Heating and ventilation equipment fault identification method and system

Abstract

The invention discloses a heating and ventilation equipment fault identification method and system, and relates to the technical field of heating and ventilation equipment.The method comprises the steps that building use behaviors, environment response data and heating and ventilation equipment operation data are collected, and a coupling model and a behavior-driven operation mode between the building behaviors and environment and equipment response are constructed; according to the method, the building behaviors, the environment responses and the equipment operation data can be integrated to carry out modeling analysis, the accuracy and the real-time performance of fault recognition are improved, and the fault recognition efficiency is improved. According to the method, abnormal behavior tracks and response dislocation can be accurately identified, potential fault reasons of equipment can be positioned in an assisted manner, rapid feedback and operation and maintenance response of fault information are realized through structured early warning and visual display, causal association analysis is introduced, historical maintenance data are effectively utilized, and the intelligence and maintenance efficiency of fault judgment are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of heating and ventilation equipment, and particularly relates to a heating and ventilation equipment fault identification method and system. BACKGROUND

[0002] Heating and ventilation equipment is widely used in modern buildings to ensure the comfort and safety of indoor environments. However, due to the variety of equipment and complex working environment, traditional equipment fault identification methods often rely on regular maintenance and manual judgment, which cannot timely and accurately detect potential equipment faults, resulting in excessive equipment downtime, increased maintenance costs, and energy waste. With the development of big data and Internet of Things technology, intelligent analysis combining building usage behavior, environmental data, and equipment operation information has become a key technology to improve the efficiency and accuracy of heating and ventilation equipment fault diagnosis.

[0003] At present, the Chinese invention patent with application number 202110192487.8 discloses a heating and ventilation equipment fault identification method, device, equipment and storage medium. Based on the processing equipment, a fuzzy search library and an exception jump library are preset, so that after the detection item name is obtained, the judgment word is selected from the fuzzy search library and compared with the detection item name, and the judgment word with the matching result is taken as the preliminary identification result. Then it is judged whether the exception word corresponding to the preliminary identification result in the exception jump library matches the detection item name. If they match, the same judgment word as the matching exception word is selected from the fuzzy search library as the final identification result. Otherwise, the preliminary identification result is taken as the final identification result. By setting this way, the processing equipment can effectively improve the processing efficiency compared with manual identification. In addition, by combining fuzzy search and exception jump functions, unique identification of the heating and ventilation equipment test item name can be realized, and the accuracy of the identification result can be improved.

[0004] The above-mentioned technology cannot real-time associate building behavior and heating and ventilation equipment operation state through multi-source data, so as to accurately identify response abnormalities and potential faults. SUMMARY

[0005] The technical problem solved by the present application is that the prior art cannot real-time associate building behavior and heating and ventilation equipment operation state through multi-source data, so as to accurately identify response abnormalities and potential faults.

[0006] To solve the above technical problems, the present application provides the following technical solutions: A heating and ventilation equipment fault identification method, comprising the following steps: Step S1, collecting building usage behavior, environmental response data and heating and ventilation equipment operation data; Step S2, constructing a coupling model between building behavior and environmental and equipment response and a behavior-driven operation mode; Step S3, identify the inconsistent behavior response area and abnormal behavior trajectory, and generate structured abnormal coding; Step S4, associate the abnormal trajectory with the equipment maintenance data, identify the potential fault type and output the fault information; Step S5, generate structured warning data, visual display and track operation and maintenance response.

[0007] Preferably, the step S1 comprises the following sub-steps: Step S101, collect building use behavior data, the building use behavior data including personnel flow frequency, personnel flow density, passing time, passing person times, lighting system on-off state and lighting system on time; Step S102, collect environmental response data, the environmental response data including environmental temperature, environmental relative humidity and environmental carbon dioxide concentration; Step S103, collect heating and ventilation equipment operation data, the heating and ventilation equipment operation data including equipment start-stop time, equipment start-stop frequency, fan running speed, temperature difference between supply air outlet and return air inlet and energy consumption data.

[0008] Preferably, the step S2 comprises the following sub-steps: Step S201, establish the time sequence correspondence between the building use behavior data and the environmental response data, the establishment logic of the time sequence correspondence being: Align the building use behavior data and the environmental response data in time, convert the building use behavior data into a standardized time sequence feature vector, take a minute as a time window unit, compare the environmental response data in the same time window, and perform sliding window comparison processing; Calculate the correlation coefficient, cross-lag time difference and cooperative fluctuation amplitude between the building use behavior data change rate and the environmental response data change rate in each time window, and record the trend change of the correlation coefficient, cross-lag time difference and cooperative fluctuation amplitude between the building use behavior data change rate and the environmental response data change rate in the continuous time period, based on the correlation coefficient, cross-lag time difference, cooperative fluctuation amplitude and trend change between the building use behavior data change rate and the environmental response data change rate, establish a dynamic coupling model between the building use behavior data and the environmental response data, and extract the driving features of the dynamic coupling model, the driving features including maximum correlation time lag, fluctuation amplitude difference value and wave peak response time length; Step S202, establish the adjustment response mapping between the environmental response data and the heating and ventilation equipment operation data, the establishment logic of the adjustment response mapping being: The environmental response data is converted into a standardized time series, time-aligned with the HVAC operation data, and a sliding window comparison is performed with a time window unit of minutes to analyze the correspondence between the parameter fluctuations of the environmental response data and the data state changes of the HVAC operation data, calculate the equipment response delay, adjustment amplitude and response rate in each time window, extract the response characteristics, including adjustment lag time, adjustment intensity change amplitude and continuous adjustment time; In step S203, the building use behavior data, the environmental response data and the equipment operation data are fused, the driving characteristics and the response characteristics are taken as joint inputs, and a building behavior-driven operation mode is constructed. The construction logic of the building behavior-driven operation mode is as follows: With a sliding time window as an analysis unit, the coordinated change sections of the building use behavior data, the environmental response data and the equipment operation data on the time axis are identified respectively, a multivariate dynamic regression model and an adaptive weight fusion mechanism are constructed, and an equipment response prediction sub-model under the driving of behavior is established; The response characteristics are taken as intermediate variables, a path analysis method is introduced to quantify the adjustment effect index of the response characteristics on the equipment operation response under the driving of building behavior changes, the adjustment effect index includes adjustment direction, intensity and lag amplitude, a linkage map is constructed, the path intensity and the response probability are dynamically updated and visually expressed in combination with a data-driven manner, and a structured building behavior response characteristic vector is output.

[0009] Preferably, the step S3 includes the following sub-steps: In step S301, a matching analysis is performed on the structured building behavior response characteristic vector between the building behavior and the equipment operation, the structured building behavior response characteristic vector and the equipment operation state sequence in a unit time window are analyzed one by one on the basis of the constructed dynamic mapping model, and the time sequence consistency is evaluated by using a synchronization rate calculation: If the structured building behavior response characteristic vector is detected to have response dislocation, a decreased synchronization rate or a correlation coefficient changing from positive to negative in consecutive time windows, it is regarded as an inconsistent behavior response area, and the corresponding spatial position identifier, starting time and duration are recorded.

[0010] Preferably, the step S3 further includes the following sub-steps: In step S302, abnormal behavior trajectories of equipment energy consumption curve deviation and response lag under a specific behavior mode are identified, clustering analysis is performed according to the structured building behavior response characteristic vector, a behavior mode clustering result is output, and a typical behavior period is screened based on the behavior mode clustering result, the typical behavior period includes high-density office, night empty load and conference concentration, and statistical analysis is performed on the equipment energy consumption curve and the operation parameters in each typical behavior period, and the logic of the statistical analysis is as follows: If the device energy consumption curve presents a sudden rise, a sustained high fluctuation or a significant response delay under the condition that the specific behavior input is unchanged, it is determined as an abnormal trajectory; By presetting the energy consumption offset threshold and the upper limit of the response time, the start and end time of the abnormal trajectory, the behavior mode label and the fluctuation amplitude are calibrated, and the abnormal behavior trajectory is output; Step S303, the response inconsistent region and the abnormal behavior trajectory are jointly structured and coded, and the coding field of the structured coding includes abnormal type, influence range, associated device number, start and end timestamp and abnormal degree score.

[0011] Preferably, the step S4 includes the following sub-steps: Step S401, obtaining the historical maintenance data and the running state data of the key components of the device, retrieving the maintenance record from the device management system, and the maintenance record includes the replacement time of the key components, the maintenance frequency, the maintenance project details, the fault description, the processing method and the actual processing time; Synchronously collecting the running state data of the current key components, including the fan, the electric valve, the sensor and the control execution unit, and the running state data includes the cumulative running time, the temperature rise curve slope, the vibration spectrum peak value and the abnormal fluctuation amplitude of the working current; Step S402, the abnormal behavior trajectory and the historical maintenance data are causally associated and analyzed, and the associated device number and the abnormal time period in the abnormal behavior trajectory output in step S303 are used as anchor points to retrieve the maintenance record and state fluctuation information of the corresponding device in the previous and subsequent periods in the maintenance database; According to the device type and the corresponding component characteristics, a causal mapping rule table is constructed to determine whether there is an abnormal situation before the occurrence of the abnormal trajectory, and the abnormal situation includes maintenance lag record, fault frequent situation or significant deterioration trend of running state index, and the causal association is output according to the abnormal situation, including behavior abnormality, component aging, lubrication loss and transmission imbalance; Step S403, judging whether the device has a hidden fault based on the abnormal situation, the energy consumption curve offset and the key component state change, and the hidden fault includes air duct blockage, fan aging, sensor accuracy decline and control logic delay; The final fault type recognition result is output combined with the causal mapping rule table, and the device identification, fault classification code, influence level score and recommended treatment measures are attached.

[0012] Preferably, the step S5 includes the following sub-steps: Step S501, converting the final fault type recognition result into structured early warning data, specifically including: Read the device identifier, fault type, risk level and recommended measures, and combine them with the spatial location coding information in the abnormal behavior trajectory to generate a standard format early warning data structure. The early warning data structure includes spatial location information, abnormal situation, hidden fault, time period and device coverage area. Step S502: Visualize the structured early warning data and synchronize it to the operation and maintenance platform. Use layer overlay to highlight the identified abnormal areas on the building floor plan and display information cards in the corresponding areas. The information cards include the equipment number, abnormal situation, hidden fault, risk level score and recommended handling solution. Step S503: Update the dynamic mapping model based on the actual response situation and track the operation and maintenance response records after the early warning information is released. The actual response situation includes the response start time, handling method, handling duration and final fault confirmation result. The actual response is compared with the structured early warning data to determine whether there are false alarms, missed alarms, and classification biases, and to correct the judgment thresholds and causal reasoning path parameters.

[0013] Preferably, the causal association analysis specifically includes: Based on the device status record data, compare the time when the adjustment signal is issued with the actual response time of the device, calculate the average response time, response variance and maximum delay time, and determine whether it exceeds the preset threshold. The number of times the equipment starts and stops within the time period of abnormal behavior trajectory is statistically analyzed, and combined with building usage behavior data, it is determined whether there are invalid responses with frequent starts and stops but no effective environmental regulation effect. Pattern recognition is performed on the energy consumption curves within the specified time period. The current energy consumption curve is normalized and aligned with the historical benchmark energy consumption curves under similar operating conditions, and the deviation index is calculated. Combined with equipment aging label data, a weighted comprehensive scoring model is used to calculate the risk level and output the judgment result.

[0014] Preferably, the abnormal behavior trajectory includes one or more abnormal manifestations, which are identified and confirmed by combining the time period of occurrence, corresponding behavior pattern, and operating status: During periods of stable personnel flow and limited changes in heat load, if the equipment energy consumption data curve shows non-periodic fluctuations and a sudden increase in energy consumption without obvious external driving conditions, the equipment is judged to be ineffective in operation and energy consumption regulation failure. The equipment frequently starts and stops without corresponding changes in building usage data, indicating that the problem stems from an abnormal start / stop logic combined with excessively high control sensitivity. When environmental response data fluctuates rapidly, if the operating status data fails to respond in a timely manner or the delay exceeds the preset adjustment response threshold, the device is judged to be in a state of adjustment lag and execution unit response failure.

[0015] A heating and ventilation equipment fault identification system comprises a data acquisition module, a behavior-driven modeling module, an anomaly detection module, a fault correlation analysis module and a warning feedback module. The data acquisition module is used to acquire building use behavior, environmental response data and heating and ventilation equipment operation data. The behavior-driven modeling module is used to build a coupling model between building behavior and environmental response and equipment response and a behavior-driven operation mode. The anomaly detection module is used to identify behavior response inconsistent areas and abnormal behavior trajectories and generate structured anomaly codes. The fault correlation analysis module is used to correlate abnormal trajectories and equipment maintenance data, identify potential fault types and output fault information. The warning feedback module is used to generate structured warning data, visually display and track operation and maintenance responses.

[0016] The present application has the following advantages: the present application can comprehensively model and analyze building behavior, environmental response and equipment operation data, improve the accuracy and real-time performance of fault identification, accurately identify abnormal behavior trajectories and response misplacement, assist in locating potential equipment fault causes, realize rapid feedback and operation and maintenance responses of fault information through structured warning and visual display, introduce causal correlation analysis, effectively utilize historical maintenance data and improve the intelligence of fault judgment and maintenance efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 A step flowchart of a heating and ventilation equipment fault identification method provided for an embodiment of the present application; Figure 2 A basic flowchart of a heating and ventilation equipment fault identification system provided for an embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to make the above objectives, characteristics and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application are described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments.

[0019] Embodiment 1, with reference to Figure 1 provides a heating and ventilation equipment fault identification method, comprising the following steps: Step S1, acquiring building use behavior, environmental response data and heating and ventilation equipment operation data.

[0020] Step S2, building a coupling model between building behavior and environmental response and equipment response and a behavior-driven operation mode.

[0021] Step S3, identifying behavior response inconsistent areas and abnormal behavior trajectories and generating structured anomaly codes.

[0022] Step S4, associate the abnormal trajectory with the equipment maintenance data, identify the potential fault type and output the fault information.

[0023] Step S5, generate structured early warning data, visual display and track operation and maintenance response.

[0024] The application can comprehensively analyze building behavior, environmental response and equipment operation data, improve the accuracy and real-time of fault identification, accurately identify abnormal behavior trajectory and response dislocation, assist in locating the potential fault cause of the equipment, realize the rapid feedback and operation and maintenance response of the fault information through structured early warning and visual display, introduce causal correlation analysis, effectively use historical maintenance data, and improve the intelligentization and maintenance efficiency of fault judgment.

[0025] Step S1 includes the following sub-steps: Step S101, collecting building use behavior data, the building use behavior data including personnel flow frequency, personnel flow density, passing time, passing person times, lighting system on-off state and lighting system on time.

[0026] Step S101 can reflect the actual use mode of the building space by collecting building use behavior data such as personnel flow frequency, density, passing time and lighting state, and provide behavior driving basis for analyzing environmental change and equipment response.

[0027] Step S102, collecting environmental response data, the environmental response data including environmental temperature, environmental relative humidity and environmental carbon dioxide concentration.

[0028] Step S102 can characterize the indoor environment state in real time by collecting environmental response data such as environmental temperature, relative humidity and carbon dioxide concentration, and provide reference for judging the relationship between equipment adjustment effect and environmental feedback.

[0029] Step S103, collecting HVAC equipment operation data, the HVAC equipment operation data including equipment start-stop time, equipment start-stop frequency, fan running speed, temperature difference between supply air outlet and return air inlet and energy consumption data.

[0030] Step S103 can comprehensively reflect the running state of the HVAC equipment by collecting equipment start-stop time, frequency, fan speed, temperature difference between supply air outlet and return air inlet and energy consumption data, and lay a data foundation for identifying equipment performance change and abnormal response.

[0031] Step S1 collects building use behavior, environmental response and HVAC equipment operation data to form a multi-dimensional raw data basis, and provides comprehensive and accurate data support for subsequent dynamic coupling modeling and fault identification.

[0032] Step S2 includes the following sub-steps: Step S201, the time sequence corresponding relationship between the building use behavior data and the environment response data is established, and the establishment logic of the time sequence corresponding relationship is: The building use behavior data and the environment response data are time-aligned, the building use behavior data is converted into a standardized time sequence feature vector, a time window unit of minutes is used, the environment response data in the same time window is used as a reference, and a sliding window comparison process is performed.

[0033] The correlation coefficient between the building use behavior data change rate and the environment response data change rate, the cross-lag time difference and the cooperative fluctuation amplitude in each time window are calculated, and the trend change of the correlation coefficient between the building use behavior data change rate and the environment response data change rate, the cross-lag time difference and the cooperative fluctuation amplitude in a continuous time period is recorded. Based on the correlation coefficient between the building use behavior data change rate and the environment response data change rate, the cross-lag time difference, the cooperative fluctuation amplitude and the trend change, a dynamic coupling model between the building use behavior data and the environment response data is established, and driving features of the dynamic coupling model are extracted. The driving features include a maximum correlation time lag, a fluctuation amplitude difference value and a peak response time length.

[0034] Step S201 can establish a dynamic coupling model and extract driving features by time-aligning the building use behavior data and the environment response data and performing sliding window analysis, can quantify the influence of building behavior on the environment state, and provide behavior driving basis for subsequent device response analysis.

[0035] Step S202, the adjustment response mapping between the environment response data and the HVAC equipment operation data is established, and the establishment logic of the adjustment response mapping is: The environment response data is converted into a standardized time sequence, time-aligned with the HVAC equipment operation data, a time window unit of minutes is used, a sliding window comparison is performed, a corresponding relationship between parameter fluctuations of the environment response data and data state changes of the HVAC equipment operation data is analyzed, a device response delay, an adjustment amplitude and a response rate in each time window are calculated, and response features are extracted. The response features include an adjustment lag time, an adjustment intensity change amplitude and a continuous adjustment time.

[0036] Step S202 can extract features such as a delay, an adjustment amplitude and a duration of a device response by adjustment response mapping analysis of the environment response data and the HVAC equipment operation data, and realize accurate association between a device operation state and an environment feedback.

[0037] Step S203, the building use behavior data, the environment response data and the equipment operation data are fused, the driving features and the response features are used as joint inputs, and a building behavior-driven operation mode is constructed. The construction logic of the building behavior-driven operation mode is: With a sliding time window as the analysis unit, the coordinated change sections of building use behavior data, environmental response data and equipment operation data on the time axis are identified respectively, a multivariate dynamic regression model and an adaptive weight fusion mechanism are constructed, and a device response prediction sub-model under the driving of behavior is established.

[0038] The response characteristics are introduced as intermediate variables to quantify the adjustment indicators of the response characteristics on the equipment operation response driven by building behavior changes by using the path analysis method. The adjustment indicators include adjustment direction, intensity and lag amplitude. A linkage map is constructed, and the path intensity and response probability are dynamically updated and visually expressed in combination with the data-driven method. A structured building behavior response feature vector is output.

[0039] Step S203 fuses the behavior-driven features and equipment response features to construct a building behavior-driven operation mode, and outputs a structured building behavior response feature vector through path analysis and dynamic regression mechanism, providing comprehensive feature support for anomaly detection and fault diagnosis.

[0040] Step S2 realizes the time sequence association of multi-dimensional data and the construction of behavior-driven operation mode by establishing the dynamic coupling relationship between building use behavior, environmental response and HVAC equipment operation data, providing high-precision feature input for subsequent anomaly detection and fault identification.

[0041] Step S3 includes the following sub-steps: Step S301, the structured building behavior response feature vector between building behavior and equipment operation is matched and analyzed. On the basis of the established dynamic mapping model, the structured building behavior response feature vector and the equipment operation state sequence in the unit time window are analyzed one by one. The time sequence consistency is evaluated by using the synchronization rate calculation: If the structured building behavior response feature vector is detected to have response dislocation, the synchronization rate decreases or the correlation coefficient changes from positive to negative in the continuous time window, it is considered as a behavior response inconsistent area, and the corresponding spatial position mark, starting time and duration are recorded.

[0042] Step S301 matches the structured building behavior response feature vector and the equipment operation state, calculates the synchronization rate and the correlation coefficient, identifies the time sequence inconsistent area between behavior driving and equipment response, and records the abnormal position and duration.

[0043] Step S302, identify the abnormal behavior trajectory of the device energy consumption curve offset and response lag in the specific behavior mode, perform clustering analysis according to the structural building behavior response feature vector occurrence, output the behavior mode clustering result, filter the typical behavior period based on the behavior mode clustering result, the typical behavior period includes high-density office, night empty load and meeting concentration, and perform statistical analysis on the device energy consumption curve and operating parameters in each typical behavior period. The logic of statistical analysis is: Compare the device energy consumption curve, operating parameters and the reference operation template. If the device energy consumption curve shows a sudden rise, continuous high fluctuation or obvious response delay under the condition that the specific behavior input is unchanged, it is determined as an abnormal trajectory.

[0044] By presetting the energy consumption offset threshold and the upper limit of the response time, the start and end time, behavior mode label and fluctuation amplitude of the abnormal trajectory are calibrated, and the abnormal behavior trajectory is output.

[0045] The abnormal behavior trajectory includes one or more abnormal behaviors, and the occurrence period, corresponding behavior mode and operating state are identified and confirmed: In the occurrence period of stable personnel flow and limited heat load change, the device energy consumption data curve appears non-periodic fluctuation and energy consumption sudden rise without obvious external driving condition, which determines that the device is invalid and the energy consumption regulation is ineffective.

[0046] The device frequently starts and stops without corresponding change in building use behavior data, which determines that the device has abnormal start-stop logic combined with high control sensitivity.

[0047] When the environmental response data occurs rapid fluctuation, the operating state data does not respond in time or the delay exceeds the preset regulation response threshold, which determines that the device has regulation lag and execution unit response failure.

[0048] Step S302 identifies the typical behavior period by clustering analysis, compares the energy consumption curve with the reference template, detects energy consumption sudden rise, continuous high fluctuation and response lag, outputs the abnormal behavior trajectory and determines the abnormal types of invalid operation, start-stop logic abnormality and regulation lag.

[0049] Step S303 jointly structures and encodes the response inconsistent area and the abnormal behavior trajectory. The encoding fields of the structured encoding include abnormal type, influence range, associated device number, start and end timestamp and abnormal degree score.

[0050] Step S303 jointly encodes the behavior response inconsistent area and the abnormal behavior trajectory to form a structured data containing abnormal type, influence range, device number and abnormal score, which provides standardized information for subsequent fault association and early warning output.

[0051] Step S3 identifies the timing consistency anomaly and energy consumption deviation by matching the building behavior response characteristics and the equipment operation state, generates a structured anomaly code, and provides a high-precision anomaly data basis for fault location and subsequent causal analysis.

[0052] Step S4 includes the following sub-steps: Step S401, obtain the historical maintenance data and key component operation state data of the equipment, call the maintenance records from the equipment management system, the maintenance records include key component replacement time, maintenance frequency, maintenance project details, fault description, treatment method and actual processing time.

[0053] Synchronize the collection of the running state data of the current key components, including fans, electric valves, sensors and control execution units, the running state data includes cumulative running time, temperature rise curve slope, vibration spectrum peak value and abnormal fluctuation amplitude of working current.

[0054] Step S401 forms a complete equipment health data chain by calling the historical maintenance records of the equipment and synchronously collecting the running state of the key components, and provides basic information for causal analysis of abnormal trajectory and equipment state.

[0055] Step S402, the abnormal behavior trajectory and the historical maintenance data are analyzed for causal correlation, and the abnormal behavior trajectory output in step S303 is used as an anchor point to search for the maintenance records and state fluctuation information of the corresponding equipment in the previous and subsequent periods in the maintenance database.

[0056] According to the equipment type and the corresponding component characteristics, a causal mapping rule table is constructed to determine whether there is an abnormal situation before the occurrence of the abnormal trajectory, the abnormal situation includes maintenance lag record, frequent failure or significant deterioration trend of operation state index, and the causal correlation is determined according to the abnormal situation, and the causal correlation includes behavior anomaly, component aging, lubrication loss and transmission imbalance.

[0057] The causal correlation analysis is as follows: Based on the equipment state record data, compare the adjustment signal sending time and the actual response time of the equipment, calculate the average response time, response variance and maximum delay time, and determine whether it exceeds the preset threshold.

[0058] The number of times of starting and stopping of the equipment in the abnormal behavior trajectory time period is counted, and whether there is an invalid response situation of frequent starting and stopping without producing effective environmental regulation effect is judged combined with the building use behavior data.

[0059] The energy consumption curve in the time period is subjected to pattern recognition, the current energy consumption curve is normalized and aligned with the historical benchmark energy consumption curve under the same running state, and the deviation index is calculated, combined with the device aging label data, a weighted comprehensive scoring model is used to calculate the risk level, and the determination result is output.

[0060] Step S402 causally correlates the abnormal behavior trajectory with the maintenance data, determines whether the abnormality is caused by component aging, lubrication loss or transmission imbalance through response delay, start-stop frequency and energy consumption deviation, etc. in combination with the causal mapping rule table, and outputs the causal correlation result and risk level.

[0061] Step S403, based on the abnormal situation, energy consumption curve deviation and key component state change, determines whether the device has a hidden fault, the hidden fault including air duct blockage, fan aging, sensor accuracy decline and control logic delay.

[0062] The final fault type identification result is output in combination with the causal mapping rule table, and the device identification, fault classification code, impact level score and recommended treatment measures are attached.

[0063] Step S403 identifies hidden faults such as air duct blockage, fan aging and sensor accuracy decline based on causal analysis and key component state, and outputs the final fault type, impact level and recommended treatment measures, providing accurate reference for equipment maintenance optimization.

[0064] Step S4 correlates the abnormal behavior trajectory with historical maintenance and key component state data, constructs a causal mapping rule, realizes accurate matching of abnormal trajectory and device hidden fault, and outputs the fault identification result with risk level and treatment suggestion, providing intelligent decision basis for equipment maintenance.

[0065] Step S5 includes the following sub-steps: Step S501 converts the final fault type identification result into structured early warning data, specifically including: Read the device identification, fault type, risk level and recommended measures, combine the spatial position coding information in the abnormal behavior trajectory, generate a standard format of early warning data structure, the early warning data structure includes spatial position information, abnormal situation, hidden fault, time period and device coverage area.

[0066] Step S501 converts the fault identification result into a standardized early warning data structure, including spatial position, abnormal type and risk level, and realizes structured storage and quick call of information.

[0067] Step S502, the structured early warning data is visualized and synchronized to the operation and maintenance platform. The abnormal area identified is highlighted on the building plan in a layer superposition manner, and an information card is displayed in the corresponding area. The information card content includes device number, abnormal condition, hidden failure, risk level score, and recommended processing scheme.

[0068] Step S502 highlights the abnormal area on the building plan through visual display, and synchronizes the information card on the operation and maintenance platform, which supports the operation and maintenance personnel to quickly locate the fault equipment and obtain processing suggestions.

[0069] Step S503, update the dynamic mapping model based on the actual response situation. The operation and maintenance response records after the early warning information is released are tracked. The actual response situation includes response start time, disposal method, disposal time length, and final fault confirmation result.

[0070] Compare the actual response situation with the structured early warning data to determine whether there is false alarm, missed alarm, and classification deviation and correct the determination threshold and causal reasoning path parameters.

[0071] Step S503 compares the early warning data based on the operation and maintenance response records, evaluates the false alarm and missed alarm situation, dynamically adjusts the determination threshold and reasoning path, and realizes the continuous optimization and precision improvement of the fault identification model.

[0072] Embodiment 2, refer to Figure 2 , provides a heating equipment fault identification system, including a data acquisition module, a behavior-driven modeling module, an anomaly detection module, a fault correlation analysis module and an early warning feedback module.

[0073] The data acquisition module is used to acquire building use behavior, environmental response data and heating equipment operation data.

[0074] The behavior-driven modeling module is used to construct the coupling model between building behavior and environment, equipment response and behavior-driven operation mode.

[0075] The anomaly detection module is used to identify inconsistent areas of behavior response and abnormal behavior trajectory, and generate structured abnormal encoding.

[0076] The fault correlation analysis module is used to correlate abnormal trajectories and equipment maintenance data, identify potential fault types and output fault information.

[0077] The early warning feedback module is used to generate structured early warning data, visual display and track operation and maintenance response.

[0078] The application improves the accuracy of equipment fault identification through multi-dimensional data acquisition and dynamic coupling modeling, can monitor the correlation between equipment operation state and environmental changes in real time, can accurately locate the potential causes of equipment failure by identifying abnormal patterns between building use behavior and equipment response, reduces misjudgment and missed judgment in traditional fault diagnosis, adopts a structured early warning mechanism and combines visual display to feedback fault information in time and support operation and maintenance personnel to respond quickly, optimizes equipment maintenance cycle and management efficiency, introduces causal correlation analysis in equipment maintenance and fault judgment, helps to extract valuable information from historical maintenance records, and realizes intelligent and accurate equipment maintenance.

[0079] Those skilled in the art will appreciate that embodiments of the application can be provided as methods, systems or computer program products. Therefore, the application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Moreover, the application can take the form of a computer program product implemented on one or more computer-usable storage media having computer-usable program code embodied in the medium. The storage media can be realized by any type of volatile or non-volatile storage devices or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk. These computer program instructions can also be stored in a computer readable storage medium that can guide the computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer readable storage medium produce the product including instruction device, which realizes the flow Figure 1 The functions specified in one or more flows and / or blocks Figure 1 The functions specified in one or more flows and / or blocks

[0080] It should be noted that the above embodiments are only used to illustrate the technical solutions of the application and not to limit it. Although the application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the application can be modified or replaced by equivalent ones without departing from the spirit and scope of the technical solutions of the application, which should be covered in the scope of the claims of the application.

Claims

1. A method for identifying faults in HVAC equipment, characterized in that, Includes the following steps: Step S1: Collect building usage behavior, environmental response data, and HVAC equipment operation data; Step S2: Construct a coupling model and behavior-driven operation mode between building behavior and environment and equipment response; Step S3: Identify areas of inconsistent behavioral responses and abnormal behavioral trajectories, and generate structured anomaly codes; Step S4: Associate the abnormal trajectory with equipment maintenance data, identify potential fault types, and output fault information; Step S5: Generate structured early warning data, visualize and track operation and maintenance responses.

2. The HVAC equipment fault identification method as described in claim 1, characterized in that, Step S1 includes the following sub-steps: Step S101: Collect building usage behavior data, which includes the frequency of personnel movement, the density of personnel movement, the passage time, the number of people passing through, the on / off status of the lighting system, and the duration of the lighting system being on. Step S102: Collect environmental response data, including ambient temperature, ambient relative humidity, and ambient carbon dioxide concentration; Step S103: Collect HVAC equipment operation data, which includes equipment start-up and shutdown time, equipment start-up and shutdown frequency, fan operating speed, temperature difference between supply air outlet and return air outlet, and energy consumption data.

3. The HVAC equipment fault identification method as described in claim 2, characterized in that, Step S2 includes the following sub-steps: Step S201: Establish a temporal correspondence between building usage behavior data and environmental response data. The logic for establishing the temporal correspondence is as follows: The building usage behavior data and environmental response data are time-aligned, the building usage behavior data is converted into a standardized time series feature vector, and a sliding window comparison process is performed with minutes as the time window unit, comparing it with the environmental response data within the same time window. Calculate the correlation coefficient, cross-lag time difference, and co-fluctuation amplitude between the rate of change of building use behavior data and the rate of change of environmental response data within each time window. Record the trend changes of the correlation coefficient, cross-lag time difference, and co-fluctuation amplitude between the rate of change of building use behavior data and the rate of change of environmental response data over a continuous period of time. Based on the correlation coefficient, cross-lag time difference, co-fluctuation amplitude, and trend changes between the rate of change of building use behavior data and the rate of change of environmental response data, establish a dynamic coupling model between building use behavior data and environmental response data, and extract the driving features of the dynamic coupling model. The driving features include the maximum correlation time lag, the difference in fluctuation amplitude, and the peak response duration. Step S202: Establish a regulation response mapping between environmental response data and HVAC equipment operation data. The logic for establishing the regulation response mapping is as follows: The environmental response data is converted into a standardized time series and time-aligned with the HVAC equipment operation data. Using minutes as the time window unit, a sliding window comparison is performed to analyze the correspondence between the parameter fluctuations of the environmental response data and the data state changes of the HVAC equipment operation data. The equipment response delay, adjustment amplitude, and response rate within each time window are calculated, and response features are extracted. The response features include adjustment lag time, adjustment intensity change amplitude, and continuous adjustment time. Step S203: Integrate building usage behavior data, environmental response data, and equipment operation data, and use driving features and response features as joint inputs to construct a building behavior-driven operation mode. The construction logic of the building behavior-driven operation mode is as follows: Using a sliding time window as the analysis unit, the collaborative change segments of building usage behavior data, environmental response data and equipment operation data on the time axis are identified respectively. A multivariate dynamic regression model and an adaptive weight fusion mechanism are constructed to establish a behavior-driven equipment response prediction sub-model. By using response features as mediating variables, path analysis is introduced to quantify the moderating effect of response features on equipment operation response driven by changes in building behavior. The moderating effect index includes the direction of adjustment, intensity, and hysteresis amplitude. A linkage map is constructed, and the path intensity and response probability are dynamically updated and visualized using a data-driven approach, outputting a structured building behavior response feature vector.

4. The HVAC equipment fault identification method as described in claim 3, characterized in that, Step S3 includes the following sub-steps: Step S301 involves matching and analyzing the structured building behavior response feature vectors between building behavior and equipment operation. Based on the constructed dynamic mapping model, a one-to-one correspondence analysis is performed between the structured building behavior response feature vectors and the equipment operation state sequence within a unit time window. The synchronization rate is used to evaluate the temporal consistency. If a misalignment, decrease in synchronization rate, or change in correlation coefficient from positive to negative is detected in the structured building behavior response feature vector within a continuous time window, it is considered an area of ​​inconsistent behavior response, and the corresponding spatial location identifier, start time, and duration are recorded.

5. The HVAC equipment fault identification method as described in claim 4, characterized in that, Step S3 further includes the following sub-steps: Step S302: Identify abnormal behavioral trajectories of equipment energy consumption curve deviation and response lag under specific behavioral patterns. Perform cluster analysis based on the structured building behavior response feature vector, output behavioral pattern clustering results, and select typical behavioral cycles based on the behavioral pattern clustering results. The typical behavioral cycles include high-density office work, nighttime idleness, and concentrated meetings. Perform statistical analysis on the equipment energy consumption curve and operating parameters within each typical behavioral cycle. The logic of the statistical analysis is as follows: Compare the equipment energy consumption curve and operating parameters with the baseline operating template. If the equipment energy consumption curve shows a sudden increase, continuous high fluctuation, or significant response delay under the condition that the specific behavioral input remains unchanged, it is judged as an abnormal trajectory. By setting preset energy consumption offset threshold and response time upper limit, the start and end time, behavior pattern label and fluctuation amplitude of abnormal trajectory are calibrated, and abnormal behavior trajectory is output. Step S303: Jointly structure and encode the inconsistent response area and the abnormal behavior trajectory. The encoded fields of the structured encoding include the abnormality type, the scope of impact, the associated device number, the start and end timestamps, and the abnormality score.

6. The HVAC equipment fault identification method as described in claim 5, characterized in that, Step S4 includes the following sub-steps: Step S401: Obtain historical maintenance data and key component operating status data of the equipment, and retrieve maintenance records from the equipment management system. The maintenance records include key component replacement time, maintenance frequency, maintenance item details, fault description, handling method and actual handling time. The system synchronously collects operational status data of key components, including fans, electric valves, sensors, and control execution units. The operational status data includes cumulative running time, temperature rise curve slope, vibration spectrum peak value, and abnormal fluctuation amplitude of operating current. Step S402: Perform causal correlation analysis between the abnormal behavior trajectory and historical maintenance data. Using the associated device number and abnormal time period in the abnormal behavior trajectory output in step S303 as anchor points, retrieve the maintenance records and status fluctuation information of the corresponding device in the previous and subsequent cycles in the maintenance database. A causal mapping rule table is constructed based on the equipment type and the corresponding component characteristics. It is determined whether there are any abnormal situations before the occurrence of abnormal trajectories. The abnormal situations include maintenance delay records, frequent failures, or significant deterioration trends in operating status indicators. Causal association is determined based on the abnormal situations, and the causal association is output. The causal association includes abnormal behavior and component aging, lack of lubrication, and transmission imbalance. Step S403: Based on abnormal conditions, energy consumption curve deviations and changes in the status of key components, determine whether there are hidden faults in the equipment. The hidden faults include air duct blockage, fan aging, sensor accuracy degradation and control logic delay. The final fault type identification result is output by combining the causal mapping rule table, along with equipment identification, fault classification code, impact level score and suggested handling measures.

7. The HVAC equipment fault identification method as described in claim 6, characterized in that, Step S5 includes the following sub-steps: Step S501: Convert the final fault type identification result into structured early warning data, specifically including: Read the device identifier, fault type, risk level and recommended measures, and combine them with the spatial location coding information in the abnormal behavior trajectory to generate a standard format early warning data structure. The early warning data structure includes spatial location information, abnormal situation, hidden fault, time period and device coverage area. Step S502: Visualize the structured early warning data and synchronize it to the operation and maintenance platform. Use layer overlay to highlight the identified abnormal areas on the building floor plan and display information cards in the corresponding areas. The information cards include the equipment number, abnormal situation, hidden fault, risk level score and recommended handling solution. Step S503: Update the dynamic mapping model based on the actual response situation and track the operation and maintenance response records after the early warning information is released. The actual response situation includes the response start time, handling method, handling duration and final fault confirmation result. The actual response is compared with the structured early warning data to determine whether there are false alarms, missed alarms, and classification biases, and to correct the judgment thresholds and causal reasoning path parameters.

8. The HVAC equipment fault identification method as described in claim 7, characterized in that, The causal relationship analysis specifically includes: Based on the device status record data, compare the time when the adjustment signal is issued with the actual response time of the device, calculate the average response time, response variance and maximum delay time, and determine whether it exceeds the preset threshold. The number of times the equipment starts and stops within the time period of abnormal behavior trajectory is statistically analyzed, and combined with building usage behavior data, it is determined whether there are invalid responses with frequent starts and stops but no effective environmental regulation effect; Pattern recognition is performed on the energy consumption curves within the specified time period. The current energy consumption curve is normalized and aligned with the historical benchmark energy consumption curves under similar operating conditions, and the deviation index is calculated. Combined with equipment aging label data, a weighted comprehensive scoring model is used to calculate the risk level and output the judgment result.

9. A method for identifying faults in HVAC equipment as described in claim 8, characterized in that, The abnormal behavior trajectory includes one or more abnormal manifestations, which are identified and confirmed by combining the time period of occurrence, corresponding behavior pattern, and operating status: During periods of stable personnel flow and limited changes in heat load, if the equipment energy consumption data curve shows non-periodic fluctuations and a sudden increase in energy consumption without obvious external driving conditions, the equipment is judged to be ineffective in operation and energy consumption regulation failure. The equipment frequently starts and stops without corresponding changes in building usage data, indicating that the problem stems from an abnormal start / stop logic combined with excessively high control sensitivity. When environmental response data fluctuates rapidly, if the operating status data fails to respond in a timely manner or the delay exceeds the preset adjustment response threshold, the device is judged to be in a state of adjustment lag and execution unit response failure.

10. A fault identification system for heating, ventilation, and air conditioning (HVAC) equipment, applied in a fault identification method for HVAC equipment as described in any one of claims 1-9, characterized in that, It includes a data acquisition module, a behavior-driven modeling module, an anomaly detection module, a fault correlation analysis module, and an early warning feedback module; The data acquisition module is used to collect building usage behavior, environmental response data, and HVAC equipment operation data. The behavior-driven modeling module is used to construct a coupled model and behavior-driven operation mode between building behavior and environment and equipment response. The anomaly detection module is used to identify areas of inconsistent behavioral responses and abnormal behavioral trajectories, and to generate structured anomaly codes. The fault correlation analysis module is used to correlate abnormal trajectories with equipment maintenance data, identify potential fault types, and output fault information. The early warning feedback module is used to generate structured early warning data, visualize and display it, and track operation and maintenance responses.

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