AI agent-driven digital twin factory construction interaction method and system

Abstract

The present application relates to the technical field of data processing analysis, and more particularly to a digital twin factory construction interaction method and system driven by an AI intelligent agent, which analyzes the disturbance degree of the insertion of an emergency order on each production line through production line dynamic data, equipment use time series data and order feature data; constructs an order adaptation degree analysis model through production line dynamic data and order feature data to analyze the adaptation degree of the emergency order and each production line; imports the disturbance degree analysis result of the insertion of the emergency order on each production line and the adaptation degree analysis result of the emergency order and each production line into a production line dynamic bearing capacity evaluation model to evaluate the dynamic bearing capacity of each production line when the emergency order is inserted; and inserts the emergency order according to the evaluation result of the dynamic bearing capacity of each production line when the emergency order is inserted. The present application can avoid production capacity conflicts or efficiency losses caused by blind order insertion, reduce resource mismatching costs and improve the emergency order delivery response speed.

Description

Technical Field

[0001] This invention relates to the field of data processing and analysis technology, and in particular to an interactive method and system for constructing a digital twin factory based on AI-driven intelligent agents. Background Technology

[0002] With the advancement of Industry 4.0 and the deepening of the concept of intelligent manufacturing, traditional manufacturing is undergoing unprecedented transformation. To cope with increasingly fierce market competition, traditional production plants urgently need to build a highly flexible and intelligently responsive production system. Digital twin technology, as a key technology for achieving deep integration of the physical and information worlds, provides an important pathway for the intelligent upgrading of factories by constructing virtual mirror images of physical entities. Traditional digital twin factory construction is often limited to static modeling and offline analysis, making it difficult to capture dynamic changes in the production process in real time. Especially when dealing with unexpected situations such as the insertion of urgent orders, its decision-making mechanism still relies on manual intervention or preset rules, lacking the ability to learn autonomously. At the same time, traditional digital twin systems have significant shortcomings in data fusion; multiple data points in the factory production process are often processed in isolation, failing to form an organic whole, making it inadequate for handling complex production scenarios such as the insertion of urgent orders.

[0003] Meanwhile, existing methods for constructing digital twin factories cannot quantify the cascading effects of sudden order insertions on production line timing conflicts and resource competition in real time. This causes the order scheduling system to frequently fall into local optimization dilemmas during dynamic conflicts. Furthermore, existing technologies typically integrate equipment capacity and order characteristics by manually setting weights, ignoring the nonlinear saturation characteristics of buffer zones on the production line and the entropy increase effect of production changeover fluctuations. This results in a consistently high misjudgment rate for highly flexible production lines during the analysis process.

[0004] To address these issues, this application presents a method and system for constructing and interacting with a digital twin factory driven by an AI agent. Summary of the Invention

[0005] To overcome the shortcomings and deficiencies of existing technologies, this invention provides an interactive method and system for constructing a digital twin factory based on AI-driven intelligent agents. By analyzing the degree of disturbance to each production line caused by the insertion of urgent orders and the compatibility between urgent orders and each production line, the method achieves an assessment of the dynamic capacity of each production line when urgent orders are inserted. Based on the assessment results of the dynamic capacity of each production line when urgent orders are inserted, the method inserts urgent orders accordingly. This avoids capacity conflicts or efficiency losses caused by blindly inserting orders, reduces resource mismatch costs, and improves the delivery response speed of urgent orders.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, embodiments of the present invention provide an interactive method for constructing a digital twin factory based on an AI agent, comprising the following steps:

[0008] S1. Obtain production line dynamic data and equipment usage time sequence data of each production line through AI intelligent agent, and at the same time obtain order characteristic data of urgent orders;

[0009] S2. Based on production line dynamic data, equipment usage time series data, and order characteristic data, construct an order insertion disturbance analysis model to analyze the disturbance degree of urgent order insertion on each production line.

[0010] S3. Based on production line dynamic data and order characteristic data, construct an order adaptability analysis model to analyze the adaptability of urgent orders with each production line;

[0011] S4. Construct a production line capacity assessment model, and import the analysis results of the disturbance degree of the insertion of emergency orders to each production line and the analysis results of the compatibility degree between emergency orders and each production line into the production line dynamic capacity assessment model to assess the dynamic capacity of each production line when emergency orders are inserted.

[0012] S5. Based on the assessment results of the dynamic carrying capacity of each production line when inserting emergency orders, insert emergency orders.

[0013] In a preferred embodiment of the present invention, step S2, analyzing the degree of disruption to each production line caused by the insertion of emergency orders, includes the following specific steps:

[0014] S21. Extract production line dynamic data, equipment usage time sequence data, and order characteristic data;

[0015] S22. Based on production line dynamic data, equipment usage time series data, and order characteristic data, construct an order insertion disturbance analysis model to analyze the disturbance degree of emergency order insertion on each production line and obtain the analysis results of the disturbance degree of emergency order insertion on each production line.

[0016] The formula for calculating the disturbance to the current production line caused by the insertion of urgent orders is as follows:

[0017] ;

[0018] In the formula, RD represents the degree of disturbance to the current production line caused by the insertion of an emergency order, Sc represents the degree of timing conflict to the current production line caused by the insertion of an emergency order, and Zz represents the degree of resource competition to the current production line caused by the insertion of an emergency order.

[0019] In a preferred embodiment of the present invention, the process of constructing the order insertion disturbance analysis model in step S22 includes the following specific steps:

[0020] S221. Based on production line dynamic data, equipment usage time sequence data, and order characteristic data, analyze the degree of time sequence conflict of the insertion of emergency orders on the current production line, and obtain the analysis results of the degree of time sequence conflict of the insertion of emergency orders on the current production line.

[0021] The formula for calculating the degree of timing conflict is as follows:

[0022] ;

[0023] In the formula, Sc represents the degree of timing conflict between the insertion of an urgent order and the current production line. , where is the historical emergency order arrival factor of the current production line in the production line dynamic data, D is the number of devices required to produce emergency orders on the current production line in the order feature data, Txd is the required time for emergency orders to be produced on the d-th device in the order feature data, Tyd is the idle time of the d-th device required to produce emergency orders in the device usage time sequence data, max() is the function to take the maximum value in parentheses, and d is any item from 1 to D;

[0024] S222. Based on production line dynamic data and order characteristic data, analyze the degree of resource competition for the current production line caused by the insertion of urgent orders, and obtain the analysis results of the degree of resource competition for the current production line caused by the insertion of urgent orders.

[0025] The formula for calculating the intensity of resource competition is as follows:

[0026] ;

[0027] In the formula, Zz represents the intensity of resource competition for the current production line caused by the insertion of an urgent order, and Pj represents the probability index of resource conflict for the j-th type required by the urgent order on the current production line. This indicates that the data within the parentheses are multiplied together, where n is the number of resource types required for urgent orders on the current production line in the order feature data, and j is any one of 1 to n;

[0028] The formula for calculating the conflict probability index is as follows:

[0029] ;

[0030] In the formula, Pj is the probability index of the use conflict of the j-th resource required by the urgent order on the current production line, rj is the demand of the j-th resource required by the urgent order on the current production line in the order feature data, Aj is the current inventory of the j-th resource required by the urgent order on the current production line in the production line dynamic data, and Uj is the historical average consumption rate of the j-th resource required by the urgent order on the current production line in the production line dynamic data.

[0031] In a preferred embodiment of the present invention, step S3, analyzing the compatibility between emergency orders and various production lines, includes the following specific steps:

[0032] S31. Extract production line dynamic data and order feature data;

[0033] S32. Based on production line dynamic data and order characteristic data, construct an order adaptability analysis model to analyze the adaptability of urgent orders with each production line and obtain the results of the adaptability analysis of urgent orders with each production line.

[0034] The formula for calculating the compatibility between urgent orders and the current production line is as follows:

[0035] ;

[0036] In the formula, SP represents the compatibility between the emergency order and the current production line, Hx represents the buffer absorption capacity of the current production line when an emergency order is inserted, Rh represents the flexible production changeover capacity of the current production line when an emergency order is inserted, Rt represents the total demand of all resources required by the emergency order on the current production line in the order feature data, and At represents the total current inventory of all resources required by the emergency order on the current production line in the production line dynamic data.

[0037] In a preferred embodiment of the present invention, the process of constructing the order compatibility analysis model in step S32 includes the following specific steps:

[0038] S321. Based on production line dynamic data, analyze the buffer absorption capacity of each production line when an emergency order is inserted, and obtain the analysis results of the buffer absorption capacity of each production line when an emergency order is inserted.

[0039] S322. Based on production line dynamic data and order characteristic data, analyze the flexible production changeover capability of each production line when an emergency order is inserted, and obtain the analysis results of the flexible production changeover capability of each production line when an emergency order is inserted.

[0040] In a preferred embodiment of the present invention, step S4, which involves constructing a production line capacity assessment model, includes the following specific steps:

[0041] S41. Extract the analysis results of the impact of emergency order insertion on each production line and the analysis results of the compatibility between emergency orders and each production line.

[0042] S42. Based on the analysis results of the disturbance degree of each production line caused by the insertion of emergency orders and the analysis results of the compatibility degree between emergency orders and each production line, the dynamic carrying capacity of each production line is evaluated when emergency orders are inserted, and the evaluation results of the dynamic carrying capacity of each production line when emergency orders are inserted are obtained.

[0043] The formula for evaluating dynamic bearing capacity is as follows:

[0044] ;

[0045] In the formula, DC represents the dynamic capacity of the production line when an emergency order is inserted.

[0046] In a preferred embodiment of the present invention, step S5 inserts emergency orders based on the evaluation results of the dynamic carrying capacity of each production line when the emergency orders are inserted, including the following specific steps:

[0047] S51. Obtain the dynamic capacity assessment results of all production lines when emergency orders are inserted;

[0048] S52. Extract the production line corresponding to the maximum value in the dynamic carrying capacity assessment results of all production lines when inserting emergency orders, and use it as the production line for inserting emergency orders.

[0049] Secondly, embodiments of the present invention also provide an interactive system for building a digital twin factory based on AI-driven intelligent agents, including:

[0050] The data acquisition module is used to acquire production line dynamic data and equipment usage time sequence data of each production line through AI intelligent agents, and at the same time acquire order characteristic data of urgent orders;

[0051] The order insertion disturbance analysis module is used to build an order insertion disturbance analysis model based on production line dynamic data, equipment usage time series data, and order characteristic data, and to analyze the disturbance of urgent orders to each production line.

[0052] The order compatibility analysis module is used to build an order compatibility analysis model based on production line dynamic data and order feature data, and to analyze the compatibility between urgent orders and each production line.

[0053] The production line dynamic capacity assessment module is used to build a production line capacity assessment model. It imports the analysis results of the disturbance of emergency orders to each production line and the analysis results of the compatibility between emergency orders and each production line into the production line dynamic capacity assessment model to assess the dynamic capacity of each production line when emergency orders are inserted.

[0054] The emergency order insertion module is used to insert emergency orders based on the assessment results of the dynamic capacity of each production line at the time of insertion.

[0055] The control module is used to control the operation of the data acquisition module, the order insertion disturbance analysis module, the order adaptation analysis module, the production line dynamic capacity assessment module, and the emergency order insertion module.

[0056] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0057] This invention analyzes the disruption caused by the insertion of urgent orders to each production line using dynamic production line data, equipment usage time-series data, and order characteristic data. It then constructs an order compatibility analysis model based on the dynamic production line data and order characteristic data to analyze the compatibility between urgent orders and each production line. The results of the analysis of the disruption caused by the insertion of urgent orders and the analysis of the compatibility between urgent orders and each production line are imported into a dynamic capacity assessment model to evaluate the dynamic capacity of each production line when urgent orders are inserted. Based on the evaluation results of the dynamic capacity of each production line when urgent orders are inserted, the urgent orders are inserted accordingly. This approach avoids capacity conflicts or efficiency losses caused by blindly inserting orders, reduces resource misallocation costs, and improves the delivery response speed of urgent orders. Attached Figure Description

[0058] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0059] Figure 1 A schematic diagram illustrating the overall process of constructing an interactive method for a digital twin factory based on AI agent-driven technology, as described in this invention.

[0060] Figure 2 A flowchart of step S2 in the AI ​​agent-driven digital twin factory construction interaction method of the present invention;

[0061] Figure 3 A flowchart of step S3 in the AI ​​agent-driven digital twin factory construction interaction method of the present invention;

[0062] Figure 4 This is a schematic diagram of the structure of the interactive system for building a digital twin factory based on AI intelligent agents according to the present invention. Detailed Implementation

[0063] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0064] Example 1

[0065] like Figure 1 As shown, this embodiment provides an interactive method for building a digital twin factory based on AI-driven intelligent agents, which specifically includes the following steps:

[0066] S1. Obtain production line dynamic data and equipment usage time sequence data of each production line through AI intelligent agent, and at the same time obtain order characteristic data of urgent orders;

[0067] S2. Based on production line dynamic data, equipment usage time series data, and order characteristic data, construct an order insertion disturbance analysis model to analyze the disturbance degree of urgent order insertion on each production line.

[0068] S3. Based on production line dynamic data and order characteristic data, construct an order adaptability analysis model to analyze the adaptability of urgent orders with each production line;

[0069] S4. Construct a production line capacity assessment model, and import the analysis results of the disturbance degree of the insertion of emergency orders to each production line and the analysis results of the compatibility degree between emergency orders and each production line into the production line dynamic capacity assessment model to assess the dynamic capacity of each production line when emergency orders are inserted.

[0070] S5. Based on the assessment results of the dynamic carrying capacity of each production line when inserting emergency orders, insert emergency orders.

[0071] In this embodiment, as Figure 2 As shown, step S2 analyzes the degree of disruption to each production line caused by the insertion of emergency orders, including the following specific steps:

[0072] S21. Extract production line dynamic data, equipment usage time sequence data, and order characteristic data;

[0073] S22. Based on production line dynamic data, equipment usage time series data, and order characteristic data, construct an order insertion disturbance analysis model to analyze the disturbance degree of emergency order insertion on each production line and obtain the analysis results of the disturbance degree of emergency order insertion on each production line.

[0074] The formula for calculating the disturbance to the current production line caused by the insertion of urgent orders is as follows:

[0075] ;

[0076] In the formula, RD represents the degree of disturbance to the current production line caused by the insertion of an emergency order, Sc represents the degree of timing conflict to the current production line caused by the insertion of an emergency order, and Zz represents the degree of resource competition to the current production line caused by the insertion of an emergency order.

[0077] For example, this embodiment addresses the fundamental deficiency of existing technologies that separately evaluate two types of disturbances by constructing a dual-disturbance coupled model of timing conflicts and resource competition. Linear terms It accurately captures the synergistic deterioration effect of "equipment congestion leading to resource stagnation"; square root term The Euclidean norm ensures that the high risk of a single dimension is not spread out. Specifically, when a conflict in a certain device surges, even if the resource competition is slow, the degree of disturbance can still be accurately captured.

[0078] In this embodiment, the construction process of the order insertion disturbance analysis model in step S22 includes the following specific steps:

[0079] S221. Based on production line dynamic data, equipment usage time sequence data, and order characteristic data, analyze the degree of time sequence conflict of the insertion of emergency orders on the current production line, and obtain the analysis results of the degree of time sequence conflict of the insertion of emergency orders on the current production line.

[0080] The formula for calculating the degree of timing conflict is as follows:

[0081] ;

[0082] In the formula, Sc represents the degree of timing conflict between the insertion of an urgent order and the current production line. , where is the historical emergency order arrival factor of the current production line in the production line dynamic data, D is the number of devices required to produce emergency orders on the current production line in the order feature data, Txd is the required time for emergency orders to be produced on the d-th device in the order feature data, Tyd is the idle time of the d-th device required to produce emergency orders in the device usage time sequence data, max() is the function to take the maximum value in parentheses, and d is any item from 1 to D;

[0083] Among them, the historical urgent order arrival factor of the current production line Where N is the number of emergency orders received by the current production line during its operating cycle in the production line dynamic data, and T is the operating cycle duration of the current production line in the production line dynamic data;

[0084] For example, this embodiment is used to quantify the intensity of production line timing conflicts caused by the insertion of urgent orders. Through in-depth analysis of time window conflicts in multi-device collaborative production, this embodiment uses an exponential decay function structure to ensure that the output value can both accurately capture high conflict risks and avoid the problem of distortion due to extreme values. (Using the numerator term...) The calculation quantifies the positive gap between the equipment demand duration of an order and the available equipment time, thus filtering out the interference of invalid negative values ​​on the calculation results. Simultaneously, the impact of differences in equipment size is eliminated by measuring equipment idle time. Specifically, this embodiment uses the historical arrival factor of emergency orders on the production line as a weighting coefficient for the conflict probability, accurately describing the actual production situation where production lines with high-frequency emergency order insertions are more sensitive to the degree of timing conflicts caused by new orders. Furthermore, this embodiment aggregates the conflicts of emergency order insertion on discrete equipment into the degree of timing conflict of emergency order insertion on the production line, breaking through the limitation of traditional scheduling systems that only focus on single-equipment conflicts. This provides a decision-making basis for dynamically adjusting equipment time windows and optimizing production cycle time, driving the digital twin system to re-plan the schedule and avoid production interruptions.

[0085] S222. Based on production line dynamic data and order characteristic data, analyze the degree of resource competition for the current production line caused by the insertion of urgent orders, and obtain the analysis results of the degree of resource competition for the current production line caused by the insertion of urgent orders.

[0086] The formula for calculating the intensity of resource competition is as follows:

[0087] ;

[0088] In the formula, Zz represents the intensity of resource competition for the current production line caused by the insertion of an urgent order, and Pj represents the probability index of resource conflict for the j-th type required by the urgent order on the current production line. This indicates that the data within the parentheses are multiplied together, where n is the number of resource types required for urgent orders on the current production line in the order feature data, and j is any one of 1 to n;

[0089] The formula for calculating the conflict probability index is as follows:

[0090] ;

[0091] In the formula, Pj is the probability index of the use conflict of the j-th resource required by the urgent order on the current production line, rj is the demand of the j-th resource required by the urgent order on the current production line in the order feature data, Aj is the current inventory of the j-th resource required by the urgent order on the current production line in the production line dynamic data, and Uj is the historical average consumption rate of the j-th resource required by the urgent order on the current production line in the production line dynamic data.

[0092] The formula for calculating the historical average utilization rate is as follows:

[0093] ;

[0094] In the formula, This represents the total consumption of the j-th type of resource required for urgent orders on the current production line within the operating cycle, as shown in the production line dynamic data.

[0095] For example, this embodiment calculates the probability of at least one resource conflict by using a formula for calculating the intensity of resource competition, thus addressing the risk of underestimating the severity of conflict in the traditional weighted average method. In the calculation formula using the conflict probability index provided in this embodiment, when resources are sufficient, i.e. When resources are scarce, a conflict probability index of 0 is used to avoid false alarms; when resources are scarce, i.e. At that time, through Precisely quantify the absolute shortage rate of resources, and simultaneously introduce This reflects the pressure of resource consumption. Furthermore, in this embodiment, when the calculated intensity of resource competition is low, the AI ​​agent can be prompted to prepare backup resources; when the calculated intensity of resource competition is high, this embodiment can mark the risk of resource deadlock and simultaneously link the supply chain system to initiate emergency procurement of resources.

[0096] In this embodiment, as Figure 3 As shown, step S3 analyzes the compatibility between emergency orders and various production lines, including the following specific steps:

[0097] S31. Extract production line dynamic data and order feature data;

[0098] S32. Based on production line dynamic data and order characteristic data, construct an order adaptability analysis model to analyze the adaptability of urgent orders with each production line and obtain the results of the adaptability analysis of urgent orders with each production line.

[0099] The formula for calculating the compatibility between urgent orders and the current production line is as follows:

[0100] ;

[0101] In the formula, SP represents the compatibility between the emergency order and the current production line, Hx represents the buffer absorption capacity of the current production line when an emergency order is inserted, Rh represents the flexible production changeover capacity of the current production line when an emergency order is inserted, Rt represents the total demand of all resources required by the emergency order on the current production line in the order feature data, and At represents the total current inventory of all resources required by the emergency order on the current production line in the production line dynamic data.

[0102] For example, in the formula for calculating the compatibility between emergency orders and the current production line provided in this embodiment, the resource matching item... The difference in dimensions is eliminated by using an absolute value ratio: when Rt = At, the maximum value of 1 is taken, which represents the ideal resource matching; however, when Rt is much greater than At, the resource matching term approaches 0, indicating that resources will be severely insufficient during urgent orders. Furthermore, this embodiment uses a formula for calculating the degree of fit to reflect that a low value for any sub-item in the formula will lead to a low overall degree of fit. Specifically, when the buffer capacity is weak, even if resources are sufficient, they cannot handle the insertion of urgent orders.

[0103] In this embodiment, the construction process of the order suitability analysis model in step S32 includes the following specific steps:

[0104] S321. Based on production line dynamic data, analyze the buffer absorption capacity of each production line when an emergency order is inserted, and obtain the analysis results of the buffer absorption capacity of each production line when an emergency order is inserted.

[0105] The formula for calculating buffer absorption capacity is:

[0106] ;

[0107] In the formula, Hx represents the buffer absorption capacity of the current production line when an urgent order is inserted, Q represents the number of buffers in the current production line in the production line dynamic data, Bq represents the maximum capacity of the q-th buffer in the current production line in the production line dynamic data, and Zq represents the number of work-in-process items implemented in the q-th buffer of the current production line when an urgent order is inserted in the production line dynamic data. The turnover efficiency index of the q-th buffer in the current production line when an urgent order is inserted;

[0108] Among them, turnover efficiency index Where vq is the actual turnover rate of the q-th buffer in the current production line when an emergency order is inserted in the production line dynamic data. The maximum designed turnover rate of the q-th buffer in the current production line in the production line dynamic data;

[0109] For example, this embodiment achieves intelligent aggregation of multi-dimensional states of all buffers on the production line through the hyperbolic tangent function. Through The real-time idle capacity ratio of the buffer is characterized by a turnover efficiency index, which quantifies how a buffer with a high turnover rate can compensate for its insufficient capacity. Furthermore, when a buffer on the production line is saturated, the remaining idle buffers can still distribute the load. Therefore, this embodiment uses a summation term to perform collaborative analysis on multiple buffers on the production line, thereby accurately quantifying the current production line's buffer absorption capacity when urgent orders are inserted. Simultaneously, this embodiment also uses the tanh function to simulate the marginal effect of actual production, avoiding the overestimation risk that occurs in traditional linear models.

[0110] S322. Based on production line dynamic data and order characteristic data, analyze the flexible production changeover capability of each production line when an emergency order is inserted, and obtain the analysis results of the flexible production changeover capability of each production line when an emergency order is inserted.

[0111] The formula for calculating flexible production changeover capacity is:

[0112] ;

[0113] In the formula, Rh is the flexible production changeover capacity of the current production line when an emergency order is inserted, D is the number of devices to be used for emergency order production on the current production line in the order feature data, Std is the standard deviation of the time consumed by all production changes of the d-th device to be used for emergency order production on the current production line in the current production line operation cycle in the production line dynamic data, and Nfd is the number of failures of the d-th device to be used for emergency order production on the current production line in the current production line operation cycle in the production line dynamic data.

[0114] For example, this embodiment quantifies the flexible production changeover capability of the current production line when an emergency order is inserted using an exponential decay function, reflecting the negative impact of fluctuations in equipment production changeover time and equipment failure frequency on the flexible production changeover capability of the current production line when an emergency order is inserted; wherein, the square term It can amplify the risks of production changeovers or equipment failures during production switching; specifically, when the time required for equipment production changeovers fluctuates significantly and failures are frequent, This will increase exponentially, significantly weakening the flexible production switching capability of the current production line when urgent orders are inserted.

[0115] In this embodiment, step S4, which involves constructing a production line capacity assessment model, includes the following specific steps:

[0116] S41. Extract the analysis results of the impact of emergency order insertion on each production line and the analysis results of the compatibility between emergency orders and each production line.

[0117] S42. Based on the analysis results of the disturbance degree of each production line caused by the insertion of emergency orders and the analysis results of the compatibility degree between emergency orders and each production line, the dynamic carrying capacity of each production line is evaluated when emergency orders are inserted, and the evaluation results of the dynamic carrying capacity of each production line when emergency orders are inserted are obtained.

[0118] The formula for evaluating dynamic bearing capacity is as follows:

[0119] ;

[0120] In the formula, DC represents the dynamic capacity of the current production line when an emergency order is inserted.

[0121] In this embodiment, step S5 inserts emergency orders based on the assessment results of the dynamic carrying capacity of each production line when the emergency order is inserted, including the following specific steps:

[0122] S51. Obtain the dynamic capacity assessment results of all production lines when emergency orders are inserted;

[0123] S52. Extract the production line corresponding to the maximum value in the dynamic carrying capacity assessment results of all production lines when inserting emergency orders, and use it as the production line for inserting emergency orders.

[0124] Example 2

[0125] like Figure 4 As shown, this embodiment provides an interactive system for building a digital twin factory based on AI-driven intelligent agents, including:

[0126] The data acquisition module is used to acquire production line dynamic data and equipment usage time sequence data of each production line through AI intelligent agents, and at the same time acquire order characteristic data of urgent orders;

[0127] The order insertion disturbance analysis module is used to build an order insertion disturbance analysis model based on production line dynamic data, equipment usage time series data, and order characteristic data, and to analyze the disturbance of urgent orders to each production line.

[0128] The order compatibility analysis module is used to build an order compatibility analysis model based on production line dynamic data and order feature data, and to analyze the compatibility between urgent orders and each production line.

[0129] The production line dynamic capacity assessment module is used to build a production line capacity assessment model. It imports the analysis results of the disturbance of emergency orders to each production line and the analysis results of the compatibility between emergency orders and each production line into the production line dynamic capacity assessment model to assess the dynamic capacity of each production line when emergency orders are inserted.

[0130] The emergency order insertion module is used to insert emergency orders based on the assessment results of the dynamic capacity of each production line at the time of insertion.

[0131] The control module is used to control the operation of the data acquisition module, the order insertion disturbance analysis module, the order adaptation analysis module, the production line dynamic capacity assessment module, and the emergency order insertion module.

[0132] The parameters and steps for implementing the corresponding functions of each unit module in the AI ​​agent-driven digital twin factory construction interaction system of the present invention described above can be referred to the parameters and steps in the embodiments of the AI ​​agent-driven digital twin factory construction interaction method above, and will not be repeated here.

[0133] The various embodiments in this invention are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. In particular, the embodiments for IoT devices and media are relatively simple in description because they are fundamentally similar to the method embodiments; relevant parts can be referred to the descriptions in the method embodiments.

[0134] The systems, media, and methods provided in the embodiments of the present invention are in one-to-one correspondence. Therefore, the systems and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the systems and media will not be repeated here.

[0135] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0136] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0137] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0138] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0139] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0140] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, 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 technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0141] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0142] The above are merely embodiments of the present invention and are not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A method for constructing and interacting with a digital twin factory driven by an AI agent, characterized in that: Includes the following steps: S1. Obtain production line dynamic data and equipment usage time sequence data of each production line through AI intelligent agent, and at the same time obtain order characteristic data of urgent orders; S2. Based on production line dynamic data, equipment usage time series data, and order characteristic data, construct an order insertion disturbance analysis model to analyze the disturbance degree of urgent order insertion on each production line. S3. Based on production line dynamic data and order characteristic data, construct an order adaptability analysis model to analyze the adaptability of urgent orders with each production line; S4. Construct a production line capacity assessment model, and import the analysis results of the disturbance degree of the insertion of emergency orders to each production line and the analysis results of the compatibility degree between emergency orders and each production line into the production line dynamic capacity assessment model to assess the dynamic capacity of each production line when emergency orders are inserted. S5. Based on the assessment results of the dynamic capacity of each production line when emergency orders are inserted, emergency orders are inserted. Step S2, which analyzes the disruption caused by the insertion of emergency orders to each production line, includes the following specific steps: S21. Extract production line dynamic data, equipment usage time sequence data, and order characteristic data; S22. Based on production line dynamic data, equipment usage time series data, and order characteristic data, construct an order insertion disturbance analysis model to analyze the disturbance degree of emergency order insertion on each production line and obtain the analysis results of the disturbance degree of emergency order insertion on each production line. The formula for calculating the disturbance to the current production line caused by the insertion of urgent orders is as follows: ； In the formula, RD represents the degree of disturbance to the current production line caused by the insertion of an emergency order, Sc represents the degree of timing conflict to the current production line caused by the insertion of an emergency order, and Zz represents the degree of resource competition to the current production line caused by the insertion of an emergency order. The construction process of the order insertion disturbance analysis model in step S22 includes the following specific steps: S221. Based on production line dynamic data, equipment usage time sequence data, and order characteristic data, analyze the degree of time sequence conflict of the insertion of emergency orders on the current production line, and obtain the analysis results of the degree of time sequence conflict of the insertion of emergency orders on the current production line. The formula for calculating the degree of timing conflict is as follows: ； In the formula, Sc represents the degree of timing conflict between the insertion of an urgent order and the current production line. , where is the historical emergency order arrival factor of the current production line in the production line dynamic data, D is the number of devices required to produce emergency orders on the current production line in the order feature data, Txd is the required time for emergency orders to be produced on the d-th device in the order feature data, Tyd is the idle time of the d-th device required to produce emergency orders in the device usage time sequence data, max() is the function to take the maximum value in parentheses, and d is any item from 1 to D; S222. Based on production line dynamic data and order characteristic data, analyze the degree of resource competition for the current production line caused by the insertion of urgent orders, and obtain the analysis results of the degree of resource competition for the current production line caused by the insertion of urgent orders. The formula for calculating the intensity of resource competition is as follows: ； In the formula, Zz represents the intensity of resource competition for the current production line caused by the insertion of an urgent order, and Pj represents the probability index of resource conflict for the j-th type required by the urgent order on the current production line. This indicates that the data within the parentheses are multiplied together, where n is the number of resource types required for urgent orders on the current production line in the order feature data, and j is any one of 1 to n; The formula for calculating the conflict probability index is as follows: ； In the formula, Pj is the probability index of the use conflict of the j-th resource required by the urgent order on the current production line, rj is the demand of the j-th resource required by the urgent order on the current production line in the order feature data, Aj is the current inventory of the j-th resource required by the urgent order on the current production line in the production line dynamic data, and Uj is the historical average consumption rate of the j-th resource required by the urgent order on the current production line in the production line dynamic data.

2. The interactive method for constructing a digital twin factory based on AI agent-driven technology according to claim 1, characterized in that, Step S3, which analyzes the compatibility between emergency orders and various production lines, includes the following specific steps: S31. Extract production line dynamic data and order feature data; S32. Based on production line dynamic data and order characteristic data, construct an order adaptability analysis model to analyze the adaptability of urgent orders with each production line and obtain the results of the adaptability analysis of urgent orders with each production line. The formula for calculating the compatibility between urgent orders and the current production line is as follows: ； In the formula, SP represents the compatibility between the emergency order and the current production line, Hx represents the buffer absorption capacity of the current production line when an emergency order is inserted, Rh represents the flexible production changeover capacity of the current production line when an emergency order is inserted, Rt represents the total demand of all resources required by the emergency order on the current production line in the order feature data, and At represents the total current inventory of all resources required by the emergency order on the current production line in the production line dynamic data.

3. The interactive method for constructing a digital twin factory based on AI agent-driven technology according to claim 2, characterized in that, The process of constructing the order suitability analysis model in step S32 includes the following specific steps: S321. Based on production line dynamic data, analyze the buffer absorption capacity of each production line when an emergency order is inserted, and obtain the analysis results of the buffer absorption capacity of each production line when an emergency order is inserted. S322. Based on production line dynamic data and order characteristic data, analyze the flexible production changeover capability of each production line when an emergency order is inserted, and obtain the analysis results of the flexible production changeover capability of each production line when an emergency order is inserted.

4. The interactive method for constructing a digital twin factory based on AI agent-driven technology according to claim 3, characterized in that, The step S4, which involves constructing a production line capacity assessment model, includes the following specific steps: S41. Extract the analysis results of the impact of emergency order insertion on each production line and the analysis results of the compatibility between emergency orders and each production line. S42. Based on the analysis results of the disturbance degree of each production line caused by the insertion of emergency orders and the analysis results of the compatibility degree between emergency orders and each production line, the dynamic carrying capacity of each production line is evaluated when emergency orders are inserted, and the evaluation results of the dynamic carrying capacity of each production line when emergency orders are inserted are obtained. The formula for evaluating dynamic bearing capacity is as follows: ； In the formula, DC represents the dynamic capacity of the production line when an emergency order is inserted.

5. The interactive method for constructing a digital twin factory based on AI agent-driven technology according to claim 4, characterized in that, Step S5 involves inserting emergency orders based on the assessment results of the dynamic capacity of each production line at the time of insertion. This includes the following specific steps: S51. Obtain the dynamic capacity assessment results of all production lines when emergency orders are inserted; S52. Extract the production line corresponding to the maximum value in the dynamic carrying capacity assessment results of all production lines when inserting emergency orders, and use it as the production line for inserting emergency orders.

6. An interactive system for constructing a digital twin factory based on AI agents, implemented according to any one of claims 1-5, characterized in that... The system includes: The data acquisition module is used to acquire production line dynamic data and equipment usage time sequence data of each production line through AI intelligent agents, and at the same time acquire order characteristic data of urgent orders; The order insertion disturbance analysis module is used to build an order insertion disturbance analysis model based on production line dynamic data, equipment usage time series data, and order characteristic data, and to analyze the disturbance of urgent orders to each production line. The order compatibility analysis module is used to build an order compatibility analysis model based on production line dynamic data and order feature data, and to analyze the compatibility between urgent orders and each production line. The production line dynamic capacity assessment module is used to build a production line capacity assessment model. It imports the analysis results of the disturbance of emergency orders to each production line and the analysis results of the compatibility between emergency orders and each production line into the production line dynamic capacity assessment model to assess the dynamic capacity of each production line when emergency orders are inserted. The emergency order insertion module is used to insert emergency orders based on the assessment results of the dynamic capacity of each production line at the time of insertion. The control module is used to control the operation of the data acquisition module, the order insertion disturbance analysis module, the order adaptation analysis module, the production line dynamic carrying capacity assessment module, and the emergency order insertion module.

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