CN117521423A - Intelligent prediction method and device for machine speed of corrugated paper processing and storage medium - Google Patents
Intelligent prediction method and device for machine speed of corrugated paper processing and storage medium Download PDFInfo
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Abstract
The application relates to the technical field of intelligent manufacturing and discloses a method, a device and a storage medium for intelligently predicting the machine speed of corrugated paper processing, wherein the method comprises the steps of acquiring production data of a target corrugated paper machine, and acquiring uplink data and downlink data of the target corrugated paper machine through a sensor; determining a first time and a second time respectively based on the uplink data and the downlink data; based on the production data, the first time, the second time, and the preset speed prediction model, the speed of the target corrugating machine at the next moment is predicted. By the method, the first time and the second time respectively corresponding to the uplink data and the downlink data are determined by acquiring the production data and the uplink data and the downlink data of the corrugated paper machine. After the production data, the first time and the second time are determined, the speed at the next moment is predicted through a preset speed prediction model, so that the production efficiency of the corrugated paper machine is improved.
Description
Technical Field
The application relates to the technical field of intelligent manufacturing, in particular to an intelligent prediction method and device for machine speed of corrugated paper processing and a storage medium.
Background
In the prior art, the traditional paper mill is difficult to monitor the running state of the corrugating machine and the production state of paper in real time, if abnormal conditions cannot be processed, the difference of the quality effects of the produced paper is large, sometimes even the corrugated paper is wrinkled and damaged, the traditional speed control mode of the corrugated paper machine is a manual adjustment method, the traditional manual adjustment method is unstable, the speed fluctuation is large, the vibration fluctuation of the corrugated paper machine is large, the corrugated paper is wrinkled and damaged, most of the time of the method for manually adjusting the speed is not up to the optimal speed of the corrugated paper machine under the conditions of the material and the breadth of certain corrugated paper, the production efficiency is influenced, and the labor cost for controlling the speed of the corrugated paper machine is also a great expense of a factory. Therefore, how to determine the processing speed of the corrugated paper machine to improve the production efficiency is a technical problem to be solved.
Disclosure of Invention
The application provides an intelligent prediction method, device and storage medium for the machine speed of corrugated paper processing, and the processing speed of a corrugated paper machine is determined to improve the production efficiency.
In a first aspect, the present application provides a method for intelligently predicting a machine speed for corrugated paper processing, the method comprising:
Acquiring production data of a target corrugated paper machine, and acquiring uplink data and downlink data of the target corrugated paper machine through a preset sensor, wherein the production data comprises material data, breadth data and stupefied data;
based on the uplink data and the downlink data, respectively determining a first time corresponding to a target position point in the uplink data and a second time corresponding to the target position point in the downlink data;
and predicting the speed of the target corrugated paper machine at the next moment based on the production data, the first time, the second time and a preset speed prediction model.
Further, predicting the speed of the target corrugating machine at the next time based on the production data, the first time, the second time, and a preset speed prediction model includes:
acquiring historical data from a preset distributed database, wherein the historical data comprises historical production data, third time corresponding to the target position point in historical uplink data and fourth time corresponding to the target position point in historical downlink data;
and training an initial speed prediction model based on each of the historical production data, each of the third time and each of the fourth time, and generating the preset speed prediction model.
Further, before obtaining the history data from the preset distributed database, the method includes:
constructing a to-be-written-in status cluster based on a relational database management instance;
deploying the synchronization agent of the preset distributed data through the to-be-written-in status cluster;
based on a to-be-backed-up Citus cluster, a slicing instruction, a synchronization relation between the to-be-backed-up Citus cluster and the to-be-written Citus cluster, and the synchronization agent, performing full data synchronization;
and when the full data synchronization is completed, performing incremental data synchronization based on the initial point of the incremental data synchronization and the synchronization relation to generate the preset distributed database.
Further, constructing a to-be-written-in status cluster based on the relational database management instance comprises:
acquiring IP and port information of DN nodes of the to-be-written-in status cluster based on the relational database management instance;
and designating socket information for DN nodes of the to-be-written-in status cluster based on the writing instruction, the IP and the port information, and constructing the to-be-written-in status cluster.
Further, training an initial speed prediction model based on the historical production data, the third time, and the fourth time, generating the preset speed prediction model includes:
Determining each historical uplink speed and each historical downlink speed according to each historical production data, each third time and each fourth time;
based on each historical uplink speed and each historical downlink speed, respectively establishing a historical uplink speed matrix and a historical downlink speed matrix;
clustering each speed based on a preset clustering algorithm, the historical uplink speed matrix and the historical downlink speed matrix in the initial speed prediction model, and dividing each historical uplink speed and each historical downlink speed into at least one speed interval;
based on each speed interval, predicting the speed of the target corrugated paper machine at the next moment.
Further, predicting a speed of the target corrugating machine at a next time based on the production data, the first time, the second time, and a preset speed prediction model, comprising:
based on the first time, the second time and the production data, respectively calculating a current uplink speed and a current downlink speed;
and predicting the speed of the target corrugated paper machine at the next moment through the current uplink speed, the current downlink speed and each speed interval by the preset speed prediction model.
Further, the preset sensor includes an inertial measurement sensor IMU sensor, and the collecting uplink data and downlink data of the target corrugated paper machine by the preset sensor includes:
acquiring acceleration data and gyroscope data of the target corrugated paper through the IMU sensor;
and respectively determining the uplink data and the downlink data according to the direction symbols of the data in the acceleration data and the gyroscope data.
Further, acquiring production data of a target corrugated paper machine, and acquiring uplink data and downlink data of the target corrugated paper machine through a preset sensor, wherein the production data comprises material data, breadth data and edge data, and then comprises the following steps:
data screening is carried out on the production data, invalid data are cleared, and the reserved production data are subjected to data management;
and carrying out numerical processing on the production data which are larger than a preset production data threshold value.
Further, the production data includes material data, breadth data, and stupefied data.
In a second aspect, the present application further provides an intelligent prediction apparatus for machine speed for corrugated paper processing, the apparatus comprising:
the data acquisition module is used for acquiring production data of the target corrugated paper machine and acquiring uplink data and downlink data of the target corrugated paper machine through a preset sensor, wherein the production data comprises material data, breadth data and stupefied data;
The time determining module is used for respectively determining a first time corresponding to a target position point in the uplink data and a second time corresponding to the target position point in the downlink data based on the uplink data and the downlink data;
and the speed prediction module is used for predicting the speed of the target corrugated paper machine at the next moment based on the production data, the first time, the second time and a preset speed prediction model.
In a third aspect, the present application also provides a computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to implement a machine speed intelligent prediction method for corrugated paper processing as described above.
The application discloses an intelligent prediction method, device and storage medium for the machine speed of corrugated paper processing, wherein the intelligent prediction method for the machine speed of corrugated paper processing comprises the steps of obtaining production data of a target corrugated paper machine, and collecting uplink data and downlink data of the target corrugated paper machine through a preset sensor, wherein the production data comprise material data, breadth data and stupefied data; based on the uplink data and the downlink data, respectively determining a first time corresponding to a target position point in the uplink data and a second time corresponding to the target position point in the downlink data; and predicting the speed of the target corrugated paper machine at the next moment based on the production data, the first time, the second time and a preset speed prediction model. By the method, the first time and the second time respectively corresponding to the uplink data and the downlink data are determined by acquiring the production data and the uplink data and the downlink data of the corrugated paper machine. After the production data, the first time and the second time are determined, the speed at the next moment is predicted through a preset speed prediction model, so that the production efficiency of the corrugated paper machine is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a machine speed intelligent prediction method for corrugated paper processing provided in a first embodiment of the present application;
FIG. 2 is a schematic flow chart of a machine speed intelligent prediction method for corrugated paper processing provided in a second embodiment of the present application;
FIG. 3 is a schematic block diagram of an intelligent prediction device for machine speed for corrugated paper processing according to an embodiment of the present application;
fig. 4 is a schematic block diagram of a computer device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The flow diagrams depicted in the figures are merely illustrative and not necessarily all of the elements and operations/steps are included or performed in the order described. For example, some operations/steps may be further divided, combined, or partially combined, so that the order of actual execution may be changed according to actual situations.
It is to be understood that the terminology used in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
The embodiment of the application provides an intelligent prediction method and device for machine speed of corrugated paper processing and a storage medium. The intelligent prediction method for the machine speed of corrugated paper processing can be applied to a server, and the first time and the second time corresponding to the uplink data and the downlink data respectively are determined by acquiring the production data, the uplink data and the downlink data of the corrugated paper machine. After the production data, the first time and the second time are determined, the speed at the next moment is predicted through a preset speed prediction model, so that the production efficiency of the corrugated paper machine is improved. The server may be an independent server or a server cluster.
Some embodiments of the present application are described in detail below with reference to the accompanying drawings. The following embodiments and features of the embodiments may be combined with each other without conflict.
Referring to fig. 1, fig. 1 is a schematic flow chart of a machine speed intelligent prediction method for corrugated paper processing according to a first embodiment of the present application. The intelligent prediction method for the machine speed of corrugated paper processing can be applied to a server and is used for determining the first time and the second time respectively corresponding to the uplink data and the downlink data by acquiring the production data, the uplink data and the downlink data of the corrugated paper machine. After the production data, the first time and the second time are determined, the speed at the next moment is predicted through a preset speed prediction model, so that the production efficiency of the corrugated paper machine is improved.
As shown in fig. 1, the intelligent prediction method for the machine speed of corrugated paper processing specifically includes steps S10 to S30.
S10, acquiring production data of a target corrugated paper machine, and acquiring uplink data and downlink data of the target corrugated paper machine through a preset sensor, wherein the production data comprises material data, breadth data and edge data;
in one embodiment, the upstream data and downstream data refer to directions of movement during the production of the corrugator machine. The movement direction is referred to herein as upstream data and downstream data.
Step S20, based on the uplink data and the downlink data, determining a first time corresponding to a target position point in the uplink data and a second time corresponding to the target position point in the downlink data respectively;
in one embodiment, the target position point is any position in the production process of the corrugated paper machine, and the time for the corrugated paper machine to pass through the position in the forward and backward directions is calculated, namely, the first time and the second time.
And step S30, predicting the speed of the target corrugated paper machine at the next moment based on the production data, the first time, the second time and a preset speed prediction model.
In one embodiment, the production data includes a distance between a target position point and a most distant position of the two ends of the target corrugated paper machine, and the speed of the current corrugated paper machine can be calculated according to the distance, the first time and the second time. And predicting the speed at the next moment by combining a preset speed prediction model and the current speed.
In one embodiment, the production data includes the following data:
1. corrugated height: the corrugated height of the common three-layer paper box corrugated paper is generally between 3mm and 5mm, and the higher the height is, the better the compression resistance of the paper box is;
2. Corrugated distance: the corrugated distance is also a factor affecting the compression resistance of the carton, generally between 5mm and 7mm, the further the distance is, the higher the strength of the carton;
3. gram weight of paperboard: the three-layer paper box corrugated paper has the gram weight of 250g/m to 350g/m, and the higher the gram weight is, the larger the thickness of the paper board is, so that the packaging of the heavy object is more stable and reliable.
The embodiment discloses a machine speed intelligent prediction method, a device and a storage medium for corrugated paper processing, wherein the machine speed intelligent prediction method for corrugated paper processing comprises the steps of obtaining production data of a target corrugated paper machine, and collecting uplink data and downlink data of the target corrugated paper machine through a preset sensor, wherein the production data comprise material data, breadth data and stupefied data; based on the uplink data and the downlink data, respectively determining a first time corresponding to a target position point in the uplink data and a second time corresponding to the target position point in the downlink data; and predicting the speed of the target corrugated paper machine at the next moment based on the production data, the first time, the second time and a preset speed prediction model. By the method, the first time and the second time respectively corresponding to the uplink data and the downlink data are determined by acquiring the production data and the uplink data and the downlink data of the corrugated paper machine. After the production data, the first time and the second time are determined, the speed at the next moment is predicted through a preset speed prediction model, so that the production efficiency of the corrugated paper machine is improved.
Based on the embodiment shown in fig. 1, before step S20, the method includes:
acquiring historical data from a preset distributed database, wherein the historical data comprises historical production data, third time corresponding to the target position point in historical uplink data and fourth time corresponding to the target position point in historical downlink data;
and training an initial speed prediction model based on each of the historical production data, each of the third time and each of the fourth time, and generating the preset speed prediction model.
Based on the embodiment shown in fig. 1, before obtaining the history data from the preset distributed database in this embodiment, the method includes:
constructing a to-be-written-in status cluster based on a relational database management instance;
deploying the synchronization agent of the preset distributed data through the to-be-written-in status cluster;
in one embodiment, citus is an extension of a relational database management instance that converts a management instance database into a distributed database, thus enabling high performance. These status supercapabilities can be used to extend the management instance database over a single status node. Or a large cluster capable of processing high transaction throughput is constructed, particularly in a multi-tenant application program, the rapid analysis query is operated, and a large amount of time series or internet of things data is processed for real-time analysis. As data size and number increase, more working nodes can be easily added to the cluster and the shards rebalanced.
Using Citus, the relational database management instance database can be extended:
1. the distributed tables are fragmented in a relational database management instance node cluster to combine their CPU, memory, storage and I/O capacities;
2. the reference table is copied to all nodes for join and foreign key from the distributed table to obtain maximum read performance;
3. the distributed query engine routes and parallelizes SELECT, DML, and other operations on the distributed tables in the cluster;
4. the column stores compressed data, so that the scanning speed is increased, and fast projection is supported on a conventional table and a distributed table;
5. by querying from any node, the full capacity of the cluster can be utilized for distributed querying.
Based on a to-be-backed-up Citus cluster, a slicing instruction, a synchronization relation between the to-be-backed-up Citus cluster and the to-be-written Citus cluster, and the synchronization agent, performing full data synchronization;
in one embodiment, a community cluster is made up of a central Coordinator Node (CN) and several Worker nodes (workers). corodinate: the coordination node, commonly called cn, stores all metadata, does not store actual data, and is directly open to users, equal to one client. works er: and the working node does not store metadata and stores actual data. And executing the query request sent by the coordination node. Are generally not directly open to the user.
And when the full data synchronization is completed, performing incremental data synchronization based on the initial point of the incremental data synchronization and the synchronization relation to generate the preset distributed database.
In one embodiment, a preset distributed database is used for storing historical data of the corrugated paper machine, and the database receives the state reported by the database synchronization agent through a management platform, such as an agent management module, a topology management module, a backup scheduling module, a statistics service module and the like, has a front-end page and can interact with a user.
Furthermore, a synchronization agent of the distributed database is preset: and the data extraction, data management and data storage are actually carried out.
In one embodiment, for example: the backup method of the distributed database in this embodiment is used in the disaster recovery platform to backup a status cluster of 3 nodes, including 1 CN (Coordinator Node) Node and 2 DN (Worker Node) nodes. The IP and ports of the CN nodes are 172.16.0.10:5432, and the IP ports of the two DN nodes are 172.16.0.20:5432 and 172.16.0.30:5432 respectively.
Further, constructing a to-be-written-in status cluster based on the relational database management instance comprises:
Acquiring IP and port information of DN nodes of the to-be-written-in status cluster based on the relational database management instance;
and designating socket information for DN nodes of the to-be-written-in status cluster based on the writing instruction, the IP and the port information, and constructing the to-be-written-in status cluster.
In one embodiment, socket is an abstraction layer between the application layer and transport layer that abstracts the complex operations of the TCP/IP layer into several simple interface provisioning application layer calls implemented processes to communicate in the network.
Socket is a communication base and is a basic operation unit for network communication supporting TCP/IP protocol. A socket can be considered an endpoint of a process between different hosts for dual communication, which forms a programming interface within a single host and throughout a network. Sockets exist in the communication domain, which is an abstraction introduced to handle general threads through socket communications. Sockets typically exchange data with sockets in the same domain (data exchanges may also cross the boundaries of the domain, but some kind of interpreter must be executed at this time), and various processes use this same domain to communicate with each other using Internet protocol clusters.
Socket can be seen as an endpoint in the respective communication connection when two network applications communicate, which is a logical concept. It is an application programming interface for interprocess communication in a network environment and is also a communication endpoint that can be named and addressed, each socket in use having its type and a process connected to it. When in communication, one network application program writes a piece of information to be transmitted into a Socket of a host where the network application program is located, and the Socket sends the piece of information to the Socket of another host through a transmission medium connected with a network interface card, so that the other party can receive the piece of information. Socket is a combination of IP address and port that provides a mechanism to deliver packets to application layer processes.
Referring to fig. 2, fig. 2 is a schematic flow chart of a machine speed intelligent prediction method for corrugated paper processing according to a second embodiment of the present application. The intelligent prediction method for the machine speed of corrugated paper processing can be applied to a server and is used for determining the first time and the second time respectively corresponding to the uplink data and the downlink data by acquiring the production data, the uplink data and the downlink data of the corrugated paper machine. After the production data, the first time and the second time are determined, the speed at the next moment is predicted through a preset speed prediction model, so that the production efficiency of the corrugated paper machine is improved.
As shown in fig. 1, the intelligent prediction method for the machine speed of corrugated paper processing specifically includes steps S01 to S04.
Step S01, determining each historical uplink speed and each historical downlink speed according to each historical production data, each third time and each fourth time;
step S02, a historical uplink speed matrix and a historical downlink speed matrix are respectively established based on each historical uplink speed and each historical downlink speed;
step S03, clustering each speed based on a preset clustering algorithm in the initial speed prediction model, the historical uplink speed matrix and the historical downlink speed matrix, and dividing each historical uplink speed and each historical downlink speed into at least one speed interval;
and step S04, predicting the speed of the target corrugated paper machine at the next moment based on each speed interval.
In one embodiment, the historical data are clustered according to a large amount of data, and at least one next-moment speed corresponding to each speed is obtained. And predicting the speed at the next moment according to the current speed and the clustering result (namely, matching with the historical data clustering result, taking the current speed as a matching standard, and obtaining the speed at the next moment with the highest probability).
The embodiment discloses a method, a device and a storage medium for intelligently predicting the machine speed of corrugated paper processing, wherein the method for intelligently predicting the machine speed of corrugated paper processing comprises the steps of determining each historical uplink speed and each historical downlink speed according to each historical production data, each third time and each fourth time; based on each historical uplink speed and each historical downlink speed, respectively establishing a historical uplink speed matrix and a historical downlink speed matrix; clustering each speed based on a preset clustering algorithm, the historical uplink speed matrix and the historical downlink speed matrix in the initial speed prediction model, and dividing each historical uplink speed and each historical downlink speed into at least one speed interval; based on each speed interval, predicting the speed of the target corrugated paper machine at the next moment. By the method, the first time and the second time respectively corresponding to the uplink data and the downlink data are determined by acquiring the production data and the uplink data and the downlink data of the corrugated paper machine. After the production data, the first time and the second time are determined, the speed at the next moment is predicted through a preset speed prediction model, so that the production efficiency of the corrugated paper machine is improved.
Based on the embodiment shown in fig. 2, in this embodiment, step S30 includes:
based on the first time, the second time and the production data, respectively calculating a current uplink speed and a current downlink speed;
and predicting the speed of the target corrugated paper machine at the next moment through the current uplink speed, the current downlink speed and each speed interval by the preset speed prediction model.
Based on the embodiment shown in fig. 1, in this embodiment, step S10 includes:
acquiring acceleration data and gyroscope data of the target corrugated paper through the IMU sensor;
and respectively determining the uplink data and the downlink data according to the direction symbols of the data in the acceleration data and the gyroscope data.
In one embodiment, the IMU (Inertial Measuring Unit, inertial measurement unit) is a sensor for detecting and measuring acceleration and rotational movement that captures acceleration and direction of the target corrugator process.
Based on the embodiment shown in fig. 1, in this embodiment, step S10 includes:
data screening is carried out on the production data, invalid data are cleared, and the reserved production data are subjected to data management;
And carrying out numerical processing on the production data which are larger than a preset production data threshold value.
Based on any of the above embodiments, in this embodiment, the production data includes material data, width data, and ridge data.
Referring to fig. 3, fig. 3 is a schematic block diagram of an intelligent predicting device for machine speed of corrugated paper processing according to an embodiment of the present application, where the intelligent predicting device for machine speed of corrugated paper processing is used to execute the foregoing intelligent predicting method for machine speed of corrugated paper processing. The intelligent predicting device for the machine speed of corrugated paper processing can be configured on a server.
As shown in fig. 3, the intelligent predicting device for machine speed of corrugated paper processing comprises:
the data acquisition module 410 is configured to acquire production data of a target corrugated paper machine, and acquire uplink data and downlink data of the target corrugated paper machine through a preset sensor, where the production data includes material data, breadth data, and edge data;
a time determining module 420, configured to determine, based on the uplink data and the downlink data, a first time corresponding to a target location point in the uplink data and a second time corresponding to the target location point in the downlink data, respectively;
The speed prediction module 430 is configured to predict a speed of the target corrugator at a next time based on the production data, the first time, the second time, and a preset speed prediction model.
Further, the intelligent predicting device for the machine speed of corrugated paper processing further comprises:
the historical data acquisition module is used for acquiring historical data from a preset distributed database, wherein the historical data comprises historical production data, third time corresponding to the target position point in historical uplink data and fourth time corresponding to the target position point in historical downlink data;
and the preset speed prediction model generation module is used for training an initial speed prediction model based on each historical production data, each third time and each fourth time to generate the preset speed prediction model.
Further, the intelligent predicting device for the machine speed of corrugated paper processing further comprises:
the to-be-written-in status cluster construction module is used for constructing to-be-written status clusters based on the relational database management instance;
the synchronous agent deployment module is used for deploying the synchronous agent of the preset distributed data through the to-be-written-in status cluster;
The full-volume data synchronization module is used for carrying out full-volume data synchronization based on a to-be-backed-up status cluster, a slicing instruction, a synchronization relation between the to-be-backed-up status cluster and the to-be-written status cluster and the synchronization agent;
and the preset distributed database production module is used for carrying out incremental data synchronization based on the initial position of the incremental data synchronization and the synchronization relation when the full data synchronization is completed, so as to generate the preset distributed database.
Further, the to-be-written-in status cluster building module further includes:
the IP and port information acquisition unit is used for acquiring the IP and port information of the DN node to be written into the Citus cluster based on the relational database management example;
a to-be-written-in status cluster construction unit for generating a write instruction based on the write instruction, the IP and the port information, and designating socket information for the DN node of the to-be-written-in status cluster, and constructing the to-be-written-in status cluster.
Further, the preset speed prediction model generating module includes:
a historical speed obtaining unit, configured to determine each historical uplink speed and each historical downlink speed according to each historical production data, each third time and each fourth time;
A history matrix generating unit, configured to respectively establish a history uplink speed matrix and a history downlink speed matrix based on each of the history uplink speeds and each of the history downlink speeds;
the speed interval generation unit is used for clustering each speed based on a preset clustering algorithm, the historical uplink speed matrix and the historical downlink speed matrix in the initial speed prediction model, and dividing each historical uplink speed and each historical downlink speed into at least one speed interval;
and the speed prediction unit is used for predicting the speed of the next moment of the target corrugated paper machine based on each speed interval.
Further, the speed prediction module 430 includes:
the current speed calculation unit is used for calculating the current uplink speed and the current downlink speed respectively based on the first time, the second time and the production data;
and the current speed prediction unit is used for predicting the speed of the target corrugated paper machine at the next moment through the current uplink speed, the current downlink speed and each speed interval by the preset speed prediction model.
Further, the data acquisition module 410 includes:
The data acquisition unit is used for acquiring acceleration data and gyroscope data of the target corrugated paper through the IMU sensor;
and the data determining unit is used for respectively determining the uplink data and the downlink data according to the direction symbols of the data in the acceleration data and the gyroscope data.
Further, the intelligent predicting device for the machine speed of corrugated paper processing further comprises:
the data processing module is used for carrying out data screening on the production data, clearing invalid data and carrying out data processing on the preserved production data;
and the numerical processing module is used for performing numerical processing on the production data which is larger than a preset production data threshold value.
It should be noted that, for convenience and brevity of description, the specific working process of the apparatus and each module described above may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
The apparatus described above may be implemented in the form of a computer program which is executable on a computer device as shown in fig. 4.
Referring to fig. 4, fig. 4 is a schematic block diagram of a computer device according to an embodiment of the present application. The computer device may be a server.
With reference to FIG. 4, the computer device includes a processor, memory, and a network interface connected by a system bus, where the memory may include a non-volatile storage medium and an internal memory.
The non-volatile storage medium may store an operating system and a computer program. The computer program comprises program instructions that, when executed, cause the processor to perform any of a number of intelligent machine speed prediction methods for corrugated paper processing.
The processor is used to provide computing and control capabilities to support the operation of the entire computer device.
The internal memory provides an environment for the execution of a computer program in the non-volatile storage medium that, when executed by the processor, causes the processor to perform any of a variety of intelligent machine speed prediction methods for corrugated paper processing.
The network interface is used for network communication such as transmitting assigned tasks and the like. Those skilled in the art will appreciate that the structures shown in FIG. 4 are block diagrams only and do not constitute a limitation of the computer device on which the present aspects apply, and that a particular computer device may include more or less components than those shown, or may combine some of the components, or have a different arrangement of components.
It should be appreciated that the processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. Wherein the general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Wherein in one embodiment the processor is configured to run a computer program stored in the memory to implement the steps of:
acquiring production data of a target corrugated paper machine, and acquiring uplink data and downlink data of the target corrugated paper machine through a preset sensor, wherein the production data comprises material data, breadth data and stupefied data;
based on the uplink data and the downlink data, respectively determining a first time corresponding to a target position point in the uplink data and a second time corresponding to the target position point in the downlink data;
And predicting the speed of the target corrugated paper machine at the next moment based on the production data, the first time, the second time and a preset speed prediction model.
In one embodiment, the method further comprises, based on the production data, the first time, the second time, and a preset speed prediction model, predicting a speed of the target corrugator machine before a next time is used to implement:
acquiring historical data from a preset distributed database, wherein the historical data comprises historical production data, third time corresponding to the target position point in historical uplink data and fourth time corresponding to the target position point in historical downlink data;
and training an initial speed prediction model based on each of the historical production data, each of the third time and each of the fourth time, and generating the preset speed prediction model.
In one embodiment, before the historical data is obtained from the preset distributed database, the method is used for realizing:
constructing a to-be-written-in status cluster based on a relational database management instance;
deploying the synchronization agent of the preset distributed data through the to-be-written-in status cluster;
based on a to-be-backed-up Citus cluster, a slicing instruction, a synchronization relation between the to-be-backed-up Citus cluster and the to-be-written Citus cluster, and the synchronization agent, performing full data synchronization;
And when the full data synchronization is completed, performing incremental data synchronization based on the initial point of the incremental data synchronization and the synchronization relation to generate the preset distributed database.
In one embodiment, a to-be-written-to-status cluster is built based on a relational database management instance for implementation:
acquiring IP and port information of DN nodes of the to-be-written-in status cluster based on the relational database management instance;
and designating socket information for DN nodes of the to-be-written-in status cluster based on the writing instruction, the IP and the port information, and constructing the to-be-written-in status cluster.
In one embodiment, training an initial speed prediction model based on the historical production data, the third time, and the fourth time generates the preset speed prediction model for implementation:
determining each historical uplink speed and each historical downlink speed according to each historical production data, each third time and each fourth time;
based on each historical uplink speed and each historical downlink speed, respectively establishing a historical uplink speed matrix and a historical downlink speed matrix;
clustering each speed based on a preset clustering algorithm, the historical uplink speed matrix and the historical downlink speed matrix in the initial speed prediction model, and dividing each historical uplink speed and each historical downlink speed into at least one speed interval;
Based on each speed interval, predicting the speed of the target corrugated paper machine at the next moment.
In one embodiment, the speed of the target corrugator machine at the next moment is predicted based on the production data, the first time, the second time and a preset speed prediction model for achieving:
based on the first time, the second time and the production data, respectively calculating a current uplink speed and a current downlink speed;
and predicting the speed of the target corrugated paper machine at the next moment through the current uplink speed, the current downlink speed and each speed interval by the preset speed prediction model.
In one embodiment, the preset sensor includes an inertial measurement sensor IMU sensor, and the preset sensor is used for collecting uplink data and downlink data of the target corrugated paper machine, so as to realize:
acquiring acceleration data and gyroscope data of the target corrugated paper through the IMU sensor;
and respectively determining the uplink data and the downlink data according to the direction symbols of the data in the acceleration data and the gyroscope data.
In one embodiment, production data of a target corrugated paper machine is acquired, and uplink data and downlink data of the target corrugated paper machine are acquired through a preset sensor, wherein the production data comprise material data, breadth data and edge data and are used for realizing the following steps:
Data screening is carried out on the production data, invalid data are cleared, and the reserved production data are subjected to data management;
and carrying out numerical processing on the production data which are larger than a preset production data threshold value.
The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, the computer program comprises program instructions, and the processor executes the program instructions to realize the intelligent machine speed prediction method for any corrugated paper processing provided by the embodiment of the application.
The computer readable storage medium may be an internal storage unit of the computer device according to the foregoing embodiment, for example, a hard disk or a memory of the computer device. The computer readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like, which are provided on the computer device.
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. An intelligent prediction method for the machine speed of corrugated paper processing is characterized by comprising the following steps:
acquiring production data of a target corrugated paper machine, and acquiring uplink data and downlink data of the target corrugated paper machine through a preset sensor, wherein the production data comprises material data, breadth data and stupefied data;
based on the uplink data and the downlink data, respectively determining a first time corresponding to a target position point in the uplink data and a second time corresponding to the target position point in the downlink data;
and predicting the speed of the target corrugated paper machine at the next moment based on the production data, the first time, the second time and a preset speed prediction model.
2. The method of claim 1, wherein predicting the speed of the target corrugating machine at the next time based on the production data, the first time, the second time, and a preset speed prediction model comprises:
acquiring historical data from a preset distributed database, wherein the historical data comprises historical production data, third time corresponding to the target position point in historical uplink data and fourth time corresponding to the target position point in historical downlink data;
And training an initial speed prediction model based on each of the historical production data, each of the third time and each of the fourth time, and generating the preset speed prediction model.
3. The intelligent prediction method for machine speed of corrugated paper processing according to claim 2, wherein before the history data is obtained from the preset distributed database, the method comprises:
constructing a to-be-written-in status cluster based on a relational database management instance;
deploying the synchronization agent of the preset distributed data through the to-be-written-in status cluster;
based on a to-be-backed-up Citus cluster, a slicing instruction, a synchronization relation between the to-be-backed-up Citus cluster and the to-be-written Citus cluster, and the synchronization agent, performing full data synchronization;
and when the full data synchronization is completed, performing incremental data synchronization based on the initial point of the incremental data synchronization and the synchronization relation to generate the preset distributed database.
4. A machine speed intelligent prediction method for corrugated paper processing according to claim 3, wherein the constructing a to-be-written-in status cluster based on the relational database management instance comprises:
acquiring IP and port information of DN nodes of the to-be-written-in status cluster based on the relational database management instance;
And designating socket information for DN nodes of the to-be-written-in status cluster based on the writing instruction, the IP and the port information, and constructing the to-be-written-in status cluster.
5. The method of claim 2, wherein the training an initial speed prediction model based on the historical production data, the third time, and the fourth time to generate the preset speed prediction model comprises:
determining each historical uplink speed and each historical downlink speed according to each historical production data, each third time and each fourth time;
based on each historical uplink speed and each historical downlink speed, respectively establishing a historical uplink speed matrix and a historical downlink speed matrix;
clustering each speed based on a preset clustering algorithm, the historical uplink speed matrix and the historical downlink speed matrix in the initial speed prediction model, and dividing each historical uplink speed and each historical downlink speed into at least one speed interval;
based on each speed interval, predicting the speed of the target corrugated paper machine at the next moment.
6. The intelligent prediction method for machine speed for corrugated paper processing according to claim 5, wherein predicting the speed of the target corrugated paper machine at the next moment based on the production data, the first time, the second time and a preset speed prediction model comprises:
based on the first time, the second time and the production data, respectively calculating a current uplink speed and a current downlink speed;
and predicting the speed of the target corrugated paper machine at the next moment through the current uplink speed, the current downlink speed and each speed interval by the preset speed prediction model.
7. The intelligent prediction method for machine speed of corrugated paper processing according to claim 1, wherein the preset sensor comprises an inertial measurement sensor IMU sensor, and the collecting uplink data and downlink data of the target corrugated paper machine by the preset sensor comprises:
acquiring acceleration data and gyroscope data of the target corrugated paper through the IMU sensor;
and respectively determining the uplink data and the downlink data according to the direction symbols of the data in the acceleration data and the gyroscope data.
8. The method for intelligently predicting the machine speed of corrugated paper processing according to any one of claims 1 to 7, wherein the acquiring production data of a target corrugated paper machine and acquiring uplink data and downlink data of the target corrugated paper machine through a preset sensor, wherein the production data comprises material data, breadth data and edge data, and then comprises:
data screening is carried out on the production data, invalid data are cleared, and the reserved production data are subjected to data management;
and carrying out numerical processing on the production data which are larger than a preset production data threshold value.
9. The utility model provides a machine speed intelligence prediction unit of corrugated paper processing which characterized in that includes:
the data acquisition module is used for acquiring production data of the target corrugated paper machine and acquiring uplink data and downlink data of the target corrugated paper machine through a preset sensor, wherein the production data comprises material data, breadth data and stupefied data;
the time determining module is used for respectively determining a first time corresponding to a target position point in the uplink data and a second time corresponding to the target position point in the downlink data based on the uplink data and the downlink data;
And the speed prediction module is used for predicting the speed of the target corrugated paper machine at the next moment based on the production data, the first time, the second time and a preset speed prediction model.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program which, when executed by a processor, causes the processor to implement the machine speed intelligent prediction method of corrugated paper processing as claimed in any one of claims 1 to 8.
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