CN111553521B - Rice product processing optimization method and device based on supply chain tracing evaluation system - Google Patents

Rice product processing optimization method and device based on supply chain tracing evaluation system Download PDF

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CN111553521B
CN111553521B CN202010325611.9A CN202010325611A CN111553521B CN 111553521 B CN111553521 B CN 111553521B CN 202010325611 A CN202010325611 A CN 202010325611A CN 111553521 B CN111553521 B CN 111553521B
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CN111553521A (en
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董尊骅
高婧
周康
刘江蓉
刘朔
杨华
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Wuhan Polytechnic University
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Abstract

The invention belongs to the technical field of rice processing, and discloses a method and a device for optimizing rice product processing based on a supply chain tracing system. The method comprises the steps of determining a target tracing database according to rice supply chain information of a target rice product and rice supply chain data; data preprocessing is carried out on data in a target tracing database to obtain optimized index data; constructing a simulation model of a processing link; determining working parameters and optimization indexes of the processing equipment according to the processing link simulation model and the optimization index data; constructing a processing optimization model according to the working parameters and the optimization indexes of the processing equipment; and determining a rice production and processing optimization strategy according to the processing optimization model, and optimizing the production and processing of the target rice product. According to the method, a high-quality target tracing database is established based on the rice supply chain information, controllable factors in a processing link are determined, a simulation model and an optimization model of the processing link are established, working parameters of processing equipment are adjusted, and the control of rice production and processing is realized.

Description

Rice product processing optimization method and device based on supply chain tracing evaluation system
Technical Field
The invention relates to the technical field of rice processing, in particular to a method and a device for optimizing rice product processing based on a supply chain tracing evaluation system.
Background
Most of the rice processing enterprises optimize the processing link in the supply chain of the rice processing enterprises from the aspects of improving the process, updating the equipment and strengthening the management, but do not analyze from the data aspect and realize the optimization control of controllable factors in the processing process, so that the limited promotion of the processing link can be obtained, and the processing process, the equipment updating and the strengthening management of the rice processing enterprises are not comprehensive, objective and incredible from a certain supply chain link or direction on the basis of no large amount of data analysis.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a method and a device for optimizing the processing of a rice product based on a supply chain retrospective evaluation system, and aims to solve the technical problem of optimizing and controlling controllable factors in the processing process of a rice processing enterprise so as to optimize the production benefit of the rice processing enterprise.
In order to achieve the purpose, the invention provides a rice product processing optimization method based on a supply chain retrospective evaluation system, which comprises the following steps:
acquiring paddy supply chain information and paddy supply chain data of a target paddy product, and determining a target tracing database according to the paddy supply chain information and the paddy supply chain data;
data preprocessing is carried out on the data in the target tracing database to obtain optimized index data;
constructing a processing link simulation model of the target rice product;
determining working parameters and optimized indexes of the processing equipment of the target rice product according to the processing link simulation model and the optimized index data;
constructing a processing optimization model of the target rice product according to the working parameters of the processing equipment and the optimization indexes;
and determining a rice production and processing optimization strategy according to the processing optimization model, and optimizing the production and processing of the target rice product according to the rice production and processing optimization strategy.
Preferably, the step of acquiring the rice supply chain information and the rice supply chain data of the target rice product and determining the target traceability database according to the rice supply chain information and the rice supply chain data specifically includes:
Acquiring paddy supply chain information and paddy supply chain data of a target paddy product, and determining a supply chain tracing evaluation system of the target paddy product according to the paddy supply chain information;
performing data extraction on the rice supply chain data of the target rice product according to the supply chain traceability evaluation system to obtain traceability data;
and determining an initial tracing database according to the tracing data, and preprocessing the initial tracing database to obtain a target tracing database.
Preferably, the step of acquiring the rice supply chain information and the rice supply chain data of the target rice product and determining the supply chain traceability evaluation system of the target rice product according to the rice supply chain information specifically includes:
acquiring paddy supply chain information and paddy supply chain data of a target paddy product, and determining a supply chain link of the target paddy product according to the paddy supply chain information;
determining an element layer of the supply chain link according to a preset element analysis model, and determining a tracing index of the element layer according to a preset index analysis model;
and establishing a supply chain retrospective evaluation system according to the supply chain links, the element layer and the retrospective indexes.
Preferably, the step of performing data preprocessing on the data in the target tracing database to obtain optimized index data specifically includes:
judging whether the data in the target tracing database meet preset conditions or not through a preset level judgment model to obtain a judgment result;
and when the judgment result does not meet the preset condition, analyzing the data in the target tracing database according to a preset multi-factor analysis of variance strategy and a joint hypothesis testing analysis strategy to obtain an analysis of variance result, and taking the analysis of variance result as optimization index data.
Preferably, after the step of determining whether the data in the target tracing database meets the preset condition by using the preset level determination model and obtaining the determination result, the method further includes:
when the judgment result meets a preset condition, determining a correlation value and a significance value among data in the target tracing database according to a preset correlation analysis strategy;
analyzing the correlation among the data in the target tracing database according to the correlation value and the significance value, and taking the correlation as a correlation analysis result;
and taking the correlation analysis result as optimization index data.
Preferably, the step of analyzing the correlation between the data in the target tracing database according to the correlation value and the significance value, and using the correlation as a correlation analysis result specifically includes:
judging whether the significance value is smaller than a preset first threshold value or not and whether the relevance value is larger than a preset second threshold value or not;
and when the significance value is smaller than a preset first threshold value and the correlation value is larger than a preset second threshold value, judging the correlation among the data in the target tracing database according to the correlation value, and taking the correlation as a correlation analysis result.
Preferably, the step of determining the working parameters and the optimization indexes of the processing equipment of the target rice product according to the processing link simulation model and the optimization index data specifically includes:
performing data analysis on the data in the target tracing database according to the processing link simulation model and the optimization index data to obtain a processing link simulation analysis result;
and determining working parameters and optimization indexes of the processing equipment of the target rice product according to the simulation analysis result of the processing link.
In addition, in order to achieve the above object, the present invention further provides a rice product processing optimization apparatus based on a supply chain retroactive evaluation system, including:
the system comprises an acquisition module, a tracking module and a tracking module, wherein the acquisition module is used for acquiring the paddy supply chain information and the paddy supply chain data of a target paddy product and determining a target tracking database according to the paddy supply chain information and the paddy supply chain data;
the processing module is used for preprocessing data in the target tracing database to obtain optimized index data;
the first construction module is used for constructing a processing link simulation model of the target rice product;
the determining module is used for determining working parameters and optimized indexes of the processing equipment of the target rice product according to the processing link simulation model and the optimized index data;
the second construction module is used for constructing a processing optimization model of the target rice product according to the working parameters of the processing equipment and the optimization indexes;
and the optimization module is used for determining a production and processing optimization strategy according to the processing optimization model and optimizing the production and processing of the target rice product according to the production and processing optimization strategy.
Preferably, the acquiring module includes a first acquiring module, a data extracting module and a data processing module:
the first acquisition module is used for acquiring the paddy supply chain information and the paddy supply chain data of a target paddy product and determining a supply chain tracing evaluation system of the target paddy product according to the paddy supply chain information;
the data extraction module is used for performing data extraction on the rice supply chain data of the target rice product according to the supply chain tracing evaluation system to obtain tracing data;
and the data processing module is used for determining an initial tracing database according to the tracing data, preprocessing the initial tracing database and obtaining a target tracing database.
Preferably, the first obtaining module includes an information obtaining module, an element analyzing module and a system establishing module:
the information acquisition module is used for acquiring the paddy supply chain information and the paddy supply chain data of a target paddy product and determining the supply chain link of the target paddy product according to the paddy supply chain information;
the element analysis module is used for determining an element layer of the supply chain link according to a preset element analysis model and determining a tracing index of the element layer according to a preset index analysis model;
And the system establishing module is used for establishing a supply chain tracing evaluation system according to the supply chain links, the element layer and the tracing indexes.
According to the method, the rice supply chain information and the rice supply chain data of a target rice product are obtained, and a target tracing database is determined according to the rice supply chain information and the rice supply chain data; data preprocessing is carried out on the data in the target tracing database, and optimized index data are obtained; constructing a simulation model of the processing link of the target rice product; determining working parameters and optimized indexes of the processing equipment of the target rice product according to the processing link simulation model and the optimized index data; constructing a processing optimization model of the target rice product according to the working parameters of the processing equipment and the optimization indexes; and determining a rice production and processing optimization strategy according to the processing optimization model, and optimizing the production and processing of the target rice product according to the rice production and processing optimization strategy. Through the mode, the high-quality target tracing database is established based on the rice supply chain information, controllable factors in the processing links of the rice processing enterprises are determined, the processing link simulation model and the optimization model are established, the working parameters of the processing equipment are adjusted, and the production benefit control of the rice processing enterprises is realized, so that the technical problem of how to realize the optimized control of the controllable factors in the processing process of the rice processing enterprises and optimize the production benefit of the rice processing enterprises is solved.
Drawings
FIG. 1 is a schematic flow chart of a first embodiment of a method for optimizing the processing of rice products based on a supply chain retrospective evaluation system according to the present invention;
FIG. 2 is a schematic view of a supply chain of a rice processing enterprise according to an embodiment of the present invention;
FIG. 3 is a table-form illustration of the operating parameters of a processing tool in accordance with an embodiment of the present invention;
FIG. 4 is a schematic flow chart of a second embodiment of the method for optimizing the processing of rice products based on a supply chain retrospective evaluation system according to the present invention;
FIG. 5 is a table format diagram of a target traceability database in an embodiment of the present invention;
fig. 6 is a block diagram illustrating a first embodiment of an optimizing apparatus for processing rice products based on a supply chain traceability system according to the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
An embodiment of the invention provides a rice product processing optimization method based on a supply chain retrospective evaluation system, and referring to fig. 1, fig. 1 is a schematic flow diagram of a first embodiment of a rice product processing optimization method based on a supply chain retrospective evaluation system.
In this embodiment, the method for optimizing the processing of rice products based on the supply chain traceability evaluation system includes the following steps:
step S10: the method comprises the steps of obtaining paddy supply chain information and paddy supply chain data of a target paddy product, and determining a target tracing database according to the paddy supply chain information and the paddy supply chain data.
It should be noted that the step of acquiring the rice supply chain information and the rice supply chain data of the target rice product and determining the target traceability database according to the rice supply chain information and the rice supply chain data includes: acquiring paddy supply chain information and paddy supply chain data of a target paddy product, and determining a supply chain tracing evaluation system of the target paddy product according to the paddy supply chain information; performing data extraction on the rice supply chain data of the target rice product according to the supply chain traceability evaluation system to obtain traceability data; and determining an initial tracing database according to the tracing data, and preprocessing the initial tracing database to obtain a target tracing database.
Specifically, the target rice product may be edible rice, and according to the rice supply chain information of the edible rice produced and processed by the rice processing enterprise, a production benefit evaluation index capable of evaluating the production benefit of the rice processing enterprise and an influence index influencing the production benefit in each supply chain are determined. The rice supply chain information can be supply chain information stored when rice processing enterprises produce edible rice; the rice supply chain data can be supply chain data stored when rice processing enterprises produce edible rice; referring to fig. 2, fig. 2 is a schematic view of a supply chain of a rice processing enterprise according to an embodiment of the present invention, the supply chain of the rice processing enterprise includes: a planting link, a harvesting link, an acquisition link, a processing link, a selling link, a storage link and a transportation link, wherein the storage link comprises storage of raw material rice after the acquisition link and storage of edible rice after the processing link; the transportation link comprises transportation and transfer of raw rice, processed products and edible rice.
Wherein, the productivity effect of the rice processing enterprise can be divided into: production cost, production consumption, production profit, production efficiency, etc., therefore, the production benefit evaluation index of the rice processing enterprise may include: an evaluation index of production cost (unit material cost, unit labor cost, unit transportation cost, unit energy consumption cost, etc.), an evaluation index of production consumption (material consumption, that is, rice yield, polished rice yield, broken rice yield, colored rice yield, bran oil yield, chaff yield, etc., energy consumption, that is, unit power consumption of each machine, etc.), an evaluation index of production profit (total profit, unit profit, etc., of each batch of rice), and an evaluation index of production efficiency (daily processing amount, monthly processing amount, etc.). Influence indexes which may influence the production benefits in each supply chain, such as influence index analysis of material consumption in the production benefits: the planting links comprise the influence indexes of the variety, planting time, soil, climate and the like of the raw material paddy; the harvesting link comprises the influence indexes such as a harvesting mode, a drying mode, drying temperature and the like; the purchasing link has no influence on the index temporarily, but can provide reference data for the production cost, the profit and the like of the rice processing enterprise; the storage link comprises the influence indexes such as storage time, storage equipment, storage conditions (temperature and humidity) and the like; the processing links comprise rice hulling, rice milling, polishing, color sorting, processing technology, processing time, processing amount and other influence indexes; the transportation link comprises influence indexes such as transportation tools, transportation conditions and the like; the marketing link has no influence on the index temporarily, but can provide reference data for the grade, variety and the like of the edible rice produced and processed by the rice processing enterprises.
Step S20: and carrying out data preprocessing on the data in the target tracing database to obtain optimized index data.
It is easy to understand that the step of performing data preprocessing on the data in the target tracing database to obtain the optimized index data specifically includes: judging whether the data in the target tracing database meet preset conditions or not through a preset level judgment model to obtain a judgment result; and when the judgment result does not meet the preset condition, analyzing the data in the target tracing database according to a preset multi-factor analysis of variance strategy and a joint hypothesis testing analysis strategy to obtain an analysis of variance result, and taking the analysis of variance result as optimization index data.
In particular toJudging whether the tracing data in the tracing database meet preset conditions or not, wherein the tracing data in the tracing database can be basic information in different supply chain links of a rice processing enterprise and influence indexes influencing production benefits in the rice processing enterprise, and the preset conditions are that the tracing data in the tracing database are divided into the same level, wherein the different levels are as follows: different levels of seasonality, variety, geographic location, etc.; and if the retroactive data in the target retroactive database can be classified into different levels, namely the judgment result does not meet the preset condition, analyzing the data in the target retroactive database according to a preset multi-factor analysis-by-variance strategy and a joint hypothesis testing analysis strategy. The preset multi-factor analysis of variance strategy is mainly divided into uncontrollable factors (such as environmental factors in a planting link) and controllable factors (such as different fertilizing amounts in the planting link and significance levels of different varieties on crop yield), and the significance levels are judged through a joint hypothesis testing analysis strategy. Suppose that: a. the iDenotes the i-th fertilizer, BjDenotes the j seed, xijIndicating the yield of the jth seed after the ith fertilizer was applied. And then carrying out a joint hypothesis testing analysis strategy to judge the significance level.
Principle of joint hypothesis testing analysis strategy: and calculating F statistic and performing F test. F statistic is the ratio of the mean squared-between sum to squared-within-group sum:
Figure GDA0003500946450000071
where SSA is the intergroup square sum and SSE is the intragroup square sum. According to the F value, if different levels of the control variable have significant effects on the observed variables, then the sum of squared differences between groups of observed variables is large, and the F value is also large; otherwise, the F value is smaller. And when the significance coefficient sig is less than 0.05, the observation variables are considered to be significantly influenced under different levels, for example, the rice yield is greatly significantly influenced by different fertilizing amounts and different varieties, the rice yield is mainly determined by the mutual action of the fertilizing amounts, the varieties and the fertilizing amounts and the varieties, a preset multi-factor variance analysis strategy is continuously carried out, and finally, which variety and which level of fertilizing amount are the optimal combination for improving the rice yield.
It should be noted that, after the step of determining whether the data in the target tracing database meets the preset condition through the preset level determination model and obtaining the determination result, the method further includes: when the judgment result meets a preset condition, determining a correlation value and a significance value among data in the target tracing database according to a preset correlation analysis strategy; analyzing the correlation among the data in the target tracing database according to the correlation value and the significance value, and taking the correlation as a correlation analysis result; and taking the correlation analysis result as optimization index data. The step of analyzing the correlation between the data in the target tracing database according to the correlation value and the significance value, and using the correlation as a correlation analysis result specifically includes: judging whether the significance value is smaller than a preset first threshold value or not and whether the relevance value is larger than a preset second threshold value or not; and when the significance value is smaller than a preset first threshold value and the correlation value is larger than a preset second threshold value, judging the correlation among the data in the target tracing database according to the correlation value, and taking the correlation as a correlation analysis result.
Specifically, if the trace data in the target trace database cannot be classified into different levels, that is, the judgment result meets a preset condition, a preset correlation analysis strategy is performed on the trace data in the target trace database, and a correlation value and a significance value between data in the target trace database are determined according to the preset correlation analysis strategy. For example, by using the correlation analysis, it is judged which variety and which level of fertilization are the optimal combination for increasing the rice yield. And analyzing the correlation among different fertilizing amounts, different varieties and rice yields according to the correlation value r and the significance value sig, wherein if the correlation value | r | is greater than 0.4 and the significance value sig is less than 0.05, the different fertilizing amounts and different varieties have strong correlation on the rice yields, which indicates that the rice yields are mainly determined by the fertilizing amounts, the varieties and the interaction of the fertilizing amounts and the varieties, otherwise, no correlation or weak correlation exists.
Step S30: and constructing a processing link simulation model of the target rice product.
It should be noted that, the constructing of the processing link simulation model of the target rice product includes: clearance stage, rice huller stage, rice milling stage, look selection stage, polishing stage and hierarchical stage, wherein, clearance stage: the working parameters of the screening machine comprise the power of the fan and the precision of the screen, and whether impurities, dust, stones, metal objects and the like in the rice are screened out or not is directly influenced; in the rice hulling stage, the rice varieties and the working parameters of the rice huller comprise roll pressure, linear speed and linear speed ratio; rice milling stage: the rice milling strength, the rice mill flow and the like can directly influence the breakage rate of the rice and the power consumption of equipment; and (3) color selection stage: mainly solves the quality problem of paddy, removes mildewed grains, heterochromatic grains and the like; and (3) polishing: the size and number of polishing areas per unit yield directly affect the breakage rate and rice yield of the rice.
Specifically, the processing link of the target rice product needs to pass through the following equipment: clearance sieve, rice huller, grain grinder, burnishing machine, look selection machine and quantitative baling press, wherein, the paddy produces through the clearance sieve: cleaning rice and impurities; the rice is produced by a rice huller: brown rice and chaff; the rice is produced by a grain grinder: white rice, fine bran, small broken rice, corn tip, and brown rice; the rice is produced by a polishing machine: rice, rice bran; the paddy is produced by a color sorter: polished rice, broken rice, porridge rice, corn are produced through a quantitative packer: and (5) fine rice.
Step S40: and determining working parameters and optimization indexes of the processing equipment of the target rice product according to the processing link simulation model and the optimization index data.
It is easy to understand that the step of determining the working parameters and the optimization indexes of the processing equipment of the target rice product according to the processing link simulation model and the optimization index data specifically comprises the following steps: performing data analysis on data in the target tracing database according to the processing link simulation model and the optimization index data to obtain a processing link simulation analysis result; and determining working parameters and optimized indexes of the processing equipment of the target rice product according to the simulation analysis result of the processing link.
Step S50: and constructing a processing optimization model of the target rice product according to the working parameters of the processing equipment and the optimization indexes.
It should be noted that, assuming the total profit x, the rice yield y, the whole meter rate c, and the unit production cost d, the model with the maximum total profit of the objective function is: x max (max x)ymax xcmax xd)max
And constructing a processing optimization model of the target rice product according to the working parameters of the processing equipment and the optimization indexes:
P1:max P
P2:min Wi,i＝1,2,3,4,5,6
Figure GDA0003500946450000091
wherein, the total profit is set as P; the energy consumption of each stage of the rice processing process is Wi；a1iI is 1,2 is the working parameter in the rice cleaning sieve; a is2iI is 1,2,3 and 4, which are working parameters of the rice varieties and the rice huller in the rice hulling stage; a is31The rice milling strength is adopted; a is32The flow rate of the grain grinding machine; a is33Is the weight of the thallium pressing; a is41The size of the polishing area; a is42The number of times of polishing; wherein c isijThe parameter values of the indexes are optimized for each processing stage, the working parameters of the processing equipment are dynamically adjusted, the optimization target of the rice production and processing is met, and the optimization control of the rice production and processing is realized.
Step S60: and determining a rice production and processing optimization strategy according to the processing optimization model, and optimizing the production and processing of the target rice product according to the rice production and processing optimization strategy.
It is easy to understand that the optimization goal of rice production process mainly includes the following three aspects: the optimal control problem is mainly embodied in that working parameters of the processing equipment are adjusted to enable economic indexes of the rice production and processing to be optimal; under the condition of ensuring the maximum profit and the minimum energy consumption of a rice processing enterprise, obtaining the highest rice yield of a target rice product; the market satisfaction of the target rice product is excellent.
Specifically, referring to fig. 3, fig. 3 is a table-form diagram of the operating parameters of the processing equipment in the embodiment of the present invention; if the working parameter of the rice cleaning sieve is a1iThe clean rice is a and the impurity is bjThe power consumption is wiReferring to fig. 3, the working parameters of other processing equipment are adjusted to minimize the power consumption, and the influence of the working parameters (control variables) of the processing equipment on the power consumption is analyzed. If the electricity consumption of a certain processing device is abnormal, the working parameters of the rice cleaning sieve are adjusted in time through the rice production and processing optimization strategy.
According to the method, the target tracing database is determined according to the rice supply chain information and the rice supply chain data; data preprocessing is carried out on the data in the target tracing database to obtain optimized index data; constructing a processing link simulation model of the target rice product; determining working parameters and optimized indexes of the processing equipment of the target rice product according to the processing link simulation model and the optimized index data; constructing a processing optimization model of the target rice product according to the working parameters of the processing equipment and the optimization indexes; and determining a rice production and processing optimization strategy according to the processing optimization model, and optimizing the production and processing of the target rice product according to the rice production and processing optimization strategy. Through the mode, the high-quality target tracing database is established based on the rice supply chain information, controllable factors in the processing links of rice processing enterprises are determined, the simulation model and the optimization model of the processing links are established, the working parameters of processing equipment are adjusted, and the production benefit control of the rice processing enterprises is realized, so that the technical problem of how to realize the optimization control of the controllable factors in the processing processes of the rice processing enterprises and optimize the production benefits of the rice processing enterprises is solved. Through the mode, the high-quality target tracing database is established based on the rice supply chain information, controllable factors in the processing links of the rice processing enterprises are determined, the processing link simulation model and the optimization model are established, the working parameters of the processing equipment are adjusted, and the production benefit control of the rice processing enterprises is realized, so that the technical problem of how to realize the optimized control of the controllable factors in the processing process of the rice processing enterprises and optimize the production benefit of the rice processing enterprises is solved.
Referring to fig. 4, fig. 4 is a schematic flow chart of a second embodiment of a rice product processing optimization method based on a supply chain retrospective evaluation system according to the present invention. Based on the first embodiment, in the step S10, the method for optimizing rice product processing based on the supply chain retrospective evaluation system of this embodiment specifically includes:
step S101: the method comprises the steps of obtaining paddy supply chain information and paddy supply chain data of a target paddy product, and determining a supply chain tracing evaluation system of the target paddy product according to the paddy supply chain information.
It should be noted that the step of acquiring the rice supply chain information and the rice supply chain data of the target rice product and determining the supply chain traceability evaluation system of the target rice product according to the rice supply chain information specifically includes: acquiring paddy supply chain information and paddy supply chain data of a target paddy product, and determining a supply chain link of the target paddy product according to the paddy supply chain information; determining an element layer of the supply chain link according to a preset element analysis model, and determining a tracing index of the element layer according to a preset index analysis model; and establishing a supply chain retrospective evaluation system according to the supply chain links, the element layer and the retrospective indexes.
Specifically, the first layer in the supply chain retrospective evaluation system is the supply chain link layer, and the mathematical expression of the supply chain link layer x is as follows:
{x1,x2,…,xm}
wherein x ismIs the mth supply chain link in the production of the processed product.
Determining the element layer of the supply chain link according to a preset element analysis model, wherein the expression forms of the element layers under different supply chains are as follows:
xn
{yn0,yn1,…,ynk}
wherein, yn0Basic information layer of the nth supply chain link for rice consumption, yn1～ynkK layers of elements affecting the productivity of the rice processing plant in n supply chain links, e.g. in the warehousing link of the supply chain, yn1～ynkThe factors such as storage time, storage equipment, and storage conditions (temperature and humidity) are shown.
Designing the tracing indexes of a basic information layer and an element layer of a supply chain link, wherein the z expression forms of the tracing index layers of different elements under different supply chain links are as follows:
xn
ynl
{znl1,znl2,…,znlp}
wherein z isnl1The method is a retroactive index collection of the factors I which influence the production benefits of rice processing enterprises in the nth supply chain link. For example, in the rice processing process, the breakage rate of the rice directly affects the production efficiency of rice processing enterprises, and if the breakage rate of the rice is increased, the supply chain traceability evaluation system can be used for finding out which elements in the rice processing link cause the increase of the breakage rate of the rice.
And establishing a supply chain retrospective evaluation system of the production and processing products of the rice processing enterprises by combining the supply chain link layer, the element layer and the retrospective index layer, namely determining the supply chain retrospective evaluation system of the target rice product, wherein the expression form of the supply chain retrospective evaluation system is as follows:
supply chain link x { x1,x2,…,xm}
Layer of elements (factor) y
Figure GDA0003500946450000121
Tracing index z
Figure GDA0003500946450000122
Wherein
Figure GDA0003500946450000123
The kth that influences the production benefit of the rice processing enterprise in the mth supply chain linkmP th of the factorkmAnd (4) tracing indexes.
Step S102: and performing data extraction on the rice supply chain data of the target rice product according to the supply chain traceability evaluation system to obtain traceability data.
It is easy to understand that, the target tracing database of the rice processing enterprise is designed according to the tracing indexes of the basic information layer and the element layer of the design supply chain link in the tracing evaluation system of the rice processing enterprise in the step S101. According to a supply chain tracing evaluation system of a rice processing enterprise, collecting all relevant tracing index data of each batch of edible rice in each supply chain, performing relevant relevance level fusion, characteristic level fusion, demand level fusion and other treatment on the tracing index data of different supply chain links at different time points and space points through a data fusion technology, obtaining the tracing data of each batch of rice in different supply chains, and designing and inputting the tracing data into an initial tracing database of the rice processing enterprise.
Step S103: and determining an initial tracing database according to the tracing data, and preprocessing the initial tracing database to obtain a target tracing database.
It should be noted that, reference is made to fig. 5 for a table expression form of the target tracing database, and fig. 5 is a table format schematic diagram of the target tracing database in the embodiment of the present invention, where the basic information of the supply chain link 1 is the basic information of the rice seeds
Figure GDA0003500946450000124
Has p10A tracing index, wherein the factor 1 of the supply chain link 1 is planting environment information
Figure GDA0003500946450000125
Has p11The trace back indicators of the rest of the information in fig. 5 can be analogized in turn. And performing data preprocessing on the initial tracing database, and checking whether data omission, data repetition, obvious data errors and the like exist in the initial tracing database, so as to obtain a high-quality initial tracing database, wherein the high-quality initial tracing database is used as a target tracing database.
The method comprises the steps of obtaining paddy supply chain information and paddy supply chain data of a target paddy product, and determining a supply chain tracing evaluation system of the target paddy product according to the paddy supply chain information; performing data extraction on the rice supply chain data of the target rice product according to the supply chain traceability evaluation system to obtain traceability data; and determining an initial tracing database according to the tracing data, and preprocessing the initial tracing database to obtain a target tracing database. Through the mode, the traditional one-sided analytical research aiming at the production benefits of the rice processing enterprises is improved and optimized, the high-quality target tracing database is established based on the rice supply chain information, the controllable factors in the processing links of the rice processing enterprises are determined, the processing link simulation model and the optimization model are established, the working parameters of the processing equipment are adjusted, the production benefits of the rice processing enterprises are controlled, and therefore the technical problem of how to realize the optimized control of the controllable factors in the processing processes of the rice processing enterprises and optimize the production benefits of the rice processing enterprises is solved.
Referring to fig. 6, fig. 6 is a block diagram illustrating a first embodiment of an optimizing apparatus for processing rice products based on a supply chain retrospective evaluation system according to the present invention.
As shown in fig. 6, the rice product processing optimization apparatus based on a supply chain retrospective evaluation system according to the embodiment of the present invention includes:
the obtaining module 10 is configured to obtain rice supply chain information and rice supply chain data of a target rice product, and determine a target tracing database according to the rice supply chain information and the rice supply chain data.
It should be noted that the step of acquiring the rice supply chain information and the rice supply chain data of the target rice product and determining the target traceability database according to the rice supply chain information and the rice supply chain data includes: acquiring paddy supply chain information and paddy supply chain data of a target paddy product, and determining a supply chain tracing evaluation system of the target paddy product according to the paddy supply chain information; performing data extraction on the rice supply chain data of the target rice product according to the supply chain traceability evaluation system to obtain traceability data; and determining an initial tracing database according to the tracing data, and preprocessing the initial tracing database to obtain a target tracing database.
Specifically, the target rice product may be edible rice, and according to the rice supply chain information of the edible rice produced and processed by the rice processing enterprise, a production benefit evaluation index capable of evaluating the production benefit of the rice processing enterprise and an influence index influencing the production benefit in each supply chain are determined. The rice supply chain information can be supply chain information stored when rice processing enterprises produce edible rice; the rice supply chain data can be supply chain data stored when rice processing enterprises produce edible rice; referring to fig. 2, fig. 2 is a schematic view of a supply chain of a rice processing enterprise according to an embodiment of the present invention, the supply chain of the rice processing enterprise includes: a planting link, a harvesting link, an acquisition link, a processing link, a selling link, a storage link and a transportation link, wherein the storage link comprises storage of raw rice after the acquisition link and storage of edible rice after the processing link; the transportation link comprises transportation and transfer of raw rice, processed products and edible rice.
Wherein, the productivity effect of rice processing enterprise can divide into: production cost, production consumption, production profit, production efficiency, etc., therefore, the production benefit evaluation index of the rice processing enterprise may include: an evaluation index of production cost (material cost per unit, labor cost per unit, transportation cost per unit, energy consumption per unit, etc.), an evaluation index of production consumption (rice output rate, polished rice rate, broken rice rate, colored rice rate, bran rate, chaff rate, etc. of each batch of rice in terms of material consumption, unit power consumption in terms of energy consumption, unit power consumption of each machine, etc.), an evaluation index of production profit (total profit, unit profit, etc. of each batch of rice), and an evaluation index of production efficiency (daily processing amount, monthly processing amount, etc.). Influence indexes which may influence the production benefits in each supply chain, such as influence index analysis of material consumption in the production benefits: the planting links comprise the influence indexes of the variety, planting time, soil, climate and the like of the raw material paddy; the harvesting link comprises the influence indexes such as a harvesting mode, a drying mode, drying temperature and the like; the purchasing link has no influence on the index temporarily, but can provide reference data for the production cost, the profit and the like of the rice processing enterprise; the storage link comprises the influence indexes such as storage time, storage equipment, storage conditions (temperature and humidity) and the like; the processing links comprise rice hulling, rice milling, polishing, color sorting, processing technology, processing time, processing amount and other influence indexes; the transportation link comprises influence indexes such as transportation tools, transportation conditions and the like; the marketing link has no influence on the index temporarily, but can provide reference data for the rice processing enterprises to produce and process the grades, varieties and the like of the edible rice.
And the processing module 20 is configured to perform data preprocessing on the data in the target tracing database to obtain optimized index data.
It is easy to understand that the step of performing data preprocessing on the data in the target tracing database to obtain the optimized index data specifically includes: judging whether the data in the target tracing database meet preset conditions or not through a preset level judgment model to obtain a judgment result; and when the judgment result does not meet the preset condition, analyzing the data in the target tracing database according to a preset multi-factor analysis of variance strategy and a joint hypothesis testing analysis strategy to obtain an analysis of variance result, and taking the analysis of variance result as optimization index data.
Specifically, whether the tracing data in the tracing database meets preset conditions is judged, the tracing data in the tracing database can be basic information in different supply chain links of a rice processing enterprise and influence indexes influencing production benefits in the rice processing enterprise, and the preset conditions are that the tracing data meets preset conditionsThe trace data in the trace database is divided into the same levels, wherein the different levels are, for example: different levels of seasonality, variety, geographic location, etc.; and if the retroactive data in the target retroactive database can be classified into different levels, namely the judgment result does not meet the preset condition, analyzing the data in the target retroactive database according to a preset multi-factor analysis-by-variance strategy and a joint hypothesis testing analysis strategy. The preset multi-factor analysis of variance strategy is mainly divided into uncontrollable factors (such as environmental factors in a planting link) and controllable factors (such as different fertilizing amounts in the planting link and significance levels of different varieties on crop yield), and the significance levels are judged through a joint hypothesis testing analysis strategy. Suppose that: a. the iDenotes the i-th fertilizer, BjDenotes the j seed, xijIndicating the yield of the jth seed after the ith fertilizer was applied. And then carrying out a joint hypothesis testing analysis strategy to judge the significance level.
Principle of joint hypothesis testing analysis strategy: and calculating F statistic and performing F test. F statistic is the ratio of the mean squared-between sum to squared-within-group sum:
Figure GDA0003500946450000151
where SSA is the intergroup square sum and SSE is the intragroup square sum. According to the F value, if different levels of the control variable have significant effects on the observed variables, then the sum of squared differences between groups of observed variables is large, and the F value is also large; otherwise, the F value is smaller. And when the significance coefficient sig is less than 0.05, the observation variables are considered to be significantly influenced under different levels, for example, the rice yield is greatly significantly influenced by different fertilizing amounts and different varieties, the rice yield is mainly determined by the mutual action of the fertilizing amounts, the varieties and the fertilizing amounts and the varieties, a preset multi-factor variance analysis strategy is continuously carried out, and finally, which variety and which level of fertilizing amount are the optimal combination for improving the rice yield.
It should be noted that, after the step of determining whether the data in the target tracing database meets the preset condition through the preset level determination model and obtaining the determination result, the method further includes: when the judgment result meets a preset condition, determining a correlation value and a significance value among data in the target tracing database according to a preset correlation analysis strategy; analyzing the correlation among the data in the target tracing database according to the correlation value and the significance value, and taking the correlation as a correlation analysis result; and taking the correlation analysis result as optimization index data. The step of analyzing the correlation between the data in the target tracing database according to the correlation value and the significance value, and using the correlation as a correlation analysis result specifically includes: judging whether the significance value is smaller than a preset first threshold value or not and whether the relevance value is larger than a preset second threshold value or not; and when the significance value is smaller than a preset first threshold value and the correlation value is larger than a preset second threshold value, judging the correlation among the data in the target tracing database according to the correlation value, and taking the correlation as a correlation analysis result.
Specifically, if the retroactive data in the target retroactive database cannot be classified into different levels, that is, the judgment result meets a preset condition, a preset correlation analysis strategy is performed on the retroactive data in the target retroactive database, and a correlation value and a significance value between data in the target retroactive database are determined according to the preset correlation analysis strategy. For example, by using the correlation analysis, it is judged which variety and which level of fertilization are the most preferable combination for increasing the rice yield. And analyzing the correlation among different fertilizing amounts, different varieties and rice yields according to the correlation value r and the significance value sig, wherein if the correlation value | r | is greater than 0.4 and the significance value sig is less than 0.05, the different fertilizing amounts and different varieties have strong correlation on the rice yields, which indicates that the rice yields are mainly determined by the fertilizing amounts, the varieties and the interaction of the fertilizing amounts and the varieties, otherwise, no correlation or weak correlation exists.
The first building module 30 is configured to build a simulation model of the processing link of the target rice product.
It should be noted that, the constructing of the processing link simulation model of the target rice product includes: clearance stage, rice huller stage, rice milling stage, look selection stage, polishing stage and hierarchical stage, wherein, clearance stage: the working parameters of the screening machine comprise the power of the fan and the precision of the screen, and whether impurities, dust, stones, metal objects and the like in the rice are screened out or not is directly influenced; in the rice hulling stage, the rice varieties and the working parameters of the rice huller comprise roll pressure, linear speed and linear speed ratio; rice milling stage: the rice milling strength, the rice mill flow and the like can directly influence the breakage rate of the rice and the power consumption of equipment; and (3) color selection stage: mainly solves the quality problem of paddy, removes mildewed grains, heterochromatic grains and the like; and (3) polishing: the size and number of polishing areas per unit yield directly affect the breakage rate and rice yield of the rice.
Specifically, the processing link of the target rice product needs to pass through the following equipment: clearance sieve, rice huller, grain grinder, burnishing machine, look selection machine and quantitative baling press, wherein, the paddy produces through the clearance sieve: cleaning rice and impurities; the rice is produced by a rice huller: brown rice and chaff; the rice is produced by a grain grinder: white rice, fine bran, small broken rice, corn tip, and brown rice; the rice is produced by a polishing machine: rice, rice bran; the paddy is produced by a color sorter: polished rice, broken rice, porridge rice, corn are produced through a quantitative packer: and (5) fine rice.
And the determining module 40 is used for determining the working parameters and the optimization indexes of the processing equipment of the target rice product according to the processing link simulation model and the optimization index data.
It is easy to understand that the step of determining the working parameters and the optimization indexes of the processing equipment of the target rice product according to the processing link simulation model and the optimization index data specifically comprises the following steps: performing data analysis on data in the target tracing database according to the processing link simulation model and the optimization index data to obtain a processing link simulation analysis result; and determining working parameters and optimized indexes of the processing equipment of the target rice product according to the simulation analysis result of the processing link.
And a second constructing module 50, configured to construct a processing optimization model of the target rice product according to the working parameters of the processing equipment and the optimization index.
It should be noted that, if the total profit x, the rice yield y, the whole meter rate c, and the unit production cost d are set, the model with the maximum total profit of the objective function is: x max (max x)ymax xcmax xd)max
And constructing a processing optimization model of the target rice product according to the working parameters of the processing equipment and the optimization indexes:
P1:max P
P2:min Wi,i＝1,2,3,4,5,6
Figure GDA0003500946450000171
wherein, the total profit is set as P; the energy consumption of each stage of the rice processing process is Wi；a1iI is 1,2 is the working parameter in the rice cleaning sieve; a is2iI is 1,2,3 and 4, which are working parameters of the rice varieties and the rice huller in the rice hulling stage; a is31The rice milling strength is adopted; a is32The flow rate of the rice mill; a is33Is the weight of the thallium pressing; a is41The size of the polishing area; a is42The number of times of polishing; wherein c isijThe parameter values of the indexes are optimized for each processing stage, the working parameters of the processing equipment are dynamically adjusted, the optimization target of the rice production and processing is met, and the optimization control of the rice production and processing is realized.
And the optimizing module 60 is configured to determine a production and processing optimizing strategy according to the processing optimizing model, and optimize the production and processing of the target rice product according to the production and processing optimizing strategy.
It is easy to understand that the optimization goal of the rice production process mainly includes the following three aspects: the optimal control problem is mainly embodied in that working parameters of the processing equipment are adjusted to enable economic indexes of the rice production and processing to be optimal; under the condition of ensuring the maximum profit and the minimum energy consumption of a rice processing enterprise, obtaining the highest rice yield of a target rice product; the market satisfaction of the target rice product is excellent.
Specifically, referring to fig. 3, fig. 3 is a table-form diagram of the operating parameters of the processing equipment in the embodiment of the present invention; if the working parameter of the rice cleaning sieve is a1iObtaining the clean rice as a and the impurity as bjThe power consumption is wiReferring to fig. 3, the working parameters of other processing equipment are adjusted to minimize the power consumption, and the influence of the working parameters (control variables) of the processing equipment on the power consumption is analyzed. If the electricity consumption of a certain processing device is abnormal, the working parameters of the rice cleaning sieve are adjusted in time through the rice production and processing optimization strategy.
In this embodiment, an obtaining module 10 is used for obtaining the rice supply chain information and the rice supply chain data of a target rice product, and determining a target tracing database according to the rice supply chain information and the rice supply chain data; the processing module 20 is configured to perform data preprocessing on the data in the target tracing database to obtain optimized index data; a first construction module 30, configured to construct a processing link simulation model of the target rice product; a determining module 40, configured to determine working parameters and optimization indexes of the processing equipment of the target rice product according to the processing link simulation model and the optimization index data; a second constructing module 50, configured to construct a processing optimization model of the target rice product according to the processing equipment operating parameters and the optimization indexes; and the optimizing module 60 is configured to determine a production and processing optimizing strategy according to the processing optimizing model, and optimize the production and processing of the target rice product according to the production and processing optimizing strategy. Through the mode, the high-quality target tracing database is established based on the rice supply chain information, controllable factors in the processing links of the rice processing enterprises are determined, the processing link simulation model and the optimization model are established, the working parameters of the processing equipment are adjusted, and the production benefit control of the rice processing enterprises is realized, so that the technical problem of how to realize the optimized control of the controllable factors in the processing process of the rice processing enterprises and optimize the production benefit of the rice processing enterprises is solved.
It should be understood that the above is only an example, and the technical solution of the present invention is not limited in any way, and in a specific application, a person skilled in the art may set the technical solution as needed, and the present invention is not limited in this respect.
It should be noted that the above-described work flows are only exemplary, and do not limit the scope of the present invention, and in practical applications, a person skilled in the art may select some or all of them to achieve the purpose of the solution of the embodiment according to actual needs, and the present invention is not limited herein.
In addition, the technical details that are not described in detail in this embodiment can be referred to the method for optimizing the processing of the rice product based on the supply chain retrospective evaluation system provided in any embodiment of the present invention, and are not described herein again.
Further, it is to be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention or portions thereof that contribute to the prior art may be embodied in the form of a software product, where the computer software product is stored in a storage medium (e.g. Read Only Memory (ROM)/RAM, magnetic disk, optical disk), and includes several instructions for enabling a terminal device (e.g. a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims (6)

1. A rice product processing optimization method based on a supply chain retrospective evaluation system is characterized by comprising the following steps of:
acquiring paddy supply chain information and paddy supply chain data of a target paddy product, and determining a target tracing database according to the paddy supply chain information and the paddy supply chain data;
data preprocessing is carried out on the data in the target tracing database, and optimized index data are obtained;
constructing a simulation model of the processing link of the target rice product;
determining working parameters and optimization indexes of the processing equipment of the target rice product according to the processing link simulation model and the optimization index data;
constructing a processing optimization model of the target rice product according to the working parameters of the processing equipment and the optimization indexes;
determining a rice production and processing optimization strategy according to the processing optimization model, and optimizing the production and processing of the target rice product according to the rice production and processing optimization strategy;
the step of obtaining the rice supply chain information and the rice supply chain data of the target rice product and determining the target tracing database according to the rice supply chain information and the rice supply chain data specifically includes:
Acquiring paddy supply chain information and paddy supply chain data of a target paddy product, and determining a supply chain tracing evaluation system of the target paddy product according to the paddy supply chain information;
performing data extraction on the rice supply chain data of the target rice product according to the supply chain tracing evaluation system to obtain tracing data;
determining an initial tracing database according to the tracing data, and preprocessing the initial tracing database to obtain a target tracing database;
the step of obtaining the rice supply chain information and the rice supply chain data of the target rice product and determining the supply chain traceability evaluation system of the target rice product according to the rice supply chain information specifically comprises the following steps:
acquiring paddy supply chain information and paddy supply chain data of a target paddy product, and determining a supply chain link of the target paddy product according to the paddy supply chain information;
determining an element layer of the supply chain link according to a preset element analysis model, and determining a tracing index of the element layer according to a preset index analysis model, wherein the element layer comprises factors influencing the production benefit of a rice processing enterprise;
And establishing a supply chain tracing evaluation system according to the supply chain links, the element layer and the tracing indexes.
2. The rice product processing optimization method based on the supply chain retrospective evaluation system as claimed in claim 1, wherein the step of preprocessing the data in the target retrospective database to obtain optimized index data specifically comprises:
judging whether the data in the target tracing database meet preset conditions or not through a preset level judgment model to obtain a judgment result;
and when the judgment result does not meet the preset condition, analyzing the data in the target tracing database according to a preset multi-factor analysis of variance strategy and a joint hypothesis testing analysis strategy to obtain an analysis of variance result, and taking the analysis of variance result as optimization index data.
3. The rice product processing optimization method based on the supply chain traceability evaluation system as claimed in claim 2, wherein the step of determining whether the data in the target traceability database satisfies the predetermined condition by the predetermined level determination model and obtaining the determination result further comprises:
when the judgment result meets a preset condition, determining a correlation value and a significance value among data in the target tracing database according to a preset correlation analysis strategy;
Analyzing the correlation between the data in the target tracing database according to the correlation value and the significance value, and taking the correlation as a correlation analysis result;
and taking the correlation analysis result as optimization index data.
4. The rice product processing optimization method based on the supply chain traceability evaluation system as claimed in claim 3, wherein the step of analyzing the correlation between the data in the target traceability database according to the correlation value and the significance value and using the correlation as the correlation analysis result comprises:
judging whether the significance value is smaller than a preset first threshold value or not and whether the relevance value is larger than a preset second threshold value or not;
and when the significance value is smaller than a preset first threshold value and the correlation value is larger than a preset second threshold value, judging the correlation among the data in the target tracing database according to the correlation value, and taking the correlation as a correlation analysis result.
5. The rice product processing optimization method based on a supply chain retrospective evaluation system as claimed in claim 1, wherein the step of determining the working parameters and the optimization index of the processing equipment of the target rice product according to the processing link simulation model and the optimization index data specifically comprises:
Performing data analysis on data in the target tracing database according to the processing link simulation model and the optimization index data to obtain a processing link simulation analysis result;
and determining working parameters and optimized indexes of the processing equipment of the target rice product according to the simulation analysis result of the processing link.
6. A rice product processing optimization device based on a supply chain retrospective evaluation system is characterized by comprising the following components:
the system comprises an acquisition module, a tracking module and a tracking module, wherein the acquisition module is used for acquiring the paddy supply chain information and the paddy supply chain data of a target paddy product and determining a target tracking database according to the paddy supply chain information and the paddy supply chain data;
the processing module is used for preprocessing data in the target tracing database to obtain optimized index data;
the first construction module is used for constructing a processing link simulation model of the target rice product;
the determining module is used for determining working parameters and optimized indexes of the processing equipment of the target rice product according to the processing link simulation model and the optimized index data;
the second construction module is used for constructing a processing optimization model of the target rice product according to the working parameters of the processing equipment and the optimization indexes;
The optimization module is used for determining a production and processing optimization strategy according to the processing optimization model and optimizing the production and processing of the target rice product according to the production and processing optimization strategy;
the acquisition module comprises a first acquisition module, a data extraction module and a data processing module:
the first acquisition module is used for acquiring the paddy supply chain information and the paddy supply chain data of a target paddy product and determining a supply chain tracing evaluation system of the target paddy product according to the paddy supply chain information;
the data extraction module is used for performing data extraction on the rice supply chain data of the target rice product according to the supply chain traceability evaluation system to obtain traceability data;
the data processing module is used for determining an initial tracing database according to the tracing data, preprocessing the initial tracing database and obtaining a target tracing database;
the first acquisition module comprises an information acquisition module, an element analysis module and a system establishment module:
the information acquisition module is used for acquiring the paddy supply chain information and the paddy supply chain data of a target paddy product and determining the supply chain link of the target paddy product according to the paddy supply chain information;
The element analysis module is used for determining an element layer of the supply chain link according to a preset element analysis model and determining a tracing index of the element layer according to a preset index analysis model, wherein the element layer comprises factors influencing the production benefit of a rice processing enterprise;
the system establishing module is used for establishing a supply chain traceability evaluation system according to the supply chain links, the element layer and the traceability indexes.
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