CN117808272A - Industrial system productivity optimization and scheduling method and system based on machine learning - Google Patents
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Abstract
The invention relates to the technical field of machine learning, in particular to an industrial system productivity optimization and scheduling method and system based on machine learning, wherein the method specifically comprises the following steps: constructing a dynamic grid visualization framework of production nodes to which an industrial system of an industrial park belongs; acquiring historical production information data of production products of an industrial system of an industrial park for one year and real-time production information data of the production products of the industrial system; constructing a machine learning model, and carrying out fusion analysis and predictive calculation of capacity threat values on historical production information data and real-time production information data; predicting the calculated productivity threat value according to the machine learning model, and judging the productivity optimization level; and verifying and evaluating the production speed of each production node, and executing the capacity scheduling strategy. The invention solves the problems of the prior art that the productivity of the industrial system is easily influenced by the number of on-duty workers, the number of fault devices and market demand information, and the optimization instantaneity and comprehensiveness of the productivity of the industrial system are poor.
Description
Technical Field
The invention relates to the technical field of machine learning, in particular to an industrial system productivity optimization and scheduling method and system based on machine learning.
Background
With the continuous development of economy, the production efficiency and quality of industrial systems are receiving more and more attention. The industrial production flow is multi-step and multi-area, and in order to meet the market demand and balance the production capacity of staff, the management departments of all industrial parks need to continuously optimize the production system, improve the production efficiency and reduce the production cost.
In the prior art, few technical schemes for integrating and analyzing the number of workers on duty, the number of equipment faults and the market demands in real time exist, and in the data analysis aiming at the market demands, consideration for changing the market demands of products due to seasonal changes is lacking; the number of market demands for seasonal products such as heating ventilation and air conditioning is not the same in different regions throughout the year.
In the prior art, as disclosed in patent with publication number CN113962439a, a cloud platform data management system based on industrial big data and a construction method thereof are disclosed, which comprises an equipment monitoring module, a controller, a procedure analysis module, an optimized layout module, an equipment acquisition module and an equipment analysis module; when the production line runs, the equipment monitoring module monitors each equipment in the production line in a product to obtain beat data of the corresponding equipment, and stores the beat data into a beat element queue; the process analysis module analyzes the beat element queue and judges whether the production beats of the corresponding equipment are normal or not.
The patent data management system is based on a cloud platform, which means that all data is stored in a cloud end in a centralized manner. Such centralized designs may increase the burden of data transmission and storage, as well as increasing the risk of system failure or network disruption. In addition, when the amount of data stored in a centralized way is huge, a certain delay may be brought to data access and processing, the real-time performance of the capacity optimization scheduling strategy is poor, and the problems described in the background art exist.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description summary and in the title of the application, to avoid obscuring the purpose of this section, the description summary and the title of the invention, which should not be used to limit the scope of the invention.
Aiming at the problems that in the prior art, the productivity of an industrial system is easily influenced by the number of on-duty workers, the number of fault devices and market demand information, and the optimization instantaneity and comprehensiveness of the productivity of the industrial system are poor, the invention provides a method and a system for optimizing and scheduling the productivity of the industrial system based on machine learning.
In order to achieve the above purpose, the technical scheme of the industrial system capacity optimizing and scheduling method based on machine learning of the invention comprises the following steps:
s1: constructing a dynamic grid visualization framework of production nodes to which an industrial system of an industrial park belongs;
s2: acquiring historical production information data of production products of an industrial system of an industrial park for one year and real-time production information data of the production products of the industrial system;
s3: constructing a machine learning model, and carrying out fusion analysis and predictive calculation of capacity threat values on the historical production information data and the real-time production information data in the step S2;
s4: predicting the calculated productivity threat value according to the machine learning model, and judging the productivity optimization level;
s5: and visually updating the productivity optimization grade data of each production node in real time, checking and evaluating the production speed of each production node, and executing a productivity scheduling strategy.
Specifically, in S1, the dynamic grid visualization framework includes:production nodes to which industrial systems of individual industrial parks belong; wherein the visualized data of each production node comprises: real-time on-duty production people number data, real-time on-duty industrial equipment quantity data, market demand order quantity data, real-time capacity threat value data, real-time capacity optimization grade data, production speed data of production nodes and capacity scheduling strategies of the production nodes.
Specifically, in S2, the historical production information data of the production products of one year on the industrial system of the industrial park includes: the production node of the industrial system produces the number data of people on duty daily in the annual production processThe method comprises the steps of carrying out a first treatment on the surface of the Daily on-stream industrial installation quantity data +.>The method comprises the steps of carrying out a first treatment on the surface of the Daily market demand order quantity dataThe method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>The>The monitoring time point of each production node on the day of the last year is +>The number of people on duty; />The>The production node monitors the time point on the day of the last yearIs->The number of industrial devices in operation; />The>The monitoring time point of each production node on the day of the last year is +>Market demand order quantity data at that time; wherein (1)>，The maximum operation working time in one day of the industrial system;
the real-time production information data of the production products of the industrial system includes: real-time on-duty production people number data acquired by production nodes of industrial systemThe method comprises the steps of carrying out a first treatment on the surface of the Real-time on-the-fly industrial installation quantity data->The method comprises the steps of carrying out a first treatment on the surface of the Real-time market demand order quantity data->The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>The >The real-time monitoring time point of each production node is +.>The number of people on duty;the>The production nodes are +.>The number of industrial devices in operation;industrial system>The real-time monitoring time point of each production node is +.>Market demand order quantity data at that time.
Specifically, S3 includes the following specific steps:
s31: building a machine learning model, comprising: an input layer, a hidden layer, 3 convolution layers, a pooling layer, a sampling layer, a full connection layer and an output layer;
s32: the method comprises the steps of extracting historical production information data of production products of one year in an industrial system of an industrial park as a training set of a machine learning model, performing data cleaning treatment on the historical production information data, and performing feature extraction training on the machine learning model through the historical production information data, wherein a convolution layer feature extraction strategy is as follows:
；
wherein,is->Characteristic matrixes of historical production information data output by the convolution layers; />Is->The number of convolution kernels in each convolution layer; />For inputting +.>Convolutional layer->A historical production information data matrix of the convolution kernels; />For inputting +.>Corresponding weight coefficient matrixes of daily on Shift production people number data, daily on operation industrial equipment quantity data and daily market demand order quantity data included in the historical production information data of the convolution layers;
S33: carrying out feature matrix compression processing on feature matrixes of historical production information data output by the 3 convolution layers through a sampling layer in a machine learning model;
s34: the characteristic matrix of the daily historical production information data in the last year after the compression processing is output through the output layer, so that the training of the convolutional neural network is completed;
s35: inputting real-time production information data of production products of the industrial system into a machine learning model which is trained mature, carrying out date matching processing on the production information data, and predicting productivity threat values of the industrial system in real time.
Specifically, in S4, the predictive calculation of the productivity threat value includes:
；
wherein,the>The real-time monitoring time point of each production node is +.>A time-to-capacity threat value; />The>The real-time monitoring time point of each production node is +.>The productivity threat weight coefficient of the number of on-duty production people; />The>The real-time monitoring time point of each production node is +.>The productivity threat weight coefficient of the number of the running industrial equipment; />The>The real-time monitoring time point of each production node is +.>The capacity threat weight coefficient of the number of the market demand orders;
Wherein,and->。
Specifically, in S4, the capacity optimization level determination includes:
when (when)Judging the +.>The production nodes are +.>When the productivity of the industrial system is the first-level surplus grade, executing step S5;
when (when)Judging the +.>The production nodes are +.>When the productivity of the industrial system is the secondary surplus grade, executing step S5;
when (when)Judging the +.>The production nodes are +.>When the industrial system is in normal operation, returning to the step S3, and continuously carrying out predictive calculation on the productivity threat value of the industrial system;
when (when)Judging the +.>The production nodes are +.>When the productivity of the industrial system is the first-level shortage grade, executing step S5;
when (when)Judging the +.>The production nodes are +.>When the productivity of the industrial system is the second-level shortage grade, executing the step S5;
wherein,and judging the threshold value for each grade of the productivity of the industrial system.
Specifically, in S5, the executing steps of the capacity scheduling policy are as follows:
s51: extraction of the first belonging to the industrial System The production nodes are +.>Capacity optimization grade data at the time;
when the capacity optimizing level data is the primary surplus level and the secondary surplus level, executing step S52;
when the capacity optimizing level data is the first shortage level and the second shortage level, executing step S53;
s52: computing the first to which the industrial system belongsThe production nodes are +.>Real-time production speed->Judging a real-time production speed threshold value;
when (when)Return call +.>The production nodes are +.>Real-time production speed->Judging a real-time production speed threshold value;
if it isThe industrial system is +.>The input material control processing is carried out by each production node; if->Continue to call back to +.>The production nodes are +.>Real-time production speed->The method comprises the steps of carrying out a first treatment on the surface of the Continuously executing real-time production speed threshold judgment; up to the +.>The production nodes are +.>Real-time production speed->In the case of industrial systems>The individual production nodes control and process input materials, send early warning information to the park productivity management room and stop circulation;
S53: computing the first to which the industrial system belongsThe production nodes are +.>Real-time production speed->Judging a real-time production speed threshold value;
when (when)Return call +.>The production nodes are +.>Real-time production speed->Judging a real-time production speed threshold value;
if it isFor the industrial systemIs>The output product control processing is carried out by each production node; if->Continue to call back to +.>The production nodes are +.>Real-time production speed->The method comprises the steps of carrying out a first treatment on the surface of the Continuously executing real-time production speed threshold judgment; up to the +.>The production nodes are +.>Real-time production speed->In the case of industrial systems>And the production nodes perform output product control processing, send early warning information to the park capacity management room and stop circulation.
Specifically, the industrial system of S52 isThe production nodes are +.>Real-time production speed->Is of the meter(s)The calculation strategy is as follows:
；
wherein,the>The production nodes are +.>The material quantity input by the industrial system;
The>The production nodes are +.>The amount of finished product output by the industrial system.
In addition, the industrial system productivity optimization and scheduling system based on machine learning comprises the following modules:
the system comprises a dynamic grid visualization module, an information data acquisition module, a machine learning module, a capacity optimization grade judgment module and a capacity scheduling module;
the dynamic grid visualization module is used for constructing a dynamic grid visualization frame of a production node to which an industrial system of the industrial park belongs;
the information data acquisition module acquires historical production information data of production products of one year in an industrial system of an industrial park and real-time production information data of production products of the industrial system;
the machine learning module is used for constructing a machine learning model, and carrying out fusion analysis and predictive calculation of capacity threat values on the historical production information data and the real-time production information data in the step S2;
the productivity optimization grade judging module predicts the calculated productivity threat value according to the machine learning model and judges the productivity optimization grade;
the capacity scheduling module is used for visually updating capacity optimization grade data of each production node in real time, verifying and evaluating production speed of each production node, and executing a capacity scheduling strategy.
A storage medium having instructions stored therein, which when read by a computer, cause the computer to perform a machine learning based industrial system capacity optimization and scheduling method as described above.
An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing a machine learning based industrial system capacity optimization and scheduling method as described above when executing the computer program.
Compared with the prior art, the invention has the following technical effects:
1. the present invention considers the problem of market demand for manufactured products changing due to seasonal variations; for example, the method aims at that the market demand numbers of seasonal products such as heating ventilation air conditioning are different in different areas throughout the year, and the method extracts the historical production information data of the industrial system of the industrial park in the last year and carries out date matching processing on the production information data of the industrial system monitored in real time, so that the market demand feature data of the same area in the same season can be effectively obtained, and the accuracy and the practicability of the historical data retrieval are improved.
2. The method comprises the steps of establishing a machine learning model, and extracting historical production information data of production products of an industrial system of an industrial park for one year and real-time production information data of production products of the industrial system; carrying out fusion analysis and predictive calculation of capacity threat values on the historical production information data and the real-time production information data; the machine learning model can comprehensively consider the influence of historical data and real-time data, and the accuracy, the instantaneity and the credibility of data prediction are improved by learning the past threat mode and rule and combining the current characteristic information to predict; the machine learning model has the characteristic of self-adaption, can continuously learn and adjust according to the continuously-changing data environment, and improves the timeliness and the robustness of prediction.
3. According to the invention, the capacity optimization grade data of each production node is visually updated in real time, the production speed of each production node is checked and estimated, the capacity scheduling strategy is executed, the production nodes with capacity needing to be optimized are screened one by one in the dynamic grid visual frame, and different capacity scheduling strategies are executed for the production nodes with different production speeds, so that the pertinence is strong, the waste of manpower and material resources is reduced, and the production efficiency of an industrial system is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
FIG. 1 is a flow chart of an industrial system capacity optimizing and scheduling method based on machine learning according to the present invention;
FIG. 2 is a schematic diagram of a machine learning based industrial system capacity optimization and scheduling system according to the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Embodiment one:
as shown in fig. 1, the method for optimizing and scheduling the capacity of the industrial system based on the machine learning according to the embodiment of the invention, as shown in fig. 1, comprises the following specific steps:
s1: constructing a dynamic grid visualization framework of production nodes to which an industrial system of an industrial park belongs;
specifically, in S1, the dynamic grid visualization framework includes:production nodes to which industrial systems of individual industrial parks belong; wherein the visualized data of each production node comprises: real-time on-duty production people number data, real-time on-duty industrial equipment quantity data, market demand order quantity data, real-time capacity threat value data, real-time capacity optimization grade data, production speed data of production nodes and capacity scheduling strategies of the production nodes.
S2: acquiring historical production information data of production products of an industrial system of an industrial park for one year and real-time production information data of the production products of the industrial system;
in S2, the historical production information data of the production product of one year on the industrial system of the industrial park includes: the production node of the industrial system produces the number data of people on duty daily in the annual production processThe method comprises the steps of carrying out a first treatment on the surface of the Daily on-stream industrial installation quantity data +.>The method comprises the steps of carrying out a first treatment on the surface of the Daily market demand order quantity dataThe method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>The>The monitoring time point of each production node on the day of the last year is +>The number of people on duty; />The>The monitoring time point of each production node on the day of the last year is +>The number of industrial devices in operation; />The>The monitoring time point of each production node on the day of the last year is +>Market demand order quantity data at that time; wherein (1)>，The maximum operation working time in one day of the industrial system;
the real-time production information data of the production products of the industrial system includes: real-time on-duty production people number data acquired by production nodes of industrial system The method comprises the steps of carrying out a first treatment on the surface of the Real-time on-the-fly industrial installation quantity data->The method comprises the steps of carrying out a first treatment on the surface of the Real-time market demand order quantity data->The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>The>The real-time monitoring time point of each production node is +.>The number of people on duty; />The>The production nodes are +.>The number of industrial devices in operation; />Industrial system>The real-time monitoring time point of each production node is +.>Market demand order quantity data at that time.
S3: constructing a machine learning model, and carrying out fusion analysis and predictive calculation of capacity threat values on the historical production information data and the real-time production information data in the step S2;
s3 comprises the following specific steps:
s31: building a machine learning model, comprising: an input layer, a hidden layer, 3 convolution layers, a pooling layer, a sampling layer, a full connection layer and an output layer;
s32: the method comprises the steps of extracting historical production information data of production products of one year in an industrial system of an industrial park as a training set of a machine learning model, performing data cleaning treatment on the historical production information data, and performing feature extraction training on the machine learning model through the historical production information data, wherein a convolution layer feature extraction strategy is as follows:
；
Wherein,is->Characteristic matrixes of historical production information data output by the convolution layers; />Is->The number of convolution kernels in each convolution layer; />For inputting +.>Convolutional layer->A historical production information data matrix of the convolution kernels; />For inputting +.>Individual rollsCorresponding weight coefficient matrixes of daily on Shift production people number data, daily on operation industrial equipment quantity data and daily market demand order quantity data included in the laminated historical production information data;
s33: carrying out feature matrix compression processing on feature matrixes of historical production information data output by the 3 convolution layers through a sampling layer in a machine learning model;
s34: the characteristic matrix of the daily historical production information data in the last year after the compression processing is output through the output layer, so that the training of the convolutional neural network is completed;
s35: inputting real-time production information data of production products of the industrial system into a machine learning model which is trained mature, carrying out date matching processing on the production information data, and predicting productivity threat values of the industrial system in real time.
S4: predicting the calculated productivity threat value according to the machine learning model, and judging the productivity optimization level;
s4, the prediction calculation of the productivity threat value comprises the following steps:
；
Wherein,the>The real-time monitoring time point of each production node is +.>A time-to-capacity threat value;the>The real-time monitoring time point of each production node is +.>The productivity threat weight coefficient of the number of on-duty production people; />The>The real-time monitoring time point of each production node is +.>The productivity threat weight coefficient of the number of the running industrial equipment; />The>The real-time monitoring time point of each production node is +.>The capacity threat weight coefficient of the number of the market demand orders;
wherein,and->。
In S4, the capacity optimization level judgment includes:
when (when)Judging the +.>The production nodes are +.>When the productivity of the industrial system is the first-level surplus grade, executing step S5;
when (when)Judging the +.>Each production node is at the real-time monitoring time pointWhen the productivity of the industrial system is the secondary surplus grade, executing step S5;
when (when)Judging the +.>The production nodes are +.>When the industrial system is in normal operation, returning to the step S3, and continuously carrying out predictive calculation on the productivity threat value of the industrial system;
When (when)Judging the +.>Each production node is at the real-time monitoring time pointWhen the productivity of the industrial system is the first-level shortage grade, executing step S5;
when (when)Judging the +.>The production nodes are +.>When the productivity of the industrial system is the second-level shortage grade, executing the step S5;
wherein,and judging the threshold value for each grade of the productivity of the industrial system.
S5: and visually updating the productivity optimization grade data of each production node in real time, checking and evaluating the production speed of each production node, and executing a productivity scheduling strategy.
In S5, the executing steps of the capacity scheduling policy are as follows:
s51: extraction of the first belonging to the industrial SystemThe production nodes are +.>Capacity optimization grade data at the time;
when the capacity optimizing level data is the primary surplus level and the secondary surplus level, executing step S52;
when the capacity optimizing level data is the first shortage level and the second shortage level, executing step S53;
s52: computing the first to which the industrial system belongsThe production nodes are +.>Real-time production speed->Judging a real-time production speed threshold value;
When (when)Return call +.>The production nodes are +.>Real-time production speed->Judging a real-time production speed threshold value;
if it isThe industrial system is +.>The input material control processing is carried out by each production node; if->Continue to call back to +.>The production nodes are +.>Real-time production speed->The method comprises the steps of carrying out a first treatment on the surface of the Continuously executing real-time production speed threshold judgment; up to the +.>The production nodes are +.>Real-time production speed->In the case of industrial systems>Each production node performs input material control processing and performs input material control processing onThe park capacity management room sends early warning information and stops circulating;
s53: computing the first to which the industrial system belongsThe production nodes are +.>Real-time production speed->Judging a real-time production speed threshold value;
when (when)Return call +.>The production nodes are +.>Real-time production speed->Judging a real-time production speed threshold value;
if it isThe industrial system is +.>The output product control processing is carried out by each production node; if->Continue to call back to +. >The production nodes are +.>When (1)Real-time production speed->The method comprises the steps of carrying out a first treatment on the surface of the Continuously executing real-time production speed threshold judgment; up to the +.>The production nodes are +.>Real-time production speed->In the case of industrial systems>And the production nodes perform output product control processing, send early warning information to the park capacity management room and stop circulation.
S52 the industrial system belongs toThe production nodes are +.>Real-time production speed->The calculation strategy of (2) is as follows:
；
wherein,the>The production nodes are +.>The material quantity input by the industrial system;
the>The production nodes are +.>The amount of finished product output by the industrial system.
Embodiment two:
as shown in fig. 2, the industrial system capacity optimizing and scheduling system based on machine learning according to the embodiment of the invention, as shown in fig. 2, includes the following modules:
the system comprises a dynamic grid visualization module, an information data acquisition module, a machine learning module, a capacity optimization grade judgment module and a capacity scheduling module;
The dynamic grid visualization module is used for constructing a dynamic grid visualization frame of a production node to which an industrial system of the industrial park belongs;
the information data acquisition module acquires historical production information data of production products of one year in an industrial system of an industrial park and real-time production information data of production products of the industrial system;
the machine learning module is used for constructing a machine learning model, and carrying out fusion analysis and predictive calculation of capacity threat values on the historical production information data and the real-time production information data in the step S2;
the productivity optimization grade judging module predicts the calculated productivity threat value according to the machine learning model and judges the productivity optimization grade;
the capacity scheduling module is used for visually updating capacity optimization grade data of each production node in real time, verifying and evaluating production speed of each production node, and executing a capacity scheduling strategy.
Embodiment III:
the present embodiment provides an electronic device including: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the processor executes the industrial system capacity optimizing and scheduling method based on machine learning by calling the computer program stored in the memory.
The electronic device may be configured or configured differently to generate a larger difference, and may include one or more processors (Central Processing Units, CPU) and one or more memories, where the memories store at least one computer program that is loaded and executed by the processors to implement a machine learning-based industrial system capacity optimization and scheduling method provided by the above method embodiments. The electronic device can also include other components for implementing the functions of the device, for example, the electronic device can also have wired or wireless network interfaces, input-output interfaces, and the like, for inputting and outputting data. The present embodiment is not described herein.
Embodiment four:
the present embodiment proposes a computer-readable storage medium having stored thereon an erasable computer program;
when the computer program runs on the computer equipment, the computer equipment is caused to execute the industrial system productivity optimization and scheduling method based on machine learning.
For example, the computer readable storage medium can be Read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), compact disk Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM), magnetic tape, floppy disk, optical data storage device, etc.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
It should be understood that determining B from a does not mean determining B from a alone, but can also determine B from a and/or other information.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions in accordance with embodiments of the present invention are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by way of wired or/and wireless networks from one website site, computer, server, or data center to another. Computer readable storage media can be any available media that can be accessed by a computer or data storage devices, such as servers, data centers, etc. that contain one or more collections of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the partitioning of units is merely one, and there may be additional partitioning in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
In summary, compared with the prior art, the technical effects of the invention are as follows:
1. the present invention considers the problem of market demand for manufactured products changing due to seasonal variations; for example, the method aims at that the market demand numbers of seasonal products such as heating ventilation air conditioning are different in different areas throughout the year, and the method extracts the historical production information data of the industrial system of the industrial park in the last year and carries out date matching processing on the production information data of the industrial system monitored in real time, so that the market demand feature data of the same area in the same season can be effectively obtained, and the accuracy and the practicability of the historical data retrieval are improved.
2. The method comprises the steps of establishing a machine learning model, and extracting historical production information data of production products of an industrial system of an industrial park for one year and real-time production information data of production products of the industrial system; carrying out fusion analysis and predictive calculation of capacity threat values on the historical production information data and the real-time production information data; the machine learning model can comprehensively consider the influence of historical data and real-time data, and the accuracy, the instantaneity and the credibility of data prediction are improved by learning the past threat mode and rule and combining the current characteristic information to predict; the machine learning model has the characteristic of self-adaption, can continuously learn and adjust according to the continuously-changing data environment, and improves the timeliness and the robustness of prediction.
3. According to the invention, the capacity optimization grade data of each production node is visually updated in real time, the production speed of each production node is checked and estimated, the capacity scheduling strategy is executed, the production nodes with capacity needing to be optimized are screened one by one in the dynamic grid visual frame, and different capacity scheduling strategies are executed for the production nodes with different production speeds, so that the pertinence is strong, the waste of manpower and material resources is reduced, and the production efficiency of an industrial system is improved.
The foregoing has shown and described the basic principles and main features of the present invention and the advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (11)
1. The industrial system productivity optimizing and scheduling method based on machine learning is characterized in that: the method comprises the following specific steps:
s1: constructing a dynamic grid visualization framework of production nodes to which an industrial system of an industrial park belongs;
S2: acquiring historical production information data of production products of an industrial system of an industrial park for one year and real-time production information data of the production products of the industrial system;
s3: constructing a machine learning model, and carrying out fusion analysis and predictive calculation of capacity threat values on the historical production information data and the real-time production information data in the step S2;
s4: predicting the calculated productivity threat value according to the machine learning model, and judging the productivity optimization level;
s5: and visually updating the productivity optimization grade data of each production node in real time, checking and evaluating the production speed of each production node, and executing a productivity scheduling strategy.
2. The machine learning based industrial system capacity optimization and scheduling method of claim 1, wherein in S1, the dynamic grid visualization framework comprises:production nodes to which industrial systems of individual industrial parks belong; wherein the visualized data of each production node comprises: real-time on-duty production people number data, real-time on-duty industrial equipment quantity data, market demand order quantity data, real-time capacity threat value data, real-time capacity optimization grade data, production speed data of production nodes and capacity scheduling strategies of the production nodes.
3. Machine learning based industrial system capacity optimization and tuning of claim 2A method, wherein in S2, the historical production information data of the production products of one year on the industrial system of the industrial park includes: the production node of the industrial system produces the number data of people on duty daily in the annual production processThe method comprises the steps of carrying out a first treatment on the surface of the Daily on-stream industrial installation quantity data +.>The method comprises the steps of carrying out a first treatment on the surface of the Daily market demand order quantity dataThe method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>The>The monitoring time point of each production node on the day of the last year is +>The number of people on duty; />The>The monitoring time point of each production node on the day of the last year is +>The number of industrial devices in operation; />The>The monitoring time point of each production node on the day of the last year is +>Market demand order quantity data at that time; wherein,，/>the maximum operation working time in one day of the industrial system;
the real-time production information data of the production products of the industrial system includes: real-time on-duty production people number data acquired by production nodes of industrial systemThe method comprises the steps of carrying out a first treatment on the surface of the Real-time on-the-fly industrial equipment quantity data The method comprises the steps of carrying out a first treatment on the surface of the Real-time market demand order quantity data->The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>The>The real-time monitoring time point of each production node is +.>The number of people on duty; />The>The production nodes are +.>The number of industrial devices in operation; />Industrial system>The real-time monitoring time point of each production node is +.>Market demand order quantity data at that time.
4. The machine learning based industrial system capacity optimization and scheduling method of claim 3, wherein S3 comprises the following specific steps:
s31: building a machine learning model, comprising: an input layer, a hidden layer, 3 convolution layers, a pooling layer, a sampling layer, a full connection layer and an output layer;
s32: the method comprises the steps of extracting historical production information data of production products of one year in an industrial system of an industrial park as a training set of a machine learning model, performing data cleaning treatment on the historical production information data, and performing feature extraction training on the machine learning model through the historical production information data, wherein a convolution layer feature extraction strategy is as follows:
；
wherein,is->Characteristic matrixes of historical production information data output by the convolution layers; / >Is->The number of convolution kernels in each convolution layer; />For inputting +.>Convolutional layer->A historical production information data matrix of the convolution kernels; />For inputting +.>Corresponding weight coefficient matrixes of daily on Shift production people number data, daily on operation industrial equipment quantity data and daily market demand order quantity data included in the historical production information data of the convolution layers;
s33: carrying out feature matrix compression processing on feature matrixes of historical production information data output by the 3 convolution layers through a sampling layer in a machine learning model;
s34: the characteristic matrix of the daily historical production information data in the last year after the compression processing is output through the output layer, so that the training of the convolutional neural network is completed;
s35: inputting real-time production information data of production products of the industrial system into a machine learning model which is trained mature, carrying out date matching processing on the production information data, and predicting productivity threat values of the industrial system in real time.
5. The machine learning based industrial system capacity optimizing and scheduling method according to claim 4, wherein in S4, the predictive calculation of the capacity threat value includes:
；
wherein,the >The real-time monitoring time point of each production node is +.>A time-to-capacity threat value;the>The real-time monitoring time point of each production node is +.>The productivity threat weight coefficient of the number of on-duty production people; />The>The real-time monitoring time point of each production node is +.>The productivity threat weight coefficient of the number of the running industrial equipment; />The>The real-time monitoring time point of each production node is +.>The capacity threat weight coefficient of the number of the market demand orders;
wherein,and->。
6. The method for optimizing and scheduling capacity of an industrial system based on machine learning according to claim 5, wherein in S4, the judging of the capacity optimizing level includes:
when (when)Judging the +.>The production nodes are +.>When the productivity of the industrial system is the first-level surplus grade, executing step S5;
when (when)Judging the +.>The production nodes are +.>When the productivity of the industrial system is the secondary surplus grade, executing step S5;
when (when)Judging the +.>The production nodes are +. >When the industrial system is in normal operation, returning to the step S3, and continuously carrying out predictive calculation on the productivity threat value of the industrial system;
when (when)Judging the +.>The production nodes are +.>When the productivity of the industrial system is the first-level shortage grade, executing step S5;
when (when)Judging the +.>The production nodes are +.>When the productivity of the industrial system is the second-level shortage grade, executing the step S5;
wherein,and judging the threshold value for each grade of the productivity of the industrial system.
7. The machine learning based industrial system capacity optimizing and scheduling method according to claim 6, wherein in S5, the capacity scheduling strategy is executed as follows:
s51: extraction of the first belonging to the industrial SystemThe production nodes are +.>Capacity optimization grade data at the time;
when the capacity optimizing level data is the primary surplus level and the secondary surplus level, executing step S52;
when the capacity optimizing level data is the first shortage level and the second shortage level, executing step S53;
s52: computing the first to which the industrial system belongsThe production nodes are +. >Real-time production speed at timeJudging a real-time production speed threshold value;
when (when)Return call +.>The production nodes are +.>Real-time production speed->Judging a real-time production speed threshold value;
if it isThe industrial system is +.>The input material control processing is carried out by each production node; if it isContinue to call back to +.>The production nodes are +.>Real-time production speed->The method comprises the steps of carrying out a first treatment on the surface of the Continuously executing real-time production speed threshold judgment; up to the +.>The production nodes are +.>Real-time production speed->In the case of industrial systems>The individual production nodes control and process input materials, send early warning information to the park productivity management room and stop circulation;
s53: computing the first to which the industrial system belongsThe production nodes are +.>Real-time production speed at timeJudging a real-time production speed threshold value;
when (when)Return call +.>The production nodes are +.>Real-time production speed->Judging a real-time production speed threshold value;
if it isThe industrial system is +. >The output product control processing is carried out by each production node; if it isContinue to call back to +.>The production nodes are +.>Real-time production speed->The method comprises the steps of carrying out a first treatment on the surface of the Continuously executing real-time production speed threshold judgment; up to the +.>The production nodes are +.>Real-time production speed->In the case of industrial systems>And the production nodes perform output product control processing, send early warning information to the park capacity management room and stop circulation.
8. The machine learning based industrial system capacity optimizing and scheduling method of claim 7, wherein the industrial system belongs to S52The production nodes are +.>Real-time production speed->The calculation strategy of (2) is as follows:
；
wherein,the>The production nodes are +.>The material quantity input by the industrial system;
the>The production nodes are +.>The amount of finished product output by the industrial system.
9. Machine learning based industrial system capacity optimization and scheduling system, realized on the basis of a machine learning based industrial system capacity optimization and scheduling method according to any of the claims 1-8, characterized in that the system comprises the following modules:
The system comprises a dynamic grid visualization module, an information data acquisition module, a machine learning module, a capacity optimization grade judgment module and a capacity scheduling module;
the dynamic grid visualization module is used for constructing a dynamic grid visualization frame of a production node to which an industrial system of the industrial park belongs;
the information data acquisition module acquires historical production information data of production products of one year in an industrial system of an industrial park and real-time production information data of production products of the industrial system;
the machine learning module is used for constructing a machine learning model, and carrying out fusion analysis and predictive calculation of capacity threat values on the historical production information data and the real-time production information data in the step S2;
the productivity optimization grade judging module predicts the calculated productivity threat value according to the machine learning model and judges the productivity optimization grade;
the capacity scheduling module is used for visually updating capacity optimization grade data of each production node in real time, verifying and evaluating production speed of each production node, and executing a capacity scheduling strategy.
10. A computer readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements a machine learning based industrial system capacity optimization and scheduling method according to any one of claims 1-8.
11. An electronic device, comprising:
a memory for storing instructions;
a processor for executing the instructions to cause the apparatus to perform operations implementing a machine learning based industrial system capacity optimization and scheduling method as claimed in any one of claims 1-8.
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