Livestock and poultry manure treatment system and method based on microbial fermentation and intelligent temperature control

Abstract

The invention relates to the technical field of agriculture and animal husbandry waste treatment, in particular to a livestock and poultry manure treatment system and method based on microbial fermentation and intelligent temperature control, and the system comprises the following steps: an activity demand analysis module, a manure pretreatment module, a temperature monitoring and regulation module, a pressure monitoring and regulation module, and a fermentation speed prediction module. According to the invention, by analyzing the breeding speed of microorganisms, identifying various environmental conditions required by microbial fermentation and pretreating excrement, the dynamic adjustment of temperature and pressure in the fermentation container is realized, the fermentation speed is effectively controlled, the reduction of fermentation efficiency caused by environmental fluctuation is reduced, and the stability of the fermentation process is ensured; the resource conversion efficiency, including organic fertilizers and biological energy sources, is improved, and in combination with the prediction of the fermentation speed and the precise measurement and calculation of the gas production rate, the fine management of the fermentation process is realized, the energy consumption and the operation cost are reduced, the automation and intelligence levels are increased, and the response to the environmental protection requirement is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural and animal husbandry waste treatment, and in particular to a livestock and poultry manure treatment system and method based on microbial fermentation and intelligent temperature control. Background Art

[0002] The field of agricultural and animal husbandry waste treatment technology involves the collection, treatment and resource utilization of livestock and poultry manure and other agricultural waste. It aims to transform agricultural and animal husbandry waste into useful resources, including organic fertilizer and energy, through physical, chemical and biological methods, reduce the pollution of waste to the environment, and improve the recycling rate of resources. It includes waste collection technology, selection and optimization of treatment processes, application and marketization of products, combining a variety of intelligent algorithms and models to improve automation and intelligence, and respond to the growing demand for environmental protection and resource recycling.

[0003] Among them, the livestock and poultry manure treatment system is used to treat the feces and urine produced by livestock and poultry breeding, covering the collection, storage, fermentation and treatment of manure, including physical separation technology such as solid-liquid separation, microbial fermentation technology such as anaerobic fermentation, and control technology such as temperature and humidity control. It controls the fermentation environment by setting temperature parameters and cooperates with selected microbial communities to accelerate the decomposition process of livestock and poultry manure, thereby achieving effective treatment and reduction of livestock and poultry manure.

[0004] Traditional livestock and poultry manure treatment technology relies on traditional physical and chemical methods. When dealing with large-scale or diversified agricultural and animal husbandry waste, it is difficult to adapt to rapidly changing environmental conditions and biodiversity. In terms of temperature and pressure control, it lacks sufficient flexibility and precision, resulting in low efficiency of the fermentation process and failure to maximize the conversion of waste into useful resources. Its application in automation and intelligence is also relatively limited, which increases the complexity and cost of operation, limits its application in large-scale agricultural and animal husbandry waste treatment, affects the recycling rate of resources, and increases the potential pollution of the treatment process to the environment. Summary of the invention

[0005] In order to solve the technical problem of poor recycling capacity in the prior art, the embodiment of the present invention provides a livestock and poultry manure treatment system and method based on microbial fermentation and intelligent temperature control. The technical solution is as follows:

[0006] On the one hand, a livestock and poultry manure treatment system based on microbial fermentation and intelligent temperature control is provided, the system comprising:

[0007] The activity demand analysis module is based on the microbial species information. By analyzing the reproduction rate of microorganisms under various conditions, it identifies the nutritional and environmental requirements of various microorganisms during fermentation activities, including temperature, pressure, and pH value, and generates fermentation demand extraction data;

[0008] The feces pretreatment module extracts data based on the fermentation requirements, detects the chemical composition of the feces, adjusts the pH value according to the activity requirements of the microorganisms, performs solid-liquid separation, and outputs the fermentation substrate pretreatment results;

[0009] The temperature monitoring and control module receives the fermentation substrate pretreatment result, monitors the temperature data in the fermentation container in real time, adjusts the temperature control parameters according to the metabolic needs of the microorganisms, and outputs the temperature control adjustment result;

[0010] The pressure monitoring and regulating module uses the temperature control adjustment result to monitor the real-time pressure in the fermentation container in real time, adjusts the pressure control parameters according to the pressure requirements of microbial growth and metabolism, and outputs the pressure balance adjustment result;

[0011] The fermentation speed prediction module uses the pressure balance adjustment result to analyze the change trend of the gas composition in the container to predict the fermentation speed of the microorganism, combines time series analysis to predict the gas production of the target fermentation container, and outputs the gas production prediction information.

[0012] As a further scheme of the present invention, the fermentation demand extraction data specifically includes microbial reproduction rate information, nutritional demand data, and environmental demand information; the fermentation matrix pretreatment results include a feces chemical composition list, pH adjustment parameters, and solid-liquid separation parameters; the temperature control adjustment results specifically refer to real-time temperature monitoring data, cooling adjustment parameters, and heating adjustment parameters; the pressure balance adjustment results include real-time pressure data, pressure adjustment parameters, and pressure adaptability analysis data; the gas production prediction information specifically includes gas composition analysis results, fermentation speed change trend, and gas production calculation results.

[0013] As a further solution of the present invention, the activity demand analysis module includes:

[0014] The growth performance analysis submodule collects the reproduction rate data of microorganisms in various environments based on the microbial species information, evaluates the growth performance under various conditions, and obtains the microbial reproduction data;

[0015] The reproduction rate analysis submodule analyzes the relationship between the microbial reproduction rate and various environmental factors, including temperature, pressure, and pH value, based on the microbial reproduction data, identifies the fermentation conditions required by the target microorganism, and obtains the environmental demand analysis results;

[0016] The nutritional demand assessment submodule analyzes and identifies the target microorganism's demand for multiple nutrients based on the environmental demand analysis results, and generates fermentation demand extraction data.

[0017] As a further solution of the present invention, the specific formula for analyzing the relationship between the microbial reproduction rate and various environmental factors is:

[0018]

[0019] Among them, r xy represents the correlation coefficient between the microbial growth rate and the target environmental factors, x i represents the observed value of the i-th data point of the target environmental factor, y i represents the observed value of the i-th data point of the microbial reproduction rate, x represents the average value of all environmental factor observations, y represents the average value of all microbial reproduction rate observations, and i represents the index of the observed data point.

[0020] As a further embodiment of the present invention, the feces pretreatment module comprises:

[0021] The chemical composition detection submodule extracts data based on the fermentation requirements, detects the chemical composition of the fecal sample, identifies the organic matter content and the nutrient content required by various microorganisms, and generates fecal chemical composition information;

[0022] The pH adjustment submodule adjusts the pH value of the stool sample based on the chemical composition information of the stool and the activity requirements of the microorganisms to obtain a pH adjustment result;

[0023] The feces solid-liquid separation submodule performs solid-liquid separation on the adjusted feces based on the pH adjustment result and the environmental requirements of the microorganisms for fermentation activities to obtain a fermentation substrate pretreatment result.

[0024] As a further solution of the present invention, the temperature monitoring and control module includes:

[0025] The temperature data acquisition submodule uses a temperature sensor to collect temperature data in the fermentation container in real time based on the fermentation substrate pretreatment result, and preprocesses the data in combination with time information to obtain temperature monitoring data;

[0026] The regulation demand analysis submodule calculates the temperature adjustment needs based on the temperature monitoring data and the temperature conditions required by microbial metabolism, and generates a temperature demand analysis result;

[0027] The real-time temperature adjustment submodule adjusts the control parameters of the temperature regulating equipment, including heating and cooling equipment, based on the temperature demand analysis result, and outputs the temperature control adjustment result.

[0028] As a further solution of the present invention, the pressure monitoring and regulating module includes:

[0029] The pressure data acquisition submodule uses the temperature control adjustment result and the pressure sensor to collect the pressure data in the fermentation container in real time, and pre-processes the data in combination with the time information to obtain the pressure monitoring data;

[0030] The adjustment target identification submodule identifies the pressure adjustment demand based on the pressure monitoring data and the demand for environmental pressure of microbial growth and metabolism, and generates a pressure demand analysis result;

[0031] The real-time pressure adjustment submodule adjusts the control parameters of the pressure regulating device based on the pressure demand analysis result and outputs a pressure balance adjustment result.

[0032] As a further embodiment of the present invention, the fermentation speed prediction module comprises:

[0033] The gas component analysis submodule detects and analyzes the gas components in the fermentation container based on the pressure balance adjustment result, identifies key gas components related to the microbial fermentation rate, identifies and records the concentration changes of multiple gas components, and obtains gas analysis data;

[0034] The time series analysis submodule calculates the fermentation speed of the microorganisms in the container in real time based on the gas analysis data and in combination with the time series analysis, and generates a fermentation speed calculation result;

[0035] The gas production capacity analysis submodule predicts the gas production of the container under the current fermentation conditions in real time based on the fermentation speed calculation result, and generates gas production prediction information.

[0036] As a further solution of the present invention, the specific formula for real-time prediction of the gas production of the container under the current fermentation conditions is:

[0037]

[0038] in, represents the predicted gas production, n represents the total number of support vectors, j is the index variable, α j is the coefficient of the jth support vector, P j is the known gas production of the jth training data point, e is the base of the natural logarithm, γ is the width parameter of the radial basis function kernel, which is used to adjust the spread of the function or the tightness of the data mapping, ∥Q j -Q∥ 2 represents the jth training sample Q j The square of the Euclidean distance from the new input sample Q in the feature space, Q j is the feature vector of the jth training sample, Q is the feature vector of the new input sample, including microbial species and environmental parameters, and b is the bias term of the model.

[0039] On the other hand, a method for treating livestock and poultry manure based on microbial fermentation and intelligent temperature control is provided. The method is applied to a livestock and poultry manure treatment system based on microbial fermentation and intelligent temperature control. The method comprises:

[0040] S1: Based on the microbial species information, collect the growth data of microorganisms under various conditions, evaluate the effects of various environmental factors on the microbial reproduction rate, including temperature, pressure and pH value, and generate fermentation demand extraction data;

[0041] S2: extracting data based on the fermentation requirements, detecting the chemical composition of feces, adjusting the pH value, matching the activity requirements of microorganisms, and performing solid-liquid separation to obtain a fermentation substrate pretreatment result;

[0042] S3: receiving the fermentation substrate pretreatment result, monitoring the temperature in the fermentation container in real time, adjusting the temperature control parameters according to the metabolic requirements of the microorganisms, and outputting the temperature control adjustment result;

[0043] S4: using the temperature control adjustment result, monitoring the real-time pressure in the container in real time, adjusting the pressure control parameters according to the growth and metabolic requirements of the microorganisms, and generating a pressure balance adjustment result;

[0044] S5: According to the pressure balance adjustment result, the change trend of the gas composition in the fermentation container is analyzed in real time, and the gas production is predicted in combination with time series analysis to obtain gas production prediction information.

[0045] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0046] By analyzing the rate of microbial reproduction, identifying the various environmental conditions required for microbial fermentation, and pre-treating feces, dynamic adjustment of temperature and pressure in the fermentation container can be achieved, effectively controlling the fermentation rate, reducing the decline in fermentation efficiency caused by environmental fluctuations, ensuring the stability of the fermentation process, and improving the efficiency of resource conversion, including organic fertilizer and bioenergy. Combined with the prediction of fermentation rate and accurate calculation of gas production, fine management of the fermentation process can be achieved, reducing energy consumption and operating costs, increasing the level of automation and intelligence, and ensuring response to environmental protection requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0048] Figure 1 is a system flow chart of the present invention;

[0049] Figure 2 It is a schematic diagram of the system framework of the present invention;

[0050] Figure 3It is a flow chart of the activity demand analysis module of the present invention;

[0051] Figure 4 This is a flow chart of the feces pretreatment module of the present invention;

[0052] Figure 5 It is a flow chart of the temperature monitoring and control module of the present invention;

[0053] Figure 6 It is a flow chart of the pressure monitoring and regulating module of the present invention;

[0054] Figure 7 It is a flow chart of the fermentation speed prediction module of the present invention;

[0055] Figure 8 It is a schematic diagram of the method steps of the present invention. DETAILED DESCRIPTION

[0056] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0057] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0058] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0059] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0060] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0061] The embodiment of the present invention provides a livestock and poultry manure treatment system based on microbial fermentation and intelligent temperature control. Figure 1 to Figure 2 The present invention provides a technical solution, a livestock and poultry manure treatment system based on microbial fermentation and intelligent temperature control, comprising:

[0062] The activity demand analysis module is based on the microbial species information. By analyzing the reproduction rate of microorganisms under various conditions, it identifies the nutritional and environmental requirements of various microorganisms during fermentation activities, including temperature, pressure, and pH value, and generates fermentation demand extraction data;

[0063] The feces pretreatment module extracts data based on fermentation requirements, detects the chemical composition of feces, adjusts the pH value according to the activity requirements of microorganisms, performs solid-liquid separation, and outputs the fermentation matrix pretreatment results;

[0064] The temperature monitoring and control module receives the fermentation substrate pretreatment results, monitors the temperature data in the fermentation container in real time, adjusts the temperature control parameters according to the metabolic needs of the microorganisms, and outputs the temperature control adjustment results;

[0065] The pressure monitoring and regulation module uses the temperature control adjustment results to monitor the real-time pressure in the fermentation container in real time, adjusts the pressure control parameters according to the pressure requirements of microbial growth and metabolism, and outputs the pressure balance adjustment results;

[0066] The fermentation rate prediction module uses the pressure balance adjustment results to analyze the changing trend of the gas composition in the container to predict the fermentation rate of the microorganisms. Combined with time series analysis, it predicts the gas production of the target fermentation container and outputs the gas production prediction information.

[0067] The fermentation demand extraction data specifically include microbial reproduction rate information, nutritional demand data, and environmental demand information. The fermentation matrix pretreatment results include a list of feces chemical composition, pH adjustment parameters, and solid-liquid separation parameters. The temperature control adjustment results specifically refer to real-time temperature monitoring data, cooling adjustment parameters, and heating adjustment parameters. The pressure balance adjustment results include real-time pressure data, pressure adjustment parameters, and pressure adaptability analysis data. The gas production prediction information specifically includes gas composition analysis results, fermentation speed change trend, and gas production calculation results.

[0068] See also Figure 2 and Figure 3 , the activity requirements analysis module includes:

[0069] The growth performance analysis submodule collects the reproduction rate data of microorganisms in various environments based on the microbial species information, evaluates the growth performance under various conditions, and obtains the microbial reproduction data;

[0070] Through high performance liquid chromatography and mass spectrometry analysis, the reproduction rate data of microorganisms under different environmental conditions are measured, including changes in temperature, humidity level, light intensity, and changes in nutrient supply. The target data ensures the accuracy and reliability of the data for each sample. After ensuring the data quality through data preprocessing, including normalization and missing value processing, the multivariate linear regression analysis model is applied to comprehensively consider the impact of various environmental variables on the growth rate of microorganisms, evaluate and compare the growth performance of microorganisms under different environmental conditions, and obtain accurate reproduction rate data. The target data provides a scientific basis for predicting the performance of microorganisms in specific environments, and also provides preliminary data support for subsequent environmental demand analysis and nutrient demand assessment.

[0071] The reproduction rate analysis submodule analyzes the relationship between microbial reproduction rate and various environmental factors, including temperature, pressure, and pH value, based on microbial reproduction data, identifies the fermentation conditions required by the target microorganisms, and obtains environmental demand analysis results;

[0072] The specific formula for analyzing the relationship between microbial reproduction rate and various environmental factors is:

[0073]

[0074] Among them, r xy represents the correlation coefficient between the microbial growth rate and the target environmental factors, x i represents the observed value of the i-th data point of the target environmental factor, y i represents the observed value of the i-th data point of the microbial reproduction rate, represents the average value of all environmental factor observations, represents the average value of all observed values ​​of microbial reproduction rate, and i represents the index of the observed data point.

[0075] formula:

[0076]

[0077] Detailed explanation of the formula and the process of formula calculation and derivation:

[0078] The formula was used to calculate the correlation between environmental factors and the growth rate of microorganisms, and the results showed the degree of linear correlation between various environmental factors and the growth rate, including temperature, pressure, and pH value;

[0079] Parameter meaning and setting value:

[0080] x i is the observed value of the environmental factor, assuming that the factor considered is temperature, x i =[20,22,21,23,24];

[0081] yi is the corresponding observed value of microbial reproduction rate, y i =[30,35,33,37,40];

[0082] is the average of all temperature observations,

[0083] is the average of all observed reproduction rates,

[0084] Substitute the parameters into the formula for calculation:

[0085]

[0086] Results xy ≈0.997 indicates that there is a positive correlation between temperature and microbial reproduction rate, which means that increased temperature leads to an increase in the reproduction rate of microorganisms. The calculation process is crucial for regulating and optimizing the fermentation environment.

[0087] The nutrient demand assessment submodule analyzes and identifies the target microorganisms’ needs for multiple nutrients based on the environmental demand analysis results, and generates fermentation demand extraction data;

[0088] Through the use of elemental analyzer and gas chromatography-mass spectrometry technology, the target microorganisms' needs for various nutrients are analyzed. By quantitatively analyzing the absorption rates of multiple trace elements and nutrients, the data are analyzed in combination with machine learning techniques such as support vector machines to identify multiple key nutrients required for microbial growth. The generated fermentation demand extraction data reflects the nutritional needs of the target microorganisms under the target environmental conditions, which is of great significance for optimizing the fermentation medium formula and improving the fermentation efficiency.

[0089] See also Figure 2 and Figure 4 , the feces pretreatment module includes:

[0090] The chemical composition detection submodule extracts data based on fermentation requirements, detects the chemical composition of fecal samples, identifies the organic matter content and the nutrient content required by various microorganisms, and generates fecal chemical composition information;

[0091] The content of various organic matter and nutrients is determined by high performance liquid chromatography and gas chromatography-mass spectrometry, including sample pretreatment, such as precipitation and filtration, optimization of chromatographic conditions, such as adjustment of the pH of the mobile phase and the ratio of the organic phase, and data analysis using quantitative analysis software. The content of various organic matter in the sample is calculated by comparing the standard curve and the sample peak area. The results are used to generate chemical composition information of feces. The process provides a basis for subsequent pH adjustment and provides key scientific data for the optimization and control of the entire feces treatment system.

[0092] The pH adjustment submodule adjusts the pH value of the stool sample based on the chemical composition information of the stool and the activity requirements of the microorganisms to obtain the pH adjustment result;

[0093] According to the survival needs of the target microorganisms, the pH value of the feces is adjusted, the amount of added acid or alkali is adjusted through an automatic titration device, the pH changes are monitored in real time, and the data is continuously recorded using a pH meter to ensure precise control of the target pH value. During the process, the rate and range of pH adjustment are adjusted according to the metabolic characteristics of the microorganisms to match the sensitivity of different microorganisms to the environmental pH. The adjusted pH value has a direct impact on the growth and metabolic activity of the microorganisms. The adjustment result will directly affect the fermentation efficiency and is a key step in optimizing the fermentation process.

[0094] The feces solid-liquid separation submodule performs solid-liquid separation on the adjusted feces based on the pH adjustment result and the environmental requirements for the fermentation activities of the microorganisms to obtain the fermentation matrix pretreatment result;

[0095] The adjusted feces are evaluated, the proportion of solid and liquid components are identified, and separation is achieved through gravity sedimentation. During the process, the physical state of the feces is monitored in real time, the size and distribution of solid particles, the pH value of the liquid and the organic matter content are recorded, and the data is collected in real time through sensors to optimize the separation parameters. By adjusting the operating parameters of the filtration equipment, the separation efficiency is effectively controlled to ensure that the expected treatment quality is achieved and an optimized fermentation matrix is ​​generated. This matrix will have a suitable moisture ratio and pH value, providing an ideal growth and reproduction environment for microorganisms, improving the fermentation efficiency of microorganisms and optimizing the subsequent energy recovery process.

[0096] See also Figure 2 and Figure 5 , the temperature monitoring and control module includes:

[0097] The temperature data acquisition submodule uses a temperature sensor to collect the temperature data in the fermentation container in real time based on the fermentation substrate pretreatment results, and preprocesses the data in combination with time information to obtain temperature monitoring data;

[0098] A digital temperature sensor is used to regularly capture the temperature changes in the fermentation container. The target sensor is connected to the data logger to ensure continuous recording of temperature data every minute. The collected data is sent to the central monitoring system through wireless transmission technology for preliminary screening of the data to exclude abnormal values ​​such as sudden temperature peaks or data transmission errors. Data processing includes smoothing and trend analysis to eliminate the impact of short-term fluctuations on the analysis. The pre-processed temperature data is integrated with time tags to ensure that each data point accurately reflects its corresponding actual sampling time, providing reliable input for subsequent temperature demand analysis.

[0099] The regulation demand analysis submodule calculates the temperature adjustment needs based on the temperature monitoring data and the temperature requirements of microbial metabolism, and generates temperature demand analysis results;

[0100] In the above content, based on the temperature monitoring data and combined with the metabolic needs of the microorganisms, the required temperature adjustment is calculated through the heat balance formula Q = m·c·ΔT to generate the temperature demand analysis results;

[0101] In the formula, Q represents the required heat, m represents the mass of the fermentation medium, c represents the specific heat capacity of the fermentation medium, and ΔT represents the difference between the target temperature and the current temperature;

[0102] Detailed explanation of the formula and the process of formula calculation and derivation:

[0103] Assume that the mass m of the fermentation medium is 1000 g, the specific heat capacity c is 4.18 J / g°C, the current temperature is 25°C, the target temperature is 35°C, and the calculated ΔT is 35-25=10°C. Calculate Q:

[0104] Q = 1000 4.18 10 = 41800;

[0105] The results showed that 41,800 joules of heat energy were required to heat the fermentation medium from 25°C to 35°C. The calculation process was used to determine the actual amount of heat energy that needed to be added during the fermentation process to ensure that the temperature regulation equipment could accurately adjust the temperature in the fermentation container to achieve the most suitable environmental conditions for microbial growth and metabolism.

[0106] The real-time temperature adjustment submodule adjusts the control parameters of the temperature regulating equipment, including heating and cooling equipment, based on the temperature demand analysis results, and outputs the temperature control adjustment results;

[0107] The operating parameters of the heating or cooling system are adjusted according to the analysis data, including the set temperature, start interval and running time. The control system uses the PID control algorithm to automatically adjust the temperature to the target set value, and performs real-time feedback adjustments to adapt to environmental changes or changes in microbial needs. The operating parameters of the temperature regulating equipment are dynamically generated by the temperature control software based on the temperature monitoring data and temperature demand analysis results. The output temperature control adjustment results ensure that the environment in the fermentation container is always in the best state, optimizing the growth of microorganisms and fermentation efficiency.

[0108] See also Figure 2 and Figure 6 , the pressure monitoring and regulation module includes:

[0109] The pressure data acquisition submodule uses the temperature control adjustment result and the pressure sensor to collect the pressure data in the fermentation container in real time, and pre-processes the data in combination with the time information to obtain the pressure monitoring data;

[0110] The multi-point pressure sensors installed inside the fermentation container are used to monitor the pressure changes during the fermentation process in real time. The target sensor captures tiny pressure changes through sensing technology and transmits the data to the central processing unit. In the central processing unit, the data is filtered and error-corrected to remove noise or abnormal readings. The collected pressure data is recorded together with the timestamp to ensure the time accuracy of each data point. By performing time series analysis on the data, the pressure change trend and possible abnormal events are evaluated. The target processed data is stored in the database for further analysis, providing real-time and historical pressure data to support subsequent pressure adjustment decisions.

[0111] The adjustment target identification submodule identifies the pressure adjustment requirements based on the pressure monitoring data and the environmental pressure requirements of microbial growth and metabolism, and generates pressure demand analysis results;

[0112] Use data analysis technology to determine the demand for environmental pressure during microbial growth and metabolism. Apply statistical analysis and machine learning models, such as support vector machines and decision tree analysis, to learn and predict the response of microorganisms to target pressure conditions from batch historical pressure data. By comparing the current monitoring data with the ideal pressure conditions predicted by the model, determine whether the pressure setting needs to be adjusted. Based on the target analysis results, generate pressure demand analysis results. The results detail the difference between the current pressure conditions and the ideal pressure conditions, providing a scientific basis for pressure regulation.

[0113] The real-time pressure adjustment submodule adjusts the control parameters of the pressure regulating device based on the pressure demand analysis results and outputs the pressure balance adjustment results;

[0114] Adjust the pressure control equipment and adjust the opening of the electric pressure regulating valve involved in algorithm control to match the required pressure level. During the adjustment process, the real-time feedback control system continuously adjusts the valve opening according to the feedback information provided by the pressure sensor to maintain the pressure in the fermentation container within the ideal range. Through dynamic adjustment strategies, such as fuzzy logic control or adaptive control strategies, the pressure adjustment process is optimized to ensure the stability of the microbial environment during the fermentation process and to ensure the optimization of fermentation efficiency and product quality.

[0115] See also Figure 2 and Figure 7 , the fermentation speed prediction module includes:

[0116] The gas composition analysis submodule detects and analyzes the gas composition in the fermentation container based on the pressure balance adjustment result, identifies the key gas components related to the microbial fermentation rate, identifies and records the concentration changes of multiple gas components, and obtains gas analysis data;

[0117] Detect the gas composition in the container and identify the concentration of key gas components such as methane and carbon dioxide in real time. Data collection is carried out through gas sensors to ensure the accuracy of detection. Real-time signal processing is performed on the collected data, including filtering, denoising and calibration to ensure data quality. Through gas chromatography analysis software, the concentration changes of target gas components are analyzed in detail, key data points are recorded, and target information is stored in the database, providing a scientific basis for the subsequent fermentation process, understanding the dynamic changes of gas composition, and providing data support for adjusting fermentation conditions.

[0118] The time series analysis submodule calculates the fermentation rate of microorganisms in the container in real time based on the gas analysis data and combines time series analysis to generate the fermentation rate calculation results;

[0119] Time series analysis techniques, such as the autoregressive moving average model, are used to evaluate the changes in microbial activity during the fermentation process in real time. The changing trend of microbial fermentation rate is predicted by calculating the relationship between microbial activity and time. The collected time series data are standardized during data processing, and statistical methods are used to identify patterns and trends in the data. The data used include temperature, pressure, and concentrations of key gas components. The fermentation rate calculation results generated will be updated in real time and used to optimize the fermentation process.

[0120] The gas production capacity analysis submodule predicts the gas production of the container under the current fermentation conditions in real time based on the fermentation speed calculation results, and generates gas production prediction information;

[0121] The specific formula for real-time prediction of the gas production of the container under the current fermentation conditions is:

[0122]

[0123] in, represents the predicted gas production, n represents the total number of support vectors, j is the index variable, α j is the coefficient of the jth support vector, P j is the known gas production of the jth training data point, e is the base of the natural logarithm, γ is the width parameter of the radial basis function kernel, which is used to adjust the spread of the function or the tightness of the data mapping, ∥Q j -Q∥ 2 represents the jth training sample Q j The square of the Euclidean distance from the new input sample Q in the feature space, Q j is the feature vector of the jth training sample, Q is the feature vector of the new input sample, including microbial species and environmental parameters, and b is the bias term of the model.

[0124] formula:

[0125]

[0126] Detailed explanation of the formula and the process of formula calculation and derivation:

[0127] The formula is used to predict the gas production during the fermentation process, optimize the management process of the fermentation process, and improve the energy recovery efficiency;

[0128] Parameter meaning and setting value:

[0129] α j is the coefficient of the jth support vector, assuming α1 = 0.3;

[0130] P j is the historical gas production, assuming P1 = 50 mL / h;

[0131] γ is the width parameter of the RBF kernel, which is set to 0.1;

[0132] Q j and Q represent the historical and current environmental parameter vectors, including temperature, pressure, and pH value, respectively. Assume that in the target measurement, Q1 = [37, 1.2, 7.0], Q = [36.5, 1.1, 7.1];

[0133] b is the bias term of the model, which is set to 5 mL / h;

[0134] The calculation process is:

[0135]

[0136] The calculation results show that under the current fermentation environment conditions, the predicted gas production per hour is about 19.616 ml. The calculation process is used to predict the future gas production of the target treatment container and provide a data basis for the adjustment of the treatment process.

[0137] See also Figure 8 A livestock and poultry manure treatment method based on microbial fermentation and intelligent temperature control, the method is applied to a livestock and poultry manure treatment system based on microbial fermentation and intelligent temperature control, the method comprising:

[0138] S1: Based on the microbial species information, collect the growth data of microorganisms under various conditions, evaluate the effects of various environmental factors on the microbial reproduction rate, including temperature, pressure and pH value, and generate fermentation demand extraction data;

[0139] S2: Extract data based on fermentation requirements, detect the chemical composition of feces, adjust the pH value, match the activity requirements of microorganisms, and perform solid-liquid separation to obtain the fermentation matrix pretreatment results;

[0140] S3: receiving the fermentation substrate pretreatment result, monitoring the temperature in the fermentation container in real time, adjusting the temperature control parameters according to the metabolic needs of the microorganisms, and outputting the temperature control adjustment result;

[0141] S4: using the temperature control adjustment result, monitoring the real-time pressure in the container in real time, adjusting the pressure control parameters according to the growth and metabolic requirements of the microorganisms, and generating a pressure balance adjustment result;

[0142] S5: According to the pressure balance adjustment result, the changing trend of the gas composition in the fermentation container is analyzed in real time, and the gas production is predicted in combination with the time series analysis to obtain the gas production prediction information.

[0143] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0144] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0145] In the present invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0146] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0147] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0148] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0149] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0150] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0151] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0152] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0153] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. Livestock and poultry manure treatment system based on microbial fermentation and intelligent temperature control, characterized in that: The system comprises: The activity demand analysis module is based on the microbial species information. By analyzing the reproduction rate of microorganisms under various conditions, it identifies the nutritional and environmental requirements of various microorganisms during fermentation activities, including temperature, pressure, and pH value, and generates fermentation demand extraction data; The feces pretreatment module extracts data based on the fermentation requirements, detects the chemical composition of the feces, adjusts the pH value according to the activity requirements of the microorganisms, performs solid-liquid separation, and outputs the fermentation substrate pretreatment results; The temperature monitoring and control module receives the fermentation substrate pretreatment result, monitors the temperature data in the fermentation container in real time, adjusts the temperature control parameters according to the metabolic needs of the microorganisms, and outputs the temperature control adjustment result; The pressure monitoring and regulating module uses the temperature control adjustment result to monitor the real-time pressure in the fermentation container in real time, adjusts the pressure control parameters according to the pressure requirements of microbial growth and metabolism, and outputs the pressure balance adjustment result; The fermentation speed prediction module uses the pressure balance adjustment result to analyze the change trend of the gas composition in the container to predict the fermentation speed of the microorganism, combines time series analysis to predict the gas production of the target fermentation container, and outputs the gas production prediction information.

2. The livestock and poultry manure treatment system based on microbial fermentation and intelligent temperature control according to claim 1 is characterized in that: The fermentation demand extraction data specifically includes microbial reproduction rate information, nutritional demand data, and environmental demand information. The fermentation matrix pretreatment results include a feces chemical composition list, pH adjustment parameters, and solid-liquid separation parameters. The temperature control adjustment results specifically refer to real-time temperature monitoring data, cooling adjustment parameters, and heating adjustment parameters. The pressure balance adjustment results include real-time pressure data, pressure adjustment parameters, and pressure adaptability analysis data. The gas production prediction information specifically includes gas composition analysis results, fermentation speed change trends, and gas production calculation results.

3. The livestock and poultry manure treatment system based on microbial fermentation and intelligent temperature control according to claim 1 is characterized in that: The activity demand analysis module includes: The growth performance analysis submodule collects the reproduction rate data of microorganisms in various environments based on the microbial species information, evaluates the growth performance under various conditions, and obtains the microbial reproduction data; The reproduction rate analysis submodule analyzes the relationship between the microbial reproduction rate and various environmental factors, including temperature, pressure, and pH value, based on the microbial reproduction data, identifies the fermentation conditions required by the target microorganism, and obtains the environmental demand analysis results; The nutritional demand assessment submodule analyzes and identifies the target microorganism's demand for multiple nutrients based on the environmental demand analysis results, and generates fermentation demand extraction data.

4. The livestock and poultry manure treatment system based on microbial fermentation and intelligent temperature control according to claim 3 is characterized in that: The specific formula for analyzing the relationship between the microbial reproduction rate and various environmental factors is: Among them, r xy represents the correlation coefficient between the microbial growth rate and the target environmental factors, x i represents the observed value of the i-th data point of the target environmental factor, y i represents the observed value of the i-th data point of the microbial reproduction rate, represents the average value of all environmental factor observations, represents the average value of all observed values ​​of microbial reproduction rate, and i represents the index of the observed data point.

5. The livestock and poultry manure treatment system based on microbial fermentation and intelligent temperature control according to claim 1 is characterized in that: The feces pretreatment module comprises: The chemical composition detection submodule extracts data based on the fermentation requirements, detects the chemical composition of the fecal sample, identifies the organic matter content and the nutrient content required by various microorganisms, and generates fecal chemical composition information; The pH adjustment submodule adjusts the pH value of the stool sample based on the chemical composition information of the stool and the activity requirements of the microorganisms to obtain a pH adjustment result; The feces solid-liquid separation submodule performs solid-liquid separation on the adjusted feces based on the pH adjustment result and the environmental requirements of the microorganisms for fermentation activities to obtain a fermentation substrate pretreatment result.

6. The livestock and poultry manure treatment system based on microbial fermentation and intelligent temperature control according to claim 1 is characterized in that: The temperature monitoring and control module includes: The temperature data acquisition submodule uses a temperature sensor to collect temperature data in the fermentation container in real time based on the fermentation substrate pretreatment result, and preprocesses the data in combination with time information to obtain temperature monitoring data; The regulation demand analysis submodule calculates the temperature adjustment needs based on the temperature monitoring data and the temperature conditions required by microbial metabolism, and generates a temperature demand analysis result; The real-time temperature adjustment submodule adjusts the control parameters of the temperature regulating equipment, including heating and cooling equipment, based on the temperature demand analysis result, and outputs the temperature control adjustment result.

7. The livestock and poultry manure treatment system based on microbial fermentation and intelligent temperature control according to claim 1 is characterized in that: The pressure monitoring and regulating module comprises: The pressure data acquisition submodule uses the temperature control adjustment result and the pressure sensor to collect the pressure data in the fermentation container in real time, and pre-processes the data in combination with the time information to obtain the pressure monitoring data; The adjustment target identification submodule identifies the pressure adjustment demand based on the pressure monitoring data and the demand for environmental pressure of microbial growth and metabolism, and generates a pressure demand analysis result; The real-time pressure adjustment submodule adjusts the control parameters of the pressure regulating device based on the pressure demand analysis result and outputs a pressure balance adjustment result.

8. The livestock and poultry manure treatment system based on microbial fermentation and intelligent temperature control according to claim 1 is characterized in that: The fermentation speed prediction module comprises: The gas component analysis submodule detects and analyzes the gas components in the fermentation container based on the pressure balance adjustment result, identifies key gas components related to the microbial fermentation rate, identifies and records the concentration changes of multiple gas components, and obtains gas analysis data; The time series analysis submodule calculates the fermentation speed of the microorganisms in the container in real time based on the gas analysis data and in combination with the time series analysis, and generates a fermentation speed calculation result; The gas production capacity analysis submodule predicts the gas production of the container under the current fermentation conditions in real time based on the fermentation speed calculation result, and generates gas production prediction information.

9. The livestock and poultry manure treatment system based on microbial fermentation and intelligent temperature control according to claim 8, characterized in that: The specific formula for real-time prediction of the gas production of the container under the current fermentation conditions is: in, represents the predicted gas production, n represents the total number of support vectors, j is the index variable, α j is the coefficient of the jth support vector, P j is the known gas production of the jth training data point, e is the base of the natural logarithm, γ is the width parameter of the radial basis function kernel, which is used to adjust the spread of the function or the tightness of the data mapping, ∥Q j -Q∥ 2 represents the jth training sample Q j The square of the Euclidean distance from the new input sample Q in the feature space, Q j is the feature vector of the jth training sample, Q is the feature vector of the new input sample, including microbial species and environmental parameters, and b is the bias term of the model.

10. A method for treating livestock and poultry manure based on microbial fermentation and intelligent temperature control, characterized in that: According to any one of claims 1 to 9, the livestock and poultry manure treatment system based on microbial fermentation and intelligent temperature control comprises: Based on the microbial species information, collect the growth data of microorganisms under various conditions, evaluate the effects of various environmental factors on the microbial reproduction rate, including temperature, pressure and pH value, and generate fermentation demand extraction data; Extracting data based on the fermentation requirements, detecting the chemical composition of feces, adjusting the pH value, matching the activity requirements of microorganisms, and performing solid-liquid separation to obtain fermentation substrate pretreatment results; Receive the fermentation substrate pretreatment result, monitor the temperature in the fermentation container in real time, adjust the temperature control parameters according to the metabolic needs of the microorganisms, and output the temperature control adjustment result; Using the temperature control adjustment result, real-time monitoring of the real-time pressure in the container is performed, and the pressure control parameters are adjusted according to the growth and metabolic requirements of the microorganisms to generate a pressure balance adjustment result; According to the pressure balance adjustment result, the change trend of the gas composition in the fermentation container is analyzed in real time, and the gas production is predicted in combination with time series analysis to obtain gas production prediction information.

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