Intelligent monitoring method, device and equipment for operation state of air compressor and medium

Abstract

The invention provides an intelligent monitoring method and device for the running state of an air compressor, equipment and a medium. The method comprises the steps that characteristic parameter data under the working condition of a full-load output state in the normal running state of the air compressor is used for constructing a multi-dimensional data matrix or an incidence relation graph; based on the multi-dimensional data matrix, a hyper-parameter method in an anomaly detection technology is adopted to construct a multi-dimensional relevance parameter data set under the operation state of the air compressor unit; the multi-dimensional relevance parameter data set is input into a machine learning model for learning, and an anomaly monitoring model used for determining the operation state of the air compressor unit is obtained; and the characteristic parameter data collected in real time under the full-load output state working condition are input into the anomaly monitoring model, and whether the operation state of the air compressor is abnormal or not is determined through vector similarity calculation according to a preset similarity threshold value condition. The method provided by the invention comprehensively and systematically carries out deep analysis and diagnosis on the operation state of the equipment, and the accuracy of equipment prediction maintenance diagnosis is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a method, device, equipment and medium for intelligently monitoring the operating status of an air compressor. Background Art

[0002] Air compressor units are important electromechanical equipment, widely used in aerospace, municipal transportation, industrial projects and other industrial sites. Due to the large number of parts and complex structure, it is very difficult to troubleshoot and analyze the failure of power equipment, and in severe cases, it will cause huge economic losses. In order to achieve predictive maintenance and predictive repair of air compressor units, the vibration state is usually monitored in real time, but the vibration state can only characterize the vibration response of electromechanical equipment. The correlation between load and equipment structure vibration response is difficult to characterize with a simple dimensional formula relationship. Therefore, it is impossible to correlate the load characteristics of the equipment. For example, when the load increases, the vibration response increases accordingly, resulting in errors or deviations in the diagnosis and analysis of the operating status, leading to misjudgment, and even unnecessary downtime and other accidents.

[0003] In view of this, there is an urgent need to provide a method that comprehensively considers the correlation between load and equipment structure vibration response to achieve intelligent monitoring of air compressor equipment status. Summary of the invention

[0004] In order to overcome the problems existing in the related art, the present disclosure provides an air compressor operating status intelligent monitoring method, device, equipment and medium to solve the technical problems in the related art.

[0005] One or more embodiments of this specification provide an intelligent monitoring method for the operating status of an air compressor, comprising the following steps:

[0006] The characteristic parameter data of the air compressor under the full load output state under normal operating conditions are used to construct a multidimensional data matrix or a correlation relationship map, wherein the characteristic parameters under the full load output state condition include the vibration load parameters and the operating load condition parameters of the compressor unit during the full load condition process;

[0007] Based on the multidimensional data matrix, the hyperparameter method in anomaly detection technology is used to construct a multidimensional correlation parameter data set under the operating state of the air compressor unit.

[0008] Inputting the multi-dimensional correlation parameter data set into the machine learning model for learning to obtain an abnormal monitoring model for determining the operating status of the air compressor unit; and

[0009] The characteristic parameter data collected in real time under the full-load output state condition is input into the abnormality monitoring model, and the vector similarity calculation is performed, and it is determined whether the operating state of the air compressor is abnormal according to the preset similarity threshold condition.

[0010] Furthermore, the vibration load parameters include vibration amplitude, waveform factor, and temperature load; the operating load condition parameters include valve opening, operating current, operating voltage, operating power factor, fan total pressure, flow, annual operating time, and annual average electricity price.

[0011] Furthermore, the step of constructing a multidimensional data matrix based on the characteristic parameter data of the air compressor under full load output state under normal operation based on the collected data specifically includes the following steps:

[0012] Step 11: At each preset moment in a preset time period, a vibration load parameter sequence and an operating load condition parameter sequence are respectively collected.

[0013] Step 12, constructing a multi-dimensional correlation matrix using time as a correlation parameter for the collected vibration load parameter data and the operating load condition parameter data, thereby obtaining a multi-dimensional correlation matrix data set.

[0014] Furthermore, the hyperparameter method is a convolutional neural network, a model based on mean and standard deviation, a model based on entropy analysis and information gain, or a Kalman filter model.

[0015] Furthermore, the machine learning model is a Clip model.

[0016] One or more embodiments of the present specification provide an intelligent monitoring device for the operation status of an air compressor, including a vibration sensor arranged at a key position of an air compressor unit and a state monitoring sensor arranged at a control system of the air compressor unit, a vibration collection sensor and a state collection sensor and an intelligent monitoring system for the operation status of the air compressor in communication connection;

[0017] The vibration sensor collects vibration load parameter data and the status monitoring sensor collects operating load condition parameter data and sends them to the air compressor operating status intelligent monitoring system;

[0018] The air compressor operation status intelligent monitoring system is provided with a first data processing module, a second data processing module, a model training module and a prediction module;

[0019] The first data processing module is used to construct a multi-dimensional data matrix or a correlation relationship map based on the vibration load parameter data and the operating load condition parameter data of the air compressor under normal operating conditions;

[0020] The second data processing module is used to construct a multi-dimensional correlation parameter data set under the operating state of the air compressor unit based on the multi-dimensional data matrix and using the hyperparameter method in the anomaly detection technology;

[0021] A model training module, used for inputting a multi-dimensional correlation parameter data set into a machine learning model for learning, and obtaining an abnormal monitoring model for determining the operating status of an air compressor unit; and

[0022] The prediction module is used to input the abnormality monitoring model according to the characteristic parameter data under the full-load output state condition collected in real time, calculate the vector similarity, and determine whether the operating state of the air compressor is abnormal according to the preset similarity threshold condition.

[0023] Furthermore, the vibration load parameters include vibration amplitude, waveform factor, and temperature load; the operating load condition parameters include valve opening, operating current, operating voltage, operating power factor, fan total pressure, flow, annual operating time, and annual average electricity price.

[0024] Furthermore, the first data processing module is configured to perform the following steps:

[0025] Step 11, collecting a vibration load parameter sequence and an operating load condition parameter sequence at each preset time within a preset time period;

[0026] Step 12, constructing a multi-dimensional correlation matrix using time as a correlation parameter for the collected vibration load parameter data and the operating load condition parameter data, thereby obtaining a multi-dimensional correlation matrix data set.

[0027] One or more embodiments of the present specification provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the intelligent monitoring method for the operating status of an air compressor as described in any one of the above when executing the computer program.

[0028] One or more embodiments of the present specification provide a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for intelligently monitoring the operating status of an air compressor as described in any one of the above items is implemented.

[0029] The present disclosure provides an intelligent monitoring method, device, equipment and medium for the operation status of an air compressor. The advantages are that the intelligent monitoring method for the operation status of an air compressor provided in the present embodiment collects characteristic parameter data under the full-load output state condition, and adopts the hyperparameter method in the abnormality detection technology to construct a multi-dimensional correlation parameter data set for the machine learning model to learn and construct a vector "dictionary" for comparison through the correlation relationship between the vibration load parameters of the air compressor group and the operation load condition parameters, so as to obtain a model that can identify the characteristic vector under the full-load output state condition under normal operation. Then, the model is used to calculate the similarity between the characteristic vector under the full-load output state condition collected in real time and the characteristic vector under the full-load output state condition learned by the model according to the preset similarity threshold condition, so as to determine whether the operation is abnormal. The method provided in the present embodiment comprehensively and systematically performs in-depth analysis and diagnosis on the operation status of the equipment, and provides the accuracy of equipment predictive maintenance diagnosis. The intelligent monitoring technology has a broader engineering practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate one or more embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0031] Figure 1 A flow chart of an intelligent monitoring method for the operating status of an air compressor provided in one or more embodiments of this specification;

[0032] Figure 2 A table of operating load condition parameters under three working conditions provided for one or more embodiments of this specification;

[0033] Figure 3 A relationship diagram between vibration load parameters and operating load condition parameters learned by a machine learning model provided in one or more embodiments of this specification;

[0034] Figure 4 A block diagram of an intelligent monitoring device for the operating status of an air compressor provided for one or more embodiments of this specification; and

[0035] Figure 5 A schematic diagram of the structure of a computer device provided for one or more embodiments of this specification. DETAILED DESCRIPTION

[0036] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below in conjunction with the drawings in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0037] The present invention is described in detail below in conjunction with specific implementation methods and the accompanying drawings.

[0038] Method Embodiment

[0039] According to an embodiment of the present invention, a method for intelligently monitoring the operating status of an air compressor is provided. Figure 1 As shown, it is a flow chart of the intelligent monitoring method for the operating status of an air compressor provided in this embodiment. The intelligent monitoring method for the operating status of an air compressor according to an embodiment of the present invention comprises the following steps:

[0040] Step S1, constructing a multidimensional data matrix or a correlation relationship map with the characteristic parameter data of the air compressor under the full-load output state under normal operation, wherein the characteristic parameters under the full-load output state include the vibration load parameters and the operating load condition parameters of the compressor unit during the full-load operation.

[0041] Step S2, based on the multidimensional data matrix, a hyperparameter method in anomaly detection technology is used to construct a multidimensional correlation parameter data set under the operating state of the air compressor unit.

[0042] Step S3, inputting the multidimensional correlation parameter data set into the machine learning model for learning, and obtaining an abnormal monitoring model for determining the operating status of the air compressor unit.

[0043] Step S4, inputting the characteristic parameter data collected in real time under the full-load output state condition into the abnormality monitoring model, calculating the vector similarity, and determining whether the operating state of the air compressor is abnormal according to a preset similarity threshold condition.

[0044] The intelligent monitoring method for the operating status of an air compressor provided in the present embodiment collects characteristic parameter data under the full-load output state condition, and adopts the hyperparameter method in the abnormality detection technology to construct a multi-dimensional correlation parameter data set for the machine learning model to learn and construct a vector "dictionary" for comparison through the correlation relationship between the vibration load parameters of the air compressor group and the operating load condition parameters, thereby obtaining a model that can identify the characteristic vector under the full-load output state condition under normal operating conditions, and then using the model according to a preset similarity threshold condition, the similarity calculation is performed between the characteristic vector under the full-load output state condition collected in real time and the characteristic vector under the full-load output state condition under normal operating conditions learned by the model, thereby determining whether the operation is abnormal. The method provided in the present embodiment comprehensively and systematically performs in-depth analysis and diagnosis on the equipment operating status, and provides the accuracy of equipment predictive maintenance diagnosis. The intelligent monitoring technology has a broader engineering practical value.

[0045] In step S1 of this embodiment, the vibration load parameters include vibration amplitude, waveform factor, temperature load, etc. Figure 2 As shown, this is a table of operating load condition parameters under three operating conditions provided in this embodiment, wherein the operating load condition parameters include valve opening, operating current, operating voltage, operating power factor, fan total pressure, flow, annual operating time and annual average electricity price and other parameters.

[0046] In this embodiment, the vibration load parameter data is acquired by collecting vibration sensors arranged at key positions of the air compressor unit, and the operating load condition parameter data is acquired by collecting status monitoring sensors arranged in the control system of the air compressor unit.

[0047] In this embodiment, based on the collected data, constructing a multidimensional data matrix from the characteristic parameter data of the air compressor under the full load output state under the normal operation state specifically includes the following steps:

[0048] Step 11: collect vibration load parameter sequence A at each preset time within a preset time period i,j and the operating load condition parameter sequence P i,j .

[0049] Step 12: Collect vibration load parameter data A i,j and operating load condition parameter data P i,j Using time as the correlation parameter, construct a multi-dimensional correlation matrix N i,j , thus obtaining a multi-dimensional correlation matrix data set.

[0050] In this embodiment, the vibration load parameter sequence A collected in step 11 i,j and the operating load condition parameter sequence P i,jPreprocessing may include standardization and normalization of dimensionless features, such as temperature data; and binarization and dummy coding of quantitative features, such as data collection time.

[0051] In step S2 of this embodiment, a hyperparameter method in anomaly detection technology is used to construct a multidimensional correlation parameter data set under the operating state of the air compressor unit based on a multidimensional correlation matrix data set, wherein the hyperparameter method can use models based on statistical features such as convolutional neural networks, models based on mean and standard deviation (such as Markowitz mean-variance model), models based on entropy analysis and information gain models (such as random forest, gradient boosting, and xgboost), and Kalman filter models.

[0052] In this embodiment, the multi-dimensional correlation parameter data set is input into the machine learning model to extract features, and the correlation between the vibration load parameters and the operating load condition parameters under normal operating conditions is learned, and the learned features are stored as corresponding feature vectors. Figure 3 As shown, it is a relationship diagram between the vibration load parameters and the operating load condition parameters learned by the machine learning model. The machine learning model can be a Clip (Contrastive language-image pre-training) model.

[0053] During the monitoring process, the characteristic parameter data collected in real time under the full-load output state condition is input into the abnormal monitoring model. The model extracts the feature vector and compares the similarity with the stored feature vector. If the similarity is lower than the preset similarity threshold, it is determined that the current air compressor is operating abnormally and triggers a warning. Among them, the similarity threshold is set to 70%-85%, and the specific threshold can be set according to needs.

[0054] By using the hyperparameter method in the above-mentioned anomaly detection technology to construct a multidimensional correlation parameter data set under the operating status of the air compressor group to train the model, it is possible to determine the correlation between the multidimensional correlation parameters when there is less data, and there is no need to make up for the data even if there is missing data. This is more suitable for the intelligent monitoring scenario of the air compressor operating status in this embodiment.

[0055] Device Embodiment

[0056] According to an embodiment of the present invention, an intelligent monitoring system for operating status of an air compressor is provided. Figure 4As shown, it is a block diagram of the intelligent monitoring system for the operation status of an air compressor provided in this embodiment. The intelligent monitoring system for the operation status of an air compressor according to the embodiment of the present invention comprises: a vibration sensor arranged at a key position of the air compressor unit and a state monitoring sensor arranged in the control system of the air compressor unit, a vibration acquisition sensor and a state acquisition sensor and an intelligent monitoring system for the operation status of the air compressor in communication connection;

[0057] The vibration sensor collects vibration load parameter data and the status monitoring sensor collects operating load condition parameter data and sends them to the air compressor operating status intelligent monitoring system;

[0058] The air compressor operation status intelligent monitoring system is provided with a first data processing module 10, a second data processing module 20, a model training module 30 and a prediction module 40;

[0059] The first data processing module 10 is used to construct a multi-dimensional data matrix or a correlation relationship map based on the vibration load parameter data and the operating load condition parameter data of the air compressor under normal operating conditions.

[0060] The second data processing module 20 is used to construct a multi-dimensional correlation parameter data set under the operating state of the air compressor unit based on the multi-dimensional data matrix and using the hyperparameter method in the abnormality detection technology.

[0061] The model training module 30 is used to input the multi-dimensional correlation parameter data set into the machine learning model for learning, so as to obtain an abnormal monitoring model for determining the operating status of the air compressor unit.

[0062] The prediction module 40 is used to input the abnormality monitoring model according to the characteristic parameter data under the full-load output state condition collected in real time, and determine whether the operating state of the air compressor is abnormal through vector similarity calculation and according to a preset similarity threshold condition.

[0063] The air compressor operating status intelligent monitoring system provided in the present embodiment collects characteristic parameter data under the full-load output state condition through sensors, and adopts the hyperparameter method in the abnormality detection technology to construct a multi-dimensional correlation parameter data set for the machine learning model to learn and construct a vector "dictionary" for comparison through the correlation relationship between the vibration load parameters of the air compressor group and the operating load condition parameters, so as to obtain a model that can identify the characteristic vector under the full-load output state condition under normal operating conditions, and then use the model to calculate the similarity between the characteristic vector under the full-load output state condition collected in real time and the characteristic vector under the full-load output state condition under normal operating conditions learned by the model according to a preset similarity threshold condition, so as to determine whether the operation is abnormal. The method provided in the present embodiment comprehensively and systematically performs in-depth analysis and diagnosis on the equipment operating status, and provides the accuracy of equipment predictive maintenance diagnosis. The intelligent monitoring technology has broader engineering practical value.

[0064] The vibration load parameters of this embodiment include vibration amplitude, waveform factor, temperature load, etc.; the operating load condition parameters include valve opening, operating current, operating voltage, operating power factor, fan total pressure, flow, annual operating time and annual average electricity price, etc.

[0065] In this embodiment, the first data processing module 10 is specifically configured to perform the following steps:

[0066] Step 11: collect vibration load parameter sequence A at each preset time within a preset time period i,j and the operating load condition parameter sequence P i,j .

[0067] Step 12: Collect vibration load parameter data A i,j and operating load condition parameter data P i,j Using time as the correlation parameter, construct a multi-dimensional correlation matrix N i,j , thus obtaining a multi-dimensional correlation matrix data set.

[0068] In this embodiment, a data preprocessing module 50 is also included to process the vibration load parameter sequence A collected by the first data processing module 10. i,j and the operating load condition parameter sequence P i,j Preprocessing may include standardization and normalization of dimensionless features, such as temperature data; and binarization and dummy coding of quantitative features, such as data collection time.

[0069] The embodiment of the present invention is an apparatus embodiment corresponding to the above-mentioned method embodiment. The specific operations of the processing steps of each module can be understood by referring to the description of the method embodiment, which will not be repeated here.

[0070] like Figure 5As shown, the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the intelligent monitoring method for the operating status of the air compressor in the above-mentioned embodiment is implemented.

[0071] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for intelligently monitoring the operating status of an air compressor in the above-mentioned embodiment is implemented; or when the computer program is executed by a processor, the method for intelligently monitoring the operating status of an air compressor in the above-mentioned embodiment is implemented.

[0072] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0073] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and the contents not described in detail in the specification of the present invention belong to the common knowledge of those skilled in the art.

Claims

1. An intelligent monitoring method for the operating status of an air compressor, characterized in that The following steps are involved: The characteristic parameter data of the air compressor under the full load output state under normal operating conditions are used to construct a multidimensional data matrix or a correlation relationship map, wherein the characteristic parameters under the full load output state include the vibration load parameters and the operating load condition parameters of the compressor unit during the full load condition; Based on the multidimensional data matrix, the hyperparameter method in anomaly detection technology is used to construct a multidimensional correlation parameter data set under the operating state of the air compressor unit. Inputting the multi-dimensional correlation parameter data set into the machine learning model for learning to obtain an abnormal monitoring model for determining the operating status of the air compressor unit; and The characteristic parameter data collected in real time under the full-load output state condition is input into the abnormality monitoring model, and the vector similarity calculation is performed, and it is determined whether the operating state of the air compressor is abnormal according to the preset similarity threshold condition.

2. The air compressor operating status intelligent monitoring method according to claim 1, characterized in that: The vibration load parameters include vibration amplitude, waveform factor, and temperature load; the operating load condition parameters include valve opening, operating current, operating voltage, operating power factor, fan total pressure, flow, annual operating time, and annual average electricity price.

3. The intelligent monitoring method for the operating status of an air compressor according to claim 1, characterized in that: Based on the collected data, constructing a multidimensional data matrix based on the characteristic parameter data of the air compressor under the full load output state under normal operation specifically includes the following steps: Step 11, collecting a vibration load parameter sequence and an operating load condition parameter sequence at each preset time within a preset time period; Step 12, constructing a multi-dimensional correlation matrix using time as a correlation parameter for the collected vibration load parameter data and the operating load condition parameter data, thereby obtaining a multi-dimensional correlation matrix data set.

4. The intelligent monitoring method for the operating status of an air compressor according to claim 1, characterized in that: The hyperparameter method is a convolutional neural network, a model based on mean and standard deviation, a model based on entropy analysis and information gain, or a Kalman filter model.

5. The intelligent monitoring method for the operating status of an air compressor according to claim 1, characterized in that: The machine learning model is a Clip model.

6. An intelligent monitoring device for the operating status of an air compressor, characterized in that It includes a vibration sensor arranged at a key position of the air compressor unit and a state monitoring sensor arranged in the air compressor unit control system, a vibration acquisition sensor and a state acquisition sensor and an intelligent monitoring system for the operation state of the air compressor in communication connection; The vibration sensor collects vibration load parameter data and the status monitoring sensor collects operating load condition parameter data and sends them to the air compressor operating status intelligent monitoring system; The air compressor operation status intelligent monitoring system is provided with a first data processing module, a second data processing module, a model training module and a prediction module; The first data processing module is used to construct a multi-dimensional data matrix or a correlation relationship map based on the vibration load parameter data and the operating load condition parameter data of the air compressor under normal operating conditions; The second data processing module is used to construct a multi-dimensional correlation parameter data set under the operating state of the air compressor unit based on the multi-dimensional data matrix and using the hyperparameter method in the anomaly detection technology; A model training module is used to input a multi-dimensional correlation parameter data set into a machine learning model for learning, so as to obtain an abnormal monitoring model for determining the operating status of an air compressor unit; as well as The prediction module is used to input the abnormality monitoring model according to the characteristic parameter data under the full-load output state condition collected in real time, calculate the vector similarity, and determine whether the operating state of the air compressor is abnormal according to the preset similarity threshold condition.

7. The intelligent monitoring device for the operation status of an air compressor according to claim 6, characterized in that: The vibration load parameters include vibration amplitude, waveform factor, and temperature load; the operating load condition parameters include valve opening, operating current, operating voltage, operating power factor, fan total pressure, flow, annual operating time, and annual average electricity price.

8. The intelligent monitoring device for the operation status of an air compressor according to claim 6, characterized in that: The first data processing module is configured to perform the following steps: Step 11, collecting a vibration load parameter sequence and an operating load condition parameter sequence at each preset time within a preset time period; Step 12, constructing a multi-dimensional correlation matrix using time as a correlation parameter for the collected vibration load parameter data and the operating load condition parameter data, thereby obtaining a multi-dimensional correlation matrix data set.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the air compressor operating status intelligent monitoring method according to any one of claims 1 to 5 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for intelligently monitoring the operating status of an air compressor according to any one of claims 1 to 5 is implemented.

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