PVB production process energy efficiency optimization system based on Apriori algorithm and edge cloud collaboration

Abstract

The invention relates to the technical field of PVB production, and particularly discloses a PVB production process energy efficiency optimization system based on Apriori algorithm and edge cloud collaboration, which is used for solving the problem of insufficient process parameter control precision of a heating kettle and a condensation kettle, and comprises a sensing layer and an edge calculation layer, the sensing layer is connected with the edge calculation layer, the edge calculation layer is connected with a data middle table layer, and the data middle table layer is connected with a data storage layer. The data middle table layer is connected with an application display layer and a rule execution and processing device, and the edge calculation layer is used for mining a preliminary frequent item set based on an improved dynamic Hash-Apriori algorithm, generating a preliminary association rule, processing and analyzing local data of a condensation kettle and a heating kettle within a short time, transmitting the processed data to the cloud analysis and storage layer, and storing the processed data in the cloud analysis and storage layer. Executing an asymptotic identification method and a multi-target heuristic evolutionary algorithm by a table layer in the data; through the dynamic Hash-Apriori algorithm, intelligent optimization of technological parameters of the heating kettle and the condensation kettle in the PVB production process is achieved.

Description

Technical Field

[0001] The present invention relates to the field of PVB production technology, and more specifically, to a PVB production process energy efficiency optimization system based on Apriori algorithm and edge cloud collaboration. Background Art

[0002] Polyvinyl butyral (PVB) is an important polymer material, widely used in safety glass, laminated glass and other fields. In the production process of PVB, the precise control of key process parameters (such as temperature, heating rate, time, etc.) has a decisive influence on the quality and energy efficiency of the product. Due to the limited technical maturity of my country's PVB production process, there is still a lot of room for improvement, especially in the accuracy, timeliness and consistency of temperature control. Traditional temperature control systems are often difficult to adapt to the complex changes in the production process, resulting in high energy consumption and unstable product quality. In the PVB production process, temperature control usually relies on fixed rule control or simple PID control. These methods cannot adjust parameters in time when dealing with dynamic production environments, which easily leads to process deviations; at the same time, the existing system lacks deep mining and correlation analysis of data, and cannot effectively identify the potential relationship between process parameters, resulting in the failure to fully realize the potential for system energy efficiency optimization. Specifically, in the condensation kettle and heating kettle of PVB production, the temperature control is not accurate enough, and overheating or insufficient heating is prone to occur, resulting in a decline in product quality. The existing control system is unable to analyze and adjust energy consumption parameters in real time, and fails to effectively reduce energy consumption. The relationship between process parameters (such as temperature, time, heating rate, etc.) is complex, and there is a lack of analysis and optimization methods based on big data, making it difficult to find the best control strategy. Summary of the invention

[0003] In order to overcome the above-mentioned defects of the prior art, the present invention provides a PVB production process energy efficiency optimization system based on the Apriori algorithm and edge-cloud collaboration. Through the dynamic hash-Apriori algorithm, the intelligent optimization of the process parameters of the heating kettle and the condensation kettle in the PVB production process is realized, thereby reducing energy consumption and improving the product quality and production efficiency of the heating kettle and the condensation kettle.

[0004] In PVB production, the dynamic production environment proposed in this solution specifically refers to the dynamic changes in the status of PVB production equipment, fluctuations in external environmental conditions, dynamic adjustments of production batches and production loads, dynamic fluctuations in energy consumption and energy supply, nonlinear changes in chemical reaction conditions, complex correlations in process parameters, and fluctuations in real-time data feedback.

[0005] Limited by the process and operation level, the current production technology in the field of PVB (polyvinyl butyral) in my country is still immature, and there is still a lot of room for technological improvement and product quality improvement, especially in terms of the timeliness, accuracy and consistency of the temperature control of the condensation kettle and the heating kettle. The Apriori algorithm is used to mine the frequent item sets between the data in the sample set, and generate the association rules of the frequent item sets, and calculate the support and confidence of each association rule; based on the preset minimum support threshold and minimum confidence threshold, the specified algorithm is used to cluster and optimize the association rules of the frequent item sets; according to the production process monitoring, the rules are used as a guide to adjust the corresponding process parameters, in order to ensure the quality of the entire production process is controllable. The asymptotic identification method is used to obtain the parameter estimation of the identification model, and the multi-objective heuristic evolutionary algorithm is designed to further optimize the model parameters. Build a platform architecture of perception layer, edge computing layer, data middle platform layer, and application display layer; develop an edge computing collection gateway to support multiple interfaces and communication protocols, and develop a high-performance analysis and application data middle platform to support the association and fusion application of multi-structured data; based on the platform system architecture of edge-cloud collaboration, analyze the functions implemented by the system, use Python to build the system's model algorithm, provide performance evaluation analysis of the control loop in multiple dimensions such as days and months, and use data mining technology to analyze the changes in PVB operation control quality and the key factors that cause the changes.

[0006] To achieve the above object, the present invention provides the following technical solutions: The PVB production process energy efficiency optimization system based on Apriori algorithm and edge cloud collaboration includes a perception layer and an edge computing layer. The perception layer and the edge computing layer are connected. The edge computing layer is connected to a data middle platform layer. The data middle platform layer is connected to an application display layer and a rule execution and processing device. The rule execution and data processing device is connected to a cloud analysis and storage layer. The edge computing layer preprocesses the real-time data of the condensation kettle and the heating kettle in the PVB production process transmitted by the perception layer, mines preliminary frequent item sets based on the improved dynamic hash-Apriori algorithm, generates preliminary association rules for processing and analyzing local data in a short time, and transmits the processed data to the cloud analysis and storage layer. The data middle platform layer summarizes the data transmitted by the edge computing layer and the historical production data, uses the improved dynamic hash-Apriori algorithm to generate global association rules, executes the asymptotic identification method and the multi-objective heuristic evolutionary algorithm, finds the optimal process parameter configuration that can reduce energy consumption while ensuring the quality of PVB production, and transmits the generated optimization results to the application display layer. The multi-objective heuristic evolutionary algorithm aims to minimize temperature deviation and energy consumption, and its objective function formula is: ; Where: is the objective function value in the multi-objective inspired evolutionary algorithm, is the set of process parameters to be optimized, , are the weight factors of temperature deviation and energy consumption, which are set according to production requirements. is the time point index during data collection. is the number of data points collected, determined according to the production cycle, For the The temperature deviation collected at each time point, For the Energy consumption collected at each time point; The application display layer displays the optimized results to the production operators and adjusts the process parameters of the condensation kettle and the heating kettle based on the optimized results.

[0007] At the data middle platform level, the specific implementation process of the asymptotic identification method is as follows: Step 1: System modeling and parameter definition: Based on the key process parameters of the condensation kettle and the heating kettle, the mathematical models of the condensation kettle and the heating kettle are established using a linear state space model; Step 2, parameter definition and collection: collect input data in the PVB production process, establish a historical database of process parameters of the heating kettle and condensation kettle, and define the parameters to be identified by the asymptotic identification method; Step 3: Parameter estimation by asymptotic identification method: Based on the core formula of asymptotic identification method, the real parameters of condensation kettle and heating kettle are gradually approached by the system input and output data through the adaptive algorithm with asymptotic convergence characteristics, and the parameters to be identified by asymptotic identification method are iteratively updated until the error between the model predicted output and the actual output is less than the set threshold, or the maximum number of iterations is reached, and the preliminary parameter estimation value is obtained as the initial value for the next step of optimization; Step 4: Multi-objective inspired evolutionary algorithm optimization parameters: Based on the preset multi-objective optimization function, energy consumption minimization and temperature deviation minimization optimization are performed, a population is initialized, each individual represents a parameter set, and the initial estimate value is generated based on the asymptotic identification method. The objective function value corresponding to each individual is calculated, and its fitness function value is obtained. Based on the fitness function value, individuals with fitness exceeding the preset threshold are selected to enter the next generation, and a new parameter set is generated through a crossover operation. Some individual parameters are randomly mutated, and the fitness calculation, selection, crossover and mutation operations are repeated to update the parameter set in the population. The process is repeated until the preset convergence condition is reached.

[0008] As a further solution of the present invention, the perception layer collects the set temperature, actual temperature, temperature deviation, heating rate, heating time, reaction time, energy consumption, and power of the heating kettle in the PVB production process, and collects the set temperature, actual temperature, temperature deviation, reaction time, pressure, energy consumption, and reaction rate of the condensation kettle in the PVB production process, and transmits the collected original data to the edge computing layer.

[0009] As a further solution of the present invention, the specific operation process of the edge computing layer generating preliminary association rules includes: Step 11, data preprocessing: remove noise and outliers in the original data, and use linear interpolation to interpolate missing values ​​to convert continuous original data into discrete data; Step 12, mining preliminary frequent item sets: define the minimum support threshold and the minimum confidence threshold, convert the preprocessed discrete data into item sets, use the initial hash function to hash map each first-order item set, generate hash values ​​and perform frequency counting, use high-order hash functions to hash map and count each second-order item set and third-order item set, prune the candidate item sets, remove the item sets that do not meet the support threshold, and filter out the item sets that meet the minimum support according to the counting results after hash mapping as preliminary frequent item sets; Step 13, generating preliminary association rules: generating preliminary association rules based on preliminary frequent item sets; Step 14, data storage: The preliminary frequent item sets and preliminary association rules are transmitted to the cloud analysis and storage layer for data storage.

[0010] As a further solution of the present invention, in the edge computing layer, the initial hash function involved in step 12 maps the values ​​of the first-order item set to a specific position in the hash table by linearly combining them and then performing a modulo operation. The formula of the initial hash function is: ; Where: is the representative value of the discretized temperature range, is the representative value of the heating rate after discretization, is the hash value of the first-order itemset, , is the prime number weight coefficient, which is set according to actual needs. is a prime number larger than the initial hash table size, It is a modular operation.

[0011] As a further solution of the present invention, in the edge computing layer, the high-order hash function involved in step 12 includes a heating kettle high-order hash function and a condensation kettle high-order hash function. The heating kettle high-order hash function maps the second-order item set and the third-order item set to a specific position of the hash table through the first heating kettle hash mapping function and the second heating kettle hash mapping function. The formula of the heating kettle high-order hash function is: ; Where: , , are the discretized temperature, discretized heating rate and discretized heating time of the heating kettle, The hash function mapping value for the heating kettle, , They are respectively the mapping value of the hash mapping function of the first heating kettle and the mapping value of the hash mapping function of the second heating kettle.

[0012] As a further solution of the present invention, in the edge computing layer, the first heating kettle hash mapping function in the high-order hash function of the heating kettle processes the discretized temperature, discretized heating rate and discretized heating time of the heating kettle through the sine function, the natural logarithm function and the exponential function with the base e, and then performs a modulo operation on the sum value. The second heating kettle hash mapping function processes the temperature, heating rate and heating time of the heating kettle through the cosine function, the arithmetic square root function and the logarithm function with the base 2, and then performs a modulo operation on the sum value. The formulas of the first heating kettle hash mapping function and the second heating kettle hash mapping function are as follows: ; ; Where: is the modulus operation of the first heating kettle function, This is the modulo operation for the second heating kettle.

[0013] As a further solution of the present invention, in the edge computing layer, the condensation kettle high-order hash function maps the second-order item set and the third-order item set to a specific position of the hash table through the first condensation kettle hash mapping function and the second condensation kettle hash mapping function. The formula of the condensation kettle high-order hash function is: ; Where: , , , are the discretized temperature, discretized pressure, discretized reaction time, and discretized energy consumption of the condensation reactor, respectively. is the mapping value of the high-order hash function of the condensation kettle, , They are the hash function mapping value of the first condensation kettle and the hash function mapping value of the second condensation kettle respectively.

[0014] As a further solution of the present invention, in the edge computing layer, the first condensation kettle hash mapping function of the condensation kettle high-order hash function processes the discretized temperature, discretized pressure, discretized reaction time, and discretized energy consumption of the condensation kettle through the inverse tangent function, logarithmic function, arithmetic square root function, and logarithmic function with base 2, and then performs a modulo operation on the sum value. The second condensation kettle hash mapping function processes the discretized temperature, discretized pressure, discretized reaction time, and discretized energy consumption of the condensation kettle through the exponential function with base e, the score function, the sine function, and the natural logarithm function. The formulas of the first condensation kettle hash mapping function and the second condensation kettle hash mapping function are as follows: ; ; Where: is the modulo operation of the first condensation kettle function, This is the modulo operation of the second condensation kettle.

[0015] As a further solution of the present invention, the specific implementation process of step 13 in the implementation process of generating preliminary association rules at the edge computing layer includes: Step 131, inputting preliminary frequent item sets: using the preliminary frequent item sets from step 12 as basic input for generating association rules; Step 132, generating the target of association rules: deriving the target of association relationship between the process parameters of the heating kettle and the condensation kettle from the preliminary frequent item set; Step 133, calculating support and confidence: inputting the process parameter combination of the condensation kettle and the heating kettle and the associated parameters derived therefrom into the support and confidence formula to obtain support and confidence; Step 134, generating candidate association rules from the preliminary frequent item sets: for each preliminary frequent item set, generating all candidate association rule combinations; Step 135, pruning: pruning the candidate association rules generated in step 134, and removing the rules that do not meet the minimum confidence threshold; Step 136, rule evaluation and sorting: sort the association rules pruned in step 135 according to support and confidence. Based on the sorting results, give priority to rules whose support and confidence are both higher than preset values.

[0016] As a further solution of the present invention, the data middle platform layer aggregates the data transmitted by the edge computing layer and the historical production data, uses the improved dynamic hash-Apriori algorithm to generate global association rules, and executes the asymptotic identification method and the multi-objective heuristic evolutionary algorithm. The specific implementation process includes: Step 21, data aggregation and integration: The data middle platform layer aggregates the real-time production data and historical production data of the heating kettle and condensation kettle transmitted by the edge computing layer, matches and integrates the real-time data and historical data to ensure time series consistency, and handles data redundancy and missing values; Step 22, mining global frequent item sets: according to the production cycle and historical data of the heating kettle and condensation kettle of the entire PVB, define the global minimum support threshold and confidence threshold, use the improved dynamic hash-Apriori algorithm, perform hash mapping on the first-order item sets, generate hash values ​​and perform frequency counting, perform multiple hash mapping on the second-order or third-order item sets, and perform pruning operations to remove item sets that do not meet the global support threshold. After hash counting and pruning, generate frequent items that meet the global support threshold as the basis for subsequent global association rule generation; Step 23, generating global association rules: according to the global frequent itemsets in step 22, generating global candidate association rules, using global data to calculate the support and confidence of each candidate association rule, and comparing them with the global support threshold and confidence threshold, removing association rules that do not meet the global minimum confidence threshold, and sorting the remaining rules by support and confidence; Step 24, executing a multi-objective inspired evolutionary algorithm: Based on the global association rules, a multi-objective inspired evolutionary algorithm is used to optimize the configuration of the process parameters of the condensation kettle and the heating kettle through a preset objective function formula, and a gradient descent method is used to solve the multi-objective optimization function to obtain the optimal process parameter configuration of the condensation kettle and the heating kettle that can minimize the temperature deviation and energy consumption while ensuring the quality of PVB production; Step 25, output and adaptive adjustment: output the optimized process parameter configuration results of the condensation kettle and the heating kettle to the application display layer, and feed back to the rule execution and processing device. The rule execution and processing device automatically adjusts the process parameters of the condensation kettle and the heating kettle according to the optimized process parameter configuration and the output process parameter configuration obtained by the asymptotic identification method.

[0017] As a further solution of the present invention, in step 25, the process parameter configuration obtained by the multi-objective inspired evolutionary algorithm and the output process parameter configuration obtained by the asymptotic identification method each account for 50% of the weight.

[0018] Compared with the prior art, the technical effect of the present invention is as follows: the present invention significantly improves the accuracy, timeliness and consistency of temperature control in the PVB production process through the collaborative work of data perception, edge computing and cloud storage. Unlike traditional fixed rules or PID control systems, the system uses a dynamic hash-Apriori algorithm to mine the potential associations between process parameters, generate frequent item sets and association rules, and then optimizes the process parameters of the heating kettle and the condensation kettle through asymptotic identification and multi-objective inspired evolutionary algorithm, and finds the optimal configuration scheme that minimizes energy consumption while ensuring product quality. Compared with the prior art, the system can analyze the relationship between complex variables such as temperature, time, and heating rate in real time, dynamically adjust process parameters, avoid overheating or insufficient heating, and solve the problems of slow response, inaccurate adjustment, and low energy efficiency of the existing temperature control system, thereby improving production efficiency and product quality stability. Through multi-dimensional data mining and intelligent algorithms, the system greatly reduces energy consumption and realizes automation and intelligent optimization of the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the present invention to clearly and completely describe the technical solutions in the present invention. Obviously, the described technical solutions are only part of the present invention, not all of it. Based on the content of the present invention, all other technical solutions obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0021] like Figure 1As shown, the PVB production process energy efficiency optimization system based on Apriori algorithm and edge cloud collaboration proposed by the present invention includes a perception layer and an edge computing layer, the perception layer and the edge computing layer are connected, the edge computing layer is connected to a data middle platform layer, the data middle platform layer is connected to an application display layer and a rule execution and processing device, the rule execution and data processing device is connected to a cloud analysis and storage layer, the edge computing layer pre-processes the real-time data of the condensation kettle and the heating kettle in the PVB production process transmitted by the perception layer, mines preliminary frequent item sets based on the improved dynamic hash-Apriori algorithm, generates preliminary association rules for processing and analyzing local data in a short time, and transmits the processed data to the cloud analysis and storage layer, the data middle platform layer summarizes the data transmitted by the edge computing layer and the historical production data, uses the improved dynamic hash-Apriori algorithm to generate global association rules, executes the asymptotic identification method and the multi-objective heuristic evolutionary algorithm, finds the optimal process parameter configuration that can reduce energy consumption while ensuring the quality of PVB production, and transmits the generated optimization results to the application display layer, the multi-objective heuristic evolutionary algorithm aims to minimize temperature deviation and energy consumption, and its objective function formula is: ; Where: is the objective function value in the multi-objective inspired evolutionary algorithm, is the set of process parameters to be optimized, , are the weight factors of temperature deviation and energy consumption, which are set according to production requirements. is the time point index during data collection. is the number of data points collected, determined according to the production cycle, For the The temperature deviation collected at each time point, For the Energy consumption collected at each time point; The application display layer displays the optimized results to the production operators and adjusts the process parameters of the condensation kettle and the heating kettle based on the optimized results.

[0022] It should be noted that at the data platform level, the specific implementation process of the asymptotic identification method is as follows: Step 1: System modeling and parameter definition: Based on the key process parameters of the condensation kettle and the heating kettle, the mathematical models of the condensation kettle and the heating kettle are established using a linear state space model; Step 2, parameter definition and collection: collect input data in the PVB production process, establish a historical database of process parameters of the heating kettle and condensation kettle, and define the parameters to be identified by the asymptotic identification method; Step 3: Parameter estimation by asymptotic identification method: Based on the core formula of asymptotic identification method, the real parameters of condensation kettle and heating kettle are gradually approached by the system input and output data through the adaptive algorithm with asymptotic convergence characteristics, and the parameters to be identified by asymptotic identification method are iteratively updated until the error between the model predicted output and the actual output is less than the set threshold, or the maximum number of iterations is reached, and the preliminary parameter estimation value is obtained as the initial value for the next step of optimization; Step 4: Multi-objective inspired evolutionary algorithm optimization parameters: Based on the preset multi-objective optimization function, energy consumption minimization and temperature deviation minimization optimization are performed, a population is initialized, each individual represents a parameter set, and the initial estimate value is generated based on the asymptotic identification method. The objective function value corresponding to each individual is calculated, and its fitness function value is obtained. Based on the fitness function value, individuals with fitness exceeding the preset threshold are selected to enter the next generation, and a new parameter set is generated through a crossover operation. Some individual parameters are randomly mutated, and the fitness calculation, selection, crossover and mutation operations are repeated to update the parameter set in the population. The process is repeated until the preset convergence condition is reached.

[0023] Through the combined application of asymptotic identification method and multi-objective inspired evolutionary algorithm, the system can accurately identify the key process parameters of condensation kettle and heating kettle, and gradually optimize temperature control and energy consumption. The asymptotic identification method gradually approaches the real parameters of the system through an adaptive algorithm to ensure the accuracy of parameter estimation; the multi-objective inspired evolutionary algorithm further optimizes these parameters to achieve the dual goals of minimizing energy consumption and minimizing temperature deviation. Through iterative calculation, selection, crossover and mutation operations, the system dynamically updates the parameter set, ensuring the continuity and adaptability of parameter optimization in a complex production environment, ultimately improving the efficiency and product quality of the PVB production process and reducing energy consumption.

[0024] It should be noted that the perception layer collects the set temperature, actual temperature, temperature deviation, heating rate, heating time, reaction time, energy consumption, and power of the heating kettle in the PVB production process, and collects the set temperature, actual temperature, temperature deviation, reaction time, pressure, energy consumption, and reaction rate of the condensation kettle in the PVB production process, and transmits the collected raw data to the edge computing layer.

[0025] It should be noted that the specific operation process of the edge computing layer to generate preliminary association rules includes: Step 11, data preprocessing: remove noise and outliers in the original data, and use linear interpolation to interpolate missing values ​​to convert continuous original data into discrete data; Step 12, mining preliminary frequent item sets: define the minimum support threshold and the minimum confidence threshold, convert the preprocessed discrete data into item sets, use the initial hash function to hash map each first-order item set, generate hash values ​​and perform frequency counting, use high-order hash functions to hash map and count each second-order item set and third-order item set, prune the candidate item sets, remove the item sets that do not meet the support threshold, and filter out the item sets that meet the minimum support according to the counting results after hash mapping as preliminary frequent item sets; Step 13, generating preliminary association rules: generating preliminary association rules based on preliminary frequent item sets; Step 14, data storage: The preliminary frequent item sets and preliminary association rules are transmitted to the cloud analysis and storage layer for data storage.

[0026] By generating preliminary association rules through the edge computing layer, the system can effectively solve the real-time data processing and analysis problems in the PVB production process. Data preprocessing ensures the integrity and accuracy of input data by removing noise and filling missing values; frequent item set mining uses an improved hash algorithm to improve the efficiency of item set mapping and calculation, especially in large-scale data sets, by pruning to remove item sets that do not meet the support and reduce computational redundancy. Based on these frequent item sets, preliminary association rules are generated, which helps to reveal the potential relationship between the process parameters of the condensation kettle and the heating kettle, and provides a basis for subsequent optimization. After the preliminary rules are stored in the cloud, further global analysis and parameter adjustment are facilitated. The entire process improves the level of intelligence of process control in PVB production, solves the defect that traditional systems cannot be dynamically adjusted and optimized, and significantly improves production efficiency and product quality.

[0027] It should be noted that in the edge computing layer, the initial hash function involved in step 12 maps the values ​​of the first-order item set to a specific position in the hash table by linearly combining them and performing a modulo operation. The formula of the initial hash function is: ; Where: is the representative value of the discretized temperature range, is the representative value of the heating rate after discretization, is the hash value of the first-order itemset, , is the prime number weight coefficient, which is set according to actual needs. is a prime number larger than the initial hash table size, It is a modular operation.

[0028] By performing linear combination and modular operations on the first-order item sets through the initial hash function, the system can efficiently map the discretized temperature and heating rate data into the hash table, realizing fast frequency counting and data processing. The introduction of prime weight coefficients ensures that the effects of temperature and heating rate on the hash value are relatively independent, reduces hash conflicts, and ensures that different item sets can be dispersedly mapped to different positions in the hash table. This method effectively improves the efficiency of large-scale data processing, lays a stable data foundation for subsequent frequent item set mining and association rule generation, and solves the problem of frequent mapping conflicts in traditional methods.

[0029] Example 1

[0030] With respect to the application of the initial hash function, the following embodiment parameter calculation is performed to illustrate in detail the technical effect of the initial hash function in solving the technical problem.

[0031] is the representative value of the discretized temperature range, is the representative value of the heating rate after discretization. Both are integer intervals. In the early stage of heating of the heating kettle, the heating rate needs to be gradually increased, but it needs to be kept under certain control to avoid a sudden temperature rise that causes a too fast reaction or instability. , As the weight of the heating kettle temperature and heating rate, at the end of the condensation kettle reaction, the temperature of the condensation kettle gradually decreases, the pressure change is small, and the smooth transition of temperature is more important. It is necessary to prevent the sudden drop in temperature from affecting the product quality. , As the weight of the condensation reactor temperature and heating rate, set the prime number of the modular operation , which is larger than the size of the hash table to ensure that the hash values ​​are evenly distributed, according to the formula of the initial hash function: ; for The remainder when divided by 31 is equal to Therefore, the first-order itemset has a hash value of 12 and is mapped to position 12 in the hash table.

[0032] It should be noted that, in the edge computing layer, the high-order hash functions involved in step 12 include the heating kettle high-order hash function and the condensation kettle high-order hash function. The heating kettle high-order hash function maps the second-order item set and the third-order item set to a specific position of the hash table through the first heating kettle hash mapping function and the second heating kettle hash mapping function. The formula of the heating kettle high-order hash function is: ; Where: , , are the discretized temperature, discretized heating rate and discretized heating time of the heating kettle, The hash function mapping value for the heating kettle, , They are respectively the mapping value of the hash mapping function of the first heating kettle and the mapping value of the hash mapping function of the second heating kettle.

[0033] It should be noted that in the edge computing layer, the first heating kettle hash mapping function in the high-order hash function of the heating kettle processes the discretized temperature, discretized heating rate and discretized heating time of the heating kettle through the sine function, the natural logarithm function and the exponential function with the base e, and then performs a modulo operation on the sum value. The second heating kettle hash mapping function processes the temperature, heating rate and heating time of the heating kettle through the cosine function, the arithmetic square root function and the logarithm function with the base 2, and then performs a modulo operation on the sum value. The formulas of the first heating kettle hash mapping function and the second heating kettle hash mapping function are as follows: ; ; Where: is the modulus operation of the first heating kettle function, This is the modulo operation for the second heating kettle.

[0034] It should be noted that in the edge computing layer, the condensation kettle high-order hash function maps the second-order item set and the third-order item set to a specific position of the hash table through the first condensation kettle hash mapping function and the second condensation kettle hash mapping function. The formula of the condensation kettle high-order hash function is: ; Where: , , , are the discretized temperature, discretized pressure, discretized reaction time, and discretized energy consumption of the condensation reactor, respectively. is the mapping value of the high-order hash function of the condensation kettle, , They are the hash function mapping value of the first condensation kettle and the hash function mapping value of the second condensation kettle respectively.

[0035] It should be noted that in the edge computing layer, the first condensation kettle hash mapping function of the condensation kettle high-order hash function processes the discretized temperature, discretized pressure, discretized reaction time, and discretized energy consumption of the condensation kettle through the inverse tangent function, logarithmic function, arithmetic square root function, and logarithmic function with base 2, and then performs a modulus operation on the sum value. The second condensation kettle hash mapping function processes the discretized temperature, discretized pressure, discretized reaction time, and discretized energy consumption of the condensation kettle through the exponential function with base e, the score function, the sine function, and the natural logarithm function. The formulas of the first condensation kettle hash mapping function and the second condensation kettle hash mapping function are as follows: ; ; Where: is the modulo operation of the first condensation kettle function, This is the modulo operation of the second condensation kettle.

[0036] In the heating kettle, the use of high-order hash functions to map the temperature, heating rate and heating time can effectively solve the technical problems of complex data processing and frequent item set mining. The nonlinear processing of these parameters through sine function, natural logarithm function and exponential function can ensure that the differences between the parameters are fully amplified, reducing the conflicts generated in the hash mapping process. In addition, the use of modular operation ensures that the generated hash values ​​are evenly distributed, avoids the concentration of data in certain specific locations, and improves the storage and retrieval efficiency of the hash table. For the second-order and third-order item sets, the combination of two different hash mappings effectively reduces the conflicts and repeated calculations that may be caused by different parameter combinations in the high-order item set, thereby improving the mining efficiency of frequent item sets. This technology enables the heating kettle to more accurately control the temperature rise rate during the production process, ensure the stability of the heating process, avoid process deviations caused by too fast or too slow, and ultimately improve the reliability of the PVB production process and product quality.

[0037] In the condensation reactor, high-order hash functions are used to map temperature, pressure, reaction time and energy consumption, and different process parameters are processed by inverse tangent function, logarithmic function, square root function and exponential function with base e. This nonlinear mapping method makes the correlation between process parameters reasonably reflected, and effectively avoids conflicts and duplications in hash mapping. The use of two hash mappings ensures that different combinations of second-order and third-order item sets can be evenly distributed in the hash table, reduces data redundancy, and improves the accuracy and speed of item set processing. In this way, the condensation reactor can more accurately control key parameters such as reaction time, temperature and pressure during the production process, ensure the stability of the reaction process, reduce energy consumption and unnecessary waste of resources, optimize production energy efficiency, and ensure the final quality of the product. This technology can effectively improve the operating efficiency of the condensation reactor under complex process conditions and reduce technical deviations and failure rates in production.

[0038] In order to illustrate the specific calculation process of the heating kettle high-order hash function and the condensation kettle high-order hash function in detail, they are respectively described through Example 2 and Example 3.

[0039] Example 2

[0040] In the heating kettle, the three process parameters of temperature, heating rate and heating time need to be hash mapped to generate corresponding high-order hash values ​​to help the rapid screening of frequent item sets and the mining of association rules. The data input is set to discretized temperature, discretized heating rate and discretized heating time as 45, 3 and 8 respectively. , the hash mapping function of the first heating kettle is , The value of is rounded to 11; The hash mapping function of the second heating kettle is ; So the high-order hash function of the heating kettle is , the result is rounded to 16.

[0041] Example 3

[0042] In the condensation reactor, the four process parameters of temperature, pressure, reaction time and energy consumption need to be hash mapped to generate high-order hash values. The discrete input data of temperature, pressure, reaction time and energy consumption are 60, 5, 10 and 100 respectively. When the first condensation kettle hash mapping function ; Second condensation kettle hash mapping function ; So the condensation kettle high-order hash function is , the rounded result is 17.

[0043] The above technical effects specifically demonstrate the application of high-order hash functions of heating kettles and condensation kettles through Examples 2 and 3, and illustrate the significant advantages of the scheme in data processing, hash conflict reduction, frequent item set mining, process control accuracy and large-scale data processing. In Example 2, the system performs hash mapping on the three process parameters of the temperature, heating rate and heating time of the heating kettle, uses sine function, natural logarithm function and exponential function for nonlinear processing, and generates hash values ​​through two different hash mapping functions. When the data input discretization temperature is 45, the heating rate is 3, and the heating time is 8, the hash result is 16. Through this hash mapping, it is ensured that multidimensional parameter data can be evenly distributed in the hash table, effectively reducing data conflicts, improving the speed of frequent item set mining, and ensuring the process control accuracy of the heating kettle. In Example 3, the four process parameters of the temperature, pressure, reaction time and energy consumption of the condensation kettle are nonlinearly processed by inverse tangent function, logarithm function, square root function, etc., and the hash value is finally calculated to be 17 after the first and second mappings. The data inputs are respectively discretized temperature 60, pressure 5, reaction time 10, and energy consumption 100. This processing method ensures the independence of the process parameters of the condensation kettle, avoids hash conflicts, makes the data evenly distributed in the hash table, and effectively improves the efficiency of the system under complex process conditions. Through the calculation process of Example 2 and Example 3, the advantages of this scheme in large-scale data processing, real-time process parameter control and frequent item set mining are verified. In particular, through two hash mappings, the system can accurately and quickly process the complex process parameters in the heating kettle and the condensation kettle, so that the accuracy and response speed of process control are greatly improved, and the production energy efficiency is further optimized, ensuring the stability of product quality and the improvement of production efficiency.

[0044] It should be noted that the specific implementation process of step 13 in the implementation process of generating preliminary association rules at the edge computing layer includes: Step 131, inputting preliminary frequent item sets: using the preliminary frequent item sets from step 12 as basic input for generating association rules; Step 132, generating the target of association rules: deriving the target of association relationship between the process parameters of the heating kettle and the condensation kettle from the preliminary frequent item set; Step 133, calculating support and confidence: inputting the process parameter combination of the condensation kettle and the heating kettle and the associated parameters derived therefrom into the support and confidence formula to obtain support and confidence; Step 134, generating candidate association rules from the preliminary frequent item sets: for each preliminary frequent item set, generating all candidate association rule combinations; Step 135, pruning: pruning the candidate association rules generated in step 134, and removing the rules that do not meet the minimum confidence threshold; Step 136, rule evaluation and sorting: sort the association rules pruned in step 135 according to support and confidence. Based on the sorting results, give priority to rules whose support and confidence are both higher than preset values.

[0045] It should be noted that the data middle platform layer aggregates the data transmitted by the edge computing layer and the historical production data, uses the improved dynamic hash-Apriori algorithm to generate global association rules, and executes the asymptotic identification method and the multi-objective heuristic evolutionary algorithm. The specific implementation process includes: Step 21, data aggregation and integration: The data middle platform layer aggregates the real-time production data and historical production data of the heating kettle and condensation kettle transmitted by the edge computing layer, matches and integrates the real-time data and historical data to ensure time series consistency, and handles data redundancy and missing values; Step 22, mining global frequent item sets: according to the production cycle and historical data of the heating kettle and condensation kettle of the entire PVB, define the global minimum support threshold and confidence threshold, use the improved dynamic hash-Apriori algorithm, perform hash mapping on the first-order item sets, generate hash values ​​and perform frequency counting, perform multiple hash mapping on the second-order or third-order item sets, and perform pruning operations to remove item sets that do not meet the global support threshold. After hash counting and pruning, generate frequent items that meet the global support threshold as the basis for subsequent global association rule generation; Step 23, generating global association rules: according to the global frequent itemsets in step 22, generating global candidate association rules, using global data to calculate the support and confidence of each candidate association rule, and comparing them with the global support threshold and confidence threshold, removing association rules that do not meet the global minimum confidence threshold, and sorting the remaining rules by support and confidence; Step 24, executing a multi-objective inspired evolutionary algorithm: Based on the global association rules, a multi-objective inspired evolutionary algorithm is used to optimize the configuration of the process parameters of the condensation kettle and the heating kettle through a preset objective function formula, and a gradient descent method is used to solve the multi-objective optimization function to obtain the optimal process parameter configuration of the condensation kettle and the heating kettle that can minimize the temperature deviation and energy consumption while ensuring the quality of PVB production; Step 25, output and adaptive adjustment: output the optimized process parameter configuration results of the condensation kettle and the heating kettle to the application display layer, and feed back to the rule execution and processing device. The rule execution and processing device automatically adjusts the process parameters of the condensation kettle and the heating kettle according to the optimized process parameter configuration and the output process parameter configuration obtained by the asymptotic identification method.

[0046] It should be noted that in step 25, the process parameter configuration obtained by the multi-objective inspired evolutionary algorithm and the output process parameter configuration obtained by the asymptotic identification method each account for 50% of the weight.

[0047] In step 25, the multi-objective heuristic evolutionary algorithm and the asymptotic identification method are used with a weight of 50% each to obtain the final process parameter configuration, aiming to combine the advantages of the two algorithms to achieve more accurate process control of the condensation reactor and the heating reactor. The asymptotic identification method provides a relatively stable preliminary parameter estimate by identifying historical data and the dynamic behavior of the system, while the multi-objective heuristic evolutionary algorithm performs optimization search based on production goals (such as minimizing energy consumption, temperature control, etc.) to provide a local optimal solution. By combining the two with a weight of 50% each, the system can maintain the stability of the historical process and dynamically respond to real-time changes, ensuring the accuracy and flexibility of the process parameters under the established goals. This method improves the adaptability and real-time adjustment capabilities of the system and optimizes production efficiency and product quality.

[0048] The present invention significantly improves the accuracy, timeliness and consistency of temperature control in the PVB production process through the collaborative work of data perception, edge computing and cloud storage. Unlike traditional fixed rules or PID control systems, the system uses a dynamic hash-Apriori algorithm to mine potential associations between process parameters, generate frequent item sets and association rules, and then optimizes the process parameters of the heating kettle and the condensation kettle through asymptotic identification and multi-objective inspired evolutionary algorithm, and finds the optimal configuration scheme that minimizes energy consumption while ensuring product quality. Compared with the existing technology, the system can analyze the relationship between complex variables such as temperature, time, and heating rate in real time, dynamically adjust process parameters, avoid overheating or insufficient heating, and solve the problems of slow response, inaccurate adjustment, and low energy efficiency of existing temperature control systems, thereby improving production efficiency and product quality stability. Through multi-dimensional data mining and intelligent algorithms, the system greatly reduces energy consumption and realizes automation and intelligent optimization of the production process.

[0049] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0050] Finally: The above description is only an implementation mode of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A PVB production process energy efficiency optimization system based on Apriori algorithm and edge cloud collaboration includes a perception layer and an edge computing layer. The perception layer and the edge computing layer are connected, and are characterized in that: The edge computing layer is connected to the data middle platform layer, which is connected to the application display layer and the rule execution and processing device. The rule execution and data processing device is connected to the cloud analysis and storage layer. The edge computing layer preprocesses the real-time data of the condensation kettle and the heating kettle in the PVB production process transmitted by the perception layer, mines the preliminary frequent item sets based on the improved dynamic hash-Apriori algorithm, generates preliminary association rules for processing and analyzing local data in a short time, and transmits the processed data to the cloud analysis and storage layer. The data middle platform layer summarizes the data transmitted by the edge computing layer and the historical production data, uses the improved dynamic hash-Apriori algorithm to generate global association rules, executes the asymptotic identification method and the multi-objective heuristic evolutionary algorithm, finds the optimal process parameter configuration that can reduce energy consumption while ensuring the quality of PVB production, and transmits the generated optimization results to the application display layer. The multi-objective heuristic evolutionary algorithm aims to minimize temperature deviation and energy consumption, and its objective function formula is: ； Where: is the objective function value in the multi-objective inspired evolutionary algorithm, is the set of process parameters to be optimized, , are the weight factors of temperature deviation and energy consumption, which are set according to production requirements. is the time point index during data collection. is the number of data points collected, determined according to the production cycle, For the The temperature deviation collected at each time point, For the Energy consumption collected at each time point; The application display layer displays the optimized results to the production operators and adjusts the process parameters of the condensation kettle and the heating kettle based on the optimized results.

2. According to claim 1, the PVB production process energy efficiency optimization system based on Apriori algorithm and edge cloud collaboration is characterized in that: The perception layer collects the set temperature, actual temperature, temperature deviation, heating rate, heating time, reaction time, energy consumption, and power of the heating kettle during the PVB production process, and collects the set temperature, actual temperature, temperature deviation, reaction time, pressure, energy consumption, and reaction rate of the condensation kettle during the PVB production process, and transmits the collected raw data to the edge computing layer.

3. According to claim 2, the PVB production process energy efficiency optimization system based on Apriori algorithm and edge-cloud collaboration is characterized in that: The specific operation process of the edge computing layer to generate preliminary association rules includes: Step 11, data preprocessing: remove noise and outliers in the original data, and use linear interpolation to interpolate missing values ​​to convert continuous original data into discrete data; Step 12, mining preliminary frequent item sets: define the minimum support threshold and the minimum confidence threshold, convert the preprocessed discrete data into item sets, use the initial hash function to hash map each first-order item set, generate hash values ​​and perform frequency counting, use high-order hash functions to hash map and count each second-order item set and third-order item set, prune the candidate item sets, remove the item sets that do not meet the support threshold, and filter out the item sets that meet the minimum support according to the counting results after hash mapping as preliminary frequent item sets; Step 13, generating preliminary association rules: generating preliminary association rules based on preliminary frequent item sets; Step 14, data storage: The preliminary frequent item sets and preliminary association rules are transmitted to the cloud analysis and storage layer for data storage.

4. According to claim 3, the PVB production process energy efficiency optimization system based on Apriori algorithm and edge-cloud collaboration is characterized in that: In the edge computing layer, the initial hash function involved in step 12 maps the values ​​of the first-order item set to a specific position in the hash table by linearly combining them and performing a modulo operation. The formula of the initial hash function is: ； Where: is the representative value of the discretized temperature range, is the representative value of the heating rate after discretization, is the hash value of the first-order itemset, , is the prime number weight coefficient, which is set according to actual needs. is a prime number larger than the initial hash table size, It is a modular operation.

5. According to claim 3, the PVB production process energy efficiency optimization system based on Apriori algorithm and edge-cloud collaboration is characterized in that: In the edge computing layer, the high-order hash functions involved in step 12 include a heating kettle high-order hash function and a condensation kettle high-order hash function. The heating kettle high-order hash function maps the second-order item set and the third-order item set to a specific position of the hash table through the first heating kettle hash mapping function and the second heating kettle hash mapping function. The formula of the heating kettle high-order hash function is: ； Where: , , are the discretized temperature, discretized heating rate and discretized heating time of the heating kettle, The hash function mapping value for the heating kettle, , They are respectively the mapping value of the hash mapping function of the first heating kettle and the mapping value of the hash mapping function of the second heating kettle.

6. According to claim 5, the PVB production process energy efficiency optimization system based on Apriori algorithm and edge-cloud collaboration is characterized in that: In the edge computing layer, the first heating kettle hash mapping function in the high-order hash function of the heating kettle processes the discretized temperature, discretized heating rate and discretized heating time of the heating kettle through the sine function, natural logarithm function and exponential function with base e, and then performs modulo operation on the sum value. The second heating kettle hash mapping function processes the temperature, heating rate and heating time of the heating kettle through the cosine function, arithmetic square root function and logarithm function with base 2, and then performs modulo operation on the sum value. The formulas of the first heating kettle hash mapping function and the second heating kettle hash mapping function are as follows: ； ； Where: is the modulus operation of the first heating kettle function, This is the modulo operation for the second heating kettle.

7. The PVB production process energy efficiency optimization system based on Apriori algorithm and edge-cloud collaboration according to claim 5 is characterized in that: In the edge computing layer, the condensation kettle high-order hash function maps the second-order item set and the third-order item set to a specific position of the hash table through the first condensation kettle hash mapping function and the second condensation kettle hash mapping function. The formula of the condensation kettle high-order hash function is: ； Where: , , , are the discretized temperature, discretized pressure, discretized reaction time, and discretized energy consumption of the condensation reactor, respectively. is the mapping value of the high-order hash function of the condensation kettle, , They are the hash function mapping value of the first condensation kettle and the hash function mapping value of the second condensation kettle respectively.

8. The PVB production process energy efficiency optimization system based on Apriori algorithm and edge-cloud collaboration according to claim 7 is characterized in that: In the edge computing layer, the first condensation kettle hash mapping function of the condensation kettle high-order hash function processes the discretized temperature, discretized pressure, discretized reaction time, and discretized energy consumption of the condensation kettle through the inverse tangent function, logarithmic function, arithmetic square root function, and logarithmic function with base 2, and then performs a modulus operation on the sum. The second condensation kettle hash mapping function processes the discretized temperature, discretized pressure, discretized reaction time, and discretized energy consumption of the condensation kettle through the exponential function with base e, the score function, the sine function, and the natural logarithm function. The formulas of the first condensation kettle hash mapping function and the second condensation kettle hash mapping function are as follows: ； ； Where: is the modulo operation of the first condensation kettle function, This is the modulo operation of the second condensation kettle.

9. The PVB production process energy efficiency optimization system based on Apriori algorithm and edge-cloud collaboration according to claim 8 is characterized in that: The specific implementation process of step 13 in the implementation process of generating preliminary association rules at the edge computing layer includes: Step 131, inputting preliminary frequent item sets: using the preliminary frequent item sets from step 12 as basic input for generating association rules; Step 132, generating the target of association rules: deriving the target of association relationship between the process parameters of the heating kettle and the condensation kettle from the preliminary frequent item set; Step 133, calculating support and confidence: inputting the process parameter combination of the condensation kettle and the heating kettle and the associated parameters derived therefrom into the support and confidence formula to obtain support and confidence; Step 134, generating candidate association rules from the preliminary frequent item sets: for each preliminary frequent item set, generating all candidate association rule combinations; Step 135, pruning: pruning the candidate association rules generated in step 134, and removing the rules that do not meet the minimum confidence threshold; Step 136, rule evaluation and sorting: sort the association rules pruned in step 135 according to support and confidence. Based on the sorting results, give priority to rules whose support and confidence are both higher than preset values.

10. The PVB production process energy efficiency optimization system based on Apriori algorithm and edge-cloud collaboration according to claim 9 is characterized in that: The data middle platform layer aggregates the data transmitted by the edge computing layer and the historical production data, uses the improved dynamic hash-Apriori algorithm to generate global association rules, and executes the specific implementation process of the multi-objective heuristic evolutionary algorithm, including: Step 21, data aggregation and integration: The data middle platform layer aggregates the real-time production data and historical production data of the heating kettle and condensation kettle transmitted by the edge computing layer, matches and integrates the real-time data and historical data to ensure time series consistency, and handles data redundancy and missing values; Step 22, mining global frequent item sets: according to the production cycle and historical data of the heating kettle and condensation kettle of the entire PVB, define the global minimum support threshold and confidence threshold, use the improved dynamic hash-Apriori algorithm, perform hash mapping on the first-order item sets, generate hash values ​​and perform frequency counting, perform multiple hash mapping on the second-order or third-order item sets, and perform pruning operations to remove item sets that do not meet the global support threshold. After hash counting and pruning, generate frequent items that meet the global support threshold as the basis for subsequent global association rule generation; Step 23, generating global association rules: according to the global frequent itemsets in step 22, generating global candidate association rules, using global data to calculate the support and confidence of each candidate association rule, and comparing them with the global support threshold and confidence threshold, removing association rules that do not meet the global minimum confidence threshold, and sorting the remaining rules by support and confidence; Step 24, executing a multi-objective inspired evolutionary algorithm: Based on the global association rules, a multi-objective inspired evolutionary algorithm is used to optimize the configuration of the process parameters of the condensation kettle and the heating kettle through a preset objective function formula, and a gradient descent method is used to solve the multi-objective optimization function to obtain the optimal process parameter configuration of the condensation kettle and the heating kettle that can minimize the temperature deviation and energy consumption while ensuring the quality of PVB production; Step 25, output and adaptive adjustment: output the optimized process parameter configuration results of the condensation kettle and the heating kettle to the application display layer, and feed back to the rule execution and processing device. The rule execution and processing device automatically adjusts the process parameters of the condensation kettle and the heating kettle according to the optimized process parameter configuration and the output process parameters obtained by the asymptotic identification method.

Images