CN116523262B

CN116523262B - Intelligent planning method, system and medium for production plan based on big data

Info

Publication number: CN116523262B
Application number: CN202310647466.XA
Authority: CN
Inventors: 李大利; 袁石安; 王毅
Original assignee: Shenzhen Pfiter Information Technology Co ltd
Current assignee: Shenzhen Pfiter Information Technology Co ltd
Priority date: 2023-06-02
Filing date: 2023-06-02
Publication date: 2023-12-22
Anticipated expiration: 2043-06-02
Also published as: CN116523262A

Abstract

The application provides a production plan intelligent planning method, system and medium based on big data. The method comprises the following steps: acquiring order demand detail information, processing to acquire order resource production and demand information and supply and demand report information, matching a plurality of production line organization model patterns according to productivity demand characteristic data, extracting dynamic response data and efficiency indexes of production line resources, respectively processing to acquire production line production scheduling response indexes and productivity efficiency coefficients, weighting the production line production scheduling response indexes and the productivity efficiency coefficients with order value expected coefficients and order priority correction factors acquired according to order supply and demand report characteristic data, acquiring order production scheduling plan evaluation coefficients, and acquiring a scheme through threshold comparison to perform production scheduling planning; and the customer order information is matched with the adaptive production line information based on the big data, and the corresponding scheduling planning scheme is obtained by processing the production related data of the production line and the order, so that the intelligent technology of analyzing the customer order according to the condition of the production line and scheduling according to the big data is realized.

Description

Intelligent planning method, system and medium for production plan based on big data

Technical Field

The application relates to the technical field of big data and production planning, in particular to an intelligent production planning method, system and medium based on big data.

Background

The capacity of the customer order is an important guarantee for enterprise profit, because the order production requirements, the customer conditions and the resources required for production of different customers are complex and various, and the matching conditions of the order requirements and the production line are due to the interference and restriction of various different elements of the capacity, the productivity and the order capacity, the production line scheduling planning has stronger complexity, variability and difficult controllability, the elements of the production line matched with the order can be reasonably matched and planned, and the effective decision scheduling is carried out by combining objective dynamic information of the customers, the supply and the demand and the production resources, so that the method is a core for determining whether the enterprise can effectively produce and reasonably produce, and the technical means for realizing accurate processing and optimized scheduling of the element information such as the customer order and the production line capacity is lacking due to the wide influencing elements of the order scheduling at present.

In view of the above problems, an effective technical solution is currently needed.

Disclosure of Invention

The invention aims to provide a production plan intelligent planning method, a production plan intelligent planning system and a production plan intelligent planning medium based on big data, which can match customer order information with adaptive production line information through the big data and process the customer order information according to production line and production related data of orders to obtain a corresponding production scheduling planning scheme, thereby realizing an intelligent technology for analyzing and scheduling customer orders according to the condition of the production line according to the big data interconnection technology.

The application also provides a production plan intelligent planning method based on big data, which comprises the following steps:

acquiring customer order information and extracting customer attribute information and order demand detail information;

analyzing and processing through a preset order supply and demand analysis model according to the order demand detail information to obtain order resource production and demand information and order supply and demand report information, and extracting productivity demand characteristic data according to the order resource production and demand information;

processing in a preset production resource allocation database according to the productivity demand characteristic data to obtain a plurality of production line organization model maps which are matched;

extracting production line resource dynamic response data and production line productivity dynamic efficiency indexes corresponding to each target production line according to the plurality of production line organization model patterns, and respectively processing according to the production line resource dynamic response data and the production line productivity dynamic efficiency indexes to obtain production line scheduling response indexes and production line productivity efficiency coefficients corresponding to each target production line;

extracting order supply and demand report feature data according to the order supply and demand report information, and processing the order supply and demand report feature data through a preset order expected value evaluation model according to the order supply and demand report feature data to obtain an order value expected coefficient;

Extracting preset customer attribute compensation factors and order element relation data according to the customer attribute information, and processing to obtain order priority correction factors;

performing aggregation weighting processing according to the production line scheduling response index and the production line productivity efficiency coefficient corresponding to each target production line and combining the order value expected coefficient and the order priority correction factor to obtain an order scheduling plan evaluation coefficient;

and carrying out threshold comparison on the order scheduling plan evaluation coefficient and a preset scheduling plan evaluation threshold set, obtaining a corresponding scheduling plan scheme according to a preset range of a comparison result, and carrying out scheduling planning on the order.

Optionally, in the big data based production plan intelligent planning method described in the present application, the analyzing and processing are performed through a preset order supply and demand analysis model according to the order demand detail information to obtain order resource production and demand information and order supply and demand report information, and extracting productivity demand feature data according to the order resource production and demand information includes:

analyzing and processing through a preset order supply and demand analysis model according to the order demand detail information to obtain order resource production and demand information and order supply and demand report information;

And extracting productivity demand characteristic data including equipment productivity demand data, raw material supply demand data and production efficiency demand data according to the order resource production demand information.

Optionally, in the big data based production plan intelligent planning method described in the present application, the processing is performed in a preset production resource configuration database according to the productivity requirement feature data, to obtain a plurality of production line organization model maps adapted to each other, including:

performing similarity comparison between the capacity demand data of the equipment, the raw material supply demand data and the production efficiency demand data in a preset production resource allocation database and capacity resource allocation data of a plurality of preset production lines;

obtaining a plurality of target production lines meeting the preset similarity comparison requirement according to the similarity comparison result;

and acquiring a plurality of corresponding production line tissue model maps according to the plurality of target production lines.

Optionally, in the big data based production plan intelligent planning method described in the present application, the extracting, according to the multiple production line organization model maps, production line resource dynamic response data and production line capacity dynamic efficiency indexes corresponding to each target production line, and processing according to the production line resource dynamic response data and the production line capacity dynamic efficiency indexes to obtain production line scheduling response indexes and production line capacity efficiency coefficients corresponding to each target production line respectively includes:

Extracting production line resource dynamic response data and production line productivity dynamic efficiency indexes corresponding to each target production line according to the production line tissue model patterns;

the production line resource dynamic response data comprises raw material supply response data, equipment operation efficiency response data, production rate response data and production effective time duty ratio data;

the production line productivity dynamic efficiency index comprises an equipment effective utilization index, a production line operation efficiency index and a supply chain efficiency index;

processing according to the raw material supply response data, the equipment operation efficiency response data, the production rate response data and the production effective time duty ratio data to obtain a production line production response index;

and processing according to the equipment effective utilization index, the production line operation efficiency index and the supply chain efficiency index to obtain the production line productivity efficiency coefficient.

Optionally, in the big data based production plan intelligent planning method described in the present application, the extracting order supply and demand report feature data according to the order supply and demand report information, and processing according to the order supply and demand report feature data through a preset order expected value evaluation model, to obtain an order value expected coefficient includes:

Extracting order supply and demand report characteristic data according to the order supply and demand report information, wherein the order supply and demand report characteristic data comprises order report nutrient data, order total profit rate data, order inventory turnover rate data and order product defective rate data;

processing according to the order supply and demand report characteristic data through a preset order expected value evaluation model to obtain an order value expected coefficient;

the calculation formula of the order value expected coefficient is as follows:

；

wherein,for order value expectations coefficients, +.>、/>、/>、/>Respectively, order report revenue data, order total profit rate data, order stock turnover rate data, order product defective rate data, and->、/>、/>、/>Is a preset characteristic coefficient.

Optionally, in the big data based production plan intelligent planning method described in the present application, the extracting preset customer attribute compensation factors and order element relation data according to the customer attribute information, and processing to obtain order priority correction factors includes:

extracting preset customer attribute compensation factors and order element relation data according to the customer attribute information;

the order element relation data comprises customer relation grade data, customer order importance grade data and customer history order volume grade data;

Processing according to the order element relation data and combining with the preset customer attribute compensation factors to obtain order priority correction factors;

the calculation formula of the order priority correction factor is as follows:

；

wherein,for order priority correction factor, +.>、/>、/>Customer relationship level data, customer order importance level data, customer history order volume level data, +.>For presetting customer attribute compensation factors, +.>、/>、/>Is a preset characteristic coefficient.

Optionally, in the big data based intelligent planning method for a production plan described in the present application, the performing, according to the production line production response index and the production line productivity coefficient corresponding to each target production line, aggregation weighting processing by combining the order value expected coefficient and the order priority correction factor to obtain an order scheduling plan evaluation coefficient includes:

performing aggregation processing according to the production line scheduling response index and the production line productivity efficiency coefficient corresponding to each target production line, and weighting by combining the order value expected coefficient and the order priority correction factor to obtain an order scheduling plan evaluation coefficient;

the calculation formula of the order scheduling plan evaluation coefficient is as follows:

；

Wherein,evaluating coefficients for order scheduling plans, +.>For order priority correction factor, +.>For order value expectations coefficients, +.>、/>The production line scheduling response index and the production line productivity efficiency coefficient of the ith target production line are respectively shown as n being the number of target production lines, < >>Is a preset characteristic coefficient.

In a second aspect, the present application provides a big data based production plan intelligent planning system, the system comprising: the system comprises a memory and a processor, wherein the memory comprises a program of a production plan intelligent planning method based on big data, and the program of the production plan intelligent planning method based on big data realizes the following steps when being executed by the processor:

Optionally, in the big data based production plan intelligent planning system described in the present application, the analyzing and processing are performed by a preset order supply and demand analysis model according to the order demand detail information to obtain order resource production and demand information and order supply and demand report information, and extracting productivity demand feature data according to the order resource production and demand information includes:

In a third aspect, the present application also provides a computer readable storage medium, in which a big data based production plan intelligent planning method program is included, which when executed by a processor, implements the steps of the big data based production plan intelligent planning method as described in any of the above.

As can be seen from the above, the production plan intelligent planning method, system and medium based on big data provided by the present application obtain order resource production and demand information and order supply and demand report information through the order demand detailed information process of obtaining the customer order information, obtain a plurality of production line organization model maps adapted according to the extracted production force demand characteristic data process, then extract the production line resource dynamic response data and the production line productivity dynamic efficiency index and process them respectively to obtain the production line production response index and the production line productivity efficiency coefficient, extract the order supply and demand report characteristic data according to the order supply and demand report information to process them to obtain the order value expected coefficient, process them according to the order element relation data to obtain the order priority correction factor, then combine the production line production response index and the production line productivity efficiency coefficient and the order value expected coefficient to obtain the order production plan evaluation coefficient, and obtain the production plan by comparing the result range with the preset threshold value; and the customer order information is matched with the adaptive production line information based on the big data, and the corresponding scheduling planning scheme is obtained by processing the production related data of the production line and the order, so that the intelligent technology for analyzing the customer order according to the condition of the production line and scheduling according to the big data interconnection technology is realized.

Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a big data based intelligent planning method for a production plan provided in an embodiment of the present application;

FIG. 2 is a flowchart of acquiring productivity requirement characteristic data of a big data-based production plan intelligent planning method according to an embodiment of the present application;

FIG. 3 is a flowchart of obtaining a plurality of production line organization model maps adapted for the big data based production plan intelligent planning method according to the embodiment of the present application;

Fig. 4 is a schematic structural diagram of a production plan intelligent planning system based on big data 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 completely with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. The components of the embodiments of the present application, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, as provided in the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, are intended to be within the scope of the present application.

It should be noted that like reference numerals and letters refer to like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.

Referring to fig. 1, fig. 1 is a flowchart of a big data based production plan intelligent planning method in some embodiments of the present application. The intelligent planning method for the production plan based on the big data is used in terminal equipment, such as a computer, a mobile phone terminal and the like. The intelligent planning method for the production plan based on the big data comprises the following steps:

s101, acquiring customer order information and extracting customer attribute information and order demand detail information;

s102, analyzing and processing through a preset order supply and demand analysis model according to the order demand detail information to obtain order resource production and demand information and order supply and demand report information, and extracting productivity demand characteristic data according to the order resource production and demand information;

s103, processing in a preset production resource configuration database according to the productivity demand characteristic data to obtain a plurality of production line organization model maps which are matched;

s104, extracting production line resource dynamic response data and production line capacity dynamic efficiency indexes corresponding to each target production line according to the plurality of production line organization model patterns, and respectively processing according to the production line resource dynamic response data and the production line capacity dynamic efficiency indexes to obtain production line scheduling response indexes and production line capacity efficiency coefficients corresponding to each target production line;

S105, order supply and demand report feature data are extracted according to the order supply and demand report information, and the order supply and demand report feature data are processed through a preset order expected value evaluation model to obtain an order value expected coefficient;

s106, extracting preset customer attribute compensation factors and order element relation data according to the customer attribute information, and processing to obtain order priority correction factors;

s107, carrying out aggregation weighting processing according to the production line scheduling response index and the production line productivity coefficient corresponding to each target production line and combining the order value expected coefficient and the order priority correction factor to obtain an order scheduling plan evaluation coefficient;

s108, comparing the threshold value according to the order scheduling plan evaluation coefficient with a preset scheduling plan evaluation threshold value set, obtaining a corresponding scheduling plan scheme according to a preset range of a comparison result, and scheduling the order.

It should be noted that, in order to implement scientific, reasonable and optimized scheduling planning by combining production capacity and production line resources according to customer order demands, matching an adapted production line and production resources according to customer orders, evaluating related information data of the adapted production line and resources, simultaneously comprehensively evaluating the evaluation of order value report forms and priority degree of customer attribute relationship, finally obtaining comprehensive scheduling plan evaluation results of customer orders, performing scheduling planning according to the evaluation results by corresponding schemes, implementing intelligent technology of analyzing and scheduling customer orders according to production line conditions according to big data interconnection technology, in order to implement intelligent planning of scheduling, firstly obtaining customer order information and extracting customer attribute information and order demand detail information, customer attribute information is customer face element information reflecting customer importance level, special identity element, rule body quantity, cooperation relation, historical cooperation compactness and the like, order demand details are order and product related information details such as order specific demand, delivery, product demand, technical index demand, special demand and the like, order resource production demand information and order supply and demand report information are obtained through analysis processing through a model according to the order demand detail information, productivity demand characteristic data are extracted according to the order resource production demand information, productivity and production data required by orders and demand conditions of related resources are reflected, screening processing is carried out in a preset production resource configuration database according to the productivity demand characteristic data to obtain a plurality of production line organization model diagrams matched with each other, namely an adaptive production line is matched through the order production data, and productivity describing the production line is obtained, the method comprises the steps of extracting dynamic response data of production line resources and dynamic efficiency indexes of production line productivity corresponding to all target production lines through a knowledge graph of related information such as resources, states and the like, respectively processing the data and the indexes to obtain corresponding production line scheduling response indexes and production line productivity efficiency coefficients, wherein the indexes and the coefficients reflect measurement parameters of related scheduling situations of orders completed by all matched production lines and efficiency parameters of scheduling productivity, extracting order supply and demand report characteristic data depicting the situations of order supply and demand reports according to order supply and demand report information, evaluating the order value expected coefficients reflecting the situations of order profit, value and benefit through a model, extracting preset customer attribute compensation factors and order element relation data according to customer attribute information and processing to obtain order priority correction factors reflecting customer order priority recognition situations, finally conducting aggregation weighting processing according to the production line scheduling response indexes and the production line productivity coefficient of all targets to obtain order scheduling plan evaluation coefficients, wherein the evaluation coefficients are order line efficiency situations, customer situations, order supply and demand comprehensive scheduling report situations are estimated according to the order value expected factors, comparing the preset order scheduling plan threshold value set with a preset order scheduling plan threshold value set, and comparing the order scheduling plan threshold value set with a preset yield plan threshold value set according to the preset order scheduling plan threshold value, and comparing the order scheduling plan threshold value set with the preset scheduling plan evaluation result set to realize the corresponding to the order scheduling plan threshold value.

Referring to fig. 2, fig. 2 is a flowchart of acquiring productivity requirement characteristic data of a big data-based production plan intelligent planning method according to some embodiments of the present application. According to the embodiment of the invention, the order resource production and demand information and the order supply and demand report information are obtained by analyzing and processing the order demand detail information through a preset order supply and demand analysis model, and the productivity demand characteristic data is extracted according to the order resource production and demand information, specifically:

s201, analyzing and processing through a preset order supply and demand analysis model according to the order demand detail information to obtain order resource production and demand information and order supply and demand report information;

s202, extracting productivity requirement characteristic data including equipment productivity requirement data, raw material supply requirement data and production efficiency requirement data according to order resource production requirement information.

It should be noted that, in order to implement the evaluation of the production capacity and resources adapted according to the customer order, the adapted production line is first matched according to the customer order condition, then the relevant data information of the production line is obtained, and the order resource production and demand information and the order supply and demand report information are obtained by performing analysis processing through a preset order supply and demand analysis model according to the order demand detailed information of the customer order information, that is, the demand detailed of the customer order is analyzed through the preset order allocation production and demand analysis model, so as to determine the production demand information of the relevant production resources of the order, such as equipment, raw materials, production line capacity, manpower and other resources, and the enumeration report information of the order supply and demand products, and then the productivity demand characteristic data including the equipment capacity requirement for production, the raw material supply requirement and the productivity relevant characteristic data of the production efficiency requirement are extracted according to the order resource production and demand information.

Referring to fig. 3, fig. 3 is a flowchart of a method for intelligent planning of a production plan based on big data to obtain a plurality of production line organization model maps adapted according to some embodiments of the present application. According to the embodiment of the invention, the processing is performed in a preset production resource configuration database according to the productivity requirement characteristic data to obtain a plurality of production line organization model maps which are matched with each other, specifically:

s301, performing similarity comparison between the production resource allocation data of the equipment capacity demand data, the raw material supply demand data and the production efficiency demand data and the capacity resource allocation data of a plurality of preset production lines in a preset production resource allocation database;

s302, obtaining a plurality of target production lines meeting preset similarity comparison requirements according to a similarity comparison result;

s303, acquiring a plurality of corresponding production line tissue model maps according to the plurality of target production lines.

It should be noted that, after the relevant data of the productivity resources for producing and supplying the customer order is defined, a suitable production line is required to be matched according to the productivity requirement to obtain a production line matched with the order product, according to a preset production resource configuration database, the database is used for matching the production line matched with the order production resource, the database is used for matching the productivity requirement characteristic data with the production resource configuration data of the preset production line to obtain a plurality of corresponding preset production lines, that is, a plurality of production lines are matched according to the productivity requirement of the order, and a plurality of target production lines matched with the production lines are matched by adopting methods such as euclidean distance similarity comparison or cosine similarity comparison, and the like, and a production line organization model map corresponding to the production line is obtained, wherein the model map is a knowledge describing the links, logics and relativity of various production line information such as the relevant productivity, resources, running conditions, fault conditions, yield, production efficiency and the like of the production line.

According to the embodiment of the invention, the production line resource dynamic response data and the production line productivity dynamic efficiency index corresponding to each target production line are extracted according to the plurality of production line organization model patterns, and the production line production response index and the production line productivity dynamic efficiency coefficient corresponding to each target production line are respectively obtained by processing according to the production line resource dynamic response data and the production line productivity dynamic efficiency index, specifically:

After a production line matched with the demand of the order production resource is matched and a corresponding organization model map is obtained, production line resource dynamic response data and production line productivity dynamic efficiency indexes corresponding to the target production line are extracted according to the production line organization model map, wherein the production line resource dynamic response data comprise production raw material supply, equipment operation efficiency, production rate, production effective time and production total period ratio response data related to production force resources, the production line productivity dynamic efficiency indexes comprise index data of production line related equipment effective utilization rate, production line total operation efficiency and supply chain efficiency, and production line production response indexes and production line productivity efficiency coefficients corresponding to all target production lines are respectively calculated according to the production line resource dynamic response data and the production line productivity dynamic efficiency indexes, and the obtained indexes and coefficients reflect related production scheduling condition detection parameters and production productivity efficiency parameters of the matched target production lines for completing the order production task, so that parameter bases are comprehensively evaluated according to the production line conditions and the customer order conditions;

the calculation formula of the production line production response index is as follows:

；

The calculation formula of the productivity efficiency coefficient of the production line is as follows:

；

wherein,、/>production line scheduling response index and production line productivity efficiency coefficient for the ith target production line, respectively, +.>、/>、/>、/>Respectively corresponding raw material supply response data, equipment operation efficiency response data, production rate response data, production effective time duty ratio data, +.>、/>、/>The effective utilization rate index, the production line operation efficiency index and the supply chain efficiency index of the corresponding equipment are respectively ∈>Is to preset the production lineBarrier correction factor (F)>、/>、/>、/>、/>、/>、/>And (3) presetting characteristic coefficients (the characteristic coefficients are obtained by inquiring a production resource configuration database).

According to the embodiment of the invention, the order supply and demand report feature data is extracted according to the order supply and demand report information, and is processed according to the order supply and demand report feature data through a preset order expected value evaluation model, so as to obtain an order value expected coefficient, specifically:

The calculation formula of the order value expected coefficient is as follows:

；

wherein,for order value expectations coefficients, +.>、/>、/>、/>Respectively, order report revenue data, order total profit rate data, order stock turnover rate data, order product defective rate data, and->、/>、/>、/>And (3) presetting characteristic coefficients (the characteristic coefficients are obtained by inquiring a production resource configuration database).

It should be noted that, in order to comprehensively evaluate the scheduling situation of the customer order to make a scheduling evaluation scheme reflecting the comprehensive information of the order, besides checking the related capacity data and efficiency index of the production line matched with the production resources of the order, the information such as the value, period, profit and the like of the order report needs to be evaluated to obtain evaluation data reflecting the value expectancy of the order, the order supply and demand report characteristic data describing the related parameter information of the order supply and demand report is extracted according to the order supply and demand report information, including the total revenue, total profit rate, inventory turnover rate required by production and order related report data reflecting the defective rate of the production estimated product in the order report, and the order value expectancy coefficient reflecting the profit, value and benefit and efficiency situation is obtained by evaluating the above data of the order through a preset expected value evaluation model.

According to the embodiment of the invention, the preset customer attribute compensation factor and the order element relation data are extracted according to the customer attribute information, and the order priority correction factor is obtained by processing, specifically:

the calculation formula of the order priority correction factor is as follows:

；

wherein,for order priority correction factor, +.>、/>、/>Customer relationship level data, customer order importance level data, customer history order volume level data, +.>For presetting customer attribute compensation factors, +.>、/>、/>And (3) presetting characteristic coefficients (the characteristic coefficients are obtained by inquiring a production resource configuration database).

It should be noted that, in addition to evaluating production line related capacity data and efficiency index matched with order production resources and order profit, value, benefit and efficiency, the customer order scheduling priority is evaluated accurately, and information such as customer relationship, customer reputation, customer attribute and the like is considered to compensate and correct the scheduling evaluation planning result, so that a compensation factor capable of correcting the order scheduling evaluation result is obtained by evaluating according to the customer attribute relationship, a preset customer attribute compensation factor reflecting the customer attribute and order element relationship data are extracted according to the customer attribute information, the order element relationship data comprises association relationship data reflecting the customer relationship level, the customer order importance level and the customer history order quantity and level, and then the order priority correction factor is obtained by processing according to the order element relationship data and the compensation factor.

According to the embodiment of the invention, the aggregate weighting processing is performed according to the production line scheduling response index and the production line productivity coefficient corresponding to each target production line in combination with the order value expected coefficient and the order priority correction factor, so as to obtain an order scheduling plan evaluation coefficient, which specifically comprises:

；

wherein,evaluation of order scheduling plansEstimating coefficient, ->For order priority correction factor, +.>For order value expectations coefficients, +.>、/>The production line scheduling response index and the production line productivity efficiency coefficient of the ith target production line are respectively shown as n being the number of target production lines, < >>And (3) presetting characteristic coefficients (the characteristic coefficients are obtained by inquiring a production resource configuration database).

Finally, the production line scheduling response index and the production line productivity coefficient of each target production line are aggregated, and weighted correction is carried out by combining the order value expected coefficient and the order priority correction factor, so that an order scheduling plan evaluation coefficient is obtained, and the evaluation coefficient is an evaluation of a comprehensive scheduling plan of the production line productivity condition, the customer relationship condition and the order value income condition of an order, accurately reflects the scheduling priority of customers and orders, so as to correspondingly obtain a scheduling planning scheme.

As shown in fig. 4, the invention also discloses a production plan intelligent planning system 4 based on big data, which comprises a memory 41 and a processor 42, wherein the memory comprises a production plan intelligent planning method program based on big data, and the production plan intelligent planning method program based on big data realizes the following steps when being executed by the processor:

According to the embodiment of the invention, the order resource production and demand information and the order supply and demand report information are obtained by analyzing and processing the order demand detail information through a preset order supply and demand analysis model, and the productivity demand characteristic data is extracted according to the order resource production and demand information, specifically:

According to the embodiment of the invention, the processing is performed in a preset production resource configuration database according to the productivity requirement characteristic data to obtain a plurality of production line organization model maps which are matched with each other, specifically:

；/>

；

wherein,、/>production line scheduling response index and production line productivity efficiency coefficient for the ith target production line, respectively, +.>、/>、/>、/>Respectively corresponding raw material supply response data, equipment operation efficiency response data, production rate response data, production effective time duty ratio data, +.>、/>、/>Respectively the effective utilization index and the production line operation efficiency of the corresponding equipmentIndex, supply chain efficiency index,/->For presetting the fault correction factor of the production line, +.>、/>、/>、/>、/>、/>、/>And (3) presetting characteristic coefficients (the characteristic coefficients are obtained by inquiring a production resource configuration database).

The calculation formula of the order value expected coefficient is as follows:

；

the calculation formula of the order priority correction factor is as follows:

；

wherein,evaluating coefficients for order scheduling plans, +.>For order priority correction factor, +.>For order value expectations coefficients, +.>、/>The production line scheduling response index and the production line productivity efficiency coefficient of the ith target production line are respectively shown as n being the number of target production lines, < >>And (3) presetting characteristic coefficients (the characteristic coefficients are obtained by inquiring a production resource configuration database).

A third aspect of the present invention provides a readable storage medium having embodied therein a big data based production plan intelligent planning method program which, when executed by a processor, implements the steps of the big data based production plan intelligent planning method as described in any one of the above.

The invention discloses a production plan intelligent planning method, a system and a medium based on big data, which are characterized in that order resource production demand information and order supply and demand report information are obtained through order demand detail information processing of obtaining customer order information, a plurality of production line organization model maps which are matched are obtained through processing according to the extracted production force demand characteristic data, then production line resource dynamic response data and production line productivity dynamic efficiency indexes are extracted and respectively processed to obtain production line production scheduling response indexes and production line productivity efficiency coefficients, order supply and demand report characteristic data are extracted according to the order supply and demand report information and processed to obtain order value expected coefficients, order priority correction factors are obtained through processing according to order element relation data, then order production scheduling plan evaluation coefficients are obtained through weighting processing by combining the production line production scheduling response indexes, the production line productivity efficiency coefficients and the order value expected coefficients, and a production scheduling plan is obtained through comparing a result range with a preset threshold; and the customer order information is matched with the adaptive production line information based on the big data, and the corresponding scheduling planning scheme is obtained by processing the production related data of the production line and the order, so that the intelligent technology for analyzing the customer order according to the condition of the production line and scheduling according to the big data interconnection technology is realized.

In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.

The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.

Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.

Alternatively, the above-described integrated units of the present invention may be stored in a readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solution of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.

Claims

1. The intelligent production plan planning method based on big data is characterized by comprising the following steps of:

2. The intelligent planning method of production plan based on big data according to claim 1, wherein the analyzing the order demand detail information through a preset order supply and demand analysis model to obtain order resource production and demand information and order supply and demand report information, extracting productivity demand feature data according to the order resource production and demand information, comprises:

3. The intelligent planning method of production plan based on big data according to claim 2, wherein the processing in a preset production resource configuration database according to the productivity requirement characteristic data to obtain a plurality of adapted production line organization model maps comprises:

4. The intelligent planning method for a production plan based on big data according to claim 3, wherein the extracting the dynamic response data of the production line resource and the dynamic efficiency index of the production line corresponding to each target production line according to the plurality of production line organization model maps, and processing the dynamic response data of the production line resource and the dynamic efficiency index of the production line respectively to obtain the production line scheduling response index and the production line productivity efficiency coefficient corresponding to each target production line comprises:

5. The intelligent planning method for big data-based production plan according to claim 4, wherein the extracting order supply and demand report feature data according to the order supply and demand report information, and processing according to the order supply and demand report feature data through a preset order expected value evaluation model, obtaining an order value expected coefficient, comprises:

the calculation formula of the order value expected coefficient is as follows:

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6. The intelligent planning method for big data based production plan of claim 5, wherein the extracting the preset customer attribute compensation factor and the order element relation data according to the customer attribute information and processing to obtain the order priority correction factor comprises:

the calculation formula of the order priority correction factor is as follows:

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7. The intelligent planning method of production plan based on big data according to claim 6, wherein the aggregate weighting process is performed according to the production line production response index and the production line productivity coefficient corresponding to each target production line in combination with the order value expected coefficient and the order priority correction factor to obtain an order scheduling plan evaluation coefficient, comprising:

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8. Big data based production plan intelligent planning system, characterized in that the system includes: the system comprises a memory and a processor, wherein the memory comprises a program of a production plan intelligent planning method based on big data, and the program of the production plan intelligent planning method based on big data realizes the following steps when being executed by the processor:

9. The intelligent planning system of claim 8, wherein the analyzing the order demand detail information by a preset order supply and demand analysis model to obtain order resource production and demand information and order supply and demand report information, and extracting productivity demand feature data according to the order resource production and demand information comprises:

10. A computer-readable storage medium, characterized in that the computer-readable storage medium contains therein a big data based production plan intelligent planning method program, which when executed by a processor, implements the steps of the big data based production plan intelligent planning method according to any of claims 1 to 7.