciation rate of each vulnerable component is obtained. The logic is as follows:

[0020] Obtain the rated life of each vulnerable component for each replacement. The rated life is the working duration of this vulnerable component under the rated power, which is obtained from the manufacturer of the replaced vulnerable component. Respectively obtain the unit life depreciation rate of each vulnerable component for each replacement. The unit life depreciation rate for each replacement represents the life depreciation rate of the unit working efficiency and unit working duration of this vulnerable component from installation to being replaced. The basis formula is as follows:

[0021]

[0022] Wherein, represents the unit life depreciation rate after the th replacement of the th vulnerable component, represents the rated life of the th vulnerable component, is the task number retrieval variable, , , is the total number of tasks after the th replacement of this vulnerable component, is the replacement number retrieval variable, , , is for the th total replacement times of the vulnerable component, is the vulnerable component number retrieval variable, , ;

[0023] Take the unit life depreciation rate in all replacement processes of each vulnerable component as the equivalent life depreciation rate of this vulnerable component. The basis formula is as follows:

[0024]

[0025] Wherein, is the equivalent life depreciation rate of the th vulnerable component, represents the unit life depreciation rate after the th replacement of the th vulnerable component, is the vulnerable component number retrieval variable, , , is the replacement number retrieval variable, , , is for the th total replacement times of the vulnerable component;

[0026] Summarize the equivalent life loss rates of each vulnerable component to construct a life loss digital twin model.

[0027] Furthermore, obtain the time of the last replacement of each vulnerable component through the maintenance log, and obtain the work data experienced by the vulnerable component since the replacement through the work log. Input the work data into the life loss digital twin model to obtain the life loss rate of each task of each vulnerable component respectively, and form a life loss sequence. The basis formula is as follows:

[0028]

[0029] Among them, represents the life loss sequence of the th vulnerable component, is the retrieval variable of the vulnerable component number, , , represents the life loss rate of the rd task, is the retrieval variable of the task number, , , represents the number of tasks experienced. [[ID=�3]]

[0030] Furthermore, construct an overall life score based on the life loss sequences of all vulnerable components. The basis formula is as follows:

[0031]

[0032] Among them, is the overall life score, represents the life loss rate of the th vulnerable component for the th task, is the retrieval variable of the vulnerable component number, , , is the retrieval variable of the task number, , , represents the number of tasks experienced, represents the high loss index of the th vulnerable component, is the coefficient;

[0033] Obtain the high loss determination threshold. The acquisition logic of the high loss index is as follows:

[0034]

[0035] Among them, represents the The high wear index of a vulnerable component is the high wear determination threshold is the mission count retrieval variable , , represents the number of operating times experienced is the vulnerable component number retrieval variable , .

[0036] Furthermore, obtain the air conditioning system parameters. The system parameters of the air conditioning system include the designed life, average number of vulnerable component replacements, and rated power of this air conditioning system. Obtain the historical data of this air conditioning system. The historical data includes the operating duration and the total number of replacements. The operating duration is the total operating duration of all tasks recorded in the work log. The total number of replacements is the sum of the number of times of replacing vulnerable components in this air conditioning system. The number of times of replacing vulnerable components is obtained through the maintenance log. Screen the tasks with operating power exceeding the rated power and their operating durations, and number them. Screen the tasks with operating power lower than 70% of the rated power and obtain the average power of these tasks. Construct the system fatigue coefficient, and the basis formula is as follows:

[0037]

[0038] Among them, is the fatigue coefficient is the operating duration is the designed life is the total number of replacements is the average number of vulnerable component replacements represents the operating power of the th task with power exceeding the rated power is the rated power is the th operating duration of the task with power exceeding the rated power is the retrieval variable of the task with power exceeding the rated power , , is the number of tasks with power exceeding the rated power is the average power is the coefficient

[0039] Further, obtain the next temperature control task of the air conditioning system, and obtain the next operating power and the next operating duration required by the air conditioning system according to the temperature control task. The operating power and the operating duration are manually set according to the performance of the air conditioning system. Input the next operating efficiency and the next operating duration into the life loss digital twin model, obtain the next life loss rate of each vulnerable component respectively, and correct the next life loss rate to obtain the corrected next life loss rate. The basis formula is as follows:

[0040]

[0041] Wherein, is the corrected next life loss rate, is the next life loss rate, is the fatigue coefficient;

[0042] Add the corrected next life loss rate into the life loss sequence to form a predicted life loss sequence. The basis formula is as follows:

[0043]

[0044] Wherein, represents the predicted life loss sequence of the th vulnerable component, represents the life loss sequence of the th vulnerable component, is the retrieval variable of the vulnerable component number, and and is the corrected next life loss rate.

[0045] Further, according to the predicted life loss sequence, form the overall life score again according to step 3, and calibrate the overall life score formed this time as the predicted wear score to generate the health score. The basis formula is as follows:

[0046]

[0047] Wherein, is the health score, is the overall life score, is the predicted wear score.

[0048] Further, set threshold I and threshold II, and threshold I is less than threshold II. Analyze according to the health score. The logic is as follows:

[0049] When the health score is greater than or equal to threshold II, it is determined that the system is healthy, indicating that the next task is satisfied;

[0050] When the health score is less than threshold II and greater than threshold I, it is determined that the system health level is low, and a warning is issued, indicating that the next task can be satisfied but the vulnerable parts need to be replaced after the task ends;

[0051] When the health score is less than or equal to threshold I, it is determined that there are potential hazards in the system, and an alarm is issued, indicating that it is not sufficient to complete the next task.

[0052] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0053] The present invention forms the equivalent life loss rate of each vulnerable part for different task amounts by obtaining the replacement time and working data of the vulnerable parts in the air-conditioning system, establishes a life loss digital twin model, constructs an overall life score, and judges the health status of the system according to the relationship between the current overall life score and the predicted wear score, reflecting the improvement effect on the health status of the air-conditioning system due to the replacement of vulnerable parts, taking into account the different life states of different vulnerable parts during task execution, and more accurately judging the health status of the air-conditioning system. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a schematic diagram of the overall method flow of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the following further describes the present invention in detail with reference to specific embodiments.

[0056] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meanings understood by those of ordinary skill in the field to which the present invention belongs. The "first", "second" and similar terms used in the present invention do not denote any order, quantity or importance, but are only used to distinguish different components. The terms such as "including" or "comprising" mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects. The terms such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "upper", "lower", "left", "right" are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0057] Example:

[0058] Please refer to Figure 1 , the present invention provides a technical solution:

[0059] Step 1: Obtain the replacement time of the vulnerable components of the air-conditioning system, obtain the working data of the air-conditioning system and the time stamps of the working data. The working data includes the running power and running duration of each task. Compare the replacement time of the vulnerable components with the time stamps of the working data to obtain the sample set of the tasks experienced after each replacement of each vulnerable component;

[0060] The said Step 1 includes the following content:

[0061] Obtain the replacement time of the vulnerable components of the air-conditioning system. The vulnerable components include air filters, fan bearings, fan belts, compressor valves, and condenser tubes. The replacement time is obtained through maintenance logs. Obtain the working data of the air-conditioning system and the time stamps of the working data. The working data and time stamps are obtained through work logs. The working data includes the running power and running duration of each task. The running power is the running power of the entire air-conditioning system. Map the working data to the replacement time. The logic of the mapping is as follows:

[0062] Compare the replacement time with the time stamps of the working data to obtain the total number of tasks experienced after each replacement of each vulnerable component, as well as the running power and running duration of each task. The running power of each vulnerable component is the running power of the entire system during this task. The running duration of each vulnerable component is the total running duration of the entire system during this task;

[0063] Air filters, fan bearings, fan belts, compressor valves, and condenser tubes are consumables in the air-conditioning system. The air filter will significantly reduce the performance of the air-conditioning system due to dust blockage. Fan bearings and fan belts will cause abnormal operation of the air-conditioning system due to fatigue damage, and may even cause the air-conditioning system to stop working. Compressor valves will cause abnormal operation of the compressor due to wear, resulting in a significant decline in the refrigerant compression effect, thus affecting the performance of the air-conditioning system. Condenser tubes are prone to bacteria growth and blockage due to long-term use, thus affecting the operating state of the air-conditioning system. Therefore, they need to be replaced regularly.

[0064] Number each vulnerable component, and respectively obtain the sample set of the tasks experienced after each replacement of each vulnerable component. The basis formula is as follows:

[0065]

[0066] Wherein, represents the sample set after the th replacement of the th vulnerable component, represents the running power of the th task, represents the running duration of the th task, Retrieve variables for consumable part numbers, , , Retrieve variables for task times, , , For this vulnerable parts The total number of tasks after replacement, Retrieve the variable for the number of replacements, , , For the The total number of replacements of wearing parts.

[0067] The system extracts the replacement time of vulnerable parts (air filters, fan bearings, fan belts, compressor valves, and condenser pipes) from the maintenance log, and extracts the timestamp data of the operating power and operating time of each task from the work log. Then, a task sample set within each component replacement cycle is established through time matching. Through strict time mapping, a precise split between the actual replacement cycle of the component and the equipment operation data is achieved, ensuring that subsequent life loss calculations only consider data from the actual service period, avoiding interference from data congestion in the result evaluation. This data partitioning method provides a clear interval definition for the subsequent establishment of a digital twin model, allowing the state evolution of each component within each replacement cycle to be described independently and in detail. It not only lays the foundation for the quantification of the state of a single vulnerable component, but also establishes a data origin for the health status assessment of the entire system, thus forming a rigorous and well-defined closed-loop connection in data transmission and calculation logic, providing a high degree of reliability and consistency for subsequent steps.

[0068] Step 2: Based on the sample set of tasks each vulnerable part undergoes after each replacement, obtain the unit life depreciation rate of each vulnerable part for each replacement. The unit life depreciation rate of each vulnerable part during all replacement processes is taken as the equivalent life depreciation rate of the vulnerable part. The equivalent life depreciation rates of all vulnerable parts are summarized to construct a life depreciation digital twin model.

[0069] The step 2 includes the following:

[0070] Based on the sample set after each replacement of each vulnerable component, the equivalent life depreciation rate of each vulnerable component is obtained respectively. The logic is as follows:

[0071] Obtain the rated life of each vulnerable component for each replacement. The rated life is the working duration of this vulnerable component under the rated power, which is obtained from the manufacturer of the replaced vulnerable component. Respectively obtain the unit life depreciation rate of each vulnerable component for each replacement. The unit life depreciation rate for each replacement represents the life depreciation rate of this vulnerable component in terms of unit working efficiency and unit working duration from installation to being replaced. The formula for the unit life depreciation rate is as follows:

[0072]

[0073] Wherein, represents the unit life depreciation rate of the th vulnerable component after the th replacement, represents the rated life of the th vulnerable component, is the task number retrieval variable, , , is the total number of tasks after the th replacement of this vulnerable component, is the replacement number retrieval variable, , , is the total number of replacements of the th vulnerable component, is the vulnerable component number retrieval variable, , ;

[0074] represents the unit life depreciation rate of the th vulnerable component during its th replacement cycle, which reflects the degree to which the component consumes its own life due to bearing the actual load in the actual work tasks from installation to replacement. counts the cumulative "workload" experienced by the component in all tasks during this replacement cycle, where represents the operating power of the th task (reflecting the actual load and energy consumption in the working environment), and represents the operating duration of this task (reflecting the length of the load acting time). The product of these two can be regarded as a quantitative indicator of the stress or wear caused by the task to the component; then represents the designed life duration of the th component under the rated power condition specified by the manufacturer, that is, the duration during which the component can work normally under the standard condition. This formula measures the degree of component life depreciation by comparing the total accumulated load in actual work with the theoretical standard life. When the operating power of each task or operation duration When it increases, the accumulated workload also rises, resulting in a decrease in the unit life loss rate; conversely, if the component mainly operates under low power and short duration, it still leads to component replacement, and its fatigue loss will be relatively large, making relatively high. The function is to normalize the actual working state to the design life. Therefore, when the rated life is longer, the accumulated workload is higher, and the unit life loss rate may be lower; conversely, it is higher. It reflects the consumption effect of the accumulated load on the component life under actual working conditions, and through the multiplication and normalization relationship between variables, quantifies the complex operating conditions into an index reflecting the life consumption efficiency, providing an intuitive basis for maintenance and prediction.

[0075] Take the unit life loss rate in all replacement processes of each vulnerable component as the equivalent life loss rate of this vulnerable component. The formula is as follows:

[0076]

[0077] Among them, is the equivalent life loss rate of the th vulnerable component, represents the unit life loss rate after the th replacement of the th vulnerable component, is the retrieval variable for the vulnerable component number, , , is the retrieval variable for the replacement times, , , is the th total replacement times of the

[0078] For the same component replaced several times, the mission experiences of the components replaced each time may be different, which may lead to deviations in the calculation of the unit life loss rate. Take the average value of the unit life loss rates of the components replaced each time as the equivalent life loss rate to reduce the deviations caused by possible quality problems of individual components and facilitate obtaining more accurate data.

[0079] Summarize the equivalent life loss rates of each vulnerable component to construct a life loss digital twin model;

[0080] By comparing the real task data with the rated life calibrated by the manufacturer, the accurate connection between "actual consumption and theoretical indicators" is achieved, which can truly reflect the wear intensity of each component under different usage conditions; averaging the data for each replacement cycle not only effectively smooths the accidental impacts caused by operation fluctuations, but also improves the consistency and reliability of the data, providing a stable and accurate historical basis for predicting trends; summarizing the life loss rates of each component and constructing a digital twin model enables the construction of a unified data platform for the next-step real-time prediction and health scoring, making the data transfer of the entire system seamlessly connected from local to overall and from history to the future, ensuring the continuity and scientificity of the internal data logic between upstream and downstream steps.

[0081] Step 3: Obtain the time of the last replacement of each vulnerable component through the maintenance log, and the working data experienced since the replacement. Input the working data into the life loss digital twin model to obtain the life loss rate of each vulnerable component for each task respectively, and form a life loss sequence for each vulnerable component. Combine the life loss sequences of each vulnerable component and the number of vulnerable components with high loss to construct an overall life score.

[0082] The said Step 3 includes the following contents:

[0083] Step 301: Obtain the time of the last replacement of each vulnerable component through the maintenance log, and obtain the working data experienced by the vulnerable component since the replacement through the work log. Input the working data into the life loss digital twin model to obtain the life loss rate of each vulnerable component for each task respectively, and form a life loss sequence. The formula is as follows:

[0084]

[0085] Among them, represents the life loss sequence of the th vulnerable component, is the retrieval variable for the vulnerable component number, , , represents the life loss rate of the th task, is the retrieval variable for the task number, , , represents the number of tasks experienced.

[0086] By collecting the task data after the last replacement and inputting the data into the digital twin model, the life loss rate corresponding to each task is obtained, thereby constructing the life loss sequence of each component. This recording method can continuously track the wear evolution process of components, capturing both the changes in instantaneous states and providing a data basis for long-term health trends; the continuous recording provides dynamic monitoring indicators for subsequent life prediction and maintenance decision-making.

[0087] Step 302: Based on the life loss sequences of all vulnerable components, construct an overall life score according to the following formula:

[0088]

[0089] Where, is the overall life score, represents the life loss rate of the th vulnerable component in the th task, is the retrieval variable for the vulnerable component number, , , is the retrieval variable for the task count, , , represents the number of tasks experienced, represents the high loss index of the th vulnerable component, is the coefficient;

[0090] represents the overall life score, quantifying the cumulative wear degree of all vulnerable components in the entire air conditioning system. The consecutive multiplications represent the product aggregation of all 5 vulnerable components, reflecting the comprehensive impact of the state of each component on the overall health during system operation; and calculates the cumulative life loss rate of the th vulnerable component in the th operation task, where each is the wear impact suffered by this component in a certain task, truly reflecting the load effect during task operation. The parameter plays a role in adjusting the contribution degree of the cumulative damage of a single component, and its magnitude determines the overall impact of the sum of component wear on S. When the sum becomes larger, it indicates that this component bears higher loads and wear in the actual working conditions, thereby increasing the corresponding value of this component part in ; otherwise, it decreases. introduces the high loss index , this indicator takes 1 when the cumulative wear reaches the preset threshold, otherwise it is 0, truly reflecting whether there is a local severe wear phenomenon during actual operation. By taking the average of these indicators and then taking the exponent, the impact of high wear phenomena in several components on the overall score is amplified, enabling potential hazards in the system's health status to be quickly reflected. It reflects the superposition effect of cumulative wear (and the risk of local high wear, and, As the actual cumulative damage of each component increases, at the same time, when the proportion of the high wear indicator becomes 1 and increases, the exponential part will cause to show a non-linear upward trend. Therefore, The higher it is, the more severe the fatigue and wear borne by the overall system, indicating that its health status may be worse, thus providing a reliable quantitative basis for maintenance and early warning decisions.

[0091] Obtain the high wear determination threshold, and the logic for obtaining the high wear indicator is as follows:

[0092]

[0093] Among them, represents the high wear indicator of the th vulnerable component, is the high wear determination threshold, is the task number retrieval variable, , , represents the number of operating times experienced, is the vulnerable component number retrieval variable, , .

[0094] represents the high wear indicator of the th vulnerable component, reflecting whether the cumulative life loss rate of this component in the tasks it has experienced exceeds a preset high wear determination threshold , thereby determining the state of being at a relatively high wear risk. represents the life loss rate of the th component in the th task, directly reflecting the load impact and wear intensity borne by this component in the actual operating environment, and the cumulative sum reflects the cumulative load loss during the entire usage cycle; the threshold is derived from engineering design and historical data, used to define the critical point between normal wear and abnormal wear under standard working conditions. When the cumulative wear reaches or exceeds this critical point, the indicator Taking a value of 1 means that there is a high risk of life loss for this component and sufficient attention needs to be paid. Conversely, when the cumulative wear is below this threshold, it takes 0, indicating that the component is still within the safe or normal wear range. The wear data continuously generated under complex actual working conditions is converted into a signal that is easy to identify and apply through a simple binary determination method, thereby playing a role in amplifying the impact of the high-loss state in subsequent overall life scoring and health prediction, helping maintenance personnel to identify potential problems in a timely manner and make decisions.

[0095] Using the life loss sequences and high-loss indicators of all components, the overall life score is generated through comprehensive calculation. The high-loss indicator introduced here further amplifies the impact of key components on the overall health of the system under abnormal wear conditions, so that the overall score can more sensitively reflect the threat of local fault evolution to the global health level. Steps 301 and 302 form a logical progressive process from point to line and from local to whole, not only quantifying the operating status of each vulnerable component, but also enabling multi-point data to be synchronously compared within the digital twin framework, constituting a data system that connects the past and the future and providing a quantitative standard for dynamic evolution for system health prediction.

[0096] Step 4: According to the parameters and historical data of the air conditioning system, screen tasks with operating power exceeding the rated power and their operating duration, and tasks with operating power lower than 70% of the rated power, and obtain the average power of these tasks, and construct the system fatigue coefficient;

[0097] The said step 4 includes the following contents:

[0098] Obtain the parameters of the air conditioning system. The system parameters of the air conditioning system include the design life, average number of replacements of vulnerable components, and rated power of this air conditioning system. Obtain the historical data of this air conditioning system. The historical data includes the operating duration and the total number of replacements. The operating duration is the total operating duration of all tasks recorded in the work log. The total number of replacements is the sum of the number of times of replacing vulnerable components of this air conditioning system. The number of times of replacing vulnerable components is obtained through the maintenance log. Screen tasks with operating power exceeding the rated power and their operating duration, and number them. Screen tasks with operating power lower than 70% of the rated power and obtain the average power of these tasks, and construct the system fatigue coefficient. The formula is as follows:

[0099]

[0100] Among them, is the fatigue coefficient, is the operating duration, is the design life, is the total number of replacements, To calculate the average number of times of replacing vulnerable parts, represents the operating power of the th task with power exceeding the rated power, is the rated power, is the operating duration of the th task with power exceeding the rated power, is the retrieval variable for tasks with power exceeding the rated power, , , is the number of tasks with power exceeding the rated power, is the average power, is the coefficient.

[0101] represents the system fatigue coefficient, which comprehensively reflects the additional fatigue and wear caused by factors such as working duration, component replacement frequency, and tasks with power exceeding the rated power and inefficient operation during the actual operation of the air-conditioning system. represents the actual operating duration of the system, while is the designed service life duration. When increases, it means that the cumulative load of the system under normal working conditions also increases. Therefore, decrease will cause to show an upward trend; is the total number of actually replaced components, is the average number of replacements, which is used to measure the impact of replacement frequency on system fatigue. When the number of replacements increases, value will increase, thereby increasing the system fatigue coefficient , thus reflecting the consumption of system life caused by frequent component replacements. accumulates the additional fatigue caused by all tasks with power exceeding the rated power . Among them, is the operating power of the th task with power exceeding the rated power, is the operating duration of this task, is the exponential factor for adjusting the effect of overloaded tasks, reflects the boosting effect on system fatigue when there are many overloaded operating tasks and long operating durations; while represents the total duration corresponding to tasks with an average operating power lower than 70% of the rated power, and its proportion is used as a subtraction term, meaning that working at a low power state can relieve system fatigue to a certain extent. quantitatively synthesizes the impact of different operating conditions on system wear, reflects the combined effect of actual operating duration, component replacement frequency, and the deviation of working state from the designed working conditions on system fatigue, and indicates that the equipment may face higher maintenance risks in future operations.

[0102] Linking the local wear condition of a single component with the overall system operating environment and working mode, and systematizing the dynamic operating condition parameters that affect the system durability and fatigue properties, a conversion channel from single-piece indicators to overall effects is formed; by introducing a fatigue coefficient to correct the additional wear caused by additional operating conditions, the deviation that may exist in predicting solely based on the local life loss rate is compensated, making the subsequent prediction more in line with the actual operating conditions; the in-depth investigation of historical data provides a correction factor for comparing future task information with historical trends in the next step, thus playing a connecting role between historical operating states and future task requirements in the entire maintenance decision-making process, enhancing the adaptability and accuracy of the prediction model.

[0103] Step 5: Obtain the next operating power and next operating duration according to the next temperature control task to be executed by the air-conditioning system, input them into the life loss digital twin model, obtain the next life loss rate of each vulnerable component respectively, and obtain the corrected next life loss rate through system fatigue coefficient correction. Add the corrected next life loss rate to the life loss sequence of each vulnerable part to form a predicted life loss sequence. Again, obtain the predicted wear score according to the overall life score, generate a health score, and judge the state of the air-conditioning system according to the relationship between the health score and threshold Ⅰ and threshold Ⅱ.

[0104] The said Step 5 includes the following contents:

[0105] Step 501: Obtain the next temperature control task of the air-conditioning system, and obtain the next operating power and next operating duration required by the air-conditioning system according to the temperature control task. The operating power and operating duration are manually set according to the performance of the air-conditioning system. Input the next operating efficiency and next operating duration into the life loss digital twin model, obtain the next life loss rate of each vulnerable component respectively, and correct the next life loss rate to obtain the corrected next life loss rate. The formula is as follows:

[0106]

[0107] Wherein, is the corrected next life loss rate, is the next life loss rate, is the fatigue coefficient;

[0108] The next life loss rate is corrected by the system fatigue coefficient. Since the air-conditioning system has been used for a long time, the consumption of its own life means the aging of other components, thus affecting the normal calculation of the next life loss rate of vulnerable components. Therefore, it is necessary to correct the next life loss rate.

[0109] Add the corrected next life loss rate to the life loss sequence to form a predicted life loss sequence, according to the following formula:

[0110]

[0111] Wherein, represents the predicted life loss sequence of the th vulnerable component, represents the life loss sequence of the th vulnerable component, is the retrieval variable of the vulnerable component number, , , is the corrected next life loss rate.

[0112] Obtain the expected operating power and duration from the temperature control tasks to be performed by the air conditioning system, input these estimated data into the digital twin model, calculate the next life loss rate, and then correct it in combination with the fatigue coefficient, so that future work tasks can be included in the system wear prediction in advance, realizing the key transformation from historical data backtracking to future state preview; the correction of the fatigue coefficient ensures that the impact of task parameters deviating from the design value can be quantitatively considered, enhancing the realistic matching degree of the prediction results;

[0113] Step 502: According to the predicted life loss sequence, form the overall life score again according to step 3, and calibrate the overall life score formed this time as the predicted wear score to generate the health score, according to the following formula:

[0114]

[0115] Wherein, is the health score, is the overall life score, is the predicted wear score.

[0116] represents the health score, which is used to reflect the quantitative evaluation of the overall health state of the air conditioning system after historical wear and predicted wear correction. is the overall life score, representing the cumulative loss situation of the system during historical operation. The higher the value, the more serious the cumulative wear of the system and the lower the remaining life; while is the predicted wear score, reflecting the possible wear trend of the system under the influence of future tasks. First, obtain a remaining health capacity based on the historical state through . When is smaller, is larger, indicating that the historical wear of the system is lighter and the health state is better; subsequently, through the exponential function Take into account the increment of predicted wear relative to the current wear. If significantly higher than (i.e., the future wear increases significantly), then tends to be large, and the exponential part decreases sharply accordingly. As a result, the overall health score will decrease, indicating that there may be a problem with the system health. On the contrary, if is close to or basically the same as the exponential term approaches such that remains at nearby, indicating that the future wear prediction of the system is relatively consistent with the historical state and the health risk is low. By combining the current health state with the future wear prediction and amplifying the impact of future wear on system health in a non-linear form, a smooth transition between historical data and predicted data is achieved. As a result, the health score can intuitively and comprehensively reflect the overall health status of the system, providing a clear quantitative basis for maintenance decisions.

[0117] Recalculate the overall life score using the updated predicted life wear sequence, then generate the corresponding predicted wear score, and finally convert it into a health score, which not only ensures a smooth transition between the data prediction result and the health assessment, but also makes the prediction result have a clear quantitative threshold meaning, providing a direct indicator for decision-making.

[0118] Step 503: Set threshold I and threshold II, and threshold I is less than threshold II. Analyze according to the health score, and the logic is as follows:

[0119] When the health score is greater than or equal to threshold II, it is determined that the system is healthy, indicating that the subsequent tasks can be satisfied;

[0120] When the health score is less than threshold II and greater than threshold I, it is determined that the system health level is low, and an early warning is given, indicating that the subsequent tasks can be satisfied but the vulnerable parts need to be replaced after the tasks are completed;

[0121] When the health score is less than or equal to threshold I, it is determined that there are potential hazards in the system, and an alarm is given, indicating that it is not sufficient to complete the subsequent tasks.

[0122] According to the set threshold I and threshold II, the health score is classified and judged, realizing a complete closed-loop from data quantification to state assessment, and then to early warning and alarm. The classification and determination method not only provides timely alarm feedback for the subsequent safe operation of the equipment at a deep level, but also provides a clear operation guide for the overall maintenance strategy, making the output from the prediction model to the practical decision-making in each step form a strong connection and closed-loop feedback.

[0123] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula that is closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0124] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0125] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, and may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0126] The above is only the specific implementation manner 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 substitutions within the technical scope disclosed in the present application, and all should be covered by the protection scope of the present application.

Claims

1. A method for health monitoring of building mechanical and electrical systems based on digital twin technology, characterized in that, The specific steps include: Step 1: Obtain the replacement time of the air conditioning system's vulnerable parts, obtain the air conditioning system's operating data and operating data timestamps. The operating data includes the operating power and operating time of each task. Compare the replacement time of the vulnerable parts with the timestamp of the operating data to obtain a sample set of tasks that each vulnerable part has experienced after each replacement. Step 2: Based on the sample set of tasks each vulnerable part undergoes after each replacement, obtain the unit life depreciation rate of each vulnerable part for each replacement. The unit life depreciation rate of each vulnerable part during all replacement processes is taken as the equivalent life depreciation rate of the vulnerable part. The equivalent life depreciation rates of all vulnerable parts are summarized to construct a life depreciation digital twin model. Step 3: Use the maintenance log to obtain the last replacement time of each vulnerable component and the work data since the replacement. Input this work data into the life loss digital twin model to obtain the life loss rate of each vulnerable component for each task. This generates a life loss sequence for each vulnerable component. Combining the life loss sequence of each vulnerable component and the number of vulnerable components with high loss, an overall life score is constructed. Step 4: Based on the air conditioning system parameters and historical data, filter out tasks with operating power exceeding the rated power and their running duration, and tasks with operating power below 70% of the rated power, obtain the average power of these tasks, and construct the system fatigue coefficient. Step 5: Based on the next temperature control task to be performed by the air-conditioning system, the next operating power and next operating time are obtained, and the life depreciation digital twin model is input to obtain the next life depreciation rate of each vulnerable component respectively. The corrected next life depreciation rate is obtained by correcting the system fatigue coefficient. The corrected next life depreciation rate is added to the life depreciation sequence of each vulnerable component to form a predicted life depreciation sequence. The predicted wear score is obtained again based on the overall life score to generate a health score. The status of the air-conditioning system is judged based on the relationship between the health score and thresholds I and II.

2. The method for health monitoring of a building electromechanical system based on digital twin technology according to claim 1, wherein: Obtain the replacement time of vulnerable parts of the air conditioning system. The vulnerable parts include air filters, fan bearings, fan belts, compressor valves, and condenser tubes. The replacement time is obtained from the maintenance log. Obtain the operating data and operating data timestamp of the air conditioning system. The operating data and timestamp are obtained from the operating log. The operating data includes the operating power and operating time of each task. The operating power is the operating power of the entire air conditioning system. Map the operating data to the replacement time. The mapping logic is as follows: Compare the replacement time and the timestamp of the working data to obtain the total number of tasks each vulnerable component has experienced after each replacement, as well as the operating power and operating time of each task. The operating power of each vulnerable component is the operating power of the entire system during this task, and the operating time of each vulnerable component is the total operating time of the entire system during this task. Each vulnerable part is numbered, and a sample set of tasks that each vulnerable part has experienced after each replacement is obtained. The formula is as follows: Among them, represents the sample set after the th replacement of the th vulnerable component, represents the operating power of the th task, represents the operating duration of the th task, is the retrieval variable for the vulnerable component number, , , are the retrieval variables for the task number, , , is the total number of tasks after the th replacement of this vulnerable component, is the retrieval variable for the replacement number, , , is the th total replacement number of the vulnerable component.

3. The method for health monitoring of building mechanical and electrical systems based on digital twin technology according to claim 2, wherein: According to the sample set after each replacement of each vulnerable component, obtain the equivalent life loss rate of each vulnerable component respectively. The logic is as follows: Obtain the rated life of each vulnerable component for each replacement. The rated life is the working duration of this vulnerable component under the rated power, which is obtained from the manufacturer of the replaced vulnerable component. Obtain the unit life loss rate of each vulnerable component for each replacement. The unit life loss rate of each replacement represents the life loss rate of this vulnerable component in terms of unit working efficiency and unit working duration from installation to being replaced. The formula is as follows: Among them, represents the unit life loss rate after the th replacement of the th vulnerable component, represents the rated life of the th vulnerable component, is the mission number retrieval variable, , , is the total mission number after the th replacement of this vulnerable component, is the replacement number retrieval variable, , , is the total replacement number of the th vulnerable component, is the vulnerable component number retrieval variable, , ; Take the unit life loss rate in all replacement processes of each vulnerable component as the equivalent life loss rate of this vulnerable component. The formula is as follows: Among them, is the equivalent life loss rate of the th vulnerable component, represents the unit life loss rate of the th vulnerable component after the th replacement, is the retrieval variable for the vulnerable component number, , , is the retrieval variable for the replacement times, , , is the th total replacement times of the vulnerable component; Summarize the equivalent life loss rates of each vulnerable component to construct a life loss digital twin model.

4. The method for health monitoring of building mechanical and electrical systems based on digital twin technology according to claim 3, wherein: Obtain the time of the last replacement of each vulnerable component through the maintenance log, and obtain the working data experienced by this vulnerable component since the replacement through the work log. Input the working data into the life loss digital twin model to obtain the life loss rate of each vulnerable component for each task respectively, and form a life loss sequence. The formula is as follows: Among them, represents the life loss sequence of the th vulnerable component, is the retrieval variable of the vulnerable component number, , , represents the life loss rate of the th mission, is the retrieval variable of the mission times, , , represents the number of missions experienced.

5. The method for health monitoring of a building electromechanical system based on digital twin technology according to claim 4, wherein: Construct an overall life score according to the life loss sequences of all vulnerable components. The formula is as follows: Among them, is the overall life score, represents the life loss rate of the th vulnerable component in the th mission, is the retrieval variable for the vulnerable component number, , , is the retrieval variable for the mission times, , , represents the number of missions experienced, represents the high loss index of the th vulnerable component, is the coefficient; Obtain the high loss determination threshold. The acquisition logic of the high loss index is as follows: Among them, represents the high wear index of the th vulnerable part, is the high wear determination threshold, is the mission count retrieval variable, , , represents the number of operating cycles experienced, is the vulnerable part number retrieval variable, , .

6. The method for health monitoring of building mechanical and electrical systems based on digital twin technology according to claim 5, characterized in that: Obtain the air-conditioning system parameters. The system parameters of the air-conditioning system include the designed life, average number of vulnerable component replacements, and rated power of this air-conditioning system. Obtain the historical data of this air-conditioning system. The historical data includes the running duration and the total number of replacements. The running duration is the total running duration of all tasks recorded in the work log. The total number of replacements is the sum of the number of times of replacing vulnerable components of this air-conditioning system. The number of times of replacing vulnerable components is obtained through the maintenance log. Screen the tasks with running power exceeding the rated power and their running durations, and number them. Screen the tasks with running power lower than 70% of the rated power and obtain the average power of these tasks. Construct a system fatigue coefficient. The formula is as follows: Among them, is the fatigue coefficient, is the operating duration, is the design life, is the replacement sum, is the average number of replacements of vulnerable parts, represents the operating power of the th task with power exceeding the rated power, is the rated power, is the operating duration of the th task with power exceeding the rated power, is the retrieval variable for tasks with power exceeding the rated power, , , is the number of tasks with power exceeding the rated power, is the average power, is the coefficient.

7. The method for health monitoring of building electromechanical systems based on digital twin technology according to claim 6, characterized in that: Obtain the next temperature control task of the air-conditioning system, and obtain the next running power and next running duration required by the air-conditioning system according to the temperature control task. The running power and running duration are manually set according to the performance of the air-conditioning system. Input the next running efficiency and next running duration into the life loss digital twin model to obtain the next life loss rate of each vulnerable component respectively, and correct the next life loss rate to obtain the corrected next life loss rate. The formula is as follows: Among them, is the corrected next life loss rate, is the next life loss rate, is the fatigue coefficient; Add the corrected next life loss rate into the life loss sequence to form a predicted life loss sequence. The formula is as follows: Among them, represents the predicted life loss sequence of the th vulnerable component, represents the life loss sequence of the th vulnerable component, is the retrieval variable of the vulnerable component number, , , is the corrected next life loss rate.

8. The method for health monitoring of building mechanical and electrical systems based on digital twin technology according to claim 7, characterized in that: According to the predicted life loss sequence, form an overall life score again according to step 3, and calibrate the overall life score formed this time as the predicted wear score to generate a health score. The formula is as follows: Among them, is the health score, is the overall life expectancy score, is the predicted wear score.

9. The method for health monitoring of a building's mechanical and electrical system based on digital twin technology according to claim 7, characterized in that: Set threshold I and threshold II, and threshold I is less than threshold II. Analyze according to the health score. The logic is as follows: When the health score is greater than or equal to threshold II, it is determined that the system is healthy, indicating that it meets the next tasks; When the health score is less than threshold II and greater than threshold I, it is determined that the system health level is low and a warning is issued, indicating that the next task can be satisfied but the vulnerable parts need to be replaced after the task is completed; When the health score is less than or equal to threshold I, it is determined that there are potential hazards in the system and an alarm is issued, indicating that it is insufficient to complete the next task.

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