Industrial park equipment operation state management method and system

Abstract

The invention relates to the technical field of equipment management, and discloses an industrial park equipment operation state management method and system. The method comprises the following steps: acquiring equipment basic information, historical fault data and real-time operation data of equipment; according to the basic information of the equipment, performing category analysis on the equipment to obtain an equipment category; according to the historical fault data and the equipment category, performing fault period prediction on the equipment based on a pre-trained support vector regression model to obtain an equipment fault period; according to the real-time operation data and the equipment category, performing health index calculation on the equipment to obtain an equipment health index; and according to the equipment category, the equipment fault period and the equipment health index, performing fault prediction to obtain a predicted fault probability, and sending the predicted fault probability to an operation and maintenance end, so that the operation and maintenance end performs equipment maintenance according to the predicted fault probability. According to the method, the equipment management efficiency of the industrial park can be improved, and the equipment state of the industrial park can be effectively analyzed and managed.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment management, and in particular to a method and system for managing the operating status of equipment in an industrial park. Background Art

[0002] With the continuous promotion and implementation of the concept of intelligent manufacturing, the information acquisition, analysis and management of equipment in industrial parks have received more and more attention.

[0003] The operating status of equipment will not only affect the working cost of the industrial park, but also the production progress of the entire industrial park. In terms of the analysis of the operating status of equipment in the current industrial park, the traditional management method of the industrial park only performs maintenance based on the historical maintenance of the equipment. The preventive maintenance method with fault tree as the core mainly makes corresponding predictions through the maintenance records of the equipment, but this method cannot consider the real-time data during the operation of the equipment. This maintenance mode belongs to scheduled maintenance and response maintenance, and it is impossible to judge in advance whether the equipment failure is about to occur.

[0004] How to accurately predict the operating status of industrial park equipment to conduct intelligent and proactive maintenance and improve the management level of equipment assets is a major focus and difficulty in the management of equipment operating status in industrial parks. However, existing technologies are still lacking in comprehensive consideration of real-time data and historical maintenance data of equipment operation for maintenance. Summary of the invention

[0005] The present invention provides a method and system for managing the operating status of equipment in an industrial park, which can comprehensively consider the historical fault data and real-time operating data of a specific equipment category, and then obtain a predicted failure probability, so that the operation and maintenance end can perform equipment maintenance based on the predicted failure probability, thereby realizing effective analysis and management of the equipment status in the industrial park.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides a method for managing the operating status of equipment in an industrial park, comprising: Obtain basic equipment information, historical fault data and real-time operation data of the equipment; According to the basic information of the equipment, the equipment is analyzed to obtain equipment categories; wherein the equipment categories include stand-alone equipment, intermittent process equipment and continuous process equipment; According to the historical fault data and the equipment category, the equipment failure cycle is predicted based on a pre-trained support vector regression model to obtain the equipment failure cycle; Calculate the health index of the equipment according to the real-time operation data and the equipment category to obtain the equipment health index; wherein the equipment health index includes the health index of a single-machine equipment, the health index of an intermittent process equipment and the health index of a continuous process equipment; According to the equipment category, the equipment failure cycle and the equipment health index, a fault prediction is performed to obtain a predicted failure probability, and the predicted failure probability is sent to the operation and maintenance end so that the operation and maintenance end performs equipment maintenance according to the predicted failure probability.

[0007] Preferably, performing category analysis on the device according to the basic information of the device to obtain the device category includes: According to the basic information of the device, a PCA dimension reduction operation is performed to obtain reduced dimension data; According to the dimension reduction data, equipment classification is performed based on a clustering algorithm to obtain equipment categories.

[0008] Preferably, performing a PCA dimensionality reduction operation according to the basic information of the device to obtain dimensionality reduction data includes: According to the basic information of the device, a matrix is ​​constructed to obtain a feature matrix; wherein the feature matrix is ​​composed of the basic information of the device, the rows of the feature matrix represent each device, and the columns of the feature matrix represent the features of the device; Standardizing the feature matrix to obtain a standardized matrix; Calculating the covariance of the standardized matrix to obtain a covariance matrix; Performing eigenvalue and vector decomposition on the covariance matrix to obtain principal components; According to the principal components, data is screened to obtain dimension-reduced data.

[0009] Preferably, the training process of the support vector regression model includes: Obtain the first failure cycle and the second failure cycle; Standardizing the first failure cycle and the second failure cycle to obtain first preprocessed data and second preprocessed data; Based on the first preprocessed data and the second preprocessed data as input data, a support vector regression model is constructed based on a kernel function, and the support vector regression model is trained to obtain a trained support vector regression model.

[0010] Preferably, the calculating of the health index of the device according to the real-time operation data and the device category to obtain the device health index includes: The health index of a single device is calculated using the following formula: ; The intermittent process equipment health index is calculated using the following formula: ; The continuous process equipment health index is calculated by the following formula: ; In the formula, It is the health index of a single device; It is the health index of intermittent process equipment; It is the continuous process equipment health index; is the running time of the i-th device; is the average time the equipment is running; is the fault status of the device during the i-th operation. If a fault occurs, the value is 1, and if no fault occurs, the value is 0; is the total number of times the device has been run.

[0011] Preferably, performing fault prediction according to the equipment category, the equipment failure cycle and the equipment health index to obtain a predicted fault probability includes: The failure probability of a single device is calculated using the following formula: ; The probability of intermittent equipment failure is calculated using the following formula: ; The probability of continuous equipment failure is calculated by the following formula: ; In the formula, is the failure probability of a single device; is the probability of intermittent equipment failure; is the probability of continuous equipment failure; The most recent equipment failure cycle; is the average value of the equipment's historical failure cycles; It is the health index of a single device; It is the health index of intermittent process equipment; It is the continuous process equipment health index; is the i-th equipment failure cycle; is the total number of historical failure cycles; is the weight of a single device; is the weight of intermittent process equipment; Weights for continuous process equipment; is the cumulative failure impact coefficient.

[0012] Preferably, the predicted failure probability is sent to an operation and maintenance end so that the operation and maintenance end performs equipment maintenance according to the predicted failure probability, including: When the predicted failure probability is greater than and equal to a first preset threshold, the failure risk level is determined to be high risk, and emergency maintenance operations are performed; When the predicted failure probability is less than the first preset threshold and greater than the second preset threshold, the failure risk level is determined to be medium risk, and preventive maintenance operations are performed; When the predicted failure probability is less than and equal to the second preset threshold, the failure risk level is determined to be low risk, and regular maintenance operations are performed.

[0013] In a second aspect, the present invention provides a management system for the operation status of equipment in an industrial park, comprising: Data acquisition module, used to obtain basic equipment information, historical fault data and real-time operation data of the equipment; A category analysis module, used to perform category analysis on the equipment according to the basic information of the equipment to obtain the equipment category; wherein the equipment category includes stand-alone equipment, intermittent process equipment and continuous process equipment; A cycle prediction module, used to predict the failure cycle of the equipment based on the historical failure data and the equipment category and based on a pre-trained support vector regression model to obtain the equipment failure cycle; A health calculation module, used to calculate the health index of the equipment according to the real-time operation data and the equipment category to obtain the equipment health index; wherein the equipment health index includes the health index of a single-machine equipment, the health index of an intermittent process equipment and the health index of a continuous process equipment; The fault prediction module is used to perform fault prediction based on the equipment category, the equipment failure cycle and the equipment health index, obtain the predicted failure probability, and send the predicted failure probability to the operation and maintenance end so that the operation and maintenance end can perform equipment maintenance based on the predicted failure probability.

[0014] In a third aspect, the present invention also provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the method for managing the operating status of industrial park equipment as described in any one of the above.

[0015] In a fourth aspect, the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned methods for managing the operating status of industrial park equipment.

[0016] Compared with the prior art, the present invention has the following beneficial effects: the embodiment of the present invention provides a method and system for managing the operation status of equipment in an industrial park. The method includes: obtaining basic equipment information, historical fault data and real-time operation data of the equipment; performing category analysis on the equipment according to the basic equipment information to obtain equipment categories; wherein the equipment categories include stand-alone equipment, intermittent process equipment and continuous process equipment; performing fault cycle prediction on the equipment based on the pre-trained support vector regression model according to the historical fault data and the equipment categories to obtain equipment fault cycles; performing health index calculation on the equipment according to the real-time operation data and the equipment categories to obtain equipment health index; wherein the equipment health index includes stand-alone equipment health index, intermittent process equipment health index and continuous process equipment health index; performing fault prediction according to the equipment category, the equipment fault cycle and the equipment health index to obtain predicted fault probability, and sending the predicted fault probability to the operation and maintenance end so that the operation and maintenance end can perform equipment maintenance according to the predicted fault probability.

[0017] In the present invention, the method can analyze the basic information, historical fault data and real-time operation data of the comprehensive equipment, and then infer the health status of the equipment and its failure cycle. Specifically, the method includes classifying the equipment, predicting the failure cycle using a support vector regression model, and calculating the health index of the equipment based on the real-time operation data. In addition, the health index and failure cycle of the equipment are combined to perform fault prediction, thereby obtaining a predicted failure probability, and finally providing the prediction result to the operation and maintenance end to perform corresponding equipment maintenance operations. The method can improve the equipment management efficiency of the industrial park and realize the effective analysis and management of the equipment status of the industrial park. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flowchart of a method for managing the operating status of industrial park equipment provided by the first embodiment of the present invention; Figure 2 It is a structural diagram of a management system for the operating status of industrial park equipment provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Reference Figure 1The first embodiment of the present invention provides a method for managing the operating status of equipment in an industrial park, comprising the following steps: S11, obtaining basic equipment information, historical fault data and real-time operation data of the equipment.

[0021] S12, performing category analysis on the device according to the basic information of the device to obtain the device category.

[0022] S13, predicting the equipment failure cycle based on the historical failure data and the equipment category and based on a pre-trained support vector regression model to obtain the equipment failure cycle.

[0023] S14, calculating the health index of the device according to the real-time operation data and the device category to obtain the device health index.

[0024] S15, performing fault prediction according to the equipment category, the equipment failure cycle and the equipment health index, obtaining a predicted failure probability, and sending the predicted failure probability to the operation and maintenance end, so that the operation and maintenance end performs equipment maintenance according to the predicted failure probability.

[0025] It is worth noting that the fault prediction of industrial park equipment uses data analysis technology and models to predict the probability of equipment failure, so as to achieve early warning and take corresponding maintenance measures. Its core purpose is to identify potential failure risks in advance through continuous monitoring and analysis of equipment operation data, and avoid production stagnation or equipment damage caused by sudden failures.

[0026] In order to facilitate the understanding of the present invention, some preferred embodiments of the present invention are further described below.

[0027] In step S11, basic device information, historical fault data, and real-time operation data of the device are obtained.

[0028] Specifically, the basic information of the equipment includes the characteristic data of the equipment, such as model, specification, operating parameters, and working environment. Optionally, this information can come from the technical manual of the equipment, documents provided by the manufacturer, or collected through the equipment management system. It is worth noting that in the subsequent steps, in order to process and classify this information more efficiently, PCA (Principal Component Analysis) dimensionality reduction operation is applied to these basic equipment information. The purpose of dimensionality reduction is to reduce the dimension of the data while retaining as much original information as possible.

[0029] Specifically, historical fault data includes records of faults that have occurred in the equipment in the past, including information such as the time, type, and severity of the faults. In the method of the present invention, historical fault data includes the first fault cycle, which is the time period when the equipment fails for the first time and is used to calibrate the initial fault cycle of the equipment; the second fault cycle refers to the time period when the equipment fails for the second time. By analyzing the difference between the first and second fault cycles, the law of equipment failure can be better understood; the fault cycle refers to the cycle data of each failure, indicating the time interval from the last failure to the next failure. These data help to establish a prediction model and infer future fault cycles. Historical fault data is used for fault cycle prediction. Specifically, by analyzing the historical fault data of the equipment, combined with the equipment category (such as stand-alone equipment, intermittent equipment, continuous equipment), a support vector regression model (SVR) is used to predict the possible future failure cycle of the equipment.

[0030] It is worth mentioning that the main applications of these historical failure data in the present invention are: used for failure cycle prediction, by analyzing the laws of historical failure cycles, predicting the future failure cycle of the equipment; used to calculate the failure probability of the equipment, especially in the failure probability calculation part of the formula, the historical failure data and the health index work together to evaluate the failure risk of the equipment.

[0031] In one feasible way, historical fault data can come from the equipment's maintenance records, fault logs, and equipment management system. These data are manually recorded by the operation and maintenance personnel or automatically generated by the equipment's own fault detection system. For each fault, the specific time of occurrence, fault type, and duration of repair are recorded. This information can be obtained through the equipment's monitoring platform or maintenance management system and form a long-term historical data archive.

[0032] Specifically, real-time operation data refers to the dynamic working status data generated by the equipment during the current or most recent operation. These data include the equipment's operating time, average operating time, and whether the equipment has failed during operation. Specifically, it includes: the equipment's operating time, which refers to the length of time the equipment runs in a specific operating cycle. For example, the specific operating time of the equipment in a working cycle; the average operating time of the equipment, which refers to the average operating time of the equipment within a certain time range. It is used to measure the overall working status and routine operation of the equipment; the fault status of the equipment's i-th operation (fault status value), each time the equipment runs, it will check whether the equipment has failed. If a fault occurs, the fault status value is 1; if no fault occurs, the fault status value is 0. This data helps evaluate the health status of the equipment and identify fault modes.

[0033] It is worth noting that in the present invention, the main uses of real-time operation data are: for calculating the health index, combining the operating time and fault status to reflect the operating status of the equipment; for failure probability prediction, combined with historical failure data, to evaluate the risk of equipment failure in the next time period.

[0034] In one feasible way, real-time operation data can be collected through various sensors on the equipment, such as temperature sensors, vibration sensors, operation time meters, pressure sensors, etc. These sensors can monitor the working status of the equipment in real time and upload the data to the equipment management system or cloud platform. Operation and maintenance personnel can view the real-time data of the equipment through these systems, conduct status assessment, and take maintenance measures according to the actual situation.

[0035] In step S12, the device is analyzed according to the basic information of the device to obtain the device category.

[0036] Preferably, the equipment categories include stand-alone equipment, intermittent process equipment and continuous process equipment.

[0037] It is worth noting that stand-alone equipment refers to those that operate independently and are not interdependent with other equipment. Such equipment usually performs a single task and does not directly affect the operation of other equipment when a failure occurs. Their notable feature is that they can complete specific tasks independently and usually do not require coordination with other equipment. Since they work independently, the failure of a stand-alone equipment will not immediately affect the operation of the entire production line. The failure will only affect the normal operation of a single device. The maintenance of stand-alone equipment is usually based on the operating status and fault records of the equipment itself, and has nothing to do with the operating status of other equipment.

[0038] It is worth noting that intermittent process equipment refers to equipment that works and stops within a specific cycle. This type of equipment is usually not in continuous operation during the production process, but starts and stops periodically according to production needs. For example, in industries such as chemicals and food processing, some equipment may only operate during specific production stages and stop operating at other stages. Their notable feature is that they operate in cycles during the production process, with a downtime period after each operation and then start again. Due to their discontinuous operation, the failure of intermittent process equipment may not be easy to detect during the non-operation period.

[0039] It is worth noting that continuous process equipment refers to equipment that runs continuously and uninterruptedly, usually working continuously for 24 hours without stopping due to periodic factors. This type of equipment is particularly common in industries such as chemicals, petroleum, and steel. Its operation mode is continuous and usually performs production tasks uninterruptedly. The characteristic of continuous process equipment is that this type of equipment hardly stops during the production process and may run for weeks, months or even longer until maintenance or overhaul is carried out. The maintenance of continuous process equipment is usually based on real-time data, and continuous status monitoring and prediction are required to detect potential failures as early as possible and take corresponding preventive measures.

[0040] Preferably, performing category analysis on the device according to the basic information of the device to obtain the device category includes: According to the basic information of the device, a PCA dimension reduction operation is performed to obtain reduced dimension data; According to the dimension reduction data, equipment classification is performed based on a clustering algorithm to obtain equipment categories.

[0041] Preferably, performing a PCA dimensionality reduction operation according to the basic information of the device to obtain dimensionality reduction data includes: According to the basic information of the device, a matrix is ​​constructed to obtain a feature matrix; wherein the feature matrix is ​​composed of the basic information of the device, the rows of the feature matrix represent each device, and the columns of the feature matrix represent the features of the device; Standardizing the feature matrix to obtain a standardized matrix; Calculating the covariance of the standardized matrix to obtain a covariance matrix; Performing eigenvalue and vector decomposition on the covariance matrix to obtain principal components; According to the principal components, data is screened to obtain dimension-reduced data.

[0042] Specifically, based on the basic information of the equipment, it is first necessary to analyze the equipment category to determine whether the equipment is a stand-alone device, an intermittent process device, or a continuous process device. This process is to ensure that different management strategies can be adopted according to different equipment types in subsequent fault prediction and health management.

[0043] Specifically, the classification analysis of devices is mainly carried out through PCA dimensionality reduction operation. It is worth mentioning that PCA (Principal Component Analysis) dimensionality reduction is a statistical method for data dimensionality reduction, which extracts the main features of devices by reducing the data dimension. The goal of this process is to extract the most important features from a large amount of device information, thereby helping the classification algorithm to better identify and distinguish device categories.

[0044] First, when performing PCA dimensionality reduction, it is necessary to extract detailed features of each device from the basic information of the device. These features form a feature matrix, where each row of the matrix represents a device and each column represents a feature of the device. The feature matrix contains various data generated by the device during operation, such as device model, operating time, load, and temperature. These data help the algorithm understand the operating status and working characteristics of the device. Next, the feature matrix is ​​standardized. Standardization is to eliminate the scale differences between different features and ensure that each feature is compared within the same range. The standardized matrix is ​​called a standardized matrix, and each column represents a standardized device feature. The standardization process can effectively prevent some features from dominating the dimensionality reduction process due to their large or small scale. After obtaining the standardized matrix, the covariance matrix will be calculated next. The covariance matrix reflects the correlation between each feature. The larger the covariance value, the stronger the relationship between the features. By calculating the covariance matrix, the algorithm can identify which features have a strong linear relationship, which helps us identify the features that need to be focused on during dimensionality reduction.

[0045] Specifically, after calculating the covariance matrix, eigenvalue and vector decomposition are performed. This process is the core step of PCA dimensionality reduction. By performing eigenvalue decomposition on the covariance matrix, we can obtain a set of principal components. These principal components are the "new coordinate axes" of the data, and they represent the most important directions in the original feature space. In the dimensionality reduction process, the principal components corresponding to the preset number of largest eigenvalues ​​are selected. These principal components contain most of the data information, while ignoring the secondary features that contribute less to the equipment classification. It is worth noting that selecting the principal components corresponding to the preset number of largest eigenvalues ​​is the data screening process. The final dimensionality reduction data is obtained. This dimensionality reduction data contains the most important feature information of the equipment, which can help the subsequent clustering algorithm to classify the equipment. The clustering algorithm will use these dimensionality reduction data to divide the equipment into different categories (such as single-machine equipment, intermittent process equipment and continuous process equipment), and provide a basis for subsequent fault prediction, health index calculation and other tasks.

[0046] In step S13, according to the historical fault data and the equipment category, the equipment failure cycle is predicted based on the pre-trained support vector regression model to obtain the equipment failure cycle Preferably, the training process of the support vector regression model includes: Obtain the first failure cycle and the second failure cycle; Standardizing the first failure cycle and the second failure cycle to obtain first preprocessed data and second preprocessed data; Based on the first preprocessed data and the second preprocessed data as input data, a support vector regression model is constructed based on a kernel function, and the support vector regression model is trained to obtain a trained support vector regression model.

[0047] Specifically, the training process of the support vector regression (SVR) model aims to use historical failure data to predict the future failure cycle of the equipment. Specifically, this process includes the following key steps.

[0048] First, we need to obtain the first failure cycle and the second failure cycle. The first failure cycle refers to the time interval between the first failure of the equipment, and the second failure cycle refers to the time interval between the second failure of the equipment. Through these two time periods, we can understand the early patterns and trends of equipment failures. These failure cycle data are the basic input of the support vector regression model, which helps the model learn the laws of equipment failure.

[0049] Next, the first failure cycle and the second failure cycle obtained need to be standardized. This is to eliminate the scale differences between the data of different failure cycles so that the data can be compared within the same range. The standardization process converts the original data into standard data with a mean of zero and a variance of one, which ensures that each data feature contributes equally to the training of the model and avoids unnecessary influence of some data features with larger scales on the model training. After standardization, the first preprocessed data and the second preprocessed data are obtained respectively.

[0050] Then, based on these two preprocessed data (i.e., the standardized first and second failure cycles) as input data, the kernel function is used to construct the support vector regression model. The kernel function is the core part of the SVR model. It maps the original data to a high-dimensional space so that an optimal hyperplane can be found in this space for regression prediction. Choosing different kernel functions (such as linear kernel and radial basis kernel) will affect the prediction ability and adaptability of the model. Through the kernel function, SVR can better capture the complex patterns of equipment failure cycles in high-dimensional space.

[0051] Finally, by training the support vector regression model, the support vector regression model learns how to predict future equipment failure cycles based on the input failure cycle data. The training process includes minimizing the error and optimizing the model parameters to ensure that the model can accurately fit the input data and has good generalization ability. After the training is completed, what is obtained is an optimized support vector regression model that can predict the failure cycle based on the historical failure data of the equipment.

[0052] In step S14, a health index of the device is calculated based on the real-time operation data and the device category to obtain a device health index.

[0053] Preferably, the equipment health index includes a stand-alone equipment health index, an intermittent process equipment health index and a continuous process equipment health index.

[0054] Preferably, the calculating of the health index of the device according to the real-time operation data and the device category to obtain the device health index includes: The health index of a single device is calculated using the following formula: ; The intermittent process equipment health index is calculated using the following formula: ; The continuous process equipment health index is calculated by the following formula: ; In the formula, It is the health index of a single device; It is the health index of intermittent process equipment; It is the continuous process equipment health index; is the running time of the i-th device; is the average time the equipment is running; is the fault status of the device during the i-th operation. If a fault occurs, the value is 1, and if no fault occurs, the value is 0; is the total number of times the device has been run.

[0055] Specifically, in the formula for calculating the health index of a single device, the running time refers to the duration of each operation of the device, while the average running time refers to the average running time of the device within a certain period of time. The deviation of each device run time from the average time is calculated. If the device run time deviates from the average run time by a large amount, the health of the device may be poor. By squaring these deviations, the impact of large deviations on the health index can be emphasized more. Fault status indicates whether the device has a fault each time it runs. If a fault occurs, the fault status is 1; if no fault occurs, the fault status is 0. The number of faults has a direct negative impact on the health index, and frequent faults can significantly reduce the health index of the device. Total runs is the number of times the device has run, that is, the number of all run cycles of the device over a period of time. The health index of the device is calculated by adding the above and dividing it by the total runs. The closer the value of the health index is to 1, the better the health of the device; if the value is lower, it means that the health of the device is poor.

[0056] Specifically, in the formula for calculating the health index of intermittent process equipment, the meanings of operating time and average operating time are the same as those of single-machine equipment, but (That is, the absolute difference in running time). Since the operation of intermittent process equipment changes periodically, using the absolute difference can more accurately reflect the volatility of the equipment each time it runs. If the running time of the equipment fluctuates greatly each time, this will affect the calculation of the health index. In addition, the meaning of the fault status and the total number of runs is the same as that of a single device. The health index of the equipment is finally obtained by summing up the absolute difference and fault status of the running time of all equipment and dividing it by the product of the total number of runs and the average running time. The closer the value of the health index is to 1, the better the health of the equipment.

[0057] Specifically, in the formula for calculating the health index of continuous process equipment, an exponential function is introduced in the calculation of the health index. This design makes the health index more sensitive to equipment failures and running time deviations. Since continuous process equipment is always running, its failures and running deviations have a greater impact on health, so the impact of failures and deviations can be made more significant through indexation. In this formula, the running time represents the duration of each operation, and the average running time represents the average running time of the equipment. Calculate the deviation of each device run time from the average run time, which is similar to the calculation method for single-machine devices, but under the action of the exponential function, the impact of the deviation on the health index is further amplified. The fault status indicates whether the device has a fault, and the total number of runs is the total number of run cycles of the device. All deviations and fault status values ​​are subjected to exponential operations to obtain the final health index. The closer the health index value is to 1, the better the health of the device. A higher fault frequency or fluctuations in run time will cause a significant decrease in the health index.

[0058] In step S15, fault prediction is performed based on the equipment category, the equipment failure cycle and the equipment health index to obtain a predicted failure probability, and the predicted failure probability is sent to the operation and maintenance end so that the operation and maintenance end performs equipment maintenance based on the predicted failure probability.

[0059] Preferably, performing fault prediction according to the equipment category, the equipment failure cycle and the equipment health index to obtain a predicted fault probability includes: The failure probability of a single device is calculated using the following formula: ; The probability of intermittent equipment failure is calculated using the following formula: ; The probability of continuous equipment failure is calculated by the following formula: ; In the formula, is the failure probability of a single device; is the probability of intermittent equipment failure; is the probability of continuous equipment failure; The most recent equipment failure cycle; is the average value of the equipment's historical failure cycles; It is the health index of a single device; It is the health index of intermittent process equipment; It is the continuous process equipment health index; is the i-th equipment failure cycle; is the total number of historical failure cycles; is the weight of a single device; is the weight of intermittent process equipment; Weights for continuous process equipment; is the cumulative failure impact coefficient.

[0060] Specifically, in fault prediction, the failure probability of stand-alone equipment, intermittent process equipment and continuous process equipment is calculated according to the equipment type, failure cycle and health index. For stand-alone equipment, the calculation of failure probability comprehensively considers the deviation between the most recent failure cycle and the average failure cycle, the health index of the equipment and the weight of the stand-alone equipment. When the difference between the most recent failure cycle and the average cycle is large and the equipment health index is low, the failure probability will increase significantly, reflecting that the equipment is at potential failure risk. For intermittent process equipment, the calculation of failure probability mainly depends on the sum of the deviations between each failure cycle and the average failure cycle, the health index of the equipment and the weight of the intermittent equipment. By accumulating the deviation values ​​of historical failure cycles and adjusting the results in combination with the health index, the failure probability of the equipment can be accurately predicted. The failure probability of intermittent equipment can reflect the impact of periodic fluctuations in the operation of the equipment on the failure risk. The failure probability of continuous process equipment is calculated by the sum of the accumulated failure cycles, and a cumulative failure influence coefficient is introduced to evaluate the impact of historical failure cycles on future failure probability. This calculation method places special emphasis on the long-term operating characteristics of the equipment. Combined with the health index and equipment category weight, it can more sensitively capture the impact of equipment health degradation or abnormal historical failure cycles on failure risks.

[0061] Preferably, the predicted failure probability is sent to an operation and maintenance end so that the operation and maintenance end performs equipment maintenance according to the predicted failure probability, including: When the predicted failure probability is greater than and equal to a first preset threshold, the failure risk level is determined to be high risk, and emergency maintenance operations are performed; When the predicted failure probability is less than the first preset threshold and greater than the second preset threshold, the failure risk level is determined to be medium risk, and preventive maintenance operations are performed; When the predicted failure probability is less than and equal to the second preset threshold, the failure risk level is determined to be low risk, and regular maintenance operations are performed.

[0062] Specifically, in the equipment maintenance process, information is sent to the operation and maintenance end based on the predicted failure probability, helping the operation and maintenance team to take appropriate maintenance measures in a timely manner. The failure probability classification standard is divided into three categories: high risk, medium risk and low risk, and each category corresponds to a different maintenance strategy to ensure the normal operation of the equipment and reduce the risk of sudden failures.

[0063] When the predicted failure probability is greater than or equal to the first preset threshold, the equipment is judged to be in a high-risk state. This means that the possibility of equipment failure is extremely high and may have a serious impact on the normal operation of the production line or system. In this case, emergency maintenance operations need to be taken immediately, including shutdown inspection, replacement of parts or comprehensive overhaul, to eliminate potential risks and restore the normal state of the equipment.

[0064] When the predicted failure probability is lower than the first preset threshold but higher than the second preset threshold, the equipment is judged to be in a medium-risk state. In this case, although the equipment has a certain failure risk, it has not reached an emergency state. In order to prevent the risk from further expanding, it is recommended to take preventive maintenance measures. This may include replacing wearing parts, adjusting operating parameters, or conducting detailed inspections, thereby delaying the occurrence of equipment failures and improving its operating stability.

[0065] When the predicted failure probability is less than or equal to the second preset threshold, the device is judged to be in a low-risk state. This means that the device is running relatively stably and the possibility of failure is low. In this case, regular maintenance can be arranged to ensure the normal operation of the equipment while avoiding waste of resources caused by excessive maintenance. Regular maintenance usually includes routine inspection, cleaning and basic maintenance.

[0066] In summary, the present invention provides a method for managing the operating status of equipment in an industrial park, including: acquiring basic equipment information, historical fault data and real-time operating data of the equipment; performing category analysis on the equipment according to the basic equipment information to obtain equipment categories; wherein the equipment categories include stand-alone equipment, intermittent process equipment and continuous process equipment; performing failure cycle prediction on the equipment based on a pre-trained support vector regression model according to the historical fault data and the equipment categories to obtain equipment failure cycles; performing health index calculation on the equipment according to the real-time operating data and the equipment categories to obtain equipment health index; wherein the equipment health index includes stand-alone equipment health index, intermittent process equipment health index and continuous process equipment health index; performing failure prediction according to the equipment category, the equipment failure cycle and the equipment health index to obtain predicted failure probability, and sending the predicted failure probability to the operation and maintenance end, so that the operation and maintenance end performs equipment maintenance according to the predicted failure probability.

[0067] In the present invention, the method can analyze the basic information, historical fault data and real-time operation data of the comprehensive equipment, and then infer the health status of the equipment and its failure cycle. Specifically, the method includes classifying the equipment, predicting the failure cycle using a support vector regression model, and calculating the health index of the equipment based on the real-time operation data. In addition, the health index and failure cycle of the equipment are combined to perform fault prediction, thereby obtaining a predicted failure probability, and finally providing the prediction result to the operation and maintenance end to perform corresponding equipment maintenance operations. The method can improve the equipment management efficiency of the industrial park and realize the effective analysis and management of the equipment status of the industrial park.

[0068] Reference Figure 2 The second embodiment of the present invention provides a management system for the operation status of equipment in an industrial park, including: Data acquisition module, used to obtain basic equipment information, historical fault data and real-time operation data of the equipment; A category analysis module, used to perform category analysis on the equipment according to the basic information of the equipment to obtain the equipment category; wherein the equipment category includes stand-alone equipment, intermittent process equipment and continuous process equipment; A cycle prediction module, used to predict the failure cycle of the equipment based on the historical failure data and the equipment category and based on a pre-trained support vector regression model to obtain the equipment failure cycle; A health calculation module, used to calculate the health index of the equipment according to the real-time operation data and the equipment category to obtain the equipment health index; wherein the equipment health index includes the health index of a single-machine equipment, the health index of an intermittent process equipment and the health index of a continuous process equipment; The fault prediction module is used to perform fault prediction based on the equipment category, the equipment failure cycle and the equipment health index, obtain the predicted failure probability, and send the predicted failure probability to the operation and maintenance end so that the operation and maintenance end can perform equipment maintenance based on the predicted failure probability.

[0069] It should be noted that the management system for the operating status of industrial park equipment provided in an embodiment of the present invention is used to execute all the process steps of the management method for the operating status of industrial park equipment in the above embodiment. The working principles and beneficial effects of the two correspond one to one, so they will not be repeated here.

[0070] An embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a program for managing the operating status of industrial park equipment. When the processor executes the computer program, the steps in the above-mentioned embodiments of the method for managing the operating status of industrial park equipment are implemented, such as Figure 1Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the period prediction module.

[0071] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0072] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0073] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0074] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0075] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0076] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0077] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for managing the operating status of equipment in an industrial park, characterized in that: include: Obtain basic equipment information, historical fault data and real-time operation data of the equipment; According to the basic information of the equipment, the equipment is analyzed to obtain equipment categories; wherein the equipment categories include stand-alone equipment, intermittent process equipment and continuous process equipment; According to the historical fault data and the equipment category, the equipment failure cycle is predicted based on a pre-trained support vector regression model to obtain the equipment failure cycle; Calculate the health index of the equipment according to the real-time operation data and the equipment category to obtain the equipment health index; wherein the equipment health index includes the health index of a single-machine equipment, the health index of an intermittent process equipment and the health index of a continuous process equipment; According to the equipment category, the equipment failure cycle and the equipment health index, a fault prediction is performed to obtain a predicted failure probability, and the predicted failure probability is sent to an operation and maintenance end so that the operation and maintenance end performs equipment maintenance according to the predicted failure probability.

2. The method for managing the operating status of industrial park equipment according to claim 1, characterized in that: The device category is analyzed based on the basic information of the device to obtain the device category, including: According to the basic information of the device, a PCA dimension reduction operation is performed to obtain reduced dimension data; According to the dimension reduction data, equipment classification is performed based on a clustering algorithm to obtain equipment categories.

3. The method for managing the operating status of industrial park equipment according to claim 2, characterized in that: The PCA dimension reduction operation is performed according to the basic information of the device to obtain the dimension reduction data, including: According to the basic information of the device, a matrix is ​​constructed to obtain a feature matrix; wherein the feature matrix is ​​composed of the basic information of the device, the rows of the feature matrix represent each device, and the columns of the feature matrix represent the features of the device; Standardizing the feature matrix to obtain a standardized matrix; Calculating the covariance of the standardized matrix to obtain a covariance matrix; Performing eigenvalue and vector decomposition on the covariance matrix to obtain principal components; According to the principal components, data is screened to obtain dimension-reduced data.

4. The method for managing the operating status of industrial park equipment according to claim 1, characterized in that: The training process of the support vector regression model includes: Obtain the first failure cycle and the second failure cycle; Standardizing the first failure cycle and the second failure cycle to obtain first preprocessed data and second preprocessed data; Based on the first preprocessed data and the second preprocessed data as input data, a support vector regression model is constructed based on a kernel function, and the support vector regression model is trained to obtain a trained support vector regression model.

5. The method for managing the operating status of industrial park equipment according to claim 1, characterized in that: The calculating the health index of the device according to the real-time operation data and the device category to obtain the device health index includes: The health index of a single device is calculated using the following formula: ； The intermittent process equipment health index is calculated using the following formula: ； The continuous process equipment health index is calculated by the following formula: ； In the formula, It is the health index of a single device; It is the health index of intermittent process equipment; It is the continuous process equipment health index; is the running time of the i-th device; is the average time the equipment is running; is the fault status of the device during the i-th operation. If a fault occurs, the value is 1, and if no fault occurs, the value is 0; is the total number of times the device has been run.

6. The method for managing the operating status of industrial park equipment according to claim 1, characterized in that: The performing of fault prediction according to the equipment category, the equipment failure cycle and the equipment health index to obtain a predicted fault probability includes: The failure probability of a single device is calculated using the following formula: ； The probability of intermittent equipment failure is calculated using the following formula: ； The probability of continuous equipment failure is calculated by the following formula: ； In the formula, is the failure probability of a single device; is the probability of intermittent equipment failure; is the probability of continuous equipment failure; The most recent equipment failure cycle; is the average value of the equipment's historical failure cycles; It is the health index of a single device; It is the health index of intermittent process equipment; It is the continuous process equipment health index; is the i-th equipment failure cycle; is the total number of historical failure cycles; is the weight of a single device; is the weight of intermittent process equipment; Weights for continuous process equipment; is the cumulative failure impact coefficient.

7. The method for managing the operating status of industrial park equipment according to claim 1, characterized in that: The method further includes: sending the predicted failure probability to an operation and maintenance end so that the operation and maintenance end performs equipment maintenance according to the predicted failure probability, including: When the predicted failure probability is greater than and equal to a first preset threshold, the failure risk level is determined to be high risk, and emergency maintenance operations are performed; When the predicted failure probability is less than the first preset threshold and greater than the second preset threshold, the failure risk level is determined to be medium risk, and preventive maintenance operations are performed; When the predicted failure probability is less than and equal to the second preset threshold, the failure risk level is determined to be low risk, and regular maintenance operations are performed.

8. A management system for the operation status of equipment in an industrial park, characterized in that: include: Data acquisition module, used to obtain basic equipment information, historical fault data and real-time operation data of the equipment; A category analysis module, used to perform category analysis on the equipment according to the basic information of the equipment to obtain the equipment category; wherein the equipment category includes stand-alone equipment, intermittent process equipment and continuous process equipment; A cycle prediction module, used to predict the failure cycle of the equipment based on the historical failure data and the equipment category and based on a pre-trained support vector regression model to obtain the equipment failure cycle; A health calculation module, used to calculate the health index of the equipment according to the real-time operation data and the equipment category to obtain the equipment health index; wherein the equipment health index includes the health index of a single-machine equipment, the health index of an intermittent process equipment and the health index of a continuous process equipment; The fault prediction module is used to perform fault prediction based on the equipment category, the equipment failure cycle and the equipment health index, obtain the predicted failure probability, and send the predicted failure probability to the operation and maintenance end so that the operation and maintenance end can perform equipment maintenance based on the predicted failure probability.

9. An electronic device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the method for managing the operating status of industrial park equipment as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the method for managing the operating status of industrial park equipment as described in any one of claims 1 to 7.

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