Industrial production scheduling planning modeling method and device, industrial production scheduling planning generation method and device and storage medium
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-08-31
- Publication Date
- 2026-03-27
AI Technical Summary
The traditional industrial production scheduling planning methods have problems of poor generalization and understanding, and information extraction and interpretation are time-consuming and expensive, and cannot be coordinated systematically.
By storing and analyzing industrial scheduling related data, determining the correlation relationship between different elements, and generating an industrial scheduling planning model based on these relationships and transformation logic, thereby realizing the automatic generation of scheduling planning.
The automatic creation of industrial scheduling planning has been realized, the efficiency and accuracy of scheduling planning has been improved, and the costs of manual intervention and information processing have been reduced.
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Abstract
Description
Industrial production scheduling modeling, generation method, device and storage medium Technical Field
[0001] The present application relates to the field of industrial technology, and in particular to an industrial production scheduling modeling, generation method, device and computer-readable storage medium.
[0002] Background of the Invention
[0003] Despite several IT reforms in recent years, Chinese manufacturing companies still have high requirements for production planning, or, more precisely, for optimizing production schedules based on capabilities, resources, demand, and expectations. To develop appropriate solutions, some solution providers schedule a series of production planning-related meetings, inviting a diverse group of participants, such as management, plant managers, order engineers, data scientists, and programming engineers. After a fragmented and time-consuming analysis of all relevant issues, personnel, and preferred approaches, they present their preferred approach and a carefully considered solution. Other solution providers, on the other hand, seek to provide generic window-based or web-based graphical user interfaces with limited functionality and / or core production logic, offering further support for predefined and / or limited functionality, such as upgrades and customized plans.
[0004] However, due to the high frequency, multi-channel, and high volatility of industrial production scheduling, traditional methods are not very generalizable and understandable. In addition, information extraction and interpretation in traditional methods are time-consuming and expensive, and there is no way to systematically coordinate this information.
[0005] Therefore, those skilled in the art are still working on finding other solutions for industrial production scheduling.
[0006] Summary of the Invention
[0007] In view of this, the embodiments of the present application provide, on the one hand, an industrial production scheduling modeling method and a generation method, and on the other hand, provide an industrial production scheduling modeling device, a generation device and a computer-readable storage medium, which can realize the automatic generation of industrial production scheduling.
[0008] To solve the above technical problems, the technical solution of this application is implemented as follows:
[0009] A method for industrial production scheduling modeling includes: storing pre-acquired industrial production scheduling related data; determining various association relationships between multiple different elements related to industrial scheduling based on pre-acquired industrial production scheduling related element information and conversation information of industrial production scheduling related personnel including demand and corresponding production scheduling results; the association relationships include: corresponding relationships of parameters pointing to industrial production scheduling related data; determining conversion logic between different association relationships and production scheduling results, and generating an industrial production scheduling model based on the conversion logic.
[0010] In one embodiment, the method further includes: creating user tags for personnel related to industrial production scheduling; and classifying and acquiring conversation information corresponding to each user tag, including demand and corresponding production scheduling results, according to the user tags.
[0011] In one embodiment, the industrial scheduling related data includes any one or any combination of machine capacity, labor shift plan, customer orders, and inventory materials.
[0012] In one embodiment, the correspondence between the parameters and industrial scheduling related data includes: the correspondence between products and the quantity of required materials, the correspondence between products and processing time, the correspondence between products and manual shifts, the correspondence between orders and order completion deadlines, and the correspondence between orders and customer priorities, any one or any combination thereof.
[0013] A method for generating an industrial production scheduling plan, comprising: obtaining industrial production scheduling demand information of a user; extracting keywords from the industrial production scheduling demand information; searching for at least one association relationship related to the keyword from a first database; the first database storing various association relationships between multiple different elements related to industrial scheduling determined based on pre-acquired industrial production scheduling related element information and conversation information of industrial production scheduling related personnel including demand and corresponding production scheduling results; if the at least one association relationship includes a corresponding relationship in which parameters point to industrial production scheduling related data, then obtaining the industrial production scheduling related data pointed to by the parameters of the corresponding relationship from a second database; an industrial production scheduling planning model determines whether there is a contradictory relationship in the at least one association relationship based on the at least one association relationship, or the acquired industrial production scheduling related data, and generates a corresponding industrial production scheduling plan when there is no contradictory relationship; the industrial production scheduling planning model is generated based on the conversion logic between different association relationships and production scheduling results.
[0014] In one embodiment, the method further includes: when it is determined that a conflicting relationship exists in the at least one association relationship, outputting error information indicating the details of the conflict.
[0015] In one embodiment, obtaining the user's industrial production scheduling demand information includes: obtaining the user's demand information in the form of a dialogue through a human-computer interaction interface.
[0016] In one embodiment, the user's industrial production scheduling demand information includes any one of the following information: scheduling each order according to a set priority; deleting at least one order in the current production schedule; and modifying a part of the current production schedule.
[0017] In one embodiment, the method further includes: updating the second database according to a user's instruction to create, modify and / or delete industrial production scheduling related data.
[0018] An industrial production scheduling modeling device includes: a first module for storing pre-acquired industrial scheduling related data; a second module for determining various association relationships between multiple different elements related to industrial scheduling based on pre-acquired industrial scheduling related element information and conversation information of industrial scheduling related personnel including demand and corresponding scheduling results; the association relationships include: corresponding relationships of parameters pointing to industrial scheduling related data; a third module for determining conversion logic between different association relationships and scheduling results, and generating an industrial production scheduling model based on the conversion logic.
[0019] An industrial production scheduling plan generation device includes: a fourth module for obtaining a user's industrial production scheduling demand information; a fifth module for extracting keywords from the demand information; a sixth module for searching at least one association relationship related to the keyword from a first database; the first database stores various association relationships between multiple different elements related to industrial scheduling determined based on pre-acquired industrial scheduling-related element information and conversation information of industrial scheduling-related personnel including demand and corresponding scheduling results; a seventh module for obtaining the industrial scheduling-related data pointed to by the parameters of the corresponding relationship from a second database when the at least one association relationship includes a corresponding relationship in which parameters point to industrial scheduling-related data; an eighth module for judging whether there is a contradictory relationship in the at least one association relationship by an industrial production scheduling planning model based on the at least one association relationship or the acquired industrial scheduling-related data, and generating a corresponding industrial production scheduling plan when there is no contradictory relationship; the industrial production scheduling planning model is generated based on the conversion logic between different association relationships and scheduling results.
[0020] Another industrial production scheduling and planning modeling device includes at least one memory and at least one processor, wherein: the at least one memory is used to store a computer program; the at least one processor is used to call the computer program stored in the at least one memory to execute the industrial production scheduling and planning modeling method as described above.
[0021] Another industrial production scheduling plan generation device includes at least one memory and at least one processor, wherein: the at least one memory is used to store a computer program; the at least one processor is used to call the computer program stored in the at least one memory to execute the industrial production scheduling plan generation method as described above.
[0022] A computer-readable storage medium stores a computer program thereon; the computer program can be executed by a processor and implement the industrial production scheduling modeling method or the industrial production scheduling generation method as described above.
[0023] It can be seen from the above technical solution that in the embodiment of the present application, since the correlation between industrial scheduling-related elements can be determined based on the historical conversation information of industrial scheduling-related personnel, and then an industrial scheduling planning model can be constructed based on the correlation and corresponding conversion logic, a scheduling plan corresponding to different user needs can be generated based on the industrial scheduling planning model, thereby realizing the automatic creation of industrial scheduling plans.
[0024] BRIEF DESCRIPTION OF THE DRAWINGS
[0025] In order to better understand the present application, the embodiments of the present application will be described in detail below with reference to the accompanying drawings so that those skilled in the art can better understand the above and other features and advantages of the present application. In the accompanying drawings:
[0026] FIG1 is an exemplary flow chart of an industrial production scheduling modeling method according to an embodiment of the present application.
[0027] FIG2 is an exemplary flow chart of a method for generating an industrial production schedule in an embodiment of the present application.
[0028] FIG3 is a schematic diagram of a human-computer interaction process in an example of the present application.
[0029] FIG4 is an exemplary structural diagram of an engineering template creation device in an embodiment of the present application.
[0030] FIG5 is an exemplary structural diagram of an engineering template generating device in an embodiment of the present application.
[0031] FIG6 is an exemplary structural diagram of another industrial production scheduling modeling device in an embodiment of the present application.
[0032] FIG7 is an exemplary structural diagram of another industrial production scheduling plan generating device in an embodiment of the present application.
[0033] The accompanying drawings are numerals as follows:
[0034] Methods of implementing this application
[0035] In an embodiment of the present application, in order to avoid the time-consuming and laborious process of organizing meetings to obtain user wishes for each production scheduling, it is considered to model the industrial production scheduling based on historical session information, and then generate a production scheduling corresponding to different user needs based on the industrial production scheduling model.
[0036] In order to make the purpose, technical solutions and advantages of this application more clear, the technical solutions of this application are described in detail below with reference to the accompanying drawings and examples.
[0037] FIG1 is an exemplary flow chart of an industrial production scheduling modeling method in an embodiment of the present application. As shown in FIG1 , the method may include the following steps:
[0038] Step S12: storing the pre-acquired industrial production scheduling related data.
[0039] In this embodiment, the industrial production scheduling related data may include any one or any combination of data such as machine capacity, labor shift plan, customer orders, and inventory materials.
[0040] Step S14, based on the pre-acquired industrial scheduling related element information and the conversation information of industrial scheduling related personnel including demand and corresponding scheduling results, determine the various association relationships between multiple different elements related to industrial scheduling; the association relationships include: the corresponding relationship of parameters pointing to industrial scheduling related data.
[0041] In this embodiment, the industrial production scheduling related element information may include: products, materials, personnel, production capacity (abbreviated as production capacity), priority and other information.
[0042] The correspondence between the parameters and industrial scheduling related data may include: the correspondence between products and the quantity of required materials, the correspondence between products and processing time, the correspondence between products and manual shifts, the correspondence between orders and order completion time limits, the correspondence between orders and customer priorities, and any other type or combination of these relationships.
[0043] The industrial production scheduling related personnel may include: management personnel, production managers, order engineers, data scientists, programming engineers, etc. For each industrial production scheduling related personnel, their historical conversation information can be expressed as: H = {(p1, r1), (p2, r2), ..., (p n ,r n )}, where p represents demand and r represents the production scheduling result feedback for the demand.
[0044] In this embodiment, user tags can also be created for personnel related to industrial production scheduling, such as management personnel, production managers, order engineers, data scientists, and programming engineers. Then, based on each user tag, conversation information corresponding to the user tag, including demand and corresponding production scheduling results, can be obtained. In this way, historical conversation information can be classified, summarized, and analyzed.
[0045] For each person involved in industrial scheduling, in addition to understanding the correspondence between different requirements and scheduling results, they can also obtain relevant user-oriented information, such as the user's background information, idiomatic phrases, language style, desired and undesirable results, risks and obstacles, etc. Based on this user-oriented information, it is possible to: accurately understand the user's true intentions, for example, by standardizing the language styles of different personnel to obtain a unique, accurate meaning; and understand the scheduling priorities corresponding to the scheduling requirements of different personnel. For example, order engineers may have customer priorities, while production managers may have order completion deadline priorities or inventory clearance priorities.
[0046] Therefore, in addition to the above-mentioned corresponding relationships, the association relationship may also include: mutual conditional constraints, such as the constraint relationship between customer priority and deadline, the constraint relationship between deadline and production capacity, and the constraint relationship between production capacity and inventory.
[0047] Step S16: determining the conversion logic between different association relationships and production scheduling results, and generating an industrial production scheduling model based on the conversion logic.
[0048] In this step, there are various methods for determining the conversion logic between different associations and production scheduling results and generating an industrial production scheduling model. For example, a tree-like industrial production scheduling model can be constructed based on executable nodes and logical nodes. Alternatively, a deep learning network can be trained using a training set consisting of historical associations and industrial production scheduling plans to obtain a trained industrial production scheduling model.
[0049] After the industrial production scheduling model is generated, the industrial production scheduling model can be used to generate an industrial production scheduling plan corresponding to the industrial production scheduling needs.
[0050] In one embodiment, the industrial production scheduling model may be implemented by a NLP (Natural Language Processing) engine.
[0051] FIG2 is an exemplary flow chart of a method for generating an industrial production schedule in an embodiment of the present application. As shown in FIG2 , the method may include the following steps:
[0052] Step S22: Obtain user demand information, including industrial production scheduling demand information.
[0053] In this step, the industrial production scheduling demand information may include: scheduling each order according to the set priority, such as order completion time limit, customer level, or inventory reduction demand; deleting at least one order in the current production schedule; modifying a part of the current production schedule, etc.
[0054] In one embodiment, a user's industrial production scheduling requirements can be obtained through a human-computer interaction interface in the form of a dialogue. For example, Figure 3 shows a schematic diagram of the human-computer interaction process in an example. As shown in Figure 3, User 1 enters the data table shown in the upper left corner of Figure 3, which includes the order number, processing time, and order completion time limit. At the same time, the user enters the voice or text input "Please prioritize these orders based on the order completion time limit." The data table and the voice or text input together constitute the user's industrial production scheduling requirements.
[0055] Step S24: extract keywords from the demand information.
[0056] In this step, to obtain the user's unique and accurate meaning, the requirement information may first be subjected to a standardized language style conversion, and then keywords may be extracted from it. Alternatively, keywords may be extracted from the requirement information and then subjected to a standardized language style conversion. The specific method of processing may be determined based on the actual situation.
[0057] Of course, in some applications, it is also possible to perform fuzzy matching in S26 without performing standardized language style conversion on the demand information or the keywords.
[0058] For the example shown in FIG3 , the extracted keywords may include: order and order completion time limit.
[0059] Step S26, searching for at least one association relationship related to the keyword from the first database; the first database stores various association relationships between multiple different elements related to industrial scheduling, which are determined based on pre-acquired industrial scheduling related element information and conversation information of industrial scheduling related personnel including demand and corresponding scheduling results.
[0060] In this step, based on the keywords, the association relationships found may include the corresponding relationships between orders and order completion deadlines.
[0061] In this step, if the at least one association relationship includes a corresponding relationship in which parameters point to industrial production scheduling related data, the industrial production scheduling related data pointed to by the parameters of the corresponding relationship are obtained from the second database.
[0062] In the example shown in Figure 3, the specific order details pointed to by the order serial number need to be extracted from the second database. In one example, the second database can be an MES (Manufacturing Execution Systems) database.
[0063] In step S28, an industrial production scheduling model determines whether there is a contradictory relationship in the at least one association relationship based on the at least one association relationship or the obtained industrial production scheduling related data, and generates a corresponding industrial production scheduling plan when there is no contradictory relationship; the industrial production scheduling model is generated based on the conversion logic between different association relationships and production scheduling results.
[0064] In this step, production can be scheduled from near to far according to the order completion time limit of each order. However, during the production scheduling process, if the time required to complete a certain order exceeds the required order completion time limit based on production capacity, then when it is determined that there is a contradiction in the production scheduling process, an error message indicating the details of the contradiction can be further output. Afterwards, return to execute step S22 to obtain the demand information adjusted by the user. If there is no contradiction in the production scheduling process, the corresponding industrial production scheduling plan can be generated. For example, for the demand information in the example shown in Figure 3, feedback can be provided as shown in the table below Figure 3, and the user can be replied to by voice or text, "Select the order completion time limit as the production scheduling target, and the production scheduling order for the input order number is 3, 5, 4, 2, 1."
[0065] If the user accepts the generated industrial production schedule, the process ends; if the user does not accept it, the process returns to step S22 to obtain the demand information adjusted by the user.
[0066] In this embodiment, the demand information may also include: system update information, such as demand information for creating, modifying and / or deleting industrial scheduling-related data. Accordingly, the second database may be updated based on the user's demand information for creating, modifying and / or deleting industrial scheduling-related data.
[0067] The above describes in detail the industrial production scheduling modeling method and generation method in the embodiments of the present application. The following describes in detail the industrial production scheduling modeling device and generation device in the embodiments of the present application. The device in the embodiments of the present application can be used to implement the corresponding method in the embodiments of the present application. For details not disclosed in detail in the device embodiments of the present application, please refer to the corresponding description in the corresponding method embodiments of the present application.
[0068] FIG4 is an exemplary structural diagram of an engineering template creation device in an embodiment of the present application. As shown in FIG4 , the device may include: a first module 401 , a second module 402 , and a third module 403 .
[0069] The first module 401 is used to store pre-acquired industrial production scheduling related data. The various association relationships between the multiple different elements related to industrial production scheduling can be stored in a second database, such as an MES database.
[0070] The second module 402 determines various associations between multiple different elements related to industrial scheduling based on pre-acquired industrial scheduling-related element information and conversation information between industrial scheduling-related personnel, including requirements and corresponding scheduling results. The associations include corresponding relationships between parameters pointing to industrial scheduling-related data. The determined various associations between the multiple different elements related to industrial scheduling may be stored in the first database.
[0071] The third module 403 is used to determine the conversion logic between different associations and production scheduling results, and generate an industrial production scheduling model based on the conversion logic. The industrial production scheduling model can be implemented in an NLP engine.
[0072] FIG5 is an exemplary structural diagram of an engineering template generation device in an embodiment of the present application. As shown in FIG5 , the device may include: a fourth module 501 , a fifth module 502 , a sixth module 503 , a seventh module 504 and an eighth module 505 .
[0073] The fourth module 501 is used to obtain user demand information, wherein the demand information includes industrial production scheduling demand information.
[0074] The fifth module 502 is used to extract keywords from the demand information.
[0075] The sixth module 503 is used to search for at least one association relationship related to the keyword from the first database; the first database stores various association relationships between multiple different elements related to industrial scheduling, which are determined based on pre-acquired industrial scheduling-related element information and conversation information of industrial scheduling-related personnel including demand and corresponding scheduling results.
[0076] The seventh module 504 is configured to obtain the industrial production scheduling related data pointed to by the parameters in the corresponding relationship from the second database when the at least one association relationship includes a corresponding relationship in which the parameters point to industrial production scheduling related data. In one embodiment, the second database may be an MES database.
[0077] The eighth module 505 is configured to have an industrial production scheduling model determine whether there is a conflict in the at least one association relationship based on the at least one association relationship or the acquired industrial production scheduling-related data, and generate a corresponding industrial production scheduling plan if no conflict exists. The industrial production scheduling model is generated based on the conversion logic between different association relationships and production scheduling results. In one embodiment, the eighth module 505 can be a natural language processing engine (NLP).
[0078] Furthermore, the demand information may also include: system update information, such as demand information for creating, modifying and / or deleting industrial scheduling-related data. Accordingly, the device may further include a ninth module 506, which is used to update the second database according to the user's demand information for creating, modifying and / or deleting industrial scheduling-related data.
[0079] In fact, the industrial production scheduling modeling device and generation device provided in the embodiments of this application can be implemented in various ways. For example, the industrial production scheduling modeling device and generation device can be compiled into a plug-in installed in a smart terminal by using an application programming interface that complies with specific rules, or can be packaged into an application for user download and use.
[0080] When compiled as a plug-in, the industrial production scheduling modeling device and the generating device can be implemented in a variety of plug-in formats, such as ocx, dll, and cab. The industrial production scheduling modeling device and the generating device provided by this implementation of the present application can also be implemented by using specific technologies, such as Flash plug-in technology, RealPlayer plug-in technology, MMS plug-in technology, MIDI plug-in technology, or ActiveX plug-in technology.
[0081] The industrial production scheduling modeling method and generation method provided by this implementation of the present application can be stored in various storage media in an instruction storage manner or an instruction set storage manner. These storage media include, but are not limited to, floppy disks, optical disks, DVDs, hard disks, flash memory, USB flash memory, CF cards, SD cards, SDHC cards, MMC cards, SM cards, memory sticks, and xD cards.
[0082] It should be clear that the operating system operating in the computer can implement the functions of any of the above embodiments not only by executing the program code read by the computer from the storage medium, but also by using instructions based on the program code to implement part or all of the actual operations.
[0083] For example, Figure 6 is an exemplary structural diagram of another industrial production scheduling modeling device in an embodiment of the present application. This device can be used to execute the industrial production scheduling modeling method shown in Figure 1, or to implement the device shown in Figure 4. As shown in Figure 6, the device may include at least one memory 61 and at least one processor 62. In addition, it may also include other components, such as communication ports, input / output controllers, and network communication interfaces. These components communicate via a bus 63, etc.
[0084] At least one memory 61 is used to store computer programs. In one example, the computer program can be understood to include the various modules of the apparatus shown in FIG4 . Additionally, at least one memory 61 can store an operating system, etc. Operating systems include, but are not limited to, Android operating system, Symbian operating system, Windows operating system, Linux operating system, etc.
[0085] At least one processor 62 is configured to invoke a computer program stored in at least one memory 61 to execute the industrial production scheduling modeling method described in the examples of this application. The processor 62 may be a CPU, a processing unit / module, an ASIC, a logic module, or a programmable gate array, and may receive and send data via a communication port.
[0086] Figure 7 is an exemplary structural diagram of another industrial production schedule generation device according to an embodiment of the present application. This device can be used to execute the industrial production schedule generation method shown in Figure 2 or to implement the device shown in Figure 5. As shown in Figure 7, the device may include at least one memory 71 and at least one processor 72. Furthermore, it may include other components, such as communication ports, input / output controllers, and network communication interfaces. These components communicate via a bus 73, etc.
[0087] At least one memory 71 is used to store computer programs. In one example, the computer program can be understood to include the various modules of the apparatus shown in FIG5 . Additionally, at least one memory 71 can store an operating system, etc. Operating systems include, but are not limited to, Android operating system, Symbian operating system, Windows operating system, Linux operating system, etc.
[0088] At least one processor 72 is configured to invoke a computer program stored in at least one memory 71 to execute the industrial production scheduling plan generation method described in the examples of this application. The processor 72 may be a CPU, a processing unit / module, an ASIC, a logic module, or a programmable gate array, and may receive and send data via a communication port.
[0089] It should be understood that "and / or" as used herein is intended to include any and all possible combinations of one or more of the associated listed items.
[0090] The number of the embodiments of the present application is only used for description and does not represent the advantages of the embodiments.
[0091] It can be seen from the above technical solution that in the embodiment of the present application, since the correlation between industrial scheduling-related elements can be determined based on the historical conversation information of industrial scheduling-related personnel, and then an industrial scheduling planning model can be constructed based on the correlation and corresponding conversion logic, a scheduling plan corresponding to different user needs can be generated based on the industrial scheduling planning model, thereby realizing the automatic creation of industrial scheduling plans.
[0092] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.
Claims
1. Industrial production scheduling modeling method, characterized in that: include: Storing pre-acquired industrial production scheduling related data; Based on the pre-acquired industrial scheduling related element information and the conversation information of the industrial scheduling related personnel including the demand and the corresponding scheduling results, various association relationships between multiple different elements related to industrial scheduling are determined; the association relationships include: the corresponding relationship between the parameters pointing to the industrial scheduling related data; Determine the conversion logic between different association relationships and production scheduling results, and generate an industrial production scheduling planning model based on the conversion logic.
2. The industrial production scheduling modeling method according to claim 1 is characterized in that: Further including: Create user tags for personnel involved in industrial scheduling; According to the user tags, the conversation information corresponding to each user tag including the demand and the corresponding production scheduling result is obtained by classification.
3. The industrial production scheduling modeling method according to claim 1 or 2, characterized in that: The industrial production scheduling related data includes: any one or any combination of machine capacity, labor shift plan, customer orders, and inventory materials.
4. The industrial production scheduling modeling method according to claim 1 or 2, characterized in that: The correspondence between the parameters and the industrial scheduling related data includes: the correspondence between products and the quantity of required materials, the correspondence between products and processing time, the correspondence between products and manual shifts, the correspondence between orders and order completion deadlines, and the correspondence between orders and customer priorities, any one or any combination thereof.
5. The industrial production scheduling generation method is characterized by: include: Obtain users' industrial production scheduling demand information; Extract keywords from the industrial production scheduling demand information; Searching for at least one association relationship related to the keyword from a first database; the first database stores various association relationships between multiple different elements related to industrial scheduling determined based on pre-acquired industrial scheduling related element information and conversation information including demand and corresponding scheduling results of industrial scheduling related personnel; If the at least one association relationship includes a corresponding relationship in which a parameter points to industrial production scheduling related data, then obtaining the industrial production scheduling related data pointed to by the parameter of the corresponding relationship from the second database; An industrial production scheduling model determines whether there is a contradictory relationship in the at least one association relationship based on the at least one association relationship or the acquired industrial production scheduling related data, and generates a corresponding industrial production scheduling plan when there is no contradictory relationship; the industrial production scheduling model is generated based on the conversion logic between different association relationships and production scheduling results.
6. The method for generating industrial production scheduling according to claim 5, characterized in that: Further including: When it is determined that there is a contradictory relationship in the at least one association relationship, error information indicating details of the contradiction is output.
7. The method for generating industrial production scheduling according to claim 5 or 6, characterized in that: The obtaining of the industrial production scheduling demand information of the user includes: Obtain user demand information in the form of dialogue through the human-computer interaction interface.
8. The method for generating industrial production scheduling according to claim 7, characterized in that: The industrial production scheduling demand information of the user includes any one of the following information: scheduling each order according to a set priority; deleting at least one order in the current production scheduling plan; modifying a part of the production scheduling plan in the current production scheduling plan.
9. The method for generating industrial production scheduling according to claim 7, characterized in that: Further including: The second database is updated according to the user's instruction to create, modify and / or delete industrial production scheduling related data.
10. Industrial production scheduling modeling device, characterized in that: include: The first module (401) is used to store pre-acquired industrial production scheduling related data; The second module (402) determines various associations between multiple different elements related to industrial scheduling based on the pre-acquired industrial scheduling related element information and the conversation information of industrial scheduling related personnel including the demand and the corresponding scheduling results; the associations include: the corresponding relationship of the parameters pointing to the industrial scheduling related data; The third module (403) is used to determine the conversion logic between different association relationships and production scheduling results, and generate an industrial production scheduling planning model based on the conversion logic.
11. An industrial production scheduling generation device, characterized in that: include: The fourth module (501) is used to obtain the industrial production scheduling demand information of the user; A fifth module (502) is used to extract keywords from the demand information; The sixth module (503) is used to search for at least one association relationship related to the keyword from the first database; the first database stores information based on pre-acquired industrial production scheduling related elements and industrial The conversation information between the production scheduling personnel, including the demand and the corresponding production scheduling results, determines the various correlations between multiple different elements related to industrial production scheduling; A seventh module (504) is used to obtain the industrial production scheduling related data pointed to by the parameters of the corresponding relationship from the second database when the at least one association relationship includes a corresponding relationship in which the parameters point to the industrial production scheduling related data; The eighth module (505) is used to determine whether there is a contradictory relationship in the at least one association relationship according to the at least one association relationship or the acquired industrial scheduling related data by an industrial production scheduling model, and generate a corresponding industrial production scheduling when there is no contradictory relationship; the industrial production scheduling model is generated based on the conversion logic between different association relationships and scheduling results.
12. Industrial production scheduling modeling device, characterized in that: The invention comprises at least one memory (61) and at least one processor (62), wherein: The at least one memory (61) is used to store a computer program; The at least one processor (62) is used to call the computer program stored in the at least one memory (61) to execute the industrial production scheduling modeling method according to any one of claims 1 to 4.
13. An industrial production scheduling and generating device, characterized in that: The invention comprises at least one memory (71) and at least one processor (72), wherein: The at least one memory (71) is used to store a computer program; The at least one processor (72) is used to call the computer program stored in the at least one memory (71) to execute the industrial production scheduling generation method according to any one of claims 5 to 9.
14. A computer-readable storage medium having a computer program stored thereon; characterized in that: The computer program can be executed by a processor and implements the industrial production scheduling modeling method according to any one of claims 1 to 4 or the industrial production scheduling generation method according to any one of claims 5 to 9.