Operation process control method and system based on man-machine material method ring total elements

Abstract

The embodiment of the invention provides an operation process control method and system based on a man-machine material method ring total element, and belongs to the technical field of operation processes. The control method comprises the following steps: acquiring operation information of an enterprise on a predetermined project; extracting structure information in the operation information to determine node information in an operation process so as to obtain an achievement of the operation process; total factor information about a man-machine material method ring of the achievement influencing the operation process is obtained; according to the total element information, judging whether the total element information affects the achievement of the operation process; under the condition of influence, constructing a prediction model of the total element information on the operation process; and optimizing the total element information of the man-machine material method ring according to the result of the prediction model. According to the control method, total element information can be considered so as to optimize flow control.

Description

Technical Field

[0001] The present invention relates to the technical field of operation flow, and in particular to an operation flow 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 optimizing their work processes. Traditional work process design often relies on manual experience, lacks systematicity and scientificity, and is difficult to adapt to rapidly changing market demands. Therefore, developing a method that can comprehensively consider multiple factors such as people, machines, materials, methods, and environment, and can automatically build and optimize work processes is of great significance for improving production efficiency and reducing costs. Summary of the invention

[0003] 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.

[0004] In order to achieve the above-mentioned purpose, an embodiment of the present invention provides a method for controlling a work process based on all the elements of man, machine, material, method and environment, and the control method comprises:

[0005] Obtain information on the company's work on the scheduled project;

[0006] Extracting structural information from the operation information to determine node information in the operation flow to obtain the results of the operation flow;

[0007] Obtain all-factor information on the human-machine-material-method-environment that affects the results of the operation process;

[0008] Based on the full-factor information, determine whether it has an impact on the outcome of the operation process;

[0009] In the case of influence, build a prediction model about the impact of all-factor information on the operation process;

[0010] According to the result of the prediction model, all the information of the man-machine-material-method loop is optimized.

[0011] Optionally, obtain information on all factors affecting the outcome of the work process, including:

[0012] Obtaining the work status of the staff member that changes over time, and converting the work status into data;

[0013] Obtain equipment operation data over time;

[0014] Get the material storage data;

[0015] Obtain various operation steps and technical specifications in the work process and convert them into data form;

[0016] Get the environment parameters in the job process.

[0017] Optionally, judging whether the full-factor information has an impact on the outcome of the operation process includes:

[0018] Obtain all-factor information about the time order of the human-machine-material-method loop;

[0019] Obtain results of operational processes over time;

[0020] According to formula (1), a vector autoregression model of each element of the man-machine-material-method loop and the result of the operation process is constructed:

[0021]

[0022] where Y t Indicates the current value of the result of the work process, Y t-1 represents the corresponding hysteresis value, X t Indicates the current value of a factor in the man-machine-material-method loop, X t-1 represents the corresponding hysteresis value, ε t and v t represents the error term, α1, α2 represent the constant term, β ij represents the regression coefficient;

[0023] Selecting a preset lag order to construct the regression model;

[0024] Assume that H0 is all X t The lag coefficient is zero, construct the F statistic:

[0025]

[0026] Among them, RSSUR represents the residual sum of squares in the autoregressive model constructed by the results of the operation process, and RSSR represents the addition of the lag term X t The residual sum of squares of the autoregressive model constructed by one factor in the human-machine-material loop after the human-machine-material loop, n represents the number of lag periods, k represents the number of parameters to be estimated in the autoregressive model constructed by the human-machine-material loop, and q represents X t The order of lag;

[0027] 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.

[0028] Optionally, where relevant, build a prediction model on the impact of all-factor information on the work process, including:

[0029] Acquiring training data on factors in the man-machine-material-method loop that affect the outcome of the operation process;

[0030] Preprocessing the training data;

[0031] Sending the preprocessed training data and the result data of the operation flow into the neural network to train the neural network;

[0032] After the training is completed, a prediction model about the impact of the full-factor information on the work process can be obtained.

[0033] Optionally, according to the result of the prediction model, all factor information of the man-machine-material-method loop is optimized, including:

[0034] The data in the man-machine-material-method loop that has an impact on 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;

[0035] Constraining the scope of data in the human-machine-material-method loop;

[0036] Randomly generate multiple candidate solutions that affect the results of the operation process within the constraints;

[0037] Updating the position of the candidate solution according to the Lévy flight strategy;

[0038] According to the updated candidate solution, it is brought into the prediction model after training to obtain the corresponding result, and to determine whether its fitness is better than the fitness of a randomly selected bird's nest;

[0039] If yes, the updated candidate solution replaces the original bird's nest;

[0040] Discover and discard inferior bird nests with a preset probability, introduce new bird nests, and calculate their fitness;

[0041] 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;

[0042] 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.

[0043] Optionally, updating the position of the candidate solution according to the Lévy flight strategy includes:

[0044] Get the initial location of the bird's nest;

[0045] The updated location of the bird's nest is calculated according to formula (3):

[0046]

[0047] in, Indicates the updated location of the bird's nest, x i represents the position of the bird's nest before the update, δ represents the step size factor, and L(λ) represents the random step size generated by the Lévy distribution.

[0048] Optionally, the random step size conforms to the probability distribution of formula (4):

[0049]

[0050] Among them, w and z are random variables that obey the standard normal distribution, and λ represents the parameter of the Lévy distribution.

[0051] On the other hand, the present invention also provides a work process control system based on all elements of man, machine, material, method and environment, the control system comprising:

[0052] Data acquisition module to obtain all-factor information about the human-machine-material-method environment;

[0053] The background control module is used to execute the above-mentioned operation process control method based on the full factors of man, machine, material, method and environment according to the full factor information.

[0054] On the one hand, the present invention also provides a processor for running a program, wherein the program, when run, is used to execute: a method for controlling an operation process based on all the elements of man, machine, material, method and environment as described above.

[0055] Through the above technical scheme, 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 first 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 the process control.

[0056] 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

[0057] 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 specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0058] Figure 1 It is a flowchart of a method for controlling a work process based on all elements of man, machine, material, method and environment according to an embodiment of the present invention;

[0059] Figure 2 It is a flowchart of screening influencing factors of a method for controlling a work process based on all elements of man, machine, material, method and environment according to one embodiment of the present invention;

[0060] Figure 3 It is a flowchart of constructing a prediction model of a method for controlling a work process based on all elements of man, machine, material, method and environment according to one embodiment of the present invention;

[0061] Figure 4 It is a flowchart of an optimization of a method for controlling a work process based on all elements of man, machine, material, method and environment according to an embodiment of the present invention;

[0062] Figure 5 It is a flowchart of updating candidate solutions of a method for controlling a work process based on all factors of man, machine, material, method and environment according to an embodiment of the present invention. DETAILED DESCRIPTION

[0063] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here 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.

[0064] 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, some existing solutions in the industry such as certain software, components, and models may be mentioned, which 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 the solution.

[0065] Figure 1 It is a flowchart of a method for controlling a work process based on all factors of man, machine, material, method and environment according to an embodiment of the present invention. In the present invention, the process of the control method may include:

[0066] In step S1, the operation information of the enterprise on the predetermined project is obtained.

[0067] In step S2, the structural information in the operation information is extracted to determine the node information in the operation flow to obtain the results of the operation flow.

[0068] In step S3, all information on the human-machine-material-method loop that affects the outcome of the work process is obtained.

[0069] In step S4, based on the all-factor information, it is determined whether it has an impact on the outcome of the work flow.

[0070] In step S5, if there is any influence, a prediction model of the effect of all factor information on the work process is constructed.

[0071] In step S6, based on the results of the prediction model, all the information of the man-machine-material-method loop is optimized.

[0072] 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. All-factor information about the man-machine-material-method loop that affects the results of the operation process can be obtained. After obtaining the all-factor information, it can be determined whether the all-factor information has an impact on the results of the operation process. In the case of an impact, a prediction model about the all-factor information on the operation process can be constructed. After obtaining the prediction model, the all-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. The control method can take into account the all-factor information to optimize the process control.

[0073] 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, and 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 storage data on materials. 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.

[0074] In one embodiment of the present invention, Figure 2 As shown, the process of screening influential factors may include:

[0075] In step S7, all element information about the time order of the man-machine-material-method loop is obtained.

[0076] In step S8, the results of the operation process changing over time are obtained.

[0077] In step S9, a vector autoregression model of each element of the man-machine-material-method loop and the result of the operation process is constructed according to formula (1):

[0078]

[0079] where Y t Indicates the current value of the result of the work process, Y t-1 represents the corresponding hysteresis value, X t Indicates the current value of a factor in the man-machine-material-method loop, X t-1 represents the corresponding hysteresis value, ε t and v t represents the error term, α1, α2 represent the constant term, β ij represents the regression coefficient.

[0080] In step S10, a preset lag order is selected to construct a regression model.

[0081] In step S11, assume that H0 is all X t The lag coefficient is zero, and the F test equation is constructed:

[0082]

[0083] Among them, RSSUR represents the residual sum of squares in the autoregressive model constructed by the results of the operation process, and RSSR represents the addition of the lag term X t The residual sum of squares of the autoregressive model constructed by one factor in the human-machine-material loop after the human-machine-material loop, n represents the number of lag periods, k represents the number of parameters to be estimated in the autoregressive model constructed by the human-machine-material loop, and q represents X t The order of lag.

[0084] In step S12, the results of the man-machine-material-method-loop and process are brought into the above model, and an F test is performed to screen factors that affect the results of the process.

[0085] 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 determined. 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 autoregression model of each element 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 H0 can be assumed to be all X tThe 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.

[0086] In one embodiment of the present invention, Figure 3 As shown, the process of building a prediction model may include:

[0087] 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 is obtained.

[0088] In step S14, the training data is preprocessed.

[0089] In step S15, the preprocessed training data and the result data of the operation flow are sent to the neural network to train the neural network.

[0090] 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.

[0091] In the present invention, after screening out the factors that affect the work process, the factors can be used as training data, and then the training data can be preprocessed to remove some noise in the training data. After the preprocessing is completed, the preprocessed training data and the data of the result of the work process can be sent to the neural network, so that the neural network can be trained to predict the result of the work process. After the training is completed, a prediction model about the impact of all factor information on the work process can be obtained, and the factors that affect the result of the work process are sent to the prediction model to obtain the corresponding prediction results.

[0092] In one embodiment of the present invention, Figure 4 As shown, the optimized process may include:

[0093] In step S17, the data in the man-machine-material-method loop that has an impact on the work process is used as a bird's nest, and the results of the corresponding work process are used as fitness, with the goal of obtaining the maximum number of results of the work process.

[0094] In step S18, the range of data in the human-machine-material-method loop is constrained.

[0095] In step S19, a plurality of candidate solutions that affect the outcome of the work flow are randomly generated within the constraints.

[0096] In step S20, the positions of the candidate solutions are updated according to the Lévy flight strategy.

[0097] 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 the fitness of a randomly selected bird's nest.

[0098] In step S22, if yes, the updated candidate solution replaces the original bird's nest.

[0099] In step S23, inferior bird nests are discovered and discarded with a preset probability, new bird nests are introduced, and their fitness is calculated.

[0100] In step S24, the position of the bird's nest is continuously iterated until the iteration reaches a maximum value, and the bird's nest with the maximum fitness is obtained.

[0101] In step S25, 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.

[0102] In the present invention, when optimizing the influential factors in the man-machine-material-method loop, the data in the man-machine-material-method loop that influences the operation process can be used as a bird's nest, and the result of the corresponding operation process can be used as fitness, and the goal can be to obtain the maximum result data of the operation process. The range of the data in the man-machine-material-method loop is constrained. Within the constraint range, multiple candidate solutions that affect the result of the operation process can be randomly generated, and then the position of the candidate solution can be updated according to the Lévy flight strategy. According to the updated position, its fitness can be judged, and then it can be judged whether it is better than the fitness of a randomly selected bird's nest. If so, the updated candidate solution can replace the original bird's nest. Then, it is possible to discover and abandon the poor bird's nest with a preset probability, and introduce a new bird's nest. By continuously iterating the position of the bird's nest until the iteration reaches the maximum, the iteration can be terminated. After the iteration is completed, the bird's nest with the largest fitness can be obtained, and then the data in the original man-machine-material-method loop of the operation process can be adjusted according to the data corresponding to the bird's nest with the largest fitness, so that the optimization of the operation process can be completed.

[0103] In one embodiment of the present invention, Figure 5 As shown, the process of updating the candidate solution may include:

[0104] In step S26, the initial location of the bird's nest is obtained.

[0105] In step S27, the updated position of the bird's nest is calculated according to formula (3):

[0106]

[0107] in, Indicates the updated location of the bird's nest, x i represents the position of the bird's nest before the update, δ represents the step size factor, and L(λ) represents the random step size generated by the Lévy distribution.

[0108] 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).

[0109] In one embodiment of the present invention, the random step size in formula (3) may conform to the probability distribution of formula (4):

[0110]

[0111] Among them, w and z are random variables that obey the standard normal distribution, and λ represents the parameter of the Lévy distribution.

[0112] On the other hand, the present invention also provides a work process control system based on the full elements of man, machine, material, method and environment, the control system comprising: a data acquisition module and a background control module. The data acquisition module is used to obtain the full element information about the man, machine, material, method and environment. The background control module is used to execute the work process control method based on the full elements of man, machine, material, method and environment as described above according to the full element information.

[0113] On the one hand, the present invention also provides a processor for running a program, wherein the program, when run, is used to execute: a method for controlling an operation process based on all the elements of man, machine, material, method and environment as described above.

[0114] Through the above technical scheme, 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 first 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 the process control.

[0115] 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 adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0116] 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 box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes 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 generate 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 1 A device that provides the functions specified in a block or multiple blocks.

[0117] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate 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 A function specified in one or more boxes.

[0118] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0119] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0120] 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.

[0121] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. 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 memory (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 technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape 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 temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0122] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0123] The above are only 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 modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should 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 comprises: 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 flow to obtain the results of the operation flow; Obtain all-factor information on the human-machine-material-method-environment that affects the results of the operation process; Based on the full-factor information, determine whether it has an impact on the outcome of the operation process; In the case of influence, build a prediction model about the impact of all-factor information on the operation process; According to the result of the prediction model, all the information of the man-machine-material-method loop is optimized.

2. The control method according to claim 1, characterized in that: Obtain all-factor information about the human-machine-material-method environment that affects the results of the operation process, including: Obtaining the work status of the staff member 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 2, characterized in that: 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 autoregression model of each element of the man-machine-material-method loop and the result of the operation process is constructed: where Y t Indicates the current value of the result of the work process, Y t-1 represents the corresponding hysteresis value, X t Indicates the current value of a factor in the man-machine-material-method loop, X t-1 represents the corresponding hysteresis value, ε t and v t represents the error term, α1, α2 represent the constant term, β ij represents the regression coefficient; Selecting a preset lag order to construct the regression model; Assume that H0 is all X t The lag coefficient is zero, and the F test equation is constructed: Among them, RSSUR represents the residual sum of squares in the autoregressive model constructed by the results of the operation process, and RSSR represents the addition of the lag term X t The residual sum of squares of the autoregressive model constructed by one factor in the human-machine-material loop after the human-machine-material loop, n represents the number of lag periods, k represents the number of parameters to be estimated in the autoregressive model constructed by the human-machine-material loop, and q represents X t 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.

4. The control method according to claim 3, characterized in that: Where relevant, a predictive model is constructed on the impact of all-factor information on the operation process, including: Acquiring training data on factors in the man-machine-material-method loop that affect the outcome of the operation process; Preprocessing the training data; Sending the preprocessed training data and the result data of the operation flow 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.

5. The control method according to claim 4, characterized in that: According to the results of the prediction model, all the information of the man-machine-material-method loop is optimized, including: The data in the man-machine-material-method loop that has an impact on 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 generate multiple candidate solutions that affect the results of the operation process within the constraints; Updating 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 to determine whether its fitness 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 inferior 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.

6. The control method according to claim 5, characterized in that: The position of the candidate solution is updated according to the Lévy flight strategy, including: Get the initial location of the bird's nest; The updated location of the bird's nest is calculated according to formula (3): in, Indicates the updated location of the bird's nest, x i represents the position of the bird's nest before the update, δ represents the step size factor, and L(λ) represents the random step size generated by the Lévy distribution.

7. The control method according to claim 6, characterized in that: The random step size conforms to the probability distribution of formula (4): Among them, w and z are random variables that obey the standard normal distribution, and λ represents the parameter of the Lévy distribution.

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

9. A processor, characterized in that: Used to run a program, wherein the program is used to execute when run: an operation process control method based on all elements of man, machine, material, method and environment as described in any one of claims 1-7.

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