CN112529310A - Production plan scheduling method, device, equipment and storage medium - Google Patents
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Abstract
The invention discloses a production plan scheduling method, a production plan scheduling device, production plan scheduling equipment and a storage medium. The method comprises the following steps: acquiring production element data; preprocessing the production element data; inputting the preprocessed production element data into a plan scheduling model to obtain a scheduling result, wherein the plan scheduling model is obtained by iteratively training a to-be-trained plan scheduling model through a training sample set, and the training sample set comprises: producing element data samples and scheduling results corresponding to the production element data samples; and carrying out production scheduling according to the scheduling result. By the technical scheme, the problems of lack of data support, insufficient plan feedback real-time performance and the like in the establishment of the enterprise production plan can be solved, the production plan scheduling efficiency is improved, and the scientificity and the rationality of the plan are improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of production line management, in particular to a production plan scheduling method, a production plan scheduling device, production plan scheduling equipment and a storage medium.
Background
The production and manufacturing process of large-scale equipment and machinery is often a complex comprehensive process, and various production workshops, production lines and work types are operated in a time and space mode in an intersecting manner. The production plan is an important basis for the enterprise to carry out overall arrangement on production tasks and production management.
At present, an enterprise production plan specifies a production plan mainly according to manual experience, various production activities are managed in a mode of coordinating dispatching parties and a field, and various work types and various production activities are roughly coordinated. On the one hand, the planning depends on the experience of the maker, and the accuracy and the performability of the planning are different due to different experiences of the maker. On the other hand, production plans are formed by buckling loops, the production rhythm of the next procedure can be influenced when a problem occurs in one procedure, the plan tracking feedback is seriously disconnected due to large workload and relatively delayed feedback time through manual feedback, changes of various plans cannot be linked in time, the production order is easily disordered, and the production progress is delayed. Therefore, under the circumstances that the business scale is continuously enlarged and the market competition is increasingly severe, the management means based on experience or anticipation is far from being adapted.
Disclosure of Invention
Embodiments of the present invention provide a production plan scheduling method, apparatus, device, and storage medium, so as to achieve that a dynamically adjusted production plan scheduling result can be obtained by inputting production real-time data into a plan scheduling model, solve the problems of lack of data support, insufficient plan feedback real-time, and the like in the formulation of an enterprise production plan, improve the efficiency of production plan scheduling, and improve the scientificity and rationality of the plan.
In a first aspect, an embodiment of the present invention provides a method for scheduling a production plan, including:
acquiring production element data;
preprocessing the production element data;
inputting the preprocessed production element data into a plan scheduling model to obtain a scheduling result, wherein the plan scheduling model is obtained by iteratively training a to-be-trained plan scheduling model through a training sample set, and the training sample set comprises: producing element data samples and scheduling results corresponding to the production element data samples;
and carrying out production scheduling according to the scheduling result.
Further, the acquiring production element data includes:
at least one of operator data, equipment data, material data, process data, and environmental data is obtained.
Further, the plan scheduling model is obtained by iteratively training a to-be-trained plan scheduling model through a training sample set, and includes:
acquiring a scheduling model of a plan to be trained;
inputting the production element data samples into the scheduling model of the plan to be trained to obtain a predicted scheduling result;
training parameters of the scheduling model of the plan to be trained according to an objective function generated by the predicted scheduling result and the scheduling result corresponding to the production factor data sample;
and iteratively executing the operation of inputting the production element data samples into the to-be-trained plan scheduling model to obtain a scheduling result until a plan scheduling model is obtained.
Further, before obtaining the scheduling model of the plan to be trained, the method further includes:
acquiring historical production element data;
and selecting a training sample set from the historical production factor data according to the target characteristics.
Further, after the operation of inputting the production element data sample into the to-be-trained plan scheduling model to obtain a scheduling result is performed iteratively until a plan scheduling model is obtained, the method further includes:
acquiring a calibration sample set, wherein data in the calibration sample set is different from data in the training sample set;
and verifying the plan scheduling model according to the verification sample set.
Further, verifying the planned scheduling model according to the verification sample set includes:
inputting the check sample set into a planning and scheduling model to obtain a scheduling result;
and adjusting the plan scheduling model according to the scheduling result.
In a second aspect, an embodiment of the present invention further provides a production plan scheduling apparatus, where the apparatus includes:
the data acquisition module is used for acquiring production element data;
the preprocessing module is used for preprocessing the production element data;
a result obtaining module, configured to input the preprocessed production element data into a plan scheduling model to obtain a scheduling result, where the plan scheduling model is obtained by iteratively training a to-be-trained plan scheduling model through a training sample set, and the training sample set includes: producing element data samples and scheduling results corresponding to the production element data samples;
and the scheduling module is used for scheduling the production plan according to the scheduling result.
Further, the data obtaining module includes:
at least one of operator data, equipment data, material data, process data, and environmental data is obtained.
Further, the result obtaining module includes:
the model obtaining unit is used for obtaining a scheduling model of the plan to be trained;
the result obtaining unit is used for inputting the production element data samples into the scheduling model of the plan to be trained to obtain a prediction scheduling result;
the model training unit is used for training the parameters of the to-be-trained plan scheduling model according to an objective function generated by the predicted scheduling result and the scheduling result corresponding to the production factor data sample;
and iteratively executing the operation of inputting the production element data samples into the to-be-trained plan scheduling model to obtain a scheduling result until a plan scheduling model is obtained.
Further, the method also comprises the following steps:
the historical data acquisition module is used for acquiring historical production element data before acquiring the scheduling model of the plan to be trained;
and the selection module is used for selecting a training sample set from the historical production element data according to the target characteristics.
Further, the method also comprises the following steps:
a calibration sample acquisition module, configured to acquire a calibration sample set after iteratively executing an operation of inputting the production element data sample into the to-be-trained plan scheduling model to obtain a scheduling result until a plan scheduling model is obtained, where data in the calibration sample set is different from data in the training sample set;
and the checking module is used for checking the plan scheduling model according to the checking sample set.
Further, the verification module is specifically configured to:
inputting the check sample set into a planning and scheduling model to obtain a scheduling result;
and adjusting the plan scheduling model according to the scheduling result.
In a third aspect, an embodiment of the present invention further provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the production plan scheduling method according to any one of the embodiments of the present invention when executing the program.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the production plan scheduling method according to any one of the embodiments of the present invention.
According to the embodiment of the invention, the production plan scheduling model is established through the historical data, the obtained production element data is input into the production plan scheduling model to obtain the scheduling result, the waste caused by the shortage or the idle of local resources can be avoided, the load balance of each procedure in the production process is ensured, the production plan scheduling efficiency is improved, the scientificity and rationality of the plan are improved, and the production process of the product is accurately and efficiently controlled.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a flowchart of a method for scheduling a production plan according to a first embodiment of the present invention;
FIG. 1a is a process flow diagram of a ship hull segment construction in one embodiment of the invention;
FIG. 1b is a flowchart of determining a scheduling result according to a first embodiment of the present invention;
FIG. 1c is a schematic diagram of a production plan scheduling method according to a first embodiment of the present invention;
fig. 2 is a schematic structural diagram of a production plan scheduling apparatus according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a computer device in a third embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present invention, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
Example one
Fig. 1 is a flowchart of a production plan scheduling method according to an embodiment of the present invention, where this embodiment is applicable to production plan making and scheduling management, and the method may be executed by a production plan scheduling apparatus according to an embodiment of the present invention, where the apparatus may be implemented in a software and/or hardware manner, as shown in fig. 1, the method specifically includes the following steps:
and S110, acquiring production element data.
Specifically, the production elements refer to various resources required in the production and storage of the product. Due to different technological processes of different products, the production element data required by different products are different. For example, an operator, a production facility, a work environment, and the like. The production element data refers to specific information of the production element, and may be, for example, the working time of an operator, the operating condition of production equipment, the area occupancy of a warehouse, and the like.
Optionally, the obtaining production element data includes:
at least one of operator data, equipment data, material data, process data, and environmental data is obtained.
The operator data may be the arrival post data of the operator, and the start time and the end time of the operation of the operator.
The equipment data may be the operating status of the production equipment for each process flow, including: an operating state, a standby state, a fault state; the accumulated running time of the production equipment of each process flow can be used, and the energy consumption state of the production equipment of each process flow can be used, such as voltage, cylinder pressure, gas consumption, rotating speed, load weight and fuel level.
The material data refers to material information required in the production process of the product, such as the type, the amount and the production number of raw materials.
The process data refers to data used and generated in a process design process, and may be, for example, machining material data, machining data, machine tool data, group classification feature data, and the like.
The environmental data refers to information of the environment required in the production process of the product, and may be, for example, a production site, temperature and humidity, noise, a particulate matter index, a carbon dioxide concentration, and the like.
Specifically, a plurality of production lines often operate simultaneously in the production and manufacturing process of the product, and each production line is divided into a plurality of processes. The production elements required by each process are different, but at least comprise at least one of operator data, equipment data, material data, process data and environment data.
Illustratively, as shown in fig. 1a, the hull segment construction is taken as an example and divided into six procedures of pretreatment, cutting, processing, welding, transportation and hoisting, wherein each procedure requires production equipment, but the production equipment and the equipment data of different procedures are different. The raw element data of the ship body section building can comprise information such as operation starting time, operation ending time, fault states, the number of cut pieces, cutting length, marking length, idle running length, operation time of loading and unloading personnel, furnace lot number two-dimensional codes of steel plates, energy consumption states, total power, active power, total current, voltage, gas flow energy consumption monitoring and the like of the cutting machine.
And S120, preprocessing the production element data.
Specifically, data preprocessing such as denoising, redundant data processing, missing value filling, abnormal value processing and the like is performed on the production factor data.
S130, inputting the preprocessed production element data into a plan scheduling model to obtain a scheduling result, wherein the plan scheduling model is obtained by iteratively training a to-be-trained plan scheduling model through a training sample set, and the training sample set comprises: the production element data samples and the corresponding scheduling results of the production element data samples.
The scheduling result may be a scheduling production line or a scheduling workshop of the process corresponding to the production element data, or may be the capacity of the process corresponding to the production element data. The productivity of each process affects the production of other processes.
Specifically, production element data acquired and preprocessed in real time are input into a plan scheduling model trained in advance, so that dynamic scheduling of a production plan is achieved through real-time data, waste caused by local resource shortage or idling and the like is avoided, and load balance of each process in the production process is guaranteed. The plan scheduling model is obtained by iteratively training the to-be-trained plan scheduling model through the training sample set, and the to-be-trained scheduling model is not limited in this respect. The training sample set includes: the production element data samples and the corresponding scheduling results of the production element data samples. The production element data sample can be extracted from historical production element data according to preset characteristic requirements.
Optionally, as shown in fig. 1b, the manner of inputting the preprocessed production element data into the plan scheduling model to obtain the scheduling result may be to identify a type of the production plan, input data corresponding to the type of the production plan collected in real time into the plan scheduling model, and output the scheduling result corresponding to the type of the production plan.
Illustratively, if the type of the production plan is identified as a cutting process, cutting process data collected in real time is input into the plan scheduling model to obtain a scheduling result corresponding to the cutting process.
And S140, carrying out production scheduling according to the scheduling result.
Specifically, each procedure in the production process is dynamically scheduled according to the scheduling result.
Optionally, the plan scheduling model is obtained by iteratively training a to-be-trained plan scheduling model through a training sample set, and includes:
acquiring a scheduling model of a plan to be trained;
inputting the production element data samples into the scheduling model of the plan to be trained to obtain a predicted scheduling result;
training parameters of the scheduling model of the plan to be trained according to an objective function generated by the predicted scheduling result and the scheduling result corresponding to the production factor data sample;
and iteratively executing the operation of inputting the production element data samples into the to-be-trained plan scheduling model to obtain a scheduling result until a plan scheduling model is obtained.
The plan scheduling model to be trained may be a linear model or a nonlinear model.
The method for obtaining the scheduling model of the plan to be trained can be to establish the model according to the relationship between the production elements and the capacity of each production line, or can be to establish the model according to the relationship between the production elements and the scheduling production lines of each production line.
For example, the number of cuts is ((cutting machine operation end time-cutting machine operation start time + operation time of the loader + operation time of the unloader)/number of cuts) × the number of planned cuts).
Specifically, production element data samples are selected from historical data, and scheduling results corresponding to the production element data samples are determined according to the historical data to form a training sample set. And inputting the production element data samples collected in real time into the scheduling model of the plan to be trained to obtain a predicted scheduling result. And generating an objective function according to a scheduling result corresponding to the production element data sample, training parameters of the to-be-trained plan scheduling model according to the predicted scheduling result and the objective function, and repeatedly executing the operation of inputting the production element data sample into the to-be-trained plan scheduling model to obtain a scheduling result until a plan scheduling model is obtained.
Optionally, before obtaining the scheduling model of the plan to be trained, the method further includes:
acquiring historical production element data;
and selecting a training sample set from the historical production factor data according to the target characteristics.
Different processes correspond to different production element data, different production element data correspond to different target characteristics, and the target characteristics can be personnel load capacity, equipment utilization rate, equipment failure rate or warehouse use area rate.
Specifically, target features corresponding to historical production element data of different procedures are determined through feature engineering such as feature processing, selection, extraction, combined dimension reduction and the like, and training samples of each procedure are determined from historical data to form a training sample set according to the target features.
Optionally, after iteratively executing the operation of inputting the production element data sample into the to-be-trained plan scheduling model to obtain a scheduling result until a plan scheduling model is obtained, the method further includes:
acquiring a calibration sample set, wherein data in the calibration sample set is different from data in the training sample set;
and verifying the plan scheduling model according to the verification sample set.
Specifically, a calibration sample set is obtained from historical data, and the calibration sample set is different from data in the training sample set. And verifying the plan scheduling model according to the verification sample set.
Optionally, verifying the planned scheduling model according to the verification sample set includes:
inputting the check sample set into a planning and scheduling model to obtain a scheduling result;
and adjusting the plan scheduling model according to the scheduling result. Specifically, the check sample set is input into a plan scheduling model to obtain a scheduling result, and the plan scheduling model is adjusted according to the scheduling result, so that the prediction result of the plan scheduling model reaches the required reliability. The method for verifying the planning and scheduling model can be fitness analysis or sensitivity analysis.
As shown in fig. 1c, the specific steps of the embodiment of the present invention are: the method comprises the steps of obtaining historical data, wherein the historical data comprises at least one of operator data, equipment data, material data, process data and environment data, conducting data preprocessing and characteristic analysis on the historical data, conducting model training and model verification according to the historical data to obtain a plan scheduling model, obtaining a scheduling result through obtaining real-time data and inputting the real-time data into the obtained plan scheduling model, and conducting real-time scheduling on a production plan.
According to the technical scheme of the embodiment, the production plan scheduling model is established through historical data, the production element data is acquired and input into the production plan scheduling model to obtain the scheduling result, the real-time load and fault conditions of personnel, equipment, materials, processes and environments of each production line of a workshop can be remotely mastered, and great convenience is provided for production plan control and resource optimization configuration; waste caused by local resource shortage or idling and the like is avoided, and load balance of each procedure in the production process is ensured; the efficiency of production plan scheduling is improved, the scientificity and rationality of the plan are improved, and the production process of the product is accurately and efficiently controlled.
Example two
Fig. 2 is a schematic structural diagram of a production plan scheduling apparatus according to a second embodiment of the present invention. The present embodiment may be applicable to the case of product production planning scheduling, where the apparatus may be implemented in a software and/or hardware manner, and the apparatus may be integrated in any device that provides a function of production planning scheduling, as shown in fig. 2, where the apparatus for production planning scheduling specifically includes: a data acquisition module 210, a pre-processing module 220, a result acquisition module 230, and a scheduling module 240.
A data obtaining module 210, configured to obtain production factor data;
a preprocessing module 220, configured to preprocess the production element data;
a result obtaining module 230, configured to input the preprocessed production factor data into a plan scheduling model to obtain a scheduling result, where the plan scheduling model is obtained by iteratively training a to-be-trained plan scheduling model through a training sample set, where the training sample set includes: producing element data samples and scheduling results corresponding to the production element data samples;
and the scheduling module 240 is configured to perform production plan scheduling according to the scheduling result.
Optionally, the data obtaining module includes:
at least one of operator data, equipment data, material data, process data, and environmental data is obtained.
Optionally, the result obtaining module includes:
the model obtaining unit is used for obtaining a scheduling model of the plan to be trained;
the result obtaining unit is used for inputting the production element data samples into the scheduling model of the plan to be trained to obtain a prediction scheduling result;
the model training unit is used for training the parameters of the to-be-trained plan scheduling model according to an objective function generated by the predicted scheduling result and the scheduling result corresponding to the production factor data sample;
and iteratively executing the operation of inputting the production element data samples into the to-be-trained plan scheduling model to obtain a scheduling result until a plan scheduling model is obtained.
Optionally, the method further includes:
the historical data acquisition module is used for acquiring historical production element data before acquiring the scheduling model of the plan to be trained;
and the selection module is used for selecting a training sample set from the historical production element data according to the target characteristics.
Optionally, the method further includes:
a calibration sample acquisition module, configured to acquire a calibration sample set after iteratively executing an operation of inputting the production element data sample into the to-be-trained plan scheduling model to obtain a scheduling result until a plan scheduling model is obtained, where data in the calibration sample set is different from data in the training sample set;
and the checking module is used for checking the plan scheduling model according to the checking sample set.
Optionally, the verification module is specifically configured to:
inputting the check sample set into a planning and scheduling model to obtain a scheduling result;
and adjusting the plan scheduling model according to the scheduling result.
The product can execute the method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
According to the technical scheme of the embodiment, the production plan scheduling model is established through historical data, the production element data is acquired and input into the production plan scheduling model to obtain the scheduling result, the real-time load and fault conditions of personnel, equipment, materials, processes and environments of each production line of a workshop can be remotely mastered, and great convenience is provided for production plan control and resource optimization configuration; waste caused by local resource shortage or idling and the like is avoided, and load balance of each procedure in the production process is ensured; the efficiency of production plan scheduling is improved, the scientificity and rationality of the plan are improved, and the production process of the product is accurately and efficiently controlled.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a computer device in a third embodiment of the present invention. FIG. 3 illustrates a block diagram of an exemplary computer device 12 suitable for use in implementing embodiments of the present invention. The computer device 12 shown in FIG. 3 is only an example and should not impose any limitation on the scope of use or functionality of embodiments of the present invention.
As shown in FIG. 3, computer device 12 is in the form of a general purpose computing device. The components of computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)30 and/or cache memory 32. Computer device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 3, and commonly referred to as a "hard drive"). Although not shown in FIG. 3, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.
The processing unit 16 executes various functional applications and data processing by executing programs stored in the system memory 28, for example, to implement the production plan scheduling method provided by the embodiment of the present invention: acquiring production element data; preprocessing the production element data; inputting the preprocessed production element data into a plan scheduling model to obtain a scheduling result, wherein the plan scheduling model is obtained by iteratively training a to-be-trained plan scheduling model through a training sample set, and the training sample set comprises: producing element data samples and scheduling results corresponding to the production element data samples; and carrying out production scheduling according to the scheduling result.
Example four
A fourth embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a production plan scheduling method according to any of the embodiments of the present invention: acquiring production element data; preprocessing the production element data; inputting the preprocessed production element data into a plan scheduling model to obtain a scheduling result, wherein the plan scheduling model is obtained by iteratively training a to-be-trained plan scheduling model through a training sample set, and the training sample set comprises: producing element data samples and scheduling results corresponding to the production element data samples; and carrying out production scheduling according to the scheduling result.
Any combination of one or more computer-readable media may be employed. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A production plan scheduling method, comprising:
acquiring production element data;
preprocessing the production element data;
inputting the preprocessed production element data into a plan scheduling model to obtain a scheduling result, wherein the plan scheduling model is obtained by iteratively training a to-be-trained plan scheduling model through a training sample set, and the training sample set comprises: producing element data samples and scheduling results corresponding to the production element data samples;
and carrying out production scheduling according to the scheduling result.
2. The method of claim 1, wherein said obtaining production element data comprises:
at least one of operator data, equipment data, material data, process data, and environmental data is obtained.
3. The method of claim 1, wherein the planning and scheduling model is obtained by iteratively training a planning and scheduling model to be trained through a training sample set, and the method comprises the following steps:
acquiring a scheduling model of a plan to be trained;
inputting the production element data samples into the scheduling model of the plan to be trained to obtain a predicted scheduling result;
training parameters of the scheduling model of the plan to be trained according to an objective function generated by the predicted scheduling result and the scheduling result corresponding to the production factor data sample;
and iteratively executing the operation of inputting the production element data samples into the to-be-trained plan scheduling model to obtain a scheduling result until a plan scheduling model is obtained.
4. The method of claim 3, prior to obtaining the planned scheduling model to be trained, further comprising:
acquiring historical production element data;
and selecting a training sample set from the historical production factor data according to the target characteristics.
5. The method of claim 3, wherein after iteratively inputting the production element data sample into the to-be-trained planning model to obtain a scheduling result until obtaining a planning model, the method further comprises:
acquiring a calibration sample set, wherein data in the calibration sample set is different from data in the training sample set;
and verifying the plan scheduling model according to the verification sample set.
6. The method of claim 5, wherein validating the planned scheduling model according to the validation sample set comprises:
inputting the check sample set into a planning and scheduling model to obtain a scheduling result;
and adjusting the plan scheduling model according to the scheduling result.
7. A production plan scheduling apparatus, comprising:
the data acquisition module is used for acquiring production element data;
the preprocessing module is used for preprocessing the production element data;
a result obtaining module, configured to input the preprocessed production element data into a plan scheduling model to obtain a scheduling result, where the plan scheduling model is obtained by iteratively training a to-be-trained plan scheduling model through a training sample set, and the training sample set includes: producing element data samples and scheduling results corresponding to the production element data samples;
and the scheduling module is used for scheduling the production plan according to the scheduling result.
8. The apparatus of claim 7, wherein the data acquisition module comprises:
at least one of operator data, equipment data, material data, process data, and environmental data is obtained.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1-6 when executing the program.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-6.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117389230A (en) * | 2023-11-16 | 2024-01-12 | 广州中健中医药科技有限公司 | Antihypertensive traditional Chinese medicine extract production control method and system |
| CN117389230B (en) * | 2023-11-16 | 2024-06-07 | 广州中健中医药科技有限公司 | Antihypertensive traditional Chinese medicine extract production control method and system |
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2020
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117389230A (en) * | 2023-11-16 | 2024-01-12 | 广州中健中医药科技有限公司 | Antihypertensive traditional Chinese medicine extract production control method and system |
| CN117389230B (en) * | 2023-11-16 | 2024-06-07 | 广州中健中医药科技有限公司 | Antihypertensive traditional Chinese medicine extract production control method and system |
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Application publication date: 20210319 |