Remote intelligent beer fermentation monitoring platform based on Internet of Things

Abstract

The invention discloses a remote intelligent beer fermentation monitoring platform based on the Internet of Things, and relates to the technical field of fermentation control, a fermentation sample collection module is arranged to collect fermentation characteristic data and progress label data of a fermented beer sample in a fermentation test environment, and a progress model training module is arranged to train a progress prediction model; a progress judgment module is arranged to obtain the expected progress after the p moment output by the progress prediction model, and collect the actual fermentation progress after the p moment, and a fermentation regulation and control module constructs the state space, the action space and the reward function of a fermentation parameter decision model, and judges whether parameter regulation and control need to be performed based on the expected progress and the actual fermentation progress. If the parameters need to be regulated and controlled, generating an action decision of a subsequent fermentation process for the to-be-monitored fermentation tank through a fermentation parameter decision model; the automation and intelligence of the fermentation process are realized, the stability of the beer quality is improved, and the labor cost is reduced.

Description

Technical Field

[0001] The invention relates to the technical field of fermentation control, and in particular to a remote intelligent beer fermentation monitoring platform based on the Internet of Things. Background Art

[0002] With the rapid development of Internet of Things (IoT) technology, the traditional beer fermentation process is expected to achieve more intelligent and automated monitoring. Beer fermentation is a complex biochemical process involving the dynamic changes of multiple key parameters such as temperature, pressure, pH value, sugar content, etc., and even small fluctuations in these parameters will directly affect the quality of the final product.

[0003] In the traditional fermentation process, operators usually need to manually collect fermentation data regularly and conduct manual analysis and judgment. This is not only time-consuming and labor-intensive, but also difficult to ensure the real-time and accuracy of the data. Manual operation is also difficult to detect and regulate abnormal conditions in a timely manner, which may lead to the fermentation process being out of control and uneven product quality. In addition, the beer brewing industry has stringent requirements for the fermentation environment. It needs to be operated in a clean and closed fermentation environment. Manual operation inevitably increases the risk of contamination.

[0004] Traditional manual monitoring and control methods can no longer meet the requirements of modern beer brewing for quality stability and production efficiency. To solve this problem, the Internet of Things technology came into being. By deploying various sensors in the fermentation tank, key parameters such as temperature, pressure, pH value, sugar content, alcohol concentration, etc. during the fermentation process can be monitored in real time, and these parameter data can be transmitted to the control center in real time through wireless networks. The control center can use big data analysis and machine learning algorithms to intelligently analyze and predict the fermentation process, detect abnormal situations in a timely manner, and make control decisions.

[0005] However, the existing IoT monitoring system mainly focuses on the real-time collection of various parameters during the fermentation process, and lacks the ability to accurately predict and intelligently control the fermentation progress. Fermentation progress is an important indicator that comprehensively reflects the fermentation state. Only by accurately predicting and dynamically controlling the fermentation progress can the automatic intelligent control of the fermentation process be truly realized.

[0006] The Chinese patent with publication number CN105467940A discloses a monitoring method for a beer fermentation tank, including the following steps: step 1, real-time detection of the temperature, CO2 gas concentration, CO2 gas flow, refrigerant flow and yeast concentration of the beer fermentation tank; step 2, using a data acquisition and processing device to collect and process the temperature signal, gas concentration signal, gas flow signal, liquid flow signal and yeast concentration signal of the beer fermentation tank; step 3, data display and fault alarm; step 4, data recording and printing; step 5, control command execution, gas flow and liquid flow adjustment, which can automatically monitor the beer fermentation tank with high control accuracy. However, this method does not take into account the influence of various parameters on the fermentation progress during the fermentation process, resulting in the problem of progress not being as expected.

[0007] To this end, the present invention proposes a remote intelligent beer fermentation monitoring platform based on the Internet of Things. Summary of the invention

[0008] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes a remote intelligent beer fermentation monitoring platform based on the Internet of Things, which realizes the automation and intelligence of the fermentation process, improves the stability of beer quality, and reduces labor costs.

[0009] To achieve the above purpose, a remote intelligent beer fermentation monitoring platform based on the Internet of Things is proposed, which includes a fermentation sample collection module, a progress model training module, a progress judgment module and a fermentation control module; wherein each module is electrically connected;

[0010] A fermentation sample collection module collects fermentation characteristic data and progress label data of fermented beer samples in a fermentation test environment in advance, and sends the fermentation characteristic data and progress label data to a progress model training module;

[0011] The progress model training module takes the fermentation feature data as input and the progress label data as output, trains the progress prediction model, and sends the trained progress prediction model to the progress judgment module;

[0012] The progress judgment module collects the parameter values ​​of various fermentation parameters in the fermentation tank to be monitored in real time, forms actual input features, inputs the actual input features into the progress prediction model, obtains the expected progress after time p output by the progress prediction model, then collects the actual fermentation progress after time p, and sends the expected progress and actual fermentation progress to the fermentation control module;

[0013] The fermentation control module constructs the state space, action space and reward function of the fermentation parameter decision model. Based on the expected progress and actual fermentation progress, it determines whether parameter control is needed. If parameter control is needed, the fermentation parameter decision model is used to generate action decisions for the subsequent fermentation process for the fermenter to be monitored.

[0014] The fermentation test environment is set up as follows:

[0015] Prepare N fermenters as test fermenters for parallel multi-batch fermentation experiments; N is the number of selected fermenters;

[0016] A parameter sensor is installed in each fermentation tank and connected to an Internet of Things data acquisition device; the parameter sensor is used to collect fermentation parameters in the test fermentation tank in real time;

[0017] An automatic control system is set up for each test fermenter to remotely adjust the control parameters in the fermenter;

[0018] The control parameters are adjustable parameters when fermenting beer in the fermentation tank.

[0019] The fermented beer sample is constructed as follows:

[0020] Manually set the raw material ratio for each test fermenter;

[0021] Prepare a set of raw materials for fermenting beer for each test fermenter according to the raw material ratio;

[0022] A different fermentation parameter combination is set for each test fermentation tank, and the fermentation parameter combination is used to start the fermentation process of the test fermentation tank, conduct a fermentation experiment, and prepare the corresponding beer paste sugar;

[0023] During the fermentation experiment, the parameter values ​​and fermentation progress of each fermentation parameter in each test fermenter are collected in real time.

[0024] The method of collecting the fermentation characteristic data and progress label data of the fermented beer sample is:

[0025] Preset the input sequence length L, sliding window step size w and prediction time p;

[0026] For each test fermenter, the start time of the fermentation experiment was set to t0;

[0027] Label each fermentation parameter as c;

[0028] For any fermentation parameter c, collect its parameter value sequence Qc sorted in time order during the fermentation experiment;

[0029] Then for any parameter value sequence Qc, starting from time t0, a set of input parameter sequences is constructed every sliding window step length w, and the length of the input parameter sequence is L;

[0030] All fermentation parameters,the input parameter sequences constructed at the same time together,compose a set of sample input features;

[0031] The sample input features constructed by all test fermenters form fermentation feature data;

[0032] For each set of sample input features, the fermentation progress after a duration of L + p from its corresponding time is used as the progress label data corresponding to the sample input features.

[0033] The method for training the progress prediction model is as follows:

[0034] In the fermentation feature data, each sample input feature of each test fermenter is used as the input of the progress prediction model. The prediction model takes the predicted value of the fermentation progress at the p + L moment after this moment as the output, takes the progress label corresponding to this moment as the prediction target, takes the difference between the fermentation progress prediction value and the progress label as the prediction error, and takes minimizing the sum of prediction errors as the training target; the progress prediction model is trained until the sum of prediction errors converges and then the training stops; the progress prediction model is a time series prediction model.

[0035] The method for actually collecting the parameter values of various fermentation parameters in the fermenter to be monitored and forming the actual input features is as follows:

[0036] Parameter sensors corresponding to various fermentation parameters are installed in the fermenter to be monitored, and the parameter values of various fermentation parameters are collected in real time through the parameter sensors;

[0037] The start time of fermentation of the fermenter to be monitored is marked as t1;

[0038] Any moment of t1 + k × w is represented as the prediction moment, where k is an integer between k0 and k1. Here, k0 is a positive integer and simultaneously satisfies t1 + k0 × w < L and t1 + (k0 - 1) × w ≥ L, and k1 is a positive integer and satisfies that at the moment of t1 + k1 × w, fermentation has ended, while at the moment of t1 + (k1 - 1) × w, fermentation has not ended;

[0039] The prediction moment is marked as t2. For any prediction moment t2, a sequence of parameter values of various fermentation parameters changing with time between the time t2 - L and t2 is collected to form the actual input features.

[0040] The method for constructing the state space, action space, and reward function of the fermentation parameter decision model is as follows:

[0041] The fermentation parameter decision model is set as an Actor - Critic model;

[0042] Each time period within each sliding window step time is represented as an acquisition period;

[0043] The state space is marked as S, and the state space S reflects the real-time state of the entire fermentation process, including the time series of parameter values ​​of various fermentation parameters in each collection cycle; the state of each collection cycle in the state space S is marked as s;

[0044] The action space is marked as A, and the action space A includes the adjustment amounts of the adjustable parameters output by the Actor model, including the temperature adjustment amount and the stirring frequency adjustment amount, and the parameter adjustment action taken by the fermentation in the next collection cycle is marked as a;

[0045] The reward function is marked as Q, and the reward value function Q=r(s, a) is used to measure the effect of performing action a in the current state s;

[0046] The intelligent agent of the fermentation parameter decision model is designed to be an Actor-Critic structure based on RNN;

[0047] The input of the agent is state s;

[0048] The Critic network outputs the state value V(s);

[0049] The Actor network outputs the action probability π(a|s);

[0050] The method of judging whether parameter adjustment is needed is as follows: if the difference between the expected progress and the actual fermentation progress is greater than a preset progress threshold, it is judged that parameter adjustment is needed.

[0051] A fermentation monitoring method of a remote intelligent beer fermentation monitoring platform based on the Internet of Things is proposed, which includes the following steps:

[0052] Step 1: Collect fermentation characteristic data and progress label data of fermented beer samples in a fermentation test environment in advance;

[0053] Step 2: Using fermentation feature data as input and progress label data as output, train a progress prediction model;

[0054] Step 3: Collect parameter values ​​of various fermentation parameters in the fermentation tank to be monitored in real time to form actual input features, input the actual input features into the progress prediction model, obtain the expected progress after time p output by the progress prediction model, and then collect the actual fermentation progress after time p;

[0055] Step 4: Construct the state space, action space and reward function of the fermentation parameter decision model. Based on the expected progress and actual fermentation progress, determine whether parameter adjustment is needed. If parameter adjustment is needed, generate action decisions for the subsequent fermentation process for the fermentation tank to be monitored through the fermentation parameter decision model.

[0056] An electronic device is proposed, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0057] The processor executes the fermentation monitoring method of the remote intelligent beer fermentation monitoring platform based on the Internet of Things by calling the computer program stored in the memory.

[0058] A computer-readable storage medium is provided, on which a rewritable computer program is stored;

[0059] When the computer program is executed on a computer device, the computer device executes the fermentation monitoring method of the remote intelligent beer fermentation monitoring platform based on the Internet of Things.

[0060] Compared with the prior art, the present invention has the following beneficial effects:

[0061] The present invention first collects a large amount of fermentation sample data to train a fermentation progress prediction model; then the fermentation progress is predicted in real time and compared with the actual progress. If an abnormal deviation occurs, regulation is triggered; during regulation, a fermentation parameter decision model based on reinforcement learning is used to automatically generate adjustment strategies for fermentation parameters such as temperature and stirring frequency, so that the fermentation progress returns to a normal state. Through modeling and intelligent regulation, the problems of uncontrolled fermentation and fluctuation in product quality in the traditional beer brewing process are effectively solved, the fermentation process is automated and intelligent, the stability of beer quality is improved, labor costs are reduced, and production efficiency is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 This is a module connection relationship diagram of the remote intelligent beer fermentation monitoring platform based on the Internet of Things in Example 1 of the present invention;

[0063] Figure 2 This is a flow chart of a fermentation monitoring method of a remote intelligent beer fermentation monitoring platform based on the Internet of Things in Example 2 of the present invention;

[0064] Figure 3 This is a schematic diagram of the structure of an electronic device in Embodiment 3 of the present invention;

[0065] Figure 4 This is a schematic diagram of the computer-readable storage medium structure in Example 4 of the present invention. DETAILED DESCRIPTION

[0066] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0067] Example 1

[0068] like Figure 1 As shown, the remote intelligent beer fermentation monitoring platform based on the Internet of Things includes a fermentation sample collection module, a progress model training module, a progress judgment module and a fermentation control module; wherein each module is electrically connected;

[0069] A fermentation sample collection module collects fermentation characteristic data and progress label data of fermented beer samples in a fermentation test environment in advance, and sends the fermentation characteristic data and progress label data to a progress model training module;

[0070] Specifically, the fermentation test environment is set as follows:

[0071] Prepare N fermenters as test fermenters for parallel multi-batch fermentation experiments; N is the number of selected fermenters;

[0072] A parameter sensor is installed in each fermentation tank and connected to an Internet of Things data acquisition device; the parameter sensor is used to collect fermentation parameters in the test fermentation tank in real time;

[0073] The fermentation parameters include several physical parameters that affect the efficiency of beer fermentation, including but not limited to temperature, raw material ratio, pressure, pH value, sugar content, alcohol concentration, carbon dioxide, etc. Each physical parameter is collected in real time using a corresponding parameter sensor, for example, temperature is collected in real time using a temperature sensor;

[0074] An automatic control system is set up for each test fermenter to remotely adjust the control parameters in the fermenter;

[0075] The control parameters are adjustable parameters when fermenting beer in the fermentation tank, including but not limited to stirring frequency, temperature, etc.

[0076] Furthermore, the fermented beer sample is constructed in the following manner:

[0077] Manually set the raw material ratio for each test fermenter;

[0078] Prepare a set of raw materials for fermenting beer for each test fermentation tank according to the raw material ratio; the raw materials include malt, yeast strains, etc.;

[0079] A different fermentation parameter combination is set for each test fermentation tank, and the fermentation parameter combination is used to start the fermentation process of the test fermentation tank, conduct a fermentation experiment, and prepare the corresponding beer paste sugar;

[0080] During the fermentation experiment, the parameter values ​​and fermentation progress of each fermentation parameter in each test fermenter are collected in real time.

[0081] In a preferred embodiment, the fermentation progress can be inferred by measuring the alcohol content, that is, firstly determining the final theoretical alcohol concentration in the fermentation process according to the ratio of each component in the raw material, and then measuring the ratio of the current alcohol concentration to the final concentration in real time as a measure of the fermentation progress;

[0082] In another preferred embodiment, the fermentation progress can also be evaluated based on the residual amount of reactants, that is, by measuring the residual concentration of glucose and comparing it with the initial glucose concentration, the consumption ratio obtained reflects the degree of advancement of the fermentation;

[0083] In another preferred embodiment, the fermentation progress can also be inferred by establishing a quantitative relationship model between metabolites and fermentation progress and measuring the concentration or production of metabolites in real time.

[0084] Furthermore, the method of collecting the fermentation characteristic data and progress label data of the fermented beer sample is:

[0085] Preset the input sequence length L, sliding window step size w and prediction time p;

[0086] For each test fermenter, the start time of the fermentation experiment was set to t0;

[0087] Label each fermentation parameter as c;

[0088] For any fermentation parameter c, collect its parameter value sequence Qc sorted in time order during the fermentation experiment;

[0089] Then for any parameter value sequence Qc, starting from time t0, a set of input parameter sequences is constructed every sliding window step length w, and the length of the input parameter sequence is L;

[0090] All fermentation parameters, the input parameter sequences constructed at the same time, together constitute a set of sample input features.

[0091] As an example, the first input parameter sequence of the fermentation parameter of temperature is the temperature value sequence within the time interval from t0 to t0+L, the second input parameter sequence is the temperature value sequence within the time interval from t0+w to t0+w+L, and so on;

[0092] Then the first sample input feature includes the parameter value sequence of all fermentation parameters in the time interval from t0 to t0+L, and the second sample input feature includes the parameter value sequence of all fermentation parameters in the time interval from t0+w to t0+w+L;

[0093] The sample input features constructed from all test fermenters constitute the fermentation feature data;

[0094] For each set of sample input features, the fermentation progress corresponding to the time after the L+p duration is used as the progress label data corresponding to the sample input feature.

[0095] For example, for the first set of sample input features, the corresponding progress label is the fermentation progress at time t0+L+p;

[0096] It can be understood that by taking the real-time sequence of various fermentation parameter values ​​as input and the fermentation progress after the predicted time p as the prediction target, the progress prediction model is trained to obtain the expected fermentation progress after the future predicted time p in the fermentation tank, so that after the future predicted time p, the actual fermentation progress can be collected and compared with the expected fermentation progress, so as to determine whether there is a problem of slow fermentation or too fast fermentation.

[0097] The progress model training module takes the fermentation feature data as input and the progress label data as output, trains the progress prediction model, and sends the trained progress prediction model to the progress judgment module;

[0098] Specifically, the training progress prediction model is as follows:

[0099] Each sample input feature of each test fermentation tank in the fermentation feature data is used as the input of the progress prediction model. The prediction model takes the predicted value of the fermentation progress at the moment p+L after the moment as the output, the progress label corresponding to the moment as the prediction target, the difference between the predicted value of the fermentation progress and the progress label as the prediction error, and minimizing the sum of the prediction errors as the training target; the progress prediction model is trained until the sum of the prediction errors reaches convergence, and the training is stopped; the progress prediction model is a time series prediction model; the time series prediction model is any one of an RNN model or an LSTM model.

[0100] The progress judgment module collects the parameter values ​​of various fermentation parameters in the fermentation tank to be monitored in real time, forms actual input features, inputs the actual input features into the progress prediction model, obtains the expected progress after time p output by the progress prediction model, then collects the actual fermentation progress after time p, and sends the expected progress and actual fermentation progress to the fermentation control module;

[0101] Specifically, the parameter values ​​of various fermentation parameters in the fermentation tank to be monitored are collected in real time to form the actual input features as follows:

[0102] Parameter sensors corresponding to various fermentation parameters are installed in the fermentation tank to be monitored, and parameter values ​​of various fermentation parameters are collected in real time through the parameter sensors;

[0103] The time when the fermentation in the monitored fermenter starts is marked as t1;

[0104] Any moment of \(t1 + k\times w\) is represented as a prediction moment, where \(k\) is an integer between \(k0\) and \(k1\). Here, \(k0\) is a positive integer and simultaneously satisfies \(t1 + k0\times w\lt L\) and \(t1+(k0 - 1)\times w\geq L\). And \(k1\) is a positive integer, and at the moment of \(t1 + k1\times w\), the fermentation has ended, while at the moment of \(t1+(k1 - 1)\times w\), the fermentation has not ended.

[0105] Mark the prediction moment as \(t2\). For any prediction moment \(t2\), collect the sequence of parameter values of various fermentation parameters changing with time between time \(t2 - L\) and \(t2\) to form the actual input features.

[0106] It can be understood that the collection of the actual input features reflects the fermentation state in the current fermenter. Thus, according to this fermentation state, the fermentation progress after a future prediction duration \(p\) can be predicted. This predicted fermentation progress can be regarded as the expected progress. And after the time reaches the future prediction duration \(p\), the actual fermentation progress can be obtained through a preset fermentation progress calculation method.

[0107] Fermentation control module: construct the state space, action space, and reward function of the fermentation parameter decision model. Based on the expected progress and the actual fermentation progress, judge whether parameter adjustment is needed. If parameter adjustment is needed, through the fermentation parameter decision model, generate action decisions for the fermenter to be monitored in the subsequent fermentation process.

[0108] Specifically, the ways to construct the state space, action space, and reward function of the fermentation parameter decision model are as follows:

[0109] The fermentation parameter decision model is set as an Actor - Critic model;

[0110] Represent the time period within each sliding window step time as the acquisition period;

[0111] Mark the state space as \(S\). The state space \(S\) reflects the real - time state of the entire fermentation process, including the sequence of parameter values of various fermentation parameters within each acquisition period. Mark the state of each acquisition period within the state space \(S\) as \(s\);

[0112] Mark the action space as \(A\). The action space \(A\) includes the adjustment amounts of adjustable parameters output by the Actor model, such as the temperature adjustment amount, stirring frequency adjustment amount, etc. Mark the parameter adjustment action taken by the fermentation in the next acquisition period as \(a\);

[0113] Mark the reward function as \(Q\). The reward value function \(Q = r(s,a)\) is used to measure the effect of executing action \(a\) in the current state \(s\);

[0114] Specifically, the reward value function \(Q\) can be specifically set as:

[0115] Q=a1×(1-|P_t-P|)-a2×∑ i hi;

[0116] Among them, P_t represents the actual fermentation progress at the current moment, and P represents the expected progress, which is used to measure whether the deviation between the fermentation progress and the theoretical progress is reduced after executing action a. The smaller the deviation, the greater the reward Q.

[0117] i is the number of the adjustable parameter, hi is the specific adjustment amount of the i-th adjustable parameter. The smaller the adjustment amount, the smaller the disturbance to the fermentation process, the better the execution effect, and the greater the reward Q;

[0118] Wherein, a1 and a2 are both preset proportional coefficients.

[0119] It can be understood that the setting of the above reward value function Q is only a simple example provided in this embodiment. In practical applications, the reward value function Q can be adaptively modified, deleted or added.

[0120] The intelligent agent of the fermentation parameter decision model is designed to be an Actor-Critic structure based on RNN;

[0121] The input of the agent is state s;

[0122] The Critic network outputs the state value V(s);

[0123] The Actor network outputs the action probability π(a|s);

[0124] Furthermore, the method of judging whether parameter adjustment is required is as follows: if the difference between the expected progress and the actual fermentation progress is greater than a preset progress threshold, it is judged that parameter adjustment is required;

[0125] Furthermore, the fermentation parameter decision model is used to generate action decisions for the subsequent fermentation process for the fermenter to be monitored as follows:

[0126] The real-time actual input features of the fermentation tank to be monitored are used as the current state s; the current state s is input to the Actor model of the fermentation parameter decision model, and the Actor model outputs actions in the action space A so as to maximize the state-behavior value output by the Critic model, and outputs action decisions on the adjustment amounts of various adjustable parameters in the fermentation tank to be monitored;

[0127] Execute action decision to obtain the next state s' and actual reward value;

[0128] The critic network outputs the state-behavior value after executing the action decision in the current state, calculates the TD error δ of the critic network, updates the critic network parameters according to δ, and minimizes the critic loss function; the state-behavior value output by the critic model is calculated by the state-behavior value function generated by the temporal difference optimization method;

[0129] Specifically, the state-behavior value function generated by the temporal difference optimization method can be:

[0130] Q(s,a)=r(s,a)+γ×V(s'), γ is the discount factor, V(s') is the estimated value when in state s'; generally, the estimated value can be replaced by Q(s',a'), that is, V(s') represents the estimated value of the state-action value function corresponding to the next state s' and action a', so as to ensure that Q(s,a) can consider the long-term instead of only the current state;

[0131] Then the TD error δ is δ=(r(s,a)+γ×V(s′)-Q(s,a)) 2 ;

[0132] The Actor model takes the state-behavior value output by the Critic model as the output target and uses the policy gradient update method to update the parameters of the Actor network model.

[0133] Example 2

[0134] like Figure 2 As shown, the fermentation monitoring method of the remote intelligent beer fermentation monitoring platform based on the Internet of Things includes the following steps:

[0135] Step 1: Collect fermentation characteristic data and progress label data of fermented beer samples in a fermentation test environment in advance;

[0136] Step 2: Using fermentation feature data as input and progress label data as output, train a progress prediction model;

[0137] Step 3: Collect the parameter values ​​of various fermentation parameters in the fermentation tank to be monitored in real time to form actual input features, input the actual input features into the progress prediction model, obtain the expected progress after time p output by the progress prediction model, and then collect the actual fermentation progress after time p; p is the preset prediction time;

[0138] Step 4: Construct the state space, action space and reward function of the fermentation parameter decision model. Based on the expected progress and actual fermentation progress, determine whether parameter adjustment is needed. If parameter adjustment is needed, generate action decisions for the subsequent fermentation process for the fermentation tank to be monitored through the fermentation parameter decision model.

[0139] Example 3

[0140] Figure 3 1 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. Figure 3 As shown, according to another aspect of the present application, an electronic device 100 is also provided. The electronic device 100 may include one or more processors and one or more memories. The memories store computer readable codes, and when the computer readable codes are executed by one or more processors, the fermentation monitoring method of the remote intelligent beer fermentation monitoring platform based on the Internet of Things as described above can be executed.

[0141] The method or device according to the embodiment of the present application can also be used by Figure 3 The electronic device architecture shown in FIG. Figure 3 As shown, the electronic device 100 may include a bus 101, one or more CPUs 102, a ROM 103, a RAM 104, a communication port 105 connected to a network, an input / output component 106, a hard disk 107, etc. The storage device in the electronic device 100, such as the ROM 103 or the hard disk 107, may store the fermentation monitoring method of the remote intelligent beer fermentation monitoring platform based on the Internet of Things provided in the present application.

[0142] Furthermore, the electronic device 100 may further include a user interface 108. Figure 3 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different devices. Figure 3 One or more components of an electronic device are shown.

[0143] Example 4

[0144] Figure 4 Schematic diagram of the computer-readable storage medium structure provided by an embodiment of the present application. Figure 4 As shown, it is a computer-readable storage medium 200 according to an embodiment of the present application. Computer-readable instructions are stored on the computer-readable storage medium 200. When the computer-readable instructions are executed by the processor, the fermentation monitoring method of the remote intelligent beer fermentation monitoring platform based on the Internet of Things according to the embodiment of the present application described with reference to the above figures can be executed. The computer-readable storage medium 200 includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory (cache), etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0145] In addition, according to the implementation of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the present application provides a non-transitory machine-readable storage medium, the non-transitory machine-readable storage medium stores machine-readable instructions, the machine-readable instructions can be run by a processor to execute instructions corresponding to the method steps provided by the present application, and when the computer program is executed by a central processing unit (CPU), the above functions defined in the method of the present application are executed.

[0146] The methods, apparatuses, and devices of the present application may be implemented in many ways. For example, the methods, apparatuses, and devices of the present application may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is for illustration only, and the steps of the method of the present application are not limited to the order specifically described above unless otherwise specified. In addition, in some embodiments, the present application may also be implemented as programs recorded in a recording medium, which include machine-readable instructions for implementing the method according to the present application. Therefore, the present application also covers recording media storing programs for executing the method according to the present application.

[0147] In addition, the parts of the above-mentioned technical solutions provided in the embodiments of the present application that are consistent with the implementation principles of the corresponding technical solutions in the prior art are not described in detail to avoid excessive redundancy.

[0148] The specific implementation modes as described above further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation mode of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0149] The above preset parameters or preset thresholds are all set by technicians in this field according to actual conditions or obtained through large-scale data simulation.

[0150] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A remote intelligent beer fermentation monitoring platform based on the Internet of Things, characterized in that: include: A fermentation sample collection module, a progress model training module, a progress judgment module, and a fermentation control module; wherein each module is electrically connected; A fermentation sample collection module collects fermentation characteristic data and progress label data of fermented beer samples in a fermentation test environment in advance, and sends the fermentation characteristic data and progress label data to a progress model training module; The progress model training module takes the fermentation feature data as input and the progress label data as output, trains the progress prediction model, and sends the trained progress prediction model to the progress judgment module; The progress judgment module collects the parameter values ​​of various fermentation parameters in the fermentation tank to be monitored in real time, forms actual input features, inputs the actual input features into the progress prediction model, obtains the expected progress after time p output by the progress prediction model, then collects the actual fermentation progress after time p, and sends the expected progress and actual fermentation progress to the fermentation control module; p is the preset prediction time; The fermentation control module constructs the state space, action space and reward function of the fermentation parameter decision model. Based on the expected progress and actual fermentation progress, it determines whether parameter control is needed. If parameter control is needed, the fermentation parameter decision model is used to generate action decisions for the subsequent fermentation process for the fermenter to be monitored.

2. The remote intelligent beer fermentation monitoring platform based on the Internet of Things according to claim 1 is characterized in that: The fermentation test environment is set up as follows: Prepare N fermenters as test fermenters for parallel multi-batch fermentation experiments; N is the number of selected fermenters; A parameter sensor is installed in each fermentation tank and connected to an Internet of Things data acquisition device; the parameter sensor is used to collect fermentation parameters in the test fermentation tank in real time; An automatic control system is set up for each test fermenter to remotely adjust the control parameters in the fermenter; The control parameters are adjustable parameters when fermenting beer in the fermentation tank.

3. The remote intelligent beer fermentation monitoring platform based on the Internet of Things according to claim 2 is characterized in that: The fermented beer sample is constructed as follows: Manually set the raw material ratio for each test fermenter; Prepare a set of raw materials for fermenting beer for each test fermenter according to the raw material ratio; A different fermentation parameter combination is set for each test fermentation tank, and the fermentation parameter combination is used to start the fermentation process of the test fermentation tank, conduct a fermentation experiment, and prepare the corresponding beer paste sugar; During the fermentation experiment, the parameter values ​​and fermentation progress of each fermentation parameter in each test fermenter are collected in real time.

4. The remote intelligent beer fermentation monitoring platform based on the Internet of Things according to claim 3 is characterized in that: The method of collecting the fermentation characteristic data and progress label data of the fermented beer sample is: Preset the input sequence length L and the sliding window step size w; For each test fermenter, the start time of the fermentation experiment was set to t0; Label each fermentation parameter as c; For any fermentation parameter c, collect its parameter value sequence Qc sorted in time order during the fermentation experiment; Then for any parameter value sequence Qc, starting from time t0, a set of input parameter sequences is constructed every sliding window step length w, and the length of the input parameter sequence is L; All fermentation parameters,the input parameter sequences constructed at the same time together,compose a set of sample input features; The sample input features constructed from all test fermenters constitute the fermentation feature data; For each set of sample input features, the fermentation progress at a time L + p duration after the corresponding time is used as the progress label data corresponding to the sample input features.

5. The remote intelligent beer fermentation monitoring platform based on the Internet of Things according to claim 4 is characterized in that: The method for training the progress prediction model is as follows: In the fermentation feature data, each sample input feature of each test fermenter is used as the input of the progress prediction model. The prediction model takes the predicted value of the fermentation progress at time p + L after this moment as the output, takes the progress label corresponding to this moment as the prediction target, takes the difference between the predicted value of the fermentation progress and the progress label as the prediction error, and takes minimizing the sum of the prediction errors as the training target; the progress prediction model is trained until the sum of the prediction errors converges and then the training stops; the progress prediction model is a time series prediction model.

6. The remote intelligent beer fermentation monitoring platform based on the Internet of Things according to claim 5 is characterized in that: The method for collecting the parameter values of each fermentation parameter in the fermenter to be monitored in real time and forming the actual input features is as follows: Install parameter sensors corresponding to each fermentation parameter in the fermenter to be monitored, and collect the parameter values of each fermentation parameter in real time through the parameter sensors; Mark the start time of fermentation of the fermenter to be monitored as t1; Represent any moment of t1 + k×w as the prediction moment, where k is an integer between k0 and k1. Here, k0 is a positive integer, and at the same time, it satisfies t1 + k0×w < L and t1 + (k0 - 1)×w ≥ L. And k1 is a positive integer, and it satisfies that at the moment of t1 + k1×w, the fermentation has ended, while at the moment of t1 + (k1 - 1)×w, the fermentation has not ended; Mark the prediction moment as t2. For any prediction moment t2, collect the sequence of parameter values of each fermentation parameter changing with time between time t2 - L and t2, and form the actual input features.

7. The remote intelligent beer fermentation monitoring platform based on the Internet of Things according to claim 6 is characterized in that: The method for constructing the state space, action space, and reward function of the fermentation parameter decision model is as follows: The fermentation parameter decision model is set as an Actor - Critic model; Represent the time period within each sliding window step time as the acquisition period; Mark the state space as S. The state space S reflects the real - time state of the entire fermentation process, including the sequence of parameter values of each fermentation parameter within each acquisition period; mark the state of each acquisition period within the state space S as s; Mark the action space as A. The action space A includes the adjustment amounts of adjustable parameters output by the Actor model, including the temperature adjustment amount and the stirring frequency adjustment amount. Mark the parameter adjustment action taken by the fermentation in the next acquisition period as a; Mark the reward function as Q. The reward value function Q = r(s, a), which is used to measure the effect of performing action a in the current state s; Design the intelligent body of the fermentation parameter decision model as an Actor - Critic structure based on RNN; The input of the intelligent body is the state s; The state value V(s) is output by the Critic network; The action probability π(a|s) is output by the Actor network.

8. The remote intelligent beer fermentation monitoring platform based on the Internet of Things according to claim 7 is characterized in that: The fermentation monitoring method of the remote intelligent beer fermentation monitoring platform based on the Internet of Things includes the following steps: Step 1: Collect fermentation feature data and progress label data of fermented beer samples in the fermentation test environment in advance; Step 2: Using fermentation feature data as input and progress label data as output, train a progress prediction model; Step 3: Collect parameter values ​​of various fermentation parameters in the fermentation tank to be monitored in real time to form actual input features, input the actual input features into the progress prediction model, obtain the expected progress after time p output by the progress prediction model, and then collect the actual fermentation progress after time p; Step 4: Construct the state space, action space and reward function of the fermentation parameter decision model. Based on the expected progress and actual fermentation progress, determine whether parameter adjustment is needed. If parameter adjustment is needed, generate action decisions for the subsequent fermentation process for the fermentation tank to be monitored through the fermentation parameter decision model.

9. An electronic device, characterized in that: include: processor and memory, wherein The memory stores a computer program that can be called by the processor; The processor executes the fermentation monitoring method of the remote intelligent beer fermentation monitoring platform based on the Internet of Things as claimed in claim 8 in the background by calling the computer program stored in the memory.

10. A computer-readable storage medium, characterized in that: A rewritable computer program is stored thereon; When the computer program is executed on a computer device, the computer device executes the fermentation monitoring method of the remote intelligent beer fermentation monitoring platform based on the Internet of Things as claimed in claim 8 in the background.

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