CN117422170A - Sterilization process optimization method and system based on pH value control - Google Patents
Sterilization process optimization method and system based on pH value control Download PDFInfo
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Abstract
The invention relates to the technical field of sterilization process optimization control, and provides a sterilization process optimization control method and system based on pH value control, wherein the method comprises the following steps: the solution data set and the flora prediction database are established, the sterilization treatment constraint is collected, similar polymerization and pH value control optimizing of flora treatment are carried out, and taste, sterilization effect and cost are optimized and evaluated, so that an optimized sterilization process is generated, the technical problem that the taste and the nutrition value of the beverage cannot be effectively considered while the sterilization effect is ensured is solved, the optimal sterilization condition is found, the damage of the taste and the nutrition value of the beverage is improved, the quality of the beverage is further ensured, the production cost is reduced to the greatest extent, and the production efficiency is improved is achieved.
Description
Technical Field
The invention relates to the technical field related to sterilization process optimization control, in particular to a sterilization process optimization method and system based on pH value control.
Background
In beverage production, microbial control in the beverage is a critical aspect, and sterilization is one of the key steps in controlling microbial numbers. The sterilization process often has problems, for example, microorganisms in the liquid are removed by a filtering method or a heating method for a certain temperature and time, so that the beverage product can reach the aseptic requirement, but the filtering method can not comprehensively and effectively kill various harmful microorganisms, and likewise, the excessive sterilization by the heating method for a certain temperature and time can cause the loss of taste and nutritive value of the beverage.
In summary, the technical problems in the prior art that the taste and the nutritive value of the beverage cannot be effectively considered while the sterilization effect is ensured.
Disclosure of Invention
The application aims to solve the technical problem that the taste and the nutritive value of the beverage cannot be effectively considered while the sterilization effect is ensured in the prior art by providing the pH value-based sterilization process control optimization method and system.
In view of the above problems, the present application provides a method and system for controlling sterilization process based on ph.
In a first aspect of the disclosure, a method for controlling sterilization process optimization based on ph is provided, wherein the method comprises: establishing a solution data set of the target solution, wherein the solution data set is obtained by connecting a processing database in a communication way, and the solution data set comprises solution component data; constructing a flora prediction database, wherein the flora prediction database is constructed by taking the solution data set and process environment data as matching data for prediction matching, and the process environment data is obtained by connecting the process database; collecting and obtaining sterilization treatment constraints of a target solution, wherein the sterilization treatment constraints comprise taste influence constraints and sterilization effect constraints; performing similar aggregation of flora treatment on the flora prediction database to generate flora treatment aggregation clusters, wherein the flora treatment aggregation clusters are provided with cluster identifiers; setting a pH value limit space based on the solution data set and the sterilization treatment constraint, and configuring a pH value control step length; taking the sterilization treatment constraint as a sterilization optimization constraint, and executing treatment optimization of the flora treatment cluster in the pH value limit space by using a pH value control step length to obtain a treatment optimization set; and performing multi-index control evaluation on the processing optimizing set, wherein the multi-index control evaluation comprises taste evaluation, sterilization effect evaluation and cost evaluation, and generating an optimized sterilization process based on a control evaluation result.
In another aspect of the disclosure, a sterilization process tuning system based on ph control is provided, wherein the system comprises: the system comprises a data acquisition module, a processing database and a processing database, wherein the data acquisition module is used for establishing a solution data set of a target solution, the solution data set is obtained through communication connection, and the solution data set comprises solution component data; the database construction module is used for constructing a flora prediction database, the flora prediction database carries out prediction matching construction by taking the solution data set and the process environment data as matching data, and the process environment data is obtained by connecting the process database; the sterilization treatment constraint module is used for acquiring sterilization treatment constraints of the target solution, wherein the sterilization treatment constraints comprise taste influence constraints and sterilization effect constraints; the similar aggregation module is used for performing similar aggregation of flora treatment on the flora prediction database to generate a flora treatment aggregation cluster, wherein the flora treatment aggregation cluster is provided with a cluster mark; the control step length configuration module is used for setting a PH value limit space based on the solution data set and the sterilization treatment constraint and configuring PH value control step length; the processing optimizing module is used for taking the sterilization processing constraint as a sterilization optimizing constraint, and executing processing optimizing of the flora processing aggregation cluster in the pH value limit space by using a pH value control step length to obtain a processing optimizing set; the control evaluation module is used for performing multi-index control evaluation on the processing optimizing set, wherein the multi-index control evaluation comprises taste evaluation, sterilization effect evaluation and cost evaluation, and an optimized sterilization process is generated based on a control evaluation result.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
as a result of the use of the solution dataset for creating the target solution; constructing a flora prediction database; acquiring sterilization treatment constraints of a target solution; performing similar aggregation of flora treatment on a flora prediction database to generate a flora treatment aggregation cluster, setting a pH limit space and configuring a pH control step length based on a solution data set and sterilization treatment constraint; performing processing optimization of the flora processing cluster in a pH value limit space according to a pH value control step length to obtain a processing optimization set; the processing optimizing set is subjected to multi-index control evaluation such as taste evaluation, sterilizing effect evaluation, cost evaluation and the like, and an optimized sterilizing process is generated based on the evaluation result, so that the optimal sterilizing condition is searched, the sterilizing effect is improved, the damage to the taste and the nutritive value of the beverage is reduced as much as possible, the quality of the beverage product is further ensured, the production cost is reduced to the maximum extent, and the production efficiency is improved.
The foregoing description is only an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.
Drawings
Fig. 1 is a schematic flow chart of a possible process for controlling the sterilization process based on the ph value according to the embodiment of the application;
fig. 2 is a schematic flow chart of a possible process for obtaining an optimized sterilization process in a sterilization process optimization method based on ph control according to an embodiment of the present application;
fig. 3 is a schematic diagram of a possible structure of a sterilization process tuning system based on ph control according to an embodiment of the present application.
Reference numerals illustrate: the system comprises a data acquisition module 100, a database construction module 200, a sterilization processing constraint module 300, a similar aggregation module 400, a control step configuration module 500, a processing optimizing module 600 and a control evaluation module 700.
Detailed Description
Exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, in which various details of the embodiments of the present invention are included to facilitate understanding, and are to be considered merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Example 1
As shown in fig. 1, an embodiment of the present application provides a method for controlling sterilization process optimization based on ph, wherein the method includes:
step-1: establishing a solution data set of the target solution, wherein the solution data set is obtained by connecting a processing database in a communication way, and the solution data set comprises solution component data;
step-2: constructing a flora prediction database, wherein the flora prediction database is constructed by taking the solution data set and process environment data as matching data for prediction matching, and the process environment data is obtained by connecting the process database;
step-3: collecting and obtaining sterilization treatment constraints of a target solution, wherein the sterilization treatment constraints comprise taste influence constraints and sterilization effect constraints;
as known, the target solution is a beverage, a solution data set is obtained from a processing database, the solution data set comprises component data of the solution, the component data is obtained through a communication connection mode, the communication connection is simply through signal transmission interaction, and a communication network is formed between the sterilization process adjusting and optimizing system based on the PH value control and the processing database;
the flora prediction database is constructed by taking the solution data set and the process environment data as matching data in a prediction matching way, specifically, taking the solution component data and the process environment data in the solution data set as matching data, and matching one solution component data for each process environment data to form a process-component matching pair; predicting the sterilizing effect of each process-component matching pair to obtain a sterilizing effect predicted value corresponding to each process environment data; and integrating the predicted bactericidal effect values corresponding to each process environment data together to construct a flora prediction database. According to different process environment data and solution composition data, a predicted value of a sterilization effect is rapidly obtained, so that a basis is provided for the optimization of a subsequent sterilization process.
Taste impact constraints and bactericidal effect constraints of the target solution are collected and recorded, wherein the taste impact constraints include, for example, impact on the taste of the beverage (using the greatest extremum constraint) and the bactericidal effect constraints include, for example, bactericidal effect on bacteria (using the smallest extremum constraint). And (3) carrying out verification, namely optimizing the sterilization process by adopting an optimization algorithm (such as a genetic algorithm, a simulated annealing algorithm and the like) so as to find process parameters capable of achieving the optimal sterilization effect under the condition of meeting the taste influence constraint and the sterilization effect constraint. In general, by creating a dataset, constructing a predictive database, and defining constraints, and then optimizing by an optimization algorithm, an effective optimization scheme can be provided for the sterilization process of the beverage solution.
Step-4: performing similar aggregation of flora treatment on the flora prediction database to generate flora treatment aggregation clusters, wherein the flora treatment aggregation clusters are provided with cluster identifiers;
step-5: setting a pH value limit space based on the solution data set and the sterilization treatment constraint, and configuring a pH value control step length;
step-6: taking the sterilization treatment constraint as a sterilization optimization constraint, and executing treatment optimization of the flora treatment cluster in the pH value limit space by using a pH value control step length to obtain a treatment optimization set;
step-7: and performing multi-index control evaluation on the processing optimizing set, wherein the multi-index control evaluation comprises taste evaluation, sterilization effect evaluation and cost evaluation, and generating an optimized sterilization process based on a control evaluation result.
Similar aggregation of flora treatment on flora prediction database: performing similarity calculation on each flora treatment in a flora prediction database, and aggregating similar flora treatments according to the similarity to form a flora treatment aggregation cluster, wherein each flora treatment aggregation cluster is provided with a cluster mark, and the general cluster mark is one or more strains which are most difficult to treat in the aggregation cluster;
setting a pH value limit space and configuring a pH value control step length: setting a pH value limit space according to the solution data set and sterilization treatment constraint, wherein the pH value limit space represents a range in which the pH value can be changed, and then configuring a pH value control step size which represents the amplitude of each adjustment of the pH value;
taking sterilization treatment constraint as sterilization optimization constraint, and performing treatment optimization of flora treatment clusters in a pH value limit space by pH value control step length: in the pH limit space, the pH value of the target solution is gradually adjusted by the pH value control step length, and each flora treatment cluster is optimized, preferably, the process optimizing is changed by different pH values, the other part is temperature control optimizing, the flora quantity and the flora type in the target solution are recorded in the adjustment process, then the optimal treatment scheme is searched, and the obtained optimal treatment schemes (such as that the first harmful flora quantity reaches the minimum value and the second harmful flora quantity reaches the minimum value) are combined together to form the treatment optimizing set.
Performing multi-index control evaluation on the processing optimizing set: and carrying out multi-index control evaluation such as taste evaluation, sterilization effect evaluation, cost evaluation and the like on each treatment scheme in the treatment optimizing set one by one, wherein the control evaluation result provides comprehensive evaluation for each treatment scheme, and the optimal sterilization process is selected from the control evaluation results according to the control evaluation result, namely the treatment scheme with the optimal taste, the optimal sterilization effect and the lowest cost. The optimized sterilization process obtained by the method has high practicability and expandability, and an effective sterilization process optimization scheme is provided for different types of solutions and products.
The embodiment of the application further comprises:
connecting the process database, and dividing process processing nodes according to the interaction data to generate N process nodes, wherein N is an integer greater than 1;
carrying out solution component matching on N process nodes based on the solution data set to obtain process solution component data;
performing process environment monitoring on the N process nodes to generate process environment data, wherein the process environment comprises temperature, humidity, pH value and flora environment;
performing process-by-process flora prediction matching through the process environment data and the process solution component data;
and constructing a flora prediction database according to the prediction matching result.
The process processing nodes are segmented according to the interaction data through connecting a process database, so that N process nodes are generated, wherein the N process nodes are matched and optimized through process environment data and solution component data, and the process processing nodes are segmented according to the interaction data, so that the N process nodes are generated specifically comprises the following steps: the method comprises the steps of collecting and recording interactive data in the process, wherein the interactive data comprise information such as process parameter changes, actions of operators, states of equipment and the like, and the information is obtained through modes such as sensors and log files; processing and analyzing the collected interaction data to identify different process processing sections, including sorting, grouping, filtering, clustering and the like of the data to find patterns and features in the data, for example, identifying different process stages or nodes by analyzing the change trend of the process parameters; dividing the whole process into N independent nodes according to the identified process treatment nodes, wherein each node represents an independent process treatment process or stage, and N is a preset integer and is set according to actual needs. Through the steps, the whole process treatment process is divided into different nodes according to the requirements of the actual process, so that the process is better understood and managed, and the method has important significance in the aspects of improving production efficiency, optimizing resource allocation, reducing cost and the like.
Carrying out solution component matching on each process node based on the solution data set to obtain process solution component data, wherein the process solution component data reflects the components and properties of solutions at different process nodes and provides an important reference basis for subsequent flora prediction matching; and monitoring the process environment of each process node to generate process environment data, wherein the process environment data comprise temperature, humidity, pH value, flora environment and the like, and reflect the influence of various factors in the process environment on the flora growth and sterilization effect.
The microbial community prediction matching of the process environment data and the process solution component data is carried out one by one, the growth condition and the sterilization effect of microbial communities under different process conditions are predicted, and the prediction matching result can provide important reference basis for subsequent microbial community treatment polymerization and pH value control optimizing; and constructing a flora prediction database according to the prediction matching result, wherein the flora prediction database can provide important data support and reference for the subsequent optimization process, so that the fine tuning of the pH value control sterilization process is realized.
The method can monitor and match solution components of a plurality of process nodes and process environments, more comprehensively consider the influence of various factors on the sterilization effect, improve the accuracy and the effect of the sterilization process, and simultaneously provide more accurate guidance for the construction of the sterilization process nodes for different types of bacteria by constructing a bacteria group prediction database.
The embodiment of the application further comprises:
establishing a flora generation prediction network, wherein the flora generation prediction network is constructed by collecting big data, and comprises a classification sub-network, M prediction sub-networks and an integration sub-network;
establishing a process data set through the process environment data and the process solution component data, and sequentially inputting the process data set into the flora to generate a prediction network;
processing input data through the classifying sub-network, matching M prediction sub-networks, and transmitting corresponding input data to the matching prediction sub-network;
and generating a flora prediction database based on a prediction result of the integrated sub-network integrated prediction sub-network, wherein the prediction result comprises a prediction strain and a prediction quantity. Constructing a flora generation prediction network by collecting big data, wherein the flora generation prediction network comprises a classification sub-network, M prediction sub-networks and an integration sub-network, the classification sub-network is responsible for classifying input data, the M prediction sub-networks are responsible for predicting different aspects, and the integration sub-network integrates prediction results of different prediction sub-networks, so that a flora prediction database is generated; and establishing a process data set through process environment data and process solution component data, and sequentially inputting the process data set into a flora generation prediction network, wherein the process data set provides important reference for classifying sub-networks and predicting sub-networks.
The method specifically comprises the steps of carrying out input data processing in a classification sub-network, matching input data with existing data, selecting a corresponding prediction sub-network to transmit the corresponding input data to the matching prediction sub-network, wherein each prediction sub-network is provided with different prediction models and algorithms, carrying out refined prediction on bacterial group generation under different process conditions, specifically comprising the steps of carrying out input data processing in the classification sub-network, matching the input data with the existing data, and selecting the corresponding prediction sub-network to transmit the corresponding input data to the matching prediction sub-network, wherein the steps specifically comprise: classifying the input data by a data classification algorithm, such as a K-nearest neighbor algorithm, a decision tree algorithm, a support vector machine algorithm and the like, and classifying the input data into different categories according to the characteristics and the attributes of the input data; matching the classified data with the existing data: the method comprises the steps of comparing the characteristics and the attributes of the classified input data with those of the existing data, and finding out similar data or the same data type; and selecting a corresponding prediction sub-network according to the matching result, and transmitting corresponding input data to the matching prediction sub-network. Each prediction subnetwork may have different prediction models and algorithms, such as linear regression models, support vector regression models, etc., for modeling and predicting different prediction problems; in the selected prediction subnetwork, the input data is processed and predicted using corresponding prediction models and algorithms to obtain predicted results for future flora generation, including data processing operations such as feature extraction, normalization, missing value filling, etc. Through the steps, input data processing is carried out in the classification sub-network, the input data is matched with the existing data, and the corresponding prediction sub-network is selected to transmit the corresponding input data to the matched prediction sub-network, wherein each prediction sub-network is provided with different prediction models and algorithms, so that the generation of the bacterial group under different process conditions is finely predicted.
And (3) integrating the prediction results of all the prediction sub-networks based on the integrated sub-networks to generate a flora prediction database, wherein the flora prediction database comprises important information such as prediction strains, prediction quantity and the like, and provides refined and comprehensive flora generation prediction results for enterprises. The method utilizes the big data technology to deeply analyze and predict the process environment data and the process solution component data, and is helpful for more accurately knowing and controlling various factors in the sterilization process, thereby improving the sterilization efficiency and protecting the product from damage.
The embodiment of the application further comprises:
performing flora influence evaluation on the flora treatment cluster, and normalizing the evaluation result to generate an initial proportion of the flora treatment cluster;
configuring a fixed tolerant space, and proportionally expanding the initial proportion by using the fixed tolerant space to generate an optimized space;
the taste constraint distribution of each flora treatment is carried out on the optimized space through the taste influence constraint, and a taste constraint distribution result is generated;
and finishing the processing optimization of the flora processing cluster through the mouthfeel constraint distribution result.
Further performing flora influence evaluation on the flora treatment cluster, generating an initial proportion, configuring a fixed tolerant space, generating an optimized space, considering taste influence constraint, and completing treatment optimization of the flora treatment cluster, including: the method comprises the steps of performing flora influence evaluation on the flora treatment clusters, wherein the flora influence evaluation comprises the steps of evaluating influence evaluation of each flora treatment cluster on the aspects of taste, flavor, color and the like of a final product (such as a beverage), normalizing the evaluation result to generate an initial proportion of the flora treatment clusters, and performing normalization treatment on different influence evaluation results to enable comparison and calculation between different evaluation results; the method comprises the steps of configuring a fixed tolerant space, wherein the fixed tolerant space is a preset optimization range, and can be used for carrying out proportional expansion on an initial proportion so as to generate an optimization space, wherein the fixed tolerant space is used for providing a search space for a subsequent optimization algorithm, namely more possible flora processing combinations can be accommodated in the fixed tolerant space, and a larger choice is provided for the subsequent optimization process.
The taste constraint distribution of each flora treatment is carried out on the optimization space through the taste constraint, and the taste constraint distribution comprises the evaluation of the influence and interaction of different flora treatments on the taste of the beverage, so as to screen out the flora treatments with the optimal combination while meeting the taste requirement; completing the processing optimization of the flora processing clusters based on the mouthfeel constraint distribution result, searching the optimal flora processing combination in an optimization space, and aggregating and sequencing the same, wherein the processing optimization method specifically comprises the steps of generating a mouthfeel constraint distribution map according to mouthfeel influence constraint and optimization space, wherein the mouthfeel constraint distribution map reflects the performances of different flora processing clusters in the aspect of mouthfeel, and the differences and the similarities in the different flora processing clusters; based on the mouthfeel constraint distribution map, optimizing the flora processing aggregation cluster by adopting an optimization algorithm (such as a genetic algorithm, a simulated annealing algorithm and the like) and performing operations including selection, crossing, variation and the like so as to find a flora processing combination with optimal performance under the condition of meeting the mouthfeel influence constraint; evaluating and verifying the optimized flora treatment combination to determine whether the combination has practical application value and production benefit, and links such as laboratory test, pilot scale test, field verification and the like can be included to prove the advantages and feasibility of the combination in the aspects of improving the sterilization effect, reducing the cost and the like: if the optimized flora treatment combination passes the evaluation and verification, the flora treatment combination can be applied to the actual production process, and the actual production effect is monitored and evaluated to further verify the feasibility and stability. Through the steps, the optimal flora treatment combination is found in the optimized space, so that the effect of a sterilization process is improved, the production cost is reduced, the requirement of taste influence constraint is met, the quality and the competitiveness of the beverage product are improved, and a larger commercial value is created for enterprises.
Correspondingly, the optimal flora treatment combination can reduce the production cost to the greatest extent and improve the production efficiency, and simultaneously ensure the quality and the taste of the beverage product. The influence of factors such as process environment, solution components and the like on the sterilization effect is considered, and the sterilization process is further optimized through taste influence constraint, so that the refinement and optimization process of flora treatment in the beverage product is realized while the sterilization effect is ensured, and the taste and the nutritional value of the beverage are furthest protected.
The embodiment of the application further comprises:
acquiring production data of the target solution, and performing time cost conversion analysis of processing based on the production data to generate a time cost conversion coefficient;
and performing time conversion by the time cost conversion coefficient, and obtaining the cost evaluation based on a conversion result.
Obtaining production data of a target solution, wherein the production data of the target solution comprise information of various parameters in the production process, such as components of the solution, process environment conditions, production time and the like; and performing time-cost conversion analysis based on the production data of the target solution, wherein the time-cost conversion analysis comprises the steps of sorting, counting and analyzing the production data to find out the relation between the time cost and each parameter, and comparing the analysis process to generate a time-cost conversion coefficient which represents the conversion relation between the time cost and other parameters.
Performing time conversion by this time cost conversion coefficient means converting other parameters into time cost, thereby being able to convert unquantifiable time into quantifiable cost; a cost evaluation is obtained based on the conversion result, which includes a plurality of angles such as a cost of the medicine, a cost of temperature control, a cost of PH control, etc., which are quantifiable so that the production cost can be more comprehensively evaluated, and simply, a non-quantifiable portion such as a time cost has been converted into a quantifiable cost by the foregoing conversion process, and thus is also included in the cost evaluation.
By analyzing production data and converting time cost, the time cost of a sterilization process is accurately estimated, an important reference basis is provided for optimizing the process, and meanwhile, the method has good practicability and expandability, can save production cost for enterprises and improve production efficiency, and provides effective technical support.
The embodiment of the application further comprises:
setting evaluation constraint proportion of taste evaluation, sterilization effect evaluation and cost evaluation;
performing weighted analysis of multi-index control evaluation through the evaluation constraint proportion to generate a control evaluation result;
and sequentially screening the control evaluation results to obtain the optimized sterilization process.
The evaluation constraint ratio of the taste evaluation, the sterilization effect evaluation, and the cost evaluation is set, including the evaluation of the importance and the influence degree of each evaluation index, so as to determine the weight of each index in the total evaluation, in short, if the importance of the taste to the beverage product is higher, the weight of the taste evaluation may be set higher.
The control evaluation result is generated by executing the weighted analysis of the multi-index control evaluation, and the weighted average or the weighted summation is carried out on the evaluation indexes such as the taste, the sterilization effect, the cost and the like of each flora treatment cluster, so that the total score of each cluster is obtained, and generally, the higher the score is, the better the performance of the cluster is.
Sequentially screening control evaluation results to obtain an optimized sterilization process, wherein the method comprises the steps of sequencing all the aggregation clusters according to the total score, and selecting the aggregation cluster with the highest score as the optimal sterilization process: if there are multiple clusters with similar overall scores, further screening and optimization of the multiple clusters may be required before the final optimized sterilization process is determined.
By taking various evaluation indexes into consideration, including taste, sterilization effect, cost and the like, and by setting different evaluation constraint proportions for weighted analysis, the effect and cost benefit of the sterilization process can be evaluated more comprehensively and objectively; through screening control evaluation results, more accurate guidance is provided for production practice, an optimized sterilization process is obtained, practicability and expandability are achieved, and flexible adjustment and optimization can be performed according to different product requirements.
As shown in fig. 2, the embodiment of the present application further includes:
performing component steady-state analysis on the solution component data, and generating control compensation parameters based on steady-state analysis results;
performing proportional compensation on the evaluation constraint through the control compensation parameters, and completing updating of weighted analysis based on the compensation result to generate an updated control evaluation result;
and obtaining the optimized sterilization process through the updated control evaluation result. The method comprises the steps of carrying out component steady-state analysis on solution component data, wherein the component steady-state analysis comprises the step of evaluating the stability of the solution component data to determine whether the data meet control requirements or whether control measures need to be taken, and identifying unstable components or influencing factors possibly existing, and the method specifically comprises the steps of collecting the solution component data, wherein the solution component data comprise the concentration, the proportion and the like of various chemical components; performing stability assessment on the collected solution composition data by comparing the data at different time points or different batches to see if the data are changing within an expected range; determining whether the data meets the control requirement: if the variation range of the data exceeds the expected range, the control requirement may not be met, and corresponding control measures need to be taken; for the data which does not meet the control requirement, possible reasons such as insufficient purity of certain components, abnormal production process and the like can be further analyzed, corresponding control measures can be taken, including the steps of adjusting the formula of the solution, optimizing the production process, replacing raw materials and the like, through the steps, possible unstable components or influencing factors can be identified, and corresponding control measures can be taken to improve the quality and stability of the solution.
Generating control compensation parameters based on the steady-state analysis results, including classifying and evaluating different components or influencing factors according to the steady-state analysis results, thereby determining the control compensation parameters of each component or influencing factor, wherein the control compensation parameters provide guidance for a subsequent control strategy; the evaluation constraint proportion is compensated by controlling the compensation parameter, and the original evaluation constraint proportion is adjusted and optimized according to the control compensation parameter, so that possible deviation or unreasonable positions of the original evaluation constraint proportion are corrected, the actual situation is reflected better, and the control requirement is met.
The updating of the weighted analysis is completed based on the compensation result, wherein the updating comprises the step of carrying out the weighted analysis again according to the adjusted evaluation constraint proportion so as to generate an updated control evaluation result, so that the final evaluation result is more accurate and reasonable; the optimized sterilization process is obtained by updating the control evaluation result, and the original sterilization process is optimized and adjusted according to the updated control evaluation result, so that a better sterilization effect and lower cost are obtained, and the whole sterilization process is more refined and efficient.
Through steady-state analysis of solution component data, the components and the stability of the solution can be more accurately known and controlled, so that an important reference basis is provided for optimizing a sterilization process; meanwhile, the evaluation constraint proportion is compensated by controlling the compensation parameter, so that the accuracy and objectivity of the evaluation result are further improved. Generally, the sterilization process is continuously optimized, the production efficiency is improved, the practicability and the expandability are combined, and references are provided for other types of sterilization processes through the steps of steady-state analysis, control compensation, update weighting analysis and the like.
In summary, the method and the system for adjusting and optimizing the sterilization process based on the pH control provided by the embodiment of the application have the following technical effects:
1. microorganisms in various types and different environments can be killed more efficiently by predicting and controlling the survival and propagation of microorganisms under different pH conditions and performing the treatment optimizing of the flora treatment cluster through the pH limit space and the pH control step length.
2. The taste and the nutritive value of the beverage can be furthest protected from loss by controlling the sterilization process optimizing method based on the pH value while guaranteeing the sterilization effect.
3. The pH value-based sterilization process control optimization method can be flexibly adjusted and optimized according to different product requirements, and can also provide references for other types of sterilization processes.
4. The evaluation constraint proportion for setting taste evaluation, sterilization effect evaluation and cost evaluation is adopted; performing weighted analysis of multi-index control evaluation through the evaluation constraint proportion to generate a control evaluation result; and sequentially screening the control evaluation results to obtain the optimized sterilization process. Considering various evaluation indexes including taste, sterilization effect, cost and the like, and performing weighted analysis by setting different evaluation constraint proportions so as to evaluate the effect and cost benefit of the sterilization process more comprehensively and objectively; through screening control evaluation results, more accurate guidance can be provided for production practice, an optimized sterilization process is obtained, practicability and expandability are achieved, and flexible adjustment and optimization can be performed according to different product requirements.
Example two
Based on the same inventive concept as the sterilization process tuning method based on the ph control in the foregoing embodiments, as shown in fig. 3, an embodiment of the present application provides a sterilization process tuning system based on the ph control, where the system includes:
a data acquisition module 100 for establishing a solution data set of the target solution, wherein the solution data set is obtained by connecting a processing database through communication, and the solution data set comprises solution composition data;
the database construction module 200 is configured to construct a flora prediction database, wherein the flora prediction database performs prediction matching construction by using the solution data set and process environment data as matching data, and the process environment data is obtained by connecting the process database;
a sterilization process constraint module 300, configured to acquire a sterilization process constraint for obtaining a target solution, where the sterilization process constraint includes a taste influence constraint and a sterilization effect constraint;
a similar aggregation module 400, configured to perform similar aggregation of the flora treatment on the flora prediction database, and generate a flora treatment aggregation cluster, where the flora treatment aggregation cluster has a cluster identifier;
the control step length configuration module 500 is used for setting a PH value limit space based on the solution data set and the sterilization treatment constraint and configuring PH value control step length;
the processing optimizing module 600 is configured to execute processing optimizing of the flora processing aggregation cluster in the ph limit space with a ph control step length by using the sterilization processing constraint as a sterilization optimizing constraint, so as to obtain a processing optimizing set;
the control evaluation module 700 is configured to perform multi-index control evaluation on the processing optimizing set, where the multi-index control evaluation includes taste evaluation, sterilization effect evaluation, and cost evaluation, and generate an optimized sterilization process based on a control evaluation result.
Further, the system further comprises:
connecting the process database, and dividing process processing nodes according to the interaction data to generate N process nodes, wherein N is an integer greater than 1;
carrying out solution component matching on N process nodes based on the solution data set to obtain process solution component data;
performing process environment monitoring on the N process nodes to generate process environment data, wherein the process environment comprises temperature, humidity, pH value and flora environment;
performing process-by-process flora prediction matching through the process environment data and the process solution component data;
and constructing a flora prediction database according to the prediction matching result.
Further, the system further comprises:
establishing a flora generation prediction network, wherein the flora generation prediction network is constructed by collecting big data, and comprises a classification sub-network, M prediction sub-networks and an integration sub-network;
establishing a process data set through the process environment data and the process solution component data, and sequentially inputting the process data set into the flora to generate a prediction network;
processing input data through the classifying sub-network, matching M prediction sub-networks, and transmitting corresponding input data to the matching prediction sub-network;
and generating a flora prediction database based on a prediction result of the integrated sub-network integrated prediction sub-network, wherein the prediction result comprises a prediction strain and a prediction quantity.
Further, the system further comprises:
performing flora influence evaluation on the flora treatment cluster, and normalizing the evaluation result to generate an initial proportion of the flora treatment cluster;
configuring a fixed tolerant space, and proportionally expanding the initial proportion by using the fixed tolerant space to generate an optimized space;
the taste constraint distribution of each flora treatment is carried out on the optimized space through the taste influence constraint, and a taste constraint distribution result is generated;
and finishing the processing optimization of the flora processing cluster through the mouthfeel constraint distribution result.
Further, the system further comprises:
acquiring production data of the target solution, and performing time cost conversion analysis of processing based on the production data to generate a time cost conversion coefficient;
and performing time conversion by the time cost conversion coefficient, and obtaining the cost evaluation based on a conversion result.
Further, the system further comprises:
setting evaluation constraint proportion of taste evaluation, sterilization effect evaluation and cost evaluation;
performing weighted analysis of multi-index control evaluation through the evaluation constraint proportion to generate a control evaluation result;
and sequentially screening the control evaluation results to obtain the optimized sterilization process.
Further, the system further comprises:
performing component steady-state analysis on the solution component data, and generating control compensation parameters based on steady-state analysis results;
performing proportional compensation on the evaluation constraint through the control compensation parameters, and completing updating of weighted analysis based on the compensation result to generate an updated control evaluation result;
and obtaining the optimized sterilization process through the updated control evaluation result.
Any of the steps of the methods described above may be stored as computer instructions or programs in a non-limiting computer memory and may be called by a non-limiting computer processor to identify any of the methods to implement embodiments of the present application, without unnecessary limitations.
Further, the first or second element may not only represent a sequential relationship, but may also represent a particular concept, and/or may be selected individually or in whole among a plurality of elements. It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the present application and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (8)
1. The pH value-based sterilization process control optimization method is characterized by comprising the following steps of:
establishing a solution data set of the target solution, wherein the solution data set is obtained by connecting a processing database in a communication way, and the solution data set comprises solution component data;
constructing a flora prediction database, wherein the flora prediction database is constructed by taking the solution data set and process environment data as matching data for prediction matching, and the process environment data is obtained by connecting the process database;
collecting and obtaining sterilization treatment constraints of a target solution, wherein the sterilization treatment constraints comprise taste influence constraints and sterilization effect constraints;
performing similar aggregation of flora treatment on the flora prediction database to generate flora treatment aggregation clusters, wherein the flora treatment aggregation clusters are provided with cluster identifiers;
setting a pH value limit space based on the solution data set and the sterilization treatment constraint, and configuring a pH value control step length;
taking the sterilization treatment constraint as a sterilization optimization constraint, and executing treatment optimization of the flora treatment cluster in the pH value limit space by using a pH value control step length to obtain a treatment optimization set;
and performing multi-index control evaluation on the processing optimizing set, wherein the multi-index control evaluation comprises taste evaluation, sterilization effect evaluation and cost evaluation, and generating an optimized sterilization process based on a control evaluation result.
2. The method of claim 1, wherein the method further comprises:
connecting the process database, and dividing process processing nodes according to the interaction data to generate N process nodes, wherein N is an integer greater than 1;
carrying out solution component matching on N process nodes based on the solution data set to obtain process solution component data;
performing process environment monitoring on the N process nodes to generate process environment data, wherein the process environment comprises temperature, humidity, pH value and flora environment;
performing process-by-process flora prediction matching through the process environment data and the process solution component data;
and constructing a flora prediction database according to the prediction matching result.
3. The method of claim 2, wherein the method further comprises:
establishing a flora generation prediction network, wherein the flora generation prediction network is constructed by collecting big data, and comprises a classification sub-network, M prediction sub-networks and an integration sub-network;
establishing a process data set through the process environment data and the process solution component data, and sequentially inputting the process data set into the flora to generate a prediction network;
processing input data through the classifying sub-network, matching M prediction sub-networks, and transmitting corresponding input data to the matching prediction sub-network;
and generating a flora prediction database based on a prediction result of the integrated sub-network integrated prediction sub-network, wherein the prediction result comprises a prediction strain and a prediction quantity.
4. The method of claim 1, wherein the method further comprises:
performing flora influence evaluation on the flora treatment cluster, and normalizing the evaluation result to generate an initial proportion of the flora treatment cluster;
configuring a fixed tolerant space, and proportionally expanding the initial proportion by using the fixed tolerant space to generate an optimized space;
the taste constraint distribution of each flora treatment is carried out on the optimized space through the taste influence constraint, and a taste constraint distribution result is generated;
and finishing the processing optimization of the flora processing cluster through the mouthfeel constraint distribution result.
5. The method of claim 1, wherein the method further comprises:
acquiring production data of the target solution, and performing time cost conversion analysis of processing based on the production data to generate a time cost conversion coefficient;
and performing time conversion by the time cost conversion coefficient, and obtaining the cost evaluation based on a conversion result.
6. The method of claim 5, wherein the method further comprises:
setting evaluation constraint proportion of taste evaluation, sterilization effect evaluation and cost evaluation;
performing weighted analysis of multi-index control evaluation through the evaluation constraint proportion to generate a control evaluation result;
and sequentially screening the control evaluation results to obtain the optimized sterilization process.
7. The method of claim 6, wherein the method further comprises:
performing component steady-state analysis on the solution component data, and generating control compensation parameters based on steady-state analysis results;
performing proportional compensation on the evaluation constraint through the control compensation parameters, and completing updating of weighted analysis based on the compensation result to generate an updated control evaluation result;
and obtaining the optimized sterilization process through the updated control evaluation result.
8. The pH value-based sterilization process tuning system is characterized by being used for implementing the pH value-based sterilization process tuning method according to any one of claims 1-7, and comprises the following steps:
the system comprises a data acquisition module, a processing database and a processing database, wherein the data acquisition module is used for establishing a solution data set of a target solution, the solution data set is obtained through communication connection, and the solution data set comprises solution component data;
the database construction module is used for constructing a flora prediction database, the flora prediction database carries out prediction matching construction by taking the solution data set and the process environment data as matching data, and the process environment data is obtained by connecting the process database;
the sterilization treatment constraint module is used for acquiring sterilization treatment constraints of the target solution, wherein the sterilization treatment constraints comprise taste influence constraints and sterilization effect constraints;
the similar aggregation module is used for performing similar aggregation of flora treatment on the flora prediction database to generate a flora treatment aggregation cluster, wherein the flora treatment aggregation cluster is provided with a cluster mark;
the control step length configuration module is used for setting a PH value limit space based on the solution data set and the sterilization treatment constraint and configuring PH value control step length;
the processing optimizing module is used for taking the sterilization processing constraint as a sterilization optimizing constraint, and executing processing optimizing of the flora processing aggregation cluster in the pH value limit space by using a pH value control step length to obtain a processing optimizing set;
the control evaluation module is used for performing multi-index control evaluation on the processing optimizing set, wherein the multi-index control evaluation comprises taste evaluation, sterilization effect evaluation and cost evaluation, and an optimized sterilization process is generated based on a control evaluation result.
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