A beer fermentation device optimization control method and system

Abstract

The present disclosure provides a method and system for optimizing control of a beer fermentation device, and relates to the technical field of optimizing control of fermentation devices. The method includes: obtaining product design indicators of beer; determining yeast varieties and expected fermentation time, and dividing the expected fermentation time into fermentation stages; tracing the expected product for N fermentation stages to generate an expected indicator sequence; obtaining fermentation control parameters; optimizing the fermentation control parameters in turn to generate an optimal fermentation control parameter sequence; collecting indicator data for a wort solution to obtain a real-time stage indicator data set; generating stage indicator deviation values ​​to obtain stage compensation data; performing compensation optimization to control beer fermentation. The present disclosure can solve the technical problem that the control accuracy of beer fermentation is insufficient in the prior art, resulting in poor beer fermentation effect, and achieve the technical effect of improving the accuracy and adaptability of beer fermentation control and improving the quality of beer fermentation.

Description

Technical Field

[0001] The present disclosure relates to the technical field of fermentation device optimization control, and in particular to a beer fermentation device optimization control method and system. Background Art

[0002] With the rise of the craft beer industry, the requirements for brewing high-quality beer are getting higher and higher. Among them, the quality of beer is mainly controlled by the fermentation process. At present, most of the existing beer fermentation methods have a long cycle. As time goes by, the adaptability of beer fermentation control becomes lower and lower, resulting in reduced beer fermentation quality and worse taste of beer. Therefore, a method is needed to solve the above problems.

[0003] In summary, the prior art has the technical problem that the control precision of beer fermentation is insufficient, resulting in poor beer fermentation effect. Summary of the invention

[0004] The present invention provides a method and system for optimizing control of a beer fermentation device, which are used to solve the technical problem in the prior art that the control accuracy of beer fermentation is insufficient, resulting in poor beer fermentation effect.

[0005] According to a first aspect of the present disclosure, a method for optimizing and controlling a beer fermentation device is provided, comprising: obtaining product design indicators of beer, wherein the product design indicators include alcohol content, wort concentration, and chromaticity; determining yeast varieties and expected fermentation time according to the product design indicators, and dividing the expected fermentation time into fermentation stages according to preset time nodes to generate N fermentation stages, wherein N is an integer greater than 3; performing expected product tracing on the N fermentation stages based on the product design indicators to generate an expected indicator sequence, wherein the expected indicator sequence includes N expected indicators, wherein the expected indicators include expected alcohol content, expected wort concentration, and expected chromaticity; obtaining fermentation control parameters of the beer fermentation device, and determining parameter adjustment thresholds, wherein the fermentation control parameters include temperature, pH value, yeast number, and quantity; according to the expected indicator sequence, the fermentation control parameters are optimized in turn in the parameter adjustment threshold value to generate an optimal fermentation control parameter sequence; based on the yeast variety and the optimal fermentation control parameter sequence, the beer fermentation device is controlled to perform beer fermentation, and indicator data of the wort solution is collected according to the preset monitoring nodes to obtain a real-time stage indicator data set, wherein the real-time stage indicator data includes real-time alcohol content, real-time wort concentration and real-time chromaticity; based on the expected indicator sequence and the real-time stage indicator data set, a stage indicator deviation value is generated, and stage compensation data is obtained according to the stage indicator deviation value; according to the stage compensation data, the optimal fermentation control parameter sequence is compensated and optimized, and beer fermentation control is performed according to the optimal compensated fermentation control parameter sequence.

[0006] According to a second aspect of the present disclosure, there is provided an optimization control system for a beer fermentation device, comprising: a product design index acquisition module, the product design index acquisition module is used to acquire product design indexes of beer, wherein the product design indexes include alcohol content, wort concentration, and chromaticity; N fermentation stage acquisition modules, the N fermentation stage acquisition modules are used to determine yeast varieties and expected fermentation duration according to the product design indexes, and divide the expected fermentation duration into fermentation stages according to preset time nodes to generate N fermentation stages, wherein N is an integer greater than 3; an expected index sequence acquisition module, the expected index sequence acquisition module is used to perform expected product traceability for the N fermentation stages based on the product design indexes to generate an expected index sequence, wherein the expected index sequence includes N expected indexes, wherein the expected indexes include expected alcohol content, expected wort concentration, and expected chromaticity; a fermentation control parameter acquisition module, the fermentation control parameter acquisition module is used to acquire fermentation control parameters of the beer fermentation device, and determine parameter adjustment thresholds, wherein the fermentation control parameters include temperature, pH value, and yeast quantity; optimal fermentation A control parameter sequence acquisition module, wherein the optimal fermentation control parameter sequence acquisition module is used to optimize the fermentation control parameters in sequence in the parameter adjustment threshold according to the expected indicator sequence to generate an optimal fermentation control parameter sequence; a real-time stage indicator data set acquisition module, wherein the real-time stage indicator data set acquisition module is used to control the beer fermentation device to perform beer fermentation based on the yeast variety and the optimal fermentation control parameter sequence, and collect indicator data of the wort solution according to preset monitoring nodes to obtain a real-time stage indicator data set, wherein the real-time stage indicator data includes real-time alcohol content, real-time wort concentration and real-time chromaticity; a stage compensation data acquisition module, wherein the stage compensation data acquisition module is used to generate a stage indicator deviation value based on the expected indicator sequence and the real-time stage indicator data set, and obtain stage compensation data according to the stage indicator deviation value; a beer fermentation control module, wherein the beer fermentation control module is used to compensate and optimize the optimal fermentation control parameter sequence according to the stage compensation data, and perform beer fermentation control according to the optimal compensation fermentation control parameter sequence.

[0007] One or more technical solutions provided in the present disclosure have at least the following technical effects or advantages: according to the method adopted in the present disclosure, by obtaining product design indicators of beer, wherein the product design indicators include alcohol content, wort concentration, and chromaticity; determining the yeast variety and expected fermentation time according to the product design indicators, and dividing the expected fermentation time into fermentation stages according to preset time nodes to generate N fermentation stages, wherein N is an integer greater than 3; tracing the expected products of the N fermentation stages based on the product design indicators to generate an expected indicator sequence, wherein the expected indicator sequence includes N expected indicators, wherein the expected indicators include expected alcohol content, expected wort concentration, and expected chromaticity; obtaining fermentation control parameters of a beer fermentation device, and determining a parameter adjustment threshold, wherein the fermentation control parameters include temperature, pH value, and yeast quantity; according to the expected indicator sequence, the fermentation control parameters are sequentially adjusted in the parameter adjustment threshold. The method comprises the steps of: optimizing the control parameters according to the yeast variety and the optimal fermentation control parameter sequence to generate an optimal fermentation control parameter sequence; controlling the beer fermentation device to perform beer fermentation based on the yeast variety and the optimal fermentation control parameter sequence, and collecting indicator data of the wort solution according to the preset monitoring nodes to obtain a real-time stage indicator data set, wherein the real-time stage indicator data includes real-time alcohol content, real-time wort concentration and real-time chromaticity; generating a stage indicator deviation value based on the expected indicator sequence and the real-time stage indicator data set, and obtaining stage compensation data according to the stage indicator deviation value; compensating and optimizing the optimal fermentation control parameter sequence according to the stage compensation data, and controlling beer fermentation according to the optimal compensated fermentation control parameter sequence, thereby solving the technical problem that the control accuracy of beer fermentation is insufficient and the beer fermentation effect is poor in the prior art, and achieving the technical effect of improving the accuracy and adaptability of beer fermentation control and improving the quality of beer fermentation.

[0008] It should be understood that the contents described in this section are not intended to indicate the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the present disclosure or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0010] Figure 1 A schematic diagram of a flow chart of a beer fermentation device optimization control method provided in an embodiment of the present disclosure;

[0011] Figure 2A schematic diagram of a flow chart of generating an expected indicator sequence in a beer fermentation device optimization control method according to an embodiment of the present disclosure;

[0012] Figure 3 A schematic structural diagram of an optimization control system for a beer fermentation device provided in an embodiment of the present disclosure.

[0013] Explanation of the accompanying drawings: product design index acquisition module 11, N fermentation stage acquisition module 12, expected index sequence acquisition module 13, fermentation control parameter acquisition module 14, optimal fermentation control parameter sequence acquisition module 15, real-time stage index data set acquisition module 16, stage compensation data acquisition module 17, beer fermentation control module 18. DETAILED DESCRIPTION

[0014] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0015] Embodiment 1

[0016] The present invention provides a method for optimizing the control of a beer fermentation device. Figure 1 Illustratively, the method comprises:

[0017] The method provided in the embodiment of the present disclosure includes:

[0018] Obtaining product design indicators of beer, wherein the product design indicators include alcohol content, wort concentration, and color;

[0019] Specifically, obtain the product design indicators of beer. Among them, the product design indicators include alcohol content, wort concentration, and chromaticity. Furthermore, the alcohol content is the volume percentage of ethanol in beer, expressed as a volume ratio at 20 degrees Celsius. For example, 50-degree beer means that at 20 degrees Celsius, 100 milliliters of beer contains 50 milliliters of ethanol. Wort concentration is used to measure the content of fermentable sugars before fermentation, and the wort concentration is the sugar content of malt in the raw materials when fermentation begins. Chromaticity is used to describe the color of beer, ranging from 1 to 40, and the higher the value, the darker the beer color.

[0020] Determine the yeast variety and expected fermentation time according to the product design index, and divide the expected fermentation time into fermentation stages according to preset time nodes to generate N fermentation stages, where N is an integer greater than 3;

[0021] Specifically, according to the alcohol content, wort concentration and chromaticity of the product design indicators, the yeast variety is determined, and the expected fermentation time of the yeast is determined. Among them, the yeast variety is the yeast variety used for brewing wine. The expected fermentation time is the time for yeast fermentation to make wine. Further, according to the preset time node, the expected fermentation time is divided into fermentation stages to generate N fermentation stages, wherein at least 3 fermentation stages are divided, and N is an integer greater than 3. For example, if the expected fermentation time is 12 hours, every 4 hours is divided into a preset time node. Furthermore, the preset time node is obtained by a technician in this field according to the actual situation.

[0022] Based on the product design indicators, the N fermentation stages are traced for expected products to generate an expected indicator sequence, wherein the expected indicator sequence includes N expected indicators, wherein the expected indicators include expected alcohol content, expected wort concentration, and expected color;

[0023] Specifically, a historical beer fermentation data set that meets the product design indicators is obtained. The historical beer fermentation data set is matched with N fermentation stages in sequence to obtain N historical fermentation stage data sets. Mean processing is performed on the N historical fermentation stage data sets respectively, and each mean calculation result is obtained as the expected indicator of each corresponding fermentation stage, thereby obtaining N expected indicators. An expected indicator sequence is constructed for the N expected indicators in the order of the fermentation stages. Among them, the expected indicators include expected alcohol content, expected wort concentration and expected color.

[0024] Acquire fermentation control parameters of a beer fermentation device and determine parameter adjustment thresholds, wherein the fermentation control parameters include temperature, pH value, and yeast quantity;

[0025] Specifically, according to the historical fermentation records, the fermentation control parameters of the beer fermentation device are obtained, and the parameter adjustment threshold is determined. Among them, the fermentation control parameters include temperature, pH value, and yeast quantity. The parameter adjustment threshold includes a temperature parameter adjustment threshold, a pH parameter adjustment threshold, and a yeast quantity parameter adjustment threshold. For example, the temperature parameter adjustment threshold is 28 to 30 degrees Celsius, the pH parameter adjustment threshold is 3 to 6, and the yeast quantity parameter adjustment threshold is 20 million to 30 million per milliliter.

[0026] According to the expected indicator sequence, the fermentation control parameters are optimized in sequence in the parameter adjustment thresholds to generate an optimal fermentation control parameter sequence;

[0027] Specifically, N beer quality evaluation functions are constructed according to the N expected indicators of the expected indicator sequence. The fermentation control parameters are randomly selected in each fermentation stage in the parameter adjustment threshold, and the fermentation control parameters are input into the beer fermentation predictor as input data to predict the fermentation situation, that is, the fermentation index. The fermentation index is evaluated by the beer quality evaluation function corresponding to the fermentation stage to obtain the evaluation result. The input data fermentation index when the evaluation result that meets the output is the highest is obtained, and the corresponding fermentation control parameter is obtained as the optimal fermentation control parameter. The N optimal fermentation control parameters of the N fermentation stages are used to construct the optimal fermentation control parameter sequence in the order of the fermentation stages.

[0028] Based on the yeast variety and the optimal fermentation control parameter sequence, the beer fermentation device is controlled to perform beer fermentation, and index data of the wort solution is collected according to the preset monitoring nodes to obtain a real-time stage index data set, wherein the real-time stage index data includes real-time alcohol content, real-time wort concentration and real-time chromaticity;

[0029] Specifically, based on the yeast variety and the optimal fermentation control parameter sequence, the optimal fermentation control parameter sequence is input to control the beer fermentation device to perform beer fermentation. N preset monitoring nodes for N fermentation stages are set, that is, the actual monitoring time is set, and the wort solution is subjected to index data collection through the corresponding sampling instrument to obtain N real-time stage index data. Then, a set of real-time stage index data for N fermentation stages is obtained, wherein the real-time stage index data includes real-time alcohol content, real-time wort concentration, and real-time chromaticity.

[0030] Generate a stage indicator deviation value based on the expected indicator sequence and the real-time stage indicator data set, and obtain stage compensation data according to the stage indicator deviation value;

[0031] Specifically, extract the real-time stage indicator data set of N fermentation stages in the real-time stage indicator data set. Extract the expected indicator sequence of N fermentation stages in the expected indicator sequence. Perform a difference comparison between the expected indicator sequence and the real-time stage indicator data set to obtain N stage indicator deviation values. Among them, the N stage indicator deviation values ​​include N alcohol content deviation values, N wort concentration deviation values, and N chromaticity deviation values. Further, obtain a preset indicator deviation threshold. Among them, the preset indicator deviation threshold is set based on the quality requirement accuracy and the indicator weight coefficient. When the quality requirement accuracy is greater, the preset indicator deviation threshold is smaller. When the indicator weight coefficient is greater, the preset indicator deviation threshold is smaller. Further, according to the alcohol content deviation threshold, wort concentration deviation threshold, and chromaticity deviation threshold of the preset indicator deviation threshold, the N alcohol content deviation values, the N wort concentration deviation values, and the N chromaticity deviation values ​​are judged to obtain a judgment result. Further, when there are N alcohol content deviation values ​​that do not meet the alcohol content deviation threshold or N wort concentration deviation values ​​that do not meet the wort concentration deviation threshold or N chromaticity deviation values ​​that do not meet any one of the chromaticity deviation thresholds, or when there are N alcohol content deviation values ​​that do not meet the alcohol content deviation threshold and N wort concentration deviation values ​​that do not meet the wort concentration deviation threshold and N chromaticity deviation values ​​that do not meet all of the chromaticity deviation thresholds, then the index deviation values ​​that do not meet the deviation thresholds are saved to obtain the stage index deviation values. Further, the historical index deviation values ​​and historical qualified compensation data in the historical beer fermentation control records are obtained, the compensation analysis model is trained, and the stage index deviation values ​​are input into the compensation analysis model for stage data compensation to obtain stage compensation data.

[0032] The optimal fermentation control parameter sequence is compensated and optimized according to the stage compensation data, and beer fermentation control is performed according to the optimal compensation fermentation control parameter sequence.

[0033] Specifically, when there is stage compensation data, the optimal fermentation control parameters of the adjacent stages in the optimal fermentation control parameter sequence are compensated according to the stage compensation data to generate the stage optimal compensation fermentation control parameters. The adjacent stage is the optimal fermentation control parameters of the next fermentation stage corresponding to the fermentation stage with the stage compensation data, so as to avoid the indicator deviation value of the previous fermentation stage from having too great an impact on the next fermentation stage. Furthermore, the indicator deviation value of each next fermentation stage of the optimal fermentation control parameter sequence is compensated according to the stage optimal compensation fermentation control parameter, and the same stage is replaced after compensation to generate the optimal compensation fermentation control parameter sequence.

[0034] Among them, this embodiment can solve the technical problem of insufficient control accuracy of beer fermentation in the prior art, resulting in poor beer fermentation effect, and achieve the technical effect of improving the accuracy and adaptability of beer fermentation control and improving the quality of beer fermentation.

[0035] The method provided in the embodiment of the present disclosure also includes:

[0036] Acquire a historical beer fermentation data set that meets the product design index;

[0037] Dividing the historical beer fermentation data set into stages according to the N fermentation stages to obtain N historical fermentation stage data sets;

[0038] The N historical fermentation stage data sets are processed in turn by averaging, and the mean calculation results are used as the expected index of each fermentation stage to obtain N expected indexes, and the expected index sequence is constructed according to the order of the fermentation stages.

[0039] like Figure 2 As shown, specifically, based on big data, a search is performed with product design indicators as constraints to obtain a historical beer fermentation data set that meets the product design indicators.

[0040] Furthermore, the historical beer fermentation data set is matched with the N fermentation stages in sequence to complete the stage data division of the historical beer fermentation data set, and obtain N historical fermentation stage data sets.

[0041] Furthermore, the data of each historical fermentation stage data set in the N historical fermentation stage data sets are processed by averaging, and each mean calculation result is obtained as the expected index of each corresponding fermentation stage, thereby obtaining N expected indexes. An expected index sequence is constructed for the N expected indexes according to the order of the fermentation stages.

[0042] Among them, based on the product design indicators, the expected product traceability is carried out for N fermentation stages to generate an expected indicator sequence. The indicators of each stage are met according to the expected indicator sequence to accurately achieve the expected indicators.

[0043] The method provided in the embodiment of the present disclosure also includes:

[0044] Selecting a first expected indicator of the first fermentation stage from the expected indicator sequence, and constructing a first beer quality evaluation function according to the first expected indicator, wherein the first expected indicator includes a first expected alcohol content, a first expected wort concentration, and a first expected chromaticity;

[0045] Randomly selecting a first fermentation control parameter from the parameter adjustment threshold, the first fermentation control parameter including a first temperature, a first pH value, and a first yeast quantity, and performing a stage index prediction on the first fermentation control parameter by a beer fermentation predictor to obtain a first fermentation index, the first fermentation index including a first predicted alcohol content, a first predicted wort concentration, and a first predicted chromaticity;

[0046] randomly selecting a second fermentation control parameter from the parameter adjustment threshold again, and obtaining a second fermentation index;

[0047] Evaluate the first fermentation index and the second fermentation index according to the first beer quality evaluation function to obtain a first evaluation result and a second evaluation result;

[0048] When the first evaluation result is greater than the second evaluation result, the first fermentation control parameter is used as the current optimal fermentation control parameter; when the first evaluation result is less than or equal to the second evaluation result, the second fermentation control parameter is used as the current optimal fermentation control parameter according to probability, wherein the probability decreases as the number of optimization searches increases;

[0049] Iterate the optimization in sequence until the current optimization times meet the preset optimization times threshold, and then output the current optimal fermentation control parameter as the first optimal fermentation control parameter;

[0050] The optimal fermentation control parameter sequence is obtained based on the first optimal fermentation control parameter.

[0051] Specifically, the first fermentation stage is the first fermentation stage in the N fermentation stages arranged in order of the fermentation stages. The first expected indicator of the first fermentation stage is selected from the expected indicator sequence. The first expected indicator includes a first expected alcohol content, a first expected wort concentration, and a first expected chromaticity. Further, a first beer quality evaluation function is constructed according to the first expected indicator.

[0052] Further, a first fermentation control parameter is randomly selected from the parameter adjustment threshold. The first fermentation control parameter includes a first temperature, a first pH value, and a first yeast quantity. Further, the beer fermentation predictor is used to predict the predicted fermentation condition of the fermentation stage through the fermentation control parameter. The first fermentation control parameter is obtained through the beer fermentation predictor, and the fermentation condition is predicted as a first fermentation index. The first fermentation index includes a first predicted alcohol content, a first predicted wort concentration, and a first predicted chromaticity.

[0053] Furthermore, a second fermentation control parameter is randomly selected from the parameter adjustment threshold again, and the second fermentation control parameter is input into the beer fermentation predictor to predict the fermentation situation and obtain a second fermentation index, wherein the second fermentation index includes a second predicted alcohol content, a second predicted wort concentration and a second predicted chromaticity.

[0054] Furthermore, the first fermentation index and the second fermentation index are evaluated respectively according to the first beer quality evaluation function of the first fermentation stage to determine whether the first fermentation index and the second fermentation index meet the requirements of the first fermentation stage, thereby obtaining a first evaluation result and a second evaluation result.

[0055] Furthermore, when the first evaluation result is greater than the second evaluation result, it means that for the first fermentation stage, the first fermentation index of the first evaluation result is better than the second fermentation index of the second evaluation result, and the first fermentation control parameter is used as the current optimal fermentation control parameter of the first fermentation stage. When the first evaluation result is less than or equal to the second evaluation result, it means that the second fermentation index of the second evaluation result is better than the first fermentation index of the first evaluation result, and the second fermentation control parameter is used as the current optimal fermentation control parameter according to probability. Among them, the probability is to improve the global search ability of the optimization in the early stage and to improve the accuracy of the optimization in the later stage. The probability decreases with the increase of the number of optimizations.

[0056] Furthermore, a fermentation control parameter is randomly selected from the parameter adjustment threshold, and the optimal fermentation control parameter is iteratively optimized in sequence, until the current optimization number meets the preset optimization number threshold, and the current optimal fermentation control parameter is output as the first optimal fermentation control parameter of the first fermentation stage. The preset optimization number threshold is obtained by a technician in this field according to the actual situation.

[0057] Furthermore, based on the method for obtaining the first optimal fermentation control parameter in the first fermentation stage, N optimal fermentation control parameters corresponding to the N fermentation stages are obtained, and the N optimal fermentation control parameters are used to construct an optimal fermentation control parameter sequence in the order of the fermentation stages.

[0058] Among them, according to the expected indicator sequence, the fermentation control parameters are optimized in turn in the parameter adjustment threshold value to generate the optimal fermentation control parameter sequence to improve the accuracy of fermentation control and improve the fermentation quality.

[0059] The method provided in the embodiment of the present disclosure also includes:

[0060] The first beer quality evaluation function expression is:

[0061] ;

[0062] in, is the beer quality of the i-th fermentation control parameter, , , is the weight coefficient of alcohol content, wort concentration and color, The first expected alcohol content, is the predicted alcohol content of the ith fermentation control parameter, is the first expected wort concentration, is the predicted wort concentration of the ith fermentation control parameter, is the first expected chromaticity, is the predicted color of the ith fermentation control parameter.

[0063] Specifically, the first beer quality evaluation function expression is:

[0064] ;

[0065] in, is the beer quality of the i-th fermentation control parameter, , , is the weight coefficient of alcohol content, wort concentration and color, The first expected alcohol content, is the predicted alcohol content of the ith fermentation control parameter, is the first expected wort concentration, is the predicted wort concentration of the ith fermentation control parameter, is the first expected chromaticity, is the predicted color of the i-th fermentation control parameter. Further, when the first expected alcohol content Prediction of alcohol content with the i-th fermentation control parameter The smaller the difference, the higher the first expected wort concentration. Prediction of wort concentration with the i fermentation control parameters The smaller the difference, the first expected chromaticity Prediction of chromaticity with the i fermentation control parameters The smaller the difference is, the better the beer quality of the i-th fermentation control parameter is. The smaller it is, the better the fermentation control parameters are, the more the beer quality meets the standards, and the better the beer quality is.

[0066] Among them, constructing the first beer quality evaluation function according to the first fermentation index can improve the efficiency and accuracy of judging the beer quality.

[0067] The method provided in the embodiment of the present disclosure also includes:

[0068] Setting preset monitoring nodes based on the N fermentation stages to obtain N preset monitoring nodes;

[0069] At N preset monitoring nodes, the wort solution is sampled and tested by an alcohol measurement kit, a pH meter, and a colorimeter to obtain N real-time stage indicator data, and the real-time stage indicator data set is generated based on the N real-time stage indicator data.

[0070] Specifically, the actual monitoring time is set based on the N fermentation stages, that is, the preset monitoring nodes are set, and N preset monitoring nodes are obtained.

[0071] Furthermore, at N preset monitoring nodes, the alcohol content measurement kit is used to sample and test the wort solution in N fermentation stages to obtain real-time alcohol content, the pH meter is used to sample and test the wort solution to obtain real-time wort concentration, and the colorimeter is used to sample and test the wort solution to obtain real-time chromaticity. N real-time stage indicator data are obtained based on the real-time alcohol content, real-time wort concentration, and real-time chromaticity, and a real-time stage indicator data set is generated based on the combination of the N real-time stage indicator data.

[0072] Among them, fermentation is controlled according to the optimal fermentation control parameter sequence, and indicator data of the wort solution is collected according to the preset monitoring nodes to obtain a real-time stage indicator data set, thereby improving the data accuracy of the real-time stage indicator data set.

[0073] The method provided in the embodiment of the present disclosure also includes:

[0074] Selecting first real-time stage indicator data from the real-time stage indicator data set, and obtaining a first stage indicator deviation value according to the first real-time stage indicator data and a first expected indicator in the expected indicator sequence, wherein the first stage indicator deviation value includes a first alcohol content deviation value, a first wort concentration deviation value, and a first chromaticity deviation value;

[0075] Obtaining a preset indicator deviation threshold, and judging the first alcohol content deviation value, the first wort concentration deviation value, and the first chromaticity deviation value according to the preset indicator deviation threshold, wherein the preset indicator deviation threshold includes an alcohol content deviation threshold, a wort concentration deviation threshold, and a chromaticity deviation threshold;

[0076] When the first alcohol content deviation value does not satisfy the alcohol content deviation threshold value and / or the first wort concentration deviation value does not satisfy the wort concentration deviation threshold value and / or the first chromaticity deviation value does not satisfy the chromaticity deviation threshold value, the indicator deviation value that does not satisfy the deviation threshold value is saved to obtain a first indicator deviation value;

[0077] The stage data is compensated according to the first indicator deviation value to obtain the stage compensation data.

[0078] Specifically, the first real-time stage indicator data of the first fermentation stage is selected from the real-time stage indicator data set, wherein the first real-time stage indicator data includes a first real-time alcohol content, a first real-time wort concentration and a first real-time chromaticity.

[0079] Further, the first expected indicator of the first fermentation stage in the expected indicator sequence is obtained. Further, the first stage indicator deviation value is obtained by performing a difference comparison between the first real-time stage indicator data and the first expected indicator. The first stage indicator deviation value includes a first alcohol content deviation value, a first wort concentration deviation value, and a first chromaticity deviation value.

[0080] Further, a preset indicator deviation threshold is obtained. The preset indicator deviation threshold is set based on the quality requirement accuracy and the indicator weight coefficient. When the quality requirement accuracy is greater, the preset indicator deviation threshold is smaller. When the indicator weight coefficient is greater, the preset indicator deviation threshold is smaller. Further, the first alcohol deviation value, the first wort concentration deviation value, and the first chromaticity deviation value are judged according to the alcohol deviation threshold, the wort concentration deviation threshold, and the chromaticity deviation threshold of the preset indicator deviation threshold to obtain a judgment result.

[0081] Further, when there is a first alcohol content deviation value that does not satisfy the alcohol content deviation threshold or the first wort concentration deviation value that does not satisfy the wort concentration deviation threshold or the first chromaticity deviation value that does not satisfy any one of the chromaticity deviation threshold, or when there is a first alcohol content deviation value that does not satisfy the alcohol content deviation threshold and the first wort concentration deviation value does not satisfy the wort concentration deviation threshold and the first chromaticity deviation value does not satisfy all of the chromaticity deviation thresholds, the indicator deviation value that does not satisfy the deviation threshold is saved to obtain the first indicator deviation value.

[0082] Furthermore, according to the historical beer fermentation control records, historical indicator deviation values ​​and historical qualified compensation data are obtained, a compensation analysis model is trained, and the first indicator deviation value is input into the compensation analysis model to perform stage data compensation to obtain stage compensation data.

[0083] Among them, the stage indicator deviation value is generated based on the expected indicator sequence and the real-time stage indicator data set, and the stage compensation data is obtained according to the stage indicator deviation value, which can improve the accuracy of beer fermentation control and improve the technical effect of beer fermentation quality.

[0084] The method provided in the embodiment of the present disclosure also includes:

[0085] Acquire a plurality of historical indicator deviation values ​​and a plurality of historical qualified compensation data, wherein the historical indicator deviation values ​​and the historical qualified compensation data have a corresponding relationship;

[0086] Constructing a compensation analysis model based on a BP neural network, and using the multiple historical indicator deviation values ​​and the multiple historical qualified compensation data as training data to perform supervised training on the compensation analysis model, so as to obtain a compensation analysis model in a converged state;

[0087] The first indicator deviation value is input into the compensation analysis model to obtain first compensation data, and the stage compensation data is obtained based on the first compensation data.

[0088] Specifically, according to the historical beer fermentation records, multiple historical indicator deviation values ​​and multiple historical qualified compensation data are obtained, and the historical indicator deviation values ​​and the historical qualified compensation data have a corresponding relationship. The multiple historical indicator deviation values ​​include one or more deviations. The multiple historical qualified compensation data are qualified records of data compensation for the multiple historical indicator deviation values.

[0089] Further, a compensation analysis model is constructed based on a BP neural network. The compensation analysis model is a neural network model that can be continuously iteratively optimized in machine learning, and is obtained by training multiple historical indicator deviation values ​​and multiple historical qualified compensation data. First, a first historical indicator deviation value is randomly selected from multiple historical indicator deviation values ​​and multiple historical qualified compensation data as the first training data to perform supervised training on the compensation analysis model, and the first output result of the compensation analysis model, the first historical qualified compensation data, is obtained. Then, the first historical qualified compensation data of the first output result is compared with the first historical indicator deviation value of the first training data. When the comparison results are consistent, supervised training of the second set of training data is performed. When the comparison results are inconsistent, the data deviation between the first output result and the first training data is calculated, and the weight parameter of the compensation analysis model is optimized according to the data deviation, and then supervised training of the second training data is performed. Iterative supervised training is performed through the training data until the output result of the compensation analysis model tends to converge, and then the output of the compensation analysis model is verified through the verification data set to obtain a preset verification accuracy index. For example: the verification accuracy index is set to an output result accuracy of 95%. When the accuracy of the output result of the compensation analysis model is greater than the preset verification accuracy index, a trained compensation analysis model is obtained.

[0090] Furthermore, the first indicator deviation value is input into the compensation analysis model, the first compensation data is obtained through the output of the compensation analysis model, and multiple compensation data are obtained based on the method for obtaining the first compensation data, and the multiple compensation data are combined in the order of fermentation stages to obtain stage compensation data.

[0091] Among them, stage data compensation is performed according to the first indicator deviation value to obtain stage compensation data, which can improve the accuracy of beer fermentation control.

[0092] The method provided in the embodiment of the present disclosure also includes:

[0093] When there is stage compensation data, the optimal fermentation control parameters of adjacent stages in the optimal fermentation control parameter sequence are compensated according to the stage compensation data to generate the stage optimal compensated fermentation control parameters;

[0094] The optimal fermentation control parameter sequence is updated according to the optimal compensatory fermentation control parameter of the stage to generate an optimal compensatory fermentation control parameter sequence.

[0095] Specifically, when there is stage compensation data, the optimal fermentation control parameters of the adjacent stages in the optimal fermentation control parameter sequence are compensated according to the stage compensation data to generate the stage optimal compensated fermentation control parameters. The adjacent stage is the optimal fermentation control parameters of the next fermentation stage corresponding to the fermentation stage with the stage compensation data, and the compensation is performed to avoid the indicator deviation value of the previous fermentation stage from having too great an impact on the next fermentation stage. Furthermore, the compensation method is to add the values ​​to the optimal control parameters of the original stage to generate the stage optimal compensated fermentation control parameters.

[0096] Furthermore, the index deviation value of each next fermentation stage of the optimal fermentation control parameter sequence is compensated according to the optimal compensatory fermentation control parameter of the stage, and after compensation, the index deviation value is replaced in the same stage to generate the optimal compensatory fermentation control parameter sequence.

[0097] Among them, the optimal fermentation control parameter sequence is compensated and optimized according to the stage compensation data to improve the accuracy of beer fermentation control.

[0098] Embodiment 2

[0099] Based on the same inventive concept as the above-mentioned beer fermentation device optimization control method, Figure 3 For illustration, the present disclosure also provides a beer fermentation device optimization control system, the system comprising:

[0100] A product design index obtaining module 11, wherein the product design index obtaining module 11 is used to obtain product design indexes of beer, wherein the product design indexes include alcohol content, wort concentration, and chroma;

[0101] N fermentation stage acquisition modules 12, the N fermentation stage acquisition modules 12 are used to determine the yeast variety and the expected fermentation time according to the product design indicators, and divide the expected fermentation time into fermentation stages according to preset time nodes to generate N fermentation stages, where N is an integer greater than 3;

[0102] An expected indicator sequence acquisition module 13, the expected indicator sequence acquisition module 13 is used to perform expected product traceability on the N fermentation stages based on the product design indicators, and generate an expected indicator sequence, wherein the expected indicator sequence includes N expected indicators, wherein the expected indicators include expected alcohol content, expected wort concentration, and expected color;

[0103] A fermentation control parameter acquisition module 14, which is used to obtain fermentation control parameters of the beer fermentation device and determine parameter adjustment thresholds, wherein the fermentation control parameters include temperature, pH value, and yeast quantity;

[0104] An optimal fermentation control parameter sequence obtaining module 15, wherein the optimal fermentation control parameter sequence obtaining module 15 is used to optimize the fermentation control parameters in the parameter adjustment thresholds in turn according to the expected indicator sequence to generate an optimal fermentation control parameter sequence;

[0105] A real-time stage indicator data set acquisition module 16, the real-time stage indicator data set acquisition module 16 is used to control the beer fermentation device to perform beer fermentation based on the yeast variety and the optimal fermentation control parameter sequence, and collect indicator data of the wort solution according to the preset monitoring nodes to obtain a real-time stage indicator data set, wherein the real-time stage indicator data includes real-time alcohol content, real-time wort concentration and real-time chromaticity;

[0106] A phase compensation data acquisition module 17, wherein the phase compensation data acquisition module 17 is used to generate a phase indicator deviation value based on the expected indicator sequence and the real-time phase indicator data set, and obtain phase compensation data according to the phase indicator deviation value;

[0107] The beer fermentation control module 18 is used to perform compensation optimization on the optimal fermentation control parameter sequence according to the stage compensation data, and perform beer fermentation control according to the optimal compensation fermentation control parameter sequence.

[0108] Furthermore, the system further comprises:

[0109] A historical beer fermentation data set acquisition module, wherein the historical beer fermentation data set acquisition module is used to acquire a historical beer fermentation data set that meets the product design index;

[0110] N historical fermentation stage data set acquisition modules, the N historical fermentation stage data set acquisition modules are used to divide the historical beer fermentation data set into stages according to the N fermentation stages to obtain N historical fermentation stage data sets;

[0111] N expected indicator acquisition modules are used to perform mean processing on the N historical fermentation stage data sets in sequence, and use the mean calculation results as the expected indicators of each fermentation stage to obtain N expected indicators, and construct the expected indicator sequence according to the order of the fermentation stages.

[0112] Furthermore, the system further comprises:

[0113] a first expected indicator acquisition module, the first expected indicator acquisition module is used to select a first expected indicator of the first fermentation stage from the expected indicator sequence, and construct a first beer quality evaluation function according to the first expected indicator, wherein the first expected indicator includes a first expected alcohol content, a first expected wort concentration, and a first expected chromaticity;

[0114] A first fermentation index obtaining module, the first fermentation index obtaining module is used to randomly select a first fermentation control parameter from the parameter adjustment threshold, the first fermentation control parameter includes a first temperature, a first pH value, and a first yeast quantity, and perform a stage index prediction on the first fermentation control parameter through a beer fermentation predictor to obtain a first fermentation index, the first fermentation index includes a first predicted alcohol content, a first predicted wort concentration, and a first predicted chromaticity;

[0115] a second fermentation index obtaining module, wherein the second fermentation index obtaining module is used to randomly select a second fermentation control parameter from the parameter adjustment threshold again and obtain a second fermentation index;

[0116] A first evaluation result obtaining module, wherein the first evaluation result obtaining module is used to evaluate the first fermentation index and the second fermentation index according to the first beer quality evaluation function to obtain a first evaluation result and a second evaluation result;

[0117] a current optimal fermentation control parameter obtaining module, wherein the current optimal fermentation control parameter obtaining module is used to use the first fermentation control parameter as the current optimal fermentation control parameter when the first evaluation result is greater than the second evaluation result, and use the second fermentation control parameter as the current optimal fermentation control parameter according to probability when the first evaluation result is less than or equal to the second evaluation result, wherein the probability decreases as the number of optimization searches increases;

[0118] a first optimal fermentation control parameter obtaining module, wherein the first optimal fermentation control parameter obtaining module is used to perform iterative optimization in sequence until the current optimization number meets a preset optimization number threshold, and then output the current optimal fermentation control parameter as the first optimal fermentation control parameter;

[0119] An optimal fermentation control parameter sequence obtaining module is used to obtain the optimal fermentation control parameter sequence based on the first optimal fermentation control parameter.

[0120] Furthermore, the system further comprises:

[0121] The first beer quality evaluation function obtaining module is used for the first beer quality evaluation function expression as follows:

[0122] ;

[0123] A first beer quality evaluation function processing module, wherein the first beer quality evaluation function processing module is used in: is the beer quality of the i-th fermentation control parameter, , , is the weight coefficient of alcohol content, wort concentration and color, The first expected alcohol content, is the predicted alcohol content of the ith fermentation control parameter, is the first expected wort concentration, is the predicted wort concentration of the ith fermentation control parameter, is the first expected chromaticity, is the predicted color of the ith fermentation control parameter.

[0124] Furthermore, the system further comprises:

[0125] N preset monitoring node acquisition modules, the N preset monitoring node acquisition modules are used to set preset monitoring nodes based on the N fermentation stages to obtain N preset monitoring nodes;

[0126] N real-time stage indicator data acquisition modules are used to sample and detect the wort solution under N preset monitoring nodes through an alcohol measurement kit, a pH meter, and a colorimeter to obtain N real-time stage indicator data, and generate the real-time stage indicator data set according to the N real-time stage indicator data.

[0127] Furthermore, the system further comprises:

[0128] A first real-time stage indicator data acquisition module, the first real-time stage indicator data acquisition module is used to select first real-time stage indicator data from the real-time stage indicator data set, and obtain a first stage indicator deviation value according to the first real-time stage indicator data and a first expected indicator in the expected indicator sequence, wherein the first stage indicator deviation value includes a first alcohol content deviation value, a first wort concentration deviation value, and a first chromaticity deviation value;

[0129] A preset indicator deviation threshold obtaining module, the preset indicator deviation threshold obtaining module is used to obtain a preset indicator deviation threshold, and judge the first alcohol content deviation value, the first wort concentration deviation value, and the first chromaticity deviation value according to the preset indicator deviation threshold, the preset indicator deviation threshold includes an alcohol content deviation threshold, a wort concentration deviation threshold, and a chromaticity deviation threshold;

[0130] A first indicator deviation value obtaining module, wherein the first indicator deviation value obtaining module is used for saving the indicator deviation value that does not meet the deviation threshold value to obtain a first indicator deviation value when the first alcohol content deviation value does not meet the alcohol content deviation threshold value and / or the first wort concentration deviation value does not meet the wort concentration deviation threshold value and / or the first chromaticity deviation value does not meet the chromaticity deviation threshold value;

[0131] A stage data compensation module, wherein the stage data compensation module is used to perform stage data compensation according to the first indicator deviation value to obtain the stage compensation data.

[0132] Furthermore, the system further comprises:

[0133] A historical qualified compensation data acquisition module, wherein the historical qualified compensation data acquisition module is used to acquire a plurality of historical indicator deviation values ​​and a plurality of historical qualified compensation data, and the historical indicator deviation values ​​and the historical qualified compensation data have a corresponding relationship;

[0134] A compensation analysis model acquisition module, the compensation analysis model acquisition module is used to construct a compensation analysis model based on a BP neural network, and use the multiple historical indicator deviation values ​​and the multiple historical qualified compensation data as training data to perform supervised training on the compensation analysis model to obtain a compensation analysis model in a converged state;

[0135] A first compensation data acquisition module is used to input the first indicator deviation value into the compensation analysis model to obtain first compensation data, and obtain the stage compensation data based on the first compensation data.

[0136] Furthermore, the system further comprises:

[0137] a module for obtaining optimal compensatory fermentation control parameters for a stage, wherein when there is stage compensation data, the module is used to compensate the optimal fermentation control parameters of adjacent stages in the optimal fermentation control parameter sequence according to the stage compensation data to generate the optimal compensatory fermentation control parameters for the stage;

[0138] The optimal compensatory fermentation control parameter sequence obtaining module is used to update the optimal fermentation control parameter sequence according to the optimal compensatory fermentation control parameters of the stage to generate the optimal compensatory fermentation control parameter sequence.

[0139] The specific example of the optimization control method of a beer fermentation device in the aforementioned embodiment 1 is also applicable to the optimization control system of a beer fermentation device in this embodiment. Through the aforementioned detailed description of the optimization control method of a beer fermentation device, those skilled in the art can clearly know the optimization control system of a beer fermentation device in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0140] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of this disclosure can be achieved, and this document is not limited here.

[0141] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A beer fermentation device optimization control method, characterized in that: The method comprises: Obtaining product design indicators of beer, wherein the product design indicators include alcohol content, wort concentration, and color; Determine the yeast variety and expected fermentation time according to the product design index, and divide the expected fermentation time into fermentation stages according to preset time nodes to generate N fermentation stages, where N is an integer greater than 3; Based on the product design indicators, the N fermentation stages are traced for expected products to generate an expected indicator sequence, wherein the expected indicator sequence includes N expected indicators, wherein the expected indicators include expected alcohol content, expected wort concentration, and expected color; Acquire fermentation control parameters of a beer fermentation device and determine parameter adjustment thresholds, wherein the fermentation control parameters include temperature, pH value, and yeast quantity; According to the expected indicator sequence, the fermentation control parameters are optimized in sequence in the parameter adjustment thresholds to generate an optimal fermentation control parameter sequence; Based on the yeast variety and the optimal fermentation control parameter sequence, the beer fermentation device is controlled to perform beer fermentation, and index data of the wort solution is collected according to the preset monitoring nodes to obtain a real-time stage index data set, wherein the real-time stage index data includes real-time alcohol content, real-time wort concentration and real-time chromaticity; Generate a stage indicator deviation value based on the expected indicator sequence and the real-time stage indicator data set, and obtain stage compensation data according to the stage indicator deviation value; The optimal fermentation control parameter sequence is compensated and optimized according to the stage compensation data, and beer fermentation control is performed according to the optimal compensation fermentation control parameter sequence.

2. The method according to claim 1, characterized in that The method of tracing the expected product source of the N fermentation stages based on the product design indicators to generate an expected indicator sequence further includes: Acquire a historical beer fermentation data set that meets the product design index; Dividing the historical beer fermentation data set into stages according to the N fermentation stages to obtain N historical fermentation stage data sets; The N historical fermentation stage data sets are processed in turn by averaging, and the mean calculation results are used as the expected index of each fermentation stage to obtain N expected indexes, and the expected index sequence is constructed according to the order of the fermentation stages.

3. The method according to claim 1, characterized in that The step of optimizing the fermentation control parameters in the parameter adjustment thresholds in turn according to the expected indicator sequence to generate an optimal fermentation control parameter sequence further includes: Selecting a first expected indicator of the first fermentation stage from the expected indicator sequence, and constructing a first beer quality evaluation function according to the first expected indicator, wherein the first expected indicator includes a first expected alcohol content, a first expected wort concentration, and a first expected chromaticity; Randomly selecting a first fermentation control parameter from the parameter adjustment threshold, the first fermentation control parameter including a first temperature, a first pH value, and a first yeast quantity, and performing a stage index prediction on the first fermentation control parameter by a beer fermentation predictor to obtain a first fermentation index, the first fermentation index including a first predicted alcohol content, a first predicted wort concentration, and a first predicted chromaticity; randomly selecting a second fermentation control parameter from the parameter adjustment threshold again, and obtaining a second fermentation index; Evaluate the first fermentation index and the second fermentation index according to the first beer quality evaluation function to obtain a first evaluation result and a second evaluation result; When the first evaluation result is greater than the second evaluation result, the first fermentation control parameter is used as the current optimal fermentation control parameter; when the first evaluation result is less than or equal to the second evaluation result, the second fermentation control parameter is used as the current optimal fermentation control parameter according to probability, wherein the probability decreases as the number of optimization searches increases; Iterate the optimization in sequence until the current optimization times meet the preset optimization times threshold, and then output the current optimal fermentation control parameter as the first optimal fermentation control parameter; The optimal fermentation control parameter sequence is obtained based on the first optimal fermentation control parameter.

4. The method according to claim 3, characterized in that The first beer quality evaluation function is constructed according to the first fermentation index, and further includes: The first beer quality evaluation function expression is: F(i)=w1|J-j i |+w2|M-m i |+w3|B-b i |; Among them, F(i) is the beer quality of the i-th fermentation control parameter, w1, w2, w3 are the weight coefficients of alcohol content, wort concentration, and chromaticity, J is the first expected alcohol content, j i is the predicted alcohol content of the i-th fermentation control parameter, M is the first expected wort concentration, m i is the predicted wort concentration of the i-th fermentation control parameter, B is the first expected color, b i is the predicted color of the ith fermentation control parameter.

5. The method according to claim 1, characterized in that The wort solution is subjected to index data collection according to preset monitoring nodes to obtain a real-time stage index data set, and further includes: Setting preset monitoring nodes based on the N fermentation stages to obtain N preset monitoring nodes; At N preset monitoring nodes, the wort solution is sampled and tested by an alcohol measurement kit, a pH meter, and a colorimeter to obtain N real-time stage indicator data, and the real-time stage indicator data set is generated based on the N real-time stage indicator data.

6. The method according to claim 1, characterized in that The generating of the stage indicator deviation value based on the expected indicator sequence and the real-time stage indicator data set, and obtaining the stage compensation data according to the stage indicator deviation value, further includes: Selecting first real-time stage indicator data from the real-time stage indicator data set, and obtaining a first stage indicator deviation value according to the first real-time stage indicator data and a first expected indicator in the expected indicator sequence, wherein the first stage indicator deviation value includes a first alcohol content deviation value, a first wort concentration deviation value, and a first chromaticity deviation value; Obtaining a preset indicator deviation threshold, and judging the first alcohol content deviation value, the first wort concentration deviation value, and the first chromaticity deviation value according to the preset indicator deviation threshold, wherein the preset indicator deviation threshold includes an alcohol content deviation threshold, a wort concentration deviation threshold, and a chromaticity deviation threshold; When the first alcohol content deviation value does not satisfy the alcohol content deviation threshold value and / or the first wort concentration deviation value does not satisfy the wort concentration deviation threshold value and / or the first chromaticity deviation value does not satisfy the chromaticity deviation threshold value, the indicator deviation value that does not satisfy the deviation threshold value is saved to obtain a first indicator deviation value; The stage data is compensated according to the first indicator deviation value to obtain the stage compensation data.

7. The method according to claim 6, characterized in that The step of performing stage data compensation according to the first indicator deviation value to obtain the stage compensation data further includes: Acquire a plurality of historical indicator deviation values ​​and a plurality of historical qualified compensation data, wherein the historical indicator deviation values ​​and the historical qualified compensation data have a corresponding relationship; Constructing a compensation analysis model based on a BP neural network, and using the multiple historical indicator deviation values ​​and the multiple historical qualified compensation data as training data to perform supervised training on the compensation analysis model, so as to obtain a compensation analysis model in a converged state; The first indicator deviation value is input into the compensation analysis model to obtain first compensation data, and the stage compensation data is obtained based on the first compensation data.

8. The method according to claim 1, characterized in that The compensation optimization of the optimal fermentation control parameter sequence according to the stage compensation data also includes: When there is stage compensation data, the optimal fermentation control parameters of adjacent stages in the optimal fermentation control parameter sequence are compensated according to the stage compensation data to generate the stage optimal compensated fermentation control parameters; The optimal fermentation control parameter sequence is updated according to the optimal compensatory fermentation control parameter of the stage to generate an optimal compensatory fermentation control parameter sequence.

9. A beer fermentation device optimization control system, characterized in that: A method for optimizing control of a beer fermentation device according to any one of claims 1 to 8, the system comprising: A product design index obtaining module, which is used to obtain product design indexes of beer, wherein the product design indexes include alcohol content, wort concentration, and chroma; N fermentation stage acquisition modules, the N fermentation stage acquisition modules are used to determine the yeast variety and the expected fermentation time according to the product design indicators, and divide the expected fermentation time into fermentation stages according to preset time nodes to generate N fermentation stages, where N is an integer greater than 3; An expected indicator sequence acquisition module, the expected indicator sequence acquisition module is used to perform expected product traceability on the N fermentation stages based on the product design indicators, and generate an expected indicator sequence, wherein the expected indicator sequence includes N expected indicators, wherein the expected indicators include expected alcohol content, expected wort concentration, and expected chromaticity; A fermentation control parameter acquisition module, which is used to obtain fermentation control parameters of the beer fermentation device and determine parameter adjustment thresholds, wherein the fermentation control parameters include temperature, pH value, and yeast quantity; An optimal fermentation control parameter sequence obtaining module, wherein the optimal fermentation control parameter sequence obtaining module is used to optimize the fermentation control parameters in sequence in the parameter adjustment threshold according to the expected indicator sequence to generate an optimal fermentation control parameter sequence; A real-time stage indicator data set acquisition module, the real-time stage indicator data set acquisition module is used to control the beer fermentation device to perform beer fermentation based on the yeast variety and the optimal fermentation control parameter sequence, and collect indicator data of the wort solution according to the preset monitoring nodes to obtain a real-time stage indicator data set, wherein the real-time stage indicator data includes real-time alcohol content, real-time wort concentration and real-time chromaticity; a stage compensation data acquisition module, the stage compensation data acquisition module being used to generate a stage indicator deviation value based on the expected indicator sequence and the real-time stage indicator data set, and to obtain stage compensation data according to the stage indicator deviation value; A beer fermentation control module is used to perform compensation optimization on the optimal fermentation control parameter sequence according to the stage compensation data, and perform beer fermentation control according to the optimal compensation fermentation control parameter sequence.

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