CN111950939A - Material management method based on intelligent manufacturing - Google Patents
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Abstract
The invention relates to the field of industrial internet and intelligent manufacturing, and discloses a material management method based on intelligent manufacturing, which comprises the following steps: the data acquisition module periodically acquires the product manufacturing information of each production workshop; the data analysis module obtains the total task time delay information of the target production task according to the product manufacturing information of all the production workshops; the material analysis module obtains estimated material inventory information in a scheduling planning period according to the total task time delay information of the target production task and the planned material demand information of the processing factory; the priority analysis module obtains the distribution priority of each material according to the estimated material inventory information and the estimated consumption rate of each material, and the supply and demand prediction module processes the distribution priority to obtain estimated supply and demand information of the materials; the instruction generation module generates a material distribution instruction according to the material pre-estimation supply and demand information adjusted by corresponding managers and the distribution priority of each material and sends the material distribution instruction to the corresponding material distribution terminal.
Description
Technical Field
The invention relates to the field of industrial internet and intelligent manufacturing, in particular to a material management method based on intelligent manufacturing.
Background
The industrial internet is a product of deep fusion of a new generation of information communication technology and modern industrial technology, is an important carrier for digitalization, networking and intellectualization of manufacturing industry, and is a high point of a new round of industry competition all over the world. The industrial internet realizes comprehensive perception, dynamic transmission and real-time analysis of industrial data by constructing a basic network connecting machines, materials, people and information systems, forms scientific decision and intelligent control, improves the manufacturing resource allocation efficiency, and becomes a new race track for lead enterprises, a new direction of global industrial layout and a new focus for manufacturing competition in the big country.
In recent years, with the increasingly international market and the increasing user demand, the structure of the user demand product is complicated, so that the uncertain factors of production are more and more, the production plan is more and more complicated due to the increase of the production period of the product, and the material demand plan is changeable. Therefore, enterprises urgently need to improve the field material management level and ensure the smoothness of production logistics. In addition, in the traditional material management mode, the raw materials are often insufficient due to the fact that the material demand plan is too coarse, and the raw materials are purchased by field scheduling personnel of each workshop department, so that the waste of the raw materials due to the excess of the materials is inevitable.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a material management method based on intelligent manufacturing, which comprises the following steps:
a data acquisition module of a supply chain management platform periodically acquires task monitoring data acquired by each sensing device in a processing factory and task feedback information sent by a management terminal, and performs data association on all the task monitoring data and the task feedback information sent by the management terminal to obtain product manufacturing information of each production workshop, wherein the product manufacturing information comprises a production workshop identifier and production state information, and the production state information comprises in-process product information, in-process product process progress information and in-process product material consumption information;
the data clustering unit of the data analysis module performs fusion clustering on the product manufacturing information of all production workshops to obtain material consumption information of a processing factory in the current time period, and updates initial material inventory information of the processing factory in the current time period according to the material consumption information to obtain real-time material inventory information; the material consumption information comprises processing material types, processing material names, processing material numbers and material consumption;
a fault prediction unit of the data analysis module obtains the material consumption rate of each material in a production period corresponding to the currently executed target production task according to the production state information of each production workshop and the target production task information of the processing factory, and obtains the prediction time delay information of each processing procedure according to the fault prediction information of each processing device in the target production task information;
a time delay prediction unit of the data analysis module obtains the total task time delay information of the target production task according to the personnel flow information in the task feedback data and the prediction time delay information of each processing procedure;
a rate correction module of the material analysis module corrects the material consumption rate of each material according to the total task time delay information to obtain the estimated consumption rate of each material in the production period corresponding to the currently executed target production task;
a demand analysis unit of a material analysis module analyzes a scheduling task plan of a processing factory to obtain planned material demand information in a corresponding scheduling plan period, wherein the scheduling task plan comprises a plurality of production tasks to be executed;
a stock pre-estimation unit of the material analysis module performs stock pre-estimation on real-time material stock information according to the planned material demand information to obtain pre-estimated material stock information in a scheduling planning period;
the priority analysis module obtains the distribution priority of each material according to the estimated material inventory information and the estimated consumption rate of each material in the production period corresponding to the currently executed target production task;
the supply and demand forecasting module acquires historical demand and historical input quantity of each material from the database, forecasts the forecast supply and demand quantity of each material according to the historical demand, the historical input quantity and the forecast material inventory information to obtain material forecast supply and demand information of a processing factory, and sends the material forecast supply and demand information to a corresponding management terminal to enable a manager to adjust the forecast supply and demand quantity of the corresponding material;
and the instruction generating module generates a material distribution instruction according to the adjusted material pre-estimation supply and demand information and the distribution priority of each material and sends the material distribution instruction to the corresponding material distribution terminal.
According to a preferred embodiment, the sensing device comprises: a two-dimensional code reader, a Radio Frequency Identification (RFID) device, an infrared sensor, a global positioning system and a laser scanner.
According to a preferred embodiment, the task feedback information includes material distribution information of the processing plant and status feedback information of the production tasks currently performed by each production shop, and the status feedback information includes personnel flow information, process completion and process material consumption.
According to a preferred embodiment, the in-process product process progress information comprises processing process progress information and information of a to-be-processed process;
the processing procedure progress information comprises processing starting time, processed time and residual processing time; the information of the procedures to be processed comprises the number of the procedures to be processed, the sequence of the procedures to be processed, the serial number of the procedures to be processed and the name of the procedures to be processed.
According to a preferred embodiment, the data obtaining module performs data association on all task monitoring data and the task feedback information sent by the management terminal to obtain the product manufacturing information of each production workshop, and the data obtaining module comprises:
the data acquisition module matches a corresponding production workshop identifier from a database according to sensing equipment information in task monitoring data, wherein the task monitoring data comprises the sensing equipment information, a monitoring task type, a data acquisition time point and task state data;
the data acquisition module classifies all task monitoring data according to the production workshop identifiers to obtain a workshop monitoring data set corresponding to each production workshop, wherein the workshop monitoring data set comprises a plurality of task monitoring data;
the data acquisition module is used for carrying out consistency verification on all task monitoring data in the workshop monitoring data set and carrying out data fusion on a plurality of task monitoring data which pass through the consistency verification so as to obtain product manufacturing information corresponding to the production workshop.
According to a preferred embodiment, the data acquisition module performing consistency verification on all task monitoring data in the plant monitoring data set comprises:
the data acquisition module acquires an equipment relation table of the sensing equipment corresponding to each task monitoring data;
the data acquisition module obtains information consistency between each sensing device according to a data confidence coefficient between each sensing device in the device relation table, wherein the data confidence coefficient is used for indicating the data mutual support degree between each sensing device;
and the data acquisition module is used for carrying out consistency verification on all task monitoring data according to the information consistency between the sensing devices.
According to a preferred embodiment, the step of performing fusion clustering on the product manufacturing information of all the production workshops by the data clustering unit of the data analysis module to obtain the material consumption information of the processing plant in the current time period comprises the following steps:
the data clustering unit carries out time sequence synchronization on the consumption information of each material in the production workshops according to the consumption information of the materials of the products in the production workshops in the product manufacturing information of each production workshop so as to obtain workshop material consumption data of each production workshop in the current time period;
and the data clustering unit performs fusion clustering on the workshop material consumption data of all the production workshops according to the material identifier of each material to obtain the material consumption information of the processing factory in the current time period.
According to a preferred embodiment, the step of obtaining the predicted time delay information of each processing procedure by the fault prediction unit of the data analysis module according to the fault prediction information of each processing device in the target production task information comprises:
the fault prediction unit acquires historical fault information and real-time equipment state data of corresponding processing equipment from a database according to the equipment identifier of each processing equipment in the target production task information;
and the fault prediction unit obtains the fault prediction information of the corresponding processing equipment according to the historical fault information and the real-time equipment state data of each processing equipment.
According to a preferred embodiment, the step of obtaining the total task time delay information of the target production task by the time delay prediction unit of the data analysis module according to the personnel flow information in the task feedback data and the predicted time delay information of each processing procedure comprises the following steps:
the time delay prediction unit obtains the personnel flow time delay information in the production period corresponding to the target production task according to the personnel flow information in the task feedback data;
and the time delay prediction unit obtains the total task time delay information of the target production task according to the personnel flow time delay information and the prediction time delay information of each processing procedure.
According to a preferred embodiment, the goal production task information includes an order number, production product information, processing equipment information, processing procedure information, and production cycle information.
The invention has the following beneficial effects:
the invention updates the material inventory information of the processing factory in real time by periodically monitoring the task progress of the production task being executed in the processing factory, predicts the material pre-estimation supply and demand information of the processing factory according to the currently executed production task and the production task planned to be executed in the processing factory, and generates a corresponding material distribution instruction for corresponding material distribution personnel to purchase and distribute each material in sequence, namely, the invention effectively reduces the inventory cost, improves the inventory turnover rate and reduces the shortage loss of the processing factory by tracking the consumption of each material in the processing factory in real time and arranging the corresponding material distribution personnel to carry out accurate distribution by combining the inventory condition.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a flow chart of a method for intelligent manufacturing-based material management, according to an exemplary embodiment.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein.
In order to better understand the technical solutions of the present invention, the following detailed descriptions of the technical solutions of the present invention are provided with the accompanying drawings and the specific embodiments, and it should be understood that the specific features in the embodiments and the examples of the present invention are the detailed descriptions of the technical solutions of the present invention, and are not limitations of the technical solutions of the present invention, and the technical features in the embodiments and the examples of the present invention may be combined with each other without conflict.
Referring to fig. 1, in one embodiment, a smart manufacturing-based materials management method may include the steps of:
s1, the data acquisition module of the supply chain management platform periodically acquires task monitoring data acquired by each sensing device in the processing factory and task feedback information sent by the management terminal, and performs data association on all the task monitoring data and the task feedback information sent by the management terminal to obtain product manufacturing information of each production workshop, wherein the product manufacturing information comprises a production workshop identifier and production state information, and the production state information comprises in-process product information, in-process product process progress information and in-process product material consumption information.
Optionally, the production shop identifier is used to uniquely identify the production shop.
Optionally, the sensing device comprises: two-dimensional code recognizer, Radio Frequency Identification (RFID) device, infrared inductor, global positioning system and laser scanner.
Optionally, the task feedback information includes material distribution information of the processing plant and state feedback information of a production task currently executed by each production workshop, where the state feedback information includes personnel flow information, process completion and process material consumption.
Optionally, the in-process product process progress information includes processing process progress information and to-be-processed process information;
the processing procedure progress information comprises processing starting time, processed time and residual processing time; the information of the procedures to be processed comprises the number of the procedures to be processed, the sequence of the procedures to be processed, the serial number of the procedures to be processed and the name of the procedures to be processed.
Specifically, the data association of the data acquisition module to the task feedback information sent by the management terminal and all the task monitoring data to obtain the product manufacturing information of each production workshop includes:
the data acquisition module matches a corresponding production workshop identifier from a database according to sensing equipment information in task monitoring data, wherein the task monitoring data comprises the sensing equipment information, a monitoring task type, a data acquisition time point and task state data;
the data acquisition module classifies all task monitoring data according to the production workshop identifiers to obtain a workshop monitoring data set corresponding to each production workshop, wherein the workshop monitoring data set comprises a plurality of task monitoring data;
the data acquisition module is used for carrying out consistency verification on all task monitoring data in the workshop monitoring data set and carrying out data fusion on a plurality of task monitoring data which pass through the consistency verification so as to obtain product manufacturing information corresponding to the production workshop.
Optionally, the consistency verification of all task monitoring data in the plant monitoring data set by the data acquisition module includes:
the data acquisition module acquires an equipment relation table of the sensing equipment corresponding to each task monitoring data;
the data acquisition module obtains information consistency between each sensing device according to a data confidence coefficient between each sensing device in the device relation table, wherein the data confidence coefficient is used for indicating the data mutual support degree between each sensing device;
and the data acquisition module is used for carrying out consistency verification on all task monitoring data according to the information consistency between the sensing devices.
Optionally, the obtaining, by the data obtaining module, the information consistency between each sensing device according to the data confidence between each sensing device in the device relationship table includes:
wherein,
is as follows
A sensing device and
the degree of information consistency between the individual sensing devices,
in order to be an index of the sensing device,
for the total number of sensing devices to be used,
is as follows
A sensing device and
the confidence in the data between the individual sensing devices,
is as follows
A sensing device and
data confidence between the sensing devices.
Optionally, the obtaining, by the data obtaining module, the information consistency between each sensing device according to the data confidence between each sensing device in the device relationship table further includes:
wherein,
in order to be an index of the sensing device,
is as follows
A sensing device and
the degree of information consistency between the individual sensing devices,
is as follows
A sensing device and
information consistency between the sensing devices.
S2, performing fusion clustering on the product manufacturing information of all production workshops by a data clustering unit of the data analysis module to obtain material consumption information of the processing factory in the current time period, and updating initial material inventory information of the processing factory in the current time period according to the material consumption information to obtain real-time material inventory information; the material consumption information comprises processing material types, processing material names, processing material numbers and material consumption.
Optionally, the current time period is an acquisition time period corresponding to all currently received task monitoring data.
Optionally, the initial material inventory information is material inventory information that has not been updated in time according to the material consumption information in the current time period.
Specifically, the step of performing fusion clustering on the product manufacturing information of all production workshops by the data clustering unit of the data analysis module to obtain the material consumption information of the processing plant in the current time period includes:
the data clustering unit carries out time sequence synchronization on the consumption information of each material in the production workshops according to the consumption information of the materials of the products in the production workshops in the product manufacturing information of each production workshop so as to obtain workshop material consumption data of each production workshop in the current time period;
and the data clustering unit performs fusion clustering on the workshop material consumption data of all the production workshops according to the material identifier of each material to obtain the material consumption information of the processing factory in the current time period.
Optionally, the time sequence synchronization is to update consumption information of each material in each production workshop in the current time period in real time according to the sequence of time.
And S3, the failure prediction unit of the data analysis module obtains the material consumption rate of each material in the production period corresponding to the currently executed target production task according to the production state information of each production workshop and the target production task information of the processing factory, and obtains the prediction time delay information of each processing procedure according to the failure prediction information of each processing device in the target production task information.
Specifically, the step of obtaining the predicted time delay information of each processing procedure by the fault prediction unit of the data analysis module according to the fault prediction information of each processing device in the target production task information includes:
the fault prediction unit acquires historical fault information and real-time equipment state data of corresponding processing equipment from a database according to the equipment identifier of each processing equipment in the target production task information;
and the fault prediction unit obtains the fault prediction information of the corresponding processing equipment according to the historical fault information and the real-time equipment state data of each processing equipment.
Optionally, the real-time equipment status data includes temperature, vibration, rotational speed, pressure and current of the processing equipment during operation.
Optionally, the target production task information includes an order number, production product information, processing equipment information, processing procedure information, and production cycle information; the processing device information includes a processing device name, a processing device type, and a device identifier.
Optionally, the target production task is a production task currently being executed by the processing plant; the target production task information is task information corresponding to a production task currently executed by the processing factory.
Alternatively, the production cycle is a planned execution cycle of a production task currently being executed by the processing plant, i.e., a planned duration of a target production task.
And S4, the time delay prediction unit of the data analysis module obtains the total task time delay information of the target production task according to the personnel flow information in the task feedback data and the predicted time delay information of each processing procedure.
Specifically, the step of obtaining the total task time delay information of the target production task by the time delay prediction unit of the data analysis module according to the personnel flow information in the task feedback data and the predicted time delay information of each processing procedure comprises the following steps:
the time delay prediction unit obtains the personnel flow time delay information in the production period corresponding to the target production task according to the personnel flow information in the task feedback data;
and the time delay prediction unit obtains the total task time delay information of the target production task according to the personnel flow time delay information and the prediction time delay information of each processing procedure.
Optionally, the staff member flow information is used to indicate the work stability of each staff member in the process plant.
And S5, correcting the material consumption rate of each material by a rate correction module of the material analysis module according to the total task time delay information to obtain the estimated consumption rate of each material in the production period corresponding to the currently executed target production task.
S6, a demand analysis unit of the material analysis module analyzes a scheduling task plan of the processing factory to obtain plan material demand information in a corresponding scheduling plan period, wherein the scheduling task plan comprises a plurality of production tasks to be executed.
Optionally, the scheduling task plan includes task material consumption information, task numbers, task start time, task completion time and start worker information; the scheduling planning period is the total time length formed by the plan execution periods of all the production tasks to be executed in the scheduling task plan.
S7, the stock pre-estimation unit of the material analysis module pre-estimates the stock of the real-time material stock information according to the planned material demand information to obtain the pre-estimated material stock information in the scheduling planning period.
And S8, the priority analysis module obtains the distribution priority of each material according to the estimated material inventory information and the estimated consumption rate of each material in the production period corresponding to the currently executed target production task.
S9, the supply and demand forecasting module obtains the historical demand and the historical input quantity of each material from the database, forecasts the forecast supply and demand quantity of each material according to the historical demand, the historical input quantity and the forecast material inventory information to obtain the material forecast supply and demand information of the processing factory, and sends the material forecast supply and demand information to the corresponding management terminal to enable the management staff to adjust the forecast supply and demand quantity of the corresponding material.
And S10, the instruction generating module generates a material distribution instruction according to the material pre-estimated supply and demand information and the distribution priority of each material and sends the material distribution instruction to the corresponding material distribution terminal.
The invention updates the material inventory information of the processing factory in real time by periodically monitoring the task progress of the production task being executed in the processing factory, predicts the material pre-estimation supply and demand information of the processing factory according to the currently executed production task and the production task planned to be executed in the processing factory, and generates a corresponding material distribution instruction for corresponding material distribution personnel to purchase and distribute each material in sequence, namely, the invention effectively reduces the inventory cost, improves the inventory turnover rate and reduces the shortage loss of the processing factory by tracking the consumption of each material in the processing factory in real time and arranging the corresponding material distribution personnel to carry out accurate distribution by combining the inventory condition.
In one embodiment, the cloud computing-based material management system comprises a supply chain management platform, a sensing device, a management terminal and a material distribution terminal. The supply chain management platform is respectively in communication connection with the sensing equipment, the management terminal and the material distribution terminal. The management terminal is the equipment that has calculation function, memory function and communication function that managers used, and it includes: smart phones, desktop computers, and notebook computers. The material distribution terminal is the equipment that has calculation function, memory function and communication function that material distribution personnel used, and it includes: smart phones, tablet computers, and smart watches.
The supply chain management platform comprises a data acquisition module, a data analysis module, a material analysis module, a priority analysis module, a supply and demand prediction module and an instruction generation module.
The data acquisition module is used for periodically acquiring task monitoring data acquired by each sensing device in a processing factory and task feedback information sent by the management terminal, and performing data association on all the task monitoring data and the task feedback information sent by the management terminal to obtain product manufacturing information of each production workshop.
The data analysis module comprises a data clustering unit, a fault prediction unit and a time delay prediction unit.
The data clustering unit is used for performing fusion clustering on the product manufacturing information of all the production workshops to obtain material consumption information of the processing factories in the current time period, and updating initial material inventory information of the processing factories in the current time period according to the material consumption information to obtain real-time material inventory information.
The failure prediction unit is used for obtaining the material consumption rate of each material in the production period corresponding to the currently executed target production task according to the production state information of each production workshop and the target production task information of the processing factory, and obtaining the prediction time delay information of each processing procedure according to the failure prediction information of each processing device in the target production task information.
And the time delay prediction unit is used for obtaining the total task time delay information of the target production task according to the personnel flow information in the task feedback data and the predicted time delay information of each processing procedure.
The material analysis module comprises a speed correction module, a demand analysis unit and an inventory pre-estimation unit.
And the rate correction module is used for correcting the material consumption rate of each material according to the task total time delay information to obtain the estimated consumption rate of each material in the production period corresponding to the currently executed target production task.
The demand analysis unit is used for analyzing a scheduling task plan of the processing factory to obtain plan material demand information in a corresponding scheduling plan period, wherein the scheduling task plan comprises a plurality of production tasks to be executed.
The stock pre-estimation unit is used for pre-estimating the stock of the real-time material stock information according to the planned material demand information to obtain the pre-estimated material stock information in the scheduling planning period.
And the priority analysis module is used for obtaining the distribution priority of each material according to the estimated material inventory information and the estimated consumption rate of each material in the production period corresponding to the currently executed target production task.
The supply and demand forecasting module is used for acquiring historical demand and historical input quantity of each material from the database, forecasting the estimated supply and demand quantity of each material according to the historical demand, the historical input quantity and the estimated material inventory information to obtain estimated supply and demand information of the materials of the processing factory, and sending the estimated supply and demand information of the materials to the corresponding management terminal to enable a manager to adjust the estimated supply and demand quantity of the corresponding materials.
The instruction generating module is used for generating a material distribution instruction according to the adjusted material pre-estimation supply and demand information and the distribution priority of each material and sending the material distribution instruction to the corresponding material distribution terminal.
An embodiment of the present invention further provides a computer-readable storage medium, on which a program is stored, where the program, when executed by a processor, implements the above-mentioned material management method based on intelligent manufacturing.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A material management method based on intelligent manufacturing is characterized by comprising the following steps:
a data acquisition module of a supply chain management platform periodically acquires task monitoring data acquired by each sensing device in a processing factory and task feedback information sent by a management terminal, and performs data association on all the task monitoring data and the task feedback information sent by the management terminal to obtain product manufacturing information of each production workshop, wherein the product manufacturing information comprises a production workshop identifier and production state information, and the production state information comprises in-process product information, in-process product process progress information and in-process product material consumption information;
the data clustering unit of the data analysis module performs fusion clustering on the product manufacturing information of all production workshops to obtain material consumption information of a processing factory in the current time period, and updates initial material inventory information of the processing factory in the current time period according to the material consumption information to obtain real-time material inventory information; the material consumption information comprises processing material types, processing material names, processing material numbers and material consumption;
a fault prediction unit of the data analysis module obtains the material consumption rate of each material in a production period corresponding to the currently executed target production task according to the production state information of each production workshop and the target production task information of the processing factory, and obtains the prediction time delay information of each processing procedure according to the fault prediction information of each processing device in the target production task information;
a time delay prediction unit of the data analysis module obtains the total task time delay information of the target production task according to the personnel flow information in the task feedback data and the prediction time delay information of each processing procedure;
a rate correction module of the material analysis module corrects the material consumption rate of each material according to the total task time delay information to obtain the estimated consumption rate of each material in the production period corresponding to the currently executed target production task;
a demand analysis unit of a material analysis module analyzes a scheduling task plan of a processing factory to obtain planned material demand information in a corresponding scheduling plan period, wherein the scheduling task plan comprises a plurality of production tasks to be executed;
a stock pre-estimation unit of the material analysis module performs stock pre-estimation on real-time material stock information according to the planned material demand information to obtain pre-estimated material stock information in a scheduling planning period;
the priority analysis module obtains the distribution priority of each material according to the estimated material inventory information and the estimated consumption rate of each material in the production period corresponding to the currently executed target production task;
the supply and demand forecasting module acquires historical demand and historical input quantity of each material from the database, forecasts the forecast supply and demand quantity of each material according to the historical demand, the historical input quantity and the forecast material inventory information to obtain material forecast supply and demand information of a processing factory, and sends the material forecast supply and demand information to a corresponding management terminal to enable a manager to adjust the forecast supply and demand quantity of the corresponding material;
and the instruction generating module generates a material distribution instruction according to the adjusted material pre-estimation supply and demand information and the distribution priority of each material and sends the material distribution instruction to the corresponding material distribution terminal.
2. The method of claim 1, wherein the sensing device comprises: the system comprises a two-dimensional code reader, a radio frequency identification device, an infrared sensor, a global positioning system and a laser scanner.
3. The method of claim 2, wherein the task feedback information includes material delivery information of the process plant and status feedback information of the production tasks currently performed by each production shop, and the status feedback information includes staff flow information, process completion and process material consumption.
4. The method of claim 3, wherein the in-process product procedure schedule information includes process procedure schedule information and to-be-processed procedure information;
the processing procedure progress information comprises processing starting time, processed time and residual processing time; the information of the procedures to be processed comprises the number of the procedures to be processed, the sequence of the procedures to be processed, the serial number of the procedures to be processed and the name of the procedures to be processed.
5. The method of claim 4, wherein the data obtaining module performs data association on all task monitoring data and the task feedback information sent by the management terminal to obtain the product manufacturing information of each production workshop comprises:
the data acquisition module matches a corresponding production workshop identifier from a database according to sensing equipment information in task monitoring data, wherein the task monitoring data comprises the sensing equipment information, a monitoring task type, a data acquisition time point and task state data;
the data acquisition module classifies all task monitoring data according to the production workshop identifiers to obtain a workshop monitoring data set corresponding to each production workshop, wherein the workshop monitoring data set comprises a plurality of task monitoring data;
the data acquisition module is used for carrying out consistency verification on all task monitoring data in the workshop monitoring data set and carrying out data fusion on a plurality of task monitoring data which pass through the consistency verification so as to obtain product manufacturing information corresponding to the production workshop.
6. The method of claim 5, wherein the data acquisition module performing consistency verification on all task monitoring data in the plant monitoring dataset comprises:
the data acquisition module acquires an equipment relation table of the sensing equipment corresponding to each task monitoring data;
the data acquisition module obtains information consistency between each sensing device according to a data confidence coefficient between each sensing device in the device relation table, wherein the data confidence coefficient is used for indicating the data mutual support degree between each sensing device;
and the data acquisition module is used for carrying out consistency verification on all task monitoring data according to the information consistency between the sensing devices.
7. The method of claim 6, wherein the data clustering unit of the data analysis module performs fused clustering on the product manufacturing information of all the production workshops to obtain the material consumption information of the processing plant in the current time period comprises:
the data clustering unit carries out time sequence synchronization on the consumption information of each material in the production workshops according to the consumption information of the materials of the products in the production workshops in the product manufacturing information of each production workshop so as to obtain workshop material consumption data of each production workshop in the current time period;
and the data clustering unit performs fusion clustering on the workshop material consumption data of all the production workshops according to the material identifier of each material to obtain the material consumption information of the processing factory in the current time period.
8. The method of claim 7, wherein the obtaining of the predicted time delay information for each process step by the fault prediction unit of the data analysis module based on the fault prediction information for each process device in the target production task information comprises:
the fault prediction unit acquires historical fault information and real-time equipment state data of corresponding processing equipment from a database according to the equipment identifier of each processing equipment in the target production task information;
and the fault prediction unit obtains the fault prediction information of the corresponding processing equipment according to the historical fault information and the real-time equipment state data of each processing equipment.
9. The method of claim 8, wherein the step of obtaining the total task delay information of the target production task by the delay prediction unit of the data analysis module according to the personnel flow information in the task feedback data and the predicted delay information of each processing procedure comprises:
the time delay prediction unit obtains the personnel flow time delay information in the production period corresponding to the target production task according to the personnel flow information in the task feedback data;
and the time delay prediction unit obtains the total task time delay information of the target production task according to the personnel flow time delay information and the prediction time delay information of each processing procedure.
10. The method of claim 9, wherein the goal production task information includes an order number, production product information, process equipment information, process procedure information, and production cycle information.
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| CN112785062A (en) * | 2021-01-26 | 2021-05-11 | 余嘉娴 | Logistics transportation path planning system based on big data |
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| CN112785150A (en) * | 2021-01-21 | 2021-05-11 | 武汉飞恩微电子有限公司 | Production line scheduling system and method based on automobile pressure sensor |
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| CN112785062B (en) * | 2021-01-26 | 2023-12-05 | 源发科技(唐山市海港区)有限责任公司 | Logistics transportation path planning system based on big data |
| CN112819187A (en) * | 2021-02-04 | 2021-05-18 | 北京戴纳实验科技有限公司 | Management method and system of shared instrument |
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| CN113283917A (en) * | 2021-05-24 | 2021-08-20 | 福州国电远控科技开发有限公司 | Quality tracing method and device for engineering raw materials |
| CN115526462A (en) * | 2022-09-13 | 2022-12-27 | 成都飞机工业（集团）有限责任公司 | Production line material matching method, device, equipment and medium |
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| CN115879743A (en) * | 2023-02-21 | 2023-03-31 | 珠海市鸿瑞信息技术股份有限公司 | Discrete manufacturing intelligent management system and method based on artificial intelligence |
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