A straw feed fermentation treatment control system and method

Abstract

The present invention discloses a control system and method for fermenting and processing straw feed, specifically relating to the technical field of production control, and is used to solve the problem that there are deficiencies in the identification and regulation of the internal imbalance phenomenon in the straw fermentation pile caused by uneven gas diffusion during the fermentation process; by collecting the environmental parameters of the fermentation pile, performing three-dimensional gas concentration field reconstruction processing, combining the fuzzy clustering algorithm to identify the stratified air mass region, measuring the pile pressure distribution and deformation characteristics, analyzing the abnormal region of the pile body structure, and marking the risk analysis region; through the three-dimensional tensor model and the fractal analysis model, respectively evaluate the time dependence of the gas exchange and metabolism process and the degree of gas diffusion equilibrium, and finally dynamically adjust the structure parameters of the fermentation pile and the operation parameters of the ventilation device based on the closed-loop control system; through the precise region identification and dynamic regulation mechanism, the uniformity of the fermentation process and the quality consistency of the straw feed are significantly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of production control, and more specifically, to a control system and method for fermenting and processing straw feed. Background Art

[0002] In a large-scale straw fermentation pile, due to the influence of local stacking pressure and pile deformation, gas diffusion is often difficult to achieve uniform distribution, which may form special regions similar to "stratified air masses". These stratified air mass regions will significantly affect the spatial distribution of the microbial community, resulting in obvious differences in the oxygen, carbon dioxide concentration and humidity in different regions of the fermentation pile. These differences further affect the utilization efficiency of nitrogen, organic acids and trace elements by microorganisms, thus leading to nutritional gradients and quality differences in the upper, middle and lower layers of the fermentation products.

[0003] In the prior art, there are deficiencies in the identification and regulation of the internal imbalance phenomenon of the straw fermentation pile caused by uneven gas diffusion during the fermentation process, which will lead to low fermentation efficiency, thus affecting the uniformity and quality consistency of the straw feed. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a control system and method for fermenting and processing straw feed to solve the problems raised in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A control method for fermenting and processing straw feed, comprising the following steps:

[0007] Collect the fermentation pile environment parameters at the straw fermentation pile, perform three-dimensional gas concentration field reconstruction processing on the fermentation pile environment parameters, and obtain the gas distribution information at each position inside the straw fermentation pile;

[0008] Use the fuzzy clustering algorithm to classify and analyze the gas distribution information, and identify the stratified air mass regions with gas concentration gradient characteristics;

[0009] Measure the stacking pressure distribution and deformation characteristics of the straw fermentation pile, analyze the potential influence of uneven gas diffusion inside the straw fermentation pile, and identify the abnormal pile body structure regions;

[0010] Mark the union of the stratified air mass regions and the abnormal pile body structure regions as the risk analysis region;

[0011] Analyze the dynamic relationship between the gas concentration and metabolites in the risk analysis region by constructing a three-dimensional tensor model, and evaluate the time dependence of the gas exchange and metabolism process;

[0012] Analyze the distribution characteristics of the gas concentration field in the risk analysis area through a fractal analysis model to evaluate the gas diffusion equilibrium degree in the local area;

[0013] Based on the time-dependence of the gas exchange and metabolism process and the gas diffusion equilibrium degree in the local area, quantitatively adjust the parameters of the straw fermentation pile structure and the ventilation device through closed-loop control.

[0014] In a preferred embodiment, collect the fermentation pile environment parameters at the straw fermentation pile, perform three-dimensional gas concentration field reconstruction processing on the fermentation pile environment parameters, and obtain the gas distribution information at each position inside the straw fermentation pile, specifically including:

[0015] Set multiple collection points within the vertical range from the bottom to the top and the horizontal range from the center to the edge inside the straw fermentation pile to collect the fermentation pile environment parameters. The fermentation pile environment parameters include oxygen concentration, carbon dioxide concentration, and humidity;

[0016] Based on the geometric shape of the straw fermentation pile and the spatial distribution of the collection points, construct a three-dimensional coordinate system matching the straw fermentation pile;

[0017] Map the fermentation pile environment parameters into the three-dimensional coordinate system to generate the gas distribution information at each position inside the straw fermentation pile; the gas distribution information includes oxygen concentration distribution, carbon dioxide concentration distribution, and humidity distribution.

[0018] In a preferred embodiment, use the fuzzy clustering algorithm to classify and analyze the gas distribution information to identify the stratified air mass area with gas concentration gradient characteristics, specifically including:

[0019] Based on the oxygen concentration distribution, carbon dioxide concentration distribution, and humidity distribution of each point in the three-dimensional space, use the parameter values of each spatial point in the gas distribution information as clustering features;

[0020] Use the fuzzy clustering algorithm to classify the spatial points in the gas distribution information according to the gas concentration gradient characteristics of the spatial points to form multiple clustering categories;

[0021] Mark the clustering category with significant gas concentration gradient characteristics and the spatial distribution characteristics of the clustering category conforming to the characteristics of the stratified air mass as the stratified air mass area.

[0022] In a preferred embodiment, measure the pile pressure distribution and deformation characteristics of the straw fermentation pile, analyze the potential impact of uneven gas diffusion in the straw fermentation pile, and identify the abnormal area of the pile body structure, specifically including:

[0023] Arrange multiple piezoresistive sensors in the vertical and horizontal directions of the straw fermentation pile to measure the pile pressure values at each position;

[0024] Use a three-dimensional scanning device to scan the surface shape of the straw fermentation pile and obtain the three-dimensional morphological characteristic data of the straw fermentation pile;

[0025] Conduct a comparative analysis of the stacking pressure value and the three-dimensional morphological characteristic data to identify the stacking pressure abnormal area and the significantly deformed area;

[0026] According to the positions of the stacking pressure abnormal area and the significantly deformed area, identify the abnormal areas of the pile structure that may affect gas diffusion.

[0027] In a preferred embodiment, analyze the dynamic relationship between the gas concentration and the metabolite in the risk analysis area by constructing a three-dimensional tensor model, and evaluate the time dependence of the gas exchange and metabolism process, specifically including:

[0028] Obtain the gas concentration data and metabolite concentration data at each position in the risk analysis area;

[0029] Map the gas concentration data and the metabolite concentration data to different dimensions of the three-dimensional tensor respectively with the time series as the dimension;

[0030] Use the time-dependent dynamic modeling algorithm to decompose the three-dimensional tensor and extract the main dynamic factors of the gas concentration and metabolite changes;

[0031] Analyze the mutual relationship between the main dynamic factors and calculate the time lag characteristics of the gas concentration and metabolite concentration changes;

[0032] Quantitatively evaluate the time dependence of the gas exchange and metabolism process according to the time lag characteristics.

[0033] In a preferred embodiment, analyze the mutual relationship between the main dynamic factors and calculate the time lag characteristics of the gas concentration and metabolite concentration changes, specifically as follows:

[0034] For each main dynamic factor, calculate the time difference between the gas concentration change and the metabolite concentration change, and the formula is as follows: ; where, represents the time lag value of the th main dynamic factor, represents the correlation function, represents the time change characteristic related to the gas concentration in the th main dynamic factor at the time point , represents the time change characteristic related to the metabolite concentration in the th main dynamic factor at the time point , represents the lag time of the time change of the gas concentration relative to the time change of the metabolite concentration, Represents a specific time point in the time series.

[0035] In a preferred embodiment, the time-dependence of gas exchange and metabolic processes is quantitatively evaluated according to the time-lag characteristics, specifically as follows:

[0036] Calculate the time-dependence index, and its expression is: ; where Represents the time-dependence index, Represents the weight of the th main dynamic factor, Greater than 0, Represents the number of main dynamic factors extracted, Is the number of the main dynamic factor.

[0037] In a preferred embodiment, the distribution characteristics of the gas concentration field in the risk analysis area are analyzed through a fractal analysis model to evaluate the gas diffusion equilibrium degree in the local area, specifically including:

[0038] Sort the gas concentration data in the risk analysis area according to spatial position and time dimension to form a gas concentration distribution matrix;

[0039] Use the fractal analysis model to perform multi-scale decomposition on the gas concentration distribution matrix;

[0040] Calculate the fractal dimension of each scale, and evaluate the gas diffusion equilibrium degree in the local area according to the change trend of the fractal dimension; the change trend of the fractal dimension is calculated by comparing the change amount of the fractal dimension under different decomposition scales.

[0041] In a preferred embodiment, based on the time-dependence of gas exchange and metabolic processes and the gas diffusion equilibrium degree in the local area, the structure of the straw fermentation pile and the parameters of the ventilation device are quantitatively adjusted through closed-loop control, specifically including:

[0042] Set the adjustment target parameters of the fermentation pile structure according to the change trend of the time-dependence index and the fractal dimension, including the pile shape parameters and the compaction degree parameters;

[0043] Set the adjustment target parameters of the ventilation device according to the change trend of the time-dependence index and the fractal dimension, including the on-off state and flow parameters of the ventilation pipeline;

[0044] Based on the adjustment target parameters of the fermentation pile structure, perform fermentation pile structure adjustment through the closed-loop control system, including changing the shape and compaction degree of the fermentation pile; based on the adjustment target parameters of the ventilation device, adjust the ventilation device through the closed-loop control system, including controlling the on-off of the ventilation pipeline and the air flow rate.

[0045] On the other hand, the present invention provides a straw feed fermentation treatment control system, including an environmental parameter acquisition module, a stratified air mass identification module, a measurement of pile pressure deformation module, a risk area marking module, a time-dependent analysis module, a diffusion equilibrium evaluation module, and a closed-loop control adjustment module;

[0046] Environmental parameter acquisition module: Collect the environmental parameters of the fermentation pile at the straw fermentation pile, perform three-dimensional gas concentration field reconstruction processing on the environmental parameters of the fermentation pile, and obtain the gas distribution information at each position inside the straw fermentation pile;

[0047] Stratified air mass identification module: Use the fuzzy clustering algorithm to classify and analyze the gas distribution information, and identify the stratified air mass area with the characteristics of gas concentration gradient;

[0048] Measurement of pile pressure deformation module: Measure the pile pressure distribution and deformation characteristics of the straw fermentation pile, analyze the potential impact of uneven gas diffusion in the straw fermentation pile, and identify the abnormal area of the pile structure;

[0049] Risk area marking module: Mark the union of the stratified air mass area and the abnormal area of the pile structure as the risk analysis area;

[0050] Time-dependent analysis module: Analyze the dynamic relationship between gas concentration and metabolites in the risk analysis area by constructing a three-dimensional tensor model, and evaluate the time dependence of the gas exchange and metabolism process;

[0051] Diffusion equilibrium evaluation module: Analyze the distribution characteristics of the gas concentration field in the risk analysis area through a fractal analysis model, and evaluate the gas diffusion equilibrium degree of the local area;

[0052] Closed-loop control adjustment module: Based on the time dependence of the gas exchange and metabolism process and the gas diffusion equilibrium degree of the local area, quantitatively adjust the straw fermentation pile structure and ventilation device parameters through closed-loop control.

[0053] The technical effects and advantages of a straw feed fermentation treatment control system and method of the present invention:

[0054] 1. By collecting environmental parameters in the fermentation pile and reconstructing the three-dimensional gas concentration field, combined with the fuzzy clustering algorithm, the stratified air mass region with gas concentration gradient characteristics is accurately identified. At the same time, by measuring the pile pressure distribution and deformation characteristics, the abnormal area of the pile structure is identified, and the union of the two is marked as the risk analysis area, improving the recognition accuracy and efficiency of the uneven gas diffusion phenomenon; by constructing a three-dimensional tensor model and a fractal analysis model, the multi-dimensional analysis of the gas concentration field and the dynamic relationship of metabolites in the risk analysis area is carried out, quantifying the time dependence of the gas exchange and metabolism process and the degree of gas diffusion equilibrium, providing a scientific basis for the internal dynamic adjustment of the fermentation pile; this method based on dynamic analysis and zoning processing solves the problem of insufficient recognition of regional differences in traditional fermentation control, thus improving the overall uniformity of the fermentation process and the consistency of feed quality.

[0055] 2. The present invention, through a closed-loop control system, dynamically adjusts the structural parameters of the fermentation pile (such as the pile shape, compaction degree) and the operating parameters of the ventilation device (such as the on-off state and flow rate of the ventilation pipeline) according to the evaluation results of the time dependence index of the gas exchange and metabolism process and the degree of gas diffusion equilibrium; this precise dynamic regulation mechanism effectively optimizes the gas flow conditions, improves the balance of the microenvironment in the fermentation pile, and significantly improves the metabolic efficiency of microorganisms in the fermentation pile by comprehensively considering the coupling relationship between microbial metabolism and gas distribution, reduces abnormal phenomena in local areas, and realizes the uniformity of the product nutritional quality and the improvement of the overall fermentation efficiency in the straw feed fermentation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a schematic diagram of a control method for straw feed fermentation treatment according to the present invention;

[0057] Figure 2 It is a schematic structural diagram of a control system for straw feed fermentation treatment according to the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0058] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0059] Example 1: Figure 1 A control method for straw feed fermentation treatment according to the present invention is given, which includes the following steps:

[0060] Collect the environmental parameters of the straw fermentation pile, perform three-dimensional gas concentration field reconstruction on the environmental parameters of the fermentation pile, and obtain the gas distribution information at each position inside the straw fermentation pile.

[0061] Use the fuzzy clustering algorithm to classify and analyze the gas distribution information, and identify the stratified air mass regions with gas concentration gradient characteristics.

[0062] Measure the pile pressure distribution and deformation characteristics of the straw fermentation pile, analyze the potential impact of uneven gas diffusion inside the straw fermentation pile, and identify the abnormal areas of the pile structure.

[0063] Mark the union of the stratified air mass region and the abnormal area of the pile structure as the risk analysis region.

[0064] Analyze the dynamic relationship between gas concentration and metabolites in the risk analysis region by constructing a three-dimensional tensor model, and evaluate the time dependence of the gas exchange and metabolism processes.

[0065] Analyze the distribution characteristics of the gas concentration field in the risk analysis region through a fractal analysis model, and evaluate the gas diffusion equilibrium degree in the local area.

[0066] Based on the time dependence of the gas exchange and metabolism processes and the gas diffusion equilibrium degree in the local area, quantitatively adjust the parameters of the straw fermentation pile structure and ventilation device through closed-loop control.

[0067] Collect the environmental parameters of the straw fermentation pile, perform three-dimensional gas concentration field reconstruction on the environmental parameters of the fermentation pile, and obtain the gas distribution information at each position inside the straw fermentation pile, specifically including:

[0068] Set multiple collection points within the vertical range from the bottom to the top and the horizontal range from the center to the edge inside the straw fermentation pile for collecting the environmental parameters of the fermentation pile. The environmental parameters of the fermentation pile include oxygen concentration, carbon dioxide concentration, and humidity:

[0069] Inside the straw fermentation pile, evenly arrange multiple collection points within the vertical range from the bottom to the top of the pile body and the horizontal direction range from the center to the edge of the pile body. The layout density of the collection points is adjusted according to the volume and complexity of the fermentation pile to ensure coverage of the key areas of the fermentation pile.

[0070] Install the following sensors at each collection point: oxygen concentration sensor (for measuring the oxygen concentration in the gas); carbon dioxide concentration sensor (for measuring the carbon dioxide concentration in the gas); humidity sensor (for measuring the humidity of the local environment).

[0071] The collection frequency can be set to once per minute to capture the dynamic changes in gas distribution during the fermentation process; the data is recorded in the form of coordinates and timestamps, and a set of parameter values is output for each collection point.

[0072] Based on the geometric shape of the straw fermentation pile and the spatial distribution of the collection points, a three-dimensional coordinate system matching the straw fermentation pile is constructed:

[0073] According to the geometric shape of the fermentation pile and the distribution of the sensor collection points, a three-dimensional coordinate system is established. The origin of this three-dimensional coordinate system is the center point at the bottom of the fermentation pile, and the spatial distribution of the pile body is defined using Cartesian coordinates, and the spatial positions of the collection points are located.

[0074] Among them, the acquisition of the geometric shape is achieved by using a laser scanning device or a three-dimensional modeling tool to measure the overall shape and boundary dimensions of the fermentation pile, ensuring that the three-dimensional coordinate system matches the actual pile body space.

[0075] Map the environmental parameters of the fermentation pile into the three-dimensional coordinate system to generate the gas distribution information at each position inside the straw fermentation pile:

[0076] For each collection point, bind its spatial position to the oxygen concentration, carbon dioxide concentration, and humidity values recorded by the sensor.

[0077] Form lattice data in the three-dimensional coordinate system, where the spatial position of each point corresponds to a set of fermentation pile environmental parameters.

[0078] It should be noted that here a three-dimensional interpolation algorithm (such as nearest neighbor interpolation or cubic spline interpolation) can be used to supplement the parameter values at the positions where the collection points are not arranged. The interpolation method calculates the estimated values of the unsampled points based on the parameter values of the surrounding sampled points to generate a complete three-dimensional data field.

[0079] Based on the mapped and interpolated data, generate the gas distribution information at each position inside the fermentation pile. The gas distribution information includes:

[0080] Oxygen concentration distribution: The oxygen concentration values at each point in the three-dimensional space, showing the concentration changes of oxygen in different regions.

[0081] Carbon dioxide concentration distribution: The carbon dioxide concentration values at each point in the three-dimensional space, revealing the intensity of gas exchange inside the pile.

[0082] Humidity distribution: The humidity values at each point in the three-dimensional space, reflecting the moisture distribution conditions in different regions.

[0083] Use the fuzzy clustering algorithm to classify and analyze the gas distribution information to identify the stratified gas mass regions with gas concentration gradient characteristics, specifically including:

[0084] Based on the oxygen concentration distribution, carbon dioxide concentration distribution, and humidity distribution at each point in the three-dimensional space, use the parameter values of each spatial point in the gas distribution information as clustering features:

[0085] After generating the gas distribution information, the oxygen concentration distribution, carbon dioxide concentration distribution, and humidity distribution at each point in the three-dimensional space are used as the basic data for classification analysis. The parameter values at each spatial point, including oxygen concentration, carbon dioxide concentration, and humidity, are used as the feature data for subsequent clustering analysis.

[0086] The feature of each spatial point is represented as a three-dimensional vector , where represents the oxygen concentration at that point, represents the carbon dioxide concentration at that point, represents the humidity at that point.

[0087] The coordinate information of the spatial points has been bound to the corresponding parameter values during the generation of the gas distribution information and does not require additional processing.

[0088] Integrate the data of all spatial points in the gas distribution information into a feature matrix , which is used as the input for the fuzzy clustering algorithm.

[0089] The feature matrix has the form: ; where is the total number of all sampling points in the three-dimensional space, and each row represents the data of a spatial point, including oxygen concentration, carbon dioxide concentration, and humidity.

[0090] Use the fuzzy clustering algorithm to classify the spatial points in the gas distribution information according to the gas concentration gradient characteristics of the spatial points, forming multiple clustering categories:

[0091] Use the fuzzy c-means clustering algorithm for clustering analysis, and its goal is to minimize the following objective function: ; where represents the clustering objective function; represents the total number of all sampling points in the three-dimensional space; represents the number of clustering categories; represents the membership degree of the th spatial point belonging to the th clustering category, with a range of [0,1]; represents the fuzzy factor, which controls the fuzziness of the membership degree, usually with a value range of [1.5,3]; represents the th feature vector of the spatial point; represents the clustering center of the th clustering category; represents the Euclidean distance between the feature vector and the clustering center.

[0092] Among them, ; ; where, is the central value of oxygen concentration for the th clustering category, is the central value of carbon dioxide concentration for the th clustering category, is the central value of humidity for the th clustering category; the central values of oxygen concentration, carbon dioxide concentration, and humidity are obtained through the fuzzy clustering algorithm. During the clustering process, according to the parameter values of all spatial points in each category, the weighted average calculation method is adopted, with the membership degree of the points as the weight, to obtain the weighted average values of oxygen concentration, carbon dioxide concentration, and humidity for the category as the corresponding central values.

[0093] In the fuzzy clustering algorithm, the clustering process is completed based on the iterative optimization of the objective function. In each iteration, according to the distance between the feature values of the current spatial points and the clustering centers of each category, the membership degree of each point is updated; at the same time, the clustering center values are recalculated based on the membership degrees of the points. This process continues until the change amount of the objective function is lower than the preset threshold or the maximum number of iterations is reached.

[0094] According to the results of fuzzy clustering, calculate the gas concentration gradient of each category: ; where, represents the gas concentration gradient, represents the maximum difference in gas concentration within the category, represents the maximum distance between spatial points within the category.

[0095] Mark the clustering category with significant gas concentration gradient characteristics and the spatial distribution characteristics of the clustering category conforming to the characteristics of stratified air masses as the stratified air mass region:

[0096] Set the gas concentration gradient threshold, and for each clustering category, compare its gas concentration gradient with the gas concentration gradient threshold:

[0097] If , then it is determined that this clustering category has significant gas concentration gradient characteristics.

[0098] If , then it is determined that this clustering category does not have significant gas concentration gradient characteristics.

[0099] Among them, is the gas concentration gradient threshold, and the gas concentration gradient threshold is the standard value used to determine whether the gas concentration change is significant, usually determined through experiments or experience. For example, when the oxygen or carbon dioxide concentration gradient change exceeds 5% / m, it can be considered that the concentration gradient is significant and is suitable as the determination basis for the stratified air mass region.

[0100] Further analyze the spatial distribution characteristics of the clustering category to determine whether it conforms to the characteristics of stratified air masses.

[0101] Analyze the spatial distribution characteristics of clustering categories, which are specifically achieved through the following methods: First, extract all spatial points in the clustering category and calculate their distribution density in the vertical direction (such as the z-axis) to observe the concentration degree and gradient change of points in height. Second, analyze the three-dimensional distribution form of spatial points in the category to determine whether there is an obvious stratification trend. For example, whether the distribution of high-density points is concentrated in a certain vertical range or shows regular changes with height. In addition, calculate the variance and centroid position of category points in the vertical direction to evaluate whether the distribution has significant hierarchical characteristics. Finally, by comparing the vertical distribution characteristics within the category with those of other categories, further confirm whether the category meets the definition of a stratified air mass, and mark the category with an obvious stratified structure as conforming to the characteristics of a stratified air mass.

[0102] When the clustering category has significant gas concentration gradient characteristics and the spatial distribution characteristics of the clustering category conform to the characteristics of a stratified air mass, it is marked as a stratified air mass region.

[0103] Measure the compaction distribution and deformation characteristics of the straw fermentation pile, analyze the potential impact of uneven gas diffusion in the straw fermentation pile, and identify abnormal regions of the pile structure, specifically including:

[0104] Install multiple piezoresistive sensors in the vertical and horizontal directions of the straw fermentation pile to measure the compaction values at each position:

[0105] The sensor layout covers the entire three-dimensional space of the fermentation pile, including the bottom of the pile, the interior of the pile, and the surface of the pile, ensuring the collection of compaction at all key positions; the layout interval of the sensors is determined according to the size of the fermentation pile and the expected range of compaction changes. A higher layout density is set for larger fermentation piles to ensure accurate compaction data collection.

[0106] Use a three-dimensional scanning device to scan the surface shape of the straw fermentation pile to obtain three-dimensional morphological characteristic data of the straw fermentation pile:

[0107] Use a laser scanning device or a structured light scanner to record the surface shape of the fermentation pile with high precision; cover the entire surface of the fermentation pile and conduct a full-range scan from the bottom to the top to ensure complete capture of the pile morphology; according to the dynamic change characteristics of the fermentation pile, set an appropriate scanning frequency, and it is recommended to scan once every 6 hours to capture the changes of the pile over time.

[0108] The three-dimensional morphological characteristic data obtained by scanning includes information such as the height distribution, surface flatness, and tilt angle of the pile body, and is saved in a digital format.

[0109] Conduct a comparative analysis of the compaction values and three-dimensional morphological characteristic data to identify abnormal compaction regions and significantly deformed regions:

[0110] Based on the spatial coordinates of the sensor layout positions and three-dimensional shapes, align the stack pressure data and shape data spatially to ensure that the two sets of data can be compared within the same spatial range.

[0111] Conduct regional gradient analysis on the stack pressure data to identify stack pressure anomaly regions. For example, calculate the change amplitude of stack pressure values between different collection points, identify high-gradient regions, and set the stack pressure gradient threshold at 20 kPa / m. Regions exceeding the stack pressure gradient threshold are marked as stack pressure anomaly regions.

[0112] Analyze the surface height distribution and inclination angle of the stack body to identify significantly deformed regions. For example, identify the deformed regions of the stack body by calculating the height difference between adjacent scan points, and set the surface height difference threshold at 5 cm. Regions exceeding the surface height difference threshold are marked as significantly deformed regions.

[0113] Based on the positions of the stack pressure anomaly regions and significantly deformed regions, identify stack body structure anomaly regions that may affect gas diffusion:

[0114] Overlay the stack pressure anomaly regions and significantly deformed regions to generate stack body structure anomaly regions. The union method can be used to identify the union of the stack pressure anomaly regions and significantly deformed regions as the stack body structure anomaly regions.

[0115] The stack body structure anomaly regions reflect the spatial position characteristics within the straw fermentation stack that may cause gas diffusion obstruction. This region usually shows areas with significant changes in stack pressure gradient or stack body deformation beyond the normal range. For example, high-pressure regions caused by excessive local compaction, or significantly deformed regions caused by stack body settlement or inclination, may hinder the uniform flow of gas inside the fermentation stack, forming local gas flow obstruction phenomena, thereby affecting the microbial metabolism and the uniformity of the distribution of fermentation products. In addition, it may also lead to too low oxygen concentration or too high carbon dioxide concentration in local regions, further triggering microbial community imbalance. By identifying these anomaly regions, it can provide a basis for optimizing the fermentation stack structure and adjusting the ventilation system to ensure the uniformity of gas distribution.

[0116] The union of the stratified air mass region and the abnormal heap structure region is marked as the risk analysis region because these two types of regions respectively reflect two key problems: uneven gas concentration distribution and abnormal heap structure. These problems often act together to cause gas flow obstruction. There is usually a significant gas concentration gradient in the stratified air mass region, which may directly affect the metabolic efficiency and fermentation uniformity of microorganisms. The abnormal heap structure region may hinder gas diffusion through deformation or abnormal heap pressure, exacerbating the stratification phenomenon. By marking the union of these two types of regions as the risk analysis region, all potential risk positions in the fermentation heap can be more comprehensively covered, providing a more targeted basis for subsequent adjustments and optimizations, avoiding risk blind spots caused by omitting a single type of abnormal region, and enhancing the overall stability of gas flow and the fermentation process.

[0117] By constructing a three-dimensional tensor model to analyze the dynamic relationship between gas concentration and metabolites in the risk analysis region and evaluate the time dependence of gas exchange and metabolism processes, specifically including:

[0118] Obtain gas concentration data and metabolite concentration data at each position in the risk analysis region:

[0119] At each position in the risk analysis region, collect oxygen concentration and carbon dioxide concentration data, which are collected in real time through the deployed sensor network and recorded in time series.

[0120] Obtain the concentration data of metabolites (such as lactic acid, acetic acid, etc.) during the fermentation process through sampling analysis methods. The sampling frequency of metabolite concentration should be consistent with the gas concentration data to ensure synchronization in the time dimension.

[0121] Organize the collected gas concentration data and metabolite concentration data into a matrix form according to the time and space dimensions, preparing to map them to a three-dimensional tensor.

[0122] Map the gas concentration data and metabolite concentration data to different dimensions of the three-dimensional tensor respectively with the time series as the dimension:

[0123] Construct a three-dimensional tensor to represent the time series change relationship between gas concentration and metabolite concentration in the risk analysis region, specifically as follows:

[0124] Tensor dimension definition: The first dimension represents the spatial position in the risk analysis region; the second dimension represents the time series, recording the parameter changes at different times; the third dimension stores the gas concentration data (oxygen concentration, carbon dioxide concentration) and metabolite concentration data respectively.

[0125] Mapping rule: After aligning the gas concentration data and metabolite concentration data according to the sampling location and time, map them to a tensor: Organize and align the gas concentration data (including oxygen concentration and carbon dioxide concentration) and metabolite concentration data (such as lactic acid, acetic acid, etc.) according to the corresponding relationship of sampling location and time series. The data at each sampling location are arranged in chronological order, and the gas concentrations and metabolite concentrations at different time points are combined to form a three-dimensional tensor, where the location, time, and parameter category correspond to the three dimensions of the tensor respectively, which can fully reflect the change relationship of gas and metabolite concentrations at different locations at different times.

[0126] Use the time-series dependent dynamic modeling algorithm to decompose the three-dimensional tensor and extract the main dynamic factors of the gas concentration and metabolite changes:

[0127] Adopt the non-negative tensor factorization (NTF) method to decompose the tensor into multiple low-dimensional factor matrices.

[0128] The objective function is: ; where is the original three-dimensional tensor; , and represent the spatial factor matrix, the time factor matrix, and the parameter factor matrix respectively; is the rank of the decomposition, representing the number of main dynamic factors extracted; represents the outer product operation of the tensor; is the number of the main dynamic factor; represents the norm of the error, usually the Euclidean norm, which is used to measure the difference between the original three-dimensional tensor and the reconstructed tensor.

[0129] is used to describe the spatial distribution characteristics of the th main dynamic factor, is used to describe the temporal variation pattern of the th main dynamic factor, is used to describe the characteristics of the th main dynamic factor in different parameter categories.

[0130] The role of this objective function is to find the optimal , and through optimization, so that their outer product combination is as close as possible to the original three-dimensional tensor, that is, minimizing the error between the reconstructed tensor and the original tensor; the decomposition result extracts the main dynamic features in the original data, corresponding to the spatial distribution ( ), temporal variation ( ), and the relationship with parameter categories ( ).

[0131] Analyze the mutual relationship between the main dynamic factors, and calculate the time lag characteristics of the changes in gas concentration and metabolite concentration:

[0132] Extract the gas concentration factor and metabolite factor from the time factor matrix.

[0133] For each main dynamic factor, calculate the time difference between the change in gas concentration and the change in metabolite concentration. The formula is as follows: ; where represents the time lag value of the th main dynamic factor, represents the correlation function (used to measure the correlation between two time series), represents the time variation characteristic related to the gas concentration in the th main dynamic factor at time point ; represents the time variation characteristic related to the metabolite concentration in the th main dynamic factor at time point ; represents the lag time of the time variation of gas concentration relative to the time variation of metabolite concentration, represents a specific time point in the time series.

[0134] Statistically analyze the distribution of the time lag values of all main dynamic factors, and identify the significantly lagged factors and their corresponding parameter relationships.

[0135] Quantitatively evaluate the time dependence of gas exchange and metabolic processes based on the time lag characteristics:

[0136] Calculate the time dependence index. The time dependence index is defined as the weighted average of all time lag values, and its expression is: ; where represents the time dependence index, represents the weight of the th main dynamic factor, is greater than 0.

[0137] The larger the time dependence index, the stronger the time dependence of the gas exchange and metabolic processes. This indicates that the change in gas concentration has a longer lag effect on the metabolite concentration, which may reflect that the regulation between gas diffusion and microbial metabolic activities within the system is not timely enough, possibly resulting in a lag in the generation or consumption rate of metabolites behind the adjustment of gas supply, thus causing a decrease in metabolic efficiency, uneven fermentation process, and even the inhibition of microbial activity in local areas.

[0138] Among them, the weights of the main dynamic factors represent the contribution degree of each dynamic factor in the overall tensor decomposition, usually calculated according to the norm of the factor matrix or the reduction of the reconstruction error. The weights are used to quantify the importance of each dynamic factor to the dynamic change patterns of gas concentration and metabolite concentration. The factors with higher weights reflect the key dynamic characteristics.

[0139] Analyze the distribution characteristics of the gas concentration field in the risk analysis area through the fractal analysis model, and evaluate the gas diffusion equilibrium degree of the local area, specifically including:

[0140] Organize the gas concentration data in the risk analysis area according to the spatial position and time dimension to form a gas concentration distribution matrix:

[0141] Obtain the time series data of oxygen concentration and carbon dioxide concentration from the sensor network in the risk analysis area to ensure that the collected data covers the entire spatial range and the complete time period.

[0142] According to the sampling position and time dimension, organize the gas concentration data into a three-dimensional data set, where each dimension represents the spatial position, time point, and gas concentration category respectively.

[0143] The first dimension represents the sampling position, indexed by the three-dimensional space coordinates; the second dimension represents the sampling order of the time points; the third dimension represents the gas concentration category, including oxygen concentration and carbon dioxide concentration.

[0144] Project the three-dimensional data onto a two-dimensional matrix, where each row represents the spatial position of a sampling point, each column represents the gas concentration value at the corresponding moment in the time series, and the element value of the matrix is the gas concentration data.

[0145] Use the fractal analysis model to perform multi-scale decomposition on the gas concentration distribution matrix:

[0146] Through the decomposition of the gas concentration distribution matrix, extract the distribution characteristics of the concentration data at different spatial scales.

[0147] Adopt a multi-scale analysis model based on fractal theory, and use the fractal dimension to reflect the distribution complexity of the gas concentration field at different spatial scales.

[0148] The decomposition process includes:

[0149] Divide the spatial units: Divide the risk analysis area into multiple small cells, and the size of each cell corresponds to a spatial scale.

[0150] Statistical concentration distribution: In each cell, statistically analyze the distribution range (such as the maximum value and the minimum value) and gradient change of the gas concentration values.

[0151] Gradually reduce the cells: Continuously decrease the size of the cells (i.e., the spatial scale), repeatedly count the characteristics of the concentration distribution, and record the concentration distribution data at different scales.

[0152] Calculate the fractal dimension for each scale, and evaluate the gas diffusion equilibrium degree of the local area according to the changing trend of the fractal dimension:

[0153] The fractal dimension reflects the complexity of the gas concentration distribution, and is used to quantify the variation law of the concentration field at different spatial scales. The larger its value, the more complex the distribution of the concentration field.

[0154] Within each decomposition scale, count the distribution frequency of the gas concentration within the spatial unit.

[0155] Use the logarithmic regression method to calculate the fractal dimension. The formula is: ; where represents the fractal dimension, represents the gas concentration distribution probability at the decomposition scale , represents the current decomposition scale.

[0156] Record the fractal dimension values at different decomposition scales to form a fractal dimension change curve. If the fractal dimension changes drastically at multiple scales, it indicates that the gas concentration field distribution in this area is uneven.

[0157] The greater the changing trend of the fractal dimension, the lower the gas diffusion equilibrium degree of the local area, indicating that the complexity differences of the gas concentration distribution at different spatial scales are significant, which may lead to blocked gas diffusion paths, insufficient oxygen supply or carbon dioxide accumulation in the local area, thus affecting the fermentation efficiency and microbial metabolic activity.

[0158] Among them, the changing trend of the fractal dimension can be calculated by comparing the change amounts of the fractal dimension at different decomposition scales to reflect the change of the complexity of the gas concentration field in the spatial distribution. The specific steps are as follows:

[0159] For each decomposition scale, calculate the corresponding fractal dimension based on the cell side length and the number of non-empty cells at the current scale, and record the fractal dimension values at all decomposition scales in sequence.

[0160] Arrange them in descending order of the decomposition scale to facilitate observing and analyzing the changing trend of the fractal dimension with the decomposition scale.

[0161] Calculate the change amplitude of the fractal dimension between adjacent decomposition scales in sequence, and use these change amounts to reflect the distribution complexity differences of the gas concentration field.

[0162] Organize the change amount of the fractal dimension into a distribution curve, and extract the main trend of the change of the fractal dimension with the decomposition scale in the curve. If the change amount of the fractal dimension increases significantly at multiple decomposition scales, it indicates that the distribution complexity of the gas concentration field is relatively high and the uniformity is poor; conversely, if the change amount is small, it means that the distribution is more uniform.

[0163] Based on the time-dependence of the gas exchange and metabolism processes and the degree of gas diffusion equilibrium in the local area, quantitatively adjust the parameters of the straw fermentation pile structure and the ventilation device through closed-loop control, specifically including:

[0164] The time-dependence index and the change trend of the fractal dimension extracted from the risk analysis area respectively reflect the dynamic relationship between gas exchange and metabolism and the degree of gas diffusion equilibrium.

[0165] If the time-dependence index is large, it indicates that the dynamic delay in gas exchange and metabolite generation is significant, and it is necessary to adjust the shape of the pile in the time-lag area and preferentially increase the gas flow path; if the time-dependence index is small, it means that the gas exchange is relatively timely, and the original structure can be maintained to avoid excessive adjustment.

[0166] If the change trend of the fractal dimension is large, it indicates that the complexity of the gas concentration distribution increases, and it is necessary to optimize the shape of the pile, reduce the local height difference or adjust the compaction degree; if the change trend of the fractal dimension is small, it means that the gas diffusion uniformity is good, and the adjustment range of the shape and compaction degree can be reduced.

[0167] Set the adjustment target parameters of the fermentation pile structure according to the change trends of the time-dependence index and the fractal dimension, including the pile shape parameters and the compaction degree parameters:

[0168] Pile shape parameters: For the area with a large time-dependence index, preferentially stretch the pile height or inclination angle to reduce the gas flow resistance; for the area with a large change trend of the fractal dimension, adjust the pile width to improve the local diffusion path.

[0169] Compaction degree parameters: For the area where diffusion is blocked, improve gas fluidity by reducing the compaction degree (such as reducing the local stacking weight); for the area where diffusion is too fast, increase the compaction degree (such as increasing the weight) to control gas leakage.

[0170] Set the adjustment target parameters of the ventilation device according to the change trends of the time-dependence index and the fractal dimension, including the on-off state and the flow rate parameters of the ventilation pipeline:

[0171] The time-dependence index is large: It is necessary to increase the ventilation volume, shorten the delay in gas exchange, preferentially open the ventilation pipelines in the time-lag area, and increase the air flow intensity.

[0172] The fractal dimension has a large change trend: it is necessary to adjust the ventilation direction for the uneven diffusion area, and preferentially open the pipelines in the high-concentration gradient area to reduce complexity.

[0173] Switch state: Open more pipelines in the area with a large time-dependence index to ensure gas exchange efficiency; preferentially open the ventilation pipelines close to the center in the area with a large change in fractal dimension to optimize gas diffusion.

[0174] Airflow rate: Increase the airflow velocity in the area with a large time-dependence index, and precisely control the airflow distribution of each pipeline in the area with a large change in fractal dimension to avoid excessive ventilation.

[0175] Based on the adjusted target parameters of the fermentation pile structure, execute the adjustment of the fermentation pile structure through a closed-loop control system, including changing the shape and compaction degree of the fermentation pile; based on the adjusted target parameters of the ventilation device, adjust the ventilation device through a closed-loop control system, including controlling the opening and closing of the ventilation pipelines and the airflow rate:

[0176] Shape adjustment: In the area with a large time-dependence index, adjust the height of the pile body through a hydraulic push rod to form a smoother vertical circulation channel for gas; in the area with a large change trend in fractal dimension, adjust the width and inclination angle of the pile body to reduce the blockage caused by uneven diffusion.

[0177] Compaction degree adjustment: Reduce the stacking weight in the high-compaction area to improve the gas diffusion path; increase the local weight or use compaction equipment in the low-compaction area to improve the gas retention ability.

[0178] During the adjustment process, monitor the shape and compaction degree of the pile body in real time, and dynamically correct the adjustment parameters by comparing the sensor data with the target values.

[0179] Switch control: For the area with a large time-dependence index, open the ventilation pipelines to preferentially remove the carbon dioxide generated by metabolism; for the area with a large change trend in fractal dimension, open the pipelines directly connected to this area to improve the concentration distribution.

[0180] Flow control: Increase the airflow velocity in the area with a large time-dependence index to accelerate gas exchange; precisely control the airflow rate in the area with a large change trend in fractal dimension, and focus on ventilating the uneven diffusion area.

[0181] Equip with flow sensors to monitor the gas flow rate of each pipeline and the regional concentration gradient in real time; compare the monitoring data with the target parameters and automatically adjust the fan power and airflow distribution ratio.

[0182] Example 2: The difference between Example 2 and Example 1 of the present invention is that this example introduces a control system for straw feed fermentation treatment.

[0183] Figure 2The structural schematic diagram of a control system for fermenting and processing straw feed according to the present invention is given. A control system for fermenting and processing straw feed includes an environmental parameter acquisition module, a layered air mass identification module, a measurement of pile pressure deformation module, a risk area marking module, a time-dependent analysis module, a diffusion equilibrium evaluation module, and a closed-loop control adjustment module.

[0184] Environmental parameter acquisition module: Collect the environmental parameters of the fermentation pile at the straw fermentation pile, perform three-dimensional gas concentration field reconstruction processing on the environmental parameters of the fermentation pile, and obtain the gas distribution information at each position inside the straw fermentation pile.

[0185] Layered air mass identification module: Use the fuzzy clustering algorithm to classify and analyze the gas distribution information, and identify the layered air mass area with the characteristics of gas concentration gradient.

[0186] Measurement of pile pressure deformation module: Measure the pile pressure distribution and deformation characteristics of the straw fermentation pile, analyze the potential impact of uneven gas diffusion inside the straw fermentation pile, and identify the abnormal area of the pile structure.

[0187] Risk area marking module: Mark the union of the layered air mass area and the abnormal area of the pile structure as the risk analysis area.

[0188] Time-dependent analysis module: Analyze the dynamic relationship between gas concentration and metabolites in the risk analysis area by constructing a three-dimensional tensor model, and evaluate the time dependence of the gas exchange and metabolism process.

[0189] Diffusion equilibrium evaluation module: Analyze the distribution characteristics of the gas concentration field in the risk analysis area through a fractal analysis model, and evaluate the degree of gas diffusion equilibrium in the local area.

[0190] Closed-loop control adjustment module: Based on the time dependence of the gas exchange and metabolism process and the degree of gas diffusion equilibrium in the local area, quantitatively adjust the parameters of the straw fermentation pile structure and ventilation device through closed-loop control.

[0191] The above formulas are all dimensionless and take their numerical calculations. The formula is obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters and threshold selection in the formula are set by those skilled in the art according to the actual situation.

[0192] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. 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 the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (such as infrared, wireless, microwave, etc.) means. 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 includes one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0193] Those of ordinary skill in the art will realize that the modules and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0194] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and modules described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0195] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there can be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or modules can be in electrical, mechanical, or other forms.

[0196] The module described as a separation component may or may not be physically separated. The component shown as a module may or may not be a physical module. It may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0197] In addition, in each embodiment of this application, each functional module can be integrated into a processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0198] If the function is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of this application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0199] As described above, this is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of changes or substitutions, which should all be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0200] Finally: The above description is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A straw feed fermentation treatment control method, characterized in that: The steps include: Collect fermentation pile environmental parameters at the straw fermentation pile, reconstruct the three-dimensional gas concentration field of the fermentation pile environmental parameters, and obtain gas distribution information at various positions inside the straw fermentation pile; Fuzzy clustering algorithm is used to classify and analyze gas distribution information and identify stratified air mass areas with gas concentration gradient characteristics. Measure the pile pressure distribution and deformation characteristics of the straw fermentation pile, analyze the potential impact of uneven gas diffusion in the straw fermentation pile, and identify abnormal areas of the pile structure; The union of the stratified gas mass area and the abnormal pile structure area is marked as the risk analysis area; The dynamic relationship between gas concentration and metabolites in the risk analysis area was analyzed by constructing a three-dimensional tensor model to evaluate the temporal dependence of gas exchange and metabolic processes. The distribution characteristics of the gas concentration field in the risk analysis area are analyzed through the fractal analysis model to evaluate the gas diffusion balance in the local area; Based on the time dependence of gas exchange and metabolic processes and the degree of gas diffusion balance in local areas, the structure of the straw fermentation pile and the parameters of the ventilation device are quantitatively adjusted through closed-loop control.

2. A straw feed fermentation processing control method according to claim 1, characterized in that: The fermentation pile environmental parameters are collected at the straw fermentation pile, and the three-dimensional gas concentration field reconstruction processing is performed on the fermentation pile environmental parameters to obtain the gas distribution information at each position inside the straw fermentation pile, including: A plurality of collection points are set in the vertical range from the bottom to the top and the horizontal range from the center to the edge of the straw fermentation pile to collect the fermentation pile environmental parameters, including oxygen concentration, carbon dioxide concentration and humidity; Based on the geometric shape of the straw fermentation pile and the spatial distribution of the collection points, a three-dimensional coordinate system matching the straw fermentation pile is constructed; The environmental parameters of the fermentation pile are mapped to a three-dimensional coordinate system to generate gas distribution information at various positions inside the straw fermentation pile; the gas distribution information includes oxygen concentration distribution, carbon dioxide concentration distribution and humidity distribution.

3. A straw feed fermentation processing control method according to claim 2, characterized in that: The fuzzy clustering algorithm is used to classify and analyze the gas distribution information and identify the stratified air mass areas with gas concentration gradient characteristics, including: Based on the oxygen concentration distribution, carbon dioxide concentration distribution and humidity distribution of each point in the three-dimensional space, the parameter value of each spatial point in the gas distribution information is used as a clustering feature; Using fuzzy clustering algorithm, the spatial points in the gas distribution information are classified according to the gas concentration gradient characteristics of the spatial points to form multiple cluster categories; Cluster categories with significant gas concentration gradient characteristics and whose spatial distribution characteristics conform to the characteristics of stratified air masses are marked as stratified air mass areas.

4. A straw feed fermentation processing control method according to claim 3, characterized in that: The pile pressure distribution and deformation characteristics of the straw fermentation pile were measured, the potential impact of uneven gas diffusion in the straw fermentation pile was analyzed, and abnormal areas of the pile structure were identified, including: Multiple pressure-sensitive sensors are arranged in the vertical and horizontal directions of the straw fermentation pile to measure the pile pressure value at each position; Scan the surface shape of the straw fermentation pile using a three-dimensional scanning device to obtain three-dimensional morphological feature data of the straw fermentation pile; Compare and analyze the pile pressure value and three-dimensional morphological feature data to identify abnormal pile pressure areas and significant deformation areas; According to the locations of abnormal stack pressure areas and significant deformation areas, abnormal stack structure areas that may affect gas diffusion are identified.

5. A straw feed fermentation processing control method according to claim 4, characterized in that: By constructing a three-dimensional tensor model to analyze the dynamic relationship between gas concentration and metabolites in the risk analysis area, the time dependence of gas exchange and metabolic processes was evaluated, including: Obtain gas concentration data and metabolite concentration data at each location in the risk analysis area; The gas concentration data and metabolite concentration data are mapped to different dimensions of the three-dimensional tensor using the time series as the dimension; The three-dimensional tensor is decomposed using a time-dependent dynamic modeling algorithm to extract the main dynamic factors of gas concentration and metabolic product changes; Analyze the relationship between the main dynamic factors and calculate the time lag characteristics of changes in gas concentration and metabolite concentration; The temporal dependence of gas exchange and metabolic processes was quantitatively assessed based on time lag characteristics.

6. A straw feed fermentation processing control method according to claim 5, characterized in that: Analyze the relationship between the main dynamic factors and calculate the time lag characteristics of the changes in gas concentration and metabolite concentration, specifically: For each main dynamic factor, the time difference between the change in gas concentration and the change in metabolite concentration is calculated using the following formula: ;in, Indicates The time lag value of the main dynamic factors, represents the correlation function, Indicates at a point in time Time The time variation characteristics of the main dynamic factors related to gas concentration, Indicates at a point in time Time The temporal variation characteristics of the main dynamic factors related to the concentration of metabolites, It represents the lag time of the time change of gas concentration relative to the time change of metabolite concentration. Represents a specific time point in a time series.

7. A straw feed fermentation processing control method according to claim 6, characterized in that: The temporal dependence of gas exchange and metabolic processes was quantitatively evaluated based on the time lag characteristics, specifically: Calculate the time dependency index, which is expressed as: ;in, represents the time dependence index, Indicates The weights of the main dynamic factors, greater than 0, Indicates the number of main dynamic factors extracted, is the number of the main dynamic factor.

8. A straw feed fermentation processing control method according to claim 7, characterized in that: The fractal analysis model is used to analyze the distribution characteristics of the gas concentration field in the risk analysis area and evaluate the degree of gas diffusion balance in the local area, including: The gas concentration data in the risk analysis area are sorted according to the spatial location and time dimensions to form a gas concentration distribution matrix; Use fractal analysis model to perform multi-scale decomposition of gas concentration distribution matrix; The fractal dimension of each scale is calculated, and the degree of gas diffusion equilibrium in the local area is evaluated according to the changing trend of the fractal dimension; the changing trend of the fractal dimension is calculated by comparing the changes in the fractal dimension at different decomposition scales.

9. A straw feed fermentation processing control method according to claim 8, characterized in that: Based on the time dependence of gas exchange and metabolic processes and the degree of gas diffusion balance in local areas, the structure of the straw fermentation pile and the parameters of the ventilation device are quantitatively adjusted through closed-loop control, including: According to the changing trends of the time dependency index and the fractal dimension, the adjustment target parameters of the fermentation pile structure are set, including the pile shape parameters and the compaction degree parameters; According to the changing trend of the time dependency index and the fractal dimension, the adjustment target parameters of the ventilation device are set, including the switch state and flow parameters of the ventilation pipeline; Based on the adjustment target parameters of the fermentation pile structure, the fermentation pile structure is adjusted through a closed-loop control system, including changing the shape and compaction degree of the fermentation pile; based on the adjustment target parameters of the ventilation device, the ventilation device is adjusted through a closed-loop control system, including controlling the switch of the ventilation pipeline and the airflow rate.

10. A straw feed fermentation processing control system, used to implement a straw feed fermentation processing control method according to any one of claims 1 to 9, characterized in that: It includes an environmental parameter acquisition module, a layered air mass identification module, a pile pressure deformation measurement module, a risk area marking module, a time-dependent analysis module, a diffusion equilibrium assessment module, and a closed-loop control adjustment module; Environmental parameter acquisition module: collects fermentation pile environmental parameters at the straw fermentation pile, performs three-dimensional gas concentration field reconstruction on the fermentation pile environmental parameters, and obtains gas distribution information at various positions inside the straw fermentation pile; Layered air mass identification module: uses fuzzy clustering algorithm to classify and analyze gas distribution information and identify layered air mass areas with gas concentration gradient characteristics; Pile pressure deformation measurement module: measures the pile pressure distribution and deformation characteristics of the straw fermentation pile, analyzes the potential impact of uneven gas diffusion in the straw fermentation pile, and identifies abnormal areas of the pile structure; Risk area marking module: marks the union of the stratified air mass area and the abnormal pile structure area as the risk analysis area; Time-dependent analysis module: By constructing a three-dimensional tensor model to analyze the dynamic relationship between gas concentration and metabolites in the risk analysis area, the time dependency of gas exchange and metabolic processes is evaluated; Diffusion balance assessment module: Analyze the distribution characteristics of the gas concentration field in the risk analysis area through the fractal analysis model, and evaluate the degree of gas diffusion balance in the local area; Closed-loop control regulation module: Based on the time dependence of gas exchange and metabolic processes and the degree of gas diffusion balance in local areas, the straw fermentation pile structure and ventilation device parameters are quantitatively adjusted through closed-loop control.

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