Intelligent factory operation management system and method

Abstract

The present invention relates to the field of intelligent monitoring and management technology, specifically to an intelligent factory operation management system and method, the system includes: a data receiving and processing module, which obtains real-time data of a factory production line, receives energy consumption, production rate and market energy price, generates a real-time data set by screening and summarizing, and performs trend analysis and pattern recognition based on the real-time data set to obtain an analysis report. In the present invention, by centrally processing the real-time data of the factory production line, highly integrated data analysis and decision support are achieved, which not only improves the response speed to changes in the production process, but also optimizes the design and execution of energy adjustment rules. Through meticulous pattern recognition, it is possible to ensure the matching of energy configuration and production strategy, and achieve improved production efficiency.

Description

Technical Field

[0001] The present invention relates to the field of intelligent monitoring and management technology, and in particular to an intelligent factory operation management system and method. Background Art

[0002] Intelligent monitoring and management technology involves the use of advanced information technology, communication technology, automation control technology, and artificial intelligence algorithms to improve the efficiency of monitoring, control, and decision-making in industrial environments. The core of this technology field is to integrate data from various sensors, cameras, and other detection equipment to optimize production processes, reduce energy consumption, and improve safety and reliability through real-time data analysis and pattern recognition. Applications include but are not limited to predictive maintenance, energy efficiency management, and resource optimization and allocation, aiming to reduce human intervention through technical means and realize automation and intelligence of production processes.

[0003] However, the existing technology still needs to be improved in terms of timeliness and accuracy. The lack of flexible and adaptive rule design also makes it difficult for existing technology to cope with the rapid changes in the market and production, thus limiting the optimization of production efficiency and cost control. These limitations not only affect the sensitivity of enterprises to market changes, but may also lead to high operating costs and improper resource allocation, reducing the overall competitiveness of enterprises. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an intelligent factory operation management system and method.

[0005] In order to achieve the above object, the present invention adopts the following technical solution: The intelligent factory operation management system includes:

[0006] The data receiving and processing module obtains real-time data of the factory production line, receives energy consumption, production rate and market energy price, generates real-time data sets through screening and aggregation, and performs trend analysis and pattern recognition based on the real-time data sets to obtain analysis reports;

[0007] A rule design and optimization module, based on the patterns identified in the analysis report, designs applicable energy adjustment rules, encodes the set of rule parameters, creates a rule base, performs rule validity tests with the parameters encoded in the rule base, and establishes an optimized rule set;

[0008] The decision algorithm implementation module performs logical operations and judgments based on the rules in the optimization rule set and the real-time data set, determines the energy allocation and production strategy, executes the decision algorithm to adjust the production line speed or energy configuration, obtains a preliminary execution plan, simulates the production environment with the preliminary execution plan, adjusts the parameters according to the test results, and obtains the final execution plan;

[0009] The production efficiency monitoring module, based on the final implementation plan, monitors the operating status and energy consumption of the production line in real time, compares the set efficiency and cost targets, records monitoring data, creates monitoring records, judges the effectiveness and deviation of the plan by analyzing the monitoring records, and generates an effect evaluation report.

[0010] Preferably, the steps of acquiring the real-time data set are:

[0011] Obtain real-time data from factory production lines, including energy consumption, production rate and market energy prices, to generate preliminary data;

[0012] Based on the preliminary data, data screening and aggregation were performed using the formula:

[0013] Calculate the weighted average of each data and introduce the standard deviation of market energy prices to obtain the aggregated data set, where: Representative Energy consumption, Representative Item production rate, Representative The market energy price, and are the weight coefficients of energy consumption and production rate, is the average market energy price, is the number of data points for energy consumption and production rate, is the number of data points of market energy prices;

[0014] Trend analysis and pattern recognition algorithms are applied to the aggregated data set to generate a real-time data set.

[0015] Preferably, the steps of obtaining the analysis report are:

[0016] Based on the real-time data set, applying statistical analysis methods to analyze key trends and patterns in the data to generate preliminary analysis results;

[0017] The preliminary analysis results are mined using the formula:

[0018] The weighted effect of each factor on production is calculated, the factors affecting production efficiency and energy consumption are sorted out, and detailed data interpretation is obtained, among which: Representative The weight coefficient of each factor, Representative The measured value of a factor, The total number of representative factors;

[0019] The detailed data is integrated into a parsable format, charts and graphical representations are added, and analytical reports are obtained.

[0020] Preferably, the steps of obtaining the optimization rule set are:

[0021] From the analysis report, identify and extract key patterns and generate preliminary rule designs;

[0022] The preliminary rule design is encoded and parameterized using the formula:

[0023] Calculate the comprehensive score of the rules to determine the priority and efficiency, and get the encoded rule set, where: Representative The effectiveness coefficient of a rule, Representative The response coefficient of the rule, represents the number of rules;

[0024] Verify the effect and performance of the encoded rule set, adjust and optimize the rules, and form an optimized rule set.

[0025] Preferably, the steps for obtaining the preliminary implementation plan are:

[0026] Combining the optimization rule set and the real-time data set, using logical operations and decision-making methods, design a preliminary strategy that matches the current production line and market demand;

[0027] The designed preliminary strategy is simulated and tested using the formula:

[0028] Calculate the performance score of the strategy in the simulation environment and obtain the simulation test results, where: Representative The adjustment depth of each strategy parameter, represents the economic efficiency coefficient, Represents the total number of strategy parameters involved in the evaluation;

[0029] The strategy parameters are adjusted according to the simulation test results to form a preliminary execution plan that matches the production needs.

[0030] Preferably, the steps for obtaining the final execution plan are:

[0031] Based on the preliminary execution plan and in combination with real-time feedback data from the production environment, the policy parameters are adjusted to generate an adjusted policy;

[0032] The adjusted strategy is simulated and tested again using the formula:

[0033] The performance score of the improved strategy is calculated by weighted average, the responsiveness and practicality of the optimization strategy are optimized, and the results of the simulation test are obtained, among which, Representative The adjustment range of the strategy parameters, represents the performance index of the corresponding parameter, Represents the total number of strategy parameters involved in the evaluation;

[0034] The strategy parameters are optimized and adjusted according to the results of the simulation test to form a final execution plan that matches the plant operation.

[0035] Preferably, the steps for obtaining the effect evaluation report are:

[0036] Based on the final implementation plan, real-time monitoring data of the production line is collected to obtain information including energy consumption, production rate, and set efficiency and cost targets, and generate a comprehensive monitoring data set;

[0037] The comprehensive monitoring data set is analyzed using the formula:

[0038] Calculate the deviation between production efficiency and target efficiency and generate quantitative indicators for effect evaluation, where: Representative The actual value of the monitoring indicator. Representative The target value of the monitoring indicator, Represents the total number of monitoring indicators.

[0039] Preferably, the step of obtaining the effect evaluation report also includes: recording the deviation and improvement instructions of each indicator according to the quantitative indicators of the effect evaluation to obtain the effect evaluation report.

[0040] A smart factory operation management method, which is executed based on the above-mentioned smart factory operation management system, comprises the following steps:

[0041] S1: Obtain real-time data from the factory production line, including energy consumption, production rate and market energy price, create a real-time data set through screening and aggregation, and use logical operations and judgments to perform trend analysis and pattern recognition on the real-time data set to obtain pattern analysis results;

[0042] S2: Based on the pattern analysis results, design energy adjustment rules, including encoding the set rule parameters, creating a rule base, using the parameters encoded in the rule base to test the effectiveness of the designed rules, optimizing the rules according to the test feedback, and obtaining an optimized rule set after optimization;

[0043] S3: Utilize the optimization rule set and the real-time data set to adjust the production line speed or energy configuration, obtain a preliminary execution plan, and perform simulation tests on the preliminary execution plan. Adjust parameters according to the simulation test results, optimize production configuration and strategy, and finally obtain an execution performance report; based on the execution performance report, monitor the production line status and energy consumption in real time, record monitoring data, and generate an effect evaluation report by analyzing the monitoring data.

[0044] Compared with the prior art, the advantages and positive effects of the present invention are:

[0045] In the present invention, by centrally processing the real-time data of the factory production line, highly integrated data analysis and decision support are achieved, which not only improves the response speed to changes in the production process, but also optimizes the design and implementation of energy adjustment rules. Through meticulous pattern recognition, it is possible to ensure the matching of energy configuration and production strategy, and achieve improved production efficiency. Through simulation testing and real-time monitoring, the implementation plan is refined and optimized to ensure that the actual application effect of the plan is consistent with expectations. Such a dynamic adjustment mechanism significantly reduces resource waste and improves economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0048] See also Figure 1 , the present invention provides a technical solution: the intelligent factory operation management system includes:

[0049] The data receiving and processing module obtains real-time data of the factory production line, receives energy consumption, production rate and market energy price, generates real-time data sets through screening and aggregation, and performs trend analysis and pattern recognition based on the real-time data sets to obtain analysis reports;

[0050] The rule design and optimization module designs applicable energy adjustment rules based on the patterns identified in the analysis report, encodes the set of rule parameters, creates a rule base, tests the validity of the rules with the parameters encoded in the rule base, and establishes an optimized rule set;

[0051] The decision algorithm implementation module performs logical operations and judgments based on the rules in the optimization rule set and the real-time data set to determine the energy allocation and production strategy, execute the decision algorithm to adjust the production line speed or energy configuration, obtain a preliminary execution plan, simulate the production environment with the preliminary execution plan, adjust the parameters according to the test results, and obtain the final execution plan;

[0052] The production efficiency monitoring module monitors the operating status and energy consumption of the production line in real time based on the final implementation plan, compares the set efficiency and cost targets, records monitoring data, creates monitoring records, analyzes monitoring records, determines the effectiveness and deviation of the plan, and generates an effect evaluation report.

[0053] The steps to obtain the real-time data set are:

[0054] Obtain real-time data from factory production lines, including energy consumption, production rate and market energy prices, to generate preliminary data;

[0055] Based on the preliminary data, data screening and aggregation are performed using the formula:

[0056] Calculate the weighted average of each data and introduce the standard deviation of market energy prices to obtain the aggregated data set, where: Representative Energy consumption, Representative Item production rate, Representative The market energy price, and are the weight coefficients of energy consumption and production rate, is the average market energy price, is the number of data points for energy consumption and production rate, is the number of data points of market energy prices;

[0057] Apply trend analysis and pattern recognition algorithms to the aggregated data sets to generate real-time data sets.

[0058] Specifically, the process of obtaining real-time data from factory production lines involves multiple steps and strategies. First, data collection equipment needs to be deployed at key locations, such as energy consumption points and production rate detection points, to ensure the real-time and accuracy of the data. The collection equipment reads the energy consumption, production rate and market energy price every second and sends the data to the central processing unit. After receiving the data, the central processing unit performs preliminary data cleaning and format standardization to remove noise and outliers, ensure data quality, perform preliminary data classification, and identify patterns and anomalies in the data. These processes ensure the quality and availability of the data before entering the next step of analysis, and generate a preliminary data set, which will directly support subsequent data screening and aggregation work;

[0059] formula The calculation process:

[0060] In the formula, and Represent the real-time energy consumption and production rate collected from the i-th production point, Represents the energy price of the jth market data point, weight parameter and Dynamically adjust based on the volatility and influence of historical data, It is the average value of market energy price. The calculation process includes the weighted energy consumption and production rate sum of all production points, plus the square root of the standard deviation of market energy price. This combination of weighting and standard deviation provides a data view that comprehensively considers production efficiency and market conditions. It is calculated through actual data:

[0061] For example , , , , , , , , the calculation process is as follows:

[0062] The results show a comprehensive data view of production efficiency and market price factors, providing real-time basis for decision-making;

[0063] The process of further processing the aggregated data set by applying trend analysis and pattern recognition algorithms requires detailed parameter settings and algorithm applications. First, the time series characteristics of each variable in the data set are determined, and the sliding window method is used to calculate the difference between each data point and the previous moment in order to capture possible trends and periodic patterns. The data points are classified using a clustering algorithm to identify data groups with similar production behaviors. These groups may represent different production efficiency levels or energy usage patterns. After that, anomaly detection algorithms are used to identify outliers in the data. These outliers may indicate equipment failures or atypical energy usage. Based on these analyses, the final real-time data set is generated. This data set will be used for further decision support and production adjustments to ensure the optimized operation of the production line.

[0064] The steps to obtain the analysis report are:

[0065] Based on real-time data sets, apply statistical analysis methods to analyze key trends and patterns in the data and generate preliminary analysis results;

[0066] To mine the preliminary analysis results, the formula is used:

[0067] The weighted effect of each factor on production is calculated, the factors affecting production efficiency and energy consumption are sorted out, and detailed data interpretation is obtained, among which: Representative The weight coefficient of each factor, Representative The measured value of a factor, The total number of representative factors;

[0068] Consolidate detailed data into a parseable format, add charts and graphical representations, and generate analytical reports.

[0069] Specifically, in the process of obtaining real-time data of the factory production line, a programmable logic controller is used to monitor energy consumption and production rate. Each production unit is equipped with an energy meter and a flow meter to record hourly energy consumption and production material flow in real time. At the same time, market energy prices are obtained by accessing Bloomberg New Energy Finance through a browser on a desktop computer connected to the Internet. These data are automatically updated every ten minutes and transmitted to the server in the data center through a wired network. The data are stored in the SQL database running on the server, and the data are preliminarily timestamped and labeled to lay the foundation for data screening and aggregation, thereby generating a preliminary data set containing the key operating parameters of the production line.

[0070] formula The calculation process:

[0071] When calculating the weighted effect of each factor, first set the weight coefficient of each factor These coefficients are obtained based on historical data analysis and reflect the relative importance of different factors in production. For example, the weight of energy consumption may be set according to its proportion in total cost, and the weight of production rate may be set according to its impact on delivery time. It is a data value read directly, such as power consumption data or production line speed count read from a PLC;

[0072] These values ​​are directly taken into the formula for calculation, and the specific values ​​are: Energy consumption Unit, production rate Item / hour, market energy price Unit / yuan, and the weights are set to , , ;

[0073] The calculation process is:

[0074] The results show that the weighted effect of the three factors is 40.33, indicating the current production efficiency and energy use efficiency.

[0075] When applying trend analysis and pattern recognition algorithms to process data sets, null values ​​and outliers are deleted, and then the main features in the data are determined by building multiple decision trees. These features are then used in the LogisticRegression model for data classification. Model training and testing are performed using labeled data obtained from the real-time system. The labeled data includes production data samples with known results, such as yield and defect rate, to ensure that the trained model can accurately predict future production trends, thereby providing effective decision support when generating the final real-time data set and enhancing the optimization and efficiency of the production process.

[0076] The steps to obtain the optimization rule set are:

[0077] From the analysis report, identify and extract key patterns and generate preliminary rule designs;

[0078] The preliminary rule design is encoded and parameterized using the formula:

[0079] Calculate the comprehensive score of the rules to determine the priority and efficiency, and get the encoded rule set, where: Representative The effectiveness coefficient of a rule, Representative The response coefficient of the rule, represents the number of rules;

[0080] Verify the effect and performance of the encoded rule set, adjust and optimize the rules, and form an optimized rule set.

[0081] Specifically, real-time data of the factory production line is obtained, including energy consumption, production rate and market energy prices, and preliminary data is generated. The energy consumption data and production rate data measured by on-site sensors are automatically uploaded to the central processing system at regular intervals, and data is synchronized through a real-time interface with market energy prices to ensure the timeliness and accuracy of the obtained data. At the same time, through real-time updating of data, the energy efficiency and production costs of the production line can be effectively monitored. Through continuous monitoring of these real-time data, abnormal situations in production can be quickly discovered, and production strategies can be adjusted in time to optimize energy use and cost control. The generation of preliminary data provides a basis for subsequent data processing and analysis.

[0082] formula The calculation process:

[0083] The formula is used to calculate the composite score of the optimized rule set, where Representative The effectiveness coefficient of a rule, Representative The response coefficient of the rule, is the total number of rules. The effectiveness coefficient and response coefficient of each rule are obtained through experiments and historical data analysis. For example, if a rule has shown a high energy saving effect in the past implementation, its effectiveness coefficient is high; the response coefficient is scored according to the impact of the rule on the production rate. The more the rule can improve the production efficiency, the higher its response coefficient.

[0084] During the calculation process, the effectiveness coefficient and response coefficient of each rule are first multiplied, and then these product values ​​are summed up and finally divided by the number of rules to obtain the average score.

[0085] There are three rules with validity coefficients of 0.8, 0.9, and 0.7, and response coefficients of 0.5, 0.4, and 0.6, respectively. The calculation process is:

[0086] The result shows that the average score of each rule is 0.4, which means that the rule set has a good overall effect and is suitable for implementation in production.

[0087] The encoded rule set is verified for effectiveness and performance tested, and the rules are further adjusted and optimized to ultimately form an optimized rule set suitable for actual operations, ensuring the feasibility and effectiveness of these rules in the production environment. The execution effect of each rule is tested by simulating actual production conditions, and data is collected to evaluate how the performance of the rules affects production efficiency and energy consumption. Based on the test results, the rule parameters are fine-tuned to ensure that the rules can remain efficient and reliable under different production conditions. The optimized rule set will be dynamically adjusted based on real-time feedback from the production line. Through continuous testing and adjustment cycles, we will eventually obtain a set of verified optimized rule sets that can significantly improve production efficiency and reduce energy consumption, ensuring the feasibility and effectiveness of these rules in the production environment.

[0088] The steps to obtain the preliminary implementation plan are:

[0089] Combine the optimization rule set with the real-time data set, apply logical operations and decision-making methods, and design a preliminary strategy that matches the current production line and market demand;

[0090] The designed preliminary strategy was simulated and tested using the formula:

[0091] Calculate the performance score of the strategy in the simulation environment and obtain the simulation test results, where: Representative The adjustment depth of each strategy parameter, represents the economic efficiency coefficient, Represents the total number of strategy parameters involved in the evaluation;

[0092] Adjust the strategy parameters based on the simulation test results to form a preliminary implementation plan that matches production needs.

[0093] Specifically, design a preliminary strategy that adapts to the current production line and market demand, taking into account specific indicators in the real-time data set, such as the hourly changes in production rate and the daily average of energy consumption, and taking into account the real-time fluctuations in market energy prices, especially the price peaks during high-demand periods, and formulate adjustment directions based on these specific data points. For example, when the ratio of energy consumption to production output increases, reduce the production speed or adjust the production period to adapt to the peak of energy prices. Conversely, increase the production rate to take advantage of low energy costs, ensuring that each adjustment is based on specific real-time data analysis rather than pre-set or estimated. Such a data-driven approach not only improves the adaptability of the strategy, but also optimizes the efficiency of resource allocation, and ultimately generates a specific preliminary strategy;

[0094] formula The calculation process:

[0095] This formula is used to estimate the performance score of the strategy in the simulated environment, where the depth is adjusted and economic efficiency coefficient It is calculated based on actual data and previous simulation test results. Specifically, the adjustment depth is measured based on the response speed and flexibility of the production line to the adjustment, while the economic benefit coefficient is calculated by analyzing past energy cost data and production efficiency data and determining the direct impact of each strategy parameter adjustment on cost savings through regression analysis.

[0096] There are three strategy parameters, and the adjustment depth is set to 0.8, 0.5, and 0.3 respectively, and the corresponding economic efficiency coefficients are 2, 1.5, and 1.0:

[0097] This score of 0.8833 represents the average performance of the strategy in the simulation test. Based on this score, we can judge the actual utility and expected economic benefits of the strategy and provide a basis for further strategy adjustments;

[0098] According to the simulation test results, the strategy parameters are optimized and adjusted. The performance of each parameter in the test results is analyzed in detail, especially those parameters with poor performance, and necessary adjustment depth corrections are made. The depth of each adjustment is determined based on the deviation between the specific performance of the parameter in the simulation test and the expected target. The economic benefit coefficient is dynamically calculated based on the impact of each parameter adjustment on the production cost. This dynamic adjustment method ensures that each parameter modification is data-driven, and ultimately forms a preliminary implementation plan that comprehensively considers production efficiency and energy optimization, comprehensively reflects the matching degree between the expected effect after adjustment and the actual production demand, and finally forms a preliminary implementation plan.

[0099] The steps to obtain the final execution plan are:

[0100] Based on the preliminary execution plan and combined with the real-time feedback data from the production environment, adjust the policy parameters and generate the adjusted policy;

[0101] The adjusted strategy is simulated and tested again, using the formula:

[0102] The performance score of the improved strategy is calculated by weighted average, the responsiveness and practicality of the optimization strategy are optimized, and the results of the simulation test are obtained, among which, Representative The adjustment range of the strategy parameters, represents the performance index of the corresponding parameter, Represents the total number of strategy parameters involved in the evaluation;

[0103] Based on the optimization results of the simulation test, the strategy parameters are adjusted to form a final execution plan that matches the factory operation.

[0104] Specifically, in real-time monitoring, the process of adjusting strategy parameters includes real-time adjustment of specific parameters such as production speed and energy configuration, based on the production line rate and energy consumption monitored by sensors. The sensors collect data every 15 seconds to obtain the average energy consumption and real-time update of the production rate. After each data update, the system automatically calculates the average production rate and energy consumption of the past minute, and uses these data to update the strategy parameters. The adjustment strategy is as follows: If the production rate is lower than the preset minimum standard, energy input will be increased to ensure that the production rate returns to the normal range. Otherwise, energy input will be reduced to reduce costs. In this way, the strategy parameters can reflect and adapt to production changes in real time to ensure optimal production efficiency and energy use.

[0105] formula The calculation process:

[0106] In this formula, Indicates the strategy adjustment range, Represents the efficiency index. These parameters are not set out of thin air, but are derived from past data analysis. For example, the production data of the past month is analyzed to determine the adjustment range of different production strategies. , and extract performance index from historical performance improvement cases . Strategy adjustment range Usually calculated based on the rate of change of production rate and energy consumption, the efficiency index It is obtained based on the production quality and cost-effectiveness evaluation after the change.

[0107] There are 5 strategies, each strategy and as follows: , , etc. Substituting these values ​​into the formula:

[0108]

[0109] The results show that the average performance score is 2.0, which means that the average benefit after the strategy adjustment is relatively balanced, which can effectively improve production efficiency while controlling costs within a reasonable range. The value 2.0 is a medium-level score, indicating that the performance after the production strategy adjustment is in an ideal state and does not require further major adjustments.

[0110] Conducting simulation tests of strategies involves a comprehensive assessment of the production line's response to strategy adjustments. This process includes monitoring the production line's operating data under different strategy parameter adjustments, such as setting different production speeds and energy configurations, and then calculating the production cost and efficiency under each configuration through simulation software. Based on the collected data, the influence of each parameter is evaluated, and data fluctuations and stability are analyzed through statistical analysis methods such as standard deviation and coefficient of variation, so as to score the effectiveness of each strategy. The scoring method is set according to the strategy's contribution to increasing production rates and reducing costs. Such simulation tests help us identify the optimal strategy configuration to ensure efficient operation and maximize cost-effectiveness in actual applications.

[0111] The steps to obtain the effect evaluation report are as follows:

[0112] Based on the final implementation plan, collect real-time monitoring data of the production line, obtain information including energy consumption, production rate, and set efficiency and cost targets, and generate a comprehensive monitoring data set;

[0113] The synthetic monitoring data set is analyzed using the formula:

[0114] Calculate the deviation between production efficiency and target efficiency and generate quantitative indicators for effect evaluation, where: Representative The actual value of the monitoring indicator. Representative The target value of the monitoring indicator, Represents the total number of monitoring indicators;

[0115] According to the quantitative indicators of effect evaluation, record the deviation and improvement instructions of each indicator to obtain the effect evaluation report.

[0116] Specifically, based on the final implementation plan, production line monitoring data is acquired, and data collection includes production line rate, energy consumption, and production line automation adjustment. These data come from each monitoring node of the production line. Each node is responsible for recording specific production line operation data, such as hourly readings of energy consumption and minute-by-minute changes in production rate. In addition, it also includes real-time evaluation of production line operating efficiency, such as by monitoring equipment operating status and output quality, as well as production line maintenance cycle data. All these data are aggregated through the automation system to generate a comprehensive monitoring data set in the production management system for further efficiency analysis and cost control.

[0117] formula The calculation process:

[0118] This formula is used to calculate the mean of the sum of squares of deviations between the actual monitoring data and the target value, where Representative The actual value of the monitoring indicator. represents the target value, is the total number of monitoring indicators;

[0119] There are three monitoring indicators, with target values ​​of 100, 200, and 300, and actual values ​​of 90, 210, and 295, respectively. The calculated sum of squared deviations is:

[0120]

[0121] Dividing by the total number of monitoring indicators 3, the average sum of squared deviations is 75. This result reflects the overall degree of deviation between the monitoring data and the target value. The smaller the deviation, the closer the operating efficiency of the production line is to the predetermined target, thereby evaluating the production efficiency.

[0122] Based on the calculation results of the quantitative indicators of effect evaluation, the deviations of various indicators are recorded and analyzed. This includes comparing the calculated deviation results with the predetermined thresholds to determine which indicators are performing poorly and need to be optimized. For example, energy consumption indicators that exceed the predetermined deviation may point to inefficient equipment or improper maintenance. Deviation analysis of production rate can help identify process bottlenecks. These analysis results are integrated into an effect evaluation report, which not only lists the specific deviation values ​​of each indicator, but also provides targeted improvement suggestions, such as optimizing energy management processes or adjusting production line layouts to improve production efficiency.

[0123] The present invention provides a smart factory operation management method, comprising the following steps:

[0124] Obtain real-time data from factory production lines, including energy consumption, production rate, and market energy prices. Create real-time data sets through screening and aggregation. Perform trend analysis and pattern recognition on real-time data sets using logical operations and judgments to obtain pattern analysis results.

[0125] Based on the pattern analysis results, energy adjustment rules are designed, including encoding the set of rule parameters, creating a rule base, using the parameters encoded in the rule base to test the effectiveness of the designed rules, optimizing the rules according to the test feedback, and obtaining an optimized rule set after optimization;

[0126] Utilize the optimization rule set and real-time data set to adjust the production line speed or energy configuration, obtain a preliminary execution plan, and conduct simulation tests on the preliminary execution plan. Adjust the parameters according to the simulation test results, optimize the production configuration and strategy, and finally obtain an execution performance report. Based on the execution performance report, monitor the production line status and energy consumption in real time, record the monitoring data, and generate an effect evaluation report by analyzing the monitoring data.

Claims

1. Intelligent factory operation management system, characterized by: The system comprises: The data receiving and processing module obtains real-time data of the factory production line, receives energy consumption, production rate and market energy price, generates real-time data sets through screening and aggregation, and performs trend analysis and pattern recognition based on the real-time data sets to obtain analysis reports; The steps for obtaining the real-time data set are: Obtain real-time data from factory production lines, including energy consumption, production rate and market energy prices, to generate preliminary data; Based on the preliminary data, data screening and aggregation were performed using the formula: ； Calculate the weighted average of each data and introduce the standard deviation of market energy prices to obtain the aggregated data set, where: Representative Energy consumption, Representative Item production rate, Representative Market energy prices, and are the weight coefficients of energy consumption and production rate, is the average market energy price, is the number of data points for energy consumption and production rate, is the number of data points of market energy prices; applying trend analysis and pattern recognition algorithms to the aggregated data set to generate a real-time data set; A rule design and optimization module, based on the patterns identified in the analysis report, designs applicable energy adjustment rules, encodes the set of rule parameters, creates a rule base, performs rule validity tests with the parameters encoded in the rule base, and establishes an optimized rule set; The decision algorithm implementation module performs logical operations and judgments based on the rules in the optimization rule set and the real-time data set, determines the energy allocation and production strategy, executes the decision algorithm to adjust the production line speed or energy configuration, obtains a preliminary execution plan, simulates the production environment with the preliminary execution plan, adjusts the parameters according to the test results, and obtains the final execution plan; The production efficiency monitoring module, based on the final implementation plan, monitors the operating status and energy consumption of the production line in real time, compares the set efficiency and cost targets, records monitoring data, creates monitoring records, judges the effectiveness and deviation of the plan by analyzing the monitoring records, and generates an effect evaluation report.

2. The intelligent factory operation management system according to claim 1, characterized in that: The steps for obtaining the analysis report are: Based on the real-time data set, applying statistical analysis methods to analyze key trends and patterns in the data to generate preliminary analysis results; The preliminary analysis results are mined using the formula: ； The weighted effect of each factor on production is calculated, the factors affecting production efficiency and energy consumption are sorted out, and detailed data interpretation is obtained, among which: Representative The weight coefficient of each factor, Representative The measured value of a factor, The total number of representative factors; Consolidate detailed data into a parseable format, add charts and graphical representations, and generate analytical reports.

3. The intelligent factory operation management system according to claim 1, characterized in that: The steps for obtaining the optimization rule set are: From the analysis report, identify and extract key patterns and generate preliminary rule designs; The preliminary rule design is encoded and parameterized using the formula: ； Calculate the comprehensive score of the rules to determine the priority and efficiency, and get the encoded rule set, where: Representative The effectiveness coefficient of a rule, Representative The response coefficient of the rule, represents the number of rules; Verify the effect and performance of the encoded rule set, adjust and optimize the rules, and form an optimized rule set.

4. The intelligent factory operation management system according to claim 1, characterized in that: The steps for obtaining the preliminary implementation plan are: Combining the optimization rule set and the real-time data set, using logical operations and decision-making methods, design a preliminary strategy that matches the current production line and market demand; The designed preliminary strategy is simulated and tested using the formula: ； Calculate the performance score of the strategy in the simulation environment and obtain the simulation test results, where: Representative The adjustment depth of each strategy parameter, represents the economic efficiency coefficient, Represents the total number of strategy parameters involved in the evaluation; The strategy parameters are adjusted according to the simulation test results to form a preliminary execution plan that matches the production needs.

5. The intelligent factory operation management system according to claim 1, characterized in that: The steps for obtaining the final execution plan are: Based on the preliminary execution plan and in combination with real-time feedback data from the production environment, the policy parameters are adjusted to generate an adjusted policy; The adjusted strategy is simulated and tested again using the formula: ； The performance score of the improved strategy is calculated by weighted average, the responsiveness and practicality of the optimization strategy are optimized, and the results of the simulation test are obtained, among which, Representative The adjustment range of the strategy parameters, represents the performance index of the corresponding parameter, Represents the total number of strategy parameters involved in the evaluation; The strategy parameters are optimized and adjusted according to the results of the simulation test to form a final execution plan that matches the plant operation.

6. The intelligent factory operation management system according to claim 1, characterized in that: The steps for obtaining the effect evaluation report are as follows: Based on the final implementation plan, real-time monitoring data of the production line is collected to obtain information including energy consumption, production rate, and set efficiency and cost targets, and generate a comprehensive monitoring data set; The comprehensive monitoring data set is analyzed using the formula: ； Calculate the deviation between production efficiency and target efficiency and generate quantitative indicators for effect evaluation, where: Representative The actual value of the monitoring indicator. Representative The target value of the monitoring indicator, Represents the total number of monitoring indicators.

7. The intelligent factory operation management system according to claim 6, characterized in that: The step of obtaining the effect evaluation report also includes: recording the deviation and improvement instructions of each indicator according to the quantitative indicators of the effect evaluation to obtain the effect evaluation report.

8. The intelligent factory operation management method of the intelligent factory operation management system according to any one of claims 1 to 7, characterized in that: The following steps are involved: Obtain real-time data from factory production lines, including energy consumption, production rate, and market energy prices. Create real-time data sets through screening and aggregation. Perform trend analysis and pattern recognition on real-time data sets using logical operations and judgments to obtain pattern analysis results. Based on the pattern analysis results, energy adjustment rules are designed, including encoding the set of rule parameters, creating a rule base, using the parameters encoded in the rule base to test the effectiveness of the designed rules, optimizing the rules according to the test feedback, and obtaining an optimized rule set after optimization; Using the optimization rule set and the real-time data set, adjusting the production line speed or energy configuration, obtaining a preliminary execution plan, and performing simulation tests on the preliminary execution plan, adjusting parameters according to the simulation test results, optimizing the production configuration and strategy, and finally obtaining an execution performance report; Based on the execution performance report, the production line status and energy consumption are monitored in real time, the monitoring data is recorded, and the effect evaluation report is generated by analyzing the monitoring data.

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