Electromechanical equipment energy-saving intelligent management and control system and method based on AI technology

Abstract

The invention relates to the field of electromechanical equipment management and control, and particularly discloses an electromechanical equipment energy-saving intelligent management and control system and method based on the AI technology. According to the method, a prediction model is constructed by analyzing variation trend curves of various representations associated with faults when the faults of the motor equipment occur, and operation data is collected; and drawing change trend curves of various representations, comparing and judging fault hidden dangers, obtaining faults of various prediction types, analyzing pre-estimation time points and pre-estimation severity degrees of the faults of the prediction types, and predicting the types, time and severity degrees of possible problems of the motor equipment before the faults actually occur. Through multi-dimensional characterization analysis, dynamic trend modeling and intelligent decision feedback, accurate prediction and energy-saving optimization of fault hidden dangers are realized. The problems of troubleshooting omission, insufficient evaluation reliability, rigid maintenance plan and the like in the prior art are solved, so that technical support is provided for efficient, safe and energy-saving operation of industrial equipment.

Description

Technical Field

[0001] The present invention relates to the field of electromechanical equipment control and management, and relates to an energy-saving intelligent control system and method for electromechanical equipment based on AI technology. Background Art

[0002] With the development of technology and the improvement of industrial automation level, electromechanical equipment, as a power source, plays an increasingly prominent role in various industrial processes. However, during long-term operation, electromechanical equipment will suffer from problems such as wear, aging, and pollution, which may lead to performance degradation, efficiency reduction, and even equipment failures, thus affecting production continuity and safety.

[0003] With the continuous improvement of industrial automation level, electromechanical equipment, as the core power source in industrial production, its operating efficiency and reliability directly affect production continuity and economic benefits. Currently, enterprises generally adopt regular maintenance and experience-based fault detection methods, which can partially reduce the risk of equipment failures, but it is difficult to cope with the dynamic changes under complex working conditions. Existing technologies mostly rely on single-parameter threshold alarms or manual experience judgments, lacking systematic analysis of the associated characteristics of multiple types of faults, resulting in insufficient fault prediction accuracy and difficulty in achieving precise maintenance. In addition, traditional maintenance strategies are often based on fixed cycles, which are prone to over-maintenance or maintenance lags, not only increasing operation and maintenance costs, but also potentially causing unplanned shutdowns due to sudden failures, seriously affecting production safety and energy efficiency optimization.

[0004] There are still significant challenges in the current field of energy conservation and fault prediction for electromechanical equipment. On the one hand, the massive data generated during equipment operation has not been fully exploited, and the correlation between fault characteristics and energy efficiency indicators has not been effectively modeled, making it difficult to detect potential faults at an early stage; on the other hand, existing prediction models are mostly based on static historical data and cannot dynamically capture the degradation trend of equipment, with limited comprehensive judgment ability for fault types, occurrence times, and severity levels. Especially in high-energy-consuming industrial scenarios, the coupling effect of equipment energy efficiency decay and potential faults further increases the operation and maintenance difficulty, and there is an urgent need for an intelligent and systematic solution. Summary of the Invention

[0005] In view of the above problems, the present invention proposes an energy-saving intelligent control system for electromechanical equipment based on AI technology, which realizes precise prediction of potential faults and energy-saving optimization through multi-dimensional characterization analysis, dynamic trend modeling, and intelligent decision feedback. It solves the problems of missed fault detection, insufficient reliability assessment, and rigid maintenance plans in the prior art, thus providing technical support for the efficient, safe, and energy-saving operation of industrial equipment.

[0006] The energy-saving intelligent control system for electromechanical equipment specifically includes: Model establishment module: It is used to analyze the changing trend curves of various characteristics associated with the faults when faults occur based on the historical operation data of each electromechanical device with various types of faults, and construct a prediction model for various types of faults of electromechanical devices.

[0007] Associated characteristic monitoring module: It is used to collect the operation data of the target electromechanical device at each historical maintenance time point, screen the numerical values of various characteristics associated with various types of faults, and draw the changing trend curves of various characteristics.

[0008] Fault type prediction module: It is used to judge whether there are potential fault hazards in the target electromechanical device according to the changing trend curve and the prediction model. If so, analyze the predicted type of fault; Time analysis module: It is used to analyze the dangerous values of various characteristics and analyze the estimated time points of the predicted type of fault in combination with the changing trend curve.

[0009] Degree analysis module: It is used to analyze the estimated severity of the predicted type of fault according to the changing trend curve.

[0010] Diagnosis feedback module: It is used to feedback the predicted type of fault, the estimated time point and the estimated severity to the operation and maintenance center for processing.

[0011] Compared with the prior art, the following beneficial effects are achieved by the electromechanical device energy-saving intelligent control system based on AI technology described in the present invention: 1. Through multi-dimensional characteristic analysis, dynamic trend modeling and intelligent decision-making feedback, the present invention realizes the accurate prediction of potential fault hazards and energy-saving optimization. It solves the problems of missed fault detection, insufficient reliability evaluation, and rigid maintenance plan in the prior art, thus providing technical support for the efficient, safe and energy-saving operation of industrial equipment.

[0012] 2. The present invention obtains the changing trend curves of various characteristics associated with various types of faults when the electromechanical device has various types of faults, constructs a prediction model for various types of faults of the electromechanical device, combines the changing trend curves of various characteristics associated with various types of faults in the target electromechanical device, and checks whether there are potential fault hazards in the target electromechanical device. It comprehensively checks the electromechanical device from the various characteristics of various types of faults, thereby avoiding missed detection, being able to predict the occurrence of potential faults, and thus realizing early intervention, reducing unplanned downtime, extending the equipment life, and improving production efficiency and safety.

[0013] 3. The present invention collects the operation data of the target electromechanical device at each historical maintenance time point, draws the changing trend curves of various characteristics associated with various types of faults in the target electromechanical device, and compares them with the prediction model of various types of faults of the electromechanical device, thereby judging whether there are potential fault hazards in the target electromechanical device, and being able to improve the reliability and accuracy of the evaluation results.

[0014] 4. The present invention obtains various predicted types of faults of the target electromechanical equipment, the estimated time points and estimated severity levels of the occurrence of various predicted types of faults, and conducts feedback, which is beneficial to formulating predictive and targeted maintenance plans for electromechanical equipment, and is also beneficial to adjusting the maintenance plan according to the prediction results, avoiding unnecessary premature maintenance or faults caused by delayed maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0016] Figure 1 It is a schematic diagram of the module composition of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0018] Please refer to Figure 1 As shown, an energy-saving intelligent control system for electromechanical equipment based on AI technology provided by the present invention includes: a model establishment module: used for analyzing the change trend curves of various characteristics associated with the faults when the faults occur according to the historical operation data of each electromechanical equipment with various types of faults, and constructing a prediction model for various types of faults of the electromechanical equipment.

[0019] As a preferred solution, the model establishment module includes: extracting the historical fault information of each electromechanical equipment stored in the database, obtaining various types of faults that have occurred in each electromechanical equipment historically, sorting and classifying them, obtaining each electromechanical equipment with various types of faults, and further obtaining the historical operation data of each electromechanical equipment with various types of faults.

[0020] It should be noted that there are various classification methods for the fault types of electromechanical equipment.

[0021] In a specific embodiment, the fault types of electromechanical equipment include but are not limited to: electrical faults, mechanical faults, control system faults, etc.

[0022] Set the time points for each maintenance of the electromechanical equipment according to the preset principles. Based on the historical operation data of each electromechanical equipment with various types of failures, filter the operation data of each electromechanical equipment with various types of failures at each historical maintenance time point before the failure, and record it as the operation data of each electromechanical equipment with various types of failures at each historical maintenance time point.

[0023] It should be noted that the time points for each maintenance of each electromechanical equipment are the same. For example, the time point for the first maintenance of each electromechanical equipment is three months later, and the time point for the second maintenance of each electromechanical equipment is six months later.

[0024] It should be noted that the failure times of each electromechanical equipment with various types of failures are different, and thus the historical maintenance time points before the failures of each electromechanical equipment with various types of failures are not all the same.

[0025] In a specific embodiment, set the interval duration for the maintenance of the electromechanical equipment, and set the time points for each maintenance of the electromechanical equipment according to the preset equal-time-interval principle. For example, the interval duration for the maintenance of the electromechanical equipment is three months, and then set the time points for each maintenance of the electromechanical equipment.

[0026] In another specific embodiment, set each maintenance stage of the electromechanical equipment, and set the maintenance interval duration corresponding to each maintenance stage of the electromechanical equipment. Respectively, at each maintenance stage of the electromechanical equipment, set the time points for each maintenance according to the preset equal-time-interval principle, and thus obtain the time points for each maintenance of the electromechanical equipment. For example, the electromechanical equipment has three maintenance stages, namely 1 - 3 years, 3 - 5 years, and over 5 years, and the maintenance interval durations corresponding to the three maintenance stages are six months, three months, and one month respectively, and further obtain the time points for each maintenance of the electromechanical equipment.

[0027] In another specific embodiment, obtain the operation data of each electromechanical equipment at each historical maintenance time point, and thus filter the operation data of each electromechanical equipment with various types of failures at each historical maintenance time point before the failure.

[0028] Based on the historical operation data of each electromechanical equipment with various types of failures, obtain the operation data of each electromechanical equipment with various types of failures at the time point of its failure.

[0029] As a preferred solution, the model establishment module further includes: setting various characterizations associated with various types of failures of the electromechanical equipment, and filtering to obtain various characterizations associated with the failures of each electromechanical equipment with various types of failures.

[0030] It should be noted that various characterizations associated with various types of failures of the electromechanical equipment are obtained based on historical experience.

[0031] In a specific embodiment, the various fault types of the electromechanical equipment include electrical faults, mechanical faults, and control system faults. Among them, the various characteristics associated with electrical faults include short-circuit current, insulation resistance, temperature, voltage fluctuation amount, and frequency change amount, etc. The various characteristics associated with mechanical faults include bearing wear degree, rotor eccentricity, shaft bending degree, shaft crack length, and vibration displacement, etc. The various characteristics associated with control system faults include voltage, current, and temperature, etc.

[0032] According to the operation data of each electromechanical equipment with various types of faults at each historical maintenance time point and the fault occurrence time point, the numerical values of the various characteristics associated with the faults of each electromechanical equipment with various types of faults at each historical maintenance time point and the fault occurrence time point are obtained.

[0033] Summarize each historical maintenance time point and the fault occurrence time point to obtain each time point. Establish a coordinate system with the time point as the abscissa and the numerical value of the characteristic as the ordinate. According to the numerical values of the various characteristics associated with the faults of each electromechanical equipment with various types of faults at each historical maintenance time point and the fault occurrence time point, mark the corresponding data points in the coordinate system, and use the method of establishing a mathematical model to draw the change trend curves of the various characteristics associated with the faults when each electromechanical equipment with various types of faults has a fault.

[0034] It should be noted that the change trend curve of the fault-associated characteristic is an upward trend curve or a downward trend curve. For example, if the fault-associated characteristic is temperature, the change trend curve of the fault-associated characteristic is an upward trend curve at this time. If the fault-associated characteristic is efficiency, the change trend curve of the fault-associated characteristic is a downward trend curve at this time.

[0035] Fit the change trend curves of the various characteristics associated with the faults when each electromechanical equipment with various types of faults has a fault to obtain the change trend curves of the various characteristics associated with the faults when each electromechanical equipment has various types of faults, and construct a prediction model for the various types of faults of the electromechanical equipment.

[0036] It should be noted that the specific process of fitting the change trend curves of the various characteristics associated with the faults when each electromechanical equipment with various types of faults has a fault is as follows: Select each marked time point in the change trend curves of the various characteristics associated with the faults when each electromechanical equipment with various types of faults has a fault according to the preset equal time interval principle.

[0037] Obtain the numerical values corresponding to the various characteristics associated with the various types of faults at each marked time point in each electromechanical equipment with the fault, and perform an average value calculation to obtain the average numerical values of the various characteristics associated with the various types of faults in the electromechanical equipment with the fault at each marked time point.

[0038] Taking the marked time points as the abscissa and the average numerical values of the characterizations as the ordinate, a coordinate system is established. According to the average numerical values of the various characterizations associated with each type of fault at each marked time point in the electromechanical equipment where the fault occurs, the corresponding data points are marked in the coordinate system. Using the method of establishing a mathematical model, the change trend curves of the various characterizations associated with each type of fault when the electromechanical equipment has a fault are plotted.

[0039] It should be noted that the same marked time points are selected for the change trend curves of the various characterizations associated with each type of fault when each electromechanical equipment has a fault.

[0040] Correlation Characterization Monitoring Module: It is used to collect the operation data of the target electromechanical equipment at each of its historical maintenance time points, screen the numerical values of the various characterizations associated with each type of fault, and plot the change trend curves of the various characterizations.

[0041] It should be noted that the method of plotting the change trend curves of the various characterizations associated with each type of fault of the target electromechanical equipment is the same in principle as the method of plotting the change trend curves of the various characterizations associated with each type of fault when each electromechanical equipment has a fault.

[0042] Fault Type Prediction Module: It is used to determine whether there are potential fault hazards in the target electromechanical equipment according to the change trend curves and the prediction model. If so, analyze the predicted type of fault.

[0043] As an optimal solution, the Fault Type Prediction Module includes: According to the prediction models of each type of fault of the electromechanical equipment, obtain the change trend curves of the various characterizations associated with each type of fault when the electromechanical equipment has a fault, and record them as the reference change trend curves of the various characterizations associated with each type of fault of the electromechanical equipment.

[0044] Compare the change trend curves of the various characterizations associated with each type of fault of the target electromechanical equipment with the reference change trend curves of the various characterizations associated with each type of fault of the electromechanical equipment to obtain the shape similarity degree between the change trend curves of the various characterizations associated with each type of fault of the target electromechanical equipment and its reference change trend curves, and record it as , indicating the number of the th fault type, , indicating the number of the th characterization item, .

[0045] Through the fuzzy function calculation formula obtain the possibility coefficient of the target electromechanical equipment for each type of fault, where represents the correction factor of the preset possibility coefficient, represents the natural constant, The weight factor characterized by the th item associated with the type of fault of the preset target electromechanical device,

[0046] As a preferred solution, the fault type prediction module further includes: comparing the possibility coefficients of various types of faults occurring in the target electromechanical device with a preset possibility coefficient warning value. If the possibility coefficients of various types of faults occurring in the target electromechanical device are all less than the preset possibility coefficient warning value, then there is no fault hidden danger in the target electromechanical device; otherwise, there is a fault hidden danger in the target electromechanical device. Record the fault types corresponding to the possibility coefficients of faults occurring in the target electromechanical device that are greater than or equal to the preset possibility coefficient warning value as the possible fault types of the target electromechanical device, and count the various types of faults that the target electromechanical device may occur, and record them as the predicted type faults of the target electromechanical device.

[0047] In this embodiment, the present invention obtains the change trend curves of the various characteristics associated with the faults when the electromechanical device has various types of faults, constructs a prediction model for various types of faults of the electromechanical device, combines the change trend curves of the various characteristics associated with the various types of faults in the target electromechanical device, checks whether there are fault hidden dangers in the target electromechanical device, comprehensively checks the electromechanical device from the various characteristics of various types of faults, thereby avoiding omission in the check, being able to predict the occurrence of potential faults, and thus realizing early intervention, reducing unplanned downtime, extending the equipment life, and improving production efficiency and safety.

[0048] In this embodiment, the present invention collects the operation data of the target electromechanical device at its historical maintenance time points, draws the change trend curves of the various characteristics associated with the various types of faults in the target electromechanical device, and compares them with the prediction models of the various types of faults of the electromechanical device, thereby judging whether there are fault hidden dangers in the target electromechanical device, and being able to improve the reliability and accuracy of the evaluation result.

[0049] Time analysis module: used to analyze the danger values of the various characteristics and analyze the estimated time points of the predicted type faults in combination with the change trend curves.

[0050] As a preferred solution, the time analysis module includes: obtaining the values of the various characteristics associated with the faults at the fault time points of each electromechanical device with various types of faults, obtaining the corresponding values of the various characteristics associated with the various types of faults when each electromechanical device has a fault, further obtaining the average value and the minimum value of the corresponding values of the various characteristics associated with the various types of faults when the electromechanical device has a fault, and recording them as respectively.

[0051] By analyzing the formula Obtain the risk values of various characteristics associated with different types of faults of the electromechanical equipment , where respectively represent the weight factors of the preset average value and the minimum value, , represents the correction amount of the preset characteristic risk value.

[0052] It should be noted that the correction amount of the characteristic risk value is a value close to zero.

[0053] In another specific embodiment, the risk values of various characteristics associated with different types of faults of the electromechanical equipment are set according to a preset principle.

[0054] As a preferred solution, the time analysis module further includes: screening the risk values of various characteristics associated with each predicted type of fault of the target electromechanical equipment according to the risk values of various characteristics associated with different types of faults of the electromechanical equipment.

[0055] According to the change trend curves of various characteristics associated with each predicted type of fault of the target electromechanical equipment, screening the change trend curves of various characteristics associated with each predicted type of fault of the target electromechanical equipment, combining with the risk values of various characteristics associated with each predicted type of fault of the target electromechanical equipment, obtaining the time points corresponding to when the characteristics in the change trend curves of various characteristics associated with each predicted type of fault of the target electromechanical equipment reach their risk values, obtaining the earliest time point when the characteristics in the change trend curves of various characteristics associated with each predicted type of fault of the target electromechanical equipment reach their risk values, and recording it as the estimated time point for the target electromechanical equipment to have each predicted type of fault.

[0056] In another specific embodiment, use a mathematical analysis method to obtain the functions corresponding to the change trend curves of various characteristics associated with each predicted type of fault of the target electromechanical equipment, and substitute the risk values of various characteristics associated with each predicted type of fault of the target electromechanical equipment to obtain the estimated time points for the target electromechanical equipment to have each predicted type of fault.

[0057] Degree analysis module: used to analyze the estimated severity of the predicted type of fault according to the change trend curve.

[0058] As a preferred solution, the degree analysis module includes: obtaining the absolute value of the maximum tangent slope of the change trend curve of various characteristics associated with each type of fault when the electromechanical equipment has each type of fault according to the prediction model of various types of faults of the electromechanical equipment, and recording it as the predicted maximum tangent slope of the change trend curve of various characteristics associated with each type of fault when the electromechanical equipment has each type of fault, and screening the predicted maximum tangent slopes of the change trend curves of various characteristics associated with each predicted type of fault of the target electromechanical equipment.

[0059] According to the change trend curves of various characteristics associated with each predicted type of fault of the target electromechanical equipment, obtain the actual maximum tangent slope of the change trend curves of various characteristics associated with each predicted type of fault of the target electromechanical equipment.

[0060] As a preferred solution, the degree analysis module further includes: obtaining the ratio between the actual maximum tangent slope of the change trend curve of each characteristic associated with each predicted type of fault of the target electromechanical equipment and the predicted maximum tangent slope of its characteristic change trend curve, and denoting it as the relative tangent slope ratio of the change trend curves of various characteristics associated with each predicted type of fault of the target electromechanical equipment, and expressing it as , indicating the number of the th predicted type of fault, , indicating the number of the th characteristic associated with the predicted type of fault, .

[0061] As a preferred solution, the degree analysis module further includes: obtaining the estimated severity of the target electromechanical equipment for each predicted type of fault through the analysis formula , where represents a preset correction factor for the estimated severity, represents a preset threshold for the relative tangent slope ratio, represents the weight factor of the th characteristic associated with the predicted type of fault and .

[0062] Diagnostic feedback module: used to feedback the predicted type of fault, the estimated time point, and the estimated severity to the operation and maintenance center for processing.

[0063] In this embodiment, the present invention obtains each predicted type of fault of the target electromechanical equipment, the estimated time point, and the estimated severity of each predicted type of fault, and conducts feedback, which is beneficial to formulating a predictive and targeted maintenance plan for the electromechanical equipment, and is beneficial to adjusting the maintenance plan according to the prediction results to avoid unnecessary premature maintenance or failures caused by delayed maintenance.

[0064] In addition, the present invention also provides an energy-saving intelligent control method for electromechanical equipment based on AI technology, specifically including: Step 1: Obtain the historical operation data of each motor equipment with various types of faults, analyze the change trend curves of various characteristics associated with the faults when each motor equipment with various types of faults fails, obtain the change trend curves of various characteristics associated with the faults when the motor equipment has various types of faults, and construct a prediction model for various types of faults of the motor equipment.

[0065] Step 2: Collect the operation data of the target motor equipment at each historical maintenance time point, screen the values of various characteristics associated with various types of faults of the target motor equipment at each historical maintenance time point, and draw the change trend curves of various characteristics associated with various types of faults of the target motor equipment.

[0066] Step 3: According to the change trend curves of various characteristics associated with various types of faults of the target motor equipment and the prediction models of various types of faults of the motor equipment, determine whether there are potential fault hazards in the target motor equipment. If so, analyze the predicted types of faults of the target motor equipment.

[0067] Step 4: Analyze the dangerous values of various characteristics associated with various types of faults of the motor equipment, and combine the change trend curves of various characteristics associated with various types of faults of the target motor equipment to analyze the estimated time points for the target motor equipment to have various predicted types of faults.

[0068] Step 5: According to the change trend curves of various characteristics associated with various predicted types of faults of the target motor equipment, analyze the estimated severity of various predicted types of faults that may occur in the target motor equipment.

[0069] Step 6: Feed back the various predicted types of faults of the target motor equipment, the estimated time points for the target motor equipment to have various predicted types of faults, and the estimated severity to the operation and maintenance management center of the target motor equipment.

[0070] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0071] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0072] Those of ordinary skill in the art can realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0073] In addition, in each embodiment of the present application, the various functional modules can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0074] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims described above.

[0075] Finally, the above description is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An intelligent energy-saving management and control system for electromechanical equipment based on AI technology, characterized in that: include: Model building module: used to analyze the change trend curves of various fault-related characteristics according to the historical operation data of various electromechanical equipment with various types of faults, and to build prediction models for various types of faults of electromechanical equipment; Correlation characterization monitoring module: used to collect the operating data of the target electromechanical equipment at each historical maintenance time point, filter the characterization values ​​associated with each type of fault, and draw the change trend curve of each characterization; Fault type prediction module: used to determine whether the target electromechanical equipment has potential fault hazards based on the change trend curve and prediction model. If so, analyze and predict the type of fault; Time analysis module: used to analyze the danger values ​​of various representations, and to analyze the estimated time point of the predicted type of failure in combination with the change trend curve; Degree analysis module: used to analyze the estimated severity of the predicted type of fault based on the change trend curve; Diagnostic feedback module: used to feed back the predicted type of failure, estimated time point and estimated severity to the operation and maintenance center for processing.

2. According to claim 1, the AI-based intelligent energy-saving management and control system for electromechanical equipment is characterized by: The model building module includes: Extract the historical fault information of each electromechanical device stored in the database, obtain various types of faults that have occurred in the history of each electromechanical device, sort and classify them, obtain each electromechanical device that has experienced various types of faults, and obtain the historical operation data of each electromechanical device that has experienced various types of faults; According to the preset principle, the time point of each maintenance of the electromechanical equipment is set, and according to the historical operation data of each electromechanical equipment with each type of fault, the operation data of each electromechanical equipment with each type of fault at each historical maintenance time point before the fault occurs are screened, and recorded as the operation data of each electromechanical equipment with each type of fault at each historical maintenance time point; According to the historical operation data of each electromechanical device having each type of fault, the operation data of each electromechanical device having each type of fault at the time point when the fault occurs is obtained.

3. According to claim 2, an AI-based intelligent management and control system for energy-saving electromechanical equipment is characterized by: The model building module also includes: Set various representations associated with various types of faults of electromechanical equipment, and filter out various representations associated with the faults of various types of electromechanical equipment; According to the operation data of each electromechanical equipment with each type of fault at each historical maintenance time point and the time point of the fault occurrence, the numerical values ​​of each representation of the fault association of each electromechanical equipment with each type of fault at each historical maintenance time point and the time point of the fault occurrence are obtained; Summarize the various historical maintenance time points and failure time points to obtain the various time points, establish a coordinate system with the time points as the horizontal axis and the numerical values ​​of the representations as the vertical axis, mark the corresponding data points in the coordinate system according to the numerical values ​​of the various representations associated with the failures of the various electromechanical equipment that have various types of failures at their various historical maintenance time points and failure time points, and use the mathematical model establishment method to draw the change trend curves of the various representations associated with the failures of the various electromechanical equipment that have various types of failures when the failure occurs; The changing trend curves of various fault-related representations of various electromechanical equipment when various types of faults occur are fitted to obtain the changing trend curves of various fault-related representations of various types of faults of electromechanical equipment when various types of faults occur, and a prediction model for various types of faults of electromechanical equipment is constructed.

4. According to claim 1, the AI-based intelligent management and control system for energy-saving electromechanical equipment is characterized by: The fault type prediction module includes: According to the prediction model of various types of mechanical and electrical equipment faults, a change trend curve of various characteristics associated with the mechanical and electrical equipment faults is obtained when various types of faults occur, and the curve is recorded as a reference change trend curve of various characteristics associated with various types of faults of the mechanical and electrical equipment; Compare the change trend curves of each representation associated with each type of fault of the target electromechanical device with the reference change trend curves of each representation associated with each type of fault of the electromechanical device, and obtain the shape similarity between the change trend curves of each representation associated with each type of fault of the target electromechanical device and the reference change trend curves of its representation; The probability coefficients of various types of failures in the target electromechanical equipment are obtained through fuzzy function analysis and calculation.

5. According to claim 4, the AI-based intelligent management and control system for energy-saving electromechanical equipment is characterized by: The fault type prediction module also includes: The probability coefficient of each type of failure of the target electromechanical equipment is compared with the preset probability coefficient warning value. If the probability coefficient of each type of failure of the target electromechanical equipment is less than the preset probability coefficient warning value, the target electromechanical equipment does not have a potential failure risk. Otherwise, the target electromechanical equipment has a potential failure risk. The failure type corresponding to the target electromechanical equipment failure probability coefficient greater than or equal to the preset probability coefficient warning value is recorded as the possible failure type of the target electromechanical equipment. The possible types of failures of the target electromechanical equipment are counted and recorded as the predicted types of failures of the target electromechanical equipment.

6. According to claim 3, the AI-based intelligent management and control system for energy-saving electromechanical equipment is characterized by: The time analysis module includes: According to the values ​​of the various fault-related representations of each electromechanical device with each type of fault at the time of its fault occurrence, the corresponding values ​​of the various fault-related representations at the time of each electromechanical device fault are obtained, and the average value and the minimum value of the various fault-related representations at the time of each electromechanical device fault are further obtained, which are recorded as ; By analyzing the formula Get the danger value of each characteristic associated with each type of fault in electromechanical equipment ,in Respectively represent the weight factors of the preset average value and minimum value, , Indicates the correction amount of the preset hazard value.

7. According to claim 1, the AI-based intelligent management and control system for energy-saving electromechanical equipment is characterized by: The time analysis module also includes: According to the danger values ​​of various representations associated with various types of faults of the electromechanical equipment, the danger values ​​of various representations associated with various predicted types of faults of the target electromechanical equipment are screened; According to the changing trend curves of various representations associated with various types of faults of the target electromechanical equipment, the changing trend curves of various representations associated with various predicted types of faults of the target electromechanical equipment are screened, and combined with the dangerous values ​​of various representations associated with various predicted types of faults of the target electromechanical equipment, the time point corresponding to the changing trend curve of various representations associated with various predicted types of faults of the target electromechanical equipment when the representation reaches its dangerous value is obtained, and the earliest time point when the representation reaches its dangerous value in the changing trend curve of the representation associated with various predicted types of faults of the target electromechanical equipment is obtained, and it is recorded as the estimated time point when each predicted type of fault of the target electromechanical equipment occurs.

8. According to claim 1, the AI-based intelligent management and control system for energy-saving electromechanical equipment is characterized by: The degree analysis module includes: According to the prediction model of each type of fault of the electromechanical equipment, the absolute value of the maximum tangent slope of each characterization change trend curve associated with each type of fault of the electromechanical equipment is obtained, and recorded as the predicted maximum tangent slope of each characterization change trend curve associated with each type of fault of the electromechanical equipment, and the predicted maximum tangent slope of each characterization change trend curve associated with each type of fault of the target electromechanical equipment is screened; According to the change trend curves of various representations associated with various predicted types of faults of the target electromechanical equipment, the actual maximum tangent slope of the change trend curves of various representations associated with various predicted types of faults of the target electromechanical equipment is obtained.

9. According to claim 8, an AI-based intelligent management and control system for energy-saving electromechanical equipment is characterized by: The degree analysis module also includes: Obtain the ratio between the actual maximum tangent slope of the change trend curve of each item characterizing the associated each predicted type of fault of the target electromechanical equipment and the predicted maximum tangent slope of its characterizing change trend curve, and record it as the relative tangent slope ratio of the change trend curve of each item characterizing the associated each predicted type of fault of the target electromechanical equipment.

10. The AI-based intelligent management and control system for energy-saving electromechanical equipment according to claim 9 is characterized in that: The degree analysis module also includes: obtaining the estimated severity of each predicted type of failure occurring in the target electromechanical equipment by analyzing a multi-weight evaluation algorithm.

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