A collaborative scheduling optimization method and system for cloud manufacturing between enterprises

Abstract

The present invention is applicable to the field of enterprise management, and provides an inter-enterprise collaborative scheduling optimization method and system for cloud manufacturing, the method comprising: collecting resource information, production task information and skill level information of targeted enterprises participating in cloud manufacturing; constructing a targeted enterprise resource capability model and a task requirement model based on the targeted enterprise resource information, production task information and personnel skill level information; setting a targeted enterprise multimodal optimization goal; generating a directional optimization instruction based on the targeted enterprise multimodal optimization goal. The method and system fully consider the differences and complex relationships between enterprises, break the limitations of traditional scheduling, optimize resource utilization from a global perspective, avoid idle or overloaded resources, and improve overall efficiency. In the face of the dynamic environment of cloud manufacturing, it is necessary to quickly adapt to changes, ensure system stability, and reduce production delays. Multi-objective optimization balances factors such as cost and delivery time.

Description

Technical Field

[0001] The present invention belongs to the field of enterprise management, and in particular relates to an inter-enterprise collaborative scheduling optimization method and system for cloud manufacturing. Background Art

[0002] With the rapid development of information technology, the manufacturing industry is evolving towards intelligence, networking and service, and cloud manufacturing has emerged as a new manufacturing model. Cloud manufacturing integrates the manufacturing resources and capabilities scattered across different enterprises to form a huge manufacturing resource pool, which is provided to users in the form of services to achieve efficient sharing and utilization of resources. However, in a cloud manufacturing environment, collaborative scheduling between multiple enterprises has become a complex and critical issue.

[0003] In the cloud manufacturing system, there are many participating enterprises, each of which has its own unique resource types, capability levels, production plans and interests. These enterprises may be widely distributed geographically, and their production equipment, process levels, personnel quality and other aspects are different. The production capacity of enterprises may change due to equipment failure, personnel changes and other reasons. Market demand may also fluctuate at any time. New orders may be added at any time, and existing orders may be cancelled or modified.

[0004] When faced with these dynamic changes, existing scheduling methods often fail to adjust scheduling plans in a timely and effective manner, resulting in confusion and delays in production plans, affecting the stability and reliability of the entire cloud manufacturing system. To address the above problems, it is urgent to develop an inter-enterprise collaborative scheduling optimization method and system for cloud manufacturing. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for inter-enterprise collaborative scheduling optimization for cloud manufacturing, aiming to solve the problems raised in the above background technology.

[0006] The present invention is implemented in this way. On the one hand, a collaborative scheduling optimization method between enterprises for cloud manufacturing is provided, and the method comprises:

[0007] Collecting targeted enterprise resource information, production task information, and skill level information of personnel involved in cloud manufacturing;

[0008] Based on targeted enterprise resource information, production task information and personnel skill level information, a targeted enterprise resource capability model and task demand model are constructed;

[0009] Set targeted enterprise multimodal optimization goals;

[0010] Generate targeted optimization instructions based on the multimodal optimization goals of the targeted enterprise.

[0011] As a further solution of the present invention, the collecting of resource information of targeted enterprises participating in cloud manufacturing, production task information, and skill level information of personnel participating in cloud manufacturing specifically includes:

[0012] Get device failure rate and equipment processing speed ;

[0013] Get the production task type ;

[0014] Get personnel skill level ;

[0015] Based on production task type , generate priority and delivery date ;

[0016] In the formula, is the device number, is the task type number, Label personnel and give them priority It can be represented by a value from 1 to 10, the higher the value, the higher the priority.

[0017] As a further solution of the present invention, the construction of a targeted enterprise resource capability model and a task requirement model based on targeted enterprise resource information, production task information and personnel skill level information specifically includes:

[0018] Based on obtaining equipment failure rate and equipment processing speed , calculate and generate equipment resource capability model ;

[0019] Among them, the equipment resource capability model The calculation process is:

[0020] ;

[0021] In the formula, It is the maximum processing speed of the same type of equipment;

[0022] Based on personnel skill level , calculate and generate personnel resource capability model ;

[0023] Among them, the human resource capability model The calculation process is:

[0024] ;

[0025] Based on production task type , Priority and delivery day , calculate and generate the task requirement model ;

[0026] Among them, the task requirement model The calculation process is:

[0027] ;

[0028] In the formula, is the current time.

[0029] As a further solution of the present invention, the setting of the targeted enterprise multimodal optimization goal specifically includes:

[0030] Setting multiple targeted enterprise optimization goals, including but not limited to a resource utilization maximization goal and an order on-time delivery rate goal;

[0031] Computing resource utilization ;

[0032] The resource utilization The calculation process is:

[0033] ;

[0034] In the formula, For equipment The maximum available state value, For personnel The maximum available state value.

[0035] As a further solution of the present invention, the setting of the targeted enterprise multimodal optimization goal specifically includes:

[0036] Calculate on-time delivery target for orders ;

[0037] The on-time delivery rate target for orders The calculation process is:

[0038] ;

[0039] In the formula, For the task Whether the delivery is on time, on time delivery is 1, and not on time delivery is 0. is the total number of tasks.

[0040] As a further solution of the present invention, the generation of directional optimization instructions based on the multimodal optimization goal of the targeted enterprise specifically includes:

[0041] Establish a dynamic adjustment mechanism and set resource utilization thresholds and order on-time delivery rate target threshold ;

[0042] Determining resource utilization Is the resource utilization rate less than the threshold? ;

[0043] If resource utilization Less than the resource utilization threshold , generating enterprise resource update demand instructions;

[0044] Determine the on-time delivery rate target for orders Is it less than the target threshold of on-time delivery rate of orders? ;

[0045] If the order on-time delivery rate target Less than the target threshold for on-time delivery rate of orders , generate task efficiency update requirement instructions.

[0046] As a further solution of the present invention, on the other hand, an inter-enterprise collaborative scheduling optimization system for cloud manufacturing is provided, the system comprising:

[0047] A collection module is used to collect resource information of targeted enterprises involved in cloud manufacturing, production task information, and skill level information of personnel involved in cloud manufacturing;

[0048] A targeted enterprise resource capability model module is used to construct a targeted enterprise resource capability model module based on targeted enterprise resource information and personnel skill level information;

[0049] Task requirement model module, used to build a task requirement model based on production task information;

[0050] The setting module is used to set the multimodal optimization goals of the targeted enterprise;

[0051] A generation module, used to generate directional optimization instructions based on the multimodal optimization goals of the targeted enterprise;

[0052] The acquisition module acquires the resource information of targeted enterprises, production task information and the skill level information of personnel involved in cloud manufacturing. Based on the targeted enterprise resource information and the skill level information of personnel, the targeted enterprise resource capability model module constructs the targeted enterprise resource capability model module. Based on the production task information, the task requirement model module constructs the task requirement model. The setting module sets the multimodal optimization target of the targeted enterprise. Based on the multimodal optimization target of the targeted enterprise, the generation module generates directional optimization instructions.

[0053] The present invention provides an inter-enterprise collaborative scheduling optimization method and system for cloud manufacturing. The method and system fully consider the differences and complex relationships between enterprises, break the limitations of traditional scheduling, optimize resource utilization from a global perspective, avoid idle or overloaded resources, and improve overall efficiency. In the face of the dynamic environment of cloud manufacturing, it can quickly adapt to changes, ensure system stability, and reduce production delays. Multi-objective optimization balances factors such as cost and delivery time, enhances enterprise competitiveness, and promotes the efficient operation of the cloud manufacturing model, thereby maximizing the collaborative value between enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a main flow chart of an inter-enterprise collaborative scheduling optimization method for cloud manufacturing.

[0055] Figure 2 The invention is a flow chart for collecting resource information of targeted enterprises, production task information and skill level information of personnel involved in cloud manufacturing in an inter-enterprise collaborative scheduling optimization method for cloud manufacturing.

[0056] Figure 3 The present invention is a flowchart for constructing a targeted enterprise resource capability model and a task requirement model based on targeted enterprise resource information, production task information and personnel skill level information in an inter-enterprise collaborative scheduling optimization method for cloud manufacturing.

[0057] Figure 4 The present invention is a flowchart for setting targeted enterprise multimodal optimization objectives in an inter-enterprise collaborative scheduling optimization method for cloud manufacturing.

[0058] Figure 5 The present invention is a flowchart for generating directional optimization instructions based on multi-modal optimization objectives of targeted enterprises in an inter-enterprise collaborative scheduling optimization method for cloud manufacturing.

[0059] Figure 6 It is the main structure diagram of an inter-enterprise collaborative scheduling optimization system for cloud manufacturing. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0061] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0062] The present invention provides a method and system for inter-enterprise collaborative scheduling optimization for cloud manufacturing, which solves the technical problems in the background technology.

[0063] like Figure 1As shown, it is a main flow chart of an inter-enterprise collaborative scheduling optimization method for cloud manufacturing provided by an embodiment of the present invention, and the inter-enterprise collaborative scheduling optimization method for cloud manufacturing includes:

[0064] Step S100: Collecting resource information of targeted enterprises participating in cloud manufacturing, production task information, and skill level information of personnel participating in cloud manufacturing;

[0065] Step S200: constructing a targeted enterprise resource capability model and a task requirement model based on targeted enterprise resource information, production task information and personnel skill level information;

[0066] Step S300: Setting a multi-modal optimization goal for a targeted enterprise;

[0067] Step S400: Generate directional optimization instructions based on the multimodal optimization goal of the targeted enterprise;

[0068] When this embodiment is applied, through the sensor network and the information management system of the targeted enterprises participating in cloud manufacturing, the resource information and production task information of the targeted enterprises participating in cloud manufacturing are first collected, and the skill level information of the personnel participating in cloud manufacturing is collected to comprehensively obtain rich information of the cloud manufacturing enterprises. The sensor network can collect the operation status data of the equipment in real time, such as the processing accuracy, processing speed and failure rate of the equipment, etc. These data directly reflect the actual performance of the equipment and provide an accurate basis for subsequent resource evaluation. At the same time, information such as personnel skill level and quantity, task type, quantity, priority and delivery time requirements are obtained from the enterprise information management system, covering the human and task dimensions of enterprise production. Then, based on the targeted enterprise resource information, production task information and personnel skill level information, a targeted enterprise resource capability model and task demand model are constructed. The resource capability model and task demand model can accurately describe the enterprise's resource availability and processing capabilities for different tasks. Through quantitative methods, the targeted enterprise multimodal optimization goals are set, and a dynamic adjustment mechanism is established. Comprehensive consideration of these goals can comprehensively improve the performance of the cloud manufacturing system. Finally, based on the targeted enterprise multimodal optimization goals, directional optimization instructions are generated; this method and system fully consider the differences and complex relationships of enterprises, break the limitations of traditional scheduling, optimize resource utilization from a global perspective, avoid resource idleness or overload, and improve overall efficiency. In the face of the dynamic environment of cloud manufacturing, it quickly adapts to changes, ensures system stability, and reduces production delays. Multi-objective optimization balances factors such as cost and delivery time, enhances enterprise competitiveness, and promotes the efficient operation of the cloud manufacturing model, thereby maximizing the collaborative value between enterprises.

[0069] like Figure 2 As shown, as a preferred embodiment of the present invention, the acquisition of the target enterprise resource information, production task information and skill level information of the personnel involved in cloud manufacturing specifically includes:

[0070] Step S101: Obtaining device failure rate and equipment processing speed ;

[0071] Step S102: Obtaining the production task type ;

[0072] Step S103: Obtaining personnel skill level ;

[0073] Step S104: Based on the production task type , generate priority and delivery date ;

[0074] In the formula, is the device number, is the task type number, Label personnel and give them priority It can be represented by a value from 1 to 10, the higher the value, the higher the priority;

[0075] In this embodiment, when applied, the device failure rate and equipment processing speed , which can be used to generate subsequent targeted enterprise resource capability models by production task types , Generate Priority and delivery date , generate subsequent task requirement models.

[0076] like Figure 3 As shown, as a preferred embodiment of the present invention, the construction of a targeted enterprise resource capability model and a task requirement model based on targeted enterprise resource information, production task information and personnel skill level information specifically includes:

[0077] Step S201: Based on obtaining the equipment failure rate and equipment processing speed , calculate and generate equipment resource capability model ;

[0078] Among them, the equipment resource capability model The calculation process is:

[0079] ;

[0080] In the formula, It is the maximum processing speed of the same type of equipment;

[0081] Step S202: Based on personnel skill level , calculate and generate personnel resource capability model ;

[0082] Among them, the human resource capability model The calculation process is:

[0083] ;

[0084] Step S203: Based on the production task type , Priority and delivery day , calculate and generate the task requirement model ;

[0085] Among them, the task requirement model The calculation process is:

[0086] ;

[0087] In the formula, is the current time;

[0088] In this embodiment, when applied, based on obtaining the equipment failure rate and equipment processing speed , calculate and generate equipment resource capability model , Equipment Resource Capability Model Indicates the enterprise's resource availability and ability to handle different tasks, based on the skill level of personnel , calculate and generate personnel resource capability model , based on the production task type , Priority and delivery day , calculate and generate the task requirement model , task requirement model Indicates the priority and time requirements of the company's current tasks.

[0089] like Figure 4 As shown, as a preferred embodiment of the present invention, the setting of the targeted enterprise multimodal optimization goal specifically includes:

[0090] Step S301: setting a plurality of targeted enterprise optimization goals, wherein the plurality of targeted enterprise optimization goals include but are not limited to a resource utilization maximization goal and an order on-time delivery rate goal;

[0091] Step S302: Calculate resource utilization ;

[0092] The resource utilization The calculation process is:

[0093] ;

[0094] In the formula, For equipment The maximum available state value, For personnel The maximum available state value;

[0095] It should be understood that multiple targeted enterprise optimization goals are set, including but not limited to resource utilization maximization goals and order on-time delivery rate goals, integrating resource capability models and human resource capability model , generating resource utilization , through resource utilization judgment and understand the deficiencies and dynamic changes in the company's resource utilization.

[0096] As a preferred embodiment of the present invention, the setting of the targeted enterprise multimodal optimization goal specifically includes:

[0097] Step S303: Calculate the order on-time delivery rate target ;

[0098] The on-time delivery rate target for orders The calculation process is:

[0099] ;

[0100] In the formula, For the task Whether the delivery is on time, on time delivery is 1, and not on time delivery is 0. is the total number of tasks;

[0101] In this embodiment, when applied, multiple targeted enterprise optimization goals are set, including but not limited to the resource utilization maximization goal and the order on-time delivery rate goal, using the task demand model , generate order on-time delivery rate target , through the order on-time delivery rate target judgment and understand the shortcomings and dynamic changes in the company's task completion level.

[0102] like Figure 5 As shown, as another preferred embodiment of the present invention, the generating of the directional optimization instruction based on the multimodal optimization goal of the targeted enterprise specifically includes:

[0103] Step S401: Establish a dynamic adjustment mechanism and set resource utilization thresholds and order on-time delivery rate target threshold ;

[0104] Step S402: Determine resource utilization Is the resource utilization rate less than the threshold? ;

[0105] Step S403: If the resource utilization Less than the resource utilization threshold , generating enterprise resource update demand instructions;

[0106] Step S404: Determine the on-time delivery rate target of the order Is it less than the target threshold of on-time delivery rate of orders? ;

[0107] Step S405: If the order on-time delivery rate target Less than the target threshold for on-time delivery rate of orders , generate task efficiency update demand instructions;

[0108] In this embodiment, a dynamic adjustment mechanism is established when applied to set a resource utilization threshold. and order on-time delivery rate target threshold , resource utilization threshold and order on-time delivery rate target threshold is the minimum value of the mode, judging resource utilization Is the resource utilization rate less than the threshold? , if resource utilization Less than the resource utilization threshold , indicating that the current resources between enterprises need to be further improved, generating enterprise resource update demand instructions, and determining the on-time delivery rate target of orders Is it less than the target threshold of on-time delivery rate of orders? If the order on-time delivery rate target Less than the target threshold for on-time delivery rate of orders , indicating that the current cooperation efficiency of enterprise components needs to be improved, and generating task efficiency update demand instructions.

[0109] like Figure 6 As shown, as another preferred embodiment of the present invention, on the other hand, an inter-enterprise collaborative scheduling optimization system for cloud manufacturing, the system includes:

[0110] The collection module 100 is used to collect the resource information of the targeted enterprises involved in cloud manufacturing, the production task information and the skill level information of the personnel involved in cloud manufacturing;

[0111] The targeted enterprise resource capability model module 200 is used to construct a targeted enterprise resource capability model module based on the targeted enterprise resource information and the personnel skill level information;

[0112] The task requirement model module 300 is used to construct a task requirement model based on production task information;

[0113] A setting module 400 is used to set a multi-modal optimization goal for a targeted enterprise;

[0114] A generation module 500 is used to generate a directional optimization instruction based on the multimodal optimization goal of the targeted enterprise;

[0115] When this embodiment is applied, the collection module 100 collects the targeted enterprise resource information, production task information and the skill level information of the personnel involved in cloud manufacturing. Based on the targeted enterprise resource information and the personnel skill level information, the targeted enterprise resource capability model module 200 constructs a targeted enterprise resource capability model module. Based on the production task information, the task requirement model module 300 constructs a task requirement model. The setting module 400 sets the targeted enterprise multimodal optimization target. Based on the targeted enterprise multimodal optimization target, the generation module 500 generates a targeted optimization instruction.

[0116] The above embodiment of the present invention provides an inter-enterprise collaborative scheduling optimization method for cloud manufacturing, and provides an inter-enterprise collaborative scheduling optimization system for cloud manufacturing. Through the sensor network and the targeted enterprise information management system participating in cloud manufacturing, firstly, the resource information and production task information of the targeted enterprises participating in cloud manufacturing are collected, and the skill level information of the personnel participating in cloud manufacturing is collected, so as to comprehensively obtain rich information of cloud manufacturing enterprises. The sensor network can collect the operation status data of the equipment in real time, such as the equipment processing accuracy, processing speed and failure rate, etc. These data directly reflect the actual performance of the equipment and provide an accurate basis for subsequent resource evaluation. At the same time, information such as personnel skill level and quantity, task type, quantity, priority and delivery requirements are obtained from the enterprise information management system, covering the human and task dimensions of enterprise production. Then, based on the targeted enterprise resource information, production task information and personnel skill level information, a targeted enterprise resource capability model and task demand model are constructed. The resource capability model and task demand model can accurately describe the enterprise's resource availability and processing capabilities for different tasks. Through quantitative methods, targeted enterprise multimodal optimization goals are set, and a dynamic adjustment mechanism is established. Comprehensive consideration of these goals can comprehensively improve the performance of the cloud manufacturing system. Finally, based on the targeted enterprise multimodal optimization goals, directional optimization instructions are generated; this method and system fully consider the differences and complex relationships between enterprises, break the limitations of traditional scheduling, optimize resource utilization from a global perspective, avoid resource idleness or overload, and improve overall efficiency. In the face of the dynamic environment of cloud manufacturing, it can quickly adapt to changes, ensure system stability, and reduce production delays. Multi-objective optimization balances factors such as cost and delivery time, enhances corporate competitiveness, and promotes the efficient operation of the cloud manufacturing model, thereby maximizing the collaborative value among enterprises. Multiple optimization goals are set from the overall perspective of the cloud manufacturing system, covering multiple dimensions such as maximizing resource utilization and maximizing on-time order delivery rate. Maximizing resource utilization can give full play to the role of the cloud manufacturing resource pool, avoid idle resources, and improve the return on resource investment. Maximizing on-time order delivery rate improves customer satisfaction and helps maintain the company's good reputation and social benefits.

[0117] In order to load the above-mentioned method and system and enable it to run smoothly, the system, in addition to the various modules mentioned above, may also include more or fewer components than described above, or a combination of certain components, or different components, for example, it may include input and output devices, network access devices, buses, processors and memories, etc.

[0118] The processor may be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the system, and various interfaces and lines are used to connect various parts.

[0119] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0120] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0121] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A collaborative scheduling optimization method among enterprises for cloud manufacturing, characterized in that: The method comprises: Collecting targeted enterprise resource information, production task information, and skill level information of personnel involved in cloud manufacturing; Based on targeted enterprise resource information, production task information and personnel skill level information, a targeted enterprise resource capability model and task demand model are constructed; Set targeted enterprise multimodal optimization goals; Generate targeted optimization instructions based on the multi-modal optimization goals of the targeted enterprise; The collection of targeted enterprise resource information, production task information, and skill level information of personnel involved in cloud manufacturing specifically includes: Get device failure rate and equipment processing speed ; Get the production task type ; Get personnel skill level ; Based on production task type , generate priority and delivery date ; In the formula, is the device number, is the task type number, Label personnel and give them priority It can be represented by a value from 1 to 10, the higher the value, the higher the priority; The construction of a targeted enterprise resource capability model and a task requirement model based on targeted enterprise resource information, production task information and personnel skill level information specifically includes: Based on obtaining equipment failure rate and equipment processing speed , calculate and generate equipment resource capability model ; Among them, the equipment resource capability model The calculation process is: ； In the formula, It is the maximum processing speed of the same type of equipment; Based on personnel skill level , calculate and generate personnel resource capability model ; Among them, the human resource capability model The calculation process is: ； Based on production task type , Priority and delivery day , calculate and generate the task requirement model ; Among them, the task requirement model The calculation process is: ； In the formula, is the current time.

2. The inter-enterprise collaborative scheduling optimization method for cloud manufacturing according to claim 1 is characterized in that: The setting of the targeted enterprise multimodal optimization goal specifically includes: Setting multiple targeted enterprise optimization goals, including but not limited to a resource utilization maximization goal and an order on-time delivery rate goal; Computing resource utilization ; The resource utilization The calculation process is: ； In the formula, For equipment The maximum available state value, For personnel The maximum available state value.

3. The inter-enterprise collaborative scheduling optimization method for cloud manufacturing according to claim 1 is characterized in that: The setting of the targeted enterprise multimodal optimization goal specifically includes: Calculate on-time delivery target for orders ; The on-time delivery rate target for orders The calculation process is: ； In the formula, For the task Whether the delivery is on time, on time delivery is 1, and not on time delivery is 0. is the total number of tasks.

4. The inter-enterprise collaborative scheduling optimization method for cloud manufacturing according to claim 1 is characterized in that: The generating of the directional optimization instruction based on the multi-modal optimization goal of the targeted enterprise specifically includes: Establish a dynamic adjustment mechanism and set resource utilization thresholds and order on-time delivery rate target threshold ; Determining resource utilization Is the resource utilization rate less than the threshold? ; If resource utilization Less than the resource utilization threshold , generating enterprise resource update demand instructions; Determine the on-time delivery rate target for orders Less than the target threshold for on-time delivery rate of orders , generate task efficiency update requirement instructions.

5. An inter-enterprise collaborative scheduling optimization system for cloud manufacturing, characterized in that: The inter-enterprise collaborative scheduling optimization method for cloud manufacturing according to any one of claims 1 to 4 is applied, wherein the system comprises: A collection module is used to collect resource information of targeted enterprises involved in cloud manufacturing, production task information, and skill level information of personnel involved in cloud manufacturing; A targeted enterprise resource capability model module is used to construct a targeted enterprise resource capability model module based on targeted enterprise resource information and personnel skill level information; Task requirement model module, used to build a task requirement model based on production task information; The setting module is used to set the multimodal optimization goals of the targeted enterprise; A generation module is used to generate directional optimization instructions based on the multimodal optimization goals of the targeted enterprise.

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