Digital twin-driven production full-process optimization method

Abstract

The invention relates to the technical field of intelligent production scheduling, and discloses a digital twin-driven production full-process optimization method, which comprises the following steps: in a first preset time period, acquiring personnel flow data and production efficiency data of K production lines in a target production system according to a fixed time interval T, and labels of the production lines with flow faults; extracting corresponding personnel flow data and production efficiency data to obtain a personnel flow data set and a corresponding production efficiency data set; based on the personnel flow data set and the corresponding production efficiency data set, constructing a characteristic relation model of personnel and production efficiency; taking the label of the mobility fault as a sample label of the corresponding sample data, and training to obtain a mobility prediction model; and adjusting the number of redundant personnel of each production line of the K production lines to obtain an updated target production system.

Description

Technical Field

[0001] The present invention relates to the field of intelligent production scheduling technology, and more specifically, to a full-process production optimization method driven by digital twins. Background Art

[0002] Digital twin is an emerging technology that enables prediction and optimization of production systems by connecting entities in the physical world and models in the virtual world in real time. For existing production systems, there is usually a certain amount of redundancy in production personnel to avoid liquidity failures in the production system caused by production personnel leaving their production positions. For the same production system, a fixed number of redundant production personnel are generally configured. However, due to differences in the physical fitness, cultural customs and other reasons of production personnel at the production site, the production efficiency and endurance of production personnel are also different. Therefore, there may be a problem of excess or insufficient redundant personnel in the production system, which increases the production cost of the production system or the uncertainty of liquidity failures in the production system. Summary of the invention

[0003] The present invention provides a digital twin-driven full-process production optimization method to solve the technical problems raised in the background technology.

[0004] The present invention provides a digital twin-driven full-process production optimization method, comprising:

[0005] Step 1: within a first preset time period, obtain the personnel flow data and production efficiency data of K production lines in the target production system at a fixed time interval T, as well as the labels of the flow failures of the production lines;

[0006] Personnel flow data includes: the maximum number of personnel required for each production line, the minimum number of personnel required, the number of personnel required to work at the post and the number of personnel actually working at the post, as well as the absenteeism rate, length of service and skill level of each person required to work;

[0007] Production efficiency data includes: the quantity of goods produced by each production line, the expected quantity of goods produced, the cumulative production time and the cumulative idle time, as well as the average yield rate of the corresponding goods produced;

[0008] Step 2: if the j-th production line does not have a mobility failure within the i-th time interval of the first preset time period, the corresponding personnel flow data and production efficiency data are extracted to obtain a personnel flow data set and a corresponding production efficiency data set, and a characteristic relationship model of personnel and production efficiency is constructed;

[0009] Step 3, extracting the personnel flow data and production efficiency data corresponding to the nth production line in the mth time interval of the first preset time period as sample data, and taking the label of the mobility failure as the sample label of the corresponding sample data, so as to train and obtain a mobility prediction model;

[0010] Step 4: Based on the feature relationship model and the mobility prediction model, the number of redundant personnel in each of the K production lines is adjusted to obtain an updated target production system.

[0011] Furthermore, the labels of liquidity failure of the production line include 0 and 1; if the production line has a liquidity failure, the label of the liquidity failure collected within the corresponding time interval is 1; if the production line does not have a liquidity failure, the label of the liquidity failure collected within the corresponding time interval is 0; wherein, the liquidity failure indicates that the quantity of goods produced per unit time of the production line is less than or equal to a preset failure threshold.

[0012] Furthermore, a characteristic relationship model between personnel and production efficiency is constructed, including:

[0013] K production lines are clustered based on their production tasks to obtain k types of production lines, and a characteristic relationship model between personnel and production efficiency is constructed for each type of production line;

[0014] The formula of the initial model of the feature relationship model is as follows: ;in, Represents the production output parameters of the feature relationship model. The production output parameters are obtained based on the production efficiency data. Represents the personnel input parameters of the feature relationship model. The personnel input parameters are obtained based on the personnel flow data. represents the first feature weight, represents the second feature weight, represents the natural base, represents feature bias;

[0015] Based on multiple production output parameters and personnel input parameters corresponding to the feature relationship model, the initial model of the feature relationship model is obtained by nonlinear least squares fitting. , and The final model of the feature relationship model is obtained by taking the value of .

[0016] Furthermore, the production output parameters include:

[0017] The production output parameters are calculated based on the production quantity of goods of each production line, the expected production quantity of goods, the cumulative production time and the cumulative idle time, as well as the average yield rate of the corresponding goods produced. The calculation formula of the production output parameters is as follows: ;

[0018] in, Indicates the quantity of goods produced, Indicates the expected production quantity of goods. represents the preset coefficient, represents the average yield rate, Indicates the cumulative production time, Indicates the accumulated idle time; the accumulated production time indicates the accumulated time from the start time to the current operation time of the production line, and the accumulated idle time indicates the accumulated time from the end time to the current end time of the production line.

[0019] Furthermore, personnel input parameters, including:

[0020] The personnel input parameters are calculated based on the maximum and minimum personnel requirements, the number of personnel required for each production line, the number of personnel actually required for each position, and the absence rate, length of service, and skill level of each required personnel. The calculation formula for the personnel input parameters is as follows: ;

[0021] in, , , and are user-defined coefficients, and , , and All are not 0, Indicates the number of people actually employed in the position. express The index of Indicates the number of people required for the position. represents the skill level of the personnel actually employed in the ith position, Indicates the maximum number of personnel required for the production line, Indicates the minimum number of personnel required for the production line, represents the length of service of the person actually employed in the ith position, It represents the absenteeism rate of the personnel actually attending the i-th position; among them, the skill level is quantified based on the person in charge of the production line.

[0022] Furthermore, the liquidity prediction model includes:

[0023] Based on the personnel flow data and production efficiency data corresponding to the nth production line in the mth time interval of the first preset time period, the personnel input parameters and the production output parameters are calculated respectively;

[0024] The personnel input parameters and production output parameters are spliced ​​to construct an initial vector, and the initial vector is normalized to obtain a flow feature vector;

[0025] The flow feature vector is used as the input of the liquidity prediction model, and the output of the liquidity prediction model is represented as the prediction label of the corresponding liquidity failure;

[0026] The liquidity prediction model includes an input layer, a hidden layer, and an output layer; and the hyperparameters of the hidden layer are optimized and updated through back propagation based on the cross entropy loss function.

[0027] Furthermore, the hidden layer of the liquidity prediction model includes R hidden units, each of which is constructed based on a one-dimensional convolutional neural network, and the calculation formula is as follows: ;

[0028] in, represents the output hidden state of the rth hidden unit, express The index of represents the one-dimensional convolution kernel of the rth hidden unit, represents the output hidden state of the r-1th hidden unit, represents the bias parameter of the rth hidden unit, Represents a convolution operation.

[0029] Furthermore, an updated target production system is obtained, including:

[0030] Step 81, initialize and generate a number of individuals, each of which represents a personnel flow data that meets the constraint conditions; wherein the number of actual personnel in the positions in the personnel flow data is randomly filled, and the remaining data in the personnel flow data changes accordingly according to the change of the actual number of personnel in the positions;

[0031] The constraints are as follows: the actual personnel at the post means that a number of personnel are randomly selected from the personnel who should be at the post as the actual personnel at the post; the personnel input parameters are calculated based on the individuals, and the production output parameters are calculated based on the individuals through the final model of the feature relationship model; among which, the personnel input parameters are greater than or equal to the preset personnel input parameter threshold, and the production output parameters are greater than or equal to the preset input parameter threshold;

[0032] Step 82, concatenate the personnel input parameters and production output parameters corresponding to the individual, and normalize them to obtain the individual vector;

[0033] Step 83, by inputting the individual vector into the liquidity prediction model, the liquidity prediction model outputs the prediction label corresponding to each individual vector;

[0034] Step 84, if the predicted label is 0, the individual is retained; if the predicted label is 1, the individual is updated;

[0035] Step 85, loop step 84 until the predicted labels of all individuals are 0;

[0036] Step 86, count the sum of the personnel input parameters and production output parameters corresponding to all individuals, and use the individual with the highest sum as the number of redundant personnel for the corresponding production line.

[0037] The beneficial effects of the present invention are as follows: by establishing a characteristic relationship model for personnel and production, as well as a prediction model for mobility failures, the efficient and stable operation of the production system is ensured, the problem of insufficient adaptability of static configuration in traditional methods is solved, the cost of redundant personnel configuration is significantly reduced, and production efficiency and system operation stability are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a flow chart of a digital twin-driven full-process production optimization method of the present invention. DETAILED DESCRIPTION

[0039] The subject matter described herein will now be discussed with reference to example embodiments. It should be understood that the discussion of these embodiments is only to enable those skilled in the art to better understand and implement the subject matter described herein, and the functions and arrangements of the elements discussed may be changed without departing from the scope of protection of the contents of this specification. Each example may omit, replace or add various processes or components as needed. In addition, the features described relative to some examples may also be combined in other examples.

[0040] like Figure 1 As shown, a digital twin-driven full-process production optimization method includes:

[0041] Step 1: within a first preset time period, obtain the personnel flow data and production efficiency data of K production lines in the target production system at a fixed time interval T, as well as the labels of the flow failures of the production lines;

[0042] Personnel flow data includes: the maximum number of personnel required for each production line, the minimum number of personnel required, the number of personnel required to work at the post and the number of personnel actually working at the post, as well as the absenteeism rate, length of service and skill level of each person required to work;

[0043] Production efficiency data includes: the quantity of goods produced by each production line, the expected quantity of goods produced, the cumulative production time and the cumulative idle time, as well as the average yield rate of the corresponding goods produced;

[0044] Step 2: if the j-th production line does not have a mobility failure within the i-th time interval of the first preset time period, the corresponding personnel flow data and production efficiency data are extracted to obtain a personnel flow data set and a corresponding production efficiency data set, and a characteristic relationship model of personnel and production efficiency is constructed;

[0045] Step 3, extracting the personnel flow data and production efficiency data corresponding to the nth production line in the mth time interval of the first preset time period as sample data, and taking the label of the mobility failure as the sample label of the corresponding sample data, so as to train and obtain a mobility prediction model;

[0046] Step 4: Based on the feature relationship model and the mobility prediction model, the number of redundant personnel in each of the K production lines is adjusted to obtain an updated target production system.

[0047] In one embodiment of the present invention, the label of the liquidity failure of the production line includes 0 and 1; if the production line has a liquidity failure, the label of the liquidity failure collected in the corresponding time interval is 1; if the production line does not have a liquidity failure, the label of the liquidity failure collected in the corresponding time interval is 0; wherein, the liquidity failure indicates that the quantity of goods produced per unit time of the production line is less than or equal to a preset failure threshold.

[0048] Specifically, the binary labels of "0" and "1" are intuitive and easy to handle, which makes the status of liquidity failure clearly identified in data processing and model building. This greatly reduces the difficulty of complex data classification during model training and enhances the efficiency of data analysis.

[0049] In one embodiment of the present invention, constructing a characteristic relationship model between personnel and production efficiency includes:

[0050] K production lines are clustered based on their production tasks to obtain k types of production lines, and a characteristic relationship model between personnel and production efficiency is constructed for each type of production line;

[0051] The formula of the initial model of the feature relationship model is as follows: ;in, Represents the production output parameters of the feature relationship model. The production output parameters are obtained based on the production efficiency data. Represents the personnel input parameters of the feature relationship model. The personnel input parameters are obtained based on the personnel flow data. represents the first feature weight, represents the second feature weight, represents the natural base, represents feature bias;

[0052] Based on multiple production output parameters and personnel input parameters corresponding to the feature relationship model, the initial model of the feature relationship model is obtained by nonlinear least squares fitting. , and The final model of the feature relationship model is obtained by taking the value of .

[0053] Specifically, by constructing a characteristic relationship model, the personnel flow data is linked to the production efficiency data, and a mathematical relationship between the two is established. The mathematical relationship provides a systematic and quantitative method to accurately describe the characteristic relationship between personnel allocation and production efficiency. In view of the differences in different production tasks, the production lines are clustered and characteristic relationship models are established separately, avoiding the errors that may be introduced by unified modeling, fully considering the differences in the requirements of different types of production lines for personnel allocation, and improving the applicability of the model in diversified production scenarios. In view of the differences in different production tasks, the production lines are clustered and characteristic relationship models are established separately, avoiding the errors that may be introduced by unified modeling. The characteristic relationship model uses nonlinear least squares method to fit the personnel input parameters and the production efficiency output parameters, thereby capturing the nonlinear dependency between the two.

[0054] In one embodiment of the present invention, the production output parameters include:

[0055] The production output parameters are calculated based on the production quantity of goods of each production line, the expected production quantity of goods, the cumulative production time and the cumulative idle time, as well as the average yield rate of the corresponding goods produced. The calculation formula of the production output parameters is as follows: ;

[0056] in, Indicates the quantity of goods produced, Indicates the expected production quantity of goods. represents the preset coefficient, represents the average yield rate, Indicates the cumulative production time, Indicates the accumulated idle time; the accumulated production time indicates the accumulated time from the start time to the current operation time of the production line, and the accumulated idle time indicates the accumulated time from the end time to the current end time of the production line.

[0057] Specifically, through the comprehensive processing of production efficiency data, a method for quantifying production output parameters is proposed, and the key factors affecting production efficiency and their effects are clarified. The main function of this method is to accurately analyze and summarize complex production data, simplify the evaluation process of production efficiency, and make production efficiency intuitively reflected by one parameter. This method helps managers quickly understand the actual operating status of the production line, especially in multiple production lines or complex scenarios, and can more effectively compare the efficiency levels of various production units. In addition, through unified production output parameters, optimizing and adjusting configuration decisions in the production process becomes more scientific and reliable.

[0058] In one embodiment of the present invention, the personnel input parameters including:

[0059] The personnel input parameters are calculated based on the maximum and minimum personnel requirements, the number of personnel required for each production line, the number of personnel actually required for each position, and the absence rate, length of service, and skill level of each required personnel. The calculation formula for the personnel input parameters is as follows: ;

[0060] in, , , and are user-defined coefficients, and , , and All are not 0, Indicates the number of people actually employed in the position. express The index of Indicates the number of people required for the position. represents the skill level of the personnel actually employed in the ith position, Indicates the maximum number of personnel required for the production line, Indicates the minimum number of personnel required for the production line, represents the length of service of the person actually employed in the ith position, It represents the absenteeism rate of the personnel actually attending the i-th position; among them, the skill level is quantified based on the person in charge of the production line.

[0061] Specifically, through the multi-dimensional comprehensive calculation of personnel flow data, a method that can quantify the degree of match between personnel allocation and production line demand is constructed. This method takes into account multiple key factors, including the actual number of positions, the number of positions required, employee skill level, length of service, and absenteeism, and accurately describes the relationship between personnel allocation and actual production needs. Its significant advantage is that it can not only reflect whether the current number of personnel allocation is reasonable, but also evaluate the comprehensive impact of personnel quality (such as skills and experience) and stability (such as absenteeism). This quantified personnel input parameter is used as an input for model prediction and optimization adjustment, providing a basic guarantee for the subsequent optimization of the entire production process.

[0062] In one embodiment of the present invention, the liquidity prediction model includes:

[0063] Based on the personnel flow data and production efficiency data corresponding to the nth production line in the mth time interval of the first preset time period, the personnel input parameters and the production output parameters are calculated respectively;

[0064] The personnel input parameters and production output parameters are spliced ​​to construct an initial vector, and the initial vector is normalized to obtain a flow feature vector;

[0065] The flow feature vector is used as the input of the liquidity prediction model, and the output of the liquidity prediction model is represented as the prediction label of the corresponding liquidity failure;

[0066] The liquidity prediction model includes an input layer, a hidden layer, and an output layer; and the hyperparameters of the hidden layer are optimized and updated through back propagation based on the cross entropy loss function.

[0067] Specifically, the mobility prediction model accurately captures the complex relationship between production parameters and personnel parameters and mobility failures based on two core data dimensions. The model fully considers the multi-dimensional influencing factors of production efficiency and personnel allocation in the production system, and extracts and analyzes the features of their interactive relationship through deep learning technology, which can reveal the potential nonlinear impact of production efficiency and personnel mobility on production line stability.

[0068] In one embodiment of the present invention, the hidden layer of the liquidity prediction model includes R hidden units, each of which is constructed based on a one-dimensional convolutional neural network, and the calculation formula is as follows: ;

[0069] in, represents the output hidden state of the rth hidden unit, express The index of represents the one-dimensional convolution kernel of the rth hidden unit, represents the output hidden state of the r-1th hidden unit, represents the bias parameter of the rth hidden unit, Represents a convolution operation.

[0070] Specifically, the hidden layer design of the one-dimensional convolutional neural network significantly enhances the model's ability to capture the complex relationship between production parameters and personnel parameters. Its role is to refine the deep correlation between multi-dimensional data, provide an efficient feature extraction mechanism for liquidity fault prediction, and improve the accuracy of prediction.

[0071] In one embodiment of the present invention, obtaining an updated target production system includes:

[0072] Step 81, initialize and generate a number of individuals, each of which represents a personnel flow data that meets the constraint conditions; wherein the number of actual personnel in the positions in the personnel flow data is randomly filled, and the remaining data in the personnel flow data changes accordingly according to the change of the actual number of personnel in the positions;

[0073] The constraints are as follows: the actual personnel at the post means that a number of personnel are randomly selected from the personnel who should be at the post as the actual personnel at the post; the personnel input parameters are calculated based on the individuals, and the production output parameters are calculated based on the individuals through the final model of the feature relationship model; among which, the personnel input parameters are greater than or equal to the preset personnel input parameter threshold, and the production output parameters are greater than or equal to the preset input parameter threshold;

[0074] Step 82, concatenate the personnel input parameters and production output parameters corresponding to the individual, and normalize them to obtain the individual vector;

[0075] Step 83, by inputting the individual vector into the liquidity prediction model, the liquidity prediction model outputs the prediction label corresponding to each individual vector;

[0076] Step 84, if the predicted label is 0, the individual is retained; if the predicted label is 1, the individual is updated;

[0077] Step 85, loop step 84 until the predicted labels of all individuals are 0;

[0078] Step 86, count the sum of the personnel input parameters and production output parameters corresponding to all individuals, and use the individual with the highest sum as the number of redundant personnel for the corresponding production line.

[0079] Specifically, the optimization method of the production system is dynamically adjusted by generating multiple individuals and iteratively optimizing. First, the generation of individuals is based on random filling, and each individual represents a possible personnel flow configuration scheme. This random initialization ensures the diversity of the optimization process and covers a wide range of schemes. Each generated individual will automatically adjust the relevant parameters according to the change of the actual number of people in the position, but it needs to meet specific constraints, such as the actual number of people in the position must be derived from the number of people who should be in the position, and ensure the rationality of the configuration. Subsequently, each individual will calculate the corresponding production output parameters based on the feature relationship model, and compare them with the thresholds of the personnel input parameters and production output parameters set by the system to screen out the configuration schemes that meet the conditions. This process can quickly eliminate the schemes that do not meet the basic production requirements and focus the optimization on more efficient and reasonable configurations. Then, all qualified individuals will be input into the liquidity prediction model to evaluate the liquidity failure risk of each configuration scheme. The model outputs a prediction label based on the relationship between production parameters and personnel parameters. If the prediction result shows a failure risk (label is 1), the individual needs to be further updated and adjusted; if there is no failure risk (label is 0), the individual is retained as a potential optimization scheme. This evaluation mechanism ensures that each round of screening can eliminate configurations with higher potential risks, while providing higher reliability for the optimized production system. During the optimization process, through repeated iterations, the parameters of the individuals are continuously adjusted so that each individual can eventually meet the requirements of no liquidity failure. This dynamic optimization mechanism combines real-time data and intelligent algorithms, enabling the production system to respond quickly to environmental changes and always maintain efficient operation. After the iteration, the configuration scheme with the highest production efficiency and the best resource utilization among all individuals will be selected as the final optimal solution. This screening process based on quantitative analysis ensures the scientific nature of the optimization scheme while avoiding the subjective errors caused by traditional empirical decision-making. Through this optimization method, the production system can effectively reduce the cost waste caused by unreasonable personnel configuration, reduce the risk of liquidity failure, and improve overall production efficiency and stability. In addition, the random initialization and dynamic iteration characteristics of this method make it highly adaptable and can be widely used in various production environments. Whether it is labor-intensive or technology-intensive industries, customized optimization can be achieved by adjusting constraints. This optimization scheme provides a scientific, flexible and efficient solution for production management, with significant practical application value.

[0080] The above describes an embodiment of the present embodiment, but the present embodiment is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present embodiment, ordinary technicians in this field can also make many forms, all of which are protected by the present embodiment.

Claims

1. A digital twin-driven production full-process optimization method, characterized in that: include: Step 1: within a first preset time period, obtain the personnel flow data and production efficiency data of K production lines in the target production system at a fixed time interval T, as well as the labels of the flow failures of the production lines; Personnel flow data includes: the maximum number of personnel required for each production line, the minimum number of personnel required, the number of personnel required to work at the post and the number of personnel actually working at the post, as well as the absenteeism rate, length of service and skill level of each person required to work; Production efficiency data includes: the quantity of goods produced by each production line, the expected quantity of goods produced, the cumulative production time and the cumulative idle time, as well as the average yield rate of the corresponding goods produced; Step 2: if the j-th production line does not have a mobility failure within the i-th time interval of the first preset time period, the corresponding personnel flow data and production efficiency data are extracted to obtain a personnel flow data set and a corresponding production efficiency data set, and a characteristic relationship model of personnel and production efficiency is constructed; Step 3, extracting the personnel flow data and production efficiency data corresponding to the nth production line in the mth time interval of the first preset time period as sample data, and taking the label of the mobility failure as the sample label of the corresponding sample data, so as to train and obtain a mobility prediction model; Step 4: Based on the feature relationship model and the mobility prediction model, the number of redundant personnel in each of the K production lines is adjusted to obtain an updated target production system.

2. The method for optimizing the entire production process driven by a digital twin according to claim 1, characterized in that: The labels of liquidity failure of the production line include 0 and 1; if the production line has a liquidity failure, the label of the liquidity failure collected in the corresponding time interval is 1; if the production line does not have a liquidity failure, the label of the liquidity failure collected in the corresponding time interval is 0; wherein, the liquidity failure indicates that the quantity of goods produced per unit time of the production line is less than or equal to the preset failure threshold.

3. The method for optimizing the entire production process driven by a digital twin according to claim 1, characterized in that: Construct a characteristic relationship model between personnel and production efficiency, including: K production lines are clustered based on their production tasks to obtain k types of production lines, and a characteristic relationship model between personnel and production efficiency is constructed for each type of production line; The formula of the initial model of the feature relationship model is as follows: ;in, Represents the production output parameters of the feature relationship model. The production output parameters are obtained based on the production efficiency data. Represents the personnel input parameters of the feature relationship model. The personnel input parameters are obtained based on the personnel flow data. represents the first feature weight, represents the second feature weight, represents the natural base, represents feature bias; Based on multiple production output parameters and personnel input parameters corresponding to the feature relationship model, the initial model of the feature relationship model is obtained by nonlinear least squares fitting. , and The final model of the feature relationship model is obtained by taking the value of .

4. The method for optimizing the entire production process driven by a digital twin according to claim 3, characterized in that: Production output parameters, including: The production output parameters are calculated based on the production quantity of goods of each production line, the expected production quantity of goods, the cumulative production time and the cumulative idle time, as well as the average yield rate of the corresponding goods produced. The calculation formula of the production output parameters is as follows: ； in, Indicates the quantity of goods produced, Indicates the expected production quantity of goods. represents the preset coefficient, represents the average yield rate, Indicates the cumulative production time, Indicates the accumulated idle time; the accumulated production time indicates the accumulated time from the start time to the current operation time of the production line, and the accumulated idle time indicates the accumulated time from the end time to the current end time of the production line.

5. The method for optimizing the entire production process driven by a digital twin according to claim 3, characterized in that: Personnel input parameters, including: The personnel input parameters are calculated based on the maximum and minimum personnel requirements, the number of personnel required for each production line, the number of personnel actually required for each position, and the absence rate, length of service, and skill level of each required personnel. The calculation formula for the personnel input parameters is as follows: ； in, , , and are user-defined coefficients, and , , and All are not 0, Indicates the number of people actually employed in the position. express The index of Indicates the number of people required for the position. represents the skill level of the personnel actually employed in the ith position, Indicates the maximum number of personnel required for the production line, Indicates the minimum number of personnel required for the production line, represents the length of service of the person actually employed in the ith position, It represents the absenteeism rate of the personnel actually attending the i-th position; among them, the skill level is quantified based on the person in charge of the production line.

6. The method for optimizing the entire production process driven by a digital twin according to claim 3, characterized in that: Liquidity prediction model, including: Based on the personnel flow data and production efficiency data corresponding to the nth production line in the mth time interval of the first preset time period, the personnel input parameters and the production output parameters are calculated respectively; The personnel input parameters and production output parameters are spliced ​​to construct an initial vector, and the initial vector is normalized to obtain a flow feature vector; The flow feature vector is used as the input of the liquidity prediction model, and the output of the liquidity prediction model is represented as the prediction label of the corresponding liquidity failure; The liquidity prediction model includes an input layer, a hidden layer, and an output layer; and the hyperparameters of the hidden layer are optimized and updated through back propagation based on the cross entropy loss function.

7. The method for optimizing the entire production process driven by a digital twin according to claim 6, characterized in that: The hidden layer of the liquidity prediction model includes R hidden units, each of which is built based on a one-dimensional convolutional neural network. The calculation formula is as follows: ； in, represents the output hidden state of the rth hidden unit, express The index of represents the one-dimensional convolution kernel of the rth hidden unit, represents the output hidden state of the r-1th hidden unit, represents the bias parameter of the rth hidden unit, Represents a convolution operation.

8. The method for optimizing the entire production process driven by a digital twin according to claim 6, characterized in that: The target production system that was updated includes: Step 81, initialize and generate a number of individuals, each of which represents a personnel flow data that meets the constraint conditions; wherein the number of actual personnel in the positions in the personnel flow data is randomly filled, and the remaining data in the personnel flow data changes accordingly according to the change of the actual number of personnel in the positions; The constraints are as follows: the actual personnel at the post means that a number of personnel are randomly selected from the personnel who should be at the post as the actual personnel at the post; the personnel input parameters are calculated based on the individuals, and the production output parameters are calculated based on the individuals through the final model of the feature relationship model; among which, the personnel input parameters are greater than or equal to the preset personnel input parameter threshold, and the production output parameters are greater than or equal to the preset input parameter threshold; Step 82, concatenate the personnel input parameters and production output parameters corresponding to the individual, and normalize them to obtain the individual vector; Step 83, by inputting the individual vector into the liquidity prediction model, the liquidity prediction model outputs the prediction label corresponding to each individual vector; Step 84, if the predicted label is 0, the individual is retained; if the predicted label is 1, the individual is updated; Step 85, loop step 84 until the predicted labels of all individuals are 0; Step 86, count the sum of the personnel input parameters and production output parameters corresponding to all individuals, and use the individual with the highest sum as the number of redundant personnel for the corresponding production line.

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