Intelligent vinasse and straw mixed feed preparation control system and method

Abstract

The invention discloses an intelligent vinasse and straw mixed feed preparation control system and method, and the system comprises a sensor module, a central processing module and a regulator module. The sensor module comprises a plurality of temperature sensors, a pH value detection device, a concentration sensor and a humidity sensor. The data acquisition module is used for monitoring and acquiring environment data during fermentation in the preparation process of the vinasse and straw mixed feed in real time; the central processing module is used for receiving and processing environment data from the sensor module, and obtaining an optimal equipment control strategy by adopting a river horse optimization algorithm and taking maximization of the nutritional value and digestion efficiency of the feed as a target; the adjusting module is used for adjusting and controlling the device and optimizing fermentation conditions according to the optimal equipment control strategy generated by the central processing module; precise adjustment of the fermentation process is realized through the closed-loop control system, so that the feed quality and the production efficiency are improved, and a large amount of energy waste in the traditional fermentation process is avoided.

Description

Technical Field

[0001] The present invention relates to a control system and method, and particularly to a preparation control system and method for intelligent mixed feed of distiller's grains and straw. Background Art

[0002] In the traditional feed production process, environmental factors such as temperature, humidity, and pH value have a significant impact on the fermentation quality. Insufficiently precise control of environmental factors may lead to low fermentation efficiency and a decrease in the nutritional value of the feed. Moreover, feed production usually consumes a large amount of energy and has a low utilization efficiency of raw materials, which limits the sustainability and economic benefits of the production process.

[0003] The traditional feed production process cannot guarantee the quality control and safety of feed products. The raw materials relied on in feed production, such as straw and distiller's grains, may have large differences in quality and composition, which will to a certain extent reduce the standardization of the production process and the stability of product quality. To ensure the quality consistency of feed products and meet the safety standards, more precise production control and quality monitoring means are needed. Summary of the Invention

[0004] Object of the Invention: The object of the present invention is to provide a preparation control system for intelligent mixed feed of distiller's grains and straw to improve the quality and production efficiency of the feed. On the other hand, a preparation control method for intelligent mixed feed of distiller's grains and straw is provided.

[0005] Technical Solution: The preparation control system for intelligent mixed feed of distiller's grains and straw according to the present invention includes:

[0006] A sensor module, including multiple temperature sensors, pH value detection devices, concentration sensors, and humidity sensors, for real-time monitoring and collecting environmental data such as temperature, pH value, concentration, and humidity during the fermentation process of the preparation of the mixed feed of distiller's grains and straw;

[0007] A central processing module, for receiving and processing the environmental data from the sensor module, adopting the hippopotamus optimization algorithm, establishing an objective function with the goal of maximizing the nutritional value and digestion efficiency of the feed, and obtaining the optimal equipment control strategy;

[0008] A regulator module, including a temperature regulating device, a PH regulating device, a reactant feeding device, and a humidity regulating device, for adjusting and controlling the devices according to the optimal equipment control strategy generated by the central processing module to optimize the fermentation conditions.

[0009] Preferably, the objective function formula is as follows:

[0010] F = w T ·f T (T) + w PH ·fPH (PH)+w C ·f C (C)+w H ·f H (H);

[0011]

[0012] f C (C)=a C ·log(1 + c);

[0013] f H (H)=a H ·tanh(H - H opt );

[0014] Wherein, F represents maximizing the nutritional value and digestion efficiency of the feed, T represents temperature, PH represents pH value, C represents concentration, H represents humidity, f T (T) represents the temperature influence function, T opt represents the optimal temperature value, β T 、a T represents the coefficient for adjusting the temperature influence, f PH (PH) represents the pH value influence function, PH opt represents the optimal pH value, a PH represents the coefficient for adjusting the pH influence, f C (C) represents the concentration influence function, a C represents the coefficient for adjusting the concentration influence, f H (H) represents the humidity influence function, H opt represents the optimal humidity value, a H represents the coefficient for adjusting the humidity influence, w T 、w PH 、w C 、w H represents the weight coefficient of the parameter pair for the objective function.

[0015] Preferably, the hippopotamus optimization algorithm includes:

[0016] (1) Initialize the hippopotamus population, where the hippopotamus population corresponds to the regulation amount of the device, and initialize the hippopotamus positions according to the collected environmental data;

[0017] (2) Update the positions of the hippopotamus members in the river or pond, where the hippopotamus members include male hippopotamuses, female hippopotamuses, and immature hippopotamuses;

[0018] (3) When the hippopotamus defends against predators, record the positions of the predators in the search space;

[0019] (4) Update the positions of escaping from predators;

[0020] (5) The male hippopotamus position formula in step 2 is improved by introducing a nonlinear periodic adjustment strategy.

[0021] Preferably, the formula for initializing the hippo population in step 1 is as follows:

[0022] χ i :x ij =ll j +r·(ul j -ll j ),i=1,2,…,N,j=1,2,…,m;

[0023]

[0024] Among them, χ i represents the position of the i-th candidate solution, x ij represents the value of the j-th question variable raised by the i-th hippopotamus, r represents a random number in the range of 0 to 1, and ll j and ul j represents the lower and upper bounds of the j-th decision variable, N represents the population size of hippos in the herd, and m represents the number of decision variables in the problem.

[0025] Preferably, the formula for updating the male hippopotamus position in step 2 is as follows:

[0026]

[0027]

[0028]

[0029] in, Indicates the position of the male hippopotamus, represents the value of the j-th question variable raised by the i-th male hippo, y1 represents a random number between 0 and 1, and Dhippo represents the position of the dominant hippo; represents a random vector between 0 and 1, r5 represents a random number between 0 and 1, and Represents an integer random number, which can be 1 or 0; I1 and I2 represent integers between 1 and 2, t represents the current iteration, and T represents the maximum number of iterations.

[0030] Preferably, the formula for updating the position of the female hippopotamus or immature hippopotamus in step 2 is as follows:

[0031]

[0032] Among them, among them, Indicates the position of the male hippopotamus, represents the value of the j-th problem variable proposed by the i-th female hippopotamus, MG i represents the average value of some randomly selected hippopotamuses, h1 and h2 represent randomly selected numbers or vectors, r7 represents a random number between 0 and 1; when r6 is greater than 0.5, it means that the immature hippopotamus has left its mother but is still within or near the herd, otherwise it means that the immature hippopotamus has left the herd; F i represents the objective function value.

[0033] Preferably, the position formula of the predator in the search space in step 3 is as follows:

[0034]

[0035] where, represents a random vector from 0 to 1, represents the predator Predator j relative to the distance vector of the target individual;

[0036]

[0037] where, represents the position of the hippopotamus facing the predator, represents the value of the j-th problem variable proposed by the i-th hippopotamus facing the predator, represents a random vector with L evy distribution, β represents a uniform random number between 2 and 4, c represents a uniform random number between 1 and 1.5, d represents a uniform random number between 2 and 3, g represents a uniform random number between -1 and 1, represents a random vector with a dimension of 1×m.

[0038] Preferably, the position formula for updating the position of the escaping predator in step 4 is as follows:

[0039]

[0040] where, and represent the lower and upper bounds of the current position of the hippopotamus, represents the position of the hippopotamus searching for the nearest safe location, s1 represents a random vector or number, r 10 r 13 represents a random number generated within the range of 0 to 1, r 12 represents a random number with a normal distribution.

[0041] Preferably, the formula for improving the position of the male hippopotamus in step 5 is as follows:

[0042]

[0043] Among them, β‘ represents the environmental factor after the improved algorithm.

[0044] A preparation control method for intelligent distiller's grains and straw mixed feed according to the present invention includes the following steps:

[0045] S1. Based on the sensor module, collect the temperature, pH value, concentration, and humidity environmental data during the fermentation of distiller's grains and straw mixed feed, and preprocess the environmental data;

[0046] S2. Based on the central processing module receiving the preprocessed environmental data, use the hippopotamus optimization algorithm, aiming to maximize the nutritional value and digestion efficiency of the feed, establish an objective function, and obtain the optimal equipment control strategy;

[0047] S3. According to the optimal equipment control strategy, use the regulator module to control the equipment to keep the fermentation conditions in the optimal state.

[0048] Beneficial effects: Compared with the prior art, the present invention has the following remarkable advantages: By using the hippopotamus optimization algorithm, the best fermentation environment parameters are obtained, and the equipment is regulated based on the best environmental parameters. Through the closed-loop control system, the precise regulation of the fermentation process is realized, thereby improving the quality and production efficiency of the feed and avoiding a large amount of energy waste in the traditional fermentation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic structural diagram of the system of the present invention;

[0050] Figure 2 It is a schematic overall flow diagram of the control method of the present invention;

[0051] Figure 3 It is a schematic diagram of the hippopotamus optimization DBO algorithm of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0052] The technical solutions of the present invention will be described in detail below with reference to the drawings.

[0053] As Figure 1 shown, a preparation control system for intelligent distiller's grains and straw mixed feed includes:

[0054] A sensor module, including multiple temperature sensors, pH value detection devices, concentration sensors, and humidity sensors, is used to monitor and collect the temperature, pH value, concentration, and humidity environmental data during the fermentation in the preparation process of distiller's grains and straw mixed feed in real time, and transmit the collected environmental data to the central processing module through a communication device in real time for further processing and analysis to ensure the accuracy and timeliness of environmental condition control;

[0055] The central processing module, which is the core part of the system, is used to receive and process the environmental data from the sensor module, store and manage it, analyze the acquired environmental data, extract useful information and trends, adopt the Hippopotamus Optimization (HO) algorithm, aim at maximizing the nutritional value and digestion efficiency of the feed, establish an objective function, comprehensively consider factors such as temperature, pH value, concentration, and humidity, obtain the optimal equipment control strategy, put forward specific adjustment suggestions and measures, and provide an operation plan for the regulator module;

[0056] The regulator module has main functions such as equipment control, feedback regulation, monitoring and alarm; it includes a temperature regulation device, a PH regulation device, a reactant feeding device, and a humidity regulation device, which are used to adjust and control the device according to the optimal equipment control strategy generated by the central processing module, ensure that the device receives feedback information from the central processing module in real time according to the optimal equipment control strategy, dynamically adjust the equipment parameters, ensure that the fermentation conditions always remain in the optimal state; monitor the operation status of the equipment, detect potential faults, and send out alarm signals in a timely manner when abnormalities occur to ensure the safe and reliable operation of the system; achieve precise regulation of the fermentation process through a closed-loop control system, thereby improving the quality and production efficiency of the feed.

[0057] As Figure 2 shown, the method adopted by the preparation control system of intelligent distiller's grains and straw mixed feed includes the following steps:

[0058] S1. Based on the sensor module, collect the environmental data of temperature, pH value, concentration, and humidity during the fermentation of distiller's grains and straw mixed feed, and preprocess the environmental data;

[0059] S2. Based on the central processing module receiving the preprocessed environmental data, adopt the Hippopotamus Optimization algorithm, aim at maximizing the nutritional value and digestion efficiency of the feed, establish an objective function, and obtain the optimal equipment control strategy;

[0060] The objective function is:

[0061] F = f(T, PH, C, H);

[0062] Its specific objective function expression form is:

[0063] F = w T ·f T (T) + w PH ·f PH (PH) + w C ·f C (C) + w H ·f H (H);

[0064]

[0065] f C (C)=a C log(1+c);

[0066] f H (H) = a H tanh(HH opt );

[0067] Among them, F represents the maximum nutritional value and digestibility of feed, T represents temperature, PH represents pH value, C represents concentration, H represents humidity, and f represents the maximum nutritional value and digestibility of feed. T (T) represents the temperature influence function, T opt represents the optimal temperature value, β T 、a T The coefficient that adjusts the temperature effect, f PH (PH) represents the pH value effect function, PH opt Indicates the optimal pH value, a PH Indicates the coefficient of adjusting pH influence, f C (C) represents the concentration influence function, a C The coefficient representing the effect of the adjusted concentration, f H (H) represents the humidity influence function, H opt Indicates the optimal humidity value, a H The coefficient for adjusting the effect of humidity, w T 、w PH 、w C 、w H Represents the weight coefficient of the parameter to the objective function.

[0068] The specific steps of the Hippo optimization algorithm are as follows:

[0069] S22, initializing the population, the adjustment device of the equipment corresponds to the hippo population, and the hippo positions are initialized according to the acquired environmental data;

[0070] χ i :x ij =ll j +r·(ul j -ll j ),i=1,2,…,N,j=1,2,…,m; (1)

[0071]

[0072] Among them, χ i represents the position of the i-th candidate solution, x ij represents the value of the j-th question variable raised by the i-th hippopotamus, r represents a random number in the range of 0 to 1, and ll j and ul jdenote the lower and upper bounds of the j-th decision variable, N represents the population size of hippos in the herd, m represents the number of decision variables in the problem, and the population matrix is constructed by Equation (2).

[0073] S22. Update the positions of hippo members in the river or pond. The position formula for male hippos is as follows:

[0074]

[0075]

[0076]

[0077] Among them, denotes the position of the male hippo, denotes the value of the j-th problem variable proposed by the i-th male hippo, y1 represents a random number between 0 and 1, and Dhippo denotes the position of the dominant hippo; denotes a random vector between 0 and 1, r5 represents a random number between 0 and 1, and denote integer random numbers, which can be either 1 or 0; I1 and I2 represent integers between 1 and 2, t represents the current iteration, and T represents the maximum number of iterations.

[0078] The position formula for female hippos or immature hippos is as follows:

[0079]

[0080]

[0081] Among them, denotes the position of the male hippo, denotes the value of the j-th problem variable proposed by the i-th female hippo, MG i denotes the average value of some randomly selected hippos, including the current considered hippo with equal probability (χ i ), h1 and h2 represent randomly selected numbers or vectors, and r7 represents a random number between 0 and 1; when r6 is greater than 0.5, it means that the immature hippo has left its mother but is still within or near the herd (Equation 7), otherwise it means that the immature hippo has left the herd. The behaviors of immature hippos and female hippos are modeled according to the equation.

[0082] The formula for updating the position of female hippos or immature hippos is as follows:

[0083]

[0084]

[0085] Among them, Equations 8 and 9 represent the position update of female or immature hippos in the group, and F i represents the objective function value.

[0086] S23. When a hippo defends against a predator, the position of the predator in the search space is represented as:

[0087]

[0088]

[0089] Among them, represents a random vector from 0 to 1, represents the distance vector of the predator Predator j relative to the target individual; during this period, the hippo adopts a defense behavior based on factor to protect itself from the predator. If the predator is less than F i , it indicates that the predator is very close to the hippo. If the predator is larger, it indicates that the predator or the invading entity is far from the hippo's territory, as shown in Equation 12:

[0090]

[0091] Among them, represents the position of the hippo facing the predator, represents the value of the j-th problem variable proposed by the i-th hippo facing the predator, represents a random vector with an L evy distribution, used for the sudden change of the predator's position when attacking the hippo. β represents a uniform random number between 2 and 4, c represents a uniform random number between 1 and 1.5, d represents a uniform random number between 2 and 3, g represents a uniform random number between -1 and 1, represents a random vector with a dimension of 1×m;

[0092] L evy The mathematical model of random movement is calculated as shown in Equation 13:

[0093]

[0094] Among them, w and v are random numbers from 0 to 1 respectively, θ is a constant (θ = 1.5), Γ is the abbreviation of the Gamma function, and σ w can be obtained from Equation 14.

[0095] S24. Update the position of escaping from the predator. When the newly created position improves the working efficiency of the device, it indicates that the hippopotamus has found a safer position near the current position and changes the position accordingly. The formula is as follows:

[0096]

[0097]

[0098]

[0099] Wherein, and represent the lower and upper bounds of the current position of the hippopotamus, represents the position of the hippopotamus searching for the nearest safe location, randomly selected from three scenarios, as shown in Formula 18.

[0100] s1 represents a random vector or number, r 10 、r 13 represent random numbers generated within the range of 0 to 1, r 12 represents a random number of normal distribution. The considered scenario leads to a more appropriate local search, making the proposed algorithm have a higher utilization quality.

[0101] S25. Improve the position formula of male hippopotamus in Step 2. Introduce a non-linear periodic adjustment strategy to enable the hippopotamus population to conduct a more sufficient global search and maximize the population diversity; in the later stage of iteration, the MHO algorithm can converge faster, further improving the optimization accuracy and convergence speed of the algorithm. The improved formula is as follows:

[0102]

[0103] Wherein, β‘ represents the environmental factor after improving the algorithm.

[0104] S3. According to the optimal device control strategy, use the regulator module to control the device to keep the fermentation conditions in the optimal state.

Claims

1. A preparation control system for intelligent distiller's grains and straw mixed feed, characterized in that, Comprising: A sensor module, including multiple temperature sensors, pH detection devices, concentration sensors and humidity sensors, for real-time monitoring and collecting environmental data such as temperature, pH value, concentration and humidity during the fermentation process of the distillers grains and straw mixed feed; A central processing module, for receiving and processing the environmental data from the sensor module, adopting the hippopotamus optimization algorithm, aiming at maximizing the nutritional value and digestion efficiency of the feed, establishing an objective function, and obtaining an optimal equipment control strategy; A regulator module, including a temperature regulating device, a PH regulating device, a reactant feeding device and a humidity regulating device, for adjusting and controlling the devices according to the optimal equipment control strategy generated by the central processing module to optimize the fermentation conditions.

2. The preparation control system according to claim 1, wherein The formula of the objective function is as follows: F = w T ·f T (T) + w PH ·f PH (PH) + w C ·f C (C) + w H ·f H (H); f C (C) = a C ·log(1 + c); f H (H) = a H ·tanh(H - H opt ); Among them, F represents maximizing the nutritional value and digestion efficiency of feed, T represents temperature, PH represents pH value, C represents concentration, H represents humidity, and f T (T) represents the temperature influence function, and T opt represents the optimal temperature value, and β T and a T represent the coefficient for adjusting the temperature influence, and f PH (PH) represents the pH value influence function, and PH opt represents the optimal pH value, and a PH represents the coefficient for adjusting the PH influence, and f C (C) represents the concentration influence function, and a C represents the coefficient for adjusting the concentration influence, and f H (H) represents the humidity influence function, and H opt represents the optimal humidity value, and a H represents the coefficient for adjusting the humidity influence, and w T and w PH and w C and w H represent the weight coefficients of the parameters for the objective function.

3. The preparation control system according to claim 1, wherein, The hippopotamus optimization algorithm includes: (1) Initializing the hippopotamus population, where the hippopotamus population corresponds to the regulation amount of the equipment, and initializing the hippopotamus position according to the collected environmental data; (2) Updating the positions of the hippopotamus members in the river or pond, where the hippopotamus members include male hippopotamuses, female hippopotamuses and immature hippopotamuses; (3) When the hippopotamus defends against predators, recording the positions of the predators in the search space; (4) Updating the positions of escaping predators; (5) Improving the male hippopotamus position formula in step 2 by introducing a non-linear periodic adjustment strategy.

4. The preparation control system according to claim 3, characterized in that, The formula for initializing the hippopotamus population in step 1 is as follows: χ i : x ij = ll j + r·(ul j - ll j ), i = 1, 2, …, N, j = 1, 2, …, m; where χ i represents the position of the i-th candidate solution, x ij represents the value of the j-th problem variable proposed by the i-th hippopotamus, r represents a random number in the range of 0 to 1, ll j and ul j represent the lower and upper bounds of the j-th decision variable, N represents the population size of the hippopotamuses in the herd, and m represents the number of decision variables in the problem.

5. The preparation control system according to claim 3, wherein The formula for updating the male hippopotamus position in step 2 is as follows: Among them, represents the position of the male hippopotamus, represents the value of the j-th question variable proposed by the i-th male hippopotamus, y1 represents a random number between 0 and 1, and Dhippo represents the position of the dominant hippopotamus; represents a random vector between 0 and 1, r5 represents a random number between 0 and 1, and represents an integer random number, which can be 1 or 0; I1 and I2 represent integers between 1 and 2, t represents the current iteration, and T represents the maximum number of iterations.

6. The preparation control system according to claim 3, wherein The formula for updating the female hippopotamus or immature hippopotamus position in step 2 is as follows: Among them, among them, represents the position of the male hippopotamus, represents the value of the j-th problem variable proposed by the i-th female hippopotamus, MG i represents the average value of some randomly selected hippopotamuses, h1 and h2 represent randomly selected numbers or vectors, r7 represents a random number between 0 and 1; when r6 is greater than 0.5, it means that the immature hippopotamus has left its mother but is still within or near the herd, otherwise it means that the immature hippopotamus has left the herd; F i represents the objective function value.

7. The preparation control system according to claim 3, characterized in that The formula for the position of the predator in the search space in step 3 is as follows: Among them, represents a random vector from 0 to 1, represents the predator Predator j the distance vector relative to the target individual; Among them, represents the position of the hippopotamus facing the predator, represents the value of the j-th problem variable proposed by the i-th hippopotamus facing the predator, represents a random vector with an L evy distribution, β represents a uniform random number between 2 and 4, c represents a uniform random number between 1 and 1.5, d represents a uniform random number between 2 and 3, and g represents a uniform random number between -1 and 1, represents a random vector with a dimension of 1×m.

8. The preparation control system according to claim 3, characterized in that The formula for updating the position of escaping predators in step 4 is as follows: Among them, and represent the lower and upper bounds of the current position of the hippopotamus. represents the position of the hippopotamus searched for finding the nearest safe location, s1 represents a random vector or number, r 10 and 13 represent random numbers generated within the range of 0 to 1, and r 12 represents a random number of the normal distribution.

9. The preparation control system according to claim 3, characterized in that The formula for improving the male hippopotamus position in step 5 is as follows: Where, β' represents the environmental factor after improving the algorithm.

10. A preparation control method for intelligent distiller's grains and straw mixed feed, characterized in that, Including the following steps: S1. Based on the sensor module, collecting environmental data such as temperature, pH value, concentration and humidity during the fermentation of the distillers grains and straw mixed feed, and preprocessing the environmental data; S2. Based on the central processing module receiving the preprocessed environmental data, adopting the hippopotamus optimization algorithm, aiming at maximizing the nutritional value and digestion efficiency of the feed, establishing an objective function, and obtaining an optimal equipment control strategy; S3. According to the optimal equipment control strategy, using the regulator module to control the equipment to keep the fermentation conditions in the optimal state.

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