Intelligent chemical production patrol control system

Abstract

The invention discloses an intelligent chemical production patrol control system, and relates to the technical field of chemical production patrol control, and the system comprises a data collection module which is responsible for collecting the operation data of chemical production equipment in real time, including the equipment temperature, pressure, flow, rotating speed and vibration; and the data processing module is used for cleaning, integrating and standardizing the collected operation data so as to eliminate noise and abnormal values in the data and improve the data quality. Equipment operation data are collected in real time through the data collection module, the prediction model module carries out fault prediction through a machine learning algorithm, potential faults of equipment can be found in time, occurrence or expansion of the faults is avoided, a maintenance plan is formulated in advance, and preventative maintenance is carried out on the equipment which may have faults, so that the equipment fault rate is reduced, and the maintenance efficiency is improved. Therefore, not only can the production efficiency of chemical enterprises be improved, but also the maintenance cost can be reduced, and the economic benefits of the enterprises can be improved.

Description

Technical Field

[0001] The invention relates to the technical field of chemical production patrol control, and in particular to an intelligent chemical production patrol control system. Background Art

[0002] Chemical production refers to the process of converting raw materials into valuable products or intermediates through chemical reactions and physical treatment processes. This process involves a series of complex processes and equipment, including raw material processing, reaction control, product separation and refining. Since the chemical production process involves many flammable, explosive, toxic or harmful substances, strict safety measures must be taken to ensure the smooth progress of the production process. It is necessary to patrol and control the chemical production system in real time. Chemical production patrol control is a crucial link in the chemical production process. It involves comprehensive monitoring and management of the production process, aiming to ensure safe, stable and efficient production. Therefore, the chemical production safety patrol control system came into being.

[0003] According to the patent application number 202211125924.5, an intelligent chemical production safety patrol and control system is disclosed, including a management module for entering the main patrol task, an allocation module for splitting the main patrol task into multiple sub-patrol tasks and assigning them to the corresponding patrol terminals, a map module, which is arranged in the patrol terminal, and the map module numbers each patrol device according to the patrol order, a monitoring module, which monitors the movement route of the patrol personnel in real time, a recording module, which is used to record the operating parameters of each device during the patrol process, a positioning module, which is used to locate the current location of the patrol terminal, and a verification module, which is used to verify the identity information of the patrol personnel using the patrol terminal.

[0004] By adopting the above scheme, each inspection equipment is numbered according to the inspection order, and the inspection personnel inspect the equipment one by one according to the number, thereby eliminating the situation of missed inspection in the traditional inspection process, and can also determine which equipment needs to be inspected this time and which equipment is not within the scope of this inspection according to the number. However, the above scheme still has certain defects when used. The inspection method of the above scheme still relies on manual inspection, which is inefficient and easy to miss potential faults. The above scheme cannot predict the possible faults of the equipment when used, and perform maintenance in advance. Faults will cause unexpected downtime and reduce production efficiency. Therefore, it is necessary to propose an intelligent chemical production inspection and control system to solve the problems in the prior art. Summary of the invention

[0005] The purpose of the present invention is to make up for the shortcomings of the prior art and provide an intelligent chemical production patrol and control system. It can collect equipment operation data, use machine learning algorithms to predict possible equipment failures, and perform maintenance in advance, thereby effectively reducing unexpected downtime and improving production efficiency.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: an intelligent chemical production patrol control system, which includes a data acquisition module responsible for real-time collection of operating data of chemical production equipment, including equipment temperature, pressure, flow, speed, and vibration;

[0007] The data processing module cleans, integrates and standardizes the collected operation data to eliminate noise and outliers in the data and improve data quality;

[0008] The prediction model module uses machine learning algorithms to build a prediction model and learn from the processed equipment operation data to predict possible equipment failures;

[0009] The maintenance control module formulates maintenance plans and generates maintenance reports based on the output results of the prediction model, and performs preventive maintenance on equipment that may fail.

[0010] Furthermore, the specific working steps of the data acquisition module are as follows:

[0011] (1) Data collection equipment configuration: Install surveillance cameras and sensors at key locations of chemical production equipment to monitor and collect data on the environment of the chemical production area and the working parameters of various equipment;

[0012] (2) Connection and communication: The data acquisition module is connected to the data acquisition equipment installed on the chemical production equipment through the Internet of Things technology, sensor network, and wireless communication;

[0013] (3) Data collection: Once the connection is established and the communication is stable, the data collection module starts to collect equipment operation data in real time;

[0014] (4) Data preprocessing: During the data collection process, the module performs some preliminary preprocessing on the collected chemical equipment working data to ensure the accuracy and reliability of the data;

[0015] (5) Data transmission: The collected and preprocessed data will be transmitted to the data processing module.

[0016] Furthermore, the specific steps of the data processing module are as follows:

[0017] (1) Data reception: The data processing module receives real-time equipment operation data transmitted from the data acquisition module;

[0018] (2) Data cleaning: The data processing module cleans the received data, performs filtering, and removes noise and interference signals to ensure the accuracy and consistency of the data;

[0019] (3) Data integration: The cleaned data needs to be integrated to convert data from different sources and in different formats into a format suitable for subsequent analysis;

[0020] (4) Data standardization: In order to eliminate the dimensional differences between different parameters, the data processing module will standardize the data.

[0021] Furthermore, the data processing module uses a filtering algorithm when filtering the collected chemical equipment data. The algorithm formula is as follows: For a set of chemical system operation data (x 1 , x 2 , ..., x n ), the output of the moving average filter (y i ) can be expressed as:

[0022] Furthermore, the data integration and data standardization in the data processing module use the K-means algorithm to classify the data. The K-means algorithm mainly involves the calculation of the Euclidean distance, and the calculation formula is: Where n is the dimension of the data (i.e. the number of features), is the data point x i The value in the pth dimension, is the cluster center c j The value in the pth dimension, d(x i ,c j ) is the data point x i To cluster center c j The K-means algorithm also involves the calculation of cluster center updates, and the calculation formula is:

[0023] Furthermore, the specific steps of the prediction model module are as follows:

[0024] (1) Model selection and construction: According to the characteristics of chemical production equipment and prediction requirements, select appropriate machine learning algorithms to build prediction models. Machine learning algorithms include support vector machines and random forests.

[0025] (2) Model training: The prediction model module will use historical operation data to train the model. By continuously adjusting the model parameters and structure, the model can accurately learn the features in the equipment operation data and identify the patterns that may lead to failures.

[0026] (3) Model evaluation and optimization: After training is completed, the prediction model module needs to evaluate the model to test its prediction performance. Based on the evaluation results, the model can be further optimized to improve the accuracy of the prediction.

[0027] (4) Real-time prediction: After model training and evaluation are completed, the prediction model module will begin to receive real-time equipment operation data from the data processing module and use the trained model to make predictions.

[0028] Furthermore, the support vector machine (SVM) algorithm in the prediction model module mainly includes the case of linear separability and the case of approximately linear separability and linear inseparability;

[0029] (1) For the linearly separable case, the decision function of the linearly separable SVM is: w^T*x+b=0, where w is the normal vector of the hyperplane, b is the intercept, and x is the input sample. This hyperplane divides the space into two parts, corresponding to different categories. After obtaining the optimal weight vector (w) and bias term (b), the new sample point can be classified by the formula (w^Tx+b). If (w^Tx+b>0), the sample point belongs to the positive class; if (w^Tx+b<0), the sample point belongs to the negative class.

[0030] (2) For the cases of approximately linearly separable and linearly inseparable, the support vector machine (SVM) algorithm is used to handle them by introducing soft margins and kernel techniques; soft margin maximization corresponds to the linear support vector machine, while the kernel technique is used to handle nonlinear cases.

[0031] Furthermore, the random forest algorithm in the prediction model module predicts or classifies data in the chemical production process by constructing multiple decision trees, thereby realizing intelligent monitoring of the production process. The construction process of the decision tree follows the principle of the decision tree algorithm. The decision tree construction process involves the calculation of the Gini index. The calculation formula of the Gini index is: Where K represents the total number of categories, p i The higher the purity (D), the higher the probability (or frequency) of the i-th category appearing in the data set. In random forests, by randomly selecting features and samples and integrating multiple decision trees, the variance of the model can be reduced and the generalization ability of the model can be improved, thereby obtaining more accurate and stable prediction results.

[0032] Furthermore, the model evaluation and optimization in the prediction model module need to be cross-validated to obtain the final evaluation result. The calculation process is as follows: assuming that the data set is divided into K subsets, the size of each subset may be the same or different, for the i-th iteration (i=1, 2, ..., K): select the i-th subset as the test set Ti, and the remaining subsets as the training set Di, use the training set Di to train the model to obtain the model Mi, evaluate the performance of the model Mi on the test set Ti, and obtain the evaluation result Ei. Finally, calculate the average value of the K evaluation results as the final evaluation result of the model performance, and the average value of the K evaluation results is calculated as follows:

[0033]

[0034] Furthermore, the specific steps of the maintenance control module are as follows:

[0035] (1) Receiving prediction results: The maintenance control module first receives the prediction results from the prediction model module;

[0036] (2) Maintenance plan formulation: Based on the prediction results, the maintenance control module will formulate a corresponding maintenance plan;

[0037] (3) Production task adjustment: In order to avoid production interruption caused by equipment failure, the maintenance control module will automatically adjust the production task according to the urgency of the maintenance plan;

[0038] (4) Maintenance execution and monitoring: During the execution of the maintenance plan, the maintenance control module will monitor the execution of the maintenance tasks to ensure that the maintenance work is carried out according to the plan;

[0039] (5) Maintenance report generation: After the maintenance task is completed, the maintenance control module will generate a maintenance report, which records in detail the equipment's maintenance history, fault type, maintenance measures, and maintenance effect information.

[0040] Compared with the existing technology, this intelligent chemical production patrol control system has the following beneficial effects:

[0041] 1. The present invention collects equipment operation data in real time through the data collection module, and the prediction model module uses machine learning algorithms to predict faults, which can timely discover potential equipment faults, avoid the occurrence or expansion of faults, formulate maintenance plans in advance, and perform preventive maintenance on equipment that may fail, thereby reducing equipment failure rates and reducing unexpected downtime. This can not only improve the production efficiency of chemical companies, but also reduce maintenance costs and improve the economic benefits of companies.

[0042] 2. The prediction model module of the present invention adopts support vector machine algorithm and random forest algorithm to perform model training on historical chemical equipment operation data, receives real-time equipment operation data from the data processing module, and uses the trained model to predict faults. The maintenance control module generates a maintenance report based on the output results of the prediction model, which is convenient for managers to understand the equipment operation status and maintenance status.

[0043] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 This is a flow chart of an intelligent chemical production patrol control system. DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. 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.

[0047] Example 1

[0048] An intelligent chemical production patrol and control system includes a data acquisition module, which is responsible for collecting the operating data of chemical production equipment in real time, including equipment temperature, pressure, flow, speed, and vibration. The module transmits the collected data to the data processing module through Internet of Things technologies such as sensor networks and wireless communications. The data acquisition module has high reliability and real-time performance to ensure the accuracy and integrity of the data.

[0049] The specific working steps of the data acquisition module are as follows: first, monitoring cameras and sensors are installed at key locations of chemical production equipment to monitor and collect the environment of the chemical production area and the working parameters of various equipment. The data acquisition module is connected to the data acquisition equipment installed on the chemical production equipment through the Internet of Things technology, sensor networks, and wireless communications to ensure stable communication with the equipment in order to receive equipment operation data in real time. Once the connection is established and the communication is stable, the data acquisition module begins to collect equipment operation data in real time. These data include the temperature, pressure, flow, speed, and vibration parameters of the equipment. The collected data needs to remain original and real-time for subsequent accurate analysis and processing. Then, during the data collection process, the module performs some preliminary preprocessing on the collected chemical equipment working data, such as removing noise and outliers, to ensure the accuracy and reliability of the data. Finally, the collected and preprocessed data will be transmitted to the data processing module. During the transmission process, the integrity and security of the data need to be ensured to prevent data loss or tampering.

[0050] The data processing module cleans, integrates and standardizes the collected operating data to eliminate noise and outliers in the data and improve data quality. This module uses advanced data processing methods and technologies, such as data filtering, to ensure data reliability and consistency. The processed data will be used for subsequent prediction model training and fault prediction.

[0051] The specific steps of the data processing module are as follows: First, the data processing module receives the real-time equipment operation data transmitted by the data acquisition module. These data are raw data, including various parameter values ​​generated by the equipment during operation. Then the data processing module cleans the received data and uses the filtering algorithm to filter and remove noise and interference signals to ensure the accuracy and consistency of the data. The algorithm formula is as follows: For a set of chemical system operation data (x 1 , x 2 , ..., x n ), the output of the moving average filter (y i ) can be expressed as: This includes detecting and processing missing values, invalid values, or values ​​that exceed a reasonable range. The cleaned data then needs to be integrated, and data from different sources and in different formats are uniformly converted into a format suitable for subsequent analysis. This involves merging, splitting, and standardizing data to form a complete and coherent data set. Finally, in order to eliminate the dimensional differences between different parameters, the data processing module will standardize the data. This is usually done by converting the data into dimensionless relative numbers so that each indicator is at the same order of magnitude, which is convenient for comprehensive comparison and evaluation. In the data processing module, data integration and data standardization use the K-means algorithm to classify the data. The K-means algorithm mainly involves the calculation of the Euclidean distance, and the calculation formula is: Where n is the dimension of the data (i.e. the number of features), is the data point x i The value in the pth dimension, is the cluster center c j The value in the pth dimension, d(x i ,c j ) is the data point x i To cluster center c j The K-means algorithm also involves the calculation of cluster center updates, and the calculation formula is:

[0052] Group similar data points into one category for better data integration and standardization. Calculation steps: Initialization: Select the number of clusters K, randomly select K points in the data set as the initial cluster centers (centroids), assign data points to the nearest cluster centers: For each data point in the data set, calculate its distance to each cluster center, assign each data point to the nearest cluster center, and thus form K initial clusters, recalculate cluster centers: For each cluster, calculate the mean of all data points within it (i.e., the new cluster center), iterative optimization: Repeat steps 2 and 3 until a certain stopping condition is met (for example, the cluster center no longer changes significantly, or the preset maximum number of iterations is reached).

[0053] Example 2

[0054] The prediction model module uses machine learning algorithms to build prediction models and learns the processed equipment operation data to predict possible equipment failures. This module can select appropriate machine learning algorithms based on actual production needs. Through continuous learning and optimization, the prediction model can accurately identify early signs of equipment failure, provide early warning, and provide strong support for preventive maintenance.

[0055] The specific steps of the prediction model module are as follows: the prediction model module first selects a suitable machine learning algorithm to build a prediction model according to the characteristics of the chemical production equipment and the prediction requirements. The machine learning algorithms include support vector machines and random forests. When selecting an algorithm, it is necessary to consider the complexity of the data, the generalization ability of the model, and the training efficiency factors. The support vector machine (SVM) algorithm mainly includes the linearly separable case and the approximately linearly separable and linearly inseparable case; for the linearly separable case, the decision function of the linearly separable SVM is: w^T*x+b=0, where w is the normal vector of the hyperplane, b is the intercept, x is the input sample; this hyperplane divides the space into two parts, corresponding to different categories. After obtaining the optimal weight vector (w) and bias term (b), the new sample point can be classified by the formula (w^Tx+b); if (w^Tx+b>0), the sample point belongs to the positive class; if (w^Tx+b<0), the sample point belongs to the negative class; for the cases of approximately linear separability and linear inseparability, the support vector machine (SVM) algorithm is processed by introducing soft margins and kernel techniques; soft margin maximization corresponds to linear support vector machines, while kernel techniques are used to handle nonlinear situations;

[0056] Once the appropriate algorithm is selected, the prediction model module will use historical operating data to train the model. This data includes parameters during normal operation of the equipment and data before past failures. By continuously adjusting the parameters and structure of the model, the model can accurately learn the features in the equipment operation data and identify patterns that may cause failures. After the training is completed, the prediction model module needs to evaluate the model to verify its prediction performance, which is usually measured by cross-validation, accuracy, recall rate, and F1 value indicators. According to the evaluation results, the model can be further optimized, such as adjusting parameters, changing the model structure, or using integrated learning methods to improve the accuracy of the prediction. When the model training and evaluation are completed, the prediction model module will begin to receive real-time equipment operation data from the data processing module and use the trained model for prediction. The prediction results include the probability of possible equipment failure, the type of failure, and the time when the failure occurs.

[0057] The random forest algorithm in the prediction model module predicts or classifies data in the chemical production process by constructing multiple decision trees, thereby realizing intelligent monitoring of the production process. The decision tree construction process follows the principles of the decision tree algorithm. The decision tree construction process involves the calculation of the Gini index. The calculation formula of the Gini index is: Where K represents the total number of categories, p i The higher the purity (D), the probability (or frequency) of the i-th category appearing in the data set; in random forest, by randomly selecting features and samples and integrating multiple decision trees, the variance of the model can be reduced and the generalization ability of the model can be improved, thereby obtaining more accurate and stable prediction results. The calculation steps are: select features that have an important impact on the prediction results from the preprocessed data, which can be achieved by calculating the importance or relevance of the features; based on the selected features, build multiple decision trees, each of which is built by random sampling of training data and random selection of features to achieve model diversity and generalization ability; integrate all the constructed decision trees together to form a random forest model; when predicting, each decision tree will give its own prediction results, and finally obtain the prediction results of the random forest by voting or averaging; use test data to evaluate the random forest model, and improve the prediction performance by adjusting parameters and optimizing the model.

[0058] Model evaluation and optimization in the prediction model module require cross-validation to obtain the final evaluation result. The calculation process is as follows: assuming that the data set is divided into K subsets, the size of each subset may be the same or different. For the i-th iteration (i=1, 2, ..., K): select the i-th subset as the test set Ti, and the remaining subsets as the training set Di. Use the training set Di to train the model to obtain the model Mi. Evaluate the performance of the model Mi on the test set Ti to obtain the evaluation result Ei. Finally, calculate the average of the K evaluation results as the final evaluation result of the model performance. The calculation formula for the average of the K evaluation results is: The steps of model evaluation and optimization are as follows: First, it is necessary to select appropriate evaluation indicators according to the specific needs of the prediction task. Common evaluation indicators include accuracy, recall, and F1 value. For some tasks, other indicators may need to be considered, such as precision and ROC curve. Then cross-validation is performed, and then the selected evaluation indicators are used to calculate the performance of the model on the test set. This usually involves calculating the number of true positive examples (TruePositive), false positive examples (FalsePositive), true negative examples (TrueNegative), and false negative examples (FalseNegative) of the prediction results, and then calculating the accuracy, recall, and F1 value indicators based on these numbers. According to the calculated evaluation indicator values, the performance of the model is analyzed. If the model performance is not good, you can consider optimizing the model, such as adjusting the model parameters, changing the model structure, or trying to use ensemble learning methods. According to the evaluation results, the model is optimized accordingly, which involves adjusting the model's hyperparameters (such as learning rate, batch size), changing the model's architecture, or using ensemble learning methods to improve the model's performance.

[0059] Example 3

[0060] The maintenance control module formulates maintenance plans and generates maintenance reports based on the output results of the prediction model, and performs preventive maintenance on equipment that may fail. The module can automatically adjust production tasks according to the urgency of the maintenance plan to avoid production interruptions due to equipment failure. At the same time, the module can generate maintenance reports that record the equipment's maintenance history, failure type, and maintenance measures in detail for management personnel's reference and analysis.

[0061] The specific steps of the maintenance control module are as follows: the maintenance control module first receives the prediction results from the prediction model module. These prediction results usually include the probability of equipment failure, the type of failure, and the expected time of failure. Then, based on the prediction results, the maintenance control module will formulate a corresponding maintenance plan, including determining the equipment that needs maintenance, the maintenance time window, the specific content of maintenance (such as replacing parts, cleaning equipment), and the priority of maintenance. When formulating a maintenance plan, it is also necessary to consider the urgency of the production task, the operating status of the equipment, and the availability of maintenance resources. In order to avoid production interruptions caused by equipment failure, the maintenance control module will automatically Automatically adjust production tasks, including reallocating production tasks to other equipment, adjusting the operating speed of the production line or suspending certain non-critical tasks. During the execution of the maintenance plan, the maintenance control module will monitor the execution of maintenance tasks to ensure that the maintenance work is carried out as planned. At the same time, it will also collect real-time operating data of the equipment during the maintenance process in order to evaluate the maintenance effect. Finally, after the maintenance task is completed, the maintenance control module will generate a maintenance report. This report records in detail the maintenance history, fault type, maintenance measures and maintenance effect information of the equipment. These reports not only help managers understand the operating status and maintenance status of the equipment, but also provide a reference for the formulation of future maintenance plans.

[0062] To sum up: the data collection module of this application collects equipment operation data in real time, and the prediction model module uses machine learning algorithms to predict faults, which can promptly detect potential equipment failures and formulate maintenance plans, perform preventive maintenance on equipment that may fail, and reduce unexpected downtime.

[0063] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the same elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. An intelligent chemical production patrol control system, characterized in that: The system includes a data acquisition module, which is responsible for collecting the operating data of chemical production equipment in real time, including equipment temperature, pressure, flow, speed, and vibration; The data processing module cleans, integrates and standardizes the collected operation data to eliminate noise and outliers in the data and improve data quality; The prediction model module uses machine learning algorithms to build a prediction model and learn from the processed equipment operation data to predict possible equipment failures; The maintenance control module formulates maintenance plans and generates maintenance reports based on the output results of the prediction model, and performs preventive maintenance on equipment that may fail.

2. The intelligent chemical production patrol control system according to claim 1 is characterized in that: The specific working steps of the data acquisition module are as follows: (1) Data collection equipment configuration: Install surveillance cameras and sensors at key locations of chemical production equipment to monitor and collect data on the environment of the chemical production area and the working parameters of various equipment; (2) Connection and communication: The data acquisition module is connected to the data acquisition equipment installed on the chemical production equipment through the Internet of Things technology, sensor network, and wireless communication; (3) Data collection: Once the connection is established and the communication is stable, the data collection module starts to collect equipment operation data in real time; (4) Data preprocessing: During the data collection process, the module performs some preliminary preprocessing on the collected chemical equipment working data to ensure the accuracy and reliability of the data; (5) Data transmission: The collected and preprocessed data will be transmitted to the data processing module.

3. The intelligent chemical production patrol control system according to claim 1 is characterized in that: The specific steps of the data processing module are as follows: (1) Data reception: The data processing module receives real-time equipment operation data transmitted from the data acquisition module; (2) Data cleaning: The data processing module cleans the received data, performs filtering, and removes noise and interference signals to ensure the accuracy and consistency of the data; (3) Data integration: The cleaned data needs to be integrated to convert data from different sources and in different formats into a format suitable for subsequent analysis; (4) Data standardization: In order to eliminate the dimensional differences between different parameters, the data processing module will standardize the data.

4. The intelligent chemical production patrol control system according to claim 3 is characterized in that: The data processing module uses a filtering algorithm to filter the collected chemical equipment data. The algorithm formula is as follows: For a set of chemical system operation data (x1, x2, ..., x n ), the output of the moving average filter (y i ) can be expressed as:

5. The intelligent chemical production patrol control system according to claim 3 is characterized in that: The data integration and data standardization in the data processing module adopt the K-means algorithm to classify the data. The K-means algorithm mainly involves the calculation of the Euclidean distance, and the calculation formula is: Where n is the dimension of the data (i.e. the number of features), is the data point x i The value in the pth dimension, is the cluster center c j The value in the pth dimension, d(x i ,c j ) is the data point x i To cluster center c j The K-means algorithm also involves the calculation of cluster center updates, and the calculation formula is:

6. The intelligent chemical production patrol control system according to claim 1 is characterized in that: The specific steps of the prediction model module are as follows: (1) Model selection and construction: According to the characteristics of chemical production equipment and prediction requirements, select appropriate machine learning algorithms to build prediction models. Machine learning algorithms include support vector machines and random forests. (2) Model training: The prediction model module will use historical operation data to train the model. By continuously adjusting the model parameters and structure, the model can accurately learn the features in the equipment operation data and identify the patterns that may lead to failures. (3) Model evaluation and optimization: After training is completed, the prediction model module needs to evaluate the model to test its prediction performance. Based on the evaluation results, the model can be further optimized to improve the accuracy of the prediction. (4) Real-time prediction: After model training and evaluation are completed, the prediction model module will begin to receive real-time equipment operation data from the data processing module and use the trained model to make predictions.

7. The intelligent chemical production patrol control system according to claim 6 is characterized in that: The support vector machine (SVM) algorithm in the prediction model module mainly includes the case of linear separability and the case of approximately linear separability and linear inseparability; (1) For the linearly separable case, the decision function of the linearly separable SVM is: w^T*x+b=0, where w is the normal vector of the hyperplane, b is the intercept, and x is the input sample. This hyperplane divides the space into two parts, corresponding to different categories. After obtaining the optimal weight vector (w) and bias term (b), the new sample point can be classified by the formula (w^Tx+b). If (w^Tx+b>0), the sample point belongs to the positive class; if (w^Tx+b<0), the sample point belongs to the negative class. (2) For the cases of approximately linearly separable and linearly inseparable, the support vector machine (SVM) algorithm is used to handle them by introducing soft margins and kernel techniques; soft margin maximization corresponds to the linear support vector machine, while the kernel technique is used to handle nonlinear cases.

8. The intelligent chemical production patrol control system according to claim 6 is characterized in that: The random forest algorithm in the prediction model module predicts or classifies data in the chemical production process by constructing multiple decision trees, thereby realizing intelligent monitoring of the production process. The construction process of the decision tree follows the principle of the decision tree algorithm. The decision tree construction process involves the calculation of the Gini index. The calculation formula of the Gini index is: Where K represents the total number of categories, p i The higher the purity (D), the higher the probability (or frequency) of the i-th category appearing in the data set. In random forests, by randomly selecting features and samples and integrating multiple decision trees, the variance of the model can be reduced and the generalization ability of the model can be improved, thereby obtaining more accurate and stable prediction results.

9. The intelligent chemical production patrol control system according to claim 6, characterized in that: The model evaluation and optimization in the prediction model module need to be cross-validated to obtain the final evaluation result. The calculation process is as follows: assuming that the data set is divided into K subsets, the size of each subset may be the same or different, for the i-th iteration (i=1, 2, ..., K): select the i-th subset as the test set Ti, and the remaining subsets as the training set Di, use the training set Di to train the model to obtain the model Mi, evaluate the performance of the model Mi on the test set Ti, and obtain the evaluation result Ei. Finally, calculate the average value of the K evaluation results as the final evaluation result of the model performance. The calculation formula for the average value of the K evaluation results is:

10. The intelligent chemical production patrol control system according to claim 1, characterized in that: The specific steps of the maintenance control module are as follows: (1) Receiving prediction results: The maintenance control module first receives the prediction results from the prediction model module; (2) Maintenance plan formulation: Based on the prediction results, the maintenance control module will formulate a corresponding maintenance plan; (3) Production task adjustment: In order to avoid production interruption caused by equipment failure, the maintenance control module will automatically adjust the production task according to the urgency of the maintenance plan; (4) Maintenance execution and monitoring: During the execution of the maintenance plan, the maintenance control module will monitor the execution of the maintenance tasks to ensure that the maintenance work is carried out according to the plan; (5) Maintenance report generation: After the maintenance task is completed, the maintenance control module will generate a maintenance report, which records in detail the equipment's maintenance history, fault type, maintenance measures, and maintenance effect information.

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