Feed fermentation parameter intelligent correction method based on Internet of Things

Abstract

The invention provides a feed fermentation parameter intelligent correction method based on the Internet of Things, and the method comprises the steps: carrying out the full-dimensional data collection from a microenvironment in a feed fermentation tank to a cross-regional macroenvironment, and generating a standardized data matrix; detecting communication time delay fluctuation characteristics based on the standardized data matrix, determining a fluctuation period and an amplitude range of a time delay abnormal point, and forming a time delay fluctuation characteristic spectrum; analyzing the influence of a raw material nutrition structure and logistics transportation time efficiency on the purity and concentration uniformity of metabolites through a regional differentiation regulation and control plan, and outputting associated feature distribution of quality control imbalance between regions; and updating an operation instruction of the feed fermentation equipment in real time according to the association feature distribution of the quality control imbalance between the regions, and obtaining equipment state operation feedback data after the operation instruction is updated.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to an intelligent correction method for feed fermentation parameters based on the Internet of Things. Background Technology

[0002] In the intersection of modern agriculture and the food industry, IoT-based intelligent monitoring technology is becoming a strategic pillar for improving production efficiency and product quality, especially in the crucial process of feed fermentation, where its importance is self-evident. The fermentation process directly affects the functionality of metabolites and the market value of the final product, and achieving precise control in complex environments is a core requirement for industry development. However, current technological solutions often struggle to adapt to the dynamic changes across multiple regions and devices when monitoring fermentation in complex environments. Existing methods focus more on data acquisition in a single environment, neglecting the systematic interference caused by cross-regional environmental differences. Especially when equipment is distributed across different climatic conditions, the diversity and unpredictability of environmental variables make data integration and process coordination extremely difficult. This limitation leads to an imbalance in quality control during production, affecting overall efficiency. A deeper analysis of the challenges in this field reveals that cross-regional environmental differences are the primary issue. Due to significant variations in climate, humidity, and temperature across different regions, sensor data exhibits high heterogeneity, making it difficult to adapt uniform parameter settings to local needs. Environmental differences further exacerbate communication latency fluctuations between devices. The instability of data transmission directly affects the real-time adjustment capability of fermentation process parameters, thereby threatening the uniformity of metabolite quality. These two factors are intertwined, forming the core obstacle for the monitoring system in dynamic environments. Therefore, how to overcome the impact of data heterogeneity and communication latency fluctuations caused by environmental differences in cross-regional fermentation environments, and achieve real-time correction of fermentation parameters and control of quality uniformity, through the construction of multi-level sensor networks and hierarchical data architecture, has become a key problem that urgently needs to be solved in this research. Summary of the Invention

[0003] This invention provides an intelligent correction method for feed fermentation parameters based on the Internet of Things, mainly including:

[0004] Data is collected across all dimensions, from the microenvironment within the feed fermentation tank to the macroenvironment across different regions, to generate a standardized data matrix.

[0005] Based on a standardized data matrix, communication latency fluctuation characteristics are detected, the fluctuation period and amplitude range of latency anomalies are determined, and a latency fluctuation characteristic map is formed.

[0006] A digital twin model of the cross-regional feed fermentation environment is constructed based on the time delay fluctuation feature map. The real-time status of the physical fermentation process is input, and the trend of microbial metabolic parameters in the virtual environment is output.

[0007] Based on the changing trends of microbial metabolic parameters in the virtual environment, the control thresholds of each region are dynamically adjusted, the adaptability of temperature control thresholds and ventilation strategies between regions is analyzed, the environmental control priority of feed fermentation process in each region is determined, and regional differentiated regulation plans are generated.

[0008] By using regionally differentiated control plans, the impact of raw material nutrient structure and logistics transportation timeliness on the purity and concentration uniformity of metabolites is analyzed, and the distribution of correlation characteristics of quality control imbalances between regions is output.

[0009] Based on the correlation characteristics of quality control imbalances between regions, the operating instructions of the feed fermentation equipment are updated in real time, and the equipment status operation feedback data after the operating instructions are updated are obtained.

[0010] Continuously monitor the quality of feed fermentation metabolites and the uniformity indicators of fermentation process, and conduct backtracking analysis of multi-level sensor data on quality deviations and fermentation process to identify environmental variables that affect the quality of metabolites.

[0011] By inputting environmental variables into the digital twin model, the predicted results of microbial metabolic parameters are obtained, the interference of environmental fluctuations on the stability and efficiency of the fermentation process is compensated, the data acquisition frequency and transmission priority in the Internet of Things architecture constructed based on the time delay fluctuation feature map are updated, and the set of operating parameters of the cross-regional fermentation environment control system is obtained.

[0012] Furthermore, the process of collecting data across all dimensions, from the microenvironment within the feed fermentation tank to the macroenvironment across different regions, and generating a standardized data matrix, includes:

[0013] Sensors are deployed inside the feed fermentation tank to collect microenvironmental parameters, recording data on temperature, humidity, and gas concentration. Monitoring nodes are arranged in different areas of the fermentation workshop to obtain differences in environmental parameters, forming a sensor data set containing timestamps and location identifiers. Outliers and missing values ​​are identified in the sensor data set, and noise is removed by smoothing. The data format is unified to generate a standardized data structure containing location and environmental category. Based on the standardized data structure, the temperature gradient and humidity change rate are calculated, and a clustering algorithm is used to output environmental feature category labels, generating a standardized data matrix.

[0014] Furthermore, the step of detecting communication delay fluctuation characteristics based on a standardized data matrix, determining the fluctuation period and amplitude range of delay anomalies, and forming a delay fluctuation feature map includes:

[0015] Timestamps and transmission records are extracted from the standardized data matrix, data packet transmission delay and packet loss rate are calculated, signal strength is obtained, and a set of communication performance parameters is formed. Anomalies are detected in the set of communication performance parameters. If the transmission delay or packet loss rate exceeds the threshold, it is marked as a delay anomaly point. Based on the delay anomaly point, Fourier transform is used to identify the fluctuation period and generate a delay fluctuation feature map.

[0016] Furthermore, the construction of a digital twin model of the cross-regional feed fermentation environment based on the time-delay fluctuation feature map, inputting the real-time status of the physical fermentation process, and outputting the trend of microbial metabolic parameter changes in the virtual environment, includes:

[0017] The delay variation pattern is extracted from the time delay fluctuation feature map, and the temperature and humidity gradient in the environmental feature distribution is combined to establish the correspondence between physical and virtual environment coordinates; according to the correspondence, the real-time data of the physical fermentation process is converted into virtual environment parameters to generate a digital twin model; real-time status data is received through the digital twin model, the microbial growth rate and metabolite generation efficiency are calculated, and the trend of microbial metabolic parameter changes in the virtual environment is output.

[0018] Furthermore, the process of dynamically adjusting the control thresholds of each region based on the changing trends of microbial metabolic parameters in the virtual environment, analyzing the compatibility of temperature control thresholds and ventilation strategies between regions, determining the environmental control priorities for the feed fermentation process in each region, and generating regionally differentiated control plans includes:

[0019] Based on the changing trends of microbial metabolic parameters in the virtual environment, the temperature control threshold and ventilation frequency are adjusted to generate environmental control parameter configuration data. The environmental control parameter configuration data drives the nutrient supply device to adjust the nutrient solution ratio and predict fermentation quality indicators. Based on the fermentation quality indicators, regional differences are assessed to determine the environmental control priority of the feed fermentation process in each region and generate a regionally differentiated regulation plan.

[0020] Furthermore, the aforementioned regional differentiated control plan analyzes the impact of raw material nutrient structure and logistics transportation timeliness on the purity and concentration uniformity of metabolites, and outputs the correlation characteristic distribution of quality control imbalances between regions, including:

[0021] Clustering algorithms are used to identify quality control imbalance patterns and generate imbalance characteristic parameters. Based on these imbalance characteristic parameters, differences in raw material nutrient structure are detected, and the nutrient structure imbalance coefficient is calculated. Combining logistics and transportation timeliness data, a correlation model between logistics timeliness and metabolite quality is established, and influencing parameters are calculated. By integrating environmental differences, nutrient structure, and logistics timeliness through these influencing parameters, the correlation characteristic distribution of quality control imbalance between regions is obtained.

[0022] Furthermore, the step of updating the operating instructions of the feed fermentation equipment in real time based on the correlation characteristic distribution of quality control imbalances between regions, and obtaining equipment status operation feedback data after the update of the operating instructions, includes:

[0023] Based on the correlation feature distribution of the quality control imbalance between the regions, the degree and location information of the imbalance are analyzed to generate operation command data including temperature control adjustment and stirring speed; the operation command data drives the device actuator to collect the device status operation feedback data after the operation command is updated, including power consumption, operating efficiency and control accuracy.

[0024] Furthermore, the continuous monitoring of feed fermentation metabolite quality and fermentation process uniformity indicators, along with backtesting analysis of multi-level sensor data on quality deviations and fermentation processes, identifies environmental variables affecting metabolite quality, including:

[0025] The purity, activity, and concentration uniformity of metabolites are detected to generate uniformity index parameters; quality deviations are identified based on the uniformity index parameters, and relevant historical sensor data are obtained; environmental parameter change patterns are extracted from the historical sensor data, the correlation strength between environmental factors and quality is calculated, and the main environmental variables affecting metabolite quality are determined.

[0026] Furthermore, the input environmental variables are fed into the digital twin model to obtain predicted results of microbial metabolic parameters, compensating for the interference of environmental fluctuations on the stability and efficiency of the fermentation process, and generating a set of operating parameters for a cross-regional fermentation environment control system, including:

[0027] By importing the main environmental variables into a digital twin model, a mapping relationship between environmental variables and quality indicators is constructed to generate quality fluctuation prediction data. Based on the prediction data, the compensation amount of environmental control parameters is adjusted to generate environmental regulation strategy parameters. By analyzing the data transmission delay pattern through the environmental regulation strategy parameters, the data acquisition frequency and transmission priority are determined to generate a set of operating parameters for the cross-regional fermentation environmental regulation system.

[0028] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0029] This invention discloses a method for cross-regional feed fermentation environment control. It acquires environmental characteristic distributions through multi-dimensional data collection, establishes a standardized data matrix, detects communication latency fluctuations, and forms a feature map. A digital twin model is constructed based on the environmental characteristic distribution to analyze the changing trends of microbial metabolic parameters, dynamically adjust control thresholds, and generate regionally differentiated control plans. The quality of metabolites is continuously monitored through closed-loop control data flow, and multi-level sensor data backtracking analysis is performed to identify environmental variables affecting quality. Finally, these environmental variables are input into the digital twin model to predict quality fluctuations and generate optimized environmental control strategies, updating data acquisition frequency and transmission priority to achieve precise control of the cross-regional fermentation environment. This invention effectively addresses the quality control imbalance caused by environmental differences, improving the stability and efficiency of the feed fermentation process. Attached Figure Description

[0030] Figure 1 This is a flowchart of an intelligent correction method for feed fermentation parameters based on the Internet of Things according to the present invention. Detailed Implementation

[0031] To further understand the content of this invention, a detailed description of the invention is provided in conjunction with the accompanying drawings and embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0032] like Figure 1 This embodiment of an IoT-based intelligent correction method for feed fermentation parameters may specifically include:

[0033] S101. Collect data from all dimensions, from the microenvironment inside the feed fermentation tank to the macroenvironment across regions, and generate a standardized data matrix.

[0034] Temperature and humidity sensors and gas concentration detection devices are deployed inside the feed fermentation tank to collect microenvironmental parameters at preset time intervals, obtaining temperature values, relative humidity percentages, and carbon dioxide concentration values. Simultaneously, environmental monitoring nodes are placed in different areas of the fermentation workshop to record differences in environmental parameters across areas, forming a raw sensor data set containing timestamps, location identifiers, and multi-dimensional environmental indicators. The raw sensor data set is preprocessed to identify outliers and missing values. Moving averages are used to smooth the temperature and humidity data, removing noise data caused by sensor malfunctions or communication interference. The data formats collected by different sensors are uniformly converted to establish a standardized data structure that includes tank location, environmental type, and numerical range. Based on the environmental parameters in the standardized data structure, the temperature gradient is obtained by dividing the temperature difference between adjacent monitoring points by the distance using the difference method. The humidity change rate is obtained by performing first-order difference on the humidity data in the time series. Temperature, humidity, and gas concentration are used as feature vectors and input into the K-means clustering algorithm to output category labels for different environmental features. The Pearson correlation coefficient between micro-environmental parameters and macro-environmental parameters is calculated. All processed data are organized into rows and columns according to time, location, environmental category, and value to generate a standardized data matrix.

[0035] Specifically, monitoring the microenvironment and macroenvironment during feed fermentation is crucial for fermentation quality.

[0036] Specifically, temperature and humidity sensors inside the fermenter typically employ PT100 platinum resistance thermometers and capacitive humidity sensors, which are characterized by high accuracy and stability. The gas concentration detection device primarily uses infrared absorption carbon dioxide sensors, capable of monitoring changes in CO2 concentration during fermentation in real time. The sensors are arranged in a three-dimensional distribution within the tank, with monitoring points at the top, middle, and bottom, and additional monitoring nodes near the inlet and outlet. This arrangement comprehensively reflects environmental gradient changes during fermentation. Monitoring the regional environment within the fermentation workshop is equally important, as the external environment directly affects the stability of fermentation conditions within the tank.

[0037] For example, environmental monitoring nodes are set up in the raw material storage area, fermentation operation area, and finished product packaging area, with each node recording the temperature, humidity, and air circulation in its respective area. Comparing this data with data from inside the tank helps analyze the extent to which environmental factors affect the fermentation process. The moving average method in the data preprocessing stage is an effective noise filtering technique.

[0038] In one possible implementation, for temperature data, two data points are selected before and after the current temperature, and the average of these five data points is calculated as the current temperature value. This effectively eliminates outliers caused by instantaneous fluctuations in the sensor. For missing data, linear interpolation is performed based on values ​​from adjacent time points to ensure data continuity. The format standardization process involves converting the output formats of sensors from different manufacturers into a unified data structure, including formatting timestamps to a standard time format, standardizing temperature to degrees Celsius, standardizing humidity to relative humidity percentage, and standardizing gas concentration to ppm. The temperature gradient calculation reflects the heat transfer pattern within the fermenter.

[0039] It should be noted that the temperature gradient value, obtained by dividing the temperature difference between adjacent monitoring points by the physical distance between the two points, can indicate the direction and intensity of heat flow. The humidity change rate is obtained by differencing humidity values ​​at consecutive time points; this indicator reflects the dynamic equilibrium between water evaporation and condensation during fermentation. The application of the K-means clustering algorithm in environmental feature classification can automatically identify similar environmental conditions.

[0040] Preferably, temperature, humidity, and CO2 concentration are used as three-dimensional feature vectors input to the algorithm. The number of clusters is set to 3-5. The algorithm iteratively calculates the distance from each data point to the cluster center, ultimately classifying environmental conditions into categories such as high-temperature and high-humidity, medium-temperature and medium-humidity, and low-temperature and low-humidity. The calculation of the Pearson correlation coefficient reveals the degree of linear correlation between micro-environmental and macro-environmental parameters. A correlation coefficient close to 1 indicates a strong positive correlation, close to -1 indicates a strong negative correlation, and close to 0 indicates no linear correlation. The generated standardized data matrix is ​​organized according to a time series, with each row representing complete environmental data at a given time point, including timestamp, location identifiers of each monitoring point, temperature and humidity values, gas concentration, environmental category label, and correlation coefficient.

[0041] S102. Based on the standardized data matrix, detect the communication delay fluctuation characteristics, determine the fluctuation period and amplitude range of delay anomalies, and form a delay fluctuation characteristic map.

[0042] The timestamp sequence and corresponding data transmission records are extracted from a standardized data matrix. The transmission delay value of each data packet is obtained by subtracting the sending timestamp from the receiving timestamp. The data packet loss rate is obtained by calculating the ratio of the number of data packets that did not receive an acknowledgment signal within a preset time window to the total number of packets sent. The signal strength values ​​recorded in the data matrix are read to form a set of communication performance parameters including transmission delay, data packet loss rate, and signal strength. Anomaly detection is performed on the set of communication performance parameters. If the transmission delay value exceeds a preset threshold or the packet loss rate is higher than the upper limit of the normal range, it is marked as a delay anomaly. The anomalies are classified according to the magnitude and duration of the transmission delay value, into instantaneous spike anomalies, continuous high delay anomalies, and periodic fluctuation anomalies, resulting in delay feature data with anomaly markers and category labels. Based on the time delay feature data with anomaly markers and category labels, the Fast Fourier Transform is used to identify the main frequency components in the transmission delay sequence, determine the fluctuation period, calculate the difference between the maximum and minimum transmission delay in each period to obtain the fluctuation amplitude, plot the change curves of transmission delay, packet loss rate and signal strength in chronological order, mark the location of anomalies and fluctuation period information, and form a time delay fluctuation feature map.

[0043] Specifically, the standardized data matrix serves as the basic data source for communication latency detection, and contains complete communication record information.

[0044] Specifically, each record contains a sending timestamp and a receiving timestamp. The difference between these two timestamps directly reflects the transmission delay of the data packet in the network. The calculation of transmission delay involves a precise time synchronization mechanism. The sending end embeds a timestamp of the sending time in the packet header, and the receiving end immediately records the receiving time upon receiving the packet. Subtracting the two timestamps yields the transmission delay value of the data packet. The statistical calculation of data packet loss rate relies on an acknowledgment mechanism.

[0045] In one possible implementation, the sender assigns a unique sequence number to each data packet, and the receiver returns an acknowledgment signal upon receiving the packet. If the sender does not receive an acknowledgment signal for a particular sequence number within a preset time window, the data packet is considered lost. The packet loss rate equals the number of lost data packets divided by the total number of packets sent; this metric directly reflects the reliability of network transmission. Signal strength values ​​are typically recorded in dBm in the data matrix, reflecting the reception quality of the wireless signal. The threshold for anomaly detection is set based on the statistical characteristics of historical data.

[0046] It should be noted that normal transmission delays typically exhibit a stable distribution. An anomaly is identified when the transmission delay of a data packet exceeds the average plus three times the standard deviation. Transient spike anomalies manifest as a sudden increase in the transmission delay of one or a few data packets, followed by a rapid return to normal. Sustained high-latency anomalies are characterized by multiple consecutive data packets maintaining a high delay level. Periodic fluctuation anomalies exhibit a regular pattern of delay increases and decreases, often related to periodic changes in network load. The Fast Fourier Transform (FFT) plays a crucial role in identifying these fluctuation periods. This algorithm transforms the time-domain transmission delay sequence to the frequency domain and determines the dominant oscillation frequencies by analyzing the peak positions in the spectrum.

[0047] For example, a significant peak in the spectrum at 0.1 Hz indicates a 10-second fluctuation period in transmission delay. This periodicity is often related to regular maintenance of network equipment, regular changes in traffic patterns, or network congestion control mechanisms. Calculating the fluctuation amplitude provides a quantitative indicator of the severity of the anomaly.

[0048] In one embodiment, for each identified fluctuation period, the maximum and minimum transmission delay values ​​within that period are identified, and the difference between them represents the fluctuation amplitude. A larger fluctuation amplitude indicates higher network performance instability, potentially impacting the service quality of real-time services. The plotting of the latency fluctuation feature map comprehensively displays multi-dimensional communication performance information. The horizontal axis represents time, and the vertical axis can simultaneously display the change curves of three indicators: transmission delay, packet loss rate, and signal strength. Anomalies are highlighted on the map with special markers, and different types of anomalies are distinguished using different symbols. Fluctuation period information is represented by adding period labels to the corresponding time periods. This visualized feature map allows network administrators to intuitively identify communication quality problems, providing a basis for network optimization decisions. By comparing the feature maps of different time periods and different network nodes, it is also possible to discover the evolution patterns of network performance and potential failure modes.

[0049] S103. Construct a digital twin model of the cross-regional feed fermentation environment based on the time delay fluctuation characteristic map, input the real-time status of the physical fermentation process, and output the trend of microbial metabolic parameters in the virtual environment.

[0050] Based on the analysis of the periodic variation pattern of communication delay using time delay fluctuation characteristic maps, and combined with temperature and humidity gradient data in the environmental feature distribution, a virtual three-dimensional coordinate system is created. The position coordinates of each monitoring point within the physical fermentation tank are mapped one-to-one with the virtual coordinates, establishing a mapping table containing spatial location, environmental parameters, and communication characteristics, thus obtaining the correspondence rules between the physical and virtual environments. Based on these correspondence rules, the geometry and internal structure of the fermentation tank are constructed in the virtual space. Real-time data on temperature, humidity, and gas concentration during the physical fermentation process are converted into virtual environmental parameters through the mapping table, forming a dynamically updated digital twin model of the cross-regional feed fermentation environment. This cross-regional digital twin model receives real-time status data from the physical fermentation process, including current temperature, humidity percentage, and carbon dioxide concentration. The growth rate is calculated based on the exponential relationship between temperature and microbial reproduction rate. Metabolite production efficiency is estimated using the ratio of substrate consumption to product production. Environmental adaptability indicators are determined by comparing the growth rate changes under different environmental parameters, and the trend of microbial metabolic parameters in the virtual environment is output.

[0051] Specifically, the latency fluctuation feature map, as a comprehensive representation of communication quality, contains periodic variation patterns that directly reflect the dynamic characteristics of network load.

[0052] Specifically, the periodic fluctuations in communication latency are often related to the batch processing mechanism of data acquisition. When multiple sensors upload data simultaneously, network congestion occurs, leading to increased latency. This periodicity is clearly shown through peaks and troughs in the graph, providing a temporal reference benchmark for subsequent virtual environment mapping. The temperature and humidity gradient data in the environmental feature distribution reveal the patterns of heat transfer and moisture diffusion within the fermenter.

[0053] In one possible implementation, the temperature gradient decreases from the center of the tank towards the outer wall, forming a typical radial distribution characteristic. This spatial distribution characteristic needs to be accurately reproduced in the virtual environment to ensure the accuracy of the digital twin model. The virtual three-dimensional coordinate system is created using a Cartesian coordinate system, with the center of the bottom of the fermenter as the origin, the vertically upward direction as the positive Z-axis, and the X and Y axes established in the horizontal plane. The mapping process between physical monitoring points and virtual coordinates involves spatial transformation and scale scaling.

[0054] It's important to note that the actual dimensions of the physical fermentation tank need to be mapped to the virtual space at a certain scale, while maintaining the relative positions of each monitoring point. The mapping table not only contains location information but also records the environmental parameters and communication latency characteristics of each monitoring point, forming a multi-dimensional correspondence. This comprehensive mapping ensures that the complexity of the physical world can be fully represented in the virtual environment. The construction process of the digital twin model begins with geometric modeling, creating a virtual container model based on the actual shape of the fermentation tank.

[0055] In one embodiment, a combination of a cylindrical body and a conical bottom is used to simulate the fermenter's shape. The internal structure includes a stirring device, inlet, outlet, and the installation locations of various sensors; these details directly affect the distribution of environmental parameters. Real-time data conversion relies on the aforementioned mapping table. Parameter values ​​such as temperature, humidity, and gas concentration are quickly located in the virtual space via table lookup, and the environmental attributes at that location are updated. The calculation of microbial growth rate is based on the principle of temperature dependence.

[0056] Specifically, within a suitable temperature range, the metabolic rate of microorganisms typically increases 2-3 times for every 10-degree Celsius increase in temperature. This exponential relationship makes temperature a key factor affecting fermentation efficiency. Once real-time temperature data is input into the model, the growth rate under current conditions can be quickly determined by consulting a pre-defined temperature-growth rate table. Estimating the efficiency of metabolite formation involves the fundamental principles of material transformation. Substrate consumption is obtained by monitoring the rate of decrease in nutrient concentration, while product formation is determined by detecting the rate of increase in the concentration of the target metabolite. The ratio of these two values ​​reflects the efficiency with which microorganisms convert substrates into products. Environmental adaptability indicators are assessed by comparing the magnitude of changes in growth rate under different environmental conditions; smaller magnitudes indicate stronger adaptability of the microorganisms to environmental changes. The output trends of microbial metabolic parameters are presented as time-series curves, including growth rate curves, metabolic efficiency curves, and environmental adaptability indicator curves. This trend information provides a scientific basis for optimizing fermentation process parameters, enabling operators to predict the development direction of the fermentation process and adjust control strategies in a timely manner.

[0057] S104. Dynamically adjust the control thresholds of each region based on the changing trends of microbial metabolic parameters in the virtual environment, analyze the compatibility of temperature control thresholds and ventilation strategies between regions, determine the environmental control priority of feed fermentation process in each region, and generate regional differentiated regulation plans.

[0058] Based on the slope and fluctuation amplitude of the microbial metabolic parameters in the virtual environment, the temperature control threshold and ventilation frequency parameters for each region are calculated. If the metabolic activity exceeds the preset threshold range, the temperature control accuracy is increased to the preset range, obtaining targeted environmental control parameter configuration data. This environmental control parameter configuration data drives the nutrient supply device, using a quantitative delivery method to adjust the nutrient solution ratio according to the climate conditions and gas composition of different regions. A decision tree algorithm is used to predict fermentation quality indicators for each region, determining the combination of nutrient supply timing and delivery amount. Based on the regional difference assessment results of the fermentation quality indicators, a priority ranking method is used to determine the importance of environmental control in each region, obtaining cross-regional temperature control threshold and ventilation strategy adaptability assessment data, and generating a regionally differentiated control plan including temperature control accuracy, ventilation duration, and nutrient delivery frequency.

[0059] In one possible implementation, key parameters such as pH, dissolved oxygen, temperature, and CO2 concentration are monitored in real time, and the slope of each indicator's change is calculated. When the pH drops from 6.8 to 6.2, the slope is -0.1 / hour, indicating a rapid increase in microbial metabolic activity. Simultaneously, fluctuations in dissolved oxygen exceeding ±2 mg / L are identified as metabolic instability, requiring immediate adjustment of the environmental control strategy. Based on these metabolic parameter trends, an adaptive algorithm is used to calculate the temperature control threshold and ventilation frequency parameters for each area in real time.

[0060] Specifically, when the microbial metabolic activity index in region A reaches 85%, the temperature control threshold is adjusted from ±1℃ to ±0.5℃, and the ventilation frequency is increased from twice per hour to four times per hour. This precise control effectively prevents localized overheating or hypoxia, ensuring the uniformity and stability of the fermentation process.

[0061] In one embodiment, when the environmental control parameter configuration data drives the nutrient supply device, the nutrient solution ratio is adjusted by quantitative dosing according to the differences in climate conditions and gas composition in different regions.

[0062] For example, in region B, where the temperature was higher and the CO2 concentration reached 8%, the system automatically reduced the nitrogen source concentration from the standard 12 g / L to 10 g / L, while increasing the buffer salt concentration to 15 g / L to maintain pH stability. This regionally differentiated nutrient allocation strategy effectively improved the overall fermentation efficiency. The decision tree algorithm played a crucial role in predicting fermentation quality indicators for each region.

[0063] It should be noted that this algorithm constructs multi-layered decision nodes based on historical data, comprehensively considering multiple factors such as temperature, pH, nutrient concentration, and microbial density. When the temperature in region C is 37℃, the pH is 6.5, and the glucose concentration is 25g / L, the decision tree predicts that the fermentation quality indicators in this region will reach an excellent level after 24 hours. Therefore, the system determines that the timing for nutrient supplementation is 6 hours after the current time point, and the amount added is 80% of the standard dosage.

[0064] Preferably, the importance of environmental control in each region is determined by prioritizing the evaluation results of regional differences in fermentation quality indicators.

[0065] In one possible implementation, region D is designated as a primary priority control region due to its fermentation quality index deviating by 15% from the target value, while region E is designated as a secondary priority region due to a deviation of 8%. This hierarchical management strategy allows the system to rationally allocate control resources, prioritizing the fermentation quality of critical regions. During the generation of data for the compatibility assessment of cross-regional temperature control thresholds and ventilation strategies, the interrelationships between adjacent regions are comprehensively analyzed.

[0066] For example, when the temperature in region F is adjusted to 35℃, the temperature in the adjacent region G will rise by 0.8℃ accordingly. Therefore, when formulating a temperature control strategy for region G, this heat conduction effect needs to be considered, and its temperature control threshold should be adjusted accordingly to 34.2℃. The generated regional differentiated control plan includes detailed parameters such as temperature control accuracy, ventilation duration, and nutrient delivery frequency.

[0067] Specifically, high-priority areas use precise temperature control of ±0.3℃, with ventilation time set to 15 minutes each time and nutrient delivery frequency of once every 4 hours. Low-priority areas use conventional temperature control of ±0.8℃, with ventilation time set to 8 minutes each time and nutrient delivery frequency of once every 8 hours.

[0068] S105. Through regional differentiated regulation plans, analyze the impact of raw material nutrient structure and logistics transportation timeliness on the purity and concentration uniformity of metabolites, and output the correlation characteristics distribution of quality control imbalances between regions.

[0069] Environmental difference data during feed fermentation in different regions were acquired using quality monitoring devices. A sensor network was used to collect information on changes in environmental parameters such as temperature, humidity, gas concentration, and pH across regions. Clustering algorithms were employed to identify quality control imbalance patterns caused by environmental differences, yielding characteristic parameters of quality control imbalance between regions. Based on these characteristic parameters, the differences in the nutritional structure of raw materials in each region were detected. Biochemical analysis methods were used to obtain data on the distribution of protein content, amino acid composition, and vitamin concentration. If the deviation in the nutritional structure of raw materials exceeded a preset threshold, a nutritional structure imbalance coefficient was calculated to determine the weighting coefficient of the influence of the raw material's nutritional structure on quality imbalance. By combining this weighting coefficient with logistics transportation timeliness monitoring data, timeliness parameters such as transportation time, temperature changes, and humidity fluctuations during the logistics process were obtained. Regression analysis was used to establish a mathematical correlation model between logistics timeliness and the purity and concentration uniformity of metabolites, calculating the quantitative influence parameters of transportation timeliness on fermentation quality. Based on the quantitative impact parameters of the mathematical correlation model, the data fusion method is used to integrate the influencing factors of three dimensions: environmental differences, nutritional structure, and logistics timeliness. The contribution coefficient of each factor to the quality of metabolites is obtained through weighted calculation, and the correlation characteristic distribution of quality control imbalance between regions is obtained. The dominant factors and spatial distribution characteristics of cross-regional quality control imbalance are output.

[0070] Specifically, the sensor network configuration of the quality monitoring device adopts a distributed deployment approach, with multiple types of sensor nodes installed in different areas of the fermentation workshop.

[0071] In one possible implementation, temperature sensors are arranged at three height levels—top, middle, and bottom—of the fermenter, with four monitoring points at each level forming a matrix coverage. Humidity sensors are installed at the air inlet, air outlet, and center of the tank to monitor moisture changes in real time. Gas concentration sensors specifically detect fluctuations in the concentrations of oxygen, carbon dioxide, and ammonia. A pH sensor is inserted into the fermentation substrate, directly contacting the material to be fermented. This multi-dimensional monitoring network can capture subtle differences in environmental parameters. When the temperature in area A remains at 35 degrees Celsius while the temperature in area B reaches 38 degrees Celsius, the system identifies a temperature gradient difference; when the humidity in area C is 65% while the humidity in area D is 80%, uneven humidity distribution is detected. Clustering algorithms analyze the spatiotemporal distribution characteristics of these environmental parameters, grouping areas with similar environmental conditions into one category and identifying environmental difference patterns that lead to imbalances in quality control. Based on the environmental difference identification results, a nutrient composition analysis device further investigates the regional differences in the nutritional structure of the raw materials.

[0072] Specifically, the biochemical analysis method uses enzymatic hydrolysis to break down protein macromolecules, and an amino acid analyzer to detect the content distribution of essential amino acids such as lysine, methionine, and threonine; high performance liquid chromatography is used to determine the concentration levels of vitamin A, vitamin E, and B vitamins; and near-infrared spectroscopy is used to rapidly detect the content ratio of crude protein, crude fat, and crude fiber.

[0073] For example, when the lysine content of raw materials in the eastern region is 1.2% while that in the western region is only 0.9%, the system calculates an amino acid structure difference coefficient of 0.33; when the vitamin E content in the southern region is 45 mg / kg while that in the northern region is 32 mg / kg, the vitamin difference coefficient is 0.41. The calculation of these nutrient imbalance coefficients provides a quantitative basis for subsequent influence weighting analysis. The determination of influence weighting coefficients is based on the nutrient structure difference analysis, and logistics transportation timeliness monitoring further reveals the influence mechanism of time factors on fermentation quality. The timeliness tracking device records the entire transportation data of raw materials from the production base to the fermentation workshop, including key nodes such as loading time, transportation route, transit stops, and arrival time.

[0074] In one embodiment, when the raw material transportation time exceeds 48 hours, protein activity decreases by 15% and amino acid structural stability decreases by 20%; when temperature fluctuations exceed 10 degrees Celsius during transportation, vitamin loss rate increases by 25%. A regression analysis method is used to establish a mathematical correlation model between transportation time and metabolite quality. The least squares method is used to fit the functional relationship between time-related parameters and purity and concentration uniformity. The model parameters include time decay coefficient, temperature influence factor, and humidity correction parameters.

[0075] P(t) = P0·e -λt ·f(T)·g(H)

[0076] P(t) represents the purity of the metabolite after transport time t, P0 represents the initial purity, λ represents the time decay coefficient, t represents the transport time, f(T) represents the temperature influence factor, and g(H) represents the humidity correction parameter.

[0077]

[0078] U represents concentration uniformity, n represents the number of measurement points, and C i This represents the concentration value at the i-th measurement point. This represents the average concentration. The formula calculates the uniformity of concentration distribution using the least squares method; a value closer to 1 indicates higher uniformity. This formula describes the variation in metabolite purity with transportation timeliness. The quantitative influence parameters of the mathematical correlation model provide a scientific basis for the final data fusion. The data fusion device uses a weighted average algorithm to integrate the influencing factors from three dimensions: environmental differences (weighted at 0.4), nutrient structure (weighted at 0.35), and logistics timeliness (weighted at 0.25).

[0079] For example, when the contribution coefficient of environmental differences in a certain region is 0.6, the contribution coefficient of trophic structure is 0.7, and the contribution coefficient of logistics timeliness is 0.8, the comprehensive contribution coefficient is calculated as 0.4×0.6+0.35×0.7+0.25×0.8=0.685. A correlation feature distribution map is generated using spatial interpolation methods. Different colored areas in the map represent the severity of quality control imbalances; darker areas indicate higher imbalances, while lighter areas indicate relative stability, providing visual decision support for cross-regional quality control.

[0080] S106. Based on the correlation characteristics distribution of quality control imbalances between regions, update the operating instructions of the feed fermentation equipment in real time and obtain the equipment status operation feedback data after the operating instructions are updated.

[0081] Based on the correlation feature distribution of quality control imbalances between regions, the degree of imbalance and spatial location information in the feature distribution are analyzed to identify the coordinates and intensity of imbalances in regions exceeding the preset range. A decision tree algorithm is used to generate operating command parameters for different equipment, resulting in equipment operating command data including temperature control, ventilation frequency, and stirring speed. This equipment operating command data drives the communication interface module, transmitting operating commands to fermentation equipment in each region using a data transmission protocol. If the command transmission is successful and the equipment responds normally, the equipment actuator is activated to begin the parameter adjustment process, obtaining initial state changes and execution confirmation feedback after the equipment receives the command. Based on the execution confirmation feedback, feedback data during equipment operation is collected in real time, recording energy consumption changes of motors, heaters, and fans, measuring operating efficiency indicators such as fermentation rate and output ratio, and obtaining control accuracy data on temperature deviation, humidity fluctuation, and pH stability output by the precision monitoring device. By combining the control accuracy data with equipment power consumption and operating efficiency indicators, multi-dimensional feedback information is integrated, and a weighted average method is used to calculate comprehensive equipment operation evaluation parameters, obtaining equipment status operation feedback data including power consumption level, efficiency indicators, and accuracy performance, forming a closed-loop control data flow from feature distribution to operating commands and then to feedback data.

[0082] Specifically, the instruction generator employs a multi-level parsing mechanism when processing associated feature distribution data, and analyzes the color depth and numerical range in the data through an imbalance identification module.

[0083] In one possible implementation, when the imbalance index of a region in the feature distribution map exceeds 0.7, the system marks that region as high imbalance; when the index is between 0.4 and 0.7, it is marked as moderate imbalance; and when the index is below 0.4, it is marked as normal. Spatial location information is obtained through coordinate mapping, for example, region A has coordinates (10, 20), region B has coordinates (30, 45), and region C has coordinates (50, 60). The threshold judgment module, based on a preset imbalance intensity standard, triggers a temperature control adjustment command when it detects an imbalance index of 0.8 and a location coordinate of (10, 20) in region A, reducing the target temperature by 3 degrees Celsius; when the imbalance index of region B is 0.6 and the location coordinates are (30, 45), it generates a ventilation frequency increase command, increasing the fan speed to 1200 revolutions per minute. The decision tree algorithm establishes command generation rules by analyzing historical operating data. The root node determines the imbalance level, the child nodes determine the specific adjustment parameters, and the leaf nodes output the final equipment operation command data. Based on the generated runtime instruction data, the communication interface module undertakes the critical data transmission task.

[0084] Specifically, the data transmission protocol adopts a standardized industrial communication format, encapsulating parameters such as temperature control, ventilation frequency, and stirring speed into data packets.

[0085] For example, when a temperature control command needs to be transmitted to the fermenter in area A, the data packet contains information such as the device address code, command type, target parameter value, and checksum. When a ventilation command is sent to the device in area B, the data packet structure remains the same, but the parameter content differs. Successful transmission is determined based on the device's response signal. When the fermentation device receives the command, it returns an acknowledgment code indicating successful reception. If no response is received within a preset time, the system determines the transmission has failed and retransmits the command. After receiving the command, the device actuators begin the parameter adjustment process: the temperature control actuator adjusts the heating element power, the ventilation actuator controls the fan speed, and the stirring actuator adjusts the stirring paddle speed. The execution confirmation feedback mechanism is implemented through a status register. When the temperature reaches the target value, the temperature control actuator sends an adjustment completion signal; when the fan speed stabilizes at the target value, the ventilation actuator provides feedback indicating successful execution. Upon receiving the execution confirmation feedback, the status monitoring module immediately initiates comprehensive data acquisition of the device's operation.

[0086] In one embodiment, the power consumption monitoring device measures the power consumption of each device in real time using current and voltage sensors. Motor power consumption data is collected every second, heater power consumption is recorded every 30 seconds, and fan power consumption is updated every 15 seconds. When the motor power consumption in area A increases from 200 watts to 250 watts, the system records a 25% increase in power consumption; when the heater power consumption decreases from 800 watts to 700 watts, it records a 12.5% ​​decrease in power consumption. The efficiency monitoring device calculates the fermentation rate using a yield sensor and a time counter. When a fermenter produces 500 kg of fermented material in 6 hours, the calculated fermentation efficiency is 83.3 kg / hour; when the production ratio increases from 85% to 90% of the raw material weight, it records a 5.9% improvement in efficiency. The accuracy monitoring device uses high-precision sensors to continuously track the stability of control parameters, controlling temperature deviation within ±0.2 degrees Celsius, humidity fluctuation within ±2%, and pH stability within ±0.1 units. The data fusion module integrates monitoring data from three dimensions: power consumption, efficiency, and accuracy, to form a comprehensive evaluation system for the equipment's operating status.

[0087] Understandably, the weighted average method assigns different weights to each parameter based on their importance: power consumption is weighted at 0.3, efficiency at 0.4, and accuracy at 0.3. When a device scores 85 for power consumption, 92 for efficiency, and 88 for accuracy, the comprehensive operational evaluation score is calculated as 0.3 × 85 + 0.4 × 92 + 0.3 × 88 = 88.7. The device status feedback data includes three core elements: power consumption level, efficiency index, and accuracy performance. This feedback data is transmitted back to the instruction generator via the data flow manager, forming a complete closed-loop control data flow and enabling a continuous cyclical optimization process from feature recognition to instruction execution and then to status feedback.

[0088] S107. Continuously monitor the quality of feed fermentation metabolites and the uniformity indicators of fermentation process. Conduct backtracking analysis of multi-level sensor data on quality deviations and fermentation process to determine environmental variables that affect the quality of metabolites.

[0089] A closed-loop control data flow drives a quality monitoring device, employing spectral detection to detect changes in metabolite purity and activity detection to measure enzyme activity and microbial vigor levels. Concentration distribution data for each region is obtained from the concentration detection device, yielding fermentation process uniformity indicators encompassing purity, activity, and concentration uniformity. Based on these uniformity indicators, quality deviations exceeding preset ranges are identified. A process status recording device tracks the temporal information of temperature changes, pH fluctuations, and stirring frequency adjustments during fermentation. If a quality deviation exceeds a preset threshold, multi-level sensor data backtracking is initiated to obtain a historical sensor data set related to the quality deviation. Using this historical sensor data set, environmental parameter change patterns over different time periods are extracted. Correlation analysis is used to calculate the correlation strength between temperature, humidity, gas concentration, pH, and metabolite quality indicators, resulting in a quantitative assessment of the relationship between environmental factors and quality. Based on this quantitative assessment, environmental parameters with correlation strengths exceeding preset levels are selected, and the impact of each environmental factor on metabolite quality is ranked. Clustering algorithms are used to classify and integrate environmental parameters with similar impact characteristics, identifying the primary and secondary environmental variables affecting metabolite quality.

[0090] Specifically, the closed-loop control data flow adopts a real-time data transmission mechanism when driving the quality monitoring device, and transmits equipment status information, environmental parameters, and process parameters synchronously to each monitoring unit through the data bus.

[0091] In one possible implementation, the spectroscopic detection method is based on the principles of molecular vibration and electronic transitions. When the protein concentration in the metabolite is 85%, a characteristic absorption peak appears at a wavelength of 280 nm. When the purity decreases to 75%, the absorption peak intensity decreases by about 12%, and an impurity peak appears at 320 nm. The activity detection method is achieved by measuring enzyme activity units. When the lactate dehydrogenase activity produced by lactic acid bacteria reaches 150 units / mL, it indicates good fermentation activity; when the activity drops below 100 units / mL, it indicates an abnormality in the fermentation process. The concentration detection device uses the density gradient centrifugation principle, assessing concentration uniformity by measuring the density difference of samples in different regions. When the concentration in region A is 18.5% and the concentration in region B is 16.2%, the concentration difference coefficient is 0.126, exceeding the uniformity standard. These multi-dimensional detection data are summarized to form uniformity index parameters, providing basic data support for subsequent quality deviation identification. The deviation identification mechanism based on uniformity index parameters uses statistical methods for anomaly detection.

[0092] Specifically, the deviation calculation module compares the measured values ​​with the standard values ​​and triggers a quality deviation alarm when the purity deviation exceeds 5%, the activity deviation exceeds 15%, or the concentration uniformity deviation exceeds 10%. The process status recording device establishes a time-series database to record temperature changes every 15 minutes, pH fluctuations every 10 minutes, and stirring frequency adjustments every 30 minutes during the fermentation process.

[0093] For example, when the purity of a batch of products drops from 85% to 78%, the system initiates backtracking analysis, tracing the changes in temperature (from 35°C to 37.5°C), pH (from 6.8 to 6.3), and stirring frequency (from 120 rpm to 150 rpm) during that period. Multi-level sensor data backtracking processing, using a sliding time window technique, integrates all sensor data from within 24 hours before and after the quality anomaly, forming a historical sensor data set containing environmental parameters, process parameters, and equipment status. Processing this historical sensor data set requires data mining techniques to reveal hidden patterns and regularities.

[0094] In one embodiment, the data processing module first smooths the time-series data to eliminate random noise interference, and then extracts the trend characteristics of temperature, humidity, gas concentration, and pH value across different time periods. Correlation analysis is used to calculate the Pearson correlation coefficients between each environmental parameter and the quality index. When the correlation coefficient between temperature and purity is -0.75, it indicates that increased temperature significantly affects product purity; when the correlation coefficient between pH value and activity is 0.68, it indicates a positive correlation between pH changes and enzyme activity; and when the correlation coefficient between stirring frequency and concentration uniformity is 0.82, it indicates that stirring intensity directly affects the uniformity of concentration distribution. This quantitative assessment reveals the specific mechanisms by which environmental factors affect fermentation quality, providing a scientific basis for precise control. In-depth analysis of the quantitative assessment results achieves accurate screening of environmental variables through influencing factor identification and weight calculation.

[0095] Understandably, the influencing factor identification module sets a correlation strength threshold of 0.6. When the correlation coefficient between an environmental parameter and a quality indicator exceeds this threshold, it is listed as a key influencing factor. The weight calculation module assigns influence weights based on the magnitude of the correlation strength: temperature has a weight of 0.35, pH value has a weight of 0.28, stirring frequency has a weight of 0.25, and humidity has a weight of 0.12. The clustering algorithm groups environmental parameters with similar influence characteristics into one category: temperature and humidity are classified as environmental control variables, pH value and dissolved oxygen value are classified as chemical environmental variables, and stirring frequency and ventilation volume are classified as physical action variables. Through this classification and integration, the main environmental variables affecting the quality of metabolites are identified as temperature, pH value, and stirring frequency, while secondary environmental variables include humidity, dissolved oxygen value, and ventilation volume, forming a systematic environmental factor influence system.

[0096] S108. Input environmental variables into the digital twin model to obtain the predicted results of microbial metabolic parameters, compensate for the interference of environmental fluctuations on the stability and efficiency of the fermentation process, update the data acquisition frequency and transmission priority in the Internet of Things architecture based on the time delay fluctuation feature map, and obtain the set of operating parameters of the cross-regional fermentation environment control system.

[0097] Based on the classification data of major and minor environmental variables affecting metabolite quality, parameters such as temperature, pH, stirring frequency, humidity, dissolved oxygen, and ventilation are imported into the simulation calculation module through the input interface of a digital twin model. A neural network algorithm is used to construct a mathematical mapping relationship between environmental variables and quality indicators, obtaining predicted data on the quality fluctuations of feed fermentation metabolites. This predicted data drives a dynamic compensation module to identify time periods and amplitudes where quality fluctuations exceed preset ranges. The compensation amount of environmental control parameters is adjusted according to the predicted quality decline trend. If the predicted purity decline exceeds a preset threshold, environmental control strategy parameters including real-time temperature control compensation, gas composition adjustment, and stirring intensity optimization are generated. Based on these environmental control strategy parameters, the delay patterns and fluctuation laws of data transmission between different regions are analyzed, and the response time distribution characteristics of each sensor node are plotted to determine the data acquisition frequency and transmission priority configuration scheme for key environmental parameters. The data acquisition frequency and transmission priority settings in the IoT architecture are updated using this configuration scheme. Multi-dimensional control parameters, including temperature control compensation coefficient, gas regulation threshold, stirring optimization parameters, acquisition frequency setting, and transmission priority ranking, are integrated to obtain a set of operating parameters for a cross-regional fermentation environmental control system, encompassing environmental control parameters and communication control parameters.

[0098] Specifically, the input interface of the digital twin model uses a standardized data format conversion mechanism when processing environmental variable classification data, mapping environmental parameters from the physical world to the virtual space for modeling and calculation.

[0099] In one possible implementation, the temperature parameter is input in degrees Celsius; when the actual temperature is 35 degrees Celsius, the model interface converts it to a standardized value of 0.7. The pH parameter is input in the range of 4.0 to 8.0; when the actual pH value is 6.5, it is converted to a standardized value of 0.625. The stirring frequency parameter is input in revolutions per minute (rpm); when the actual frequency is 150 rpm, it is converted to a standardized value of 0.5. The simulation calculation module constructs a virtual fermentation environment based on digital twin technology, describing the microbial metabolic process through physical modeling equations. When the temperature increases by 1 degree Celsius, the model calculates that the fermentation rate increases by 8%, and the product purity decreases by 2.3%. The neural network algorithm adopts a multilayer perceptron structure. The input layer contains 6 neurons corresponding to 6 environmental variables, the hidden layer has 12 neurons for feature extraction, and the output layer contains 3 neurons to predict purity, activity, and concentration uniformity, respectively. Backpropagation training establishes a nonlinear mathematical mapping relationship between environmental variables and quality indicators. The dynamic compensation mechanism based on the predicted data reflects the system's forward-looking control capability.

[0100] Specifically, the compensation calculation device quantifies the compensation requirement by comparing the deviation between the predicted and target values. When the predicted purity is 82% and the target purity is 85%, the calculated purity deviation is -3%, triggering a temperature reduction compensation strategy. When the predicted activity is 140 units / mL and the target activity is 160 units / mL, the deviation is -12.5%, triggering a pH adjustment compensation mechanism. Real-time temperature control compensation achieves precise control by adjusting the heating power and cooling airflow. When the predicted temperature deviation is +2 degrees Celsius, the system reduces the heating power to 75% and increases the cooling airflow to 120%. Gas composition adjustment controls the supply ratio of oxygen and carbon dioxide. When the predicted dissolved oxygen is insufficient, the aeration rate is increased to 2.5 cubic meters per minute. Stirring intensity optimization adjusts the stirring speed and stirring time. When the predicted concentration is uneven, the stirring intensity is increased to 180 rpm and the stirring time is extended to 45 minutes per hour. The synergistic effect of this multi-dimensional compensation strategy significantly improves the stability of the fermentation process and the consistency of product quality. The implementation of environmental control strategy parameters needs to consider the timeliness requirements of data transmission, therefore the role of delay monitoring devices is crucial.

[0101] In one embodiment, the latency monitoring device measures the data transmission latency from each sensor node to the control center using network probing technology. The data transmission latency is 50 milliseconds in area A, 80 milliseconds in area B, and 120 milliseconds in area C. This latency difference directly affects the synchronization of control commands. The feature extraction module analyzes the temporal distribution characteristics of the transmission latency, identifying influencing factors such as network congestion periods, equipment response bottlenecks, and communication protocol efficiency. When the network load exceeds 75%, the transmission latency increases to 1.8 times its original value. The priority allocation algorithm determines the transmission priority based on the importance and timeliness requirements of environmental parameters. Temperature and pH data are set to the highest priority, transmitted once per second; stirring frequency and ventilation volume are set to medium priority, transmitted once every 30 seconds; humidity and pressure data are set to low priority, transmitted once per minute. The parameter integration module undertakes the crucial task of unified management of dispersed control parameters.

[0102] Understandably, the temperature control compensation coefficient reflects the sensitivity of temperature regulation. When the compensation coefficient is set to 1.2, it means that a 1-degree Celsius temperature deviation requires a 20% adjustment of the heating power. The gas regulation threshold sets the critical condition for triggering regulation; when the dissolved oxygen concentration is below 6 mg / L, the aeration program is activated. The stirring optimization parameters include the speed adjustment range and time control strategy. The speed can be adjusted within the range of 100-200 rpm, and the stirring time can vary within the range of 20-60 minutes per hour. The acquisition frequency is determined based on the parameter change rate; rapidly changing temperature parameters are acquired at high frequency, while slowly changing humidity parameters are acquired at low frequency. The transmission priority sorting ensures that critical data is transmitted first: temperature control data occupies 40% of the bandwidth, gas regulation data occupies 30% of the bandwidth, and other parameters share the remaining 30% of the bandwidth. The cross-regional fermentation environment control system's operating parameter set integrates all control dimensions, achieving a leap from single-point control to systemic coordination.

[0103] The above are only some preferred embodiments of the present invention, but the present invention is not limited thereto, and many improvements and modifications can be made. Any improvements and modifications made based on the basic principles of the present invention should be considered to fall within the protection scope of the present invention.

Claims

1. A method for intelligent correction of feed fermentation parameters based on the Internet of Things, characterized in that, The method includes: Data is collected across all dimensions, from the microenvironment within the feed fermentation tank to the macroenvironment across different regions, to generate a standardized data matrix. Based on a standardized data matrix, communication latency fluctuation characteristics are detected, the fluctuation period and amplitude range of latency anomalies are determined, and a latency fluctuation characteristic map is formed. A digital twin model of the cross-regional feed fermentation environment is constructed based on the time delay fluctuation feature map. The real-time status of the physical fermentation process is input, and the trend of microbial metabolic parameters in the virtual environment is output. Based on the changing trends of microbial metabolic parameters in the virtual environment, the control thresholds of each region are dynamically adjusted, the adaptability of temperature control thresholds and ventilation strategies between regions is analyzed, the environmental control priority of feed fermentation process in each region is determined, and regional differentiated regulation plans are generated. By using regionally differentiated control plans, the impact of raw material nutrient structure and logistics transportation timeliness on the purity and concentration uniformity of metabolites is analyzed, and the distribution of correlation characteristics of quality control imbalances between regions is output. Based on the correlation characteristics of quality control imbalances between regions, the operating instructions of the feed fermentation equipment are updated in real time, and the equipment status operation feedback data after the operating instructions are updated are obtained. Continuously monitor the quality of feed fermentation metabolites and the uniformity indicators of fermentation process, and conduct backtracking analysis of multi-level sensor data on quality deviations and fermentation process to identify environmental variables that affect the quality of metabolites. By inputting environmental variables into the digital twin model, the predicted results of microbial metabolic parameters are obtained, compensating for the interference of environmental fluctuations on the stability and efficiency of the fermentation process, and generating a set of operating parameters for a cross-regional fermentation environment control system.

2. The method for intelligent correction of feed fermentation parameters based on the Internet of Things according to claim 1, characterized in that, The process involves comprehensive data collection from the microenvironment within the feed fermentation tank to the macroenvironment across different regions, generating a standardized data matrix, including: Sensors are deployed inside the feed fermentation tank to collect microenvironmental parameters, recording data on temperature, humidity, and gas concentration. Monitoring nodes are arranged in different areas of the fermentation workshop to obtain differences in environmental parameters, forming a sensor data set containing timestamps and location identifiers. Outliers and missing values ​​are identified in the sensor data set, and noise is removed by smoothing. The data format is unified to generate a standardized data structure containing location and environmental category. Based on the standardized data structure, the temperature gradient and humidity change rate are calculated, and a clustering algorithm is used to output environmental feature category labels, generating a standardized data matrix.

3. The method for intelligent correction of feed fermentation parameters based on the Internet of Things according to claim 1, characterized in that, The method of detecting communication delay fluctuation characteristics based on a standardized data matrix, determining the fluctuation period and amplitude range of delay anomalies, and forming a delay fluctuation feature map includes: Timestamps and transmission records are extracted from the standardized data matrix, data packet transmission delay and packet loss rate are calculated, signal strength is obtained, and a set of communication performance parameters is formed. Anomalies are detected in the set of communication performance parameters. If the transmission delay or packet loss rate exceeds the threshold, it is marked as a delay anomaly point. Based on the delay anomaly point, Fourier transform is used to identify the fluctuation period and generate a delay fluctuation feature map.

4. The method for intelligent correction of feed fermentation parameters based on the Internet of Things according to claim 1, characterized in that, The process of constructing a digital twin model of a cross-regional feed fermentation environment based on time-delay fluctuation characteristic maps, inputting the real-time status of the physical fermentation process, and outputting the changing trends of microbial metabolic parameters in the virtual environment includes: The delay variation pattern is extracted from the time delay fluctuation feature map, and the temperature and humidity gradient in the environmental feature distribution is combined to establish the correspondence between physical and virtual environment coordinates; according to the correspondence, the real-time data of the physical fermentation process is converted into virtual environment parameters to generate a digital twin model; real-time status data is received through the digital twin model, the microbial growth rate and metabolite generation efficiency are calculated, and the trend of microbial metabolic parameter changes in the virtual environment is output.

5. The method for intelligent correction of feed fermentation parameters based on the Internet of Things according to claim 1, characterized in that, The process involves dynamically adjusting the control thresholds for each region based on the changing trends of microbial metabolic parameters in the virtual environment, analyzing the compatibility of temperature control thresholds and ventilation strategies between regions, determining the environmental control priorities for the feed fermentation process in each region, and generating regionally differentiated control plans, including: Based on the changing trends of microbial metabolic parameters in the virtual environment, the temperature control threshold and ventilation frequency are adjusted to generate environmental control parameter configuration data. The environmental control parameter configuration data drives the nutrient supply device to adjust the nutrient solution ratio and predict fermentation quality indicators. Based on the fermentation quality indicators, regional differences are assessed to determine the environmental control priority of the feed fermentation process in each region and generate a regionally differentiated regulation plan.

6. The method for intelligent correction of feed fermentation parameters based on the Internet of Things according to claim 1, characterized in that, The aforementioned regional differentiated control plan analyzes the impact of raw material nutrient structure and logistics transportation timeliness on the purity and concentration uniformity of metabolites, and outputs the correlation characteristic distribution of quality control imbalances between regions, including: Clustering algorithms are used to identify quality control imbalance patterns and generate imbalance characteristic parameters. Based on these imbalance characteristic parameters, differences in raw material nutrient structure are detected, and the nutrient structure imbalance coefficient is calculated. Combining logistics and transportation timeliness data, a correlation model between logistics timeliness and metabolite quality is established, and influencing parameters are calculated. By integrating environmental differences, nutrient structure, and logistics timeliness through these influencing parameters, the correlation characteristic distribution of quality control imbalance between regions is obtained.

7. The method for intelligent correction of feed fermentation parameters based on the Internet of Things according to claim 1, characterized in that, The method involves updating the operating instructions of the feed fermentation equipment in real time based on the correlation characteristics distribution of quality control imbalances between regions, and obtaining equipment status operation feedback data after the update of the operating instructions, including: Based on the correlation feature distribution of the quality control imbalance between the regions, the degree and location information of the imbalance are analyzed to generate operation command data including temperature control adjustment and stirring speed; the operation command data drives the device actuator to collect the device status operation feedback data after the operation command is updated, including power consumption, operating efficiency and control accuracy.

8. The method for intelligent correction of feed fermentation parameters based on the Internet of Things according to claim 1, characterized in that, The continuous monitoring of feed fermentation metabolite quality and fermentation process uniformity indicators, along with backtesting analysis of multi-level sensor data on quality deviations and fermentation processes, identifies environmental variables affecting metabolite quality, including: The purity, activity, and concentration uniformity of metabolites are detected to generate uniformity index parameters; quality deviations are identified based on the uniformity index parameters, and relevant historical sensor data are obtained; environmental parameter change patterns are extracted from the historical sensor data, the correlation strength between environmental factors and quality is calculated, and the main environmental variables affecting metabolite quality are determined.

9. The method for intelligent correction of feed fermentation parameters based on the Internet of Things according to claim 8, characterized in that, The input environmental variables are fed into the digital twin model to obtain predicted results of microbial metabolic parameters, compensating for the interference of environmental fluctuations on the stability and efficiency of the fermentation process, and generating a set of operating parameters for a cross-regional fermentation environment control system, including: By importing the main environmental variables into a digital twin model, a mapping relationship between environmental variables and quality indicators is constructed to generate quality fluctuation prediction data. Based on the prediction data, the compensation amount of environmental control parameters is adjusted to generate environmental regulation strategy parameters. By analyzing the data transmission delay pattern through the environmental regulation strategy parameters, the data acquisition frequency and transmission priority are determined to generate a set of operating parameters for the cross-regional fermentation environmental regulation system.

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