Method for optimizing production process of security interface of electronic equipment based on fuzzy logic

Abstract

The invention discloses an electronic equipment security interface production process optimization method based on fuzzy logic, which comprises the following steps: S1, designing and constructing a multi-level fuzzy rule base which comprises a first level, a second level and a third level; s2, the first level adopts a genetic algorithm to generate and optimize an initial fuzzy rule library; s3, optimizing the fuzzy rule base by adopting a particle swarm optimization algorithm at the second level; s4, the third level adopts an ant colony algorithm to optimize specific process parameters in the fuzzy rule base; s5, acquiring data in the production process in real time through a data acquisition and monitoring technology, constructing a data set, and transmitting the data set to a central processing unit; s6, constructing a personalized fuzzy rule base; and S7, displaying the key data and the optimization result in the production process through a visualization technology. According to the method, the multi-level fuzzy rule base and the intelligent optimization algorithm are utilized, and efficient optimization of the electronic equipment security interface production process is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of electronic equipment manufacturing, and in particular to a method for optimizing the production process of an electronic equipment security interface based on fuzzy logic. Background Art

[0002] With the continuous development of electronic equipment manufacturing technology, the functional complexity and integration of electronic equipment are also constantly improving. Especially in the production process involving safety interfaces, the requirements for process control are becoming more and more stringent. These safety interfaces must not only ensure the stable operation of electronic equipment under various environmental conditions, but also meet the requirements of high reliability, high performance and low energy consumption. In order to achieve these goals, the optimization of production processes is particularly important. However, in the prior art, there are still many deficiencies in the production process optimization method for electronic equipment safety interfaces, which cannot fully meet the needs of modern electronic manufacturing industry for efficient and high-precision production control.

[0003] In traditional production processes, fixed rules or simple experience-based adjustment methods are usually used to control production parameters. Although these methods can work in some cases, since the process parameters in the production process involve many variables, such as temperature, pressure, material flow rate, equipment load, etc., the relationship between these variables is complex and uncertain, making it difficult for traditional methods to maintain the best process control state in a dynamically changing production environment. In addition, traditional methods often lack flexibility and adaptability when dealing with changes in production demand, fluctuations in equipment status, or external environmental disturbances, which can easily lead to problems such as low production efficiency, unstable product quality, and excessive energy consumption.

[0004] In order to make up for these shortcomings, in recent years, some intelligent process control methods have gradually been applied to the field of electronic equipment manufacturing, such as fuzzy logic control, genetic algorithm optimization, particle swarm optimization and ant colony optimization and other intelligent optimization algorithms. However, these methods usually only optimize at a single level or in a local range, lacking a global consideration of the entire production process. For example, although fuzzy logic can handle uncertainty and ambiguity, the design and optimization of its rule base often rely on expert experience and are difficult to adapt to complex and changing production environments. Although intelligent algorithms such as genetic algorithms, particle swarm optimization and ant colony optimization have strong global search capabilities, they are difficult to achieve comprehensive optimization from the global to the details in the production process due to the lack of comprehensive consideration of multiple levels and multiple objectives. In addition, the data processing methods in the prior art are mostly static processing, which cannot dynamically adapt to the volatility and real-time nature of data in the production process, resulting in limited response speed and control accuracy of the system.

[0005] In the existing technology, data collection and monitoring technology also has some limitations. Although the development of industrial Internet of Things and sensor technology has made real-time data collection possible, these data are usually not optimized and directly applied to production control systems, which may cause information overload or data noise to affect system decisions. In addition, data standardization processing mostly uses fixed standardization formulas, which cannot adapt to the dynamic characteristics of data changing over time. This can easily lead to the inadaptability of control parameters when the production environment changes rapidly, thereby affecting production efficiency and product quality.

[0006] Based on the above analysis, the existing technology has the following main defects in the optimization of the production process of the safety interface of electronic equipment. First, the lack of a multi-level optimization strategy that combines global and local optimization makes it difficult to achieve global optimization in a complex production environment. Secondly, the application of existing intelligent optimization algorithms is mostly limited to single-objective optimization, lacks multi-objective comprehensive optimization capabilities, and cannot take into account the balance between production speed, product quality and energy consumption. Thirdly, data processing technology is insufficient in dealing with the volatility and dynamic characteristics of real-time data, and traditional static data processing methods are difficult to provide real-time and accurate production control support. Finally, the existing production control system lacks personalized optimization strategies when dealing with equipment differences and production task diversity, and cannot be flexibly adjusted according to the status of specific equipment or the requirements of production tasks.

[0007] Therefore, how to provide an optimization method for the production process of electronic device security interfaces based on fuzzy logic is an urgent problem that needs to be solved by those skilled in the art. Summary of the invention

[0008] One purpose of the present invention is to propose an optimization method for the production process of electronic device security interfaces based on fuzzy logic. The present invention realizes the global and local optimization of the production process of electronic device security interfaces by introducing intelligent optimization technologies such as multi-level fuzzy rule base, genetic algorithm, particle swarm optimization algorithm and ant colony algorithm. The system can dynamically adapt to changes in the production environment and accurately control key parameters in the production process. At the same time, the present invention adopts adaptive dynamic data processing technology and personalized rule base construction to ensure that the production process runs efficiently under multi-objective optimization conditions, and has the advantages of strong adaptability, high precision, low energy consumption and high production efficiency.

[0009] The method for optimizing the production process of an electronic device security interface based on fuzzy logic according to an embodiment of the present invention comprises the following steps:

[0010] S1. Design and construct a multi-level fuzzy rule base, wherein the multi-level fuzzy rule base includes a first level, a second level and a third level;

[0011] S2. The first level uses genetic algorithm to generate and optimize the initial fuzzy rule base. The input variables include total production demand, overall equipment health status, energy consumption rate and production period. The output variables include global production speed setting, equipment utilization allocation and energy consumption optimization strategy, which are used to achieve global control of the overall production speed, energy consumption and output balance.

[0012] S3. The second level uses particle swarm optimization algorithm to optimize the fuzzy rule base. The input variables include the temperature, pressure, material flow rate and production line load of each production line. The output variables include production line speed adjustment, temperature setting and pressure regulation. It is used to optimize the operating parameters of a single production line to ensure that each production line operates in the best state.

[0013] S4. The third level uses ant colony algorithm to optimize specific process parameters in the fuzzy rule base. The input variables include ingredient ratio, equipment fine-tuning parameters and process errors. The output variables include ratio optimization, equipment fine-tuning instructions and process parameter correction, which are used to accurately control production process parameters to ensure further improvement of product quality and production efficiency.

[0014] S5. Through data acquisition and monitoring technology, real-time data from the production process is obtained, a multidimensional data set is constructed, and the multidimensional data set is transmitted to the central processing unit;

[0015] S6. Use multidimensional data sets to build a personalized fuzzy rule base, automatically generate and optimize the exclusive rule base according to the individual differences of equipment and task classification, and make adaptive adjustments based on real-time data during the production process;

[0016] S7. Display key data and optimization results in the production process through visualization technology, generate a real-time graphical interface, and support quick decision-making and problem troubleshooting.

[0017] Optionally, the S2 specifically includes:

[0018] S21, initialize the population and generate several candidate rule sets, each rule in the candidate rule set is composed of a combination of input variables and output variables, where the input variables include the total output demand D total 、Overall health status of equipment equipment , energy consumption rate E rate and production period T production , the output variables include the global production speed setting S global , equipment utilization allocation U utilization and energy consumption optimization strategy C consumption ;

[0019] S22. Perform fitness evaluation on each candidate rule set, and calculate the fitness value of each rule set through the fitness function f(x):

[0020]

[0021] Among them, w1, w2, w3 and w4 represent the pre-set weight coefficients, and f(x) represents the fitness value of the rule set in balancing production speed, energy consumption and output. The larger the value, the better the optimization effect of the rule set.

[0022] S23, performing a selection operation according to the fitness value, retaining the rule set with a higher fitness value, and performing a crossover operation, wherein the crossover operation generates a new rule set by exchanging the values ​​of some input variables and output variables in two rule sets;

[0023] S24, performing a mutation operation on the rule set after the selection and crossover operations, wherein the mutation operation dynamically adjusts the mutation range based on the population fitness:

[0024]

[0025] Among them, X i,j represents the jth variable of the ith rule set after mutation, X i,j represents the jth variable of the i-th rule set before mutation, δ(t) represents the coefficient of variation, which is dynamically adjusted with the number of generations t, rand(-1,1) represents a random number between -1 and 1, and f avg represents the average fitness of the current population, f max Represents the maximum fitness in the current population;

[0026] Used to control the intensity of mutation. When the overall fitness of the population tends to be stable, the intensity of mutation will gradually weaken, thereby improving the convergence and stability of the algorithm;

[0027] S25, repeating steps S22-S24 until the fitness value of the rule set in the population converges to a preset threshold or reaches a predetermined number of iterations, and finally obtaining an optimized fuzzy rule base;

[0028] S26. Apply the optimized fuzzy rule base to the production process and adjust the global production speed setting S in real time global , equipment utilization allocation U utilization and energy consumption optimization strategy C consumption .

[0029] Optionally, the S3 specifically includes:

[0030] S31, initialize the particle swarm, set the size of the particle swarm to N, and randomly generate the initial position X of each particle i and speed V i , where i represents the number of the particle and the initial position X iIndicates the temperature T of each production line i 、Pressure P i , material flow rate F i and production line load L i The initial value, initial velocity V i Indicates the temperature T of each production line i 、Pressure P i , material flow rate F i and production line load L i The initial rate of change of

[0031] S32, define the fitness function f1(X i ), used to evaluate the current position X of each particle i The corresponding production line operation status:

[0032]

[0033] Among them, T opt represents the optimal target value of temperature, P opt Indicates the optimal target value of pressure, F opt Indicates the optimal target value of material flow rate, L opt represents the optimal target value of the production line load, and v1, v2, v3 and v4 represent weight coefficients;

[0034] S33, according to the fitness function value, the individual optimal position A of each particle i (t) and the global optimal position G(t), record the fitness value of each particle in the current iteration, and update the position of the particle with a better fitness value to the individual optimal position A i (t), and update the optimal position of all particles to the global optimal position G(t);

[0035] S34. Update the position and speed of the particle, the speed of the particle V i (t+1) and position X i The update formula for (t+1) is:

[0036] V i (t+1)=ω·V i (t)+c1·r1·(A i (t)-X i (t))+c2·r2·(G(t)-X i (t));

[0037] X i (t+1)=X i (t)+V i (t+1);

[0038] Among them, ω represents the inertia weight, which is used to control the influence of the current velocity of the particle, c1 and c2 represent learning factors, which represent the influence of the particle's own experience and the group's experience respectively, and r1 and r2 represent random numbers between 0 and 1;

[0039] S35. In each iteration, the inertia weight ω is dynamically adjusted, and as the number of iterations increases, ω is gradually reduced. At the same time, c1 and c2 are adaptively adjusted to enhance the collaborative optimization capability of the particle swarm.

[0040] S36, iteratively update the particle swarm, and make the particle swarm gradually converge through multiple iterations until the fitness function f1(X i ) converges to the set threshold or reaches the maximum number of iterations, and finally finds the fitness function f1(X i )The smallest particle position X i , as the optimized production line operating parameters, including optimal production line speed adjustment, temperature setting and pressure regulation.

[0041] Optionally, the S4 specifically includes:

[0042] S41, initialize the ant colony, set the size M of the ant colony and the maximum number of iterations C max , randomly distributed initial pheromone concentration τ ij (0), each ant represents a process parameter combination X j , including the ingredient ratio R j 、Equipment fine-tuning parameters A j and process error E j , where i represents the ant number and j represents the parameter combination number;

[0043] S42, define the multi-objective fitness function f2(X j ), used to evaluate each parameter combination X j Impact on production process:

[0044]

[0045] Among them, R opt Indicates the optimal target value of the ingredient ratio, A opt represents the optimal target value of the device fine-tuning parameters, E opt represents the optimal target value of process error, α1, β1 and γ1 represent weight coefficients;

[0046] S43, entropy-based adaptive selection mechanism calculates the selection probability P of each ant on each path ij (t), the selection probability is based on the pheromone concentration τ ij (t), heuristic factor η ij and the uncertainty of the current path H ij(t) Calculation:

[0047]

[0048] Among them, α(t) represents the weight coefficient of pheromone concentration, β(t) represents the weight coefficient of heuristic factor, γ(t) represents the weight factor of information entropy, k represents the index of other paths available for ants to choose, allowed represents the set of paths that the current ant has not chosen, t represents the number of iterations, τ ij (t) represents the current pheromone concentration on path ij, η ij represents the heuristic factor, H ij (t) represents the information entropy on path ij, which is used to describe the uncertainty of path selection:

[0049]

[0050] Among them, l represents the index variable, P il (t) represents the probability that the ant chooses path il;

[0051] S44, each ant chooses a ij (t) Select a path and determine a process parameter combination X j , through the multi-objective fitness function f2(X j ) Evaluate the effect and record the best process parameter combination found by each ant;

[0052] S45, update the pheromone concentration, use the adaptive pheromone volatilization mechanism and dynamic increment to update the pheromone concentration τ on each path ij (t+1):

[0053] τ ij (t+1)=(1-ρ(t))·τ ij (t)+Δτ ij (t);

[0054] Among them, ρ(t) represents the pheromone volatility coefficient, Δτ ij (t) represents the pheromone increment on path ij, τ ij (t) represents the pheromone on path ij;

[0055]

[0056] Among them, ∈ represents the stability coefficient, σ(X j ) represents the volatility of the current parameter combination;

[0057] S46, repeat steps S43-S45 until the maximum number of iterations C is reached max Or the fitness function converges, and finally determines the optimal process parameter combination Including the optimal ratio of ingredients Equipment fine-tuning parameters and process error control And apply the optimization results to the actual production process to ensure further improvement of product quality and production efficiency.

[0058] Optionally, the S5 specifically includes:

[0059] S51. Arrange a sensor network and install a variety of sensors at key locations of the production line, including temperature sensors, pressure sensors, speed sensors, flow sensors and vibration sensors, to collect multi-dimensional data of temperature, pressure, speed, flow and equipment vibration in real time during the production process;

[0060] S52. Define the symbolic representation of multidimensional data collected by sensors, T c (t) represents the temperature data stream, P c (t) represents the pressure data flow, V c (t) represents the velocity data flow, F c (t) represents the traffic data flow, S c (t) represents the equipment vibration data stream, where t represents the time index;

[0061] S53, preprocessing the multidimensional data collected in real time, including data denoising, missing value filling and outlier detection, using a low-pass filter to denoise the original data to obtain a filtered temperature data stream T f (t), filtered pressure data stream P f (t), filtered velocity data stream V f (t), filtered traffic data stream F f (t) and the filtered equipment vibration data stream S f (t);

[0062] S54, using adaptive dynamic normalization technology to normalize the pre-processed multidimensional data:

[0063]

[0064] Where X(t) represents any preprocessed data stream, μ X (t-τ, t) represents the dynamic mean of the data stream in the time window [t-τ, t], τ represents the size of the time window, σ X (t-τ,t) represents the dynamic standard deviation of the data flow in the time window [t-τ,t], σ ΔX (t) represents the volatility of the data stream X(t) at time point t, and λ3 represents the weight coefficient for adjusting the impact of volatility;

[0065] Get the normalized temperature data stream T dyn_norm (t), normalized pressure data stream P dyn_norm (t), normalized velocity data stream V dyn_norm (t), normalized traffic data flow F dyn_norm (t) and the normalized equipment vibration data stream S dyn_norm (t);

[0066] S55. Construct a multidimensional data set D(t) according to the time index t of the data stream after dynamic standardization processing:

[0067] D(t)={T dyn_norm (t),P dyn_norm (t),V dyn_norm (t),F dyn_norm (t),S dyn_norm (t)};

[0068] S56. Perform high-speed data transmission via industrial Ethernet or wireless network to transmit the constructed multidimensional data set D(t) to the central processing unit in real time.

[0069] Optionally, the S6 specifically includes:

[0070] S61, extract key features from the multidimensional data set D(t) transmitted to the central processing unit in real time, and determine the input variable set of the personalized fuzzy rule base as the temperature data stream T according to the historical operation data, current status and task requirements of each device. dyn_norm (t), pressure data stream P dyn_norm (t), speed data flow V dyn_norm (t), traffic data flow F dyn_norm (t) and equipment vibration data stream S dyn_norm (t);

[0071] S62, define the output variable set of the personalized fuzzy rule base, the output variable includes the production speed setting S output (t), Equipment utilization adjustment U output (t), Energy consumption optimization strategy C output (t), process parameter fine-tuning A output (t) and production quality target Q output (t);

[0072] S63, construct a dynamic adaptive fuzzy membership function, map the input variables to fuzzy sets, each input variable X dyn_norm (t) through the dynamic membership function μ Ai Mapping to fuzzy set A i middle:

[0073]

[0074] Among them, c i (t) represents the fuzzy set A i The dynamic center value, b i (t) represents the dynamic width parameter of the fuzzy set, σ X (t) represents the volatility of the input variable, λ4 represents the weight coefficient, and m(t) represents the shape control parameter of the dynamic membership function;

[0075] S64, generating an initial fuzzy rule base according to the membership degree of the input variable and the initial rule set of the output variable;

[0076] S65, using multi-objective genetic algorithm to optimize the initial fuzzy rule base, for multiple optimization objectives, through the complex fitness function f multi_obj (R) Evaluate and evolve the fuzzy rule base:

[0077]

[0078] Where R represents the fuzzy rule, T represents the evaluation time period, ω1(t) represents the dynamic weight coefficient of the production speed at time t, ω2(t) represents the dynamic weight coefficient of the equipment utilization at time t, ω3(t) represents the dynamic weight coefficient of the energy optimization at time t, ω4(t) represents the dynamic weight coefficient of the production quality target at time t, S target (t) represents the production speed setting target value at time t, U target (t) represents the target value of equipment utilization adjustment at time t, C target (t) represents the target value of the energy consumption optimization strategy at time t, Q target (t) represents the production quality target value at time t;

[0079] S66. According to the fuzzy rule base optimized by the multi-objective genetic algorithm, the process parameters in the production process are adjusted in real time to adapt to the individual differences of the equipment and the task requirements, ensuring that each device and each task operates in the optimal state, thereby improving production efficiency, reducing energy consumption and improving product quality.

[0080] The beneficial effects of the present invention are:

[0081] First, by designing and building a multi-level fuzzy rule base, we have achieved comprehensive optimization control from global to local, from macro to micro. The multi-level structure enables the system to handle global production speed setting, single production line parameter optimization, and refined control of specific process parameters, thereby ensuring optimal control at different levels. This hierarchical optimization method can not only maintain the overall coordination of the system in a complex production environment, but also accurately control the key nodes in production, thereby effectively responding to the challenges of dynamic changes and uncertainties.

[0082] Secondly, the present invention introduces three intelligent optimization algorithms: genetic algorithm, particle swarm optimization algorithm and ant colony algorithm. By combining the advantages of these algorithms, efficient generation and dynamic optimization of the rule base are achieved. The genetic algorithm is responsible for global optimization of the initial rule base to ensure that the system has strong adaptability and evolutionary capabilities in the initial stage; the particle swarm optimization algorithm is used to adjust the production line parameters in real time to ensure that each production line operates in the best state; the ant colony algorithm further optimizes specific process parameters to ensure that the micro-control in the production process reaches the optimal level. Through the organic combination of this intelligent optimization algorithm, the present invention not only improves the global search capability and convergence speed of the system, but also enhances the adaptability and flexibility of the rule base, so that the system can operate efficiently under multi-objective optimization.

[0083] In addition, the present invention overcomes the limitations of traditional static data processing methods by introducing adaptive dynamic data processing technology. Adaptive dynamic standardization technology can dynamically adjust according to the volatility and time-varying characteristics of real-time data to ensure the accuracy and timeliness of data processing results. This data processing method not only improves the system's response speed to real-time changes, but also enhances the accuracy of control decisions, thereby effectively improving the stability and consistency of the production process.

[0084] More importantly, the present invention proposes a method for constructing a personalized fuzzy rule base, which can automatically generate and optimize a dedicated rule base based on the individual differences of each device and the requirements of specific production tasks. Through this innovative design, the system can tailor the optimal control strategy for different devices and tasks to ensure the best production results under various conditions. This personalized optimization method greatly enhances the adaptability and flexibility of the system, enabling the production process to better cope with complex and changing production environments while meeting diverse product requirements.

[0085] Finally, the present invention uses visualization technology to present key data and optimization results in the production process in a graphical interface, supporting rapid decision-making and problem troubleshooting. This real-time visualization function not only improves the operability and user experience of the system, but also provides production managers with a more intuitive and convenient means of production monitoring and adjustment, further ensuring the efficiency and controllability of the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0087] Figure 1 This is a flow chart of the method for optimizing the production process of the electronic device security interface based on fuzzy logic proposed by the present invention;

[0088] Figure 2 A schematic diagram of the structure of a multi-level fuzzy rule base for the fuzzy logic-based electronic device security interface production process optimization method proposed by the present invention;

[0089] Figure 3 The present invention provides a personalized fuzzy rule base construction and optimization flow chart for the fuzzy logic-based electronic device security interface production process optimization method proposed in the present invention. DETAILED DESCRIPTION

[0090] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0091] refer to Figure 1-3 , a method for optimizing the production process of electronic equipment safety interface based on fuzzy logic, comprising the following steps:

[0092] S1. Design and construct a multi-level fuzzy rule base, wherein the multi-level fuzzy rule base includes a first level, a second level and a third level;

[0093] S2. The first level uses genetic algorithm to generate and optimize the initial fuzzy rule base. The input variables include total production demand, overall equipment health status, energy consumption rate and production period. The output variables include global production speed setting, equipment utilization allocation and energy consumption optimization strategy, which are used to achieve global control of the overall production speed, energy consumption and output balance.

[0094] S3. The second level uses particle swarm optimization algorithm to optimize the fuzzy rule base. The input variables include the temperature, pressure, material flow rate and production line load of each production line. The output variables include production line speed adjustment, temperature setting and pressure regulation. It is used to optimize the operating parameters of a single production line to ensure that each production line operates in the best state.

[0095] S4. The third level uses ant colony algorithm to optimize specific process parameters in the fuzzy rule base. The input variables include ingredient ratio, equipment fine-tuning parameters and process errors. The output variables include ratio optimization, equipment fine-tuning instructions and process parameter correction, which are used to accurately control production process parameters to ensure further improvement of product quality and production efficiency.

[0096] S5. Through data acquisition and monitoring technology, real-time data from the production process is obtained, a multidimensional data set is constructed, and the multidimensional data set is transmitted to the central processing unit;

[0097] S6. Use multidimensional data sets to build a personalized fuzzy rule base, automatically generate and optimize the exclusive rule base according to the individual differences of equipment and task classification, and make adaptive adjustments based on real-time data during the production process;

[0098] S7. Display key data and optimization results in the production process through visualization technology, generate a real-time graphical interface, and support quick decision-making and problem troubleshooting.

[0099] In this implementation, S2 specifically includes:

[0100] S21, initialize the population and generate several candidate rule sets, each rule in the candidate rule set is composed of a combination of input variables and output variables, where the input variables include the total output demand D total 、Overall health status of equipment equipment , energy consumption rate E rate and production period T production , the output variables include the global production speed setting S global , equipment utilization allocation U utilization and energy consumption optimization strategy C consumption ;

[0101] S22. Perform fitness evaluation on each candidate rule set, and calculate the fitness value of each rule set through the fitness function f(x):

[0102]

[0103] Among them, w1, w2, w3 and w4 represent the pre-set weight coefficients, and f(x) represents the fitness value of the rule set in balancing production speed, energy consumption and output. The larger the value, the better the optimization effect of the rule set.

[0104] S23, performing a selection operation according to the fitness value, retaining the rule set with a higher fitness value, and performing a crossover operation, wherein the crossover operation generates a new rule set by exchanging the values ​​of some input variables and output variables in two rule sets;

[0105] S24, performing a mutation operation on the rule set after the selection and crossover operations, wherein the mutation operation dynamically adjusts the mutation range based on the population fitness:

[0106]

[0107] Among them, X i,j represents the jth variable of the ith rule set after mutation, X i,j represents the jth variable of the i-th rule set before mutation, δ(t) represents the coefficient of variation, which is dynamically adjusted with the number of generations t, rand(-1,1) represents a random number between -1 and 1, and f avg represents the average fitness of the current population, f max Represents the maximum fitness in the current population;

[0108] Used to control the intensity of mutation. When the overall fitness of the population tends to be stable, the intensity of mutation will gradually weaken, thereby improving the convergence and stability of the algorithm;

[0109] S25, repeating steps S22-S24 until the fitness value of the rule set in the population converges to a preset threshold or reaches a predetermined number of iterations, and finally obtaining an optimized fuzzy rule base;

[0110] S26. Apply the optimized fuzzy rule base to the production process and adjust the global production speed setting S in real time global , equipment utilization allocation U utilization and energy consumption optimization strategy C consumption .

[0111] In this implementation, S3 specifically includes:

[0112] S31, initialize the particle swarm, set the size of the particle swarm to N, and randomly generate the initial position X of each particle i and speed V i , where i represents the number of the particle and the initial position X i Indicates the temperature T of each production line i 、Pressure P i , material flow rate F i and production line load L i The initial value, initial velocity V i Indicates the temperature T of each production line i 、Pressure P i , material flow rate F i and production line load L i The initial rate of change of

[0113] S32, define the fitness function f1(X i), used to evaluate the current position X of each particle i The corresponding production line operation status:

[0114]

[0115] Among them, T opt represents the optimal target value of temperature, P opt Indicates the optimal target value of pressure, F opt Indicates the optimal target value of material flow rate, L opt represents the optimal target value of the production line load, and v1, v2, v3 and v4 represent weight coefficients;

[0116] S33, according to the fitness function value, the individual optimal position A of each particle i (t) and the global optimal position G(t), record the fitness value of each particle in the current iteration, and update the position of the particle with a better fitness value to the individual optimal position A i (t), and update the optimal position of all particles to the global optimal position G(t);

[0117] S34. Update the position and speed of the particle, the speed of the particle V i (t+1) and position X i The update formula for (t+1) is:

[0118] V i (t+1)=ω·V i (t)+c1·r1·(A i (t)-X i (t))+c2·r2·(G(t)-X i (t));

[0119] X i (t+1)=X i (t)+V i (t+1);

[0120] Among them, ω represents the inertia weight, which is used to control the influence of the current velocity of the particle, c1 and c2 represent learning factors, which represent the influence of the particle's own experience and the group's experience respectively, and r1 and r2 represent random numbers between 0 and 1;

[0121] S35. In each iteration, the inertia weight ω is dynamically adjusted, and as the number of iterations increases, ω is gradually reduced. At the same time, c1 and c2 are adaptively adjusted to enhance the collaborative optimization capability of the particle swarm.

[0122] S36, iteratively update the particle swarm, and make the particle swarm gradually converge through multiple iterations until the fitness function f1(X i) converges to the set threshold or reaches the maximum number of iterations, and finally finds the fitness function f1(X i )The smallest particle position X i , as the optimized production line operating parameters, including optimal production line speed adjustment, temperature setting and pressure regulation.

[0123] In this implementation manner, the S4 specifically includes:

[0124] S41, initialize the ant colony, set the size M of the ant colony and the maximum number of iterations C max , randomly distributed initial pheromone concentration τ ij (0), each ant represents a process parameter combination X j , including the ingredient ratio R j 、Equipment fine-tuning parameters A j and process error E j , where i represents the ant number and j represents the parameter combination number;

[0125] S42, define the multi-objective fitness function f2(X j ), used to evaluate each parameter combination X j Impact on production process:

[0126]

[0127] Among them, R opt Indicates the optimal target value of the ingredient ratio, A opt represents the optimal target value of the device fine-tuning parameters, E opt represents the optimal target value of process error, α1, β1 and γ1 represent weight coefficients;

[0128] S43, entropy-based adaptive selection mechanism calculates the selection probability P of each ant on each path ij (t), the selection probability is based on the pheromone concentration τ ij (t), heuristic factor η ij and the uncertainty of the current path H ij (t) Calculation:

[0129]

[0130] Among them, α(t) represents the weight coefficient of pheromone concentration, β(t) represents the weight coefficient of heuristic factor, γ(t) represents the weight factor of information entropy, k represents the index of other paths available for ants to choose, allowed represents the set of paths that the current ant has not chosen, t represents the number of iterations, τ ij (t) represents the current pheromone concentration on path ij, η ij represents the heuristic factor, H ij(t) represents the information entropy on path ij, which is used to describe the uncertainty of path selection:

[0131]

[0132] Among them, l represents the index variable, P il (t) represents the probability that the ant chooses path il;

[0133] S44, each ant chooses a ij (t) Select a path and determine a process parameter combination X j , through the multi-objective fitness function f2(X j ) Evaluate the effect and record the best process parameter combination found by each ant;

[0134] S45, update the pheromone concentration, use the adaptive pheromone volatilization mechanism and dynamic increment to update the pheromone concentration τ on each path ij (t+1):

[0135] τ ij (t+1)=(1-ρ(t))·τ ij (t)+Δτ ij (t);

[0136] Among them, ρ(t) represents the pheromone volatility coefficient, Δτ ij (t) represents the pheromone increment on path ij, τ ij (t) represents the pheromone on path ij;

[0137]

[0138] Among them, ∈ represents the stability coefficient, σ(X j ) represents the volatility of the current parameter combination;

[0139] S46, repeat steps S43-S45 until the maximum number of iterations C is reached max Or the fitness function converges, and finally determines the optimal process parameter combination Including the optimal ratio of ingredients Equipment fine-tuning parameters and process error control And apply the optimization results to the actual production process to ensure further improvement of product quality and production efficiency.

[0140] In this implementation manner, S5 specifically includes:

[0141] S51. Arrange a sensor network and install a variety of sensors at key locations of the production line, including temperature sensors, pressure sensors, speed sensors, flow sensors and vibration sensors, to collect multi-dimensional data of temperature, pressure, speed, flow and equipment vibration in real time during the production process;

[0142] S52. Define the symbolic representation of multidimensional data collected by sensors, T c (t) represents the temperature data stream, P c (t) represents the pressure data flow, V c (t) represents the velocity data flow, F c (t) represents the traffic data flow, S c (t) represents the equipment vibration data stream, where t represents the time index;

[0143] S53, preprocessing the multidimensional data collected in real time, including data denoising, missing value filling and outlier detection, using a low-pass filter to denoise the original data to obtain a filtered temperature data stream T f (t), filtered pressure data stream P f (t), filtered velocity data stream V f (t), filtered traffic data stream F f (t) and the filtered equipment vibration data stream S f (t);

[0144] S54, using adaptive dynamic normalization technology to normalize the pre-processed multidimensional data:

[0145]

[0146] Where X(t) represents any preprocessed data stream, μ X (t-τ, t) represents the dynamic mean of the data stream in the time window [t-τ, t], τ represents the size of the time window, σ X (t-τ,t) represents the dynamic standard deviation of the data flow in the time window [t-τ,t], σ ΔX (t) represents the volatility of the data stream X(t) at time point t, and λ3 represents the weight coefficient for adjusting the impact of volatility;

[0147] Get the normalized temperature data stream T dyn_norm (t), normalized pressure data stream P dyn_norm (t), normalized velocity data stream V dyn_norm (t), normalized traffic data flow F dyn_norm (t) and the normalized equipment vibration data stream S dyn_norm (t);

[0148] S55. Construct a multidimensional data set D(t) according to the time index t of the data stream after dynamic standardization processing:

[0149] D(t)={T dyn_norm (t),P dyn_norm (t),V dyn_norm (t),F dyn_norm (t),S dyn_norm (t)};

[0150] S56. Perform high-speed data transmission via industrial Ethernet or wireless network to transmit the constructed multidimensional data set D(t) to the central processing unit in real time.

[0151] In this implementation manner, S6 specifically includes:

[0152] S61, extract key features from the multidimensional data set D(t) transmitted to the central processing unit in real time, and determine the input variable set of the personalized fuzzy rule base as the temperature data stream T according to the historical operation data, current status and task requirements of each device. dyn_norm (t), pressure data stream P dyn_norm (t), speed data flow V dyn_norm (t), traffic data flow F dyn_norm (t) and equipment vibration data stream S dyn_norm (t);

[0153] S62, define the output variable set of the personalized fuzzy rule base, the output variable includes the production speed setting S output (t), Equipment utilization adjustment U output (t), Energy consumption optimization strategy C output (t), process parameter fine-tuning A output (t) and production quality target Q output (t);

[0154] S63, construct a dynamic adaptive fuzzy membership function, map the input variables to fuzzy sets, each input variable X dyn_norm (t) through the dynamic membership function μ Ai Mapping to fuzzy set A i middle:

[0155]

[0156] Among them, c i (t) represents the fuzzy set A i The dynamic center value, b i (t) represents the dynamic width parameter of the fuzzy set, σ X(t) represents the volatility of the input variable, λ4 represents the weight coefficient, and m(t) represents the shape control parameter of the dynamic membership function;

[0157] S64, generating an initial fuzzy rule base according to the membership degree of the input variable and the initial rule set of the output variable;

[0158] S65, using multi-objective genetic algorithm to optimize the initial fuzzy rule base, for multiple optimization objectives, through the complex fitness function f multi_obj (R) Evaluate and evolve the fuzzy rule base:

[0159] f multi_obj (R)

[0160]

[0161] Where R represents the fuzzy rule, T represents the evaluation time period, ω1(t) represents the dynamic weight coefficient of the production speed at time t, ω2(t) represents the dynamic weight coefficient of the equipment utilization at time t, ω3(t) represents the dynamic weight coefficient of the energy optimization at time t, ω4(t) represents the dynamic weight coefficient of the production quality target at time t, S target (t) represents the production speed setting target value at time t, U target (t) represents the target value of equipment utilization adjustment at time t, C target (t) represents the target value of the energy consumption optimization strategy at time t, Q target (t) represents the production quality target value at time t;

[0162] S66. According to the fuzzy rule base optimized by the multi-objective genetic algorithm, the process parameters in the production process are adjusted in real time to adapt to the individual differences of the equipment and the task requirements, ensuring that each device and each task operates in the optimal state, thereby improving production efficiency, reducing energy consumption and improving product quality.

[0163] Embodiment 1:

[0164] In order to verify the feasibility of the present invention in implementation, the present invention is applied to an electronic equipment manufacturer, which mainly produces high-performance security interfaces for smartphones and industrial control equipment. Due to the volatility of market demand and the diversity of product models, the production line needs to have a high degree of flexibility and adaptability to cope with the production of products of different batches and specifications. The problem faced by this manufacturer is how to improve production efficiency, reduce energy consumption, and cope with the challenges brought by equipment aging and changes in the production environment while ensuring product quality.

[0165] In actual application, the manufacturer first designed and built a multi-level fuzzy rule base, through which the production process was optimized from global to local. The generation and optimization of the first-level rule base mainly rely on genetic algorithms, which output global production speed settings, equipment utilization allocation and energy consumption optimization strategies based on input variables such as total production demand, overall equipment health status, energy consumption rate and production schedule. After optimization, the global production speed setting increased by an average of 12%, equipment utilization increased by 15%, and energy consumption decreased by about 8%.

[0166] In the second level of optimization, the manufacturer used the particle swarm optimization algorithm to dynamically optimize key parameters such as temperature, pressure, material flow rate and production line load of each production line. Through this optimization, the operating parameters of each production line can be dynamically adjusted according to real-time data to ensure that the production line operates in the best state. Specifically, within a one-month production cycle, the product qualification rate increased from the original 95% to 97.5%, and the failure rate of the production line decreased by about 20%, further reducing the uncontrollable factors in the production process.

[0167] The third level of optimization uses the ant colony algorithm to fine-tune specific process parameters such as ingredient ratio, equipment fine-tuning parameters and process errors. Through these optimizations, the micro-quality indicators of the product, such as the connection strength, wear resistance and oxidation resistance of the interface, have been significantly improved. After two months of production data comparison, the average connection strength of the optimized interface increased by 10%, the wear resistance increased by 8%, and the oxidation resistance increased by about 7%. The improvement of these indicators has greatly enhanced the competitiveness of the product in the market.

[0168] In terms of data collection and monitoring, manufacturers use temperature, pressure, speed, flow and vibration sensors placed throughout the production line to obtain multi-dimensional data in real time during the production process. The accuracy and timeliness of data collection are guaranteed through the adaptive dynamic standardization technology proposed in the present invention. Specifically, the manufacturer's data analysis team compared the data processing efficiency before and after the implementation of the present invention and found that the data transmission delay was reduced by about 15%, while the impact of data noise was reduced by more than 20%, thereby significantly improving the response speed and decision-making accuracy of the production control system.

[0169] In addition, manufacturers pay special attention to the construction of personalized fuzzy rule bases. Due to the different service life and status of production equipment, the personalized rule base proposed in the present invention can automatically generate fuzzy rules that adapt to its specific needs based on the historical data and real-time status of each device. Taking two devices in different states in the factory as an example, device A is a new device with a good operating condition; device B has been used for five years and has a certain aging phenomenon. In actual production, the personalized rule base can set a more aggressive production speed and a lower energy consumption strategy for device A, and set a more conservative production speed and more stable operating parameters for device B. After three months of production practice, the production efficiency of device A increased by 18% and the energy consumption decreased by about 10%; while device B reduced the production speed by 5%, the product quality remained stable, and the equipment failure rate decreased by about 25%.

[0170] In order to verify the beneficial effects of the present invention, the manufacturer compared the production data before and after the application of the present invention within one year.

[0171] Table 1 Comparison of the implementation effect of intelligent production optimization system

[0172]

[0173]

[0174] Table 1 clearly shows the significant advantages and improvement effects of the present invention in the production process of electronic device security interfaces. By comparing the key indicators before and after implementation, we can fully understand the actual benefits brought by the present invention.

[0175] First, from the perspective of global production speed setting and equipment utilization, after the implementation of the present invention, these two indicators have increased by 12% and 15% respectively. This shows that by introducing a multi-level fuzzy rule base and intelligent optimization algorithm, the overall operating efficiency of the production line has been greatly improved, the utilization of the equipment has been better optimized, and the production bottleneck caused by uneven production line load or unreasonable allocation of equipment resources has been avoided.

[0176] In terms of energy consumption, the energy consumption after implementation was reduced by 8%, showing the strong ability of the present invention in optimizing energy utilization and reducing production costs. This improvement not only helps to reduce production costs, but also reflects the potential of the present invention in supporting green manufacturing and sustainable development.

[0177] In terms of product quality, the product qualification rate increased from 95% to 97.5% after implementation, and the production line failure rate decreased by 20%. This shows that the invention effectively improves the consistency and reliability of products through refined process parameter optimization, and reduces unplanned downtime and fault repair time in the production process.

[0178] From the specific performance indicators of the product, the interface connection strength, wear resistance and oxidation resistance have been improved by 10%, 8% and 7% respectively. These improvements directly improve the quality and service life of the electronic equipment safety interface, enabling it to better cope with harsh use environments and long-term work pressure.

[0179] In terms of data processing and control response, data transmission delay was reduced by 15%, data noise impact was reduced by 20%, and the response speed of the production control system was significantly improved. This shows that the present invention has strong capabilities in real-time data processing and control optimization, and can maintain efficient and accurate control in a dynamically changing production environment.

[0180] In the application of specific equipment, the production efficiency of equipment A increased by 18%, and the energy consumption decreased by 10%; the failure rate of equipment B decreased by 25%. This shows that the personalized fuzzy rule base of the present invention can be customized and optimized according to the status of different equipment, so that new equipment and old equipment can operate in their respective best conditions and give full play to their potential.

[0181] Finally, from the perspective of overall production efficiency, after the implementation of the present invention, production efficiency increased by 20%, product qualification rate further increased by 2.5%, energy consumption decreased by 15%, and production adjustment time decreased by 30%. These data show that the present invention can not only significantly improve the overall operating efficiency of the production line, but also effectively reduce energy consumption and reduce the time cost of production adjustment, thereby greatly improving the competitiveness and economic benefits of the production line at a comprehensive level.

[0182] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for optimizing the production process of electronic equipment security interfaces based on fuzzy logic, characterized in that: The steps include: S1. Design and construct a multi-level fuzzy rule base, wherein the multi-level fuzzy rule base includes a first level, a second level and a third level; S2. The first level uses genetic algorithm to generate and optimize the initial fuzzy rule base. The input variables include total production demand, overall equipment health status, energy consumption rate and production period. The output variables include global production speed setting, equipment utilization allocation and energy consumption optimization strategy. S3. The second level uses particle swarm optimization algorithm to optimize the fuzzy rule base. The input variables include the temperature, pressure, material flow rate and production line load of each production line. The output variables include production line speed adjustment, temperature setting and pressure regulation. S4, the third level uses ant colony algorithm to optimize specific process parameters in the fuzzy rule base, the input variables include ingredient ratio, equipment fine-tuning parameters and process errors, and the output variables include ratio optimization, equipment fine-tuning instructions and process parameter correction; S5. Through data acquisition and monitoring technology, real-time data from the production process is obtained, a multidimensional data set is constructed, and the multidimensional data set is transmitted to the central processing unit; S6. Use multidimensional data sets to build a personalized fuzzy rule base, automatically generate and optimize the exclusive rule base according to the individual differences of equipment and task classification, and make adaptive adjustments based on real-time data during the production process; S7. Display key data and optimization results in the production process through visualization technology, generate a real-time graphical interface, and support quick decision-making and problem troubleshooting.

2. The method for optimizing the production process of electronic device security interface based on fuzzy logic according to claim 1, characterized in that: The S2 specifically includes: S21, initialize the population and generate several candidate rule sets, each rule in the candidate rule set is composed of a combination of input variables and output variables, where the input variables include the total output demand D total 、Overall health status of equipment equipment , energy consumption rate E rate and production period T production , the output variables include the global production speed setting S global , equipment utilization allocation U utilization and energy consumption optimization strategy C consumption ; S22. Perform fitness evaluation on each candidate rule set, and calculate the fitness value of each rule set through the fitness function f(x): Wherein, w1, w2, w3 and w4 represent pre-set weight coefficients; S23, performing a selection operation according to the fitness value, retaining the rule set with a higher fitness value, and performing a crossover operation, wherein the crossover operation generates a new rule set by exchanging the values ​​of some input variables and output variables in two rule sets; S24, performing a mutation operation on the rule set after the selection and crossover operations, wherein the mutation operation dynamically adjusts the mutation range based on the population fitness: Among them, X′ i,j represents the jth variable of the ith rule set after mutation, X i,j represents the jth variable of the i-th rule set before mutation, δ(t) represents the coefficient of variation, which is dynamically adjusted with the number of generations t, rand(-1,1) represents a random number between -1 and 1, and f avg represents the average fitness of the current population, f max Represents the maximum fitness in the current population; S25, repeating steps S22-S24 until the fitness value of the rule set in the population converges to a preset threshold or reaches a predetermined number of iterations, and finally obtaining an optimized fuzzy rule base; S26. Apply the optimized fuzzy rule base to the production process and adjust the global production speed setting S in real time global , equipment utilization allocation U utilization and energy consumption optimization strategy C consumption .

3. The method for optimizing the production process of electronic device security interface based on fuzzy logic according to claim 1, characterized in that: The S3 specifically includes: S31, initialize the particle swarm, set the size of the particle swarm to N, and randomly generate the initial position X of each particle i and speed V i , where i represents the number of the particle and the initial position X i Indicates the temperature T of each production line i 、Pressure P i , material flow rate F i and production line load L i The initial value, initial velocity V i Indicates the temperature T of each production line i 、Pressure P i , material flow rate F i and production line load L i The initial rate of change of S32, define the fitness function f1(X i ), used to evaluate the current position X of each particle i The corresponding production line operation status: Among them, T opt represents the optimal target value of temperature, P opt Indicates the optimal target value of pressure, F opt Indicates the optimal target value of material flow rate, L opt represents the optimal target value of the production line load, and v1, v2, v3 and v4 represent weight coefficients; S33, according to the fitness function value, the individual optimal position A of each particle i (t) and the global optimal position G(t), record the fitness value of each particle in the current iteration, and update the position of the particle with a better fitness value to the individual optimal position A i (t), and update the optimal position of all particles to the global optimal position G(t); S34. Update the position and speed of the particle, the speed of the particle V i (t+1) and position X i The update formula for (t+1) is: V i (t+1)=ω·V i (t)+c1·r1·(A i (t)-X i (t))+c2·r2·(G(t)-X i (t)); X i (t+1)=X i (t)+V i (t+1); Among them, ω represents the inertia weight, c1 and c2 represent learning factors, representing the influence of the particle's own experience and the group's experience respectively, and r1 and r2 represent random numbers between 0 and 1; S35, in each iteration, dynamically adjust the inertia weight ω, and gradually reduce ω as the number of iterations increases, while adaptively adjusting c1 and c2; S36, iteratively update the particle swarm, and make the particle swarm gradually converge through multiple iterations until the fitness function f1(X i ) converges to the set threshold or reaches the maximum number of iterations, and finally finds the fitness function f1(X i )The smallest particle position X i , as the optimized production line operating parameters, including optimal production line speed adjustment, temperature setting and pressure regulation.

4. The method for optimizing the production process of electronic device security interface based on fuzzy logic according to claim 1, characterized in that: The S4 specifically includes: S41, initialize the ant colony, set the size M of the ant colony and the maximum number of iterations C max , randomly distributed initial pheromone concentration τ ij (0), each ant represents a process parameter combination X j , including the ingredient ratio R j 、Equipment fine-tuning parameters A j and process error E j , where i represents the ant number and j represents the parameter combination number; S42, define the multi-objective fitness function f2(X j ), used to evaluate each parameter combination X j Impact on production process: Among them, R opt Indicates the optimal target value of the ingredient ratio, A opt represents the optimal target value of the device fine-tuning parameter, E opt represents the optimal target value of the process error, α1, β1 and γ1 represent weight coefficients; S43, entropy-based adaptive selection mechanism calculates the selection probability P of each ant on each path ij (t), the selection probability is based on the pheromone concentration τ ij (t), heuristic factor η ij and the uncertainty of the current path H ij (t) Calculation: Among them, α(t) represents the weight coefficient of pheromone concentration, β(t) represents the weight coefficient of heuristic factor, γ(t) represents the weight factor of information entropy, k represents the index of other paths available for ants to choose, allowed represents the set of paths that the current ant has not chosen, t represents the number of iterations, τ ij (t) represents the current pheromone concentration on path ij, η ij represents the heuristic factor, H ij (t) represents the information entropy on path ij: Among them, l represents the index variable, P il (t) represents the probability that the ant chooses path il; S44, each ant chooses a ij (t) Select a path and determine a process parameter combination X j , through the multi-objective fitness function f2(X j ) Evaluate the effect and record the best process parameter combination found by each ant; S45, update the pheromone concentration, use the adaptive pheromone volatilization mechanism and dynamic increment to update the pheromone concentration τ on each path ij (t+1): t ij (t+1)=(1-ρ(t))·τ ij (t)+Δτ ij (t); Among them, ρ(t) represents the pheromone volatility coefficient, Δτ ij (t) represents the pheromone increment on path ij, τ ij (t) represents the pheromone on path ij; Among them, ∈ represents the stability coefficient, σ(X j ) represents the volatility of the current parameter combination; S46, repeat steps S43-S45 until the maximum number of iterations C is reached max Or the fitness function converges, and finally determines the optimal process parameter combination Including the optimal ratio of ingredients Equipment fine-tuning parameters and process error control 5. The method for optimizing the production process of electronic device security interface based on fuzzy logic according to claim 1, characterized in that: The S5 specifically includes: S51. Arrange a sensor network and install a variety of sensors at key locations of the production line, including temperature sensors, pressure sensors, speed sensors, flow sensors and vibration sensors, to collect multi-dimensional data of temperature, pressure, speed, flow and equipment vibration in real time during the production process; S52. Define the symbolic representation of multidimensional data collected by sensors, T c (t) represents the temperature data stream, P c (t) represents the pressure data flow, V c (t) represents the velocity data flow, F c (t) represents the traffic data flow, S c (t) represents the equipment vibration data stream, where t represents the time index; S53, preprocessing the multidimensional data collected in real time, including data denoising, missing value filling and outlier detection, using a low-pass filter to denoise the original data to obtain a filtered temperature data stream T f (t), filtered pressure data stream P f (t), filtered velocity data stream V f (t), filtered traffic data stream F f (t) and the filtered equipment vibration data stream S f (t); S54, using adaptive dynamic normalization technology to normalize the pre-processed multidimensional data: Where X(t) represents any preprocessed data stream, μ X (t-τ, t) represents the dynamic mean of the data stream in the time window [t-τ, t], τ represents the size of the time window, σ X (t-τ,t) represents the dynamic standard deviation of the data flow in the time window [t-τ,t], σ ΔX (t) represents the volatility of the data stream X(t) at time point t, and λ3 represents the weight coefficient for adjusting the impact of volatility; Get the normalized temperature data stream T dyn_norm (t), normalized pressure data stream P dyn_norm (t), normalized velocity data stream V dyn_norm (t), normalized traffic data flow F dyn_norm (t) and the normalized equipment vibration data stream S dyn_norm (t); S55. Construct a multidimensional data set D(t) according to the time index t of the data stream after dynamic standardization processing: D(t)={T dyn_norm (t),P dyn_norm (t),V dyn_norm (t),F dyn_norm (t),S dyn_norm (t)}; S56. Perform high-speed data transmission via industrial Ethernet or wireless network to transmit the constructed multidimensional data set D(t) to the central processing unit in real time.

6. The method for optimizing the production process of electronic device security interface based on fuzzy logic according to claim 1, characterized in that: The S6 specifically includes: S61, extract key features from the multidimensional data set D(t) transmitted to the central processing unit in real time, and determine the input variable set of the personalized fuzzy rule base as the temperature data stream T according to the historical operation data, current status and task requirements of each device. dyn_norm (t), pressure data stream P dyn_norm (t), speed data flow V dyn_norm (t), traffic data flow F dyn_norm (t) and equipment vibration data stream S dyn_norm (t); S62, define the output variable set of the personalized fuzzy rule base, the output variable includes the production speed setting S output (t), Equipment utilization adjustment U output (t), Energy consumption optimization strategy C output (t), process parameter fine-tuning A output (t) and production quality target Q output (t); S63, construct a dynamic adaptive fuzzy membership function, map the input variables to fuzzy sets, each input variable X dyn_norm (t) Through the dynamic membership function Mapping to fuzzy set A i middle: Among them, c i (t) represents the fuzzy set A i The dynamic center value, b i (t) represents the dynamic width parameter of the fuzzy set, σ X (t) represents the volatility of the input variable, λ4 represents the weight coefficient, and m(t) represents the shape control parameter of the dynamic membership function; S64, generating an initial fuzzy rule base according to the membership degree of the input variable and the initial rule set of the output variable; S65, using multi-objective genetic algorithm to optimize the initial fuzzy rule base, for multiple optimization objectives, through the complex fitness function f multi_obj (R) Evaluate and evolve the fuzzy rule base: Where R represents the fuzzy rule, T represents the evaluation time period, ω1(t) represents the dynamic weight coefficient of the production speed at time t, ω2(t) represents the dynamic weight coefficient of the equipment utilization at time t, ω3(t) represents the dynamic weight coefficient of the energy optimization at time t, ω4(t) represents the dynamic weight coefficient of the production quality target at time t, S target (t) represents the production speed setting target value at time t, U target (t) represents the target value of equipment utilization adjustment at time t, C target (t) represents the target value of the energy consumption optimization strategy at time t, Q target (t) represents the production quality target value at time t; S66. According to the fuzzy rule base optimized by the multi-objective genetic algorithm, the process parameters in the production process are adjusted in real time.

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