CN114047729B - Natural plant processing control method, system, computer device and storage medium - Google Patents
Natural plant processing control method, system, computer device and storage medium Download PDFInfo
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Abstract
The application relates to a natural plant processing control method, a natural plant processing control system, a natural plant processing control computer device and a natural plant processing control storage medium. The method comprises the following steps: acquiring industrial big data in a historical time period; acquiring control instructions of all natural plant processing subsystems; constructing an intelligent model associated with each natural plant processing subsystem according to natural plant knowledge, control instructions and industrial big data; the corresponding natural plant processing subsystem is controlled according to the smart model associated with each natural plant processing subsystem. By adopting the method, a large amount of natural plant processing data are processed and analyzed, and the central control subsystem can timely respond to each natural plant processing subsystem, so that the degree of automation and the control accuracy of the natural plant processing system are improved, and the processing efficiency of the natural plant processing system is further improved.
Description
Technical Field
The application relates to the technical field of processing control, in particular to a natural plant processing control method, a natural plant processing control system, computer equipment and a storage medium.
Background
Along with the approach of the processing, management and decision-making of the enterprise products to digitization, informatization and intelligence, the capability of the processing system of the enterprise for thinking and working like a person is gradually becoming the development direction of the domestic raw material processing enterprise. However, the organic combination of the natural plant processing control and the modern science and technology is not tight at present.
In the related art, the natural plant processing system only collects and stores data generated in industrial processing, and although a part of enterprises have accumulated a large amount of processing data, the data are not processed, so that the intangible assets accumulated by the enterprises are not well utilized, the degree of automation of the existing natural plant processing system is low, the control theory is single, and each natural plant processing subsystem in the natural plant processing system cannot respond and accurately control in time, so that the problem of low processing efficiency is caused. Thus, there is an urgent need for a natural plant processing system treatment method.
Disclosure of Invention
Based on this, it is necessary to provide a natural plant processing control method, system, computer device and storage medium in view of the above technical problems.
A method of controlling natural plant processing, the method comprising:
Acquiring industrial big data in a historical time period, wherein the industrial big data is used for representing data generated by each natural plant processing subsystem in the processing process;
Acquiring control instructions of all natural plant processing subsystems;
Constructing an intelligent model associated with each natural plant processing subsystem according to natural plant knowledge, control instructions and industrial big data;
the corresponding natural plant processing subsystem is controlled according to the smart model associated with each natural plant processing subsystem.
In one embodiment, each of the natural plant processing subsystems is a raw material storage and transportation subsystem, an on-site plant production subsystem, a finished product storage and transportation subsystem, respectively, and accordingly, each of the natural plant processing subsystems generates data during processing, including: raw material data generated by the raw material storage and transportation subsystem in the processing process, production data generated by the on-site workshop production subsystem in the processing process and finished product data generated by the finished product storage and transportation subsystem in the processing process.
In one embodiment, building an intelligent model associated with each natural plant processing subsystem based on natural plant knowledge, control instructions, and industrial big data, comprises:
Respectively carrying out data selection, data cleaning and data transformation on the industrial big data to obtain processed industrial big data;
classifying the processed industrial big data according to the control instruction of each natural plant processing subsystem to obtain the input data of each intelligent model;
and constructing each intelligent model according to the natural plant knowledge and the input data of each intelligent model.
In one embodiment, the data selection, data cleaning and data transformation are performed on the industrial big data respectively to obtain processed industrial big data, including:
selecting control data related to a natural plant processing subsystem from the industrial big data according to the control instruction, and acquiring the selected industrial big data;
removing error data in the selected industrial big data to obtain cleaned industrial big data;
and carrying out data standard transformation processing on the cleaned industrial big data to obtain the processed industrial big data.
In one embodiment, classifying the processed industrial big data according to the control instruction of each natural plant processing subsystem to obtain the input data of each intelligent model includes:
According to the type of the data in the processed industrial big data, extracting the characteristics of the processed industrial big data to obtain different characteristic variables;
And classifying the characteristic variables according to the control instructions of each natural plant processing subsystem to obtain the input data of each intelligent model.
In one embodiment, controlling each natural plant processing subsystem according to an intelligent model associated with the corresponding natural plant processing subsystem comprises:
acquiring output data of each intelligent model according to each intelligent model;
and controlling the corresponding natural plant processing subsystem according to the output data.
In one embodiment, after controlling each natural plant processing subsystem according to the smart model associated with the corresponding natural plant processing subsystem, the method comprises:
acquiring data generated at the current moment of each natural plant processing subsystem;
And matching the data generated at the current moment with intelligent models associated with all the natural plant processing subsystems to obtain an optimized intelligent model.
A natural plant processing control system, the system comprising: the system comprises an industrial internet subsystem, a raw material storage and transportation subsystem, a field workshop production subsystem, a finished product storage and transportation subsystem, a central control subsystem and a big data cloud platform subsystem;
the industrial internet subsystem is used for connecting processing, storing and transporting equipment, production lines, factories, suppliers, products and clients through the internet and is responsible for collecting data to form industrial big data and communicating and storing information and commands;
The raw material storage and transportation subsystem is used for storing and distributing various natural plant processing raw materials, transporting the raw materials to the on-site workshop production subsystem and uploading raw material information to the industrial Internet subsystem;
The on-site workshop production subsystem is used for controlling corresponding production equipment and flow of a workshop production line, transporting finished products which are processed and packaged to the finished product storage and transportation system, and uploading production information on the production line to the industrial Internet subsystem;
The finished product storage and transportation subsystem is used for transporting, distributing, counting, storing and warehousing the processed finished products, and uploading the finished product information to the industrial Internet subsystem;
The central control subsystem is used for uniformly issuing working commands of the raw material storage and transportation subsystem, the on-site workshop production subsystem and the finished product storage and transportation subsystem so as to coordinate the normal and stable operation of each subsystem and ensure that each natural plant processing subsystem can be controlled;
the big data cloud platform subsystem is used for establishing a mathematical intelligent model associated with each natural plant processing subsystem by combining natural plant knowledge judgment by means of AI technical means such as data mining, machine learning and the like on the basis of industrial big data provided by the industrial Internet subsystem, and providing more optimized control strategies, key process points and process parameters for the central control subsystem.
A computer device comprising a memory storing a computer program and a processor which when executing the computer program performs the steps of:
Acquiring industrial big data in a historical time period, wherein the industrial big data is used for representing data generated by each natural plant processing subsystem in the processing process;
Acquiring control instructions of all natural plant processing subsystems;
Constructing an intelligent model associated with each natural plant processing subsystem according to natural plant knowledge, control instructions and industrial big data;
the corresponding natural plant processing subsystem is controlled according to the smart model associated with each natural plant processing subsystem.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
Acquiring industrial big data in a historical time period, wherein the industrial big data is used for representing data generated by each natural plant processing subsystem in the processing process;
Acquiring control instructions of all natural plant processing subsystems;
Constructing an intelligent model associated with each natural plant processing subsystem according to natural plant knowledge, control instructions and industrial big data;
the corresponding natural plant processing subsystem is controlled according to the smart model associated with each natural plant processing subsystem.
The method, the system, the computer equipment and the storage medium for controlling the natural plant processing acquire industrial big data in a historical time period, wherein the industrial big data are used for representing data generated by each natural plant processing subsystem in the processing process; acquiring control instructions of all natural plant processing subsystems; constructing an intelligent model associated with each natural plant processing subsystem according to natural plant knowledge, control instructions and industrial big data; the corresponding natural plant processing subsystem is controlled according to the smart model associated with each natural plant processing subsystem.
Compared with the prior art, a large amount of processing and manufacturing data accumulated by a natural plant processing enterprise can not be fully utilized, so that the degree of automation of the existing natural plant processing system is low and the control theory is single.
Drawings
FIG. 1 is a diagram of an application environment of a natural plant processing control method in one embodiment;
FIG. 2 is a schematic flow chart of a natural plant processing control method according to an embodiment;
FIG. 3 is a block diagram of a natural plant processing control system in another embodiment;
Fig. 4 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
It will be understood that the terms "first," "second," and the like, as used herein, may be used to describe various terms, but are not limited by these terms unless otherwise specified. These terms are only used to distinguish one term from another. For example, the third and fourth preset thresholds may be the same or different without departing from the scope of the application.
The processing method of the natural plant processing system provided by the application can be applied to an application environment shown in figure 1. Wherein the terminal 101 communicates with the server 102 via a network. The terminal 101 collects industrial big data over a historical period of time. Industrial big data in the history period is sent to the server 102. The server 102 builds an intelligent model associated with each natural plant processing subsystem based on the natural plant knowledge and the industrial big data; the corresponding natural plant processing subsystem is controlled according to the smart model associated with each natural plant processing subsystem. The terminal 101 may be, but not limited to, various personal computers, notebook computers, smartphones, tablet computers, and portable wearable devices, and the server 102 may be implemented by a stand-alone server or a server cluster formed by a plurality of servers.
In one embodiment, as shown in fig. 2, there is provided a processing method of a natural plant processing system, which is illustrated by taking an example that the method is applied to the terminal in fig. 1, including the steps of:
201. Acquiring industrial big data in a historical time period, wherein the industrial big data is used for representing data generated by each natural plant processing subsystem in the processing process;
202. Acquiring control instructions of all natural plant processing subsystems;
203. Constructing an intelligent model associated with each natural plant processing subsystem according to natural plant knowledge, control instructions and industrial big data;
204. The corresponding natural plant processing subsystem is controlled according to the smart model associated with each natural plant processing subsystem.
For the natural plant processing system in the application, the system comprises an industrial internet subsystem, each natural plant processing subsystem, a central control subsystem and a big data cloud platform subsystem. The industrial internet subsystem is responsible for connecting processing, storage and transportation equipment, production lines, factories, suppliers, products and clients through the internet. Moreover, the industrial internet subsystem is also responsible for acquiring, storing and transmitting data.
In addition, each natural plant processing subsystem is responsible for specific processes in the natural plant processing and uploads data generated during the processing to the industrial internet subsystem. The central control subsystem is responsible for issuing unified working commands to each natural plant processing subsystem so as to coordinate the normal and stable operation of each natural plant processing subsystem and control the processing of each natural plant processing subsystem in real time.
The big data cloud platform subsystem is based on industrial big data provided by the industrial Internet subsystem, an intelligent model associated with each natural plant processing subsystem is established by means of technical means such as data mining and machine learning, and the central control subsystem can control the corresponding natural plant processing subsystem through the intelligent model.
In step 201 described above, the history period refers to a period of time before the current time. Specifically, the industrial Internet subsystem acquires industrial big data of a plurality of months before the current moment, and transmits the acquired industrial big data to the big data cloud platform subsystem through the communication module. As to the communication module used for transmitting data, embodiments of the present invention are not limited thereto, and include, but are not limited to: WIFI, GPRS, 3G or 4G, bluetooth, zigBee, field bus and Ethernet communication module.
For example, the industrial internet subsystem acquires industrial big data of 3 months before the current moment, and then the industrial internet subsystem transmits the industrial big data of three months to the big data cloud platform subsystem through the Ethernet communication module.
In step 202 described above, the control command refers to the control target of the central control subsystem to the natural plant processing subsystem, and the control targets of the different natural plant processing subsystems are also different.
In the step 203, the natural plant knowledge refers to the related knowledge of the natural plant stored in the big data cloud platform subsystem; the smart model associated with each natural plant processing subsystem means that the central control subsystem can implement control of the corresponding natural plant processing subsystem based on the smart model. Specifically, after the big data cloud platform subsystem processes the obtained industrial big data, the big data cloud platform subsystem is combined with the control instruction and the natural plant knowledge stored by the big data cloud platform subsystem to judge, and an intelligent model associated with each natural plant processing subsystem is built.
In step 204, the central control subsystem may adjust the control strategy of each natural plant processing subsystem according to the intelligent model associated with each natural plant processing subsystem, so as to control the key process points and process parameters in the natural plant processing subsystem.
According to the method provided by the embodiment of the invention, the accumulated large amount of processing data are processed, and the intelligent model associated with each natural plant processing subsystem is constructed according to the accumulated large amount of processing data, so that the central control subsystem can timely respond to each natural plant processing subsystem, the degree of automation and the control accuracy of the natural plant processing system can be improved, and the processing efficiency of the natural plant processing system is further improved.
In one embodiment, each of the natural plant processing subsystems is a feedstock storage and transportation subsystem, an on-site plant production subsystem, a finished product storage and transportation subsystem, respectively; accordingly, data generated during processing by each of the natural plant processing subsystems includes: raw material data generated by the raw material storage and transportation subsystem in the processing process, production data generated by the on-site workshop production subsystem in the processing process and finished product data generated by the finished product storage and transportation subsystem in the processing process.
Specifically, the raw material storage and transportation subsystem is responsible for storing and distributing various natural plant processing raw materials, transporting the natural plant processing raw materials to the on-site workshop production subsystem, and uploading raw material data generated in the process to the industrial internet subsystem.
The on-site workshop production subsystem is responsible for controlling corresponding production equipment and flow of a workshop production line, transporting the finished products which are produced and packaged to the finished product storage and transportation subsystem, and uploading production data on the production line to the industrial Internet subsystem.
The finished product storage and transportation subsystem is responsible for transporting, distributing and statistically storing finished products obtained by processing, and uploading finished product data generated in the process to the industrial Internet subsystem.
According to the method provided by the embodiment of the invention, the raw material storage and transportation subsystem, the on-site workshop production subsystem and the finished product storage and transportation subsystem upload data to the industrial Internet subsystem, so that the industrial Internet subsystem can acquire a large amount of industrial big data, and a data basis is provided for the establishment of an intelligent model.
In one embodiment, building an intelligent model associated with each natural plant processing subsystem based on natural plant knowledge, control instructions, and industrial big data, comprises:
301. respectively carrying out data selection, data cleaning and data transformation on the industrial big data to obtain processed industrial big data;
302. Classifying the processed industrial big data according to the control instruction of each natural plant processing subsystem to obtain the input data of each intelligent model;
303. and constructing each intelligent model according to the natural plant processing knowledge and the input data of each intelligent model.
In step 301, the data selection, data cleaning and data transformation are performed on the industrial big data, respectively, to obtain valuable information from the industrial big data, and provide useful guidance and decision services for the processing of the natural plant processing system.
In step 302 described above, the control command for each natural plant processing subsystem refers to the control command issued by the central control subsystem for that natural plant processing subsystem.
In the step 303, data mining, machine learning, etc. are performed on the input data of each intelligent model through AI technology means, and then the judgment is performed by combining with the natural plant knowledge in the big data cloud platform subsystem, so that each intelligent model can be constructed.
According to the method provided by the embodiment of the invention, the central control subsystem can timely respond and accurately control each natural plant processing subsystem by classifying huge data generated by the natural plant processing subsystem in the processing process and then constructing the corresponding intelligent model.
In one embodiment, the data selection, data cleaning and data transformation are respectively performed on the industrial big data to obtain processed industrial big data, including:
401. Selecting corresponding control data from the industrial big data according to the control instruction, and acquiring the selected industrial big data;
402. removing erroneous data in the selected industrial big data to obtain cleaned industrial big data;
403. And carrying out data standard transformation processing on the cleaned industrial big data to obtain the processed industrial big data.
In the step 401, according to the control instructions of all the natural plant processing subsystems, the control data related to all the natural plant processing subsystems are selected from the industrial big data, and the selected industrial big data, for example, the industrial big data includes data such as temperature, pressure, frequency and rotation speed, and the data required by the control instructions of all the natural plant processing subsystems are the temperature, pressure and frequency, so that only the selected temperature, pressure and frequency are used as the selected industrial big data.
In the step 402, the selected industrial large data may have error data such as format, content, logic, etc., for example, the temperature data is 50 ℃, 56 ℃, 400 ℃, 62 ℃, etc., so that the error data of 400 ℃ needs to be removed to obtain the cleaned industrial large data.
In step 402, the embodiment of the present invention does not specifically limit the standard transformation process, including but not limited to: feature binarization, feature normalization, continuous feature variation, qualitative feature dummy coding, and the like.
According to the method provided by the embodiment of the invention, the accuracy of input data can be improved by carrying out data selection, data cleaning and data transformation processing on industrial big data, so that the accurate control accuracy of the intelligent model is improved.
In one embodiment, classifying the processed industrial big data according to the control instructions of each natural plant processing subsystem to obtain the input data of each intelligent model comprises:
501. According to the type of the data in the processed industrial big data, extracting the characteristics of the processed industrial big data to obtain different characteristic variables;
502. And classifying the characteristic variables according to the control instruction of each model to obtain the input data of each intelligent model.
Specifically, the control command of each natural plant processing subsystem has a corresponding control target according to the respective processing task, and the processing task of each natural plant processing subsystem may include one or more than two kinds, for example, the task of the raw material storage and transportation subsystem is to mix the processing raw materials according to the best raw material ratio, complete the preparation of the processing raw materials in the shortest time, and the like.
According to the method provided by the embodiment of the invention, the processed industrial big data is classified through the big data cloud platform subsystem to obtain the input data of each intelligent model, so that the accurate control of each natural plant processing subsystem can be realized, the processing efficiency of the natural plant processing system is improved, and the labor cost is further reduced.
In one embodiment, controlling each natural plant processing subsystem according to an intelligent model associated with the corresponding natural plant processing subsystem comprises:
601. acquiring output data of each intelligent model according to each intelligent model;
602. and controlling the corresponding natural plant processing subsystem according to the output data.
Specifically, the central control subsystem obtains output data of each intelligent model according to each intelligent model, and matches the output data to a corresponding natural plant processing subsystem according to the output data, so that the natural plant processing subsystem is controlled by the output data.
According to the method provided by the embodiment of the invention, the central control subsystem can control different natural plant processing subsystems through different intelligent models, so that the control and decision making capability of the central control subsystem is improved.
In one embodiment, after controlling each natural plant processing subsystem according to the smart model associated with the corresponding natural plant processing subsystem, the method comprises:
701. Acquiring data generated at the current moment of each natural plant processing subsystem;
702. And matching the data generated at the current moment with intelligent models associated with all the natural plant processing subsystems to obtain an optimized intelligent model.
Specifically, the industrial internet subsystem acquires data generated by each natural plant processing subsystem at the current moment, analyzes the data as industrial big data in a historical time period, and then matches the analyzed data with the intelligent model so as to optimize the intelligent model.
According to the method provided by the embodiment, the intelligent model is optimized by acquiring the data generated at the current moment and analyzing and processing the data generated at the current moment, so that the control efficiency of the intelligent model can be improved, and the processing efficiency of the natural plant processing system is improved.
In one embodiment, as shown in fig. 3, there is provided a natural plant processing control system comprising: an industrial internet subsystem 311, a raw materials storage and transportation subsystem 312, a field shop production subsystem 313, a finished product storage and transportation subsystem 314, a central control subsystem 315, and a big data cloud platform subsystem 316;
The industrial internet subsystem 311 is used for connecting processing, storage and transportation equipment, production lines, factories, suppliers, products and clients through the internet to form industrial big data, and is responsible for collecting data to form industrial big data and communicating and storing information and commands;
A raw material storage and transportation subsystem 312 for storage and distribution of various natural plant processing raw materials, transporting the raw materials to an on-site workshop production subsystem 313, and uploading raw material information to an industrial internet subsystem 311;
The on-site workshop production subsystem 313 is used for controlling corresponding production equipment and flow of a workshop production line, conveying finished products which are processed and packaged to the finished product storage and transportation system 314, and uploading production information on the production line to the industrial internet subsystem 311;
The finished product storage and transportation subsystem 314 is used for transporting, distributing, statistically storing and warehousing the finished products obtained by processing, and uploading the finished product information to the industrial internet subsystem 311;
the central control subsystem 315 is used for uniformly issuing working commands of the raw material storage and transportation subsystem 312, the on-site workshop production subsystem 313 and the finished product storage and transportation subsystem 314 so as to coordinate the normal and stable operation of the subsystems and ensure that each natural plant processing subsystem can be controlled;
The big data cloud platform subsystem 316 is configured to establish a mathematical intelligent model associated with each natural plant processing subsystem by combining natural plant knowledge judgment with the aid of AI technical means such as data mining and machine learning based on industrial big data provided by the industrial internet subsystem 311, and provide more optimized control strategies, key process points and process parameters for the central control subsystem 315.
The big data cloud platform subsystem 316 includes a big data integration layer, a big data analysis layer, a data modeling, an application scene layer, and an intelligent model.
In the natural plant processing process, the equipment control has the characteristics of real-time performance and accuracy, the required data types are not large, but the data volume is huge, and the data sensing, analysis, decision making and control are required to form an information flow of a real-time closed loop.
Specifically, the industrial big data are data resources generated by various sensors and instrument equipment, control, workshop production records, enterprises and the like; data generated, collected and processed primarily for industrial equipment and industrial software, and intermediate process control and quality inspection data.
The big data integration layer is used for collecting and storing data from different sources in a centralized way and providing accurate basic data for the big data analysis layer and the application scene layer.
The big data analysis layer comprises data preparation and feature extraction. Aiming at specific business targets, a large data set of natural plant processing is flexibly organized, various large data analysis mining algorithms and technologies are flexibly selected and combined for use, valuable information and knowledge are obtained from the large data, and decision services are provided for production guidance and management modes.
Wherein, the data preparation is to provide correct, predetermined format data for solving the business problem; data preparation includes data selection, data cleansing, etc., and attempts are made to combine various methods during processing. Feature extraction refers to the automatic extraction of simple and abstract features and the combination of more complex features by using some AI algorithms, such as deep learning. In the exploratory analysis process, different methods are tried to convert the data, and the quality of feature extraction is evaluated by data modeling.
Data modeling refers to building an algorithm library, inserting the prepared data into each model at the time of application, matching the best model.
The application scene layer is distributed in the whole life cycle of natural plant processing design, research and development, production manufacturing and supply sales, and determines the upper limit of big data value. And aiming at different application scenes, organizing corresponding data and constructing various applications.
The intelligent model comprises an intelligent model A, an intelligent model B and an intelligent model C, and the intelligent models are respectively constructed corresponding to the raw material storage and transportation subsystem 312, the field workshop production subsystem 313 and the finished product storage and transportation subsystem 314, and are independently constructed according to different natural plant processing subsystem data, so that the data extrusion is avoided, and the constructed model can timely and accurately respond to and control the natural plant processing subsystem.
According to the system provided by the embodiment of the invention, the data of each natural plant processing subsystem is processed, analyzed, excavated and modeled correspondingly in the natural plant processing system through the big data cloud platform subsystem 316, and the model of each natural plant processing subsystem is respectively constructed while a large amount of manufacturing data is fully utilized, so that timely and accurate response and control of the natural plant processing subsystem are realized, and the processing efficiency of the natural plant processing system is further improved.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 4. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used to store a preset threshold. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program when executed by a processor implements a natural plant processing control system.
It will be appreciated by persons skilled in the art that the architecture shown in fig. 4 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements are applicable, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
acquiring industrial big data in a historical time period, wherein the industrial big data is used for representing data generated by each natural plant processing subsystem in the processing process;
Acquiring control instructions of all natural plant processing subsystems;
Constructing an intelligent model associated with each natural plant processing subsystem according to natural plant knowledge, control instructions and industrial big data;
the corresponding natural plant processing subsystem is controlled according to the smart model associated with each natural plant processing subsystem.
In one embodiment, the processor, when executing the computer program: each natural plant processing subsystem is respectively a raw material storage and transportation subsystem, an on-site workshop production subsystem and a finished product storage and transportation subsystem, and correspondingly, data generated in the processing process of each natural plant processing subsystem comprises: raw material data generated by the raw material storage and transportation subsystem in the processing process, production data generated by the on-site workshop production subsystem in the processing process and finished product data generated by the finished product storage and transportation subsystem in the processing process.
In one embodiment, the processor when executing the computer program further performs the steps of:
Respectively carrying out data selection, data cleaning and data transformation on the industrial big data to obtain processed industrial big data;
classifying the processed industrial big data according to the control instruction of each natural plant processing subsystem to obtain the input data of each intelligent model;
and constructing each intelligent model according to the natural plant knowledge and the input data of each intelligent model.
In one embodiment, the processor when executing the computer program further performs the steps of:
selecting control data related to a natural plant processing subsystem from the industrial big data according to the control instruction, and acquiring the selected industrial big data;
removing error data in the selected industrial big data to obtain cleaned industrial big data;
and carrying out data standard transformation processing on the cleaned industrial big data to obtain the processed industrial big data.
In one embodiment, the processor when executing the computer program further performs the steps of:
According to the type of the data in the processed industrial big data, extracting the characteristics of the processed industrial big data to obtain different characteristic variables;
And classifying the characteristic variables according to the control instructions of each natural plant processing subsystem to obtain the input data of each intelligent model.
In one embodiment, the processor when executing the computer program further performs the steps of:
acquiring output data of each intelligent model according to each intelligent model;
and controlling the corresponding natural plant processing subsystem according to the output data.
In one embodiment, the processor when executing the computer program further performs the steps of:
acquiring data generated at the current moment of each natural plant processing subsystem;
And matching the data generated at the current moment with intelligent models associated with all the natural plant processing subsystems to obtain an optimized intelligent model.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring industrial big data in a historical time period, wherein the industrial big data is used for representing data generated by each natural plant processing subsystem in the processing process;
Acquiring control instructions of all natural plant processing subsystems;
Constructing an intelligent model associated with each natural plant processing subsystem according to natural plant knowledge, control instructions and industrial big data;
the corresponding natural plant processing subsystem is controlled according to the smart model associated with each natural plant processing subsystem.
In one embodiment, the computer program when executed by the processor further performs the steps of: each natural plant processing subsystem is respectively a raw material storage and transportation subsystem, a field workshop production subsystem, a finished product storage and transportation subsystem, and correspondingly, data generated in the processing process of each natural plant processing subsystem comprises: raw material data generated by the raw material storage and transportation subsystem in the production process, production data generated in the production process and finished product data generated by the finished product storage and transportation subsystem.
In one embodiment, the computer program when executed by the processor further performs the steps of:
Respectively carrying out data selection, data cleaning and data transformation on the industrial big data to obtain processed industrial big data;
classifying the processed industrial big data according to the control instruction of each natural plant processing subsystem to obtain the input data of each intelligent model;
and constructing each intelligent model according to the natural plant knowledge and the input data of each intelligent model.
In one embodiment, the computer program when executed by the processor further performs the steps of:
selecting control data related to a natural plant processing subsystem from the industrial big data according to the control instruction, and acquiring the selected industrial big data;
removing error data in the selected industrial big data to obtain cleaned industrial big data;
and carrying out data standard transformation processing on the cleaned industrial big data to obtain the processed industrial big data.
In one embodiment, the computer program when executed by the processor further performs the steps of:
According to the type of the data in the processed industrial big data, extracting the characteristics of the processed industrial big data to obtain different characteristic variables;
And classifying the characteristic variables according to the control instructions of each natural plant processing subsystem to obtain the input data of each intelligent model.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring output data of each intelligent model according to each intelligent model;
and controlling the corresponding natural plant processing subsystem according to the output data.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring data generated at the current moment of each natural plant processing subsystem;
And matching the data generated at the current moment with intelligent models associated with all the natural plant processing subsystems to obtain an optimized intelligent model.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can be in various forms such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (10)
1. A method of controlling natural plant processing, the method comprising:
Acquiring industrial big data in a historical time period, wherein the industrial big data is used for representing data generated by each natural plant processing subsystem in the processing process;
acquiring control instructions of the natural plant processing subsystems; wherein, the control instructions of different natural plant processing subsystems correspond to different processing tasks;
respectively carrying out data selection, data cleaning and data transformation on the industrial big data to obtain processed industrial big data;
according to the type of the data in the processed industrial big data, extracting the characteristics of the processed industrial big data to obtain different characteristic variables;
Classifying the characteristic variables according to control instructions of the central control subsystem aiming at each natural plant processing subsystem to acquire input data of each intelligent model;
Constructing each intelligent model according to natural plant knowledge and input data of each intelligent model;
And adjusting the control strategy of the corresponding natural plant processing subsystem according to the output data of the intelligent model associated with each natural plant processing subsystem so as to realize the control of key process points and process parameters in the natural plant processing subsystem.
2. The method of claim 1, wherein each of the natural plant processing subsystems is a feedstock storage and transportation subsystem, an on-site plant production subsystem, a finished product storage and transportation subsystem, respectively, and wherein the data generated by each of the natural plant processing subsystems during processing comprises: raw material data generated by the raw material storage and transportation subsystem in the processing process, production data generated by the on-site workshop production subsystem in the processing process, and finished product data generated by the finished product storage and transportation subsystem in the processing process.
3. The method of claim 1, wherein the performing data selection, data cleaning, and data transformation on the industrial big data, respectively, to obtain processed industrial big data, comprises:
Selecting control data related to a natural plant processing subsystem from the industrial big data according to the control instruction, and acquiring the selected industrial big data;
removing error data in the selected industrial big data to obtain cleaned industrial big data;
And carrying out data standard transformation processing on the cleaned industrial big data to obtain the processed industrial big data.
4. A method according to claim 3, wherein the standard transformation process comprises: feature binarization, feature normalization, continuous feature variation, and qualitative feature dummy coding.
5. The method of claim 1, wherein adjusting the control strategy for each natural plant processing subsystem based on the output data of the smart model associated with each natural plant processing subsystem, after effecting control of key process points and process parameters in the natural plant processing subsystem, further comprises:
acquiring data generated at the current moment of each natural plant processing subsystem;
And matching the data generated at the current moment with the intelligent models associated with the natural plant processing subsystems to obtain optimized intelligent models.
6. The method of claim 1, wherein the acquiring industrial big data over the historical period of time comprises:
Industrial big data in a historical time period is obtained through any one mode of WIFI, GPRS, 3G or 4G, bluetooth, zigBee, field bus and Ethernet communication module.
7. A natural plant processing control system, the system comprising: the system comprises an industrial internet subsystem, a raw material storage and transportation subsystem, a field workshop production subsystem, a finished product storage and transportation subsystem, a central control subsystem and a big data cloud platform subsystem;
the industrial internet subsystem is used for connecting processing, storing and transporting equipment, production lines, factories, suppliers, products and clients through the internet and is responsible for collecting data to form industrial big data and communicating and storing information and commands;
The raw material storage and transportation subsystem is used for storing and distributing various natural plant processing raw materials, transporting the raw materials to the on-site workshop production subsystem and uploading raw material information to the industrial Internet subsystem;
The on-site workshop production subsystem is used for controlling corresponding production equipment and flow of a workshop production line, transporting finished products which are processed and packaged to the finished product storage and transportation system, and uploading production information on the production line to the industrial Internet subsystem;
The finished product storage and transportation subsystem is used for transporting, distributing, counting, storing and warehousing the processed finished products, and uploading the finished product information to the industrial Internet subsystem;
The central control subsystem is used for uniformly issuing working commands of the raw material storage and transportation subsystem, the on-site workshop production subsystem and the finished product storage and transportation subsystem so as to coordinate the normal and stable operation of each subsystem and ensure that each natural plant processing subsystem can be controlled;
The big data cloud platform subsystem is used for acquiring control instructions of the raw material storage and transportation subsystem, the on-site workshop production subsystem and the finished product storage and transportation subsystem from the central control subsystem; wherein, the control instructions of different natural plant processing subsystems correspond to different processing tasks; the natural plant processing subsystem is respectively a raw material storage and transportation subsystem, a field workshop production subsystem and a finished product storage and transportation subsystem; respectively carrying out data selection, data cleaning and data transformation on the industrial big data to obtain processed industrial big data; the industrial big data comprises raw material information, production information and finished product information; according to the type of the data in the processed industrial big data, extracting the characteristics of the processed industrial big data to obtain different characteristic variables; classifying the characteristic variables according to control instructions of the central control subsystem aiming at each natural plant processing subsystem to acquire input data of each intelligent model; constructing each intelligent model according to natural plant knowledge and input data of each intelligent model;
The central control subsystem is also used for adjusting the control strategy of the corresponding natural plant processing subsystem according to the output data of the intelligent model associated with each natural plant processing subsystem so as to realize the control of key process points and process parameters in the natural plant processing subsystem.
8. The system of claim 7, wherein the big data cloud platform subsystem comprises a big data integration layer, a big data analysis layer, data modeling, an application scenario layer, and an intelligent model.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109154588A (en) * | 2016-05-17 | 2019-01-04 | 自动化合作关系（剑桥）有限公司 | Automatic biological process development |
| CN109559096A (en) * | 2018-11-22 | 2019-04-02 | 贵州省食品安全检测应用工程技术研究中心有限公司 | A kind of ecology cloud platform data analysis application system |
| CN110520464A (en) * | 2016-10-28 | 2019-11-29 | 康宝有限公司 | Biocomposite material, biological plastics material and the method for manufacturing biological plastics material |
| CN111667162A (en) * | 2020-05-28 | 2020-09-15 | 广东工业大学 | Traditional chinese medicine or natural plant extract intelligent manufacturing system based on industry internet |
| CN112163688A (en) * | 2019-08-16 | 2021-01-01 | 重庆华森制药股份有限公司 | Full-flow intelligent manufacturing system for multi-dosage form Chinese and western medicine products and construction method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3493001A1 (en) * | 2017-11-29 | 2019-06-05 | Siemens Aktiengesellschaft | Cloud-based method and system for optimising tuning of an industrial plant |
-
2021
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109154588A (en) * | 2016-05-17 | 2019-01-04 | 自动化合作关系（剑桥）有限公司 | Automatic biological process development |
| CN110520464A (en) * | 2016-10-28 | 2019-11-29 | 康宝有限公司 | Biocomposite material, biological plastics material and the method for manufacturing biological plastics material |
| CN109559096A (en) * | 2018-11-22 | 2019-04-02 | 贵州省食品安全检测应用工程技术研究中心有限公司 | A kind of ecology cloud platform data analysis application system |
| CN112163688A (en) * | 2019-08-16 | 2021-01-01 | 重庆华森制药股份有限公司 | Full-flow intelligent manufacturing system for multi-dosage form Chinese and western medicine products and construction method |
| CN111667162A (en) * | 2020-05-28 | 2020-09-15 | 广东工业大学 | Traditional chinese medicine or natural plant extract intelligent manufacturing system based on industry internet |
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