A method and system for controlling operation flow based on all factors of man, machine, material, method and environment

Abstract

The embodiment of the present invention provides a method and system for controlling an operation process based on the full elements of man, machine, material, method and environment, belonging to the field of operation process technology. The control method includes: obtaining the operation information of the enterprise on the predetermined project; extracting the structural information in the operation information to determine the node information in the operation process to obtain the results of the operation process; obtaining the full element information about the man, machine, material, method and environment that affects the results of the operation process; judging whether it has an impact on the results of the operation process based on the full element information; if there is an impact, constructing a prediction model about the full element information on the operation process; and optimizing the full element information of the man, machine, material, method and environment based on the results of the prediction model. The control method can take into account the full element information to optimize the process control.

Description

Technical Field

[0001] The present invention relates to the technical field of operation processes, and in particular to an operation process control method and system based on all the elements of man, machine, material, method and environment. Background Art

[0002] With the rise of Industry 4.0 and smart manufacturing, enterprises have a growing demand for process optimization.

[0003] Growing day by day. Traditional operation process design often relies on manual experience and lacks systematic and

[0004] Therefore, it is difficult to develop a method that can comprehensively consider the

[0005] It takes into account multiple factors such as people, machines, materials, methods, and environment, and can automatically build and optimize the operation process.

[0006] This method is of great significance for improving production efficiency and reducing costs. Summary of the Invention

[0007] The purpose of the embodiments of the present invention is to provide a method and system for controlling an operation process based on all factors of man, machine, material, method and environment. The control method can take all factor information into consideration to optimize the control factors of the process.

[0008] To achieve the above objectives, an embodiment of the present invention provides a method for controlling an operation process based on all the elements of man, machine, material, method and environment. The method includes:

[0009] Obtain information on the company's work on the scheduled project;

[0010] Extracting structural information from the operation information to determine node information in the operation process to obtain the results of the operation process;

[0011] Obtain all-factor information on the human-machine-material-method-environment that affects the results of the operation process;

[0012] Determine, based on the full-factor information, whether it has an impact on the outcome of the operation process;

[0013] Where there is an impact, a prediction model of the impact of all-factor information on the work process is constructed;

[0014] Based on the results of the prediction model, all factor information of the man-machine-material-method loop is optimized.

[0015] Optionally, obtain comprehensive information on the human-machine-material-method-environment that influences the outcome of the work process, including:

[0016] Obtaining the work status of workers that changes over time, and converting the work status into data;

[0017] Obtain equipment operation data over time;

[0018] Get the material storage data;

[0019] Obtain various operation steps and technical specifications in the work process and convert them into data form;

[0020] Get the environment parameters in the job process.

[0021] Optionally, judging whether the full-factor information has an impact on the outcome of the operation process includes:

[0022] Obtain all-factor information about the time order of the human-machine-material-method loop;

[0023] Obtain results of operational processes over time;

[0024] According to formula (1), a vector autoregressive model is constructed for each element of the human-machine-material-method loop and the results of the operation process:

[0025] , formula (1)

[0026] in The current value representing the result of the job flow, represents the corresponding hysteresis value, Indicates the current value of a factor in the man-machine-material-method loop. represents the corresponding hysteresis value, and represents the error term, represents a constant term, represents the regression coefficient;

[0027] Selecting a preset lag order to construct the regression model;

[0028] Assumptions For all The lag coefficient is zero, construct the F statistic:

[0029] , formula (2)

[0030] in, represents the sum of squared residuals in the autoregressive model constructed based on the results of the workflow. Indicates adding a lag term The residual sum of squares of the autoregressive model constructed with one factor in the human-machine-material-method loop, represents the sample size, It represents the number of parameters to be estimated in the autoregressive model constructed by the human-machine-material-method loop. express the order of lag;

[0031] The results of the man-machine-material-method-environment and process are brought into the above model and an F test is performed to screen the factors that affect the results of the process.

[0032] Optionally, where relevant, a prediction model is constructed based on the impact of all-factor information on the operational process, including:

[0033] Acquiring training data on factors in the man-machine-material-method loop that affect the outcome of the work process;

[0034] Preprocessing the training data;

[0035] The pre-processed training data and the result data of the operation process are fed into the neural network to train the neural network;

[0036] After the training is completed, a prediction model about the impact of the full-factor information on the work process can be obtained.

[0037] Optionally, based on the results of the prediction model, all factor information of the man-machine-material-method loop is optimized, including:

[0038] The data in the man-machine-material-method loop that affects the operation process is used as a bird's nest, and the result of the corresponding operation process is used as fitness, with the goal of obtaining the maximum number of results of the operation process;

[0039] Constraining the scope of data in the human-machine-material-method loop;

[0040] Randomly generating a plurality of candidate solutions that affect the outcome of the operation process within the constraints;

[0041] Update the position of the candidate solution according to the Lévy flight strategy;

[0042] According to the updated candidate solution, it is brought into the prediction model after training to obtain the corresponding result, and its fitness is judged whether it is better than the fitness of a randomly selected bird's nest;

[0043] If yes, the updated candidate solution replaces the original bird's nest;

[0044] Discover and discard suboptimal bird nests with a preset probability, introduce new bird nests, and calculate their fitness;

[0045] The position of the bird's nest is iterated continuously until the iteration reaches the maximum value, and the bird's nest with the highest fitness is obtained;

[0046] According to the data in the man-machine-material-method loop corresponding to the bird's nest with the greatest fitness, the data in the original man-machine-material-method loop of the operation process is adjusted to complete the optimization of the operation process.

[0047] Optionally, updating the position of the candidate solution according to the Lévy flight strategy includes:

[0048] Get the initial location of the bird's nest;

[0049] The updated nest position is calculated according to formula (3):

[0050] , formula (3)

[0051] in, Indicates the updated location of the bird's nest, Indicates the location of the bird's nest before the update. represents the step size factor, Represents the random step size generated by the Lévy distribution.

[0052] Optionally, the random step size complies with the probability distribution of formula (4):

[0053] , formula (4)

[0054] in, and is a random variable that follows a standard normal distribution, Denotes the parameter of the Lévy distribution.

[0055] On the other hand, the present invention also provides an operation process control system based on the full elements of man, machine, material, method and environment, the control system comprising:

[0056] Data acquisition module to obtain all-factor information about the human-machine-material-method-environment;

[0057] The background control module is used to execute the above-mentioned operation process control method based on the full elements of man, machine, material, method and environment according to the full element information.

[0058] In one aspect, the present invention further provides a processor for running a program, wherein the program, when run, is used to execute: an operation process control method based on the full elements of man, machine, material, method and environment as described above.

[0059] Through the above technical solution, the present invention provides a method and system for controlling an operation process based on the full elements of man, machine, material, method and loop. By obtaining the operation information of the enterprise on the predetermined project, the structural information in the operation information can be extracted, so as to determine the node information in the operation process to obtain the results of the operation process. The full element information about the man, machine, material, method and loop that affects the results of the operation process can be obtained. After obtaining the full element information, it can be determined whether the full element information has an impact on the results of the operation process. In the case of an impact, a prediction model of the full element information on the operation process can be constructed. After obtaining the prediction model, the full element information of the man, machine, material, method and loop can be adjusted according to the results of the prediction model to optimize the best results of the operation process. The control method can take into account the full element information to optimize process control.

[0060] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following detailed description, they are used to explain the embodiments of the present invention, but do not constitute a limitation of the embodiments of the present invention. In the accompanying drawings:

[0062] Figure 1 This is a flowchart of a method for controlling an operation process based on all the elements of man, machine, material, method and environment according to one embodiment of the present invention;

[0063] Figure 2 This is a flowchart of screening influencing factors in a method for controlling an operation process based on all elements of man, machine, material, method and environment according to one embodiment of the present invention;

[0064] Figure 3 This is a flowchart of a prediction model construction method for a work process control method based on all factors of man, machine, material, method and environment according to one embodiment of the present invention;

[0065] Figure 4 This is a flowchart of an optimized operation process control method based on all factors of man, machine, material, method and environment according to one embodiment of the present invention;

[0066] Figure 5 It is a flowchart of updating candidate solutions of an operation process control method based on all factors of man, machine, material, method and environment according to one embodiment of the present invention. DETAILED DESCRIPTION

[0067] The following describes the specific implementation of the embodiment of the present invention in detail with reference to the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the embodiment of the present invention and is not used to limit the embodiment of the present invention.

[0068] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application are in compliance with the relevant provisions of national laws and regulations. In the embodiments of this application, certain software, components, models, and other existing solutions in the industry may be mentioned. These should be considered as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of this application, but it does not mean that the applicant has or will necessarily use such solutions.

[0069] Figure 1 This is a flowchart of a method for controlling an operation process based on the human-machine-material-method-environmental-factor according to one embodiment of the present invention. In the present invention, the process of the control method may include:

[0070] In step S1, the enterprise's operation information on the predetermined project is obtained.

[0071] In step S2, the structural information in the operation information is extracted to determine the node information in the operation process to obtain the results of the operation process.

[0072] In step S3, all factor information on the human-machine-material-method-environment that affects the results of the work process is obtained.

[0073] In step S4, based on the full factor information, it is determined whether it has an impact on the results of the work process.

[0074] In step S5, if there is any influence, a prediction model of the effect of all factor information on the work process is constructed.

[0075] In step S6, all factor information of the man-machine-material-method loop is optimized according to the results of the prediction model.

[0076] In the present invention, when optimizing, the operation information of the enterprise on the predetermined project can be obtained, and then the structural information in the operation information can be extracted, so that the node information in the operation process can be determined to obtain the results of the operation process. The full-factor information about the man-machine-material-method loop that affects the results of the operation process can be obtained. After obtaining the full-factor information, it can be determined whether the full-factor information has an impact on the results of the operation process. In the case of an impact, a prediction model of the full-factor information on the operation process can be constructed. After obtaining the prediction model, the full-factor information of the man-machine-material-method loop can be adjusted according to the results of the prediction model to optimize the best results of the operation process. This control method can take into account the full-factor information to optimize process control.

[0077] In one embodiment of the present invention, when obtaining all-factor information about the human-machine-material-method loop, the working status of the staff that changes over time can be obtained. The working status can be factors such as the staff's operating time, work intensity, heart rate, and blood pressure. After obtaining the working status data, the working status data can be converted into data that changes over time. Obtain data on equipment that changes over time, such as temperature, vibration, current, and other data. Obtain material storage data. Obtain various operating steps and technical specifications in the operation process, and different operating steps and technical specifications can be converted into certain data through expert evaluation. Obtain environmental parameters in the operation process.

[0078] In one embodiment of the present invention, Figure 2 As shown, the process of screening influential factors may include:

[0079] In step S7, all-factor information on the time order of the human-machine-material-method loop is obtained.

[0080] In step S8, the results of the operation process changing over time are obtained.

[0081] In step S9, a vector autoregressive model is constructed based on formula (1) for each element of the human-machine-material-method loop and the results of the operation process:

[0082] , formula (1)

[0083] in The current value representing the result of the job flow, represents the corresponding hysteresis value, Indicates the current value of a factor in the man-machine-material-method loop. represents the corresponding hysteresis value, and represents the error term, represents a constant term, represents the regression coefficient.

[0084] In step S10, a preset lag order is selected to construct a regression model.

[0085] In step S11, it is assumed that For all The lag coefficient is zero, and the F test equation is constructed:

[0086] , formula (2)

[0087] in, represents the sum of squared residuals in the autoregressive model constructed based on the results of the workflow. Indicates adding a lag term The residual sum of squares of the autoregressive model constructed with one factor in the human-machine-material-method loop, represents the sample size, It represents the number of parameters to be estimated in the autoregressive model constructed by the human-machine-material-method loop. express The order of lag.

[0088] In step S12, the results of the man-machine-material-method-environment and process are brought into the above model, and an F test is performed to screen factors that affect the results of the process.

[0089] In the present invention, after obtaining the full-factor information about the man-machine-material-method loop, the influence of the full-factor information of the man-machine-material-method loop on the result of the operation process can be judged. If the influence is large, it can be determined that it is a factor affecting the result of the operation process. If one or two of them have little influence, they can be eliminated and the factors in the man-machine-material-method loop that have an influence can be obtained. When judging, the full-factor information of the shut-down man-machine-material-method loop can be obtained. After obtaining the full-factor information, the results of the operation process changing over time can be obtained, and the results can be output. According to formula (1), a vector autoregressive model of each factor in the man-machine-material-method loop and the result of the operation process can be constructed. The regression model can be constructed by selecting a preset lag order, and then it can be assumed that For all The lag coefficient is zero, and the F test equation is constructed. By screening the F statistic, the factors that affect the results of the operation process can be screened. This formula (1) indicates whether one factor is the cause of another factor. Therefore, by constructing an autoregressive model, the factors that affect the results of the operation process can be screened.

[0090] In one embodiment of the present invention, Figure 3 As shown, the process of building a prediction model may include:

[0091] In step S13 , training data of factors in the man-machine-material-method loop that have an impact on the results of the work flow are obtained.

[0092] In step S14, the training data is preprocessed.

[0093] In step S15, the pre-processed training data and the result data of the operation process are sent to the neural network to train the neural network.

[0094] In step S16, after the training is completed, a prediction model about the impact of all-factor information on the work process can be obtained.

[0095] In the present invention, after identifying factors that influence the workflow, these factors can be used as training data. This training data can then be preprocessed to remove any noise. After preprocessing, the preprocessed training data and workflow results data can be fed into a neural network, which can then be trained to predict workflow outcomes. After training, a prediction model for the impact of all factor information on the workflow can be generated. By feeding the factors that influence workflow outcomes into this prediction model, corresponding prediction results can be obtained.

[0096] In one embodiment of the present invention, Figure 4 As shown, the optimized process may include:

[0097] In step S17, the data in the man-machine-material-method loop that affects the operation process is used as a bird's nest, and the results of the corresponding operation process are used as fitness, with the goal of obtaining the maximum number of results of the operation process.

[0098] In step S18, the range of the data in the human-machine-material-method loop is constrained.

[0099] In step S19 , a plurality of candidate solutions that affect the outcome of the work flow are randomly generated within the constraints.

[0100] In step S20 , the positions of the candidate solutions are updated according to the Lévy flight strategy.

[0101] In step S21, the updated candidate solution is brought into the trained prediction model to obtain the corresponding result, and it is determined whether its fitness is better than that of a randomly selected bird's nest.

[0102] In step S22, if yes, the updated candidate solution replaces the original bird's nest.

[0103] In step S23, poor quality bird nests are discovered and discarded with a preset probability, new bird nests are introduced, and their fitness is calculated.

[0104] In step S24, the position of the bird's nest is iterated continuously until the iteration reaches a maximum value, and the bird's nest with the maximum fitness is obtained.

[0105] In step S25, the data in the original man-machine-material-method loop of the operation process is adjusted according to the data in the man-machine-material-method loop corresponding to the bird's nest with the greatest fitness, so as to complete the optimization of the operation process.

[0106] In the present invention, when optimizing the influencing factors within the human-machine-material-method loop, the data within the human-machine-material-method loop that influences the workflow can be used as nests, and the corresponding workflow results can be used as fitness. The goal is to maximize the workflow result data. The range of the data within the human-machine-material-method loop is constrained. Within the constrained range, multiple candidate solutions that influence the workflow results can be randomly generated. The positions of the candidate solutions can then be updated according to the Lévy flight strategy. Based on the updated positions, their fitness can be determined. Whether it outperforms the fitness of a randomly selected nest can be determined. If so, the updated candidate solution can replace the original nest. Suboptimal nests can then be discovered and discarded with a preset probability, and new nests introduced. The nest positions are iterated until the iteration reaches a maximum, and then the iteration ends. After the iteration is complete, the nest with the highest fitness can be obtained. Based on the data corresponding to the nest with the highest fitness, the data in the original human-machine-material-method loop of the workflow can be adjusted, thereby optimizing the workflow.

[0107] In one embodiment of the present invention, Figure 5 As shown, the process of updating candidate solutions may include:

[0108] In step S26, the initial location of the bird's nest is obtained.

[0109] In step S27, the updated location of the bird's nest is calculated according to formula (3):

[0110] , formula (3)

[0111] in, Indicates the updated location of the bird's nest, Indicates the location of the bird's nest before the update. represents the step size factor, Represents the random step size generated by the Lévy distribution.

[0112] In the present invention, when updating the candidate solution, the initial position of the bird's nest can be obtained first, and then the new position of the bird's nest can be calculated according to formula (3).

[0113] In one embodiment of the present invention, the random step size in formula (3) may conform to the probability distribution of formula (4):

[0114] , formula (4)

[0115] in, and is a random variable that follows a standard normal distribution, Denotes the parameter of the Lévy distribution.

[0116] In another aspect, the present invention further provides a work process control system based on the full elements of human-machine-material-method-environmental integration. The control system comprises a data acquisition module and a background control module. The data acquisition module is configured to acquire information regarding the full elements of the human-machine-material-method-environmental integration. The background control module is configured to execute the above-described work process control method based on the full elements of human-machine-material-method-environmental integration based on the full elements of the human-machine-material-method-environmental integration.

[0117] In one aspect, the present invention further provides a processor for running a program, wherein the program, when run, is used to execute: an operation process control method based on the full elements of man, machine, material, method and environment as described above.

[0118] Through the above technical solution, the present invention provides a method and system for controlling an operation process based on the full elements of man, machine, material, method and loop. By obtaining the operation information of the enterprise on the predetermined project, the structural information in the operation information can be extracted, so as to determine the node information in the operation process to obtain the results of the operation process. The full element information about the man, machine, material, method and loop that affects the results of the operation process can be obtained. After obtaining the full element information, it can be determined whether the full element information has an impact on the results of the operation process. In the case of an impact, a prediction model of the full element information on the operation process can be constructed. After obtaining the prediction model, the full element information of the man, machine, material, method and loop can be adjusted according to the results of the prediction model to optimize the best results of the operation process. The control method can take into account the full element information to optimize process control.

[0119] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0120] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0121] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0122] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0123] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0124] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0125] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can be implemented using any method or technology for information storage. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change RAM (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media, such as modulated data signals and carrier waves.

[0126] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0127] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A method for controlling an operation process based on all the elements of man, machine, material, method and environment, characterized in that: The control method includes: Obtain information on the company's work on the scheduled project; Extracting structural information from the operation information to determine node information in the operation process to obtain the results of the operation process; Obtain all-factor information on the human-machine-material-method-environment that affects the results of the operation process; Determine, based on the full-factor information, whether it has an impact on the outcome of the operation process; Where there is an impact, a prediction model of the impact of all-factor information on the work process is constructed; Optimizing all the elements of the human-machine-material-method loop according to the results of the prediction model; Based on the full-factor information, determine whether it has an impact on the outcome of the operation process, including: Obtain all-factor information about the time order of the human-machine-material-method loop; Obtain results of operational processes over time; According to formula (1), a vector autoregressive model is constructed for each element of the human-machine-material-method loop and the results of the operation process: , Formula (1) in The current value representing the result of the job flow, represents the corresponding hysteresis value, Indicates the current value of a factor in the man-machine-material-method loop. represents the corresponding hysteresis value, and represents the error term, represents a constant term, represents the regression coefficient; Select the preset lag order to build the regression model; Assumptions For all The lag coefficient is zero, and the F test equation is constructed: , Formula (2) in, represents the sum of squared residuals in the autoregressive model constructed based on the results of the workflow. Indicates adding a lag term The residual sum of squares of the autoregressive model constructed with one factor in the human-machine-material-method loop, represents the sample size, It represents the number of parameters to be estimated in the autoregressive model constructed by the human-machine-material-method loop. express the order of lag; The results of the man-machine-material-method-environment and process are brought into the above model and an F test is performed to screen the factors that affect the results of the process.

2. The control method according to claim 1, characterized in that: Obtain comprehensive information on the human-machine-material-method-environment that influences the outcome of the operational process, including: Obtaining the work status of workers that changes over time, and converting the work status into data; Obtain equipment operation data over time; Get the material storage data; Obtain various operation steps and technical specifications in the work process and convert them into data form; Get the environment parameters in the job process.

3. The control method according to claim 1, wherein: Where relevant, a prediction model is constructed based on the impact of all-factor information on the operational process, including: Acquiring training data on factors in the man-machine-material-method loop that affect the outcome of the work process; Preprocessing the training data; The pre-processed training data and the result data of the operation process are fed into the neural network to train the neural network; After the training is completed, a prediction model about the impact of the full-factor information on the work process can be obtained.

4. The control method according to claim 3, characterized in that: Based on the results of the prediction model, all the elements of the human-machine-material-method loop are optimized, including: The data in the man-machine-material-method loop that affects the operation process is used as a bird's nest, and the result of the corresponding operation process is used as fitness, with the goal of obtaining the maximum number of results of the operation process; Constraining the scope of data in the human-machine-material-method loop; Randomly generating a plurality of candidate solutions that affect the outcome of the operation process within the constraints; Update the position of the candidate solution according to the Lévy flight strategy; According to the updated candidate solution, it is brought into the prediction model after training to obtain the corresponding result, and its fitness is judged whether it is better than the fitness of a randomly selected bird's nest; If yes, the updated candidate solution replaces the original bird's nest; Discover and discard suboptimal bird nests with a preset probability, introduce new bird nests, and calculate their fitness; The position of the bird's nest is iterated continuously until the iteration reaches the maximum value, and the bird's nest with the highest fitness is obtained; According to the data in the man-machine-material-method loop corresponding to the bird's nest with the greatest fitness, the data in the original man-machine-material-method loop of the operation process is adjusted to complete the optimization of the operation process.

5. The control method according to claim 4, characterized in that: The positions of candidate solutions are updated according to the Lévy flight strategy, including: Get the initial location of the bird's nest; The updated nest position is calculated according to formula (3): , Formula (3) in, Indicates the updated location of the bird's nest, Indicates the location of the bird's nest before the update. represents the step size factor, Represents the random step size generated by the Lévy distribution.

6. The control method according to claim 5, characterized in that: The random step size conforms to the probability distribution of formula (4): , Formula (4) in, and is a random variable that follows a standard normal distribution, Denotes the parameter of the Lévy distribution.

7. A work process control system based on the full elements of man, machine, material, method and environment, characterized in that: The control system includes: Data acquisition module to obtain all-factor information about the human-machine-material-method-environment; A background control module is used to execute an operation process control method based on the full elements of man, machine, material, method and environment as described in any one of claims 1 to 6 according to the full element information.

8. A processor, characterized in that: Used to run a program, wherein the program is used to execute when it is run: an operation process control method based on all elements of man, machine, material, method and environment as described in any one of claims 1 to 6.

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