CN114493199B

CN114493199B - Intelligent cloud scheduling method based on small-sized cross-medium enterprises

Info

Publication number: CN114493199B
Application number: CN202210026302.0A
Authority: CN
Inventors: 郭航瑞; 饶云波; 吴俊君; 杨自强; 周望; 慕通泽; 王奕文
Original assignee: University of Electronic Science and Technology of China
Current assignee: University of Electronic Science and Technology of China
Priority date: 2022-01-11
Filing date: 2022-01-11
Publication date: 2023-04-18
Anticipated expiration: 2042-01-11
Also published as: CN114493199A

Abstract

The invention discloses an intelligent cloud production scheduling method based on a cross-medium small enterprise, and belongs to the field of enterprise production scheduling planning and scheduling. The invention makes the scheduling plan from the global aspect, improves the efficiency and the scientificity of the scheduling plan, and simultaneously reduces the scheduling cost, thereby integrating all related enterprise information with low cost; relevant constraint conditions and optimization conditions are considered according to actual conditions, so that the scheduling plan is more scientific and reasonable, and the production efficiency is improved. The characteristics of the complex and changeable demand orders of small and medium-sized enterprises are considered, corresponding strategies are adopted aiming at the dynamic insertion of the orders, the corrected scheduling plan is updated and revised in time, and the extra cost in the production process is reduced as much as possible. By combining the intelligent cloud platform and utilizing the strong computing power of cloud computing, the scheduling efficiency is greatly improved.

Description

Intelligent cloud scheduling method based on small-sized cross-medium enterprises

Technical Field

The invention belongs to the field of enterprise scheduling plan scheduling, and particularly relates to an intelligent cloud scheduling method based on cross-medium small enterprises.

Background

With the continuous progress of industrial production technology, the automation level of production activities is higher and higher, and industrial 4.0 is the target of industrial production in various countries. The core characteristic of the industry 4.0 is interconnection, and the supply, manufacture, sale information datamation and intelligence in production are realized by using an internet of things information system, so that the rapid and effective product supply is achieved, and a highly flexible personalized and digitalized production mode of products and services is established.

In a conventional industrial system, an Enterprise Resource Planning (ERP) system is generally adopted to manage project organization. The ERP system is a management platform which is established on the basis of information technology, integrates the information technology and advanced management thought, and provides decision-making means for enterprise employees and decision-making layers by using a systematized management thought. However, with the significant increase of the complexity of Manufacturing products, the ERP System cannot meet the requirements of the current industrial automation production, and gradually becomes a functional module responsible for enterprise management, and the production activities of the enterprise are handled by the MES (Manufacturing Execution System) System and the APS (Advanced Planning and Scheduling) System. The MES system is a production informatization management system facing to a workshop execution layer of a manufacturing enterprise, integrates product data management in a front-end product design and process definition stage with production data management in a rear-end manufacturing stage, and realizes closed-loop collaborative full-life cycle management of product design, a production process and maintenance service. The APS system is a system for solving the problems of production scheduling and production scheduling, and can synchronize and monitor all resources in real time, and can realize an effective and accurate production plan no matter on materials, machines and equipment, personnel management, customer demand supply and the like.

However, the MES + APS model is relatively high in complexity and cost of system management, and is only suitable for large-scale production of some large-scale enterprises, but is not suitable for small and medium-scale enterprises due to the influence of factors such as turnover capital, production scale, production process, automation degree and cost investment; on the other hand, large enterprises are usually "sold in production", that is, products are produced to promote the product sale, and are dominant in the market supply and demand relationship, while medium and small enterprises are usually "sold in production", that is, products are produced according to the product sale, and are passive in the market supply and demand relationship. The result is that production orders for large enterprises tend to be large and predictable, while orders for small and medium enterprises are small and complex. With the improvement of living standard of people, the personalized demand is more and more, and the mode of 'bringing products by sale' of small and medium-sized enterprises is more and more common, so the production requirements of the enterprises are higher and higher, and the MES + APS mode is difficult to exert good effect under the condition of limited resources and scale.

Disclosure of Invention

In view of the problems of the method in the scheduling planning of the small and medium-sized enterprises, the invention provides an intelligent cloud scheduling method based on the small and medium-sized enterprises, so as to solve the scheduling planning problem in the collaborative production of the small and medium-sized enterprises.

The invention provides an intelligent cloud production scheduling method based on a cross-medium small enterprise, which comprises the following steps:

the resource demander calls a demand order publishing service to publish demand order information, wherein the demand order information comprises: the product name, the product structure, the manufacturing process of the product, the required quantity of the product, the estimated cost and the required delivery date;

the production supplier calls a production capacity information publishing service to publish capacity information (namely, production capacity information), wherein the capacity information comprises: the name of the produced product, the adopted manufacturing process, the yield in unit time, the working time per day, the storage of production raw materials, the work price in unit time, the product storage warehouse address, the transportation mode and the cost information;

a scheduling server (cloud platform) periodically calls an order summarizing service to summarize all currently published demand order information and determine the relationship types among the demand orders, wherein the relationship types comprise a parent-child relationship, a brother relationship and a no relationship; and sending a demand task model creation event;

the scheduling server calls a demand task model creating service to create a demand task model and sends out a demand task model ready event under the condition that the demand task model creating event is monitored; the demand task model includes: order number, order name, father order, son order, production raw material, production process, product quantity, delivery date and requirement enterprise;

the scheduling server calls a capacity summarizing service to summarize all currently issued capacity information and sends out a resource capacity model establishing event; under the condition that the resource capacity model creating event is monitored, calling resource capacity model creating service to create resource capacity models of all production units and sending out resource capacity model ready events; the resource capability model includes: production unit name, production process, unit productivity, unit cost and raw material storage; the production unit represents a production site or a production plant of a certain production type;

the scheduling server calls a constraint and optimization condition acquisition service to acquire a constraint condition and an optimization condition of scheduling and sends a data ready event under the condition that a demand task model ready event and a resource capacity model ready event are monitored; and under the condition that the data ready event is monitored, calling a scheduling determining service to obtain initial scheduling information and sending a scheduling confirmation event according to the demand task model, the resource capacity model and a preset scheduling strategy, wherein the scheduling information is used for providing a production plan for a demand order in the demand task model, namely a cross-enterprise collaborative production plan.

And the scheduling server calls a scheduling confirmation service under the condition that the scheduling confirmation event is monitored to be ready, negotiations and confirmations of the scheduling information are respectively carried out with the corresponding production supplier and the corresponding resource demand side in the initial scheduling information, and the final scheduling information is obtained and the corresponding production supplier and the corresponding resource demand side are informed based on the negotiation and confirmation results.

Further, the determination method of the relationship type between orders is as follows: if the order A is one of the production raw materials of the order B or the order A is one of the raw materials of the order B after being processed by a specified degree, the order A is called a child order of the order B, and the order B is called a parent order of the order A; if the order A and the order B are the sub-orders of the order C together, the order A and the order B are called as brother orders; if order A and order B are not parent-child and sibling, then there is no relationship between A and B.

Further, in order to ensure that the scheduling plan better meets the actual situation, a constraint condition needs to be set before the scheduling determination service is called to start scheduling, where the constraint condition includes:

1. the planned production start date must be later than the earliest start date of the order;

2. the planned production completion date must be earlier than the delivery date plus a maximum acceptable pull-in;

3. the sum of daily production time of each production unit must be less than the upper limit of the daily working capacity of the unit;

4. the production task must meet the minimum maximum capacity of the production unit;

5. the production task must be produced within the working time of the production unit;

6. the parent of the workpiece must have been produced;

7. the predecessor tasks of the production task must have been completed;

8. the sum of the production quantity of all batches of production tasks on each production unit is equal to the required quantity of the tasks;

meanwhile, in order to maximize the yield in actual production, optimization conditions are also considered, and the optimization conditions include:

1. minimum hold off time, minimizing the sum of hold off times for all orders.

2. Minimum production costs, minimizing the sum of all enterprises (production suppliers) production costs, including product production costs, storage costs, and transfer transportation costs.

3. The minimum production span minimizes the time span between the first production unit to start and the last finished production unit to finish in all enterprises (production suppliers).

Further, after the scheduling server completes the creation of the demand task model, the scheduling server calls a product model construction service, creates a product model according to the relation type between the orders and the demand task model, and sends a product model ready event; the product model is represented by adopting a tree diagram structure, wherein a root node is a final product, child nodes of the root node are child orders for directly producing the final product, and child nodes of the child nodes are child orders for directly producing the child nodes. So as to more intuitively present the logical structure of the visual output whole product to the user.

Further, in order to make the platform know which orders on the product structure tree have finished producing or are producing, the production supplier needs to report the key nodes of production to the production scheduling server in real time during the production process, i.e. information is reported when each order starts producing and is finished.

Furthermore, in view of the complexity and variability of the demand orders of small and medium-sized enterprises, the demand orders are often dynamically inserted in the middle, and the insertion time can be divided into three types: before, during and after the abortion; namely, the scheduling server monitors dynamically inserted orders in real time, and adopts different processing strategies according to the order insertion time:

when the scheduling server detects that the inserted demand order information exists before the scheduling determining service is called, calling an order summarizing service to confirm the type of the relation between the order and other orders, calling a demand task model establishing service to establish a demand task model of the order, calling a product model establishing service to add the current demand task model into the product model, and calling a scheduling determining service to perform scheduling;

when the scheduling server detects that the scheduling determining service has the inserted demand order information in the process of calling the scheduling determining service, suspending the scheduling determining service, calling the order summarizing service to confirm the relationship type between the order and other orders, calling the demand task model creating service to create a demand task model of the order, calling the product model building service to add the current demand task model into the product model, and calling the scheduling determining service to perform scheduling;

when the scheduling server detects that the inserted demand order information exists after calling the scheduling determination service, marking nodes which are already produced or are being produced on a current product model, calling the order summarizing service to confirm the relation type between the order and other orders, calling the demand task model creating service to create a demand task model of the order, calling the product model building service to add the current demand task model into the product model, calling the scheduling determination service to perform scheduling, and not including the marked nodes during scheduling.

The technical scheme provided by the invention at least has the following beneficial effects:

(1) All relevant enterprise information is integrated at low cost, a scheduling plan is formulated from the global aspect, efficiency and scientificity of the scheduling plan are improved, and meanwhile the scheduling cost is reduced.

(2) Relevant constraint conditions and optimization conditions are considered according to practical conditions on the basis of a scheduling plan commonly used in the industry, so that the scheduling plan is more scientific and reasonable, and the production efficiency is improved.

(3) The characteristics of the complex and changeable demand orders of small and medium-sized enterprises are considered, corresponding strategies are adopted aiming at the dynamic insertion of the orders, the corrected scheduling plan is updated and revised in time, and the extra cost in the production process is reduced as much as possible.

(4) By combining an intelligent cloud platform and utilizing the strong computing power of cloud computing, the scheduling efficiency is greatly improved.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

FIG. 1 is a flow chart of the method of the present invention.

Fig. 2 is a diagram of a complex product tree structure.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

The embodiment of the invention provides an intelligent cloud scheduling method based on a cross-medium small-sized enterprise, which is used for realizing scheduling plan planning of cross-enterprise production of complex products by using an intelligent cloud platform, and is shown in figure 1, and the method comprises the following steps:

step 1: the supply and demand enterprise calls the corresponding information publishing service respectively to publish the corresponding demand order and the production capacity condition, and the method comprises the following steps:

the demand enterprise issues demand order information, and the content comprises a demand product name, a product structure, a manufacturing process of the product, a demand quantity of the product, a predicted cost, a required delivery date and the like. In addition, enterprises need to provide information on product receiving warehouse addresses, transportation modes and costs to a platform (intelligent cloud platform).

The supply enterprise issues the production capacity condition, and the content comprises the name of the produced product, the adopted manufacturing process, the yield per unit time, the storage of production raw materials in working time every day, the working price per unit time and the like. In addition, enterprises need to provide product storage warehouse address, transportation means, and cost information to the platform.

And 2, step: the intelligent cloud platform calls corresponding services to classify and summarize the order information and the capacity information and establish a corresponding model, and the method comprises the following steps:

and summarizing the order information of all the demand enterprises, and analyzing the relation among the demand orders, including parent-child relation, brother relation and no relation. The specific relationship is illustrated as follows:

if the order A is one of the production raw materials of the order B or one of the raw materials after simple processing, the order A is called as a child order of the order B, and the order B is called as a parent order of the order A; if the order A and the order B are the sub-orders of the order C together, the order A and the order B are called as brother orders; if order A and order B are not parent-child and sibling, then there is no relationship between A and B.

And establishing a demand task model according to the order information, wherein the demand task model comprises information such as order numbers, order names, parent orders, child orders, production raw materials, production processes, product quantity, delivery dates, demand enterprises and the like.

In addition, the logical structure of the whole product is shown for more intuition. The embodiment of the invention also comprises the following steps: and constructing a product model according to the relation between the orders and the demand task model, and representing the product model by using a tree diagram structure. The root node of the tree is the final product, the child nodes of the tree are child orders for directly producing the product, similarly, the child nodes of the child nodes are child orders for directly producing the child nodes, the logical structure of the whole product is shown by progressing layer by layer, and the logical structure is shown in fig. 2.

The production capacity information of all supply enterprises is collected, and a resource capacity model of each enterprise production unit is established, wherein the production units represent production places or production workshops with relatively independent production types. The resource capacity model comprises information such as production unit name, production process, unit capacity, unit cost, raw material reserve and the like.

And step 3: the intelligent cloud platform calls a scheduling determining service, calculates according to corresponding algorithm rules to obtain a cross-enterprise collaborative production plan, and comprises the following steps:

and (3) converting the industrial scheduling problem into a mathematical problem according to the demand task model and the resource capacity model established in the step (2), and selecting a proper scheduling algorithm as a basic algorithm of the cloud scheduling.

At present, industrial scheduling algorithms tend to be mature, wherein some relatively simple algorithms, such as a shortest delivery date algorithm is a task with the earliest scheduled delivery date, a shortest construction period algorithm is a task with the shortest scheduled consumption construction period, and the like, and complex algorithms, such as a neural network, a simulated annealing method, a genetic algorithm, a tabu search method and the like, are adopted, and a proper algorithm is selected according to actual conditions.

In order to ensure that the scheduling plan is more in line with the actual situation, before starting scheduling, constraints need to be set, and the constraints include:

2. the planned production completion date must be earlier than the delivery date plus the maximum acceptable pull-in;

6. the parent of the workpiece must have been produced;

7. the predecessor tasks of the production task must have been completed;

8. the sum of the production quantities of all batches of the production tasks on each production unit is equal to the required quantity of the tasks;

9. the sum of the working time of all batches of a production task on each production unit is equal to the total working time of the task.

Meanwhile, in order to maximize the yield in actual production, optimization conditions need to be considered, and the optimization conditions include:

1. minimum hold off time, minimizing the sum of hold off times for all orders.

2. Minimum production cost, the sum of the production cost of all enterprises is minimized, including the production cost, the storage cost and the transfer transportation cost of products.

3. And the minimum production span ensures that the start-up time of the first start-up production unit and the finish time span of the last finished production unit in all enterprises are minimum.

And 4, step 4: the intelligent cloud platform calls a scheduling confirmation service to send the preliminary scheduling meter to a production enterprise, informs a demand enterprise of relevant information, and requires to repeat the plan within a specified time to determine whether the enterprise approves the scheduling plan or has other modification suggestions and suggestions.

And when all the enterprises confirm no errors, informing the final scheduling plan to all the related enterprises.

Further, in order to make the platform know which orders on the product structure tree have finished producing or are producing, the supply enterprise needs to report the key nodes of production to the platform in real time during the production process, i.e. information is filled when each order starts producing and is finished.

It should be noted that, in view of the complexity of the demand orders of small and medium-sized enterprises, there is often dynamic insertion of demand orders in the middle, and the insertion time can be divided into three categories: before, during and after the birth.

For an inserted order before scheduling, only a demand task model is established for the order, the model is added to a product structure tree according to the relation between the models, and finally scheduling is carried out.

For an inserted order in the scheduling, when the order is detected, the current scheduling is immediately stopped, a demand task model is established for the order, the model is added to a product structure tree according to the relation between the models, and finally the scheduling is performed.

For the order inserted after scheduling, the product of which nodes on the current product structure tree is produced or is being produced needs to be counted, the nodes are marked, then a demand task model is established for the inserted order, the model is added to the product structure tree according to the relation between the models, finally scheduling is carried out, and during scheduling, the production plan is not arranged for the marked nodes.

In order to obtain a scheduling plan of cross-enterprise collaborative production of complex products with high efficiency, the plan is a collaborative production plan with small delay, low cost and short span obtained by combining constraint and optimization strategies on the basis of considering the requirements and the production energy of each enterprise. Referring to fig. 1 and fig. 2, as a possible implementation manner, a specific working process of the intelligent cloud scheduling method based on the cross-medium small enterprise provided by the embodiment of the present invention includes:

s1: the demand enterprise issues demand order information, and the content comprises a demand product name, a product structure, a manufacturing process of the product, a demand quantity of the product, a predicted cost, a required delivery date and the like. In addition, the enterprise needs to provide the platform with product receiving warehouse address, transportation mode and cost information.

S2: the supply enterprise issues the generation capacity condition, and the content comprises the name of the produced product, the adopted manufacturing process, the yield per unit time, the storage of production raw materials in working time every day, the working price per unit time and the like. In addition, enterprises need to provide product storage warehouse address, transportation means, and cost information to the platform.

S3: the platform collects the order information of all demand enterprises and analyzes the relation among the demand orders according to the information in the orders. Establishing a demand task model according to order information:

order o = (product p, quantity q, order origination time s1, delivery time s2, order priority pri, parent order fo, child order co, business e);

product p = (name n, process pc, raw material r, sub-product ratio rr);

s4: and constructing a structural tree diagram of the complex product, as shown in FIG. 2.

The root node of the tree is the final product, its child nodes are the child orders a1, a2, a3 of the product, similarly, the child node of a1 is the child orders b1, b2 of a1, the child node of a2 is the child orders c1, c2 of a2, the child node of a3 is the child order d1 of a3, the child node of b1 is the child order e1 of b1, the child node of c1 is the child orders f1, f2 of c1, and the child node of d1 is the child order g1 of d 1. If a node has no child node, the node represents a raw material for production and can be directly used without complex processing.

S5: the platform collects the production capacity information of all supply enterprises, and establishes a resource capacity model of each enterprise production unit:

production unit g = (minimum take-up minG, maximum take-up maxG, unit hour quote up, production type pt, raw material stock sr, enterprise e);

production unit capacity gc = (production unit g, process pc, daily capacity dc);

s6: and selecting a proper scheduling algorithm according to the actual production situation, such as a shortest delivery date algorithm, a shortest procedure algorithm, a neural network, a simulated annealing method, a genetic algorithm, a tabu search method and the like.

S7: setting a constraint condition:

6. the parent of the workpiece must have been produced;

7. the predecessor tasks of the production task must have been completed;

S8: setting an optimization condition:

1. minimum hold off time, minimizing the sum of hold off times for all orders.

2. Minimum production costs, minimizing the sum of all enterprises' production costs, including product production costs and transfer transportation costs.

3. And the minimum production span minimizes the start-up time of the first production unit and the completion time span of the last finished production unit in all enterprises.

S9: and calculating the scheduling plan to obtain a primary draft of the scheduling plan.

S10: and sending the part related to the respective enterprise in the initial draft of the production plan to the corresponding enterprise.

S11: and correspondingly modifying according to the feedback condition of the enterprise, and if no feedback exists, determining that the enterprise agrees the scheduling plan.

S12: and sending the final draft of the modified production plan to the enterprise.

S13: and monitoring dynamic insertion orders in real time, and adopting different strategies according to different insertion occasions. For an inserted order before scheduling, only a demand task model is established for the order, the model is added to a product structure tree according to the relation between the models, and finally scheduling is carried out. For an inserted order in the scheduling, when the order is detected, the current scheduling is immediately stopped, a demand task model is established for the order, the model is added to a product structure tree according to the relation between the models, and finally the scheduling is performed. For the order inserted after scheduling, the product of which nodes on the current product structure tree is produced or is being produced needs to be counted, the nodes are marked, then a demand task model is established for the inserted order, the model is added to the product structure tree according to the relation between the models, finally scheduling is carried out, and during scheduling, the production plan is not arranged for the marked nodes.

Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

What has been described above are merely some embodiments of the present invention. It will be apparent to those skilled in the art that various changes and modifications can be made without departing from the inventive concept thereof, and these changes and modifications can be made without departing from the spirit and scope of the invention.

Claims

1. An intelligent cloud production scheduling method based on cross-medium small enterprises is characterized by comprising the following steps:

the resource demand side calls demand order releasing service to release demand order information, wherein the demand order information comprises: the product name, the product structure, the manufacturing process of the product, the required quantity of the product, the estimated cost and the required delivery date;

the production supplier calls a production capacity information publishing service to publish the capacity information, wherein the capacity information comprises: the name of the produced product, the adopted manufacturing process, the yield in unit time, the working time per day, the storage of production raw materials, the work price in unit time, the product storage warehouse address, the transportation mode and the cost information;

the production scheduling server periodically calls an order summarizing service to summarize all currently published demand order information and determines the relationship types among the demand orders, wherein the relationship types comprise a parent-child relationship, a brother relationship and a no relationship; and sending a demand task model creation event;

the scheduling server calls a demand task model creating service to create a demand task model and sends a demand task model ready event under the condition that the demand task model creating event is monitored; the demand task model includes: order number, order name, father order, son order, production raw material, production process, product quantity, delivery date and requirement enterprise;

the scheduling server calls a capacity summarizing service to summarize all currently issued capacity information and sends out a resource capacity model establishing event; under the condition that the resource capacity model creating event is monitored, calling resource capacity model creating service to create resource capacity models of all production units and sending out resource capacity model ready events; the resource capability model includes: production unit name, production process, unit productivity, unit cost and raw material storage; the production unit represents a production site or a production workshop of a certain production type;

the scheduling server calls a constraint and optimization condition acquisition service to acquire a constraint condition and an optimization condition of scheduling and sends a data ready event under the condition that a demand task model ready event and a resource capacity model ready event are monitored; under the condition that the data ready event is monitored, calling a scheduling determining service to obtain initial scheduling information and send a scheduling confirmation event according to a demand task model, a resource capacity model and a preset scheduling strategy, wherein the scheduling information is used for providing a production plan for a demand order in the demand task model;

the scheduling server calls a scheduling confirmation service under the condition that a scheduling confirmation event is monitored to be ready, negotiates and confirms the scheduling information with the corresponding production supplier and resource demander in the initial scheduling information respectively, obtains final scheduling information based on negotiation and confirmation results and informs the corresponding production supplier and resource demander;

wherein the content of the first and second substances,

the determination method of the relationship type between orders is as follows: if the order A is one of the production raw materials of the order B or the order A is one of the raw materials of the order B after being processed by a specified degree, the order A is called as a child order of the order B, and the order B is called as a parent order of the order A; if the order A and the order B are the sub-orders of the order C together, the order A and the order B are called as brother orders; if the order A and the order B are not in a parent-child relationship and a brother relationship, the order A and the order B have no relationship;

the constraint conditions include:

the planned production start date is later than the earliest start date of the order;

the planned production completion date is earlier than the delivery date plus the maximum acceptable pull-in period;

the total daily production time of each production unit is less than the upper limit of the daily working capacity of the unit;

the production task meets the minimum maximum carrying capacity of the production unit;

the production task is produced within the working time of the production unit;

the parent of the workpiece has been produced;

the predecessor tasks of the production task have been completed;

the sum of the production quantities of all batches of the production tasks on each production unit is equal to the required quantity of the tasks;

the optimization conditions include:

the minimum pull-off time is used for minimizing the sum of the pull-offs of all orders;

minimum production costs, minimizing the sum of production costs of production suppliers, including product production costs, storage costs, and transfer transportation costs;

the minimum production span is used for minimizing the start-up time of the first start-up production unit and the completion time span of the last completed production unit in the production supplier;

after the scheduling server completes the establishment of the demand task model, the scheduling server calls a product model construction service, establishes a product model according to the relation type between orders and the demand task model and sends a product model ready event; the product model is represented by adopting a tree diagram structure, wherein a root node is a final product, child nodes of the root node are child orders for directly producing the final product, and child nodes of the child nodes are child orders for directly producing the child nodes, so that a logic structure of the whole product can be visually presented and output to a user;

the scheduling server monitors dynamic order insertion in real time, and adopts different processing strategies according to the order insertion time:

2. The method of claim 1, wherein a production provider needs to report key nodes of production to a scheduling server in real time during production.