Resource cooperative control method and system based on proxy AI

Abstract

The invention discloses a resource cooperative control method and system based on proxy AI. The method comprises the following steps: an enterprise management system transmits a production plan to a manufacturing execution system; the manufacturing execution system determines a production task demand based on the production plan and the production scheduling model; the production control system determines a production control scheme based on the equipment occupation condition, the production task demand and the task planning model, issues first production planning control of the production control scheme to the resource controller, and issues second production planning control of the production control scheme to the production equipment; the resource controller determines target resource allocation according to the first production planning control, the current computing network resource allocation condition and a resource configuration model so as to send a resource determination message; the production equipment receives the resource determination message and controls execution of the task according to the second production plan; according to the technical scheme, intelligent cooperation and accurate resource allocation between systems can be realized, and an industrial production line is promoted to move towards intelligence, collaboration and high efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of resource collaborative control, and in particular to a resource collaborative control method and system based on agent-type AI. Background Art

[0002] Industrial Internet promotes the digital transformation of manufacturing industry and enhances the competitiveness of the industry. Currently, Industrial Internet has entered a new stage of large-scale development. In this process, the production line is the core link of industrial manufacturing, and its intelligent and efficient operation is crucial. However, with the expansion of production scale and the increase of product complexity, the traditional production line control mode has increasingly exposed many drawbacks, such as rigid resource allocation, slow response, poor coordination, etc., which makes it difficult to meet the urgent needs of modern industry for high-precision and high-flexibility production.

[0003] At the same time, it has brought a new opportunity for change to the industrial field. Artificial intelligence is an artificial intelligence system with autonomous decision-making and action capabilities. It can autonomously plan tasks and perform operations according to preset goals in a complex and changing environment, and support real-time adjustment of strategies to cope with uncertainty. However, due to the dynamic and changing requirements of various tasks on the production line for computing power and network bandwidth, the unified management of network edge cloud devices is very complicated, and computing power is often idle during certain periods of time and bandwidth is insufficient at critical moments. In addition, the lack of deep integration of computing network resource management and industrial application scenarios restricts the maximization of synergy efficiency. Summary of the invention

[0004] In view of this, the present invention provides a resource collaborative control method and system based on agent-type AI, which can realize intelligent collaboration and precise allocation of resources between systems, and effectively promote industrial production lines to move towards a high-level stage of intelligence, collaboration and efficiency.

[0005] According to one aspect of the present invention, an embodiment of the present invention provides a resource collaborative control method based on agent-type AI, which is applied to a resource collaborative control system based on agent-type AI, wherein the resource collaborative control system comprises: an enterprise management system, a manufacturing execution system, a production control system, a resource controller and production equipment; wherein the manufacturing execution system comprises a first slave agent-type AI module, wherein the first slave agent-type AI module comprises a production scheduling model; the production control system comprises a second slave agent-type AI module, wherein the second slave agent-type AI module comprises a task planning model; the resource controller comprises a third slave agent-type AI module, wherein the third slave agent-type AI module comprises a resource configuration model;

[0006] Accordingly, the resource collaborative control method based on agent-based AI includes:

[0007] The enterprise management system receives a production plan issued by a user, and transmits the production plan to the manufacturing execution system;

[0008] The manufacturing execution system determines the production task demand based on the production plan and the scheduling model;

[0009] The production control system determines the equipment occupancy of the production equipment, determines the production control scheme based on the equipment occupancy, the production task requirements and the task planning model, and sends the first production planning control of the production control scheme to the resource controller, and sends the second production planning control of the production control scheme to the production equipment; wherein the first production planning control is the computing power requirement and network requirement of the production equipment required for the production plan, and the second production planning control is the production operation of the required production line;

[0010] The resource controller obtains the current computing network resource allocation status of the production equipment, determines the target resource allocation corresponding to the production equipment respectively according to the first production planning control, the current computing network resource allocation status and the resource configuration model, and sends a resource confirmation message to the production equipment after the target resource allocation is successful;

[0011] The production equipment receives the resource confirmation message and controls the execution of tasks according to the second production plan.

[0012] According to another aspect of the present invention, an embodiment of the present invention further provides a resource collaborative control system based on agent-type AI, the resource collaborative control system comprising: an enterprise management system, a manufacturing execution system, a production control system, a resource controller and production equipment; wherein the manufacturing execution system comprises a first slave agent-type AI module, the first slave agent-type AI module comprises a production scheduling model; the production control system comprises a second slave agent-type AI module, the second slave agent-type AI module comprises a task planning model; the resource controller comprises a third slave agent-type AI module, the third slave agent-type AI module comprises a resource configuration model;

[0013] Wherein, the enterprise management system is used to receive the production plan issued by the user and transmit the production plan to the manufacturing execution system;

[0014] The manufacturing execution system is used to determine the production task requirements based on the production plan and the production scheduling model;

[0015] The production control system is used to determine the equipment occupancy of the production equipment, determine the first production planning control and the second production planning control based on the equipment occupancy, the production task requirements and the task planning model, and send the first production planning control to the resource controller, and send the second production planning control to the production equipment; wherein the first production planning control is the computing power requirement and network requirement of the production equipment required for the production plan, and the second production planning control is the production operation of the required production line;

[0016] The resource controller is used to obtain the current network resource allocation status of the production equipment, determine the target resource allocation corresponding to the production equipment respectively according to the first production planning control, the current network resource allocation status and the resource configuration model, and send a resource confirmation message to the production equipment after the target resource allocation is successful;

[0017] The production equipment is used to receive the resource confirmation message and control the execution of tasks according to the second production plan.

[0018] The technical effect of the present invention is that the enterprise management system transmits the production plan to the manufacturing execution system; the manufacturing execution system determines the production task requirements based on the production plan and the scheduling model, the production control system determines the production control plan based on the equipment occupancy, the production task requirements and the task planning model, and sends the first production planning control of the production control plan to the resource controller, and sends the second production planning control of the production control plan to the production equipment; the resource controller determines the target resource allocation according to the first production planning control, the current computing network resource allocation and the resource configuration model to send a resource determination message; the production equipment receives the resource determination message, and executes the task according to the second production planning control. Through the mutual cooperation of the above-mentioned enterprise management system, production control system, resource controller and production equipment, each system is configured with a corresponding slave agent AI module, and a distributed agent AI module is used to coordinate the production process and computing network resources to achieve intelligent collaboration and precise resource allocation, and promote industrial production lines to move towards intelligence, collaboration and efficiency.

[0019] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 A flowchart of a resource collaborative control method based on agent-based AI provided by one embodiment of the present invention;

[0022] Figure 2 A schematic diagram of the structure of a distributed agent-type AI provided by one embodiment of the present invention;

[0023] Figure 3 A flowchart of another resource collaborative control method based on agent-based AI provided by an embodiment of the present invention;

[0024] Figure 4 A schematic diagram of a flow chart of a process of generating a production description character string in a manufacturing execution system provided in one embodiment of the present invention;

[0025] Figure 5 A schematic diagram of the architecture of a resource collaborative control system based on agent-type AI provided by one embodiment of the present invention;

[0026] Figure 6 A schematic diagram of the architecture of another resource collaborative control system based on agent-type AI provided by an embodiment of the present invention;

[0027] Figure 7 A schematic diagram of the relationship between a master agent AI module and slave agent AI modules corresponding to each system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] In one embodiment, Figure 1 A flowchart of a resource collaborative control method based on agent-type AI is provided for one embodiment of the present invention. This embodiment can be applied to the collaborative control of production lines and computing network resources in the industrial field. The method can be executed by a resource collaborative control system based on agent-type AI.

[0031] like Figure 1 As shown, the resource collaborative control method based on agent-type AI in this embodiment is applied to a resource collaborative control system based on agent-type AI, and the resource collaborative control system includes: an enterprise management system, a manufacturing execution system, a production control system, a resource controller and production equipment; wherein, the manufacturing execution system includes a first slave agent-type AI module, and the first slave agent-type AI module includes a production scheduling model; the production control system includes a second slave agent-type AI module, and the second slave agent-type AI module includes a task planning model; the resource controller includes a third slave agent-type AI module, and the third slave agent-type AI module includes a resource configuration model.

[0032] In this embodiment, the resource collaborative control method based on agent-based AI specifically includes the following steps:

[0033] S110. The enterprise management system receives the production plan issued by the user, and transmits the production plan to the manufacturing execution system.

[0034] Among them, the production plan can be understood as a production plan for producing a certain product on a certain production line in the industrial field.

[0035] The resource controller in this embodiment can be understood as a controller of a network, computing power, or computing power network, etc., and is used to allocate resources to the network and computing power, etc.

[0036] In this embodiment, the enterprise management system records various information such as production orders, contracts, materials, etc., which can be used to implement various information management of the enterprise. Of course, the user can use the enterprise management system to formulate a production plan for a certain product based on orders, etc. The enterprise management system receives the production plan issued by the user, and then transmits the production plan to the manufacturing execution system so that the manufacturing execution system can perform subsequent processing. In this embodiment, the interaction between the enterprise information management system and the manufacturing execution system is to improve production efficiency and information fluency. This interaction can achieve seamless flow of data and reduce information islands, thereby providing real-time production data and business analysis, and optimizing the overall business process. For example, in a flexible production and manufacturing scenario, users can provide various customized product requirements, and the system of this embodiment can automatically adjust the production process or resources according to the new order.

[0037] S120. The manufacturing execution system determines the production task requirements based on the production plan and scheduling model.

[0038] Among them, the production task requirements may specifically include: production order number, production content description, customization requirement description and production deadline. Of course, the production task requirements may also be increased according to user needs, and this embodiment does not limit this.

[0039] In this embodiment, the manufacturing execution system is an integrated software system designed for the manufacturing industry, which aims to improve the execution efficiency of the entire production process, coordinate various production stages, monitor production activities in real time and provide detailed production data. The scheduling model is a local model configured in the manufacturing execution system. The local model can be a pre-trained scheduling model. The scheduling model can be a deep neural network architecture for generating production task requirements. The training of the scheduling model can include first collecting data and using the collected data as a training set for training to optimize model parameters until the model parameters reach the optimal level to obtain a pre-trained scheduling model. Among them, the collected data can include but is not limited to collecting historical production plan information corresponding to the production plan, equipment / resource information, process route and process information, and production time data (preparation time, processing time), etc.

[0040] In this embodiment, the manufacturing execution system includes a first slave agent AI module, the first slave agent AI module includes a production scheduling model, the manufacturing execution system receives the production plan, decomposes the production plan into multiple demand statements, and then inputs the multiple demand statements into the production scheduling model in the first slave agent AI module, thereby outputting a specific production task demand description, and sending the production task demand description to the production control system. It can be understood that when the manufacturing execution system receives the production plan, it will optimize and adjust these plans according to factors such as the current production status, equipment capacity, and raw material inventory to ensure the smooth progress of production. Specifically, the first slave agent AI deployed on the manufacturing execution system decomposes the production order into a production scheduling plan, with the old production scheduling plan as input and the new production scheduling plan as output. In some embodiments, in addition to the production scheduling model, the first slave agent AI module may also include a supervision model, which can be used to supervise and review the relevant data of the input production scheduling model, thereby optimizing the data. Specifically, the manufacturing execution system uses the production plan, historical production plan and / or historical supervision feedback information of the second supervision model in the production control system as the first initial information, and then fills it into the production product requirement template to obtain the corresponding first production description text, divides the first production description text into at least two first implementation requirement statements, and adds the first implementation requirement statement to the first production queue, and then uses the first supervision model to review the first implementation requirement statement in the first production queue. If the review is qualified, the first implementation requirement statement is taken out from the first production queue in turn, and the first implementation requirement statement is input into the scheduling model to obtain a new first implementation requirement. The first supervision model is used to conduct a second review of the new first implementation requirement, and the new first implementation requirements that pass the second review are combined to form the production task requirement, until all the first implementation requirement statements in the first production queue are processed; if unqualified, the first production queue is cleared and re-executed.

[0041] In this embodiment, the manufacturing execution system will send the optimized production plan to the production control system. The production control system is a system close to the actual production process and can convert the production instructions from the manufacturing execution system into signals that can be understood by the production equipment, thereby controlling the operation of the equipment.

[0042] S130. The production control system determines the equipment occupancy status of the production equipment, determines the production control plan based on the equipment occupancy status, production task requirements and task planning model, and sends the first production planning control of the production control plan to the resource controller, and sends the second production planning control of the production control plan to the production equipment.

[0043] Among them, the equipment occupancy status can be understood as whether the production equipment is occupied. The production equipment may include but is not limited to network controllers, edge computing devices, production line equipment, and cloud computing services.

[0044] In this embodiment, the first production planning control is the computing power demand and network demand of the production equipment required for the production plan, and the second production planning control is the production operation of the required production line. It can be understood that the production control plan consists of two parts, one part describes the production process's demand for the computing power and network of each device, and the description is sent to the resource controller; the other part describes the specific production operation of the production line, and the description is sent to the production equipment. It should be noted that in this embodiment, the issuance of the first production planning control and the second production planning control is a parallel operation, which can be understood as that after obtaining the production control plan, the first production planning control and the second production planning control are simultaneously issued to the corresponding equipment.

[0045] In one embodiment, the first production planning control includes at least: service number, application type, computing power requirements and network requirements; wherein, the computing power requirements include at least the total calculation delay, the number of computing units and the input data size; the network requirements include at least: the total transmission delay, the minimum bandwidth and the maximum jitter; the second production planning control includes at least: the production equipment address, the application port number, the operation parameters and the execution time. It should be noted that after the task planning model gives the production operation, it needs to be executed by applications such as production equipment and edge computing. If these applications communicate remotely, the network is required. In this process, in order to be able to schedule resources more accurately, it is necessary to clarify the application type. For example, the computing power and network requirements of image recognition are relatively large, and the requirements of sensors are relatively small.

[0046] In this embodiment, the production control system includes a second slave agent AI module, and the second slave agent AI module includes a task planning model. The task planning model can be understood as a local model configured in the production control system. The local model can be a pre-trained task planning model for outputting a production control plan. The task planning model is a model of a deep neural network architecture, and its pre-training can also include first collecting historical related data, and using the collected historical data as a training set for training to optimize model parameters until the model parameters reach the optimal level to obtain a pre-trained task planning model. In some embodiments, in addition to the task planning model, the second slave agent AI module can also include a supervision model, which can be used to supervise and review the relevant data of the input task planning model, thereby optimizing the data. It can be understood that the agent AI of the production control system is a module that further decomposes the production plan into production tasks for each device. The module has a high degree of intelligence and automation capabilities, and can automatically generate the optimal production task plan based on the complexity of the production plan and the production capacity of the equipment.

[0047] Specifically, the production control system uses the equipment occupancy status, production task requirements, historical equipment occupancy status, and / or the historical supervision feedback information of the third supervision model in the resource controller as the second initial information, determines the second production description text based on the second initial information, divides the second production description text into at least two second implementation requirement statements, and adds the second implementation requirement statements to the second production queue, uses the second supervision model to review the second implementation requirement statements in the second production queue, and if the review is qualified, then takes out the second implementation requirement statements in turn, inputs the second implementation requirement statements into the task planning model to obtain at least two new second implementation requirements, uses the second supervision model to conduct a second review of the new second implementation requirements, combines the new second implementation requirements that pass the second review to form the first production planning control and the second production planning control, and re-examines the new second implementation requirements that fail the second review until all the second implementation requirement statements in the second production queue are processed.

[0048] S140. The resource controller obtains the current computing network resource allocation status of the production equipment, and determines the target resource allocation corresponding to the production equipment according to the first production planning control, the current computing network resource allocation status and the resource configuration model, and sends a resource confirmation message to the production equipment after the target resource allocation is successful.

[0049] Among them, the target resource allocation can be understood as the resource allocation situation that the production equipment ultimately corresponds to.

[0050] In this embodiment, the resource configuration model can be understood as a local model configured in the resource controller, and the local model can be a pre-trained resource configuration model for outputting the target resource allocation corresponding to each production device. The resource configuration model is a model of a deep neural network architecture, and its pre-training can also include first collecting historical relevant data, and using the collected historical data as a training set for training to optimize model parameters, until the model parameters reach the optimal level to obtain a pre-trained resource configuration model.

[0051] In this embodiment, the resource controller performs network configuration and computing power configuration. After receiving the computing power and network requirements, the resource controller creates a deterministic service for each step of the production process. The deterministic service is a closed-loop business chain composed of multiple computing and communication processes. The local slave agent AI module of the resource controller determines the target resource allocation corresponding to the production equipment according to the requirements of the first part of the plan and the allocation of computing network resources under management, the current computing network resource allocation and the resource configuration model according to the first production plan control, and then generates specific network, edge and cloud configurations. After the resources are allocated successfully, the resource controller sends a resource confirmation message to the production equipment.

[0052] In this embodiment, the resource controller includes a third slave agent AI module, and the third slave agent AI module includes a resource configuration model. Of course, in addition to the resource configuration model, the resource controller may also include a supervision model, which can be used to review the relevant data input into the resource configuration model. It can be understood that the third slave agent AI module is a module for adjusting the computing network resource scheduling plan according to the production plan, and can adjust the computing power, bandwidth, execution order, etc. according to the complexity of the production plan and the production capacity of the equipment, so that the total execution delay and jitter of all links are controlled within the expected value range. Specifically, the resource controller uses the current computing network resource allocation situation, the first production planning control and / or the historical computing network resource allocation situation as the third initial information, determines the corresponding third production description text based on the third initial information, divides the third production description text into at least two third implementation requirement statements, uses the third supervision model to review the third implementation requirement statements, and if the review is qualified, takes out the third implementation requirement statements in turn, inputs the third implementation requirement statements into the task planning model to obtain at least two third new implementation requirements, uses the third supervision model to conduct a second review of the third new implementation requirements, and forms the target resource allocation for the third new implementation requirements that pass the second review, until each third implementation requirement statement in the third production queue is processed.

[0053] S150. The production equipment receives the resource confirmation message and executes the task according to the second production plan control.

[0054] In this embodiment, the production equipment receives the resource confirmation message and executes the task according to the second production planning control. It can be understood that after receiving these two messages, the production equipment starts to execute specific production operations and computing network configuration; the network controller executes specific network configuration; the edge computing device and cloud computing service execute specific supporting applications and computing network configuration. More specifically, the network controller executes network configuration and regularly reports network status information; the production line equipment executes production operations and regularly reports production status information; the edge computing device runs the edge computing tasks issued by the resource controller and regularly reports the edge computing task status information; the cloud computing service runs the cloud computing tasks issued by the resource controller and regularly reports the cloud computing task status information.

[0055] According to the technical solution of the embodiment of the present invention, the enterprise management system transmits the production plan to the manufacturing execution system; the manufacturing execution system determines the production task requirements based on the production plan and the scheduling model, the production control system determines the production control plan based on the equipment occupancy, the production task requirements and the task planning model, and sends the first production planning control of the production control plan to the resource controller, and sends the second production planning control of the production control plan to the production equipment, and then the resource controller determines the target resource allocation according to the first production planning control, the current computing network resource allocation and the resource configuration model to send a resource determination message, and finally the production equipment receives the resource determination message and executes the task according to the second production planning control. Through the mutual cooperation of the above-mentioned enterprise management system, production control system, resource controller and production equipment, each system is configured with a corresponding slave agent AI module, and a distributed agent AI module is used to coordinate the production process and computing network resources to achieve intelligent collaboration and precise resource allocation, so as to promote the industrial production line to move towards intelligence, collaboration and efficiency.

[0056] In one embodiment, the resource collaborative control system further includes: a master-agent AI module; the master-agent AI module includes a global model; accordingly, the resource collaborative control method further includes:

[0057] The production scheduling model in the first slave agent AI module, the task planning model in the second slave agent AI module, and the resource allocation model in the third slave agent AI module respectively feed back the corresponding model parameters to the global model in the master agent AI module, so that the global model optimizes the model parameters of the production scheduling model, the task planning model, and the resource allocation model according to the corresponding model parameters, so as to update the production scheduling model, the task planning model, and the resource allocation model with optimized parameters to the corresponding systems.

[0058] The master agent AI module can be deployed in other systems, computer devices or electronic devices other than the systems where the first slave agent AI module, the second slave agent AI module and the third slave agent AI module are deployed. In this embodiment, the global model aggregates the models of all slave agent AI modules and performs overall training optimization, and then sends the trained local models in each slave agent AI, so that each local model maintains consistency regularly.

[0059] In this embodiment, the resource collaborative control system also includes: a master-agent AI module, which includes a global model. The global model can be an interactive large model, which is used to manage the production scheduling model in the first slave-agent AI module, the task planning model in the second slave-agent AI module, and the resource allocation model in the third slave-agent AI module. The management can include but is not limited to updating the model parameters of the production scheduling model in the first slave-agent AI module, the task planning model in the second slave-agent AI module, and the resource allocation model in the third slave-agent AI module, respectively, so as to better optimize the model optimization capabilities of each model. Specifically, the production scheduling model in the first slave agent AI module can feed back the corresponding model parameters to the global model in the master agent AI module, and the global model optimizes the model parameters to update the production scheduling model after parameter tuning to the first slave agent AI module in the manufacturing execution system; similarly, the task planning model in the second slave agent AI module and the resource allocation model in the third slave agent AI module will also feed back the corresponding model parameters to the global model in the master agent AI module, so that the global model can optimize the model parameters of the task planning model and the resource allocation model according to the corresponding model parameters, so as to update the task planning model and the resource allocation model after parameter tuning to the second slave agent AI module and the third slave agent AI module respectively.

[0060] It should be noted that the parameters of the global model mainly include input layer parameters, hidden layer parameters, output layer parameters, activation function parameters, loss function parameters, optimizer parameters and regularization parameters, which are the sum of the association parameters between neurons in the neural network and the internal parameters of neurons.

[0061] In this embodiment, when the global model in the master-agent AI module performs parameter tuning, personalized federated learning or centralized tuning strategies can be used for parameter tuning. For example, gradient transfer-based tuning (centralized supervision) requires the slave agent to upload the gradient of the local training data to the master agent, and the master agent calculates the global gradient direction and generates the tuned parameters. The master agent then sends the adjusted parameters to the slave agent. Of course, other parameter tuning methods in the prior art may also be used, and this embodiment does not limit this.

[0062] In one embodiment, the master agent AI module further includes a global supervision model; accordingly, the first slave agent AI module further includes: a first supervision model; the second slave agent AI module further includes a second supervision model; the third slave agent AI module includes a third supervision model; accordingly, the resource collaborative control method further includes:

[0063] The first supervisory model in the first slave agent AI module, the second supervisory model in the second slave agent AI module, and the third supervisory model in the third slave agent AI module respectively feed back corresponding supervisory parameters to the global supervisory model in the master agent AI module, so that the global supervisory model optimizes the corresponding supervisory model according to the corresponding supervisory parameters.

[0064] Among them, the first supervision model, the second supervision model and the third supervision model are all models of neural network architecture, illustratively, hidden Markov, support vector machine, etc. In this embodiment, the supervision parameters corresponding to each supervision model may include but are not limited to demand templates, demand parameters, supervision algorithms, heuristic function thresholds, etc., and can be stored in the format of JSON or XML files.

[0065] In this embodiment, the master agent AI module manages each slave agent AI module, and each slave agent AI module has a sequential execution relationship, and the output of the predecessor agent AI module is the input of the successor agent AI module. The model of the master agent AI module is used to train and update the models of multiple slave agent AI modules. At the same time, the feedback results of the supervisory module of the successor agent AI are used to adjust and optimize the predecessor agent AI supervisory module. Exemplarily, the first slave agent AI module is the predecessor node of the second slave agent AI module, and the second slave agent AI module is the predecessor node of the third slave agent AI module; the second slave agent AI module is the successor node of the first slave agent AI module.

[0066] In this embodiment, the main task of supervised learning is to learn a mapping function from existing labeled data so that for a given input, the corresponding output can be obtained. In supervised learning, a labeled data set is usually used for training, and each sample in the training set has an input and a corresponding output. During the training process, the model learns the mapping relationship between input and output, thereby learning a function that can map new inputs to the correct output.

[0067] In this embodiment, the first supervisory model in the first slave agent AI module, the second supervisory model in the second slave agent AI module, and the third supervisory model in the third slave agent AI module will respectively check the rationality of the corresponding production description string requirements and evaluate whether the existing resources can meet the requirements of the production task. If the required computing power and network exceed the existing resources, the resource shortage situation will be fed back to the predecessor node supervision module, and the predecessor node supervision module will add the feedback information to the context information to affect the subsequent production description string generation. At the same time, the production description string of the service is regenerated. It can be understood that the second slave agent AI module performs a first assessment based on the review results of the second supervisory model to determine whether the computing power resources and network resources can meet the needs of the production task. If the needs of the production task are not met, the result of the first assessment is fed back to the first supervisory model in the first slave agent AI module as the first feedback information, so that the first slave agent AI module uses the first feedback information as the historical supervisory feedback information of the first supervisory model; similarly, the third slave agent AI module performs a second assessment based on the review results of the third supervisory model to determine whether the computing power resources and network resources can meet the needs of the production task. If the needs of the production task are not met, the result of the second assessment is fed back to the historical supervisory feedback information of the second supervisory model as the second feedback information.

[0068] In this embodiment, the master-agent AI module also includes a global supervision model, the first slave-agent AI module also includes: a first supervision model, the second slave-agent AI module also includes a second supervision model, and the third slave-agent AI module includes a third supervision model. In this embodiment, the first supervision model in the first slave-agent AI module, the second supervision model in the second slave-agent AI module, and the third supervision model in the third slave-agent AI module respectively feed back corresponding supervision parameters to the global supervision model in the master-agent AI module, so that the global supervision model optimizes the corresponding supervision model according to the corresponding supervision parameters. Specifically, the method for optimizing the supervision parameters can be gradient correction supervision.

[0069] It can be understood that the master-agent AI module is responsible for the management, training and updating of the supervisory model and the global model, and completes the unified planning logic through the master-agent AI module. The resource scheduling plan may include the allocation and scheduling of computing power, network and other resources. Through reasonable resource scheduling, the continuity, efficiency and certainty of the production process can be ensured. At the same time, the master-agent AI module also has global optimization capabilities, which can dynamically adjust and optimize the global model based on the supervision results of the slave-agent AI module on each device.

[0070] In this embodiment, the master-agent AI module maintains the entire architecture system, regularly evaluates the operation of the entire production line, and updates the execution templates and supervision parameters of each slave-agent AI module in each system. The slave-agent AI takes the requirements of the predecessor node agent AI as input, and after multiple rounds of review and optimization by the local supervision module, it outputs the local configuration and the requirements of the successor node. In addition, the master-agent AI module can also be responsible for coordinating and managing the communication and computing tasks between the slave-agent AI modules. Ensure that the slave-agent AI module policies on multiple devices tend to be consistent. The main functions include model aggregation, model update and distribution, parameter transfer and encryption, task management and scheduling, visualization and monitoring, etc.

[0071] In one embodiment, to better understand the structure of distributed agent AI, Figure 2 A schematic diagram of the structure of a distributed agent-type AI provided by an embodiment of the present invention is shown in FIG. Figure 2 The slave agent AI in is a slave agent AI model, representing the first slave agent AI module, the second slave agent AI module and the third slave agent AI module. Each slave agent AI module includes a corresponding local supervision model and a local model, that is, in the above embodiment, the first slave agent AI module includes a first supervision model and a production scheduling model; the second slave agent AI module includes a second supervision model and a task planning model; the third slave agent AI module includes a third supervision model and a resource allocation model. In this embodiment, the master agent AI module is responsible for the management, training and updating of the supervision models and local models in each other system. The resource scheduling plan may include the allocation and scheduling of computing power, network and other resources. Through reasonable resource scheduling, the continuity, efficiency and certainty of the production process can be ensured. At the same time, the master agent AI also has global optimization capabilities, and can dynamically adjust and optimize the global model according to the supervision results of the slave agent AI on each device.

[0072] In one embodiment, Figure 3 A flowchart of another resource collaborative control method based on agent-type AI provided for one embodiment of the present invention. In this embodiment, based on the above embodiments, the manufacturing execution system determines the production task requirements based on the production plan and scheduling model; the production control system determines the equipment occupancy of the production equipment, and determines the production control plan based on the equipment occupancy, production task requirements and task planning model; the resource controller obtains the current computing network resource allocation of the production equipment, and determines the target resource allocation corresponding to the production equipment according to the first production planning control, the current computing network resource allocation and the resource configuration model; and the production equipment receives the resource determination message and further refines the execution task according to the second production planning control.

[0073] like Figure 3As shown, the resource collaborative control method based on agent-based AI in this embodiment may specifically include the following steps:

[0074] S310: The enterprise management system receives the production plan issued by the user and transmits the production plan to the manufacturing execution system.

[0075] S320. The manufacturing execution system determines the production task requirements based on the production plan and scheduling model.

[0076] In one embodiment, S320 may specifically include: S3201-S3206, specifically:

[0077] S3201. The manufacturing execution system uses the production plan and the first context information as the first initial information.

[0078] In this embodiment, the manufacturing execution system receives the production plan, and then uses the production plan and the first context information as the first initial information. The first context information includes: historical production plans and / or historical supervision feedback information of the second supervision model in the production control system. The historical supervision feedback information can be understood as feedback of the historical review results of the second supervision model.

[0079] S3202: Fill the first initial information into the production product requirement template to obtain the corresponding first production description text.

[0080] The first production description text is the production description text information corresponding to the first initial information. The requirement template can also be called a demand template, and the requirement template can be supplemented with more necessary information, such as the size and weight of the product to be produced.

[0081] In this embodiment, corresponding keywords are first set according to user needs, and then initial information is filled into the production product requirement template through the keywords to form an implementable first production description text.

[0082] S3203. Divide the first production description text into at least two first implementation requirement statements, and add the first implementation requirement statements to the first production queue.

[0083] In this embodiment, the first production description text can be divided into at least two first implementation requirement statements according to various separators (e.g., line breaks, html tags, etc.), and the first implementation requirement statements are added to the first production queue to wait for the review of the requirement statements. The first implementation requirement statements are multiple implementation requirement statements corresponding to the division of the first production description text.

[0084] S3204. Use the first supervision model to review the first implementation requirement statement in the first production queue, and determine whether the first implementation requirement statement is qualified. If qualified, execute S3205; if unqualified, execute S3206.

[0085] In this embodiment, the first supervisory model in the first slave agent AI module is used to review the first implementation requirement statement in the first production queue. Specifically, the review may include but is not limited to reviewing the syntax, semantics and rationality of the requirement statements in the generation queue, and then judging whether the first implementation requirement statement is qualified. If qualified, the first slave agent AI module loops through the statements in the generation queue and inputs each statement into the production scheduling model. Each time an implementation requirement is input, the requirement is written into the production description string to be output. The production scheduling model will feedback multiple new implementation requirements, and the first supervisory model will review these implementation requirements.

[0086] S3205. When the first production queue is not empty, take out the first implementation requirement statements from the first production queue in turn, input the first implementation requirement statements into the production scheduling model to obtain new first implementation requirements, use the first supervision model to conduct a secondary review of the new first implementation requirements, mark the new first implementation requirements that pass the secondary review as the first leaf nodes, each first leaf node forms a production task requirement, and re-add the new first implementation requirements that fail the secondary review to the first production queue, return to the step of using the first supervision model to review the first implementation requirement statements in the production queue until all the first implementation requirement statements in the first production queue are processed.

[0087] Among them, the first leaf node is a character string. All the first leaf nodes are spliced ​​together to become the production task requirement, and then sent to the production description string of the next slave agent module (the second slave agent AI module).

[0088] In this embodiment, if qualified, the first implementation requirement statement is taken out from the first production queue in turn, the first implementation requirement statement is input into the scheduling model to obtain a new first implementation requirement, the new first implementation requirement is reviewed twice using the first supervision model, the new first implementation requirement that passes the second review is marked as the first leaf node, each first leaf node forms a production task requirement, and the new first implementation requirement that fails the second review is re-added to the first production queue, and the step of reviewing the first implementation requirement statement in the production queue using the first supervision model is returned until each first implementation requirement statement in the first production queue is processed; if unqualified, the first production queue is cleared, and the step of taking the production plan and the first context information as the first initial information is returned and re-executed. Exemplarily, the input from the agent module in the manufacturing execution system is an order content of the enterprise information system, and the slave agent AI supervision module forms a more complete production description according to the required template of the production product of model XX, such as which parts, processing steps, etc. are required. Then the production description is input into the large model to obtain the production task requirement, such as when to start production, which manufacturing system to produce, raw material inventory processing, etc.

[0089] S3206. Clear the first production queue, return to the step of using the production plan and the first context information as the first initial information, and execute again.

[0090] S330. The production control system determines the equipment occupancy status of the production equipment, and determines the first production planning control and the second production planning control based on the equipment occupancy status, production task requirements, and the task planning model.

[0091] In one embodiment, S330 may specifically include: S3301-S3305, specifically:

[0092] S3301. The production control system uses the equipment occupancy status and the second context information as the second initial information.

[0093] In this embodiment, the production control system uses the equipment occupancy and the second context information as the second initial information to perform subsequent operations. The second context information includes: production task requirements, historical equipment occupancy, and / or historical supervision feedback information of the third supervision model in the resource controller.

[0094] It should be noted that the role of the second supervisory model included in the second slave agent AI module in the production control system is similar to the role of the first supervisory model included in the first slave agent AI module in the manufacturing execution system, and this embodiment will not be specifically introduced. Similarly, the third supervisory model included in the third slave agent AI module in the resource controller described later is also the same. It can be understood that the production control system, the manufacturing execution system and the resource controller are in the same process of processing information, but the information content processed is different.

[0095] S3302. Determine a second production description text based on the second initial information, divide the second production description text into at least two second implementation requirement statements, and add the second implementation requirement statements to the second production queue.

[0096] In this embodiment, the second production description text is the second initial information filled into the production product requirement template to obtain the production description text.

[0097] In this embodiment, the corresponding keywords are first set according to user needs, and then the second initial information is filled into the production product requirement template through the keywords to form an implementable second production description text, and then the second production description text is divided into multiple second implementation requirement statements according to certain requirements, and the second implementation requirement statements are added to the second production queue.

[0098] S3303. Use the second supervision model to review the second implementation requirement statement in the second production queue, and determine whether the second implementation requirement statement is qualified. If qualified, execute S3304; if unqualified, execute S3305.

[0099] In this embodiment, the second supervision model in the second slave agent AI module is used to review the second implementation requirement statement in the second production queue, and determine whether the second implementation requirement statement is qualified. If it is qualified and the second production queue is not empty, the second implementation requirement statement is taken out from the second production queue in turn, and the second implementation requirement statement is input into the task planning model to obtain at least two new second implementation requirements. The new second implementation requirements are reviewed for the second time using the second supervision model, and the new second implementation requirements that pass the second review are marked as second leaf nodes, and each second leaf node forms the first production planning control and the second production planning control, and the new second implementation requirements that fail the second review are re-added to the second production queue, and the step of using the second supervision model to review the second implementation requirement statements in the second production queue is returned until each second implementation requirement statement in the second production queue is processed. For example, the input of the manufacturing system from the agent module is the production schedule. The supervision model will form the production steps composed of production tasks based on the production schedule and the product requirement template. This production product requirement template should be designed according to the factory production line. Each production task corresponds to a specific link of the production line. The output text content is the production task and its detailed requirements. For example, visual inspection equipment requires camera monitoring and transmitting images to edge computing. After the edge computing makes a decision, the result is transmitted to the robotic arm.

[0100] S3304. When the second production queue is not empty, take out the second implementation requirement statements from the second production queue in turn, input the second implementation requirement statements into the task planning model to obtain at least two new second implementation requirements, use the second supervision model to conduct a secondary review of the new second implementation requirements, mark the new second implementation requirements that pass the secondary review as second leaf nodes, form the first production planning control and the second production planning control for each second leaf node, and add the new second implementation requirements that fail the secondary review back to the second production queue, and return to the step of using the second supervision model to review the second implementation requirement statements in the second production queue until all second implementation requirement statements in the second production queue are processed.

[0101] In this embodiment, the second slave agent AI module performs a first assessment of whether the computing power resources and network resources can meet the needs of the production task based on the review results of the second supervisory model. If the needs of the production task are not met, the result of the first assessment is fed back to the first supervisory model in the first slave agent AI module as the first feedback information, so that the first slave agent AI module uses the first feedback information as the historical supervisory feedback information of the first supervisory model.

[0102] S3305. Clear the second production queue, return to the step of using the device occupancy status and the second context information as the second initial information, and execute again.

[0103] S340. The resource controller obtains the current computing network resource allocation status of the production equipment, and determines the target resource allocation corresponding to each production equipment according to the first production planning control, the current computing network resource allocation status and the resource configuration model.

[0104] In one embodiment, S340 may specifically include: S3401-S3405, specifically:

[0105] S3401. The resource controller uses the current computing network resource allocation status and the third context information as the third initial information.

[0106] In this embodiment, the resource controller uses the current computing network resource allocation situation and the third context information as the third initial information. The third context information includes: the first production planning control and / or the historical computing network resource allocation situation.

[0107] S3402. Determine a corresponding third production description text based on the third initial information, divide the third production description text into at least two third implementation requirement statements, and add the third implementation requirement statement to the third production queue.

[0108] In this embodiment, corresponding keywords are first set according to user needs, and then the third initial information is filled into the production product requirement template through keywords to form an implementable third production description text, and then the third production description text is divided into multiple third implementation requirement statements according to certain requirements, and the third implementation requirement statements are added to the second production queue.

[0109] S3403. Use the third supervision model to review the third implementation requirement statement in the third production queue, and determine whether the third implementation requirement statement is qualified. If qualified, execute S3404; if unqualified, execute S3405.

[0110] In this embodiment, the third supervisory model in the third slave agent AI module is used to review the third implementation requirement statement in the third production queue, and determine whether the third implementation requirement statement is qualified. If qualified, then when the third production queue is not empty, the third implementation requirement statement is taken out from the third production queue in turn, and the third implementation requirement statement is input into the task planning model to obtain at least two third new implementation requirements, and the third supervisory model is used to conduct a secondary review of the third new implementation requirement, and the third new implementation requirement that passes the secondary review is marked as a third leaf node, and the third leaf node is used to form a target resource allocation, and the third new implementation requirement that fails the secondary review is re-added to the third production queue, and the step of using the third supervisory model to review the third implementation requirement statement in the third production queue is returned until each third implementation requirement statement in the third production queue is processed; if unqualified, the third production queue is cleared, and the step of using the current computing network resource allocation and the third context information as the third initial information is returned and re-executed. Exemplarily, the slave agent module input of the resource controller is a production task, such as what signal the robot arm performs assembly operation after receiving, and the slave agent model outputs the required network configuration and computing power configuration according to the local model, and the output is resource configuration information.

[0111] S3404. When the third production queue is not empty, take out the third implementation requirement statements from the third production queue in turn, input the third implementation requirement statements into the task planning model to obtain at least two third new implementation requirements, use the third supervision model to conduct a secondary review of the third new implementation requirements, mark the third new implementation requirements that pass the secondary review as third leaf nodes, form target resource allocations with the third leaf nodes, and add the third new implementation requirements that fail the secondary review back to the third production queue, return to the step of using the third supervision model to review the third implementation requirement statements in the third production queue, until each third implementation requirement statement in the third production queue is processed.

[0112] In this embodiment, the third slave agent AI module performs a second evaluation on whether the computing power resources and network resources can meet the needs of the production task based on the review results of the third supervisory model. If the needs of the production task are not met, the result of the second evaluation is fed back as the second feedback information to the historical supervisory feedback information of the second supervisory model.

[0113] S3405. Clear the third production queue, return to the step of using the current computing network resource allocation status and the third context information as the third initial information, and execute again.

[0114] For example, to better understand the production description string generation process in each process, Figure 4A flowchart of a production description string generation process in a manufacturing execution system provided for one embodiment of the present invention. In this embodiment, the manufacturing execution system determines the production task requirements based on the production plan and scheduling model as an example for explanation. The production description string in this embodiment can be understood as the string corresponding to the production task requirements in the above embodiment. The local supervision model in this embodiment can be understood as the first supervision model in the above embodiment. The next slave agent AI module in this embodiment can be understood as the second slave agent AI module in the above embodiment.

[0115] a1. Initial information is formed by input and context information.

[0116] a2. Divide the production description text into multiple implementation requirements and input the implementation requirements into the generation queue.

[0117] a3. Use the first supervision model to review the implementation requirements in the generation queue.

[0118] a4. Determine whether the implementation requirements are qualified. If not, execute a5. If qualified, execute a6.

[0119] a5. Clear the generation queue and return to a1.

[0120] a6. Determine whether the generation queue is empty. If so, execute a7; if not, execute a8.

[0121] a7. Send the production description string to the next slave agent AI module.

[0122] a8. First, take out the first demand statement in the generation queue from the agent AI module, input the demand statement into the production scheduling model, and then concatenate it to the production description string to be output.

[0123] a9. The first supervision model reviews multiple new implementation requirements fed back by the production scheduling model.

[0124] a10. Determine whether the new implementation requirement has been processed. If so, return to a6; if not, execute a11.

[0125] a11. Select an implementation requirement and determine whether it meets the implementation conditions. If so, execute a12; if not, execute a13.

[0126] a12. Mark as a leaf node and return to a10 until all leaf nodes are obtained, and each leaf node is used as a production task requirement.

[0127] a13. Rejoin the generation queue and return to a10.

[0128] S350: The network controller executes network configuration and periodically reports network status information.

[0129] In this embodiment, the network controller is software or equipment for managing deterministic networks, supports local deployment of slave Agentic AI modules, manages network devices, maintains network topology, and regularly reports network status information.

[0130] S360, production line equipment performs production operations and regularly reports production status information.

[0131] In this embodiment, the production equipment receives production tasks as input from the proxy module and receives operation instructions as output.

[0132] S370. The edge computing device runs the edge computing task issued by the resource controller and regularly reports the edge computing task status information.

[0133] In this embodiment, the edge computing device is an edge computing device that executes deterministic applications, including but not limited to edge servers, edge controllers, edge all-in-one machines, etc. It supports the deployment of Agentic AI on the device, running deterministic computing tasks and regularly reporting computing task status information.

[0134] S380. The cloud computing service runs the cloud computing tasks issued by the resource controller and regularly reports cloud computing task status information.

[0135] In this embodiment, the cloud computing service is a cloud service that executes deterministic applications, supports the deployment of slave Agentic AI in the service, runs deterministic computing tasks and regularly reports computing task status information. The input of the slave agent module of edge computing and cloud services is production tasks, and the output is operation instructions and computing power configuration information.

[0136] The technical solution of the embodiment of the present invention, through a distributed structure, can deploy special agent-type AI modules on different systems, form a consistent management strategy through centralized control, and ultimately form an intelligent management and operation and dynamic optimization solution, while taking into account the reduction of management and operation costs and agile regulation and optimization, thereby further realizing intelligent collaboration between devices and precise allocation of resources, and promoting industrial production lines to move towards intelligence, collaboration, and efficiency.

[0137] In one embodiment, Figure 5 A schematic diagram of the architecture of an agent-based AI-based resource collaborative control system provided for one embodiment of the present invention.

[0138] like Figure 5As shown, the resource collaborative control system includes: an enterprise management system 510, a manufacturing execution system 520, a production control system 530, a resource controller 540 and a production equipment 550; wherein the manufacturing execution system 520 includes a first slave agent AI module, and the first slave agent AI module includes a production scheduling model; the production control system 530 includes a second slave agent AI module, and the second slave agent AI module includes a task planning model; the resource controller 540 includes a third slave agent AI module, and the third slave agent AI module includes a resource configuration model;

[0139] The enterprise management system 510 is used to receive the production plan issued by the user and transmit the production plan to the manufacturing execution system 520;

[0140] Manufacturing execution system 520, used to determine production task requirements based on production plans and scheduling models;

[0141] The production control system 530 is used to determine the equipment occupancy of the production equipment, determine the first production planning control and the second production planning control based on the equipment occupancy, the production task requirements and the task planning model, and send the first production planning control to the resource controller 540 and send the second production planning control to the production equipment 550; wherein the first production planning control is the computing power requirement and network requirement of the production equipment required for the production plan, and the second production planning control is the production operation of the required production line;

[0142] The resource controller 540 is used to obtain the current computing network resource allocation of the production equipment, determine the target resource allocation corresponding to the production equipment according to the first production planning control, the current computing network resource allocation and the resource configuration model, and send a resource confirmation message to the production equipment after the target resource allocation is successful;

[0143] The production device 550 is used to receive the resource confirmation message and control the execution of the task according to the second production plan.

[0144] In one embodiment, Figure 6 The following is a schematic diagram of the architecture of another resource collaborative control system based on agent-based AI provided by an embodiment of the present invention. Figure 6As shown, the resource collaborative control system includes: an enterprise management system 610, a manufacturing execution system 620, a production control system 630, a resource controller 640, production equipment 650 and a master-agent AI module 660; wherein, the manufacturing execution system 620 includes a first slave-agent AI module, the first slave-agent AI module includes a scheduling model and a first supervision model; the production control system 630 includes a second slave-agent AI module, the second slave-agent AI module includes a task planning model and a second supervision model; the resource controller 640 includes a third slave-agent AI module, the third slave-agent AI module includes a resource allocation model and a third supervision model; the master-agent AI module 660 includes a global model and a global supervision model.

[0145] 1) Interaction between enterprise information management system and MES (manufacturing execution system): When MES receives the production plan, it will optimize and adjust these plans based on the current production status, equipment capacity, raw material inventory and other factors to ensure smooth production. The slave agent AI deployed on MES breaks down the order into production schedules, with the old production schedule as input and the new production schedule as output.

[0146] 2) Interaction between MES and production control system: MES will send the optimized production plan to the production control system. The agent AI of the production control system is a module that further decomposes the production plan into production tasks for each device. This module has a high degree of intelligence and automation capabilities and can automatically generate the optimal production task plan based on the complexity of the production plan and the production capacity of the equipment.

[0147] 3) Resource scheduling of the resource controller: The resource controller's slave agent AI is a module used to adjust the computing network resource scheduling plan based on the production plan. It can adjust the computing power, bandwidth and execution order according to the complexity of the production plan and the production capacity of the equipment, so that the total execution delay and jitter of all links are controlled within the expected value range.

[0148] 4) Control of edge-end cloud devices: The edge-end cloud devices directly execute remote control commands. Optionally, the requirements are translated and executed from the agent AI.

[0149] 5) Distributed agent AI global control: The master agent AI is responsible for the management, training and updating of the supervision modules and models, and completes the unified planning logic through the master agent. The resource scheduling plan may include the allocation and scheduling of computing power, network and other resources. Through reasonable resource scheduling, the continuity, efficiency and certainty of the production process can be ensured. At the same time, the master agent AI also has global optimization capabilities, which can dynamically adjust and optimize the global model based on the supervision results of the slave agent AI on each device.

[0150] In this embodiment, in order to better understand the relationship between the master agent AI module and the slave agent AI modules corresponding to each system, Figure 7 A schematic diagram of the relationship between a master agent AI module and slave agent AI modules corresponding to each system provided in an embodiment of the present invention.

[0151] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0152] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A resource collaborative control method based on agent-based AI, characterized in that: Applied to a resource collaborative control system based on agent-type AI, the resource collaborative control system includes: an enterprise management system, a manufacturing execution system, a production control system, a resource controller and production equipment; wherein the manufacturing execution system includes a first slave agent-type AI module, the first slave agent-type AI module includes a production scheduling model; the production control system includes a second slave agent-type AI module, the second slave agent-type AI module includes a task planning model; the resource controller includes a third slave agent-type AI module, the third slave agent-type AI module includes a resource configuration model; Accordingly, the resource collaborative control method based on agent-based AI includes: The enterprise management system receives a production plan issued by a user, and transmits the production plan to the manufacturing execution system; The manufacturing execution system determines the production task demand based on the production plan and the scheduling model; The production control system determines the equipment occupancy of the production equipment, determines the production control scheme based on the equipment occupancy, the production task requirements and the task planning model, and sends the first production planning control of the production control scheme to the resource controller, and sends the second production planning control of the production control scheme to the production equipment; wherein the first production planning control is the computing power requirement and network requirement of the production equipment required for the production plan, and the second production planning control is the production operation of the required production line; The resource controller obtains the current computing network resource allocation status of the production equipment, determines the target resource allocation corresponding to the production equipment respectively according to the first production planning control, the current computing network resource allocation status and the resource configuration model, and sends a resource confirmation message to the production equipment after the target resource allocation is successful; The production equipment receives the resource confirmation message and controls the execution of tasks according to the second production plan.

2. The resource collaborative control method according to claim 1, characterized in that: The resource collaborative control system further includes: a master-agent AI module; the master-agent AI module includes a global model; Accordingly, the resource collaborative control method further includes: The production scheduling model in the first slave agent AI module, the task planning model in the second slave agent AI module, and the resource allocation model in the third slave agent AI module respectively feed back corresponding model parameters to the global model in the master agent AI module, so that the global model optimizes the model parameters of the production scheduling model, the task planning model, and the resource allocation model according to the corresponding model parameters, so as to update the production scheduling model, task planning model, and resource allocation model after parameter optimization to the corresponding systems.

3. The resource collaborative control method according to claim 2, characterized in that: The master agent AI module also includes a global supervision model; correspondingly, the first slave agent AI module also includes: a first supervision model; the second slave agent AI module also includes a second supervision model; the third slave agent AI module includes a third supervision model; Accordingly, the resource collaborative control method further includes: The first supervisory model in the first slave-agent AI module, the second supervisory model in the second slave-agent AI module, and the third supervisory model in the third slave-agent AI module respectively feed back corresponding supervisory parameters to the global supervisory model in the master-agent AI module, so that the global supervisory model optimizes the corresponding supervisory model according to the corresponding supervisory parameters.

4. The resource collaborative control method according to claim 1, characterized in that: The manufacturing execution system determines the production task requirements based on the production plan and the scheduling model, including: The manufacturing execution system uses the production plan and the first context information as the first initial information; wherein the first context information includes: historical production plans and / or historical supervisory feedback information of the second supervisory model in the production control system; Filling the first initial information into a production product requirement template to obtain a corresponding first production description text; Splitting the first production description text into at least two first implementation requirement statements, and adding the first implementation requirement statements to a first production queue; Using a first supervisory model to review a first implementation requirement statement in the first production queue, and determining whether the first implementation requirement statement is qualified; If qualified, then when the first production queue is not empty, take out the first implementation requirement statements from the first production queue in turn, input the first implementation requirement statements into the production scheduling model to obtain new first implementation requirements, use the first supervision model to conduct a second review of the new first implementation requirements, mark the new first implementation requirements that pass the second review as first leaf nodes, each of the first leaf nodes forms the production task requirements, and re-add the new first implementation requirements that fail the second review to the first production queue, and return to the step of using the first supervision model to review the first implementation requirement statements in the production queue until each of the first implementation requirement statements in the first production queue is processed; If unqualified, clearing the first production queue, returning to the step of taking the production plan and the first context information as the first initial information, and re-executing; The production task requirements include at least: production order number, production content description, customization requirement description and production deadline.

5. The resource collaborative control method according to claim 1, characterized in that: The production control system determines the equipment occupancy of the production equipment, and determines the first production planning control and the second production planning control based on the equipment occupancy, the production task requirements and the task planning model, including: The production control system uses the equipment occupancy and the second context information as the second initial information; wherein the second context information includes: production task requirements, historical equipment occupancy, and / or historical supervision feedback information of the third supervision model in the resource controller; Determine a second production description text according to the second initial information, divide the second production description text into at least two second implementation requirement statements, and add the second implementation requirement statements to a second production queue; Using a second supervisory model to review the second implementation requirement statement in the second production queue, and determining whether the second implementation requirement statement is qualified; If qualified, then when the second production queue is not empty, take out the second implementation requirement statements from the second production queue in turn, input the second implementation requirement statements into the task planning model to obtain at least two new second implementation requirements, use the second supervision model to conduct a secondary review of the new second implementation requirements, mark the new second implementation requirements that pass the secondary review as second leaf nodes, form the first production planning control and the second production planning control with each second leaf node, and re-add the new second implementation requirements that fail the secondary review to the second production queue, and return to the step of using the second supervision model to review the second implementation requirement statements in the second production queue until the processing of each second implementation requirement statement in the second production queue is completed; If unqualified, the second production queue is cleared, and the process returns to the step of using the device occupancy status and the second context information as the second initial information and is executed again.

6. The resource collaborative control method according to claim 1, characterized in that: The resource controller obtains the current network computing resource allocation of the production equipment, and determines the target resource allocation corresponding to each of the production equipment according to the first production planning control, the current network computing resource allocation and the resource configuration model, including: The resource controller uses the current computing network resource allocation situation and the third context information as the third initial information; wherein the third context information includes: the first production planning control and / or the historical computing network resource allocation situation; Determine a corresponding third production description text according to the third initial information, divide the third production description text into at least two third implementation requirement statements, and add the third implementation requirement statements to a third production queue; Using a third supervisory model to review a third implementation requirement statement in the third production queue, and determining whether the third implementation requirement statement is qualified; If qualified, then if the third production queue is not empty, take out the third implementation requirement statements from the third production queue in turn, input the third implementation requirement statements into the task planning model to obtain at least two third new implementation requirements, use the third supervision model to conduct a secondary review of the third new implementation requirements, mark the third new implementation requirements that pass the secondary review as third leaf nodes, form target resource allocation with the third leaf nodes, and re-add the third new implementation requirements that fail the secondary review to the third production queue, and return to the step of using the third supervision model to review the third implementation requirement statements in the third production queue until each third implementation requirement statement in the third production queue is processed; If unqualified, the third production queue is cleared, and the process returns to the step of using the current computing network resource allocation status and the third context information as the third initial information and is executed again.

7. The resource collaborative control method according to any one of claims 4 to 6, characterized in that: The second slave agent AI module performs a first evaluation based on the review result of the second supervisory model to determine whether the computing resources and network resources can meet the requirements of the production task. If the requirements of the production task are not met, the result of the first evaluation is fed back to the first supervisory model in the first slave agent AI module as first feedback information, so that the first slave agent AI module uses the first feedback information as historical supervisory feedback information of the first supervisory model. The third slave agent AI module performs a second evaluation based on the review results of the third supervisory model to determine whether the computing power resources and network resources can meet the needs of the production task. If the needs of the production task are not met, the result of the second evaluation is fed back as the second feedback information to the historical supervisory feedback information of the second supervisory model.

8. The resource collaborative control method according to claim 1, characterized in that: The first production planning control includes at least: service number, application type, computing power requirement and network requirement; wherein the computing power requirement includes at least total calculation delay, number of computing units and input data size; the network requirement includes at least total transmission delay, minimum bandwidth and maximum jitter; The second production planning control includes at least: production equipment address, application port number, operation parameters and execution time.

9. The resource collaborative control method according to claim 1, characterized in that: The production equipment at least includes a network controller, a production line device, an edge computing device, and a cloud computing service; accordingly, the production equipment receives the resource determination message and controls the execution of tasks according to the second production plan, including: The network controller performs network configuration and regularly reports network status information; The production line equipment performs production operations and regularly reports production status information; The edge computing device runs the edge computing tasks issued by the resource controller and regularly reports edge computing task status information; The cloud computing service runs the cloud computing tasks issued by the resource controller and regularly reports cloud computing task status information.

10. A resource collaborative control system based on agent-based AI, characterized in that: The resource collaborative control system includes: an enterprise management system, a manufacturing execution system, a production control system, a resource controller and production equipment; wherein the manufacturing execution system includes a first slave agent AI module, the first slave agent AI module includes a production scheduling model; the production control system includes a second slave agent AI module, the second slave agent AI module includes a task planning model; the resource controller includes a third slave agent AI module, the third slave agent AI module includes a resource configuration model; Wherein, the enterprise management system is used to receive the production plan issued by the user and transmit the production plan to the manufacturing execution system; The manufacturing execution system is used to determine the production task requirements based on the production plan and the scheduling model; The production control system is used to determine the equipment occupancy of the production equipment, determine the first production planning control and the second production planning control based on the equipment occupancy, the production task requirements and the task planning model, and send the first production planning control to the resource controller, and send the second production planning control to the production equipment; wherein the first production planning control is the computing power requirement and network requirement of the production equipment required for the production plan, and the second production planning control is the production operation of the required production line; The resource controller is used to obtain the current network resource allocation status of the production equipment, determine the target resource allocation corresponding to the production equipment respectively according to the first production planning control, the current network resource allocation status and the resource configuration model, and send a resource confirmation message to the production equipment after the target resource allocation is successful; The production equipment is used to receive the resource confirmation message and control the execution of tasks according to the second production plan.

Images