Fault prediction and prevention method and system in intelligent equipment transformation technology

Abstract

The invention relates to the field of industrial intellectualization, and provides a fault prediction and prevention method and system in an intellectualized equipment transformation technology, and the method comprises the steps: obtaining real-time data of intellectualized equipment, carrying out the filtering processing, screening an abnormal entropy value, extracting an abnormal parameter set, constructing a fault prediction frame based on the abnormal entropy value, and carrying out the fault prediction. The method comprises the following steps: predicting a fault probability, determining a residual operation life and calculating an equipment operation and maintenance period, then evaluating an operation state to obtain a state evaluation parameter, querying a fault type and analyzing occurrence time, constructing a preventive maintenance strategy in combination with a current state, generating a maintenance task list and querying a maintenance task, and analyzing a prevention target in the maintenance task. And identifying prevention obstruction factors, and finally generating an operation report based on the monitored operation feedback data. According to the invention, the operation stability of intelligent equipment can be improved.

Description

Technical Field

[0001] The present invention relates to the field of industrial intelligence, and in particular to a fault prediction and prevention method and system in intelligent equipment transformation technology. Background Art

[0002] In today's industrial field, the application of intelligent equipment is becoming more and more extensive, involving many industries such as manufacturing, energy, transportation, etc. In the intelligent equipment transformation technology, effective fault prediction and prevention methods are crucial, which is of great significance for ensuring the reliable operation of equipment and extending the service life of equipment.

[0003] At present, traditional intelligent equipment fault detection mainly relies on regular maintenance and post-repair. This method not only consumes a lot of time and manpower costs, but also often fails to detect potential faults in time, resulting in increased equipment downtime and affecting production progress. In addition, some conventional fault detection methods are difficult to meet the increasingly complex structural and functional requirements of intelligent equipment, resulting in the inability to accurately predict and prevent failures of new intelligent equipment. Therefore, a fault prediction and prevention method in intelligent equipment transformation technology is needed to improve the operating stability of intelligent equipment. Summary of the invention

[0004] The present invention provides a fault prediction and prevention method and system in intelligent equipment transformation technology, the main purpose of which is to improve the operating stability of intelligent equipment.

[0005] Acquire real-time data during the operation of intelligent equipment, filter the real-time data to obtain filtered real-time data, screen abnormal entropy parameters corresponding to the filtered real-time data, perform feature extraction on the abnormal entropy parameters, and obtain an abnormal parameter set;

[0006] Based on the abnormal parameter set, a fault prediction framework corresponding to the intelligent device is constructed, the fault prediction framework is used to predict the fault probability corresponding to the intelligent device, the remaining operating life corresponding to the intelligent device is determined according to the fault probability, and the equipment operation and maintenance period corresponding to the intelligent device is calculated based on the remaining operating life, wherein the calculation of the equipment operation and maintenance period corresponding to the intelligent device based on the remaining operating life includes:

[0007] The equipment operation and maintenance cycle corresponding to the intelligent equipment is calculated using the following formula:

[0008]

[0009] Wherein, WT represents the equipment operation and maintenance cycle corresponding to the intelligent equipment, k represents the adjustment coefficient corresponding to the intelligent equipment, L represents the remaining operating life, t represents the time variable, f(t) represents the weight coefficient at the time variable t, R(t) represents the reliability coefficient at the time t, and dt represents the small increment of the time variable t;

[0010] Based on the equipment operation and maintenance cycle, the operating status of the intelligent equipment is evaluated to obtain status evaluation parameters, and based on the status evaluation parameters, the fault type of the intelligent equipment is queried, and the occurrence time corresponding to the fault type is analyzed;

[0011] Based on the fault type and the occurrence time, combined with the current state of the intelligent device, a preventive maintenance strategy corresponding to the intelligent device is constructed, based on the preventive maintenance strategy, a maintenance task list corresponding to the intelligent device is generated, and the maintenance tasks in the maintenance task list are queried;

[0012] Analyze the prevention goals in the maintenance task, identify the prevention obstacles in the prevention goals, monitor the operation feedback data of the intelligent equipment based on the prevention obstacles, and generate an operation report corresponding to the intelligent equipment based on the operation feedback data.

[0013] Optionally, the screening of abnormal entropy parameters corresponding to the filtered real-time data includes:

[0014] Generate an entropy value sequence corresponding to the filtered real-time data;

[0015] Based on the entropy value sequence, calculating the average entropy value corresponding to the filtered real-time data;

[0016] Based on the average entropy value, determining the entropy value fluctuation range of the filtered real-time data;

[0017] Based on the entropy value fluctuation range, the abnormal entropy parameter corresponding to the filtered real-time data is screened.

[0018] Optionally, the calculating, based on the entropy value sequence, an average entropy value corresponding to the filtered real-time data includes:

[0019] The average entropy value corresponding to the filtered real-time data is calculated using the following formula:

[0020]

[0021] in, represents the average entropy value corresponding to the filtered real-time data, N represents the total number of entropy values ​​in the entropy value sequence, i represents the number index of entropy values ​​in the entropy value sequence, H i represents the i-th entropy value in the entropy value sequence.

[0022] Optionally, constructing a fault prediction framework corresponding to the intelligent device based on the abnormal parameter set includes:

[0023] Analyze the parameter distribution law corresponding to the abnormal parameter set;

[0024] Based on the parameter distribution law, determining the importance of the parameters in the abnormal parameter set;

[0025] Based on the importance, filtering key abnormal parameters corresponding to the abnormal parameter set;

[0026] Based on the key abnormal parameters, generating a fault feature vector corresponding to the intelligent device;

[0027] Based on the fault feature vector, determining a fault classification standard corresponding to the intelligent device;

[0028] Based on the fault classification standard, a fault prediction framework corresponding to the intelligent device is constructed.

[0029] Optionally, determining the remaining operating life corresponding to the intelligent device according to the failure probability includes:

[0030] Based on the failure probability, obtaining historical operation data corresponding to the intelligent device;

[0031] Analyzing performance degradation trends in the historical operating data;

[0032] Based on the performance degradation trend, predict the time span from the performance degradation of the intelligent device to failure;

[0033] Analyzing the uncertainties corresponding to the time span;

[0034] Based on the uncertain factors, the remaining operating life corresponding to the intelligent device is determined.

[0035] Optionally, the evaluating the operating state corresponding to the intelligent device based on the device operation and maintenance cycle to obtain a state evaluation parameter includes:

[0036] Based on the equipment operation and maintenance cycle, query the original operation data corresponding to the intelligent equipment;

[0037] Removing invalid data from the original operation data to obtain valid operation data;

[0038] Analyzing the degree of deviation between the effective operation data and the historical normal operation data corresponding to the intelligent device;

[0039] According to the degree of deviation, marking abnormal data segments in the valid operation data;

[0040] Based on the abnormal data segment, calculating the performance degradation amplitude corresponding to the intelligent device;

[0041] Based on the performance degradation amplitude, the operating state corresponding to the intelligent device is evaluated to obtain a state evaluation parameter.

[0042] Optionally, the calculating, based on the abnormal data segment, a performance degradation amplitude corresponding to the intelligent device includes:

[0043] The performance degradation amplitude corresponding to the intelligent device is calculated using the following formula:

[0044]

[0045] Wherein, XF represents the performance degradation amplitude corresponding to the intelligent device, m represents the number of data points in the abnormal data segment, j represents the index of the data point in the abnormal data segment, and P nj represents the actual value of the performance indicator corresponding to the jth data point in the abnormal data segment, P hj It represents the expected value of the performance indicator corresponding to the jth data point under normal operating conditions.

[0046] Optionally, querying the fault type of the intelligent device based on the status evaluation parameter includes:

[0047] Based on the state evaluation parameters, analyzing the operation fluctuations corresponding to the intelligent equipment;

[0048] Extracting operation fluctuation characteristics corresponding to the operation fluctuation situation;

[0049] Query the fluctuation index of the fault in the operation fluctuation characteristics;

[0050] Matching the fluctuation index with a preset fault index library to obtain matching data;

[0051] Filtering a subset of fault indicators with higher similarity in the matching data;

[0052] Based on the fault indicator subset, the fault type of the intelligent device is queried.

[0053] Optionally, the constructing a preventive maintenance strategy corresponding to the intelligent device based on the fault type and the occurrence time in combination with the current state of the intelligent device includes:

[0054] Based on the fault type and occurrence time, analyzing the fault development trend corresponding to the intelligent device;

[0055] Extracting key change nodes in the fault development trend;

[0056] Query the node operation parameters corresponding to the key change node;

[0057] Comparing the node operating parameters with normal standard parameters to obtain difference parameters;

[0058] Analyze the difference maintenance items corresponding to the difference parameters;

[0059] Based on the differential maintenance items and in combination with the current status of the intelligent device, a preventive maintenance strategy corresponding to the intelligent device is constructed.

[0060] Optionally, in order to solve the above problem, the present invention provides a fault prediction and prevention system in intelligent equipment transformation technology, the system comprising:

[0061] A feature extraction module is used to obtain real-time data during the operation of intelligent equipment, filter the real-time data to obtain filtered real-time data, screen abnormal entropy parameters corresponding to the filtered real-time data, perform feature extraction on the abnormal entropy parameters, and obtain an abnormal parameter set;

[0062] An operation and maintenance cycle calculation module is used to construct a fault prediction framework corresponding to the intelligent device based on the abnormal parameter set, use the fault prediction framework to predict the fault probability corresponding to the intelligent device, determine the remaining operating life corresponding to the intelligent device according to the fault probability, and calculate the equipment operation and maintenance cycle corresponding to the intelligent device based on the remaining operating life, wherein the calculation of the equipment operation and maintenance cycle corresponding to the intelligent device based on the remaining operating life includes:

[0063] The equipment operation and maintenance cycle corresponding to the intelligent equipment is calculated using the following formula:

[0064]

[0065] Wherein, WT represents the equipment operation and maintenance cycle corresponding to the intelligent equipment, k represents the adjustment coefficient corresponding to the intelligent equipment, L represents the remaining operating life, t represents the time variable, f(t) represents the weight coefficient at the time variable t, R(t) represents the reliability coefficient at the time t, and dt represents the small increment of the time variable t;

[0066] A time analysis module is used to evaluate the operating status of the intelligent device based on the equipment operation and maintenance cycle, obtain status evaluation parameters, query the fault type of the intelligent device based on the status evaluation parameters, and analyze the occurrence time corresponding to the fault type;

[0067] A task query module is used to construct a preventive maintenance strategy corresponding to the intelligent device based on the fault type and the occurrence time, combined with the current state of the intelligent device, generate a maintenance task list corresponding to the intelligent device based on the preventive maintenance strategy, and query the maintenance tasks in the maintenance task list;

[0068] The report generation module is used to analyze the prevention goals in the maintenance task, identify the prevention obstacles in the prevention goals, monitor the operation feedback data of the intelligent equipment based on the prevention obstacles, and generate an operation report corresponding to the intelligent equipment based on the operation feedback data.

[0069] Firstly, the present invention can reflect the current operating status of the equipment, including the changes of various parameters, by acquiring real-time data during the operation of intelligent equipment, which helps to timely discover abnormal conditions of the equipment, so as to take measures in advance to avoid the occurrence of faults. At the same time, the present invention constructs a fault prediction framework corresponding to the intelligent equipment based on the abnormal parameter set, and can make full use of the information provided by the abnormal parameter set to conduct a comprehensive and in-depth analysis of the operating status of the intelligent equipment, so as to more accurately predict potential faults. The present invention evaluates the operating status corresponding to the intelligent equipment based on the equipment operation and maintenance cycle to obtain status evaluation parameters, which helps to timely discover potential problems in the equipment and take targeted measures to intervene before the problems worsen, so as to effectively prevent the occurrence of equipment failures and ensure the stable operation of the equipment. Based on the fault type and the occurrence time, combined with the current state of the intelligent device, the present invention constructs a preventive maintenance strategy corresponding to the intelligent device, which can fully and deeply understand the evolution of the operating status of the equipment, clearly grasp the entire process from normal to failure of the equipment, and reasonably arrange maintenance personnel and prepare the required parts and tools according to the fault type, occurrence time and current state, thereby greatly improving maintenance efficiency and reducing equipment downtime. Furthermore, by analyzing the prevention goals in the maintenance tasks, the present invention can accurately determine the focus and key links of maintenance work, reasonably allocate resources, avoid waste and mismatch of resources, and help predict the future failure modes and performance degradation trends of the equipment, make preparations in advance, and facilitate timely discovery of deficiencies in the maintenance strategy, and make adjustments and improvements. Therefore, the fault prediction and prevention method and system in the intelligent equipment transformation technology proposed by the present invention can improve the operating stability of intelligent equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 A flow chart of a method for predicting and preventing faults in intelligent equipment transformation technology provided by an embodiment of the present invention;

[0071] Figure 2A schematic diagram of a module of a fault prediction and prevention system in an intelligent equipment transformation technology provided in an embodiment of the present invention.

[0072] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0073] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0074] The embodiment of the present application provides a fault prediction and prevention method in the intelligent equipment transformation technology. The execution subject of the fault prediction and prevention method in the intelligent equipment transformation technology includes but is not limited to at least one of the electronic devices such as the server, the terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the fault prediction and prevention method in the intelligent equipment transformation technology can be executed by software or hardware installed on the terminal device or the server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.

[0075] Reference Figure 1 FIG. 1 is a flow chart of a method for predicting and preventing faults in intelligent equipment transformation technology provided in one embodiment of the present invention. In this embodiment, the method for predicting and preventing faults in intelligent equipment transformation technology includes:

[0076] S1. Acquire real-time data during the operation of intelligent equipment, filter the real-time data to obtain filtered real-time data, screen abnormal entropy parameters corresponding to the filtered real-time data, perform feature extraction on the abnormal entropy values, and obtain an abnormal parameter set.

[0077] The present invention can reflect the current operating status of the equipment, including changes in various parameters, by acquiring real-time data during the operation of the intelligent equipment, which helps to promptly discover abnormal conditions in the equipment, thereby taking measures in advance to avoid the occurrence of failures.

[0078] Among them, the intelligent equipment refers to equipment that uses advanced information technology and intelligent control technology to achieve automated and intelligent operation, such as intelligent robots, automated production lines, intelligent sensors, etc.; the real-time data refers to various real-time data generated by intelligent equipment during operation, including but not limited to equipment operating parameters (such as temperature, pressure, speed, current, etc.), working status (such as on, off, operating mode, etc.), fault information (such as fault code, fault time, etc.) and environmental data (such as humidity, air quality, etc.). Optionally, the acquisition of real-time data during the operation of intelligent equipment can be achieved through sensor technology, such as: temperature sensors, pressure sensors, speed sensors, etc., to collect equipment operating parameters in real time.

[0079] Furthermore, the present invention obtains filtered real-time data by filtering the real-time data, which can effectively remove noise and interference in the real-time data, making the data more accurate and reliable, helping to accurately judge the operating trends and potential problems of the equipment, reducing the impact of abnormal data on fault prediction and prevention, and improving the accuracy and reliability of fault diagnosis.

[0080] Among them, the filtered real-time data refers to the data obtained after filtering the real-time data acquired during the operation of the intelligent equipment. It is achieved by removing or reducing factors such as noise, interference and abnormal fluctuations in the original real-time data, so as to make the data smoother, more stable and reliable. Optionally, the filtering of the real-time data can be achieved through a digital filter, such as: a low-pass filter, a high-pass filter, a band-pass filter, etc.

[0081] Furthermore, the present invention can help capture abnormal patterns and potential fault signs in the data by screening the abnormal entropy parameters corresponding to the filtered real-time data, and can promptly discover abnormal conditions in equipment operation and provide early warning of faults, thereby taking corresponding measures to avoid production stagnation and economic losses caused by equipment failures.

[0082] Among them, the preset threshold refers to an entropy value limit set according to experience or specific standards, which is used to determine whether the entropy value is abnormal; the abnormal entropy refers to the entropy value that exceeds the preset threshold, and these entropy values ​​indicate that there are abnormal conditions or potential fault information in the filtered real-time data.

[0083] As an embodiment of the present invention, the screening of abnormal entropy parameters corresponding to the filtered real-time data includes: generating an entropy value sequence corresponding to the filtered real-time data; based on the entropy value sequence, calculating an average entropy value corresponding to the filtered real-time data; based on the average entropy value, determining an entropy value fluctuation range of the filtered real-time data; based on the entropy value fluctuation range, screening abnormal entropy parameters corresponding to the filtered real-time data.

[0084] Among them, the entropy value sequence refers to the arrangement of a series of entropy values ​​obtained by processing the filtered real-time data according to certain rules; the average entropy value refers to the average value of all entropy values ​​in the entropy value sequence, which reflects the overall entropy level of the filtered real-time data; the entropy value fluctuation range refers to the variation range of the entropy value in the entropy value sequence, which can usually be determined by calculating the difference between the maximum and minimum entropy values.

[0085] Furthermore, the generation of the entropy value sequence corresponding to the filtered real-time data can be achieved through an information entropy algorithm, such as: calculating the entropy value according to the probability of occurrence of each data point in the filtered real-time data, thereby obtaining an entropy value sequence; the calculation of the average entropy value corresponding to the filtered real-time data can be achieved through the following algorithm; the determination of the entropy value fluctuation range of the filtered real-time data can be achieved through the NumPy library, such as: using the max and min functions in the NumPy library to find the maximum and minimum values ​​of the entropy value sequence, and then determine the entropy value fluctuation range; the screening of abnormal entropy parameters corresponding to the filtered real-time data can be achieved through an anomaly detection algorithm, such as: an anomaly detection algorithm based on statistics, an anomaly detection algorithm based on machine learning, etc.

[0086] As an embodiment of the present invention, the calculating, based on the entropy value sequence, the average entropy value corresponding to the filtered real-time data includes:

[0087] The average entropy value corresponding to the filtered real-time data is calculated using the following formula:

[0088]

[0089] in, represents the average entropy value corresponding to the filtered real-time data, N represents the total number of entropy values ​​in the entropy value sequence, i represents the number index of entropy values ​​in the entropy value sequence, H i represents the i-th entropy value in the entropy value sequence.

[0090] In detail, the average entropy value refers to the result obtained by summing all entropy values ​​in the entropy value sequence and dividing it by the total number N of entropy values. It reflects the overall entropy level of the filtered real-time data and is a measure of data uncertainty. Moreover, the larger the average entropy value, the higher the uncertainty of the data; the smaller the average entropy value, the lower the uncertainty of the data; the entropy value refers to a measure of the degree of confusion or disorder of the filtered real-time data at the i-th position. The higher the entropy value, the more chaotic and disordered the data at that position is, and the less information it contains; the lower the entropy value, the more ordered the data at that position is, and the more information it contains.

[0091] S2. Based on the abnormal parameter set, a fault prediction framework corresponding to the intelligent device is constructed, and the fault prediction framework is used to predict the fault probability corresponding to the intelligent device. According to the fault probability, the remaining operating life corresponding to the intelligent device is determined, and based on the remaining operating life, the equipment operation and maintenance period corresponding to the intelligent device is calculated.

[0092] The present invention constructs a fault prediction framework corresponding to the intelligent device based on the abnormal parameter set, and can make full use of the information provided by the abnormal parameter set to conduct a comprehensive and in-depth analysis of the operating status of the intelligent device, thereby more accurately predicting potential faults.

[0093] The fault prediction framework refers to a model or system for predicting intelligent equipment failures based on fault feature vectors and fault classification standards, which can perform fault prediction and early warning based on real-time data.

[0094] As an embodiment of the present invention, the fault prediction framework corresponding to the intelligent device is constructed based on the abnormal parameter set, including: analyzing the parameter distribution law corresponding to the abnormal parameter set; determining the importance of the parameters in the abnormal parameter set based on the parameter distribution law; screening the key abnormal parameters corresponding to the abnormal parameter set based on the importance; generating a fault feature vector corresponding to the intelligent device based on the key abnormal parameters; determining the fault classification standard corresponding to the intelligent device based on the fault feature vector; and constructing a fault prediction framework corresponding to the intelligent device based on the fault classification standard.

[0095] Among them, the parameter distribution law refers to the distribution of the abnormal parameter set in various dimensions, including the parameter value range, central tendency, degree of dispersion, etc.; the importance refers to the influence of each parameter in the abnormal parameter set on the intelligent equipment fault prediction, which is usually determined through data analysis and expert experience; the key abnormal parameters refer to the parameters in the abnormal parameter set that have an important influence on the equipment fault prediction, and the changes in these parameters directly lead to the occurrence of equipment failure; the fault feature vector refers to a vector composed of key abnormal parameters, which is used to describe the fault characteristics of intelligent equipment; the fault classification standard refers to the basis for classifying the faults of intelligent equipment, which is usually determined according to factors such as the nature and severity of the fault.

[0096] Furthermore, the analysis of the parameter distribution law corresponding to the abnormal parameter set can be achieved through data visualization tools, such as Matplotlib, Seaborn and other tools; the determination of the importance of the parameters in the abnormal parameter set can be achieved through Python tools, such as using the Scikit-learn library in Python to perform principal component analysis to determine the importance of each parameter in the abnormal parameter set; the screening of key abnormal parameters corresponding to the abnormal parameter set can be achieved through machine learning algorithms, such as decision trees, random forests and other algorithms; the generation of the fault feature vector corresponding to the intelligent device can be achieved through feature engineering methods, such as combining, transforming and other operations on key abnormal parameters to generate a fault feature vector; the determination of the fault classification standard corresponding to the intelligent device can be achieved through a clustering analysis algorithm, such as performing cluster analysis on fault data and determining the fault classification standard based on the clustering results; the construction of the fault prediction framework corresponding to the intelligent device can be achieved through a deep learning framework, such as TensorFlow, PyTorch and other models.

[0097] The present invention uses the fault prediction framework to predict the failure probability corresponding to the intelligent equipment, and can give early warning of equipment failures, so that relevant personnel have enough time to take preventive measures to avoid the occurrence of failures or reduce the losses caused by failures. The equipment maintenance plan can be reasonably arranged, and timely understanding of the equipment failure probability helps to optimize the production process, avoid production interruptions due to equipment failures, and reduce maintenance costs.

[0098] Among them, the failure probability refers to the probability of an intelligent device failing within a specific time period in the future. It is a value calculated by analyzing the historical data, real-time operation data and fault prediction framework of the device. The size of the failure probability can reflect the health status and potential risk level of the device. The higher the probability, the greater the probability of the device failing. Optionally, the use of the fault prediction framework to predict the corresponding failure probability of the intelligent device can be achieved through a Monte Carlo simulation method, such as: randomly taking values ​​for various operating parameters of the device, simulating the operation of the device multiple times, counting the number of failures, and thus estimating the failure probability.

[0099] Furthermore, by determining the remaining operating life of the intelligent equipment according to the failure probability, the present invention can more accurately plan equipment maintenance and replacement plans, avoid waste of resources due to premature replacement, or sudden equipment failure affecting production due to late replacement, and ensure that the equipment can fully perform its functions while it can still operate stably, thereby ensuring the continuity of production.

[0100] The remaining operating life refers to the remaining time that the intelligent device is expected to operate normally in the current state.

[0101] As an embodiment of the present invention, determining the remaining operating life corresponding to the intelligent device according to the failure probability includes: obtaining historical operating data corresponding to the intelligent device based on the failure probability; analyzing the performance degradation trend in the historical operating data; predicting the time span from the performance degradation of the intelligent device to failure based on the performance degradation trend; analyzing the uncertainty factors corresponding to the time span; and determining the remaining operating life corresponding to the intelligent device based on the uncertainty factors.

[0102] Among them, the historical operation data refers to the relevant information generated and recorded by the intelligent equipment during its past operation, including the equipment's operating time, workload, maintenance records, number and type of faults, operating environment conditions, etc.; the performance degradation trend refers to the direction and pattern of the gradual decline in performance of the intelligent equipment during use, which is usually obtained by analyzing and summarizing the changes in the key performance indicators of the equipment; the time span refers to the estimated length of time from the current moment to the performance degradation of the intelligent equipment to the occurrence of a failure; the uncertainties refer to various variables and unknown factors that may affect the accuracy of the prediction of the remaining operating life of the intelligent equipment, such as sudden changes in the external environment, potential problems inside the equipment that have not been taken into consideration, etc.

[0103] Furthermore, the acquisition of historical operating data corresponding to the intelligent device can be achieved by querying a relational database, such as extracting historical operating data of the intelligent device in a relational database (such as MySQL, Oracle); the analysis of the performance degradation trend in the historical operating data can be achieved through the Scikit-learn library, such as using PolynomialFeatures combined with linear regression in the Scikit-learn library to capture the nonlinear performance degradation trend; the prediction of the time span from the performance degradation of the intelligent device to failure can be achieved through a time series prediction model, such as ARIMA, SARIMA and other models; the analysis of the uncertainty factors corresponding to the time span can be achieved through Python tools, such as using the Sobol sensitivity analysis library in Python to analyze the sensitivity of uncertain factors; the determination of the remaining operating life corresponding to the intelligent device can be achieved through R language, such as using the reliability package of R language to determine the remaining operating life of the device.

[0104] The present invention calculates the equipment operation and maintenance cycle corresponding to the intelligent equipment based on the remaining operating life, which can help enterprises plan the investment of equipment operation and maintenance resources more accurately and avoid waste or shortage of resources. It can effectively improve the availability and stability of equipment, perform maintenance and repairs at appropriate times, reduce the probability of equipment failure, and ensure the continuity and stability of production.

[0105] The equipment operation and maintenance cycle refers to the time interval required for maintenance and operation management of intelligent equipment, which reflects the ideal length of time between two operation and maintenance operations when the equipment is operating normally.

[0106] As an embodiment of the present invention, the calculating, based on the remaining operating life, the equipment operation and maintenance cycle corresponding to the intelligent equipment includes:

[0107] The equipment operation and maintenance cycle corresponding to the intelligent equipment is calculated using the following formula:

[0108]

[0109] Among them, WT represents the equipment operation and maintenance cycle corresponding to the intelligent equipment, k represents the adjustment coefficient corresponding to the intelligent equipment, L represents the remaining operating life, t represents the time variable, f(t) represents the weight coefficient at the time variable t, R(t) represents the reliability coefficient at time t, and dt represents the small increment of the time variable t.

[0110] In detail, the adjustment coefficient refers to the parameter used to correct and adapt the formula for calculating the equipment operation and maintenance cycle; the weight coefficient refers to the different weight values ​​assigned to the time variable t, which reflects the differences in the importance of various factors to the equipment operation and maintenance cycle at different time points; the reliability coefficient refers to a numerical value that describes the reliability of the intelligent equipment to maintain normal operation at a specific time t. The higher the reliability coefficient R(t), the greater the reliability of the equipment to operate normally at that time point, and vice versa.

[0111] S3. Based on the equipment operation and maintenance cycle, evaluate the operating status of the intelligent equipment to obtain status evaluation parameters, query the fault type of the intelligent equipment based on the status evaluation parameters, and analyze the occurrence time of the fault type.

[0112] The present invention evaluates the corresponding operating status of the intelligent device based on the equipment operation and maintenance cycle to obtain status evaluation parameters, which helps to timely discover potential problems with the equipment and take targeted measures to intervene before the problems worsen, thereby effectively preventing the occurrence of equipment failures and ensuring the stable operation of the equipment.

[0113] The state assessment parameters refer to a set of quantitative indicators and qualitative descriptions used to comprehensively describe and measure the operating status of intelligent equipment.

[0114] As an embodiment of the present invention, the operation status corresponding to the intelligent device is evaluated based on the device operation and maintenance cycle to obtain the status evaluation parameters, including: based on the device operation and maintenance cycle, querying the original operation data corresponding to the intelligent device; removing invalid data in the original operation data to obtain valid operation data; analyzing the degree of deviation between the valid operation data and the historical normal operation data corresponding to the intelligent device; marking the abnormal data segments in the valid operation data according to the degree of deviation; based on the abnormal data segments, calculating the performance degradation amplitude corresponding to the intelligent device; based on the performance degradation amplitude, evaluating the operation status corresponding to the intelligent device to obtain the status evaluation parameters.

[0115] Among them, the original operation data refers to all operation-related data directly collected from the intelligent equipment without processing and screening; the effective operation data refers to the useful data that can truly reflect the operation status of the equipment after screening and processing, and removing invalid, erroneous or interfering information; the historical normal operation data refers to the representative data set recorded by the intelligent equipment in the previous normal operation state; the degree of deviation refers to the measurement of the difference between the effective operation data and the historical normal operation data in various key indicators; the abnormal data segment refers to the continuous data segment in the effective operation data that is marked as having a large deviation from the normal situation; the performance degradation amplitude refers to the quantitative value of the amplitude of the decline in the performance of the intelligent equipment from the normal state to the current state.

[0116] Furthermore, the query of the original operating data corresponding to the intelligent device can be implemented through a data acquisition interface, such as: using the OPC UA interface of industrial equipment to obtain the original operating data; removing invalid data in the original operating data can be implemented through a data cleaning tool, such as: OpenRefine and other tools; analyzing the degree of deviation between the effective operating data and the historical normal operating data corresponding to the intelligent device can be implemented through a clustering algorithm, such as: clustering the effective operating data and the historical normal operating data respectively, and comparing the differences in clustering results; marking the abnormal data segments in the effective operating data can be implemented through an isolation forest algorithm, such as: identifying abnormal data segments by constructing an isolated tree; calculating the performance degradation amplitude corresponding to the intelligent device can be implemented through the following calculation formula; evaluating the operating status corresponding to the intelligent device can be implemented through a fuzzy comprehensive evaluation method, such as: fuzzifying qualitative evaluation indicators to comprehensively obtain evaluation data of the operating status.

[0117] As an embodiment of the present invention, the calculating, based on the abnormal data segment, the performance degradation amplitude corresponding to the intelligent device includes:

[0118] The performance degradation amplitude corresponding to the intelligent device is calculated using the following formula:

[0119]

[0120] Wherein, XF represents the performance degradation amplitude corresponding to the intelligent device, m represents the number of data points in the abnormal data segment, j represents the index of the data point in the abnormal data segment, and P nj represents the actual value of the performance indicator corresponding to the jth data point in the abnormal data segment, P hj It represents the expected value of the performance indicator corresponding to the jth data point under normal operating conditions.

[0121] In detail, the performance degradation amplitude refers to a numerical value used to quantify the degree of performance degradation of intelligent equipment, which reflects the extent of performance degradation of the equipment when an abnormal data segment occurs, relative to the normal operating state; the actual value of the performance indicator refers to the numerical value of the specific performance indicator measured in the abnormal data segment during the operation of the intelligent equipment; the expected value of the performance indicator refers to the theoretical or empirical numerical value of the performance indicator expected to be achieved for the corresponding data point under normal operating conditions.

[0122] The present invention queries the fault type of the intelligent device based on the status assessment parameters, and can quickly and accurately locate the equipment fault, greatly shortening the troubleshooting time, improving maintenance efficiency, and helping to prepare the required maintenance tools and parts in advance, take preventive measures in advance, and avoid further deterioration of the equipment fault.

[0123] Among them, the fault type refers to the specific type and form of fault that occurs in intelligent equipment, such as hardware fault, software fault, mechanical fault, etc.

[0124] As an embodiment of the present invention, querying the fault type of the intelligent device based on the state evaluation parameters includes: analyzing the operation fluctuation situation corresponding to the intelligent device based on the state evaluation parameters; extracting the operation fluctuation characteristics corresponding to the operation fluctuation situation; querying the fluctuation index of the fault in the operation fluctuation characteristics; matching the fluctuation index with a preset fault index library to obtain matching data; screening a subset of fault indicators with higher similarity in the matching data; and querying the fault type of the intelligent device based on the fault indicator subset.

[0125] Among them, the operation fluctuation situation refers to the fluctuation of various performance parameters and operating states of intelligent equipment during operation; the operation fluctuation feature refers to the key attributes extracted from the operation fluctuation situation that can reflect the operating laws and characteristics of the equipment; the fluctuation index refers to the specific quantitative value or feature identifier used to measure the operation fluctuation feature that indicates a fault; the matching data refers to the relevant result data obtained after comparing the fluctuation index with the preset fault index library, including similarity, matching items and other information; the fault index subset refers to a collection of a part of fault indicators with higher similarity screened out from the matching data.

[0126] Furthermore, the analysis of the operation fluctuation situation corresponding to the intelligent device can be achieved through time series analysis methods, such as moving average, exponential smoothing and other methods; the extraction of the operation fluctuation characteristics corresponding to the operation fluctuation situation can be achieved through Python tools, such as using the PyWavelets library for wavelet transform in Python; the query of the fluctuation index of the fault in the operation fluctuation characteristics can be achieved through a pattern recognition algorithm, such as a decision tree, a support vector machine, etc.; the matching of the fluctuation index with a preset fault index library can be achieved through a similarity algorithm, such as cosine similarity, Euclidean distance and other algorithms; the screening of the fault index subset with higher similarity in the matching data can be achieved through a sorting algorithm, such as bubble sort, quick sort and other algorithms; the query of the fault type existing in the intelligent device can be achieved through a deep learning model, such as a convolutional neural network, a recurrent neural network and other models.

[0127] By analyzing the occurrence time corresponding to the fault type, the present invention helps to accurately trace the source of the fault and understand at which stage of equipment operation the problem occurs, thereby providing key clues for in-depth analysis of the cause of the fault.

[0128] The occurrence time refers to a specific time point or time period when the fault type first occurs or recurs. Optionally, the analysis of the occurrence time corresponding to the fault type may be implemented by a data mining algorithm, such as an Apriori algorithm.

[0129] S4. Based on the fault type and the occurrence time, combined with the current state of the intelligent device, a preventive maintenance strategy corresponding to the intelligent device is constructed; based on the preventive maintenance strategy, a maintenance task list corresponding to the intelligent device is generated, and the maintenance tasks in the maintenance task list are queried.

[0130] The present invention constructs a preventive maintenance strategy corresponding to the intelligent device based on the fault type and the occurrence time, combined with the current state of the intelligent device, which can fully and deeply understand the evolution of the operating status of the equipment, clearly grasp the entire process from normal to failure of the equipment, and can reasonably arrange maintenance personnel and prepare required parts and tools according to the fault type, occurrence time and current state, thereby greatly improving maintenance efficiency and reducing equipment downtime.

[0131] The preventive maintenance strategy refers to a series of targeted maintenance plans, measures and methods developed based on the current status of the equipment, fault development trends and differential maintenance items in order to avoid similar failures in intelligent equipment or reduce the likelihood of failures.

[0132] As an embodiment of the present invention, based on the fault type and the occurrence time, combined with the current state of the intelligent device, a preventive maintenance strategy corresponding to the intelligent device is constructed, including: based on the fault type and the occurrence time, analyzing the fault development trend corresponding to the intelligent device; extracting the key change nodes in the fault development trend; querying the node operating parameters corresponding to the key change nodes; comparing the node operating parameters with normal standard parameters to obtain difference parameters; analyzing the difference maintenance items corresponding to the difference parameters; based on the difference maintenance items, combined with the current state of the intelligent device, constructing a preventive maintenance strategy corresponding to the intelligent device.

[0133] Among them, the fault development trend refers to the direction and degree of change of the fault type over time, including the evolution of the severity, frequency, impact range, etc. of the fault; the key change node refers to a specific time point or state transition point that is of great significance in the fault development trend and can significantly affect the direction or degree of fault development; the node operating parameters refer to the various operating indicators and data of the intelligent equipment at the key change nodes, such as temperature, pressure, speed, etc.; the difference parameters refer to the different values ​​or characteristics between the node operating parameters and the normal standard parameters; the difference maintenance items refer to the specific items and contents that need special maintenance, adjustment or improvement reflected by the difference parameters;.

[0134] Furthermore, the analysis of the fault development trend corresponding to the intelligent equipment can be achieved through a time series prediction model, such as ARIMA model, SARIMA model and other models; the extraction of key change nodes in the fault development trend can be achieved through a clustering analysis algorithm, such as K-Means clustering algorithm; the query of node operation parameters corresponding to the key change nodes can be achieved through a principal component analysis algorithm, such as using the principal component analysis (PCA) algorithm to extract the main characteristic parameters; the comparison of the node operation parameters with the normal standard parameters can be achieved through a hypothesis testing method, such as t-test, variance analysis and other methods; the analysis of the difference maintenance items corresponding to the difference parameters can be achieved through a fishbone diagram analysis method, such as when analyzing the reasons for the decline in equipment performance, a fishbone diagram can be used to find out factors such as human operation, equipment aging, and environmental changes; the construction of the preventive maintenance strategy corresponding to the intelligent equipment can be achieved through a hierarchical analysis method, such as using AHP to determine the optimal maintenance strategy when considering factors such as equipment maintenance cost, equipment reliability, and maintenance personnel skills.

[0135] Based on the preventive maintenance strategy, the present invention generates a maintenance task list corresponding to the intelligent equipment, which helps to prepare the resources required for maintenance in advance, including human, material and financial resources, to ensure that the maintenance work can be carried out smoothly, reduce equipment downtime, and ensure the normal operation of the equipment, thereby minimizing production losses caused by equipment failure.

[0136] The maintenance task list refers to a preventive maintenance strategy for intelligent equipment, which lists in detail a series of specific maintenance operations, required resources, execution time, responsible persons and other information. Optionally, the generation of the maintenance task list corresponding to the intelligent equipment can be achieved through project management tools, such as Microsoft Project, Trello and other tools.

[0137] Furthermore, by querying the maintenance tasks in the maintenance task list, the present invention can clarify work priorities and responsibilities, help plan equipment resource arrangements in advance, ensure that maintenance work can be carried out efficiently and orderly, facilitate tracking and supervision of the execution of maintenance tasks, timely discover potential problems and risks, and take corresponding adjustment measures.

[0138] Among them, the maintenance task refers to the specific operations and work content that need to be performed in order to maintain the normal operation of intelligent equipment, prevent failures or repair existing problems. Optionally, the query of the maintenance tasks in the maintenance task list can be implemented through a database query language, such as: using SQL statements to filter out maintenance tasks with specific conditions from the database storing the maintenance task list.

[0139] S5. Analyze the prevention goals in the maintenance task, identify the prevention obstacles in the prevention goals, monitor the operation feedback data of the intelligent equipment based on the prevention obstacles, and generate an operation report corresponding to the intelligent equipment based on the operation feedback data.

[0140] By analyzing the prevention objectives in the maintenance tasks, the present invention can accurately determine the focus and key links of the maintenance work, reasonably allocate resources, avoid waste and mismatch of resources, help predict future failure modes and performance degradation trends of equipment, make preparations in advance, and facilitate timely discovery of deficiencies in maintenance strategies for adjustment and improvement.

[0141] Among them, the prevention goal refers to the specific results and expected effects that are expected to be achieved in the maintenance task, aimed at avoiding equipment failure, performance degradation or other adverse conditions. Optionally, the analysis of the prevention goal in the maintenance task can be achieved through the goal decomposition method, such as: decomposing the overall prevention goal into multiple specific and measurable sub-goals to facilitate clearer analysis.

[0142] Furthermore, by identifying the prevention obstacles in the prevention goals, the present invention can predict in advance the potential problems that may affect the maintenance tasks in achieving the prevention goals, thereby formulating targeted response strategies, optimizing the maintenance process and improving the maintenance effect, and ensuring the stable operation and long-term reliability of the intelligent equipment.

[0143] Among them, the prevention obstacles refer to those specific factors that are finally determined to be able to clearly hinder the achievement of prevention goals, including various internal and external obstacles.

[0144] As an embodiment of the present invention, the identification of prevention hindering factors in the prevention target includes: analyzing the target environment corresponding to the prevention target; extracting potential adverse factors in the target environment; evaluating the degree of influence of the potential adverse factors on the prevention target; screening hindering factors among the potential adverse factors based on the degree of influence; and identifying prevention hindering factors in the prevention target based on the hindering factors.

[0145] Among them, the target environment refers to the sum of various external and internal conditions related to the prevention target, including the physical environment of equipment operation, work flow, personnel operation and other aspects; the potential adverse factors refer to various factors that have a negative impact on the realization of the prevention target in the target environment, such as equipment aging, improper operation, environmental changes, etc.; the degree of impact refers to the magnitude of the negative effects of potential adverse factors on the prevention target, which can be evaluated in a quantitative or qualitative way; the hindering factor refers to the factor among the potential adverse factors that has a greater impact on the realization of the prevention target and becomes the main obstacle to achieving the prevention target.

[0146] Furthermore, the analysis of the target environment corresponding to the prevention target can be achieved through a system analysis method, such as Petri, ISM and other analysis methods; the extraction of potential adverse factors in the target environment can be achieved through an impact analysis method, such as using Excel spreadsheets for FMEA analysis; the evaluation of the impact of the potential adverse factors on the prevention target can be achieved through a hierarchical analysis tool, such as using Expert Choice and other AHP analysis tools; the screening of the hindering factors in the potential adverse factors can be achieved through a simulation analysis tool, such as Crystal Ball and other tools; the identification of the prevention hindering factors in the prevention target can be achieved through a data mining tool, such as Weka and other tools.

[0147] Based on the prevention obstacle factors, the present invention monitors the operation feedback data of the intelligent device, and can timely discover whether the intelligent device has a situation that causes the prevention target to be blocked during operation, so as to quickly take countermeasures to avoid further deterioration of the problem.

[0148] Among them, the operation feedback data refers to various data information generated by intelligent equipment during operation that can reflect its current status, performance and prevention of obstacles. Optionally, the monitoring of the operation feedback data of the intelligent equipment can be achieved through network monitoring tools, such as: SolarWinds and other network monitoring tools, real-time monitoring of the network traffic, connection status, etc. of the equipment.

[0149] The present invention generates an operation report corresponding to the intelligent device based on the operation feedback data, which helps to reasonably allocate resources. According to the predicted equipment requirements and potential problems, it promotes the intelligence and refinement of equipment management, continuously improves the equipment management level, and ensures the stable and efficient operation of the intelligent equipment.

[0150] Among them, the operation report refers to a comprehensive report on the operation status, performance trend, failure risk and corresponding maintenance suggestions of the equipment in the future, which is obtained through analysis and prediction methods based on the operation feedback data of the intelligent equipment. In detail, the operation report covers many aspects. In terms of operation status, it will clearly point out the current working mode, load condition, operation efficiency and other information of the equipment, so that users can clearly understand the operation status of the equipment at this moment. For performance trends, the report predicts the performance change trend of the equipment in the future by analyzing historical data and trend extrapolation, such as whether there will be performance degradation, efficiency reduction, etc. In the part of failure risk, the probability of various failures of the equipment will be evaluated based on abnormal signals in the feedback data, changes in key indicators and comparison with known failure modes, and the type and location of the failure will be specifically pointed out. Optionally, the generation of the operation report corresponding to the intelligent equipment can be achieved through data visualization tools, such as using visualization tools such as Tableau or PowerBI to display key indicators and trend forecasts of the equipment.

[0151] Firstly, the present invention can reflect the current operating status of the equipment, including the changes of various parameters, by acquiring real-time data during the operation of intelligent equipment, which helps to timely discover abnormal conditions of the equipment, so as to take measures in advance to avoid the occurrence of faults. At the same time, the present invention constructs a fault prediction framework corresponding to the intelligent equipment based on the abnormal parameter set, and can make full use of the information provided by the abnormal parameter set to conduct a comprehensive and in-depth analysis of the operating status of the intelligent equipment, so as to more accurately predict potential faults. The present invention evaluates the operating status corresponding to the intelligent equipment based on the equipment operation and maintenance cycle to obtain status evaluation parameters, which helps to timely discover potential problems in the equipment and take targeted measures to intervene before the problems worsen, so as to effectively prevent the occurrence of equipment failures and ensure the stable operation of the equipment. Based on the fault type and the occurrence time, combined with the current state of the intelligent device, the present invention constructs a preventive maintenance strategy corresponding to the intelligent device, which can fully and deeply understand the evolution of the operating status of the equipment, clearly grasp the entire process from normal to failure of the equipment, and reasonably arrange maintenance personnel and prepare the required parts and tools according to the fault type, occurrence time and current state, thereby greatly improving maintenance efficiency and reducing equipment downtime. Furthermore, by analyzing the prevention goals in the maintenance tasks, the present invention can accurately determine the focus and key links of maintenance work, reasonably allocate resources, avoid waste and mismatch of resources, and help predict the future failure modes and performance degradation trends of the equipment, make preparations in advance, and facilitate timely discovery of deficiencies in the maintenance strategy, and make adjustments and improvements. Therefore, the fault prediction and prevention method and system in the intelligent equipment transformation technology proposed by the present invention can improve the operating stability of intelligent equipment.

[0152] like Figure 2 , which is a functional module diagram of a fault prediction and prevention system in an intelligent equipment transformation technology provided in one embodiment of the present invention.

[0153] The fault prediction and prevention system 200 in the intelligent equipment transformation technology of the present invention can be installed in an electronic device. According to the functions implemented, the fault prediction and prevention system 200 in the intelligent equipment transformation technology can include a feature extraction module 201, an operation and maintenance cycle calculation module 202, a time analysis module 203, a task query module 204 and a report generation module 205. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0154] In this embodiment, the functions of each module / unit are as follows:

[0155] The feature extraction module 201 is used to obtain real-time data during the operation of the intelligent device, filter the real-time data to obtain filtered real-time data, filter abnormal entropy parameters corresponding to the filtered real-time data, and perform feature extraction on the abnormal entropy parameters to obtain an abnormal parameter set;

[0156] The operation and maintenance cycle calculation module 202 is used to construct a fault prediction framework corresponding to the intelligent device based on the abnormal parameter set, use the fault prediction framework to predict the fault probability corresponding to the intelligent device, determine the remaining operating life corresponding to the intelligent device according to the fault probability, and calculate the equipment operation and maintenance cycle corresponding to the intelligent device based on the remaining operating life, wherein the calculation of the equipment operation and maintenance cycle corresponding to the intelligent device based on the remaining operating life includes:

[0157] The equipment operation and maintenance cycle corresponding to the intelligent equipment is calculated using the following formula:

[0158]

[0159] Wherein, WT represents the equipment operation and maintenance cycle corresponding to the intelligent equipment, k represents the adjustment coefficient corresponding to the intelligent equipment, L represents the remaining operating life, t represents the time variable, f(t) represents the weight coefficient at the time variable t, R(t) represents the reliability coefficient at the time t, and dt represents the small increment of the time variable t;

[0160] The time analysis module 203 is used to evaluate the operation status of the intelligent device based on the device operation and maintenance cycle, obtain the status evaluation parameters, query the fault type of the intelligent device based on the status evaluation parameters, and analyze the occurrence time of the fault type;

[0161] The task query module 204 is used to construct a preventive maintenance strategy corresponding to the intelligent device based on the fault type and the occurrence time, combined with the current state of the intelligent device, generate a maintenance task list corresponding to the intelligent device based on the preventive maintenance strategy, and query the maintenance tasks in the maintenance task list;

[0162] The report generation module 205 is used to analyze the prevention goals in the maintenance task, identify the prevention obstacles in the prevention goals, monitor the operation feedback data of the intelligent equipment based on the prevention obstacles, and generate an operation report corresponding to the intelligent equipment based on the operation feedback data.

[0163] In detail, the modules described in the fault prediction and prevention system 200 in the intelligent equipment transformation technology described in the embodiment of the present invention adopt the same technical means as the fault prediction and prevention method in the intelligent equipment transformation technology described in the accompanying drawings when used, and can produce the same technical effects, which will not be repeated here.

[0164] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A fault prediction and prevention method in intelligent equipment transformation technology, characterized in that: The method comprises: Acquire real-time data during the operation of intelligent equipment, filter the real-time data to obtain filtered real-time data, screen abnormal entropy parameters corresponding to the filtered real-time data, perform feature extraction on the abnormal entropy parameters, and obtain an abnormal parameter set; Based on the abnormal parameter set, a fault prediction framework corresponding to the intelligent device is constructed, the fault prediction framework is used to predict the fault probability corresponding to the intelligent device, the remaining operating life corresponding to the intelligent device is determined according to the fault probability, and the equipment operation and maintenance period corresponding to the intelligent device is calculated based on the remaining operating life, wherein the calculation of the equipment operation and maintenance period corresponding to the intelligent device based on the remaining operating life includes: The equipment operation and maintenance cycle corresponding to the intelligent equipment is calculated using the following formula: Wherein, WT represents the equipment operation and maintenance cycle corresponding to the intelligent equipment, k represents the adjustment coefficient corresponding to the intelligent equipment, L represents the remaining operating life, t represents the time variable, f(t) represents the weight coefficient at the time variable t, R(t) represents the reliability coefficient at the time t, and dt represents the small increment of the time variable t; Based on the equipment operation and maintenance cycle, the operating status of the intelligent equipment is evaluated to obtain status evaluation parameters, and based on the status evaluation parameters, the fault type of the intelligent equipment is queried, and the occurrence time corresponding to the fault type is analyzed; Based on the fault type and the occurrence time, combined with the current state of the intelligent device, a preventive maintenance strategy corresponding to the intelligent device is constructed, based on the preventive maintenance strategy, a maintenance task list corresponding to the intelligent device is generated, and the maintenance tasks in the maintenance task list are queried; Analyze the prevention goals in the maintenance task, identify the prevention obstacles in the prevention goals, monitor the operation feedback data of the intelligent equipment based on the prevention obstacles, and generate an operation report corresponding to the intelligent equipment based on the operation feedback data.

2. The fault prediction and prevention method in the intelligent equipment transformation technology according to claim 1, characterized in that: The screening of abnormal entropy parameters corresponding to the filtered real-time data includes: Generate an entropy value sequence corresponding to the filtered real-time data; Based on the entropy value sequence, calculating the average entropy value corresponding to the filtered real-time data; Based on the average entropy value, determining the entropy value fluctuation range of the filtered real-time data; Based on the entropy value fluctuation range, the abnormal entropy parameter corresponding to the filtered real-time data is screened.

3. The fault prediction and prevention method in the intelligent equipment transformation technology according to claim 2 is characterized in that: The calculating, based on the entropy value sequence, an average entropy value corresponding to the filtered real-time data comprises: The average entropy value corresponding to the filtered real-time data is calculated using the following formula: in, represents the average entropy value corresponding to the filtered real-time data, N represents the total number of entropy values ​​in the entropy value sequence, i represents the number index of entropy values ​​in the entropy value sequence, H i represents the i-th entropy value in the entropy value sequence.

4. The fault prediction and prevention method in the intelligent equipment transformation technology according to claim 1 is characterized in that: The constructing a fault prediction framework corresponding to the intelligent device based on the abnormal parameter set includes: Analyze the parameter distribution law corresponding to the abnormal parameter set; Based on the parameter distribution law, determining the importance of the parameters in the abnormal parameter set; Based on the importance, filtering key abnormal parameters corresponding to the abnormal parameter set; Based on the key abnormal parameters, generating a fault feature vector corresponding to the intelligent device; Based on the fault feature vector, determining a fault classification standard corresponding to the intelligent device; Based on the fault classification standard, a fault prediction framework corresponding to the intelligent device is constructed.

5. The fault prediction and prevention method in the intelligent equipment transformation technology according to claim 1 is characterized in that: Determining the remaining operating life of the intelligent device according to the failure probability includes: Based on the failure probability, obtaining historical operation data corresponding to the intelligent device; Analyzing performance degradation trends in the historical operating data; Based on the performance degradation trend, predict the time span from the performance degradation of the intelligent device to failure; Analyzing the uncertainties corresponding to the time span; Based on the uncertain factors, the remaining operating life corresponding to the intelligent device is determined.

6. The fault prediction and prevention method in the intelligent equipment transformation technology according to claim 1, characterized in that: The step of evaluating the operating state of the intelligent device based on the device operation and maintenance cycle to obtain a state evaluation parameter includes: Based on the equipment operation and maintenance cycle, query the original operation data corresponding to the intelligent equipment; Removing invalid data from the original operation data to obtain valid operation data; Analyzing the degree of deviation between the effective operation data and the historical normal operation data corresponding to the intelligent device; According to the degree of deviation, marking abnormal data segments in the valid operation data; Based on the abnormal data segment, calculating the performance degradation amplitude corresponding to the intelligent device; Based on the performance degradation amplitude, the operating state corresponding to the intelligent device is evaluated to obtain a state evaluation parameter.

7. The fault prediction and prevention method in the intelligent equipment transformation technology according to claim 6 is characterized in that: The calculating, based on the abnormal data segment, the performance degradation amplitude corresponding to the intelligent device includes: The performance degradation amplitude corresponding to the intelligent device is calculated using the following formula: Wherein, XF represents the performance degradation amplitude corresponding to the intelligent device, m represents the number of data points in the abnormal data segment, j represents the index of the data point in the abnormal data segment, and P nj represents the actual value of the performance indicator corresponding to the jth data point in the abnormal data segment, P hj It represents the expected value of the performance indicator corresponding to the jth data point under normal operating conditions.

8. The fault prediction and prevention method in the intelligent equipment transformation technology according to claim 1, characterized in that: The querying of the fault type of the intelligent device based on the status evaluation parameter includes: Based on the state evaluation parameters, analyzing the operation fluctuations corresponding to the intelligent equipment; Extracting operation fluctuation characteristics corresponding to the operation fluctuation situation; Query the fluctuation index of the fault in the operation fluctuation characteristics; Matching the fluctuation index with a preset fault index library to obtain matching data; Filtering a subset of fault indicators with higher similarity in the matching data; Based on the fault indicator subset, the fault type of the intelligent device is queried.

9. The fault prediction and prevention method in the intelligent equipment transformation technology according to claim 1, characterized in that: The constructing of a preventive maintenance strategy corresponding to the intelligent device based on the fault type and the occurrence time in combination with the current state of the intelligent device includes: Based on the fault type and occurrence time, analyzing the fault development trend corresponding to the intelligent device; Extracting key change nodes in the fault development trend; Query the node operation parameters corresponding to the key change node; Comparing the node operating parameters with normal standard parameters to obtain difference parameters; Analyze the difference maintenance items corresponding to the difference parameters; Based on the differential maintenance items and in combination with the current status of the intelligent device, a preventive maintenance strategy corresponding to the intelligent device is constructed.

10. The fault prediction and prevention system in intelligent equipment transformation technology is characterized by: Used to execute the fault prediction and prevention method in the intelligent equipment transformation technology according to any one of claims 1 to 9, the system comprises: A feature extraction module is used to obtain real-time data during the operation of intelligent equipment, filter the real-time data to obtain filtered real-time data, screen abnormal entropy parameters corresponding to the filtered real-time data, perform feature extraction on the abnormal entropy parameters, and obtain an abnormal parameter set; An operation and maintenance cycle calculation module is used to construct a fault prediction framework corresponding to the intelligent device based on the abnormal parameter set, use the fault prediction framework to predict the fault probability corresponding to the intelligent device, determine the remaining operating life corresponding to the intelligent device according to the fault probability, and calculate the equipment operation and maintenance cycle corresponding to the intelligent device based on the remaining operating life, wherein the calculation of the equipment operation and maintenance cycle corresponding to the intelligent device based on the remaining operating life includes: The equipment operation and maintenance cycle corresponding to the intelligent equipment is calculated using the following formula: Wherein, WT represents the equipment operation and maintenance cycle corresponding to the intelligent equipment, k represents the adjustment coefficient corresponding to the intelligent equipment, L represents the remaining operating life, t represents the time variable, f(t) represents the weight coefficient at the time variable t, R(t) represents the reliability coefficient at the time t, and dt represents the small increment of the time variable t; A time analysis module is used to evaluate the operating status of the intelligent device based on the equipment operation and maintenance cycle, obtain status evaluation parameters, query the fault type of the intelligent device based on the status evaluation parameters, and analyze the occurrence time corresponding to the fault type; A task query module is used to construct a preventive maintenance strategy corresponding to the intelligent device based on the fault type and the occurrence time, combined with the current state of the intelligent device, generate a maintenance task list corresponding to the intelligent device based on the preventive maintenance strategy, and query the maintenance tasks in the maintenance task list; The report generation module is used to analyze the prevention goals in the maintenance task, identify the prevention obstacles in the prevention goals, monitor the operation feedback data of the intelligent equipment based on the prevention obstacles, and generate an operation report corresponding to the intelligent equipment based on the operation feedback data.

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