CN118248245A - Chlorella-based prawn feed preparation optimization method and system - Google Patents
Chlorella-based prawn feed preparation optimization method and system Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 33
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Abstract
The invention discloses a chlorella-based prawn feed preparation optimization method and system, comprising the following steps: the method comprises the steps of obtaining preparation information of prawn feeds with different nutrition specifications based on big data retrieval, extracting nutrition indexes corresponding to the prawn feeds with different specifications, and analyzing the nutrition efficacy of the prawn feeds to obtain nutrition efficacy analysis information; according to the preparation information of the prawn feed, analyzing the fish meal efficacy, and quantifying the fish meal efficacy to obtain fish meal efficacy quantification information; acquiring efficacy capability information of the chlorella, analyzing nutrition conversion capability of nutritional indexes replaced by the chlorella, and analyzing fish meal preparation retention quantity of unreplaced indexes of the chlorella; obtaining information of a current preparation scheme, constructing a preparation optimization model, performing preparation optimization on the prawn feed through the preparation optimization model, and selecting an optimal preparation optimization scheme. Ensures the quality and stability of the final feed and reduces the preparation cost of the prawn feed.
Description
Technical Field
The invention relates to the technical field of prawn feed preparation, in particular to a prawn feed preparation optimization method and system based on chlorella.
Background
With the rapid development of the global aquaculture industry, the prawns are taken as important economic aquatic products, the cultivation scale of the prawns is continuously enlarged, and the demands for feeds are also increasing. The traditional prawn feed mostly uses fish meal, bean pulp and the like as main raw materials, but the raw materials have the problems of high cost, limited resources, environmental pollution and the like.
Chlorella is used as a single-cell green algae, and has wide application prospect in the field of aquaculture due to the characteristics of rich nutrition, rapid growth, strong environmental adaptability and the like. The chlorella is rich in various nutritional components such as protein, fat, carbohydrate, vitamins, minerals and the like, and the amino acid composition of the protein is close to the requirements of prawns. Meanwhile, the method has the characteristics of short growth period, environmental friendliness and the like, and becomes one of research hotspots in the feed industry. The feed produced by taking the chlorella as the raw material can meet the growth requirement of the prawns, is hopeful to reduce the dependence on traditional components such as fish meal, reduces the production cost and realizes sustainable development.
However, the preparation of shrimp feed based on chlorella still has some technical challenges. For example, further researches are required in the aspects of mixing proportion of chlorella and traditional feed ingredients, processing technology and the like so as to ensure the quality and stability of the final feed. Therefore, how to better utilize chlorella to prepare prawn feed is an important problem.
Disclosure of Invention
The invention overcomes the defects of the prior art, and provides a chlorella-based prawn feed preparation optimization method and system, which aim at reducing the preparation cost of the prawn feed.
In order to achieve the above purpose, the first aspect of the present invention provides a method for optimizing the preparation of a prawn feed based on chlorella, comprising:
The method comprises the steps of obtaining preparation information of prawn feeds with different nutrition specifications based on big data retrieval, extracting nutrition indexes corresponding to the prawn feeds with different specifications, and analyzing the nutrition efficacy of the prawn feeds to obtain nutrition efficacy analysis information;
according to the preparation information of the prawn feed, analyzing the fish meal efficacy, and quantifying the fish meal efficacy to obtain fish meal efficacy quantification information;
Acquiring efficacy capability information of the chlorella, analyzing nutrition conversion capability of nutritional indexes replaced by the chlorella, and analyzing fish meal preparation retention quantity of unreplaced indexes of the chlorella;
Obtaining information of a current preparation scheme, constructing a preparation optimization model, performing preparation optimization on the prawn feed through the preparation optimization model, and selecting an optimal preparation optimization scheme.
In this scheme, based on big data retrieval obtain the shrimp feed preparation information of different nutrition specifications, the nutrition efficiency of each shrimp feed of analysis specifically includes:
Retrieving and acquiring prawn feed preparation information of different nutritional specifications based on big data, and extracting nutritional indexes and corresponding nutritional specifications and component characteristics according to the prawn feed preparation information;
Calculating pearson correlation coefficients between the component characteristics and the nutrition indexes to form a pearson correlation coefficient matrix, and setting a pearson correlation coefficient threshold value based on a breakpoint detection method;
performing threshold processing on the pearson correlation coefficient matrix according to the pearson correlation coefficient threshold, and setting zero for data smaller than the pearson correlation coefficient threshold in the matrix to update the pearson correlation coefficient matrix to obtain an update matrix;
And extracting corresponding nutrition indexes according to the updated matrix, acquiring nutrition specifications corresponding to the nutrition indexes, correlating the nutrition indexes, the nutrition specifications and the component characteristics, and analyzing the nutrition efficacy of the target prawn feed to obtain nutrition efficacy analysis information.
In this scheme, according to the shrimp feed preparation information, carry out fish meal efficiency ability analysis to quantify fish meal efficiency, specifically include:
Acquiring nutrition efficacy analysis information, and extracting nutrition efficacy characteristics corresponding to the fish meal according to the nutrition efficacy analysis information, wherein the nutrition efficacy characteristics comprise nutrition indexes and nutrition specifications, so as to obtain fish meal nutrition efficacy characteristic information;
Obtaining the component characteristics and the content characteristics of different nutritional specifications of the target nutritional index according to the preparation information of the prawn feed, and calculating the nutritional content of the target nutritional index of the prawn feed per unit weight to obtain nutritional content analysis information;
Analyzing whether nutritional indexes corresponding to the fish meal are related to other components according to the nutritional efficacy characteristic information and the nutritional efficacy analysis information of the fish meal, and performing quantitative analysis on the fish meal efficacy if the nutritional indexes are not related to other components;
Carrying out fish meal efficacy quantification analysis according to the nutritional content analysis information, and taking the content of fish meal components in the unit weight of the shrimp feed as a fish meal efficacy quantification standard to obtain first efficacy quantification information;
If other components are related, analyzing the efficacy contribution rate of the fish meal under the target nutrition index, and obtaining the related component information of the target nutrition index;
acquiring the component structure of the prawn feed per unit weight according to the related component information, calculating the component proportion of the fish meal, and calculating the efficacy contribution rate of the fish meal to the target nutrition index by combining the nutrition content analysis information to obtain second efficacy quantification information;
and combining the first efficacy quantification information and the second efficacy quantification information to form fish meal efficacy quantification information.
In this scheme, obtain the efficiency ability information of chlorella, the nutrition conversion ability of the nutrition index that the analysis chlorella replaced to the fish meal preparation reserve of the index is not replaced to the analysis chlorella specifically includes:
Acquiring the efficacy capability information of the chlorella, carrying out chlorella replacement index analysis by combining the nutrition efficacy analysis information, judging the replacement index of the chlorella according to the efficacy capability of the chlorella, and obtaining replacement index information;
Setting a unit standard value of each replacement index according to the replacement index information, acquiring the corresponding chlorella use content by utilizing big data retrieval, and analyzing the nutrition conversion capability of the chlorella and the corresponding replacement index to obtain nutrition conversion capability information;
acquiring fish meal efficacy quantification information, extracting fish meal efficacy quantification values of corresponding indexes through the replacement index information, and analyzing the replacement relationship between chlorella and fish meal by combining the nutritional conversion capability information to obtain replacement relationship analysis information;
Extracting associated nutrition indexes of the chlorella through the efficacy information of the chlorella, extracting associated nutrition indexes of fish meal through the fish meal efficacy quantification information, and judging the unreplaced indexes of the chlorella to obtain unreplaced index information;
Extracting a fish meal efficacy quantized value corresponding to the non-replacement index according to the fish meal efficacy quantized information, and taking the fish meal efficacy quantized value as the preparation reserved quantity of the fish meal in the target non-replacement index to obtain fish meal preparation reserved quantity information of each non-replacement index.
In this scheme, the obtaining of the current preparation scheme information, the construction of the preparation optimization model, and the preparation optimization of the prawn feed by the preparation optimization model specifically includes:
acquiring information of a current preparation scheme, and extracting nutrition index requirements of the current preparation scheme and proportion of components used in feed preparation to obtain characteristic information of the current preparation scheme;
Constructing a preparation optimization model based on SA and PSO algorithms, acquiring fish meal efficacy quantization information, nutrition conversion capability information and fish meal preparation retention information, and training the preparation optimization model to obtain a preparation optimization model which meets expectations;
Inputting the characteristic information of the current preparation scheme into the preparation optimization model for preparation optimization, setting an objective function, and setting a punishment condition according to the fish meal preparation reserve quantity information;
initializing a population according to the characteristic information of the current preparation scheme, generating an initial particle swarm, calculating the fitness of each particle in the initial particle swarm, and judging with a preset threshold value to obtain a current optimal solution;
Presetting initial receiving probability and descending rate, taking the current optimal solution as an initial solution of an SA algorithm, judging whether to accept according to the initial receiving probability, and carrying out punishment judgment according to set punishment conditions;
And carrying out acceptance probability adjustment by combining the descending rate and the punishment judgment result, carrying out iterative optimization until the model converges, obtaining a final solution, and generating an initial preparation optimization scheme through the final solution.
In this scheme, the selecting an optimal preparation scheme specifically includes:
acquiring initial preparation optimization schemes, extracting features of each initial preparation optimization scheme, and extracting preparation component proportion of each initial preparation optimization scheme to obtain optimized preparation component proportion information;
acquiring the price of the components used at the current moment according to the optimized preparation and use component proportion information, and calculating the preparation cost of each initial preparation scheme to obtain the preparation cost information of the optimized scheme;
taking the preparation cost information of the optimization scheme as weight, carrying out weighted calculation on each initial preparation optimization scheme, and sequencing weighted calculation results to obtain preparation optimization scheme sequencing information;
presetting a selection threshold, judging the preparation optimization scheme ordering information and the selection threshold, and acquiring a final preparation optimization scheme according to a judging result to perform prawn feed preparation optimization.
The second aspect of the invention provides a chlorella-based prawn feed preparation optimization system, which comprises: the device comprises a memory and a processor, wherein the memory contains a chlorella-based prawn feed preparation optimization method program, and the chlorella-based prawn feed preparation optimization method program realizes the following steps when executed by the processor:
The method comprises the steps of obtaining preparation information of prawn feeds with different nutrition specifications based on big data retrieval, extracting nutrition indexes corresponding to the prawn feeds with different specifications, and analyzing the nutrition efficacy of the prawn feeds to obtain nutrition efficacy analysis information;
according to the preparation information of the prawn feed, analyzing the fish meal efficacy, and quantifying the fish meal efficacy to obtain fish meal efficacy quantification information;
Acquiring efficacy capability information of the chlorella, analyzing nutrition conversion capability of nutritional indexes replaced by the chlorella, and analyzing fish meal preparation retention quantity of unreplaced indexes of the chlorella;
Obtaining information of a current preparation scheme, constructing a preparation optimization model, performing preparation optimization on the prawn feed through the preparation optimization model, and selecting an optimal preparation optimization scheme.
The invention discloses a chlorella-based prawn feed preparation optimization method and system, comprising the following steps: the method comprises the steps of obtaining preparation information of prawn feeds with different nutrition specifications based on big data retrieval, extracting nutrition indexes corresponding to the prawn feeds with different specifications, and analyzing the nutrition efficacy of the prawn feeds to obtain nutrition efficacy analysis information; according to the preparation information of the prawn feed, analyzing the fish meal efficacy, and quantifying the fish meal efficacy to obtain fish meal efficacy quantification information; acquiring efficacy capability information of the chlorella, analyzing nutrition conversion capability of nutritional indexes replaced by the chlorella, and analyzing fish meal preparation retention quantity of unreplaced indexes of the chlorella; obtaining information of a current preparation scheme, constructing a preparation optimization model, performing preparation optimization on the prawn feed through the preparation optimization model, and selecting an optimal preparation optimization scheme. Ensures the quality and stability of the final feed and reduces the preparation cost of the prawn feed.
Drawings
In order to more clearly illustrate the technical solutions of embodiments or examples of the present invention, the drawings that are required to be used in the embodiments or examples of the present invention will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to the drawings without inventive efforts for those skilled in the art.
FIG. 1 is a flowchart of a method for optimizing the preparation of a prawn feed based on chlorella according to an embodiment of the invention;
FIG. 2 is a flowchart of optimizing a preparation scheme according to an embodiment of the present invention;
FIG. 3 is a block diagram of a chlorella-based prawn feed preparation optimization system according to an embodiment of the invention;
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, without conflict, the embodiments of the present application and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
FIG. 1 is a flowchart of a method for optimizing the preparation of a prawn feed based on chlorella according to an embodiment of the invention;
As shown in fig. 1, the invention provides a chlorella-based prawn feed preparation optimization method, which comprises the following steps:
s102, obtaining preparation information of prawn feeds with different nutrition specifications based on big data retrieval, extracting nutrition indexes corresponding to the prawn feeds with different specifications, and analyzing the nutrition efficacy of the prawn feeds to obtain nutrition efficacy analysis information;
S104, analyzing the fish meal efficacy according to the preparation information of the prawn feed, and quantifying the fish meal efficacy to obtain fish meal efficacy quantification information;
S106, acquiring efficacy capability information of the chlorella, analyzing nutrition conversion capability of nutritional indexes replaced by the chlorella, and analyzing fish meal preparation retention quantity of unreplaced indexes of the chlorella;
S110, obtaining current preparation scheme information, constructing a preparation optimization model, performing preparation optimization on the prawn feed through the preparation optimization model, and selecting an optimal preparation optimization scheme.
The invention provides a chlorella-based prawn feed preparation optimization method and system, which are characterized in that firstly, prawn feed preparation information with different nutritional specifications is obtained through big data retrieval, and the correlation among nutritional indexes, preparation components and nutritional specifications is analyzed, so that the corresponding preparation components of the nutritional indexes with different specifications in the prawn feed are known. Then, aiming at the fish meal component, analyzing the efficacy capacity of the fish meal component, judging the nutrition index corresponding to the fish meal component and the relation between the fish meal under unit weight and the nutrition index, thereby quantifying the efficacy capacity of the fish meal. And then, analyzing which nutritional indexes can replace the fish meal according to the functional capability of the chlorella, judging the conversion relation between the chlorella and the replacement indexes, and simultaneously analyzing the preparation reserve quantity of the fish meal for the nutritional indexes which cannot be replaced. And finally, constructing a preparation optimization model, optimizing the current preparation scheme to obtain a preparation optimization scheme which accords with the nutrition index and the nutrition specification of the original preparation scheme, selecting the optimal preparation scheme according to the preparation cost, ensuring the quality and the stability of the preparation optimization scheme of the prawn feed, and reducing the preparation cost.
Further, in a preferred embodiment of the present invention, the method for obtaining the preparation information of the prawn feed with different nutritional specifications based on big data retrieval, and analyzing the nutritional efficacy of each prawn feed specifically includes:
Retrieving and acquiring prawn feed preparation information of different nutritional specifications based on big data, and extracting nutritional indexes and corresponding nutritional specifications and component characteristics according to the prawn feed preparation information;
Calculating pearson correlation coefficients between the component characteristics and the nutrition indexes to form a pearson correlation coefficient matrix, and setting a pearson correlation coefficient threshold value based on a breakpoint detection method;
performing threshold processing on the pearson correlation coefficient matrix according to the pearson correlation coefficient threshold, and setting zero for data smaller than the pearson correlation coefficient threshold in the matrix to update the pearson correlation coefficient matrix to obtain an update matrix;
And extracting corresponding nutrition indexes according to the updated matrix, acquiring nutrition specifications corresponding to the nutrition indexes, correlating the nutrition indexes, the nutrition specifications and the component characteristics, and analyzing the nutrition efficacy of the target prawn feed to obtain nutrition efficacy analysis information.
Firstly, retrieving and obtaining prawn feed preparation information with different nutritional specifications based on big data, and extracting nutritional indexes and corresponding nutritional specifications and component characteristics according to the prawn feed preparation information. And then, calculating pearson correlation coefficients between each component characteristic and each nutrition index, measuring the correlation between different component characteristics and each nutrition index, constructing a pearson correlation coefficient matrix, setting a pearson correlation coefficient threshold value based on a breakpoint detection method, and analyzing the preparation components with large correlation with each nutrition index. Finally, the nutrition indexes, the nutrition specifications and the component characteristics are correlated to obtain nutrition efficacy analysis information, so that the preparation components corresponding to the nutrition indexes under different nutrition specifications are known.
Further, in a preferred embodiment of the present invention, the analyzing the fish meal efficiency according to the preparation information of the prawn feed, and quantifying the fish meal efficiency specifically includes:
Acquiring nutrition efficacy analysis information, and extracting nutrition efficacy characteristics corresponding to the fish meal according to the nutrition efficacy analysis information, wherein the nutrition efficacy characteristics comprise nutrition indexes and nutrition specifications, so as to obtain fish meal nutrition efficacy characteristic information;
Obtaining the component characteristics and the content characteristics of different nutritional specifications of the target nutritional index according to the preparation information of the prawn feed, and calculating the nutritional content of the target nutritional index of the prawn feed per unit weight to obtain nutritional content analysis information;
Analyzing whether nutritional indexes corresponding to the fish meal are related to other components according to the nutritional efficacy characteristic information and the nutritional efficacy analysis information of the fish meal, and performing quantitative analysis on the fish meal efficacy if the nutritional indexes are not related to other components;
Carrying out fish meal efficacy quantification analysis according to the nutritional content analysis information, and taking the content of fish meal components in the unit weight of the shrimp feed as a fish meal efficacy quantification standard to obtain first efficacy quantification information;
If other components are related, analyzing the efficacy contribution rate of the fish meal under the target nutrition index, and obtaining the related component information of the target nutrition index;
acquiring the component structure of the prawn feed per unit weight according to the related component information, calculating the component proportion of the fish meal, and calculating the efficacy contribution rate of the fish meal to the target nutrition index by combining the nutrition content analysis information to obtain second efficacy quantification information;
and combining the first efficacy quantification information and the second efficacy quantification information to form fish meal efficacy quantification information.
The method is characterized in that firstly, the nutritional efficacy characteristics corresponding to the fish meal are extracted through the nutritional efficacy analysis information, wherein the nutritional efficacy characteristics comprise nutritional indexes and nutritional specifications, and the nutritional efficacy characteristic information of the fish meal is obtained. And then, according to the preparation information of the prawn feed, obtaining the component characteristics and the content characteristics of the target nutrition indexes with different nutrition specifications, and calculating the nutrition content of the target nutrition indexes of the prawn feed per unit weight to obtain nutrition content analysis information, namely the degree to which the target nutrition indexes can be provided by the prawn feed per unit weight. Various nutritional indexes in the prawn feed can be provided by a single preparation component, so that whether the nutritional indexes corresponding to the fish meal are related to other components or not is analyzed according to the characteristic information and the analysis information of the nutritional efficacy of the fish meal. And if other components are not related, carrying out quantitative analysis on the fish meal efficacy. And carrying out fish meal efficacy quantification analysis according to the nutritional content analysis information, and taking the content of fish meal components in the unit weight of the shrimp feed as a fish meal efficacy quantification standard to obtain first efficacy quantification information. If a certain nutritional index is provided by combining fish meal with other ingredients, the efficacy contribution rate of the fish meal under the target nutritional index is analyzed. And obtaining the component structure of the prawn feed per unit weight by obtaining the associated component information of the target nutrition index, calculating the component ratio of the fish meal, and calculating the efficacy contribution rate of the fish meal to the target nutrition index by combining the nutrition content analysis information to obtain second efficacy quantification information. Finally, combining the first efficacy quantification information and the second efficacy quantification information to form fish meal efficacy quantification information. Better understand the effect and contribution of fish meal in prawn feed, and provide basis and guidance for formulating more optimized feed formulation.
Further, in a preferred embodiment of the present invention, the obtaining the efficacy capability information of the chlorella, analyzing the nutritional conversion capability of the nutritional index replaced by the chlorella, and analyzing the fish meal preparation retention amount of the unreplaced index of the chlorella specifically includes:
Acquiring the efficacy capability information of the chlorella, carrying out chlorella replacement index analysis by combining the nutrition efficacy analysis information, judging the replacement index of the chlorella according to the efficacy capability of the chlorella, and obtaining replacement index information;
Setting a unit standard value of each replacement index according to the replacement index information, acquiring the corresponding chlorella use content by utilizing big data retrieval, and analyzing the nutrition conversion capability of the chlorella and the corresponding replacement index to obtain nutrition conversion capability information;
acquiring fish meal efficacy quantification information, extracting fish meal efficacy quantification values of corresponding indexes through the replacement index information, and analyzing the replacement relationship between chlorella and fish meal by combining the nutritional conversion capability information to obtain replacement relationship analysis information;
Extracting associated nutrition indexes of the chlorella through the efficacy information of the chlorella, extracting associated nutrition indexes of fish meal through the fish meal efficacy quantification information, and judging the unreplaced indexes of the chlorella to obtain unreplaced index information;
Extracting a fish meal efficacy quantized value corresponding to the non-replacement index according to the fish meal efficacy quantized information, and taking the fish meal efficacy quantized value as the preparation reserved quantity of the fish meal in the target non-replacement index to obtain fish meal preparation reserved quantity information of each non-replacement index.
It should be noted that, first, the nutritional conversion capability of the replacement nutritional index is analyzed by obtaining the efficacy capability information of the chlorella, and this step is to know the replacement capability and retention characteristics of the chlorella in the feed.
Firstly, acquiring the energy efficiency information of the chlorella, and carrying out chlorella replacement index analysis by combining the energy efficiency information and the nutrition efficiency analysis information to judge which nutrition indexes in the prawn feed can be replaced by the chlorella. And then, according to the set unit standard value, acquiring the corresponding chlorella use content by utilizing big data retrieval, and analyzing the nutrition conversion capability of the chlorella and the corresponding replacement index to obtain nutrition conversion capability information, namely the degree to which the chlorella with unit weight can provide the corresponding nutrition index. And then, acquiring fish meal efficacy quantification information, and extracting fish meal efficacy quantification values of corresponding indexes through replacing the index information. And analyzing the replacement relationship between the chlorella and the fish meal by combining the nutrition conversion capability information to obtain replacement relationship analysis information, thereby obtaining the replacement relationship between the chlorella and the fish meal in each replacement nutrition index. Because the chlorella and the fish meal are different substances, the nutrition provided by the chlorella and the fish meal also have certain difference, so that the chlorella and the fish meal can not completely replace the use of the fish meal when the preparation is optimized, and the reserved quantity of the fish meal needs to be analyzed. Extracting the associated nutrition index of the chlorella according to the efficacy information of the chlorella, extracting the associated nutrition index of the fish meal according to the fish meal efficacy quantization information, and judging the unreplaced index of the chlorella to obtain the unreplaced index information. And finally, extracting a fish meal efficacy quantized value corresponding to the non-replacement index according to the fish meal efficacy quantized information, and taking the fish meal efficacy quantized value as the preparation reserved quantity of the fish meal in the target non-replacement index to obtain fish meal preparation reserved quantity information of each non-replacement index.
Further, in a preferred embodiment of the present invention, the obtaining the current preparation scheme information, constructing a preparation optimization model, and performing preparation optimization on the prawn feed through the preparation optimization model specifically includes:
acquiring information of a current preparation scheme, and extracting nutrition index requirements of the current preparation scheme and proportion of components used in feed preparation to obtain characteristic information of the current preparation scheme;
Constructing a preparation optimization model based on SA and PSO algorithms, acquiring fish meal efficacy quantization information, nutrition conversion capability information and fish meal preparation retention information, and training the preparation optimization model to obtain a preparation optimization model which meets expectations;
Inputting the characteristic information of the current preparation scheme into the preparation optimization model for preparation optimization, setting an objective function, and setting a punishment condition according to the fish meal preparation reserve quantity information;
initializing a population according to the characteristic information of the current preparation scheme, generating an initial particle swarm, calculating the fitness of each particle in the initial particle swarm, and judging with a preset threshold value to obtain a current optimal solution;
Presetting initial receiving probability and descending rate, taking the current optimal solution as an initial solution of an SA algorithm, judging whether to accept according to the initial receiving probability, and carrying out punishment judgment according to set punishment conditions;
And carrying out acceptance probability adjustment by combining the descending rate and the punishment judgment result, carrying out iterative optimization until the model converges, obtaining a final solution, and generating an initial preparation optimization scheme through the final solution.
It should be noted that, first, the information of the current preparation scheme is obtained, which includes the nutrition index requirement of the current preparation scheme and the proportion of the components used in the preparation of the feed, so as to obtain the characteristic information of the current preparation scheme. Next, a preparation optimization model is constructed based on SA and PSO algorithms. And training by combining fish meal efficacy quantification information, nutrition conversion capability information and fish meal preparation retention information to obtain a preparation optimization model which meets expectations. And then, inputting the characteristic information of the current preparation scheme into a preparation optimization model for preparation optimization, setting an objective function, and setting punishment conditions according to the fish meal preparation retention amount information so as to ensure that the fish meal cannot be completely replaced by chlorella in the optimization scheme and ensure that the nutrition indexes of the preparation optimization scheme are the same as those of the initial scheme. Then, the initial receiving probability and the descending rate are preset, and the current optimal solution is taken as an initial solution. Judging whether to accept or not according to the preset initial acceptance probability, and carrying out punishment judgment through the set punishment conditions. And then, carrying out acceptance probability adjustment according to the descending rate and the punishment judgment result, and carrying out iterative optimization until the model converges, thereby finally obtaining the optimal solution. Finally, generating an initial preparation optimization scheme through a final solution, thereby obtaining the preparation optimization scheme containing the nutrition indexes in the original preparation scheme.
Further, in a preferred embodiment of the present invention, the selecting the optimal preparation scheme specifically includes:
acquiring initial preparation optimization schemes, extracting features of each initial preparation optimization scheme, and extracting preparation component proportion of each initial preparation optimization scheme to obtain optimized preparation component proportion information;
acquiring the price of the components used at the current moment according to the optimized preparation and use component proportion information, and calculating the preparation cost of each initial preparation scheme to obtain the preparation cost information of the optimized scheme;
taking the preparation cost information of the optimization scheme as weight, carrying out weighted calculation on each initial preparation optimization scheme, and sequencing weighted calculation results to obtain preparation optimization scheme sequencing information;
presetting a selection threshold, judging the preparation optimization scheme ordering information and the selection threshold, and acquiring a final preparation optimization scheme according to a judging result to perform prawn feed preparation optimization.
It should be noted that, the initial preparation optimization scheme only meets the basic requirement of unchanged nutrition index, and the optimized preparation optimization scheme also needs to consider the optimized preparation cost. Firstly, obtaining initial preparation optimization schemes, extracting features of each initial preparation optimization scheme, and extracting preparation component proportions of each initial preparation optimization scheme. The proportion information refers to the proportion of each component in the feed preparation process, such as the use proportion of fish meal, soybean meal and the like. And then, according to the optimized preparation use component proportion information, the price of each use component at the current moment is required to be obtained, and the preparation cost of each initial preparation scheme is calculated to obtain the preparation cost information of the optimized scheme. And taking the preparation cost information of the optimized scheme as weight, carrying out weighted calculation on each initial preparation optimized scheme, and sequencing the schemes according to the weighted calculation result. And finally, presetting a selection threshold, wherein the selection threshold is the preparation cost of the initial prawn feed preparation scheme, and comparing and judging the preparation optimization scheme sequencing information with the selected threshold. And determining a final preparation optimization scheme according to the judging result so as to improve the production efficiency and the feed quality, and controlling the preparation cost within an acceptable range.
FIG. 2 is a flowchart of optimizing a preparation scheme according to an embodiment of the present invention;
As shown in fig. 2, the present invention provides a preparation scheme optimization flow chart comprising:
S202, acquiring information of a current preparation scheme, extracting nutrition index requirements of the current preparation scheme and proportion of components used in feed preparation, and inputting the nutrition index requirements and the proportion of components used in feed preparation into a preparation optimization model for preparation optimization to obtain an initial preparation optimization scheme;
s204, extracting the preparation use component proportion of each initial preparation optimization scheme, acquiring the price of each use component at the current moment, and calculating the preparation cost of each initial preparation optimization scheme to obtain the preparation cost information of the optimization scheme;
s206, taking the preparation cost information of the optimization scheme as weight, carrying out weighted calculation on each initial preparation optimization scheme, and sequencing weighted calculation results;
S208, judging the preparation optimization scheme ordering information and the selection threshold value, and acquiring a final preparation optimization scheme according to a judging result to perform prawn feed preparation optimization.
In addition, the preparation optimization method of the chlorella-based prawn feed provided by the invention further comprises the following steps:
acquiring the preparation scheme information of the prawn feed at the current moment, and extracting the adopted component characteristics of the preparation of the prawn feed at the current moment to obtain the adopted component characteristic information;
Constructing a price prediction model based on SARIMA, acquiring historical prices of all the adopted components according to the adopted component characteristic information, performing model training, acquiring the prices of all the adopted components at the current moment, and inputting the prices into the price prediction model for prediction to obtain price prediction information;
acquiring the last purchase price of each adopted component, calculating the deviation from the price prediction information, judging with a preset threshold value, and defining the adopted component larger than the preset threshold value as a component needing to be replaced to obtain component information needing to be replaced;
Acquiring nutrition efficacy information of the components to be replaced according to the information of the components to be replaced, and performing big data retrieval according to the nutrition efficacy information of the components to be replaced to acquire replaceable components and corresponding nutrition efficacy, so as to acquire the information of the replaceable components;
performing component conversion relation analysis according to the nutritional efficacy information of the components to be replaced and the replaceable component information to obtain conversion relation analysis information;
Inputting the conversion relation analysis information, the component analysis information to be replaced, the replaceable component analysis information and the prawn feed preparation scheme information at the current moment into the preparation optimization model for preparation component optimization to obtain an initial preparation component optimization scheme;
and acquiring real-time prices of components adopted in preparation in each initial preparation component optimization scheme, calculating the cost of each initial preparation component optimization scheme, carrying out weighting calculation on each scheme, and selecting an optimal preparation component optimization scheme for recommendation according to a weighting calculation result.
It should be noted that, for the preparation of prawn feed, the price of the adopted preparation components fluctuates with the market change, and when the price of the preparation components increases, the preparation still adopts the original preparation scheme, which may increase the preparation cost. Therefore, the price change trend of each preparation component is judged by predicting the price of each preparation component, so that when the price of certain preparation components is too high, the preparation components are replaced, the preparation cost is kept or reduced, and meanwhile, the quality and the stability of the preparation of the prawn feed are ensured.
Fig. 3 is a schematic diagram of a chlorella-based prawn feed preparation optimizing system 3 according to an embodiment of the present invention, which includes: the device comprises a memory 31 and a processor 32, wherein the memory 31 contains a chlorella-based prawn feed preparation optimizing method program, and the chlorella-based prawn feed preparation optimizing method program realizes the following steps when executed by the processor 32:
The method comprises the steps of obtaining preparation information of prawn feeds with different nutrition specifications based on big data retrieval, extracting nutrition indexes corresponding to the prawn feeds with different specifications, and analyzing the nutrition efficacy of the prawn feeds to obtain nutrition efficacy analysis information;
according to the preparation information of the prawn feed, analyzing the fish meal efficacy, and quantifying the fish meal efficacy to obtain fish meal efficacy quantification information;
Acquiring efficacy capability information of the chlorella, analyzing nutrition conversion capability of nutritional indexes replaced by the chlorella, and analyzing fish meal preparation retention quantity of unreplaced indexes of the chlorella;
Obtaining information of a current preparation scheme, constructing a preparation optimization model, performing preparation optimization on the prawn feed through the preparation optimization model, and selecting an optimal preparation optimization scheme.
In the several embodiments provided by the present 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 computer 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 optical disk, or the like, which can store program codes.
Or the above-described integrated units of the invention may be stored in a computer-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 solutions 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.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A chlorella-based prawn feed preparation optimization method is characterized by comprising the following steps:
The method comprises the steps of obtaining preparation information of prawn feeds with different nutrition specifications based on big data retrieval, extracting nutrition indexes corresponding to the prawn feeds with different specifications, and analyzing the nutrition efficacy of the prawn feeds to obtain nutrition efficacy analysis information;
according to the preparation information of the prawn feed, analyzing the fish meal efficacy, and quantifying the fish meal efficacy to obtain fish meal efficacy quantification information;
Acquiring efficacy capability information of the chlorella, analyzing nutrition conversion capability of nutritional indexes replaced by the chlorella, and analyzing fish meal preparation retention quantity of unreplaced indexes of the chlorella;
Obtaining information of a current preparation scheme, constructing a preparation optimization model, performing preparation optimization on the prawn feed through the preparation optimization model, and selecting an optimal preparation optimization scheme.
2. The chlorella-based prawn feed preparation optimization method of claim 1, wherein the large data retrieval-based prawn feed preparation information of different nutritional specifications is obtained, and the nutritional efficacy of each prawn feed is analyzed, specifically comprising:
Retrieving and acquiring prawn feed preparation information of different nutritional specifications based on big data, and extracting nutritional indexes and corresponding nutritional specifications and component characteristics according to the prawn feed preparation information;
Calculating pearson correlation coefficients between the component characteristics and the nutrition indexes to form a pearson correlation coefficient matrix, and setting a pearson correlation coefficient threshold value based on a breakpoint detection method;
performing threshold processing on the pearson correlation coefficient matrix according to the pearson correlation coefficient threshold, and setting zero for data smaller than the pearson correlation coefficient threshold in the matrix to update the pearson correlation coefficient matrix to obtain an update matrix;
And extracting corresponding nutrition indexes according to the updated matrix, acquiring nutrition specifications corresponding to the nutrition indexes, correlating the nutrition indexes, the nutrition specifications and the component characteristics, and analyzing the nutrition efficacy of the target prawn feed to obtain nutrition efficacy analysis information.
3. The chlorella-based prawn feed preparation optimization method of claim 1, wherein the fish meal efficacy analysis is performed according to the prawn feed preparation information, and the fish meal efficacy is quantified, specifically comprising:
Acquiring nutrition efficacy analysis information, and extracting nutrition efficacy characteristics corresponding to the fish meal according to the nutrition efficacy analysis information, wherein the nutrition efficacy characteristics comprise nutrition indexes and nutrition specifications, so as to obtain fish meal nutrition efficacy characteristic information;
Obtaining the component characteristics and the content characteristics of different nutritional specifications of the target nutritional index according to the preparation information of the prawn feed, and calculating the nutritional content of the target nutritional index of the prawn feed per unit weight to obtain nutritional content analysis information;
Analyzing whether nutritional indexes corresponding to the fish meal are related to other components according to the nutritional efficacy characteristic information and the nutritional efficacy analysis information of the fish meal, and performing quantitative analysis on the fish meal efficacy if the nutritional indexes are not related to other components;
Carrying out fish meal efficacy quantification analysis according to the nutritional content analysis information, and taking the content of fish meal components in the unit weight of the shrimp feed as a fish meal efficacy quantification standard to obtain first efficacy quantification information;
If other components are related, analyzing the efficacy contribution rate of the fish meal under the target nutrition index, and obtaining the related component information of the target nutrition index;
acquiring the component structure of the prawn feed per unit weight according to the related component information, calculating the component proportion of the fish meal, and calculating the efficacy contribution rate of the fish meal to the target nutrition index by combining the nutrition content analysis information to obtain second efficacy quantification information;
and combining the first efficacy quantification information and the second efficacy quantification information to form fish meal efficacy quantification information.
4. The chlorella-based prawn feed preparation optimization method of claim 1, wherein the obtaining of the efficacy capability information of the chlorella, the analysis of the nutritional conversion capability of the chlorella replacement nutritional index, and the analysis of the fish meal preparation retention of the chlorella non-replacement index specifically comprises:
Acquiring the efficacy capability information of the chlorella, carrying out chlorella replacement index analysis by combining the nutrition efficacy analysis information, judging the replacement index of the chlorella according to the efficacy capability of the chlorella, and obtaining replacement index information;
Setting a unit standard value of each replacement index according to the replacement index information, acquiring the corresponding chlorella use content by utilizing big data retrieval, and analyzing the nutrition conversion capability of the chlorella and the corresponding replacement index to obtain nutrition conversion capability information;
acquiring fish meal efficacy quantification information, extracting fish meal efficacy quantification values of corresponding indexes through the replacement index information, and analyzing the replacement relationship between chlorella and fish meal by combining the nutritional conversion capability information to obtain replacement relationship analysis information;
Extracting associated nutrition indexes of the chlorella through the efficacy information of the chlorella, extracting associated nutrition indexes of fish meal through the fish meal efficacy quantification information, and judging the unreplaced indexes of the chlorella to obtain unreplaced index information;
Extracting a fish meal efficacy quantized value corresponding to the non-replacement index according to the fish meal efficacy quantized information, and taking the fish meal efficacy quantized value as the preparation reserved quantity of the fish meal in the target non-replacement index to obtain fish meal preparation reserved quantity information of each non-replacement index.
5. The chlorella-based prawn feed preparation optimization method of claim 1, wherein the obtaining of the current preparation scheme information constructs a preparation optimization model, and the preparation optimization model is used for prawn feed preparation optimization, and the method specifically comprises the following steps:
acquiring information of a current preparation scheme, and extracting nutrition index requirements of the current preparation scheme and proportion of components used in feed preparation to obtain characteristic information of the current preparation scheme;
Constructing a preparation optimization model based on SA and PSO algorithms, acquiring fish meal efficacy quantization information, nutrition conversion capability information and fish meal preparation retention information, and training the preparation optimization model to obtain a preparation optimization model which meets expectations;
Inputting the characteristic information of the current preparation scheme into the preparation optimization model for preparation optimization, setting an objective function, and setting a punishment condition according to the fish meal preparation reserve quantity information;
initializing a population according to the characteristic information of the current preparation scheme, generating an initial particle swarm, calculating the fitness of each particle in the initial particle swarm, and judging with a preset threshold value to obtain a current optimal solution;
Presetting initial receiving probability and descending rate, taking the current optimal solution as an initial solution of an SA algorithm, judging whether to accept according to the initial receiving probability, and carrying out punishment judgment according to set punishment conditions;
And carrying out acceptance probability adjustment by combining the descending rate and the punishment judgment result, carrying out iterative optimization until the model converges, obtaining a final solution, and generating an initial preparation optimization scheme through the final solution.
6. The chlorella-based prawn feed preparation optimization method of claim 1, which is characterized by comprising the following steps of:
acquiring initial preparation optimization schemes, extracting features of each initial preparation optimization scheme, and extracting preparation component proportion of each initial preparation optimization scheme to obtain optimized preparation component proportion information;
acquiring the price of the components used at the current moment according to the optimized preparation and use component proportion information, and calculating the preparation cost of each initial preparation scheme to obtain the preparation cost information of the optimized scheme;
taking the preparation cost information of the optimization scheme as weight, carrying out weighted calculation on each initial preparation optimization scheme, and sequencing weighted calculation results to obtain preparation optimization scheme sequencing information;
presetting a selection threshold, judging the preparation optimization scheme ordering information and the selection threshold, and acquiring a final preparation optimization scheme according to a judging result to perform prawn feed preparation optimization.
7. A chlorella-based prawn feed preparation optimization system is characterized in that the system comprises: the device comprises a memory and a processor, wherein the memory contains a chlorella-based prawn feed preparation optimization method program, and the chlorella-based prawn feed preparation optimization method program realizes the following steps when executed by the processor:
The method comprises the steps of obtaining preparation information of prawn feeds with different nutrition specifications based on big data retrieval, extracting nutrition indexes corresponding to the prawn feeds with different specifications, and analyzing the nutrition efficacy of the prawn feeds to obtain nutrition efficacy analysis information;
according to the preparation information of the prawn feed, analyzing the fish meal efficacy, and quantifying the fish meal efficacy to obtain fish meal efficacy quantification information;
Acquiring efficacy capability information of the chlorella, analyzing nutrition conversion capability of nutritional indexes replaced by the chlorella, and analyzing fish meal preparation retention quantity of unreplaced indexes of the chlorella;
Obtaining information of a current preparation scheme, constructing a preparation optimization model, performing preparation optimization on the prawn feed through the preparation optimization model, and selecting an optimal preparation optimization scheme.
8. The chlorella-based prawn feed preparation optimizing system of claim 7, wherein the large data retrieval-based prawn feed preparation information of different nutritional specifications is obtained, and the nutritional efficacy of each prawn feed is analyzed, specifically comprising:
Retrieving and acquiring prawn feed preparation information of different nutritional specifications based on big data, and extracting nutritional indexes and corresponding nutritional specifications and component characteristics according to the prawn feed preparation information;
Calculating pearson correlation coefficients between the component characteristics and the nutrition indexes to form a pearson correlation coefficient matrix, and setting a pearson correlation coefficient threshold value based on a breakpoint detection method;
performing threshold processing on the pearson correlation coefficient matrix according to the pearson correlation coefficient threshold, and setting zero for data smaller than the pearson correlation coefficient threshold in the matrix to update the pearson correlation coefficient matrix to obtain an update matrix;
And extracting corresponding nutrition indexes according to the updated matrix, acquiring nutrition specifications corresponding to the nutrition indexes, correlating the nutrition indexes, the nutrition specifications and the component characteristics, and analyzing the nutrition efficacy of the target prawn feed to obtain nutrition efficacy analysis information.
9. The chlorella-based prawn feed preparation optimizing system of claim 7, wherein the analyzing fish meal efficacy according to the prawn feed preparation information and quantifying fish meal efficacy specifically comprises:
Acquiring nutrition efficacy analysis information, and extracting nutrition efficacy characteristics corresponding to the fish meal according to the nutrition efficacy analysis information, wherein the nutrition efficacy characteristics comprise nutrition indexes and nutrition specifications, so as to obtain fish meal nutrition efficacy characteristic information;
Obtaining the component characteristics and the content characteristics of different nutritional specifications of the target nutritional index according to the preparation information of the prawn feed, and calculating the nutritional content of the target nutritional index of the prawn feed per unit weight to obtain nutritional content analysis information;
Analyzing whether nutritional indexes corresponding to the fish meal are related to other components according to the nutritional efficacy characteristic information and the nutritional efficacy analysis information of the fish meal, and performing quantitative analysis on the fish meal efficacy if the nutritional indexes are not related to other components;
Carrying out fish meal efficacy quantification analysis according to the nutritional content analysis information, and taking the content of fish meal components in the unit weight of the shrimp feed as a fish meal efficacy quantification standard to obtain first efficacy quantification information;
If other components are related, analyzing the efficacy contribution rate of the fish meal under the target nutrition index, and obtaining the related component information of the target nutrition index;
acquiring the component structure of the prawn feed per unit weight according to the related component information, calculating the component proportion of the fish meal, and calculating the efficacy contribution rate of the fish meal to the target nutrition index by combining the nutrition content analysis information to obtain second efficacy quantification information;
and combining the first efficacy quantification information and the second efficacy quantification information to form fish meal efficacy quantification information.
10. The chlorella-based prawn feed preparation optimizing system of claim 7, wherein the obtaining of the efficacy capability information of the chlorella, analyzing the nutritional conversion capability of the chlorella replacement nutritional index, and analyzing the fish meal preparation retention of the chlorella non-replacement index, specifically comprises:
Acquiring efficacy capability information of the chlorella, analyzing nutrition conversion capability of nutritional indexes replaced by the chlorella, and analyzing fish meal preparation retention quantity of unreplaced indexes of the chlorella;
Acquiring the efficacy capability information of the chlorella, carrying out chlorella replacement index analysis by combining the nutrition efficacy analysis information, judging the replacement index of the chlorella according to the efficacy capability of the chlorella, and obtaining replacement index information;
Setting a unit standard value of each replacement index according to the replacement index information, acquiring the corresponding chlorella use content by utilizing big data retrieval, and analyzing the nutrition conversion capability of the chlorella and the corresponding replacement index to obtain nutrition conversion capability information;
acquiring fish meal efficacy quantification information, extracting fish meal efficacy quantification values of corresponding indexes through the replacement index information, and analyzing the replacement relationship between chlorella and fish meal by combining the nutritional conversion capability information to obtain replacement relationship analysis information;
Extracting associated nutrition indexes of the chlorella through the efficacy information of the chlorella, extracting associated nutrition indexes of fish meal through the fish meal efficacy quantification information, and judging the unreplaced indexes of the chlorella to obtain unreplaced index information;
Extracting a fish meal efficacy quantized value corresponding to the non-replacement index according to the fish meal efficacy quantized information, and taking the fish meal efficacy quantized value as the preparation reserved quantity of the fish meal in the target non-replacement index to obtain fish meal preparation reserved quantity information of each non-replacement index.
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| CN113935233A (en) * | 2021-09-28 | 2022-01-14 | 暨南大学 | Feed formula optimization method, system, computer and storage medium |
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