CN-122015229-A - Air conditioning equipment fault early warning and intelligent operation and maintenance service system

CN122015229ACN 122015229 ACN122015229 ACN 122015229ACN-122015229-A

Abstract

The invention discloses an air conditioning equipment fault early warning and intelligent operation and maintenance service system, and relates to the technical field of intelligent operation and maintenance and fault diagnosis. The air conditioning equipment fault early warning and intelligent operation and maintenance service system comprises a multidimensional data acquisition module, an intelligent data preprocessing module, an accurate fault early warning module, a dynamic operation and maintenance decision module, a cloud collaborative management module, a user interaction terminal module and a fault diagnosis knowledge base module, wherein the multidimensional data acquisition module is used for comprehensively acquiring operation state parameters of the air conditioning equipment. The air conditioning equipment fault early warning and intelligent operation and maintenance service system has comprehensive and accurate data acquisition, namely, through the multi-dimensional sensor layout and a regular calibration mechanism, the comprehensive acquisition of the running state, the environmental parameters, the electrical performance and the full life cycle basic information of the air conditioning equipment is realized, the accuracy of acquired data is improved to more than 98%, and the problems of incomplete data acquisition and poor accuracy of the existing system are solved.

Inventors

CHEN YIN

Assignees

东莞祥科智控装备有限公司

Dates

Publication Date: 20260512
Application Date: 20260126

Claims (10)

1. The fault early warning and intelligent operation and maintenance service system of the air conditioning equipment is characterized by comprising a multi-dimensional data acquisition module, an intelligent data preprocessing module, an accurate fault early warning module, a dynamic operation and maintenance decision module, a cloud collaborative management module, a user interaction terminal module and a fault diagnosis knowledge base module; The multi-dimensional data acquisition module is used for acquiring the running state parameters, the environment influence parameters and the full life cycle basic information of the air conditioning equipment in all directions and transmitting acquired data to the intelligent data preprocessing module in real time; The intelligent data preprocessing module is used for carrying out layered cleaning, self-adaptive denoising, standardized conversion and multi-source data fusion processing on the acquired data, outputting a high-quality standardized data set, and respectively transmitting the high-quality standardized data set to the accurate fault early warning module and the cloud collaborative management module; The accurate fault early warning module is used for analyzing the standardized data set in real time based on the improved deep learning fusion model, identifying the type and the grade of the equipment fault risk, generating early warning information containing fault positioning and risk trend, and synchronously pushing the early warning information to the cloud collaborative management module and the user interaction terminal module; The dynamic operation and maintenance decision module relies on the fault diagnosis knowledge base module, combines the historical data, the early warning information and the equipment basic information stored by the cloud collaborative management module to generate a targeted operation and maintenance scheme, and implements a feedback dynamic optimization scheme according to the operation and maintenance; the cloud collaborative management module is used for storing full data, early warning information and operation and maintenance schemes, realizing data interaction and collaborative scheduling among the modules and simultaneously providing a data safety protection function; The user interaction terminal module is used for displaying differentiated information to users with different roles and supporting user operation instruction input and operation and maintenance process tracing; the fault diagnosis knowledge base module stores typical fault cases, fault feature bases and operation and maintenance scheme templates of the air conditioning equipment and provides knowledge support for fault identification and operation and maintenance decision.
2. The air conditioning equipment fault early warning and intelligent operation and maintenance service system according to claim 1, wherein the multi-dimensional data acquisition module comprises a core component sensor group, an environment sensing unit, an electrical parameter acquisition unit and an information input and calibration unit; The core component sensor group comprises a vibration sensor, a temperature sensor and a pressure sensor which are arranged on the compressor, the evaporator, the condenser and the throttling device and is used for collecting real-time operation parameters of the core component; the environment sensing unit is used for collecting temperature, humidity, dust concentration and air pressure data of an air conditioner running environment; The electric parameter acquisition unit is used for acquiring the running current, voltage, power and power factor of the air conditioner; The information input and calibration unit is used for inputting equipment model, factory parameters, installation information and historical maintenance records, and carrying out periodic calibration on sensor acquisition data.
3. The system for air conditioning equipment fault early warning and intelligent operation and maintenance service according to claim 1, wherein the intelligent data preprocessing module comprises the following processing flows: firstly, adopting a layered cleaning strategy based on a3 sigma criterion and an isolated forest algorithm to remove abnormal data, and adopting interpolation filling or migration filling based on similar working conditions for missing data according to data types; Step two, adopting a self-adaptive wavelet threshold denoising algorithm, dynamically adjusting a wavelet basis function and the number of decomposition layers according to the data noise intensity, and realizing accurate denoising; Thirdly, converting the data of different dimension into a standard normal distribution interval by adopting a Z-score standardization method; and fourthly, adopting a multisource data fusion algorithm based on an attention mechanism to assign dynamic weights to the operation parameters, the environment parameters and the basic information, fusing the dynamic weights and outputting a standardized data set.
4. The air conditioning equipment fault early warning and intelligent operation and maintenance service system according to claim 1, wherein the improved deep learning fusion model in the accurate fault early warning module is a CNN-BiLSTM-Attention fusion model, and the model comprises a CNN feature extraction layer, a BiLSTM time sequence analysis layer, an Attention enhancement layer and a risk decision layer which are connected in sequence; The CNN feature extraction layer is used for extracting spatial features and local associated features in the data; the BiLSTM time sequence analysis layer is used for capturing the long-term and short-term time dependence of the data; The attention enhancing layer is used for enhancing the weight of the key fault characteristics; The risk decision layer outputs a fault risk level (no risk, low risk, medium risk, high risk) and a corresponding fault type.
5. The system for air conditioning equipment fault early warning and intelligent operation and maintenance service according to claim 4, wherein the training process of the improved deep learning fusion model comprises the following steps: S1, constructing a labeling data set containing normal operation data, potential fault data and typical fault data, and dividing the labeling data set into a training set and a testing set according to the proportion of 8:2; s2, initializing model parameters, setting a learning rate to be 0.001-0.003, and setting iteration times to be 120-150 and batch sizes to be 32-64; S3, model training is carried out by adopting AdamW optimizers and Focal Loss functions, and overfitting is prevented through an early-stop mechanism; S4, verifying the performance of the model by using the test set, and finishing training when the fault identification accuracy is more than or equal to 96% and the recall is more than or equal to 95%, otherwise, adjusting the model structure and the parameters to retrain.
6. The system for air conditioning equipment fault early warning and intelligent operation and maintenance service according to claim 1, wherein the process of generating the operation and maintenance scheme by the dynamic operation and maintenance decision module is as follows: s1, receiving early warning information of an accurate fault early warning module, and calling matched fault characteristics and cases from a fault diagnosis knowledge base module; S2, analyzing root causes of faults by combining historical operation data, maintenance records and current operation states of equipment stored by the cloud collaborative management module; S3, generating a targeted scheme comprising fault processing steps, required spare parts, tool lists and operation specifications based on fault types, equipment service years, operation environments and user operation and maintenance cost budget; And S4, receiving a scheme implementation effect fed back by operation and maintenance personnel, performing iterative optimization on the scheme, and updating the scheme to the fault diagnosis knowledge base module.
7. The system for early warning of faults and intelligent operation and maintenance of air conditioning equipment according to claim 1 is characterized in that the cloud collaborative management module comprises a distributed data storage unit, a real-time data interaction unit, a data encryption and authority management unit and a data backup unit; The distributed data storage unit adopts a MySQL and MongoDB hybrid storage architecture to respectively store structured data and unstructured data; the real-time data interaction unit adopts an MQTT communication protocol to realize low-delay data transmission; the data encryption and authority management unit adopts an AES-256 encryption algorithm to transmit and store the data, and distributes user authorities based on a role-based access control strategy; The data backup unit adopts a remote multi-copy backup mechanism, and ensures the safety and the integrity of the data.
8. The air conditioning equipment fault early warning and intelligent operation and maintenance service system according to claim 1, wherein the user interaction terminal module comprises an operation and maintenance personnel Web management end, a common user mobile end APP and an equipment management background; The Web management end of the operation and maintenance personnel supports real-time monitoring of equipment operation data, checking of fault early warning details, editing and issuing of operation and maintenance schemes, recording and updating of a knowledge base; The common user mobile terminal APP supports fault early warning reminding, simple operation and maintenance suggestion checking, maintenance application submitting and operation and maintenance progress inquiring; the equipment management background supports system parameter configuration, sensor calibration management and module state monitoring.
9. The system for early warning and intelligent operation and maintenance of air conditioning equipment according to claim 1, wherein the fault diagnosis knowledge base module adopts an incremental updating mechanism, and comprises a fault feature base, a case base and a scheme template base; the fault feature library stores typical fault feature parameter thresholds of air conditioning equipment of different models; The case stock stores historical fault processing cases and effect evaluation data; The scheme template library stores standardized operation and maintenance scheme templates aiming at different fault types and equipment models, and can be dynamically adjusted according to actual working conditions.
10. The system for early warning of faults and intelligent operation and maintenance of air conditioning equipment according to claim 1, wherein the information input and calibration unit has an automatic calibration reminding function, and generates periodic calibration reminding according to the use duration of the sensor, the ambient humidity and the stability of collected data, and records a calibration result; when the deviation of the collected data of the sensor exceeds a preset threshold, a sensor fault early warning is sent out to prompt replacement or maintenance.

Description

Air conditioning equipment fault early warning and intelligent operation and maintenance service system Technical Field The invention relates to the technical field of intelligent operation and maintenance and fault diagnosis, in particular to an air conditioning equipment fault early warning and intelligent operation and maintenance service system. Background The air conditioning equipment is used as core equipment for indoor environment adjustment, is widely applied to various places such as houses, commercial complexes, industrial plants, medical institutions and the like, the running stability of the air conditioning equipment directly influences the environment comfort level and the normal development of production activities, and with the increase of the service life of the air conditioning equipment and the complex change of the running environment, the problems of compressor faults, pipeline leakage, refrigeration efficiency reduction, electric system faults and the like frequently occur, the faults not only can cause the shutdown of the air conditioning equipment, influence the use experience of users, but also can cause permanent damage of the equipment due to the expansion of faults, increase the maintenance cost and even cause safety accidents; The operation and maintenance of the current air conditioning equipment mainly depend on the traditional manual inspection and post-maintenance modes, and have obvious limitations that the manual inspection is low in efficiency, needs to input a large amount of labor cost, is limited by the experience of inspection personnel, and is difficult to find potential fault hidden trouble of the equipment; The post-maintenance mode can only remedy after the fault occurs, and early warning cannot be performed in advance, so that the equipment has long downtime and normal use is affected; In order to solve the problems, a part of enterprises put forward a simple air conditioner operation and maintenance system, but the existing system still has three main core defects that data acquisition is incomplete, most systems only acquire a small amount of key electrical parameters or temperature data, parameters which are critical to fault diagnosis such as core component vibration, running environment dust concentration, pipeline pressure change and the like are ignored, a data calibration mechanism is lacked, and the accuracy of acquired data is difficult to guarantee; The fault early warning accuracy is low, the traditional threshold judgment or simple machine learning model is mostly adopted in the existing system, complex spatial characteristics and long-period time dependency relations in data cannot be effectively captured, and false early warning or missing early warning is easy to occur; The operation and maintenance scheme lacks pertinence, most systems adopt standardized scheme templates, and personalized information such as equipment model, service life, operation environment, historical fault records and the like is not fully combined, so that the applicability of the operation and maintenance scheme is poor, the maintenance efficiency is low, and the operation and maintenance cost is high; Therefore, aiming at the problems of incomplete data acquisition, low fault early warning accuracy and lack of pertinence of an operation and maintenance scheme of the existing air conditioner operation and maintenance system, an intelligent service system capable of realizing comprehensive data acquisition, accurate fault early warning and personalized operation and maintenance decision is developed, and the intelligent service system has important practical significance and application value for improving the operation and maintenance efficiency of air conditioner equipment, reducing the operation and maintenance cost and guaranteeing the safe and stable operation of the equipment. Disclosure of Invention The invention aims to at least solve one of the technical problems in the prior art, and provides an air conditioning equipment fault early warning and intelligent operation and maintenance service system which can solve the problems. The technical scheme includes that the air conditioning equipment fault early warning and intelligent operation and maintenance service system comprises a multidimensional data acquisition module, an intelligent data preprocessing module, an accurate fault early warning module, a dynamic operation and maintenance decision module, a cloud collaborative management module, a user interaction terminal module and a fault diagnosis knowledge base module; The multi-dimensional data acquisition module is used for acquiring the running state parameters, the environment influence parameters and the full life cycle basic information of the air conditioning equipment in all directions and transmitting acquired data to the intelligent data preprocessing module in real time; The intelligent data preprocessing module is used for carrying out layered clean