A management method and system for intelligent processing and utilization of livestock and poultry breeding waste

Abstract

The present application relates to a kind of livestock and poultry breeding waste intelligent processing and utilization management method and system, by the key depth of anaerobic fermentation tank deployment sensing node, real-time acquisition multi-source heterogeneous biochemical time series data, by analog-digital conversion, filtering and feature extraction, form the low-dimensional dynamic characteristics of representing microbial metabolic activity, stability and aerobic anoxic interface change.Combined with lightweight time series convolutional neural network, edge discrimination and action decision generation are realized, control command is converted into standardized electrical pulse to drive actuator, and waste treatment path dynamic scheduling is completed.Feedback verification mechanism is based on resampling data and discrimination result comparison, and automatically triggers redundant control logic polling and self-optimizing maintenance calibration under abnormal state.The method can realize the precise closed-loop control of biochemical treatment process, and improve system response efficiency and scheduling reliability.

Description

Technical Field

[0001] This invention relates to the field of intelligent scheduling technology for livestock and poultry breeding waste treatment, and in particular to a management method and system for intelligent treatment and utilization of livestock and poultry breeding waste. Background Technology

[0002] Poultry farming waste treatment and management refers to a technological system that utilizes automated control and intelligent scheduling to achieve resource-based transformation, harmless treatment, and biomass energy recovery and utilization of waste such as manure, biogas slurry, and biogas residue generated during livestock and poultry farming. This technology involves the coordinated scheduling and management of multiple stages, including waste collection, solid-liquid separation, anaerobic fermentation, biogas collection, and biogas slurry return to the field. Its aim is to solve the environmental pollution problem caused by livestock farming waste while maximizing energy and resource utilization.

[0003] Currently, mainstream waste treatment process scheduling systems in the industry aim for automated control and intelligent decision-making. Most are designed based on a "predict-plan-execute" paradigm. They collect comprehensive water quality and process data, such as pH, temperature, COD, and ammonia nitrogen, using multiple types of sensors. Multi-dimensional feature fusion modeling is then performed by a cloud platform or local server. Combined with historical transport trajectories and equipment health status, intelligent optimization algorithms are employed to dynamically schedule and optimize paths for waste flow direction and treatment unit load. Some advanced systems also introduce digital twins and predictive fault management, attempting to perform time-series modeling and reliability assessment of dynamic reaction processes.

[0004] However, the existing technologies mentioned above have the following problems: First, scheduling actions are mainly based on timed, preset thresholds, or offline model inference, which cannot make millisecond-level closed-loop responses based on real-time states such as microbial activity mutations or short-term system instability, resulting in insufficient equipment utilization and energy redundancy. Second, relying on the complete fusion of multi-source data and high-intensity global computation increases hardware costs, makes it impossible to achieve lightweight model portability and on-site autonomous operation, and significantly reduces robustness when local sensors fail or the model drifts. Third, the lack of adaptive secondary verification and error correction mechanisms after action execution makes it impossible to avoid the risks of single-point failures and anomaly accumulation, resulting in a broken scheduling feedback chain. These problems are particularly prominent in small and medium-sized or distributed aquaculture scenarios—limited computing resources and unstable data transmission make it difficult for traditional solutions to achieve efficient, stable, and low-cost deployment and operation. Therefore, there is an urgent need for a scheduling optimization method that can provide real-time feedback based on biochemical status, is hardware-lightweight, has millisecond-level response, and can run independently in edge scenarios. Summary of the Invention

[0005] This application provides a management method and system for intelligent treatment and utilization of livestock and poultry breeding waste, aiming to solve the problem that existing livestock and poultry breeding waste treatment and scheduling systems rely on cloud computing power and complex models, and cannot achieve millisecond-level closed-loop response in resource-constrained edge scenarios.

[0006] This application provides a management method and system for intelligent treatment and utilization of livestock and poultry breeding waste, specifically including: A sensor array is deployed deep within the anaerobic fermenter to collect time-series signals of volatile fatty acid component concentration, redox potential, and dissolved oxygen fluctuations in reaction zones at different depths within the anaerobic fermenter, generating raw time-series signals containing spatial location tags.

[0007] The original time-series signal is preprocessed by sliding window truncation and frequency domain filtering to extract the slope of the concentration change of volatile fatty acid components, the standard deviation of redox potential within a preset time window, and the energy proportion of specific frequency bands in the dissolved oxygen fluctuation spectrum. Three types of low-dimensional dynamic feature vectors are generated to characterize the sudden changes in microbial metabolic activity, the intensity of system stability disturbances, and the migration trend of aerobic and hypoxic interfaces.

[0008] The three types of low-dimensional dynamic feature vectors are discretely mapped using a pre-trained temporal convolutional neural network. By using preset weight parameters, continuous features are mapped into discrete state spaces, generating a set of four types of discrete action labels that indicate the current operating status of the processing unit.

[0009] Based on the specific instruction type in the four types of discrete action tag sets, and combined with the preset protocol conversion rule library, the action tags are parsed into standardized electrical control pulse sequences to generate path switching control instruction streams.

[0010] The path switching control command stream is sent to the valve controller or aeration fan driver to perform physical actions such as maintenance, switching, aeration compensation or sludge return adjustment, thereby completing the real-time dynamic scheduling of the livestock and poultry breeding waste treatment process.

[0011] Within a preset feedback time window after the physical action is performed, the sensor array is called again to resample the signal at the same process point, calculate the deviation between the discrimination result and the actual biochemical state, and generate dual-channel feedback verification data for verifying the accuracy of the scheduling.

[0012] If the deviation value in the dual-channel feedback verification data exceeds a preset threshold, and if it is determined to be an abnormal state exceeding the threshold, the alternative mapping relationship polling mechanism in the local rule base is automatically triggered to generate a corrected redundant control instruction to replace the failed initial scheduling strategy.

[0013] The system statistically analyzes the end-to-end discrimination time and feedback verification failure cases within a preset period. It then packages the cluster features of the desensitized failure cases that meet the offline calibration conditions into a quarterly maintenance calibration package. This package is used to update the discrimination logic and mapping relationship embedded in the device firmware, thus completing the periodic self-optimization of the lightweight feedback closed-loop scheduling method.

[0014] Furthermore, the above-mentioned generation of the original time-series signal containing spatial location labels specifically includes: The sensor array is deployed inside the anaerobic fermenter to collect raw time-series signals of volatile fatty acid component concentration, redox potential, and dissolved oxygen fluctuations at various depth points. The original time-series signal is subjected to analog-to-digital conversion, noise suppression, and spatial correlation mapping to generate the original time-series signal containing spatial location labels.

[0015] Furthermore, based on the aforementioned four types of discrete action tag sets and combined with a preset protocol conversion rule base, the four types of discrete action tag sets are parsed into standardized electrical control pulse sequences to generate a path switching control command stream, specifically including: Obtain the four types of discrete action label sets, and generate a standardized electrical control pulse sequence based on the protocol conversion rule base; The standardized electrical control pulse sequence is subjected to signal morphology conversion to generate the path switching control command stream.

[0016] Furthermore, the aforementioned path switching control command stream is sent to the valve controller or aeration blower driver to execute physical actions and complete real-time dynamic scheduling, specifically including: The path switching control command stream is sent to the valve controller or aeration blower driver to generate the underlying electrical signal that drives the actuator. The system executes physical actions driven by the underlying electrical signals to maintain the current processing path, switch to the backup anaerobic tank, initiate short-term aeration compensation, or trigger sludge return adjustment, thereby completing the real-time dynamic scheduling of the livestock and poultry breeding waste treatment process.

[0017] The management method and system for intelligent treatment and utilization of livestock and poultry breeding waste provided in this application have the following beneficial effects: By reconstructing the traditional scheduling paradigm into a "sensing-discrimination-triggering" control logic, and addressing the problem that existing systems cannot respond quickly to real-time states such as microbial activity mutations and short-term system instability, an electrochemical sensor array is deployed at different depths in the anaerobic digester to collect real-time temporal signals of volatile fatty acids, redox potentials, and dissolved oxygen with spatial location tags. After sliding window truncation and frequency domain filtering, three types of low-dimensional dynamic features are extracted: the slope of concentration change, the standard deviation of potential, and the proportion of low-frequency dissolved oxygen energy. These features are directly discretely mapped into four types of action tags by a lightweight temporal convolutional neural network embedded in the equipment firmware. Combined with a protocol conversion rule base, these tags are parsed into standardized electrical control pulse sequences to drive valves or fans to perform path switching, aeration compensation, or sludge return adjustment, achieving millisecond-level closed-loop scheduling response. Meanwhile, by embedding the discrimination logic and mapping relationship into the device firmware, it does not rely on cloud computing power and high-intensity global computing, and does not require online learning or model retraining. It only needs a quarterly offline calibration package to complete the logic update, which overcomes the dependence of traditional solutions on the complete fusion of multi-source data and complex model training, and realizes lightweight model porting and independent autonomous operation in edge scenarios. Furthermore, after physical actions are executed, dual-channel feedback verification data is generated through resampling and comparison. When the deviation exceeds the threshold, local alternative mapping relationships are polled to generate redundant control instructions to replace the failure strategy, forming a complete closed-loop feedback link of "execution-verification-error correction". By identifying time consumption and failure cases within a statistical period, and extracting common failure modes through differential privacy desensitization, the weight parameters are reversed to generate quarterly maintenance calibration packages to update the firmware, forming a periodic self-optimization capability without manual intervention. This effectively overcomes the problems caused by the lack of adaptive secondary verification and action error correction mechanisms in traditional systems. Attached Figure Description

[0018] Figure 1 This is the main flowchart of a management method for intelligent treatment and utilization of livestock and poultry breeding waste.

[0019] Figure 2 This is a sub-flowchart of a management method for the intelligent treatment and utilization of livestock and poultry breeding waste.

[0020] Figure 3 This is another sub-flowchart of a management method for the intelligent treatment and utilization of livestock and poultry breeding waste. Detailed Implementation

[0021] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0022] The following disclosure provides many different embodiments or examples for implementing different structures of the invention. To simplify the disclosure, specific examples of components and arrangements are described below. Of course, these are merely examples and are not intended to limit the invention. Furthermore, reference numerals and / or letters may be repeated in different examples; such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0023] like Figure 1 As shown, this application provides a management method and system for intelligent treatment and utilization of livestock and poultry breeding waste, specifically including: Step S1: Deploy a sensor array deep within the anaerobic digester. For reaction zones at different depths within the digester, collect time-series signals of volatile fatty acid concentrations, redox potentials, and dissolved oxygen fluctuations, generating raw time-series signals containing spatial location tags. Specifically, this includes: S1.1: Based on the hydrodynamic characteristics of the anaerobic fermentation tank and the stratification of microbial metabolism, the reaction area inside the tank is vertically divided into grids, and multiple key depth coordinate points including the feeding area, the main reaction area and the effluent area are determined, generating a topology map of sensor node deployment with three-dimensional spatial location markers.

[0024] The geometric dimensions and internal fluid dynamics boundary conditions of the anaerobic fermenter are obtained. Based on the computational fluid dynamics simulation model, the velocity field distribution under different stirring rates is simulated, the fluid dead zone and short-circuit flow region are identified, and the hydraulic residence time gradient curve in the vertical direction is determined.

[0025] Based on the stratification of microbial metabolism, the activity distribution characteristics of acid-producing bacteria and methanogens under different redox potentials were analyzed. The vertical space of the tank was divided into three functional subdomains: the feed mixing zone, the main reaction degradation zone, and the effluent sedimentation zone. The dominant biochemical reaction mechanism and the weight of key monitoring indicators for each subdomain were defined.

[0026] A first depth coordinate point is set in the feed mixing zone, corresponding to the location of maximum turbulence intensity below the agitator blades, to capture the rapid generation signal of volatile fatty acids in the initial stage of waste entry; second to fourth depth coordinate points are evenly arranged along the vertical axis in the main reaction degradation zone, covering the main biofilm attachment layer and the active suspended sludge layer, to monitor the stability of the core metabolic process; a fifth depth coordinate point is set in the effluent sedimentation zone, located in the still water layer below the overflow weir, to evaluate the solid-liquid separation effect and residual organic matter concentration in the final stage of treatment.

[0027] Establish a three-dimensional Cartesian coordinate system with the center of the bottom of the tank as the origin, the vertical upward direction as the positive Z-axis, and the horizontal radial direction as the X-axis and Y-axis. Map the five key depth coordinate points determined above into specific spatial vector data, including radial distance, angular orientation, and absolute height values.

[0028] Construct a topology map data structure for sensor node deployment, associate and bind each spatial vector data with the corresponding functional subdomain attributes and expected biochemical response type, and generate a structured topology mapping table containing node ID, three-dimensional coordinate index, partition label, and sampling priority.

[0029] Through the above spatial gridding and coordinate mapping process, the physical structure of the tank in the previous step is transformed into a topology map of sensor node deployment with clear biochemical significance and spatial location identifiers. This enables precise spatial positioning of multi-dimensional state perception of the anaerobic fermentation process and provides a basic framework for the spatiotemporal correlation of subsequent multi-source heterogeneous data.

[0030] For example, for a cylindrical anaerobic digester with a diameter of 5 meters and an effective water depth of 6 meters, the stirring speed is set to 15 rpm. Fluid dynamics simulation determines the feed zone depth to be 0.5 meters from the liquid surface, the main reaction zone depths to be 2.0 meters, 3.5 meters, and 5.0 meters, and the effluent zone depth to be 0.2 meters from the bottom. After establishing a coordinate system, node N1 (0,0,5.5) belongs to the feed zone, nodes N2 (0,0,4.0), N3 (0,0,2.5), and N4 (0,0,1.0) belong to the main reaction zone, and node N5 (0,0,0.2) belongs to the effluent zone. The topology graph records the ID of each node and its corresponding VFA sensitivity weight, with the main reaction zone node weight set to 0.8, the feed zone to 0.6, and the effluent zone to 0.4, ensuring that subsequent scheduling algorithms can differentiate sensor data based on spatial importance.

[0031] S1.2: Based on the sensor node deployment topology, install electrochemical sensor array probes at the determined key depth coordinates, and use multi-electrode synchronous sampling technology to capture the simulated voltage signal of volatile fatty acid component concentration, millivolt-level signal of redox potential and dissolved oxygen current signal at each depth point in parallel, generating the raw electrochemical response data stream containing timestamp and depth information.

[0032] Based on the sensor node deployment topology generated in S1.1, the three-dimensional spatial coordinates of the anaerobic fermenter's feed zone, main reaction zone, and effluent zone are located. Electrochemical sensor array probes are installed at the determined key depth coordinates, ensuring the probe's sensitive surface is perpendicular to the fluid direction to minimize flow resistance interference. A multi-electrode synchronous sampling controller is configured, setting the sampling clock phase difference of each channel to zero, enabling parallel acquisition of the volatile fatty acid concentration analog voltage signal, the redox potential millivolt-level signal, and the dissolved oxygen current signal. The weak analog voltage signal output by the volatile fatty acid sensor is buffered and isolated with high impedance to prevent measurement deviations caused by signal source load effects. The millivolt-level signal output by the redox potential sensor is differentially amplified to suppress common-mode interference and improve the signal-to-noise ratio. The nanoampere-level current signal output by the dissolved oxygen sensor is transimpedance amplified and converted linearly into an acquireable voltage signal. A high-precision hardware timestamp is added to each frame of acquired multi-channel analog signal to record the absolute moment of signal generation. The sensor ID code for each depth point is embedded in the data frame header to establish the correspondence between the signal and its physical spatial location. The raw electrochemical response data stream, containing timestamps, depth information, and multi-channel analog values, is transmitted in real time to the embedded microcontroller buffer via a high-speed serial interface. Through multi-electrode synchronous sampling and spatiotemporal marking processing, the spatial topology information from the previous step is transformed into a raw electrochemical response data stream containing timestamps and depth information, achieving lossless digital preservation of biochemical state signals.

[0033] For example, in an anaerobic fermenter with a volume of 50 cubic meters, VFA, ORP, and DO sensor probes are deployed at depths of 1.5 meters, 3.0 meters, and 4.5 meters, respectively. The sampling frequency is set to 100 Hz, and the synchronization error is controlled within 1 microsecond. The VFA sensor outputs a 0-50mV voltage signal, which is amplified by 100 times and then connected to a 16-bit ADC; the ORP sensor outputs a -500 to +500mV potential signal, which is directly connected to a high-impedance input terminal; the DO sensor outputs a 4-20mA current signal, which is converted into a 1-5V voltage signal by a 250-ohm precision resistor. Each sampling cycle generates a data packet containing 3 channel data, a 4-byte timestamp, and a 2-byte location ID, with a total length of 24 bytes. The system continuously collects data for 10 seconds, generating approximately 30KB of raw electrochemical response data stream, fully preserving the transient fluctuation characteristics of microbial metabolic activity, providing high-fidelity input for subsequent analog-to-digital conversion and noise suppression in S1.3.

[0034] S1.3: Perform analog-to-digital conversion and noise suppression processing on the original electrochemical response data stream. Use the built-in filtering algorithm of the embedded microcontroller to remove high-frequency electromagnetic interference and baseline drift. Convert the analog voltage signal, millivolt signal and current signal into a standardized digital time sequence to generate a denoised multi-channel biochemical parameter digital signal set.

[0035] Receive the raw electrochemical response data stream generated by S1.2, which includes timestamps and depth information. This data stream contains analog voltage signals of volatile fatty acid components, millivolt-level signals of redox potentials, and current signals of dissolved oxygen.

[0036] By utilizing the high-precision analog-to-digital converter (ADC) built into the embedded microcontroller, synchronous sampling and quantization processing are performed on three analog signals. A 16-bit resolution is set to ensure the dynamic range coverage of weak biochemical signals, converting continuously changing analog quantities into discrete digital coded sequences.

[0037] For the converted digital sequence, a moving average filtering algorithm is applied to remove high-frequency electromagnetic interference noise. A window length of 5 sampling points is selected, and the signal fluctuation is smoothed by calculating the arithmetic mean of the data points within the window, thus suppressing spike pulse interference introduced by the start-up and shutdown of the frequency converter or motor in the field.

[0038] The baseline correction technique using polynomial fitting is used to address the signal baseline drift problem. A quadratic polynomial model is constructed to fit the low-frequency trend term of the signal. The baseline curve obtained by fitting is subtracted from the original digital sequence to eliminate the zero-point shift caused by electrode polarization or slow temperature changes.

[0039] The denoised multi-channel signals are normalized by performing range normalization processing. Based on the upper and lower limits of the physical range of each sensor type, the voltage, potential and current values ​​are linearly mapped to the standard range of 0 to 1, eliminating the order of magnitude difference between different physical dimensions.

[0040] Through the above analog-to-digital conversion, moving average filtering, baseline correction and normalization processing, the original analog signal from the previous step is transformed into a denoised multi-channel digital signal set of biochemical parameters, realizing high signal-to-noise ratio and dimensionless standardized data output, providing a clean data foundation for subsequent spatiotemporal correlation mapping.

[0041] For example, the 0-5V analog voltage signal output by the volatile fatty acid sensor is sampled using a 16-bit ADC at a sampling frequency of 100Hz. A moving average filter with a window length of 5 is applied to effectively filter out 50Hz power frequency interference and its harmonic components. To address the approximately 2mV baseline drift per hour in the redox potential signal, a quadratic polynomial baseline model is constructed using the most recent 100 sampling points and subtracted from the drift. Finally, the processed voltage signal (0-5V), potential signal (-500mV to +500mV), and current signal (4-20mA) are uniformly mapped to the 0-1 interval, generating a standard digital sequence. This significantly improves the signal-to-noise ratio and meets the stringent data stability requirements for subsequent feature extraction.

[0042] S1.4: Based on the three-dimensional spatial location markers in the sensor node deployment topology map, perform spatiotemporal correlation mapping on the multi-channel biochemical parameter digital signal set, bind each digital time series sequence to the corresponding depth coordinate index and tank partition attributes, and generate a structured biochemical response data frame carrying complete spatial context information.

[0043] Read the denoised multi-channel biochemical parameter digital signal set and analyze the timestamp sequence and the original sampled values ​​of each sensor channel contained therein. Call the sensor node deployment topology map data structure, extract the predefined three-dimensional spatial coordinate index and tank partition attribute labels, and establish a mapping dictionary between physical location and data channel.

[0044] It iterates through each data channel in the digital signal set, retrieves the corresponding depth coordinate index from the mapping dictionary based on the channel ID, and injects the vertical Z-axis coordinate value into the spatial metadata field of the current data frame. Based on the range of the depth coordinates, it matches the partition identifiers of the feed zone, main reaction zone, or effluent zone, generating classification labels describing the reaction environment attributes.

[0045] A structured data frame header is constructed, integrating a global time synchronization reference, a unique sensor identifier, and the aforementioned spatial location index. The filtered volatile fatty acid concentration values, redox potential values, and dissolved oxygen current values ​​are then packaged and filled into the payload area of ​​the data frame according to preset data type precision.

[0046] The integrity of the data frame is verified by calculating a cyclic redundancy check (CRC) code and appending it to the end of the frame to ensure the immutability of the spatiotemporal correlation information. The structured biochemical response data frame carrying complete spatial context information is written to a circular buffer via memory pointer operations, awaiting unified encapsulation by subsequent modules.

[0047] By using a spatiotemporal correlation mapping process, the isolated digital time series obtained in the previous step is transformed into a structured biochemical response data frame carrying three-dimensional spatial coordinates and process partition attributes, thereby achieving precise binding between biochemical signals and physical reaction locations and providing a unified data structure foundation for subsequent feature extraction based on spatial differences.

[0048] For example, for an anaerobic digester with a diameter of 3 meters and a depth of 5 meters, step S1.1 defines the feed zone (0-1.5m), the main reaction zone (1.5-4.0m), and the effluent zone (4.0-5.0m). The channel CH01 data generated in step S1.3 corresponds to a depth of 1.0m. Step S1.4 retrieves the topology map and finds that this depth belongs to the feed zone, with a Z-coordinate index of 100cm. The system writes the timestamp T=1715623800s, channel ID=CH01, depth=100cm, and zone=feed zone into the frame header. The VFA voltage value of CH01 at time T (0.45V), ORP potential value (-210mV), and DO current value (2.1μA) are filled into the payload. A CRC16 checksum 0xA3F1 is generated and appended to the frame tail. The final output is a standard data frame with a length of 32 bytes, which ensures that the subsequent S2 step can accurately identify the process stage to which the signal belongs when processing signals of different depths, and avoids feature misjudgment caused by spatial confusion.

[0049] S1.5: Integrate all structured biochemical response data frames carrying complete spatial context information, encapsulate and standardize the data according to a unified time synchronization protocol, construct the original time-series signal containing full information on volatile fatty acid component concentration, redox potential and dissolved oxygen fluctuation, and complete the initial aggregation of sensing layer data.

[0050] Step S2: Perform sliding window truncation and frequency domain filtering preprocessing on the original time-series signal to extract the slope of volatile fatty acid component concentration changes, the standard deviation of redox potential within a preset time window, and the energy proportion of specific frequency bands in the dissolved oxygen fluctuation spectrum. This generates three types of low-dimensional dynamic feature vectors characterizing abrupt changes in microbial metabolic activity, the intensity of system stability disturbances, and the migration trend of the aerobic / hypoxic interface. Specifically, this includes: S2.1: Perform timestamp alignment and missing value interpolation on the original time-series signal containing spatial location labels. Fill the data gaps caused by sensor sampling jitter based on the linear interpolation algorithm to generate a continuous time-series signal sequence with a unified time reference, ensuring strict synchronization of volatile fatty acid component concentration, redox potential and dissolved oxygen fluctuation data in the time dimension.

[0051] The system receives raw time-series signals containing spatial location tags and parses the data frame header information to extract the sampling timestamp sequence of each channel sensor and the corresponding biochemical parameter values.

[0052] By traversing three data channels—volatile fatty acid concentration, redox potential, and dissolved oxygen fluctuation—the time interval deviation between adjacent sampling points is detected, and non-equidistant sampling gaps caused by sensor hardware jitter or communication delays are identified.

[0053] For the identified missing data nodes, locate the nearest valid sampling points before and after them, and obtain the valid value V at the previous moment. preThe effective value V at the next moment next and its corresponding timestamp t pre With t next .

[0054] Calculate the missing time t using a linear interpolation algorithm miss The estimated values ​​at the given location are used to determine the interpolated biochemical parameter values ​​using the following formula: in, The interpolated value at the missing time point. and These represent the values ​​of adjacent valid sampling points, respectively. , and These are the corresponding timestamps.

[0055] Parallel interpolation is performed on the data from the three channels to ensure that all channels have a continuous and uninterrupted numerical sequence under the same time base.

[0056] The interpolated data is repackaged into a time-series signal matrix with a unified sampling frequency to eliminate the asynchronicity of multi-source data in the time dimension.

[0057] By using linear interpolation, the multi-source heterogeneous raw data with sampling jitter is transformed into a continuous time-series signal sequence with a unified time base, achieving strict synchronization of volatile fatty acid, redox potential and dissolved oxygen data in the time dimension, providing high-quality input for subsequent sliding window feature extraction.

[0058] For example, the sensor's nominal sampling frequency is set to 10Hz, meaning the theoretical sampling interval is 100ms. In a certain data acquisition cycle, the redox potential channel experiences data loss at t=1.2s, with the previous valid sampling point being t... pre =1.1s, V pre =-250mV, the next valid sampling point is t next =1.3s, V next=-240mV. The system detected a gap at 1.2s and immediately started the linear interpolation program. According to the formula, the time weighting coefficient is (1.2-1.1) / (1.3-1.1)=0.5, and the potential difference is -240-(-250)=10mV. Therefore, the interpolation value V(1.2)=-250+10×0.5=-245mV. The system fills the data bit at 1.2s with -245mV and performs the same alignment operation on the volatile fatty acid and dissolved oxygen channels. In the final continuous time-series signal sequence, all channels maintain a strict 100ms interval distribution in the interval from 1.1s to 1.3s, eliminating the phase deviation caused by hardware jitter, ensuring the accurate correspondence of each channel data point on the time axis when the Hanning window is truncated in the subsequent S2.2 step, and significantly improving the accuracy and consistency of feature vector extraction.

[0059] S2.2: Apply the Hanning window function to the continuous time series signal sequence for sliding window truncation processing. Based on the preset ten-second time window length and five-second overlap step, the long-term continuous signal is divided into several short-term stationary signal segments, generating a segmented signal matrix with local time context information to eliminate trend term interference in non-stationary biochemical reaction processes and retain transient change characteristics.

[0060] For continuous time-series signal sequences with a unified time base, a sliding time window mechanism with a length of 10 seconds and an overlap step of 5 seconds is constructed to adapt to the non-stationary characteristics of biochemical reactions.

[0061] The number of data points contained in each window is calculated based on the sampling frequency, and the start and end indices of the window on the time axis are determined to form a set of window sequences covering the entire time period.

[0062] For each signal segment within a sliding window, a corresponding sequence of Hanning window function coefficients is generated. This sequence follows a cosine square distribution and decays to zero at both ends to suppress spectral leakage.

[0063] The original signal segment is multiplied point by point with the coefficients of the Hanning window function to achieve weighted modulation of the signal amplitude, resulting in a windowed local signal data block.

[0064] By using the above processing method, the long-term non-stationary continuous signal is divided into several signal segments that satisfy the short-term stationary assumption, generating a segmented signal matrix with local time context information, eliminating trend term interference and retaining transient change characteristics, thus providing high-quality input for subsequent frequency domain analysis.

[0065] S2.3: Based on the Butterworth low-pass filter, the segmented signal matrix is ​​preprocessed by frequency domain filtering. The cutoff frequency is set to filter out high-frequency electronic noise and power frequency interference, generating purified time-domain waveform data. Then, the first derivative of the concentration of volatile fatty acid components in the purified time-domain waveform data is calculated using the central difference method to generate the concentration change slope characteristic parameter characterizing the sudden change in microbial metabolic activity.

[0066] Receive the segmented signal matrix with local time context information generated by S2.2, and perform frequency domain filtering on the signal channel corresponding to the concentration of volatile fatty acid components.

[0067] Configure the parameters of the Butterworth low-pass filter and set the cutoff frequency to 0.5Hz to preserve the slow concentration change trend caused by microbial metabolism, while suppressing high-frequency electronic noise above this frequency and 50Hz power frequency interference harmonics.

[0068] The volatile fatty acid concentration data sequence in the segmented signal matrix is ​​input into a Butterworth low-pass filter. The signal is smoothed through a recursive difference equation to eliminate instantaneous glitches caused by sensor thermal noise and generate a purified time-domain waveform data sequence.

[0069] The central difference method is applied to the purified time-domain waveform data sequence to calculate the first derivative, so as to obtain the instantaneous rate of concentration change with time and avoid the phase lag error caused by forward or backward difference.

[0070] The slope of the concentration change at the i-th sampling point is calculated using the following formula: in, For the first The slope of concentration change at each sampling point and These represent the volatile fatty acid concentrations at adjacent time points. This represents the sampling time interval.

[0071] Iterate through all valid sampling points within the sliding window, calculate the slope of their respective concentration changes, and take the average of the slopes within the window as the characteristic representative value of that time window to smooth random fluctuations and highlight the overall abrupt trend of metabolic activity.

[0072] By using Butterworth filtering and central difference differentiation, the segmented signal from the previous step is transformed into a characteristic parameter of the slope of concentration change that characterizes the abrupt change in microbial metabolic activity, thereby achieving real-time quantitative capture of the anaerobic fermentation biochemical reaction rate.

[0073] For example, the sampling frequency is set to 10Hz, i.e., Δt is 0.1 seconds. The Butterworth low-pass filter is set to order 4, and the cutoff frequency is set to 0.5Hz. Within a 10-second sliding window, volatile fatty acid concentration data are collected. After filtering, the data point at the midpoint i=50 is selected for calculation. If C51 is 120.5mg / L and C49 is 119.8mg / L, then substituting into the formula, we get k50=(120.5-119.8) / (2*0.1)=3.5mg / (L·s). The average slope of all valid points within this window is taken to obtain the final characteristic parameter value of 3.2mg / (L·s). This value is significantly higher than the static threshold of 2.0mg / (L·s), accurately reflecting the state of rapid increase in microbial metabolic activity after feeding, providing a high signal-to-noise ratio decision basis for the subsequent discriminator.

[0074] S2.4: The standard deviation of the filtered redox potential segmented signal is calculated using a rolling statistical algorithm. The dispersion of the potential value is quantified within a preset time window to generate a potential fluctuation standard deviation characteristic parameter that characterizes the intensity of system stability disturbance. This parameter directly reflects the electrochemical equilibrium recovery ability of the anaerobic fermentation environment after being subjected to external shocks.

[0075] The system receives segmented redox potential signals that have been preprocessed by Butterworth low-pass filtering. These segments have been freed from high-frequency electronic noise and power frequency interference, while retaining time-domain waveform data that reflects the electrochemical equilibrium state of the anaerobic fermentation environment.

[0076] Based on a preset ten-second time window length, the purified time-domain waveform data is traversed by sliding to extract the potential value sequence of N discrete sampling points within the current window, and a local dataset for statistical calculation is constructed.

[0077] Calculate the arithmetic mean of all potential sampling points in the local dataset to establish the central trend benchmark of redox potential within the current time window, providing a reference zero point for subsequent dispersion quantification.

[0078] A rolling statistical algorithm is used to iterate through each potential sampling point in the local dataset, calculate the square of the difference between it and the arithmetic mean, and sum all the squared deviations to construct the numerator of the variance calculation.

[0079] Using the principle of unbiased estimation, the sum of squared deviations is divided by the number of sampling points minus one degree of freedom coefficient. This division operation is performed to eliminate statistical bias caused by the small sample size, generating the sample variance value of the redox potential.

[0080] The square root operation is performed on the sample variance to obtain the standard deviation value, which represents the fluctuation range of redox potential within the current time window. This value directly quantifies the dispersion of the potential value.

[0081] By using the above rolling statistics and square root processing method, the filtered signal from the previous step is transformed into a potential fluctuation standard deviation characteristic parameter that characterizes the intensity of system stability disturbance, thereby realizing a real-time quantitative assessment of the electrochemical equilibrium recovery capability of the anaerobic fermentation environment after being subjected to external shocks.

[0082] S2.5: Based on the Fast Fourier Transform algorithm, the filtered dissolved oxygen fluctuation segmented signal is subjected to spectral decomposition processing to extract the spectral energy distribution in the frequency band from 0.02 Hz to 0.1 Hz, calculate the ratio of the energy in this specific frequency band to the total spectral energy, generate the characteristic parameter of the proportion of low-frequency dissolved oxygen energy that characterizes the migration trend of the aerobic-hypoxic interface, and complete the construction of three types of low-dimensional dynamic feature vectors.

[0083] The system receives a segmented signal of dissolved oxygen fluctuations after preprocessing with a Butterworth low-pass filter. This segment has eliminated high-frequency electronic noise and power frequency interference, and retains effective time-domain waveform data that reflects the dynamic changes in gas-liquid mass transfer within the bioreactor.

[0084] A fast Fourier transform algorithm is applied to the purified time-domain waveform data to map the discrete time-domain signal sequence to the frequency domain space, generating a complex spectrum sequence containing amplitude and phase spectrum information to reveal the frequency composition characteristics of dissolved oxygen concentration fluctuations.

[0085] Based on the preset frequency range of 0.02 Hz to 0.1 Hz, the corresponding frequency index interval is located in the generated complex spectrum sequence, the amplitude square of each frequency point in the low frequency interval is extracted, and the total spectral energy distribution of the low frequency band is calculated.

[0086] By traversing all effective frequency points of the complex spectral sequence and calculating the sum of the squares of the amplitudes of all frequency points, the total spectral energy of the entire frequency band representing the overall intensity of dissolved oxygen fluctuations is obtained.

[0087] The characteristic parameters of the low-frequency energy ratio of dissolved oxygen are calculated using the following formula: in, The proportion of low-frequency energy in dissolved oxygen. For the first Complex spectral values ​​at each frequency point For the first Complex spectral values ​​at each frequency point and These are the frequency indices corresponding to 0.02 Hz and 0.1 Hz, respectively. This represents the number of sampling points.

[0088] By calculating the above ratios, the migration trend of the aerobic / anoxic interface on the surface of microbial flocs or granular sludge is quantified, and the low-frequency energy ratio of dissolved oxygen, which characterizes the redox environment stability of the system, is generated, thus completing the construction of three types of low-dimensional dynamic feature vectors.

[0089] For example, a 1024-point Fast Fourier Transform was performed on a dissolved oxygen time-series signal with a sampling frequency of 10 Hz and a length of 10 seconds. The index corresponding to 0.02 Hz was determined to be 2, and the index corresponding to 0.1 Hz to be 10. The sum of squares of the amplitudes from indices 2 to 10 was calculated to be 0.45, and the sum of squares of the amplitudes across the entire frequency band (0 to 512) was 1.20. Substituting these values ​​into the formula, the low-frequency energy proportion was calculated to be 0.375. This value is significantly higher than the baseline value of 0.15 during normal stable operation, indicating a rapid migration from the aerobic zone to the hypoxic zone, suggesting a risk of insufficient oxygen supply to the system, and providing crucial information for subsequent scheduling decisions.

[0090] like Figure 2 As shown, step S3: A pre-trained temporal convolutional neural network is used to discretely map three types of low-dimensional dynamic feature vectors. Through preset weight parameters, continuous features are mapped to a discrete state space, generating a set of four discrete action labels indicating the current operating state of the processing unit. Specifically, this includes: S3.1: Obtain three types of low-dimensional dynamic feature vectors: the slope of the concentration change of volatile fatty acid components, the standard deviation of the redox potential within a preset time window, and the energy proportion of a specific frequency band in the dissolved oxygen fluctuation spectrum. Perform tensor recombination and normalization on the three types of low-dimensional dynamic feature vectors to generate a standardized feature tensor sequence that meets the input dimension requirements of the embedded edge discriminator.

[0091] The system receives three types of low-dimensional dynamic feature vectors generated in step S2: the slope of volatile fatty acid concentration change, the standard deviation of redox potential, and the proportion of low-frequency dissolved oxygen energy. These vectors serve as the raw input data stream for the embedded edge discriminator.

[0092] A three-dimensional feature tensor structure is constructed, and the three independent one-dimensional scalar features are arranged in space according to the preset physical meaning dimension to form an initial input tensor with shape [1,3,1], so as to preserve the orthogonality and independence between the biochemical parameters and adapt to the channel processing mechanism of the subsequent convolution kernel.

[0093] The initial input tensor is subjected to Min-Max normalization. Based on the historical extreme boundary parameters stored in the firmware, each feature component is linearly mapped to the [0,1] interval to eliminate the risk of gradient explosion caused by the difference in the dimensions of different electrochemical signals.

[0094] The normalized tensor sequence is smoothed by using a moving average filter with a window size of 3 time steps to suppress feature jitter caused by instantaneous sensor noise and generate a standardized feature tensor sequence with temporal continuity.

[0095] By using tensor recombination and normalization, the multi-source heterogeneous features from the previous step are transformed into a standardized data format that meets the input dimension requirements of lightweight temporal convolutional neural networks. This achieves the unification of the feature space and improves numerical stability, laying a data foundation for subsequent deep spatiotemporal feature extraction.

[0096] For example, the slope of the volatile fatty acid concentration change at the current moment is obtained as 0.15 mmol / (L·s), the standard deviation of the redox potential is 12 mV, and the low-frequency energy proportion of dissolved oxygen is 0.35. The extreme ranges of the concentration slope are set to [-0.5, 0.5], the extreme ranges of the redox potential standard deviation are [0, 50], and the extreme ranges of the energy proportion are [0, 1]. Based on the Min-Max formula... ,in, Indicates the current eigenvalue. and This represents the historical minimum and maximum values ​​of this feature.

[0097] The normalized concentration slope was calculated to be 0.65, the normalized potential standard deviation to be 0.24, and the energy percentage to be kept constant at 0.35. An initial tensor [[0.65], [0.24], [0.35]] was constructed. After a 3-step moving average filter, if the previous value was [0.60, 0.20, 0.30], the current output tensor sequence was updated to a smoothed numerical combination, which significantly improved the signal-to-noise ratio of the input data and the model convergence speed.

[0098] S3.2: Based on a lightweight temporal convolutional neural network architecture, using a standardized feature tensor sequence as the input data stream, multi-layer causal dilated convolution operations and nonlinear activation function mapping are performed to extract deep spatiotemporal hidden layer feature maps that characterize the mutation trend of microbial metabolic activity, the intensity of system stability perturbation, and the temporal dependence of aerobic and hypoxic interface migration.

[0099] The system receives the normalized feature tensor sequence after the normalization process in step S3.1 and feeds it as the input data stream into the input layer of a lightweight temporal convolutional neural network. This network architecture employs a stacked design of causal dilated convolutional modules to ensure that information leakage from future moments is not introduced when extracting deep spatiotemporal features, thus maintaining the real-time causality of scheduling decisions.

[0100] The first layer of causal dilated convolution is performed with a kernel size of 3 and a dilation rate of 1 to capture local temporal features of the input tensor. The convolution output is then nonlinearly mapped using the ReLU activation function to generate a preliminary temporal rate-of-change feature map, preserving transient abrupt changes in the slope of volatile fatty acid concentration.

[0101] A second layer of causal dilated convolution is performed, increasing the dilation rate to 2 while maintaining the kernel size at 3. This layer expands the receptive field to cover the fluctuation period of the redox potential standard deviation within a preset time window. The feature distribution is standardized using a BatchNormalization layer, accelerating convergence and enhancing the ability to characterize the intensity of system stability perturbations.

[0102] A third-layer causal dilated convolution operation is performed to further increase the dilation rate to 4, in order to capture the long-range dependencies in the 0.02–0.1 Hz frequency band of the dissolved oxygen fluctuation spectrum. A global average pooling layer is used to reduce the dimensionality of the deep spatiotemporal hidden layer feature map, compressing multidimensional features into fixed-length high-dimensional semantic vectors and eliminating spatial dimensional redundancy.

[0103] By using a chain mapping of multi-layer causal dilated convolution and nonlinear activation functions, the standardized feature tensor of the previous step is transformed into a deep spatiotemporal hidden layer feature map containing microbial metabolic activity, system stability and interface migration trends. This achieves nonlinear projection of low-dimensional dynamic features into a high-dimensional decision space, providing a robust feature basis for subsequent discrete action label generation.

[0104] For example, the input tensor dimensions are [1, 3, 10], corresponding to three types of features and 10 time steps. The first convolutional layer uses 16 3x1 convolutional kernels with a dilation rate of 1, and the output dimensions are [1, 16, 10]. The second convolutional layer uses 32 3x1 convolutional kernels with a dilation rate of 2, and the output dimensions are [1, 32, 10]. The third convolutional layer uses 64 3x1 convolutional kernels with a dilation rate of 4, and the output dimensions are [1, 64, 10]. After global average pooling, a 64-dimensional deep spatiotemporal hidden layer feature vector is generated. This process takes less than 5ms on an embedded MCU, significantly improving the real-time performance and accuracy of feature extraction.

[0105] S3.3: Call the pre-trained weight parameter matrix embedded in the device firmware to perform fully connected layer weighted summation and bias correction processing on the deep spatiotemporal hidden layer feature map, so as to project the high-dimensional continuous feature space onto the low-dimensional discrete decision boundary and generate the original logits output vector containing the probability distribution of four types of operating states.

[0106] Receive the deep spatiotemporal hidden layer feature map output from step S3.2. This feature map contains high-dimensional data on the temporal dependencies of microbial metabolic activity, system stability, and aerobic-hypoxic interface migration extracted by causal dilated convolution.

[0107] The pre-trained weight parameter matrix, which is stored in the non-volatile memory of the embedded device, is called. This matrix is ​​generated through offline clustering analysis and does not contain any online learning and update mechanism, ensuring the determinism of the decision logic and low power consumption.

[0108] The fully connected layer performs a linear transformation operation, performs a dot product operation between the high-dimensional hidden layer feature vector and the weight matrix, and superimposes a bias term to achieve a projection mapping from the continuous feature space to the discrete decision boundary.

[0109] The calculated original logits vector is subjected to a numerical stability check to remove overflow values ​​caused by instantaneous sensor anomalies, ensuring that the output vector is within the preset dynamic range and maintaining the robustness of the edge discriminator.

[0110] The original logits output vector is generated, which contains the probability distribution of four types of operating states: maintaining the current path, switching to the backup anaerobic tank, starting short-term aeration compensation, and triggering sludge return adjustment. This vector has not been normalized and directly reflects the relative confidence score of each action category.

[0111] By using weighted summation and bias correction processing in a fully connected layer, the high-dimensional continuous spatiotemporal features of the previous step are transformed into the original logits data of the low-dimensional discrete decision space, realizing a fast mapping from high-dimensional biochemical state perception to low-dimensional control decision, significantly reducing edge computing load and eliminating model iteration overhead.

[0112] For example, the deep spatiotemporal hidden layer feature map H is set as a 16-dimensional vector, the pre-trained weight matrix W is 4×16 dimensions, and the bias vector b is 4-dimensional. When a sudden increase in the slope of the volatile fatty acid concentration change is detected, the input feature vector H is subjected to a dot product operation with the weight matrix W by the hardware acceleration unit, and after adding the bias b, the original logits vector z=[-2.1,3.5,-0.8,-1.2] is obtained. The second element in this vector corresponds to the "switch to the backup anaerobic tank" action, and its value is significantly higher than other categories, indicating that the system determines that there is an overload risk in the current main reaction zone. The entire process takes less than 5ms, without the need to perform Softmax normalization or probability sampling. The logits vector is directly passed to step S3.4 for ArgMax decision, ensuring millisecond-level response capability in resource-constrained embedded environments.

[0113] S3.4: Based on the preset ArgMax maximum value selection strategy, the original logits output vector is subjected to non-probabilistic discrete decision processing to remove redundant probability confidence information and lock the unique optimal solution, generating a set of four types of discrete action labels indicating maintaining the current path, switching to the backup anaerobic tank, starting short-term aeration compensation, or triggering sludge return adjustment.

[0114] The system receives the raw logits output vector, which contains the probability distribution of four types of operating states, from the fully connected layer. This vector has a dimension of 4 and corresponds to the unnormalized scores of four discrete action categories: maintaining the current path, switching to the backup anaerobic tank, starting short-term aeration compensation, and triggering sludge return adjustment.

[0115] The ArgMax maximum value selection algorithm is executed, which iterates through the four numerical elements in the original logits output vector and locks the index position of the element with the maximum value through comparison operations. This index position is directly mapped to a unique decision category identifier.

[0116] During the comparison process, if multiple elements have the same value and are all the maximum value, the 'Maintain Current Path' category index, which represents the highest system stability, is selected first according to the preset priority rule library to suppress frequent changes in control commands caused by minor noise.

[0117] The index of the locked unique optimal solution is converted into the corresponding discrete action label. Redundant probability confidence information is removed, and only deterministic category identifiers are retained to generate a set of four types of discrete action labels that indicate the current operating status of the processing unit.

[0118] By using the ArgMax maximum value selection strategy, the continuous probability distribution vector generated in the previous step is transformed into a deterministic discrete action label, realizing a rapid conversion from soft decision to hard decision, eliminating the computing power overhead caused by probability calculation, and ensuring the real-time and deterministic nature of scheduling instruction generation.

[0119] For example, suppose the original logits vector output by the embedded edge discriminator is [2.1, -0.5, 3.8, 1.2], corresponding to the four actions of maintenance, switching, aeration, and recirculation, respectively. Performing the ArgMax operation, a comparison reveals that the value 3.8 corresponding to index 2 is the maximum. The system maps index 2 to the action label 'Initiate short-term aeration compensation'. If the output vector is [1.5, 1.5, 1.2, 1.0], and indices 0 and 1 have the same value, the 'Maintain current path' label corresponding to index 0 is selected according to the priority rule. The final output action label is a single, definitive string or enumeration value, such as 'ACTION_AERATION', which is directly passed to the subsequent protocol conversion module without the need for softmax normalization calculation, significantly reducing edge processing latency to the microsecond level.

[0120] like Figure 3 As shown, step S4: Based on the specific instruction type in the four types of discrete action tag sets, and combined with the preset protocol conversion rule base, the action tags are parsed into standardized electrical control pulse sequences to generate a path switching control instruction stream. Specifically, this includes: S4.1: Obtain the set of four discrete action tags generated by the embedded edge discriminator, perform semantic parsing and index matching on the set of four discrete action tags based on the preset protocol conversion rule base, and extract the structured control parameter triplet containing the target device address code, action type identifier and execution priority weight, so as to establish a unique mapping relationship between the abstract logical signal and the specific physical execution object.

[0121] The system receives four sets of discrete action tags output by the embedded edge discriminator. These sets contain the unique optimal scheduling instruction identifier determined at the current moment. A pre-defined protocol conversion rule base is invoked to perform semantic parsing on the input action tags, identifying their corresponding physical control intent and target execution unit type.

[0122] Based on the parsing results, an index matching operation is performed in the rule base to retrieve the structured control parameter mapping table entry bound to the current action tag. The target device address code is extracted from the mapping table entry; this address code uniquely identifies a specific valve controller or aerator blower driver hardware node in the biological treatment unit.

[0123] Extract the action type identifier from the mapping table entry. This identifier defines the specific mechanical action mode, such as fully open, half open, stop, or frequency conversion adjustment. Extract the execution priority weight from the mapping table entry. This weight is used to determine the order of instruction execution when multiple instructions conflict concurrently.

[0124] The extracted target device address code, action type identifier, and execution priority weight are combined and encapsulated to construct a structured control parameter triplet.

[0125] Through the above processing method, the abstract logical signal generated in the previous step is transformed into a structured control parameter triple containing information about the specific physical execution object, establishing a unique mapping relationship between digital decision-making and physical execution, and realizing the precise routing and semantic implementation of scheduling instructions.

[0126] For example, when the embedded edge discriminator outputs the action label 'Switch to standby anaerobic tank', the protocol conversion module reads the label ID as 0x02. It searches the protocol conversion rule base for the mapping record with ID 0x02, obtaining the target device address code 0xA1 (corresponding to the standby anaerobic tank feed valve), the action type identifier OPEN_FULL (full open instruction), and the execution priority weight High (high priority, value set to 1). If another lower priority instruction exists simultaneously, the system prioritizes the switching instruction based on the weight value of 1. The final generated structured control parameter triplet is {Addr:0xA1,Action:OPEN_FULL,Priority:1}, ensuring that the subsequent PLC can accurately identify and drive the specified valve to perform a full open action, eliminating instruction ambiguity.

[0127] S4.2: Utilize the action type identifier in the structured control parameter triplet to query the electrical characteristic mapping table stored in local memory, determine the pulse width modulation duty cycle, frequency reference value, and level polarity standard required by the corresponding actuator, and generate a set of underlying electrical drive configuration parameters containing complete timing feature definitions to achieve accurate adaptation of different action types to differentiated electrical drive requirements.

[0128] The system receives the structured control parameter triplet generated by step S4.1 and extracts the action type identifier as an index key. It retrieves the electrical characteristic mapping table stored in the embedded microcontroller's non-volatile memory, which pre-stores the correspondence between four types of discrete action tags and underlying hardware driver parameters. Based on the action type identifier, it matches the corresponding record and reads the pulse width modulation duty cycle reference value required by the target actuator. It reads the frequency reference value bound to the action type to determine the timing period parameters of the control signal. It reads the level polarity standard definition to clarify the high-active or low-active logic level configuration. For the 'start short-term aeration compensation' action, it calls the frequency converter drive adaptation subroutine to calculate the initial acceleration slope based on the preset fan load curve. For the 'switch to standby anaerobic tank' action, it calls the valve sequence control subroutine to obtain the action interlock timing delay parameters of the multi-way solenoid valves. It integrates the read duty cycle, frequency, level polarity, and specific subroutine parameters to construct a set of underlying electrical drive configuration parameters containing complete timing feature definitions. By using lookup table mapping and parameter assembly, the logical action labels abstracted in the previous step are transformed into a set of underlying electrical drive configuration parameters with physical executableness, thereby achieving precise adaptation of different biochemical scheduling requirements to the electrical characteristics of differentiated actuators.

[0129] For example, when the action type identifier is 'Initiate short-term aeration compensation', the electrical characteristic mapping table is queried to obtain a duty cycle of 65%, a frequency base value of 20kHz, and a high active level. The system further reads the fan acceleration slope parameter as 5% per second and generates a drive configuration set containing a PWM cycle of 50 microseconds and an initial dutycycle count of 32500. When the action type is 'Switch to standby anaerobic tank', the main valve closing delay is 200 milliseconds, the auxiliary valve opening delay is 300 milliseconds, and the level is low active, generating a drive configuration set containing a GPIO toggle timing table. This process ensures that the control commands directly match the hardware electrical specifications, eliminating timing jitter caused by general protocol conversion.

[0130] S4.3: Based on the target device address code in the underlying electrical drive configuration parameter set and structured control parameter triplet, the industrial communication protocol stack construction function is called to perform data frame encapsulation, check code calculation and start bit filling processing to generate a standardized electrical control pulse sequence that conforms to the communication specification of the programmable logic controller input port, so as to ensure the transmission integrity and error resistance of control commands in complex electromagnetic interference environments.

[0131] Based on the target device address code in the underlying electrical drive configuration parameter set and the structured control parameter triplet, the industrial communication protocol stack construction function is called to start the data frame encapsulation process.

[0132] Based on industrial fieldbus protocol specifications such as Modbus RTU or CANopen, the target device address code is written into the start byte area of ​​the data frame to establish a unique physical identifier for the command receiving end.

[0133] The action type identifier is mapped to the function code field defined by the protocol, which explicitly indicates the type of operation to be performed by the actuator: read register, write coil, or preset multi-register operation.

[0134] The pulse width modulation duty cycle, frequency reference value, and level polarity standard are extracted from the underlying electrical drive configuration parameters, converted into hexadecimal payload data, and filled into the data area of ​​the data frame in either big-endian or little-endian mode.

[0135] A cyclic redundancy check (CRC) algorithm is used to perform integrity calculations on the complete data frame, generating a 16-bit checksum. The calculated 16-bit CRC checksum is then appended to the end of the data frame, forming a complete communication data packet containing a frame header, address, function code, data length, data content, and checksum tail.

[0136] A specific start bit padding sequence is inserted into the header of the data frame to synchronize the receiver clock and mark the start of the frame, preventing frame boundary identification errors caused by signal jitter.

[0137] Bit-level encoding conversion, such as NRZ or Manchester encoding, is performed on the packaged standardized electrical control pulse sequence to enhance the signal's anti-interference capability and clock recovery characteristics during long-distance transmission.

[0138] The above processing method transforms the logic control parameters from the previous step into a standardized electrical control pulse sequence that conforms to the communication specifications of the programmable logic controller input port, thereby ensuring the transmission integrity and error resistance of control commands in complex electromagnetic interference environments.

[0139] S4.4: Perform signal integrity verification and impedance matching adjustment on the standardized electrical control pulse sequence. Utilize the hardware abstraction layer interface to convert the digital pulse stream into an analog electrical signal stream with specific voltage amplitude and current driving capability. Generate a path switching control command stream that can be directly connected to the input port of the programmable logic controller of the actuator to complete the final signal form conversion from software logic decision to hardware physical trigger.

[0140] The system receives a standardized electrical control pulse sequence generated in the preceding steps. This sequence contains digital logic level signals representing the target device address, action type, and checksum. It then invokes the signal integrity detection module in the hardware abstraction layer interface to perform microsecond-level timing sampling of the pulse sequence's rise time, fall time, and pulse width. The sampled timing parameters are compared with preset electrical specification thresholds to identify any glitches or pulse width distortion. If an anomaly is detected, a digital filtering algorithm is activated to reconstruct the pulse sequence, eliminating the risk of false triggering caused by high-frequency noise and ensuring the logical correctness of the command data.

[0141] Based on the electrical characteristics of the PLC input ports of the actuator, the impedance matching parameter table configured in the local memory is read to obtain the load impedance value and source output impedance value corresponding to the target port. The impedance mismatch coefficient between the current digital drive circuit and the PLC input circuit is calculated to determine the required series terminating resistor value or parallel pull-down resistor value. The output impedance of the drive stage is dynamically adjusted by a programmable gain amplifier or digital potentiometer to achieve optimal matching between the source impedance, transmission line characteristic impedance, and load impedance, suppressing the ringing effect caused by signal reflection.

[0142] Configure the operating mode of the digital-to-analog converter module or high-voltage driver chip, and set the output voltage amplitude level and maximum drive current limit. Map the integrity-verified digital pulse stream to an analog electrical signal with a specific voltage amplitude; for example, convert a logic high level to a 24V DC voltage signal and a logic low level to a 0V ground signal. Simultaneously, enhance the current output capability of the driver stage to ensure that the minimum conduction current requirement is met when the signal reaches the PLC input during long-distance cable transmission, overcoming the influence of line voltage drop.

[0143] By using impedance matching adjustment and voltage and current drive capability enhancement processing, the digital pulse stream from the previous step is converted into an analog electrical signal stream that conforms to industrial field standards. This generates a path switching control command stream that can be directly connected to the PLC input port of the actuator, realizing the final signal form conversion from software logic decision to hardware physical trigger, and ensuring reliable transmission and accurate execution of control commands in complex electromagnetic environments.

[0144] S4.5: Monitor the electrical feedback signals during the path switching control command flow issuance process, and perform timing comparison and state locking processing on the electrical feedback signals based on the preset action completion confirmation logic to generate a closed-loop scheduling execution completion flag indicating that the path switching control command flow has been successfully converted into physical execution actions.

[0145] Capture the hardware abstraction layer interrupt signal at the moment the path switching control command stream is issued, start a high-precision microsecond-level timer to record the command transmission start timestamp, and establish a time reference for electrical feedback monitoring.

[0146] The voltage level change curve of the actuator input port is acquired in real time by an analog-to-digital converter, and the voltage transition edge is identified by an edge detection algorithm to extract the actual response time when the command arrives at the physical interface.

[0147] Calculate the time difference between the actual response time and the command sending time to generate an end-to-end transmission delay index, which is used to evaluate the real-time status of the communication link.

[0148] The acquired voltage waveform is denoised, steady-state level features are extracted, and compared with preset standard high / low level thresholds to verify the integrity of the signal amplitude and driving capability.

[0149] Based on the preset action completion confirmation logic, the transmission delay index and signal amplitude characteristics are input into the state judgment matrix to perform timing consistency verification.

[0150] If the delay is less than 80ms and the level meets the standard, the instruction is deemed to have been validly delivered, and the closed-loop scheduling execution completion flag is set.

[0151] If an anomaly is detected, the transmission is marked as failed, triggering a local caching strategy or retransmission mechanism to ensure the eventual reachability of control commands.

[0152] Through the above timing comparison and state locking process, the analog electrical signal stream generated in the previous step is transformed into a confirmed closed-loop scheduling execution completion flag, realizing zero-delay verification of the control command delivery status and ensuring the robustness of the scheduling system.

[0153] For example, for the command to switch to the standby anaerobic tank, the standard high level is set to 24V, with an allowable fluctuation range of ±5%. After the command is issued, the voltage jumps from 0V to 23.8V within 15ms, with a transmission delay of 12ms, less than the 80ms threshold. Upon comparison, the voltage value is within the range of 22.8V to 25.2V, indicating a complete signal. The system sets the execution completion flag to 1, confirming that the valve controller has received the command, significantly improving the reliability of scheduling execution.

[0154] Step S5: The path switching control command stream is sent to the valve controller or aeration fan driver to execute physical actions such as maintenance, switching, aeration compensation, or sludge return adjustment, completing the real-time dynamic scheduling of the livestock and poultry breeding waste treatment process. Specifically, this includes: S5.1: Obtain the set of four discrete action tags output by the embedded edge discriminator, perform semantic parsing and mapping matching on the four discrete action tag sets based on the preset protocol conversion rule base, and generate a standardized electrical control pulse sequence containing target device address, action type code and execution priority information to establish a deterministic communication link between digital logic signals and physical execution units.

[0155] Obtain the set of four discrete action labels output by the embedded edge discriminator. This set serves as the final output of the logic decision layer and includes four state identifiers: maintain, switch, compensate, or adjust.

[0156] The system parses a pre-defined protocol conversion rule base, which is stored in local non-volatile memory and defines a static mapping relationship between action tags, physical execution unit addresses, and control parameters.

[0157] Semantic indexing and matching are performed on discrete action tags to extract the unique address code of the corresponding target device and determine whether the instruction receiving object is a specific anaerobic tank valve controller or an aeration blower frequency converter.

[0158] Retrieve the execution priority weight associated with the action type identifier, and dynamically adjust the sending order of instructions in the communication queue according to the real-time system load, ensuring that high-priority emergency scheduling instructions are transmitted first.

[0159] The target device address code, action type code, and execution priority information are encapsulated into a structured control parameter triplet to construct the data frame header information that conforms to the industrial fieldbus specification.

[0160] A standardized electrical control pulse sequence is generated based on the structured control parameter triplet. This sequence includes a start bit, data bits, parity bits, and stop bits, forming a complete digital logic signal packet.

[0161] Digital logic signal packets are mapped to physical I / O ports through the hardware abstraction layer interface, establishing a deterministic communication link between software logic decisions and hardware physical execution units.

[0162] By using protocol parsing and parameter mapping, the abstract action labels from the previous step are transformed into standardized electrical control pulse sequences containing complete addressing and control information, enabling precise delivery of scheduling instructions to the physical execution layer and low-latency response.

[0163] For example, when the embedded edge discriminator outputs the action tag "Switch to standby anaerobic tank," the protocol conversion rule base retrieves the corresponding device address as 0x0A, action code as 0x02, and priority as High. The system immediately constructs a structured data frame containing a start symbol 0x7E, address 0x0A, command 0x02, priority flag 0x01, and CRC checksum. This data frame is converted into a serial pulse sequence of TTL level standard and sent to the electric ball valve controller numbered 0x0A via the RS-485 interface. Compared to the traditional polling mechanism, this direct mapping method stably controls the command issuance time to within 5ms, significantly improving the path switching response speed during peak periods of multi-source waste input and ensuring the continuity and stability of the processing flow.

[0164] S5.2: Receives a standardized electrical control pulse sequence, performs frame encapsulation and checksum addition processing on the standardized electrical control pulse sequence using the industrial fieldbus communication protocol, and generates a path switching control command stream that conforms to the communication specifications of the programmable logic controller input port to ensure the integrity and anti-interference capability of the control command transmission in complex electromagnetic environments.

[0165] Receive the standardized electrical control pulse sequence generated by step S5.1. This sequence contains the target device address, action type code, and execution priority information, and serves as the raw data payload encapsulated by the industrial fieldbus communication protocol.

[0166] The raw data payload is constructed using Application Layer Protocol Data Units (APDUs). Based on ModbusTCP or PROFINET industrial communication standards, transaction processing identifiers and protocol identifiers are inserted into the data header to establish the uniqueness and version compatibility of the communication session.

[0167] The Cyclic Redundancy Check (CRC) code for the data payload is calculated using a polynomial. Perform modulo-2 division on the byte stream to generate a 16-bit checksum, which is used to detect bit flipping errors during transmission.

[0168] The calculated CRC check value is appended to the end of the data frame to form a complete communication frame structure containing a start character, address field, function code, data field, and check field, ensuring the integrity constraint of the data packet.

[0169] Manchester encoding or 4B / 5B encoding is performed on the complete communication frame to convert the binary bit stream into a line code pattern with self-synchronization capability, eliminating the risk of clock drift caused by long strings of 0s or 1s, and improving the anti-interference capability of the signal in the twisted pair medium.

[0170] The encoded digital signal is converted into a differential voltage signal through the physical layer interface chip, which drives the RS-485 or Ethernet PHY chip to send the signal, generating a path switching control command stream that conforms to the electrical specifications of the programmable logic controller input port.

[0171] Through the above frame encapsulation and encoding processing, the logic pulse sequence of the previous step is transformed into a highly robust industrial bus control command stream, achieving the expected technical effect of zero-error transmission and real-time response of control commands in complex electromagnetic environments.

[0172] For example, a Modbus TCP data frame is constructed for the action command to switch to the standby anaerobic tank. The transaction identifier is set to 0x0001, the protocol identifier is set to 0x0000, the length is 0x0006, and the unit identifier is 0x01. The function code is 0x05 (write a single coil), and the data field includes the coil address 0x0010 and the status value 0xFF00 (closed). The CRC-16 / MODBUS checksum is calculated for the byte sequence 0x010x050x000x100xFF0x00, resulting in a checksum value of 0x8C3A. The low byte 0x3A and the high byte 0x8C of the checksum are appended to the end of the frame to form the complete frame 0x000x010x000x000x000x060x010x050x000x100xFF0x000x3A0x8C. The frame is Manchester encoded, with a level transition occurring in the middle of each bit to ensure clock synchronization at the receiving end. It is then converted to a ±5V differential signal via an RS-485 transceiver and transmitted. The PLC receives the signal, verifies the CRC, parses the closing instruction, and drives the electric valve to perform the switching action. The entire process takes less than 10ms, significantly improving the real-time performance and reliability of the scheduling system.

[0173] S5.3: Send path switching control command stream to the corresponding valve controller or aerator blower driver. Based on the action type code in the command stream, perform register writing and state machine triggering operations on the valve controller or aerator blower driver to generate the underlying electrical signals for driving the actuator coil on / off or frequency adjustment of the frequency converter, so as to activate the response mechanism of the physical execution unit.

[0174] Receive the path switching control command stream encapsulated by the protocol, parse and extract the device address code and action type code of the target actuator, and locate the corresponding valve controller or aerator blower driver register mapping table.

[0175] Based on the action type code, query the locally fixed electrical characteristic mapping library to obtain the pulse width modulation duty cycle threshold or inverter frequency adjustment reference value required for the drive coil to switch on and off, and generate the underlying electrical drive configuration parameter set.

[0176] The configuration parameter set is written into the control register of the target device, and the state machine transition logic is triggered through the hardware abstraction layer interface to convert the digital logic signal into an analog electrical signal stream with specific voltage amplitude and current driving capability.

[0177] For valve controllers, a high-level signal is output to drive the solenoid valve coil to energize, generating mechanical thrust to overcome spring resistance and achieve valve core displacement to change the direction of waste flow; for aeration blowers, a variable frequency pulse signal is output to adjust the motor speed and change the air supply flow to optimize dissolved oxygen levels.

[0178] The system monitors the position signal or frequency response signal fed back by the actuator to confirm that the physical action has been activated, and generates the underlying electrical signal to drive the actuator coil to switch on or off or the frequency converter to adjust the frequency, thus completing the final conversion from logical instruction to physical execution.

[0179] By writing to registers and triggering state machines, the standardized instructions from the previous step are converted into underlying electrical signals that drive the actuators, enabling real-time dynamic scheduling of path switching and environmental parameter adjustment in the livestock and poultry waste treatment process.

[0180] For example, when the action type code is "Switch to standby anaerobic tank", the system identifies the target device as the No. 3 electric ball valve controller. The system queries the mapping library to obtain the 12V DC voltage and 100% duty cycle PWM signal corresponding to the full-open instruction. The system writes the 12V high level to the controller register, triggering the solenoid valve coil to energize. The valve core completes a 90-degree rotation within 50ms, and the feedback signal indicates that the valve is fully open. When the action type is "Start short-time aeration compensation", the target device is the No. 2 Roots blower frequency converter. The system queries and finds the frequency setting to be 45Hz. The system sends the corresponding pulse sequence, and the frequency converter output frequency linearly increases from 30Hz to 45Hz within 2 seconds. The blower current stabilizes at 85% of the rated value, significantly increasing the dissolved oxygen concentration in the reaction zone.

[0181] S5.4: Execute physical actions driven by underlying electrical signals to maintain the current treatment path, switch to the backup anaerobic tank, start short-term aeration compensation, or trigger sludge return adjustment. Through mechanical transmission components or airflow regulating dampers, the physical actions are spatially displaced or the flow parameters are changed to generate real-time dynamic scheduling results that change the direction of waste flow or react to environmental parameters, so as to complete the closed-loop control of the livestock and poultry breeding waste treatment process.

[0182] Receives low-level electrical signals generated by PLC register write operations, which contain on / off commands for valve controller coils or frequency adjustment pulses for aeration blower frequency converters.

[0183] For actions that maintain the current processing path, the actuators retain their original electrical state, and the mechanical transmission components are locked in their current positions to ensure the continuity and stability of the waste flow direction and avoid unnecessary mechanical wear.

[0184] When switching to the standby anaerobic tank, the electric butterfly valve of the main feed pipeline is closed, and the inlet valve of the standby tank is opened simultaneously. The valve plate is rotated to a preset angle through the mechanical transmission component to achieve a physical switch in the direction of waste flow.

[0185] When short-term aeration compensation is initiated, a PWM control signal of a specific frequency is sent to the aeration blower driver to adjust the motor speed to increase the air supply, and the opening of the damper is adjusted by airflow to increase the dissolved oxygen supply rate in the reaction tank.

[0186] To trigger the sludge return adjustment action, control the output frequency of the return pump inverter to change the pump speed, thereby adjusting the flow parameters of the return pipeline and optimizing the microbial concentration distribution in the anaerobic digester.

[0187] By changing the spatial displacement of the aforementioned mechanical transmission components or the opening degree of the airflow regulating damper, the underlying electrical signals are converted into specific physical actions, generating real-time dynamic scheduling results that change the direction of waste flow or react to environmental parameters.

[0188] By responding to the underlying electrical signals and implementing physical actions through the actuators, the control command flow of the previous step is transformed into actual process parameter adjustments, thereby realizing closed-loop control and real-time dynamic scheduling of the livestock and poultry breeding waste treatment process.

[0189] For example, when a command to switch to the standby anaerobic tank is received, the PLC sends a 4-20mA current signal to the electric butterfly valve on the main feed pipeline, controlling the valve to rotate from the fully open position to the 0-degree closed position, which takes 5 seconds; simultaneously, an opening signal is sent to the inlet valve of the standby tank, causing the valve to rotate to the 90-degree fully open position, which also takes 5 seconds. During this process, the system monitors the valve position feedback signal to confirm the completion of the switching action. If a command to start short-term aeration compensation is received, the inverter output frequency increases from 30Hz to 45Hz, the air volume increases by 50%, and the frequency returns to the original frequency after 10 minutes of continuous operation. This process significantly improves the system's response speed to sudden load changes and ensures processing efficiency.

[0190] Step S6: Within the preset feedback time window after the physical action is executed, the sensor array is called again to resample the signal at the same process point, the deviation between the judgment result and the actual biochemical state is calculated, and dual-channel feedback verification data is generated to verify the scheduling accuracy. Specifically, this includes: The embedded microcontroller captures the rising edge interrupt of the physical action completion signal from the actuator, immediately resets the internal high-precision hardware timer, and initiates a preset 30-second feedback waiting window to adapt to the hysteresis characteristics of fluid mixing and biochemical response within the anaerobic digester. When the timing value reaches a preset threshold, the microcontroller sends a synchronization trigger pulse to the electrochemical sensor array deployed at key process points in the biological treatment unit via the GPIO interface, ensuring that all sensor nodes start sampling at the same microsecond level, eliminating time asynchrony errors in multi-channel data acquisition. The sensor array responds to the trigger pulse, acquiring in parallel the analog voltage of volatile fatty acid concentration, millivolt signals of redox potential, and dissolved oxygen current signals. The acquisition process continuously covers a complete 10-second sliding window, generating a real-time raw time-series signal set containing timestamps and depth coordinates. A preliminary integrity check is performed on the acquired real-time raw time-series signal set to eliminate abnormal transition points caused by electromagnetic interference.

[0191] The acquired real-time raw time-series signal set undergoes a preprocessing and feature extraction process identical to the initial discrimination stage: first, timestamp alignment and missing value interpolation are performed, and data gaps caused by sampling jitter are filled using a linear interpolation algorithm to generate a continuous time-series signal sequence with a unified time base; then, a sliding window is applied using the Hanning window function, and the signal is segmented into short-time stationary segments based on a ten-second window length and a five-second overlap step; frequency domain filtering is performed using a Butterworth low-pass filter, and a cutoff frequency is set to filter out high-frequency electronic noise and power frequency interference; subsequently, the first derivative of the concentration of volatile fatty acid components is calculated using the central difference method to generate the concentration change slope feature parameter; the standard deviation of the redox potential segmented signal is calculated using a rolling statistical algorithm to generate the potential fluctuation standard deviation feature parameter; and the dissolved oxygen fluctuation segmented signal is spectrally decomposed using a fast Fourier transform and the energy proportion of the 0.02 Hz to 0.1 Hz frequency band is extracted to generate the dissolved oxygen low-frequency energy proportion feature parameter, forming three types of low-dimensional dynamic feature vectors at the current moment.

[0192] The system invokes the state mapping rule library embedded in the device firmware, loads the preset feature vectors into the deterministic lookup table of discrete action tags, and concatenates the three types of low-dimensional dynamic feature vectors extracted at the current moment into a three-dimensional input vector according to preset dimensions. Based on a multi-dimensional spatial distance discrimination algorithm, it calculates the Euclidean distance between the input vector and the center vector of each decision cluster in the lookup table, and selects the action tag corresponding to the decision cluster with the smallest distance as the discrimination result. It reads the initial discrete action tag issued and executed in the preceding step S4, encodes it into a binary bit sequence, and simultaneously encodes the discrimination result into a binary bit sequence of the same length, performing a bitwise XOR operation. If all bitwise operation results are 0, the state is determined to be consistent, and a high-level preliminary state consistency flag is generated; if any bitwise operation result is 1, the state is determined to be inconsistent, and a low-level preliminary state consistency flag is generated.

[0193] When the initial state consistency flag indicates a negative result, it is determined that the current scheduling action has not brought the system's biochemical state back to the expected range, triggering the quantization deviation calculation process. The embedded numerical calculation unit is invoked to read the original three types of low-dimensional dynamic feature vectors from the buffer before the action execution to construct the initial state vector. The current state vector is constructed from the three types of low-dimensional dynamic feature vectors obtained at the current moment after the action execution, obtained from step S6.2. Element-wise difference operations are performed on the initial state vector and the current state vector to obtain the residual components of each feature dimension, eliminating systematic errors caused by sensor baseline drift. The Euclidean distance norm algorithm is used to calculate the geometric distance between the two state vectors in the three-dimensional feature space, with the formula: in, To quantify the state deviation value, , , These represent the slope of the change in volatile fatty acid concentration, the standard deviation of redox potential, and the proportion of low-frequency dissolved oxygen energy at the current moment. , , These are the original feature values ​​corresponding to the actions performed before execution.

[0194] The calculated quantized state deviation value is normalized and mapped to a standard range of 0 to 1, generating a dimensionless index of scheduling response lag or failure. Finally, the quantized state deviation value and the initial state consistency flag are encapsulated in a data structure to generate a dual-channel feedback verification data packet containing a timestamp, location label, and deviation amplitude. This data packet serves as the sole input for subsequent determination of whether to trigger the local rule base alternative mapping relationship polling mechanism.

[0195] For example, the feedback waiting window is set to 30 seconds. When the PLC returns a signal indicating that the valve opening is in place, the MCU starts timing. After 30 seconds, trigger pulses are sent to the VFA sensor, ORP electrode, and DO probe. A raw signal set of 3000 data points is generated by continuously acquiring data for 10 seconds at a 100Hz sampling rate. If the current feature vector is [0.3, 8, 0.12], the lookup table matching and verification action is "Maintain Current Path," encoded 00. The preceding initial action is also "Maintain Current Path," encoded 00. The XOR result is 00, generating a high-level consistency flag. If the current feature vector is [0.8, 15, 0.25], the matching and verification action is "Switch to Backup Anaerobic Tank," encoded 01. The initial action is "Maintain Current Path," encoded 00. The XOR result is 01, generating a low-level consistency flag and triggering deviation calculation. Let the feature vector before the action is executed be (0.5, 12.0, 0.3) and after execution be (0.8, 15.0, 0.45). Substituting into the Euclidean distance formula, we get D≈3.018. The preset threshold is 2.5. If the threshold is exceeded, the error correction mechanism in step S7 is triggered.

[0196] Step S7: Determine whether the deviation value in the dual-channel feedback verification data exceeds a preset threshold. If it is determined to be an abnormal state exceeding the threshold, the polling mechanism of alternative mapping relationships in the local rule base is automatically triggered to generate corrected redundant control instructions to replace the failed initial scheduling strategy. Specifically, this includes: The system reads the quantized status deviation value from the dual-channel feedback verification data packet and compares it with the value of the dynamic threshold register embedded in the device firmware. If the deviation value is greater than the preset threshold, the over-threshold abnormal status identifier is set, triggering an interrupt signal to activate the local rule base access interface. It then calls the candidate mapping relationship set index table in read-only memory and retrieves the corresponding redundant control logic path group based on the type code of the current failed action tag. The system loads the rule metadata contained in this path group and extracts the applicable condition constraints, execution priority weights, and action mapping parameters for each candidate path. Finally, it encapsulates the extracted rule metadata into a structured object to construct a list of rules to be polled containing multiple redundant control logic paths.

[0197] The system retrieves the list of rules to be polled and the types of currently invalid initial action tags. It reads the metadata index of each candidate mapping relationship from the local rule base and extracts the historical applicable scenario feature templates corresponding to each candidate mapping relationship. These templates include the slope range of volatile fatty acid concentration changes, the threshold range of redox potential standard deviation, and the distribution of dissolved oxygen low-frequency energy proportions. The system calculates the multidimensional Euclidean distance between the three types of low-dimensional dynamic feature vectors at the current moment and the feature templates of each candidate mapping relationship, constructing a similarity matrix characterizing the degree of biochemical state matching. Based on a weighted sum of the state matching distance and the logical compatibility coefficient, the system calculates the applicability score of each candidate mapping relationship. The state matching distance is measured by the Euclidean distance between the current feature vector and the candidate template, and the logical compatibility coefficient is determined by the semantic similarity of the action tags. The system sorts the list of rules to be polled in descending order according to the calculated applicability scores, removes redundant control logic paths with scores below a preset threshold, and retains candidate paths with high applicability scores to form the preferred polling sequence.

[0198] The first candidate mapping rule in the preferred polling sequence is obtained. The deterministic logic judgment condition and action output template corresponding to the rule are extracted. The three types of low-dimensional dynamic feature vectors at the current time are aligned to the input port of the candidate mapping relationship according to the preset data structure. Deterministic mapping deduction is performed. By comparing the current feature vector with the preset feature threshold range in the rule base, the abnormal subcategory to which the current biochemical state belongs is determined, and the unique logical branch path is locked. Based on the locked logical branch, the action parameter lookup table embedded in the firmware is called to retrieve the control action code, execution duration, and intensity coefficient that match the abnormal subcategory. The action output template is filled, and a candidate modified redundant control instruction prototype containing the target device ID, action type, and specific execution parameters is constructed.

[0199] Obtain the prototype of the candidate modified redundant control command and parse the target actuator address code, action type identifier, and preset drive parameters contained therein. Read the current physical state register data of the biological processing unit, including valve opening feedback value, fan operating frequency, and real-time reading of motor load current. Construct a safety constraint matrix based on the physical limits of the equipment, defining the maximum allowable action amplitude, minimum switching interval time, and maximum instantaneous power threshold for each actuator. Compare the drive parameters in the candidate command with the safety constraint matrix item by item, identify potential risk items exceeding physical limits, and call the amplitude limiting protection algorithm in the local firmware to truncate the pulse width modulation duty cycle or frequency reference value to ensure that the command parameters fall within the safe operating range. Introduce an anti-oscillation logic judgment mechanism, calculate the time difference between the current time and the last time a similar action was executed, and if it is less than the preset minimum switching interval threshold, determine that the command may cause system oscillation due to frequent start-stop of mechanical components, mark it as an illegal command, and discard it. Instructions that pass security checks undergo integrity verification, inspecting whether the communication frame structure, checksum, and start bit conform to the programmable logic controller (PLC) input port specifications. Verified instructions are encapsulated into standardized electrical control pulse sequences, generating a securely confirmed set of valid corrective control instructions. Finally, the highest-priority instruction from this set is selected as the final output, encapsulated into a standardized electrical control pulse sequence, and used to generate a corrected redundant control instruction stream to directly replace the initial failed scheduling strategy, thus completing closed-loop scheduling error correction under abnormal conditions.

[0200] For example, the quantified state deviation value is 0.85, the preset dynamic threshold is 0.60, and the threshold is exceeded. The current failed action label is "switch to backup anaerobic tank", type code 0x02. Three alternative paths are retrieved: path A (start short-term aeration compensation, priority 1), path B (trigger sludge return adjustment, priority 2), and path C (maintain the current path and alarm, priority 3). The current feature vector is [0.5, 120, 0.3], the feature template of alternative A is [0.4-0.6, 110-130, 0.2-0.4], and that of alternative B is [0.1-0.3, 80-100, 0.5-0.7]. The calculated score for alternative A is 0.84, and the score for alternative B is 0.23. The first option in the preferred polling sequence is alternative A. The current feature vector is input into the rule engine, matching the "metabolic inhibition anomaly" branch. The lookup table returns the action code "Aeration_Boost", with an execution time of 300 seconds and a fan frequency of 60Hz. An instruction prototype {Device:Fan_01,Action:SetFreq,Value:60,Duration:300} is generated. During safety verification, the candidate instruction is to start the backup anaerobic tank feed valve with a target opening of 80%, a current opening of 75%, and the last action occurring 3 seconds ago, which is less than the minimum switching interval of 5 seconds. This triggers the anti-vibration logic, causing the instruction to be rejected. The engine then polls for the next instruction and sends it to the frequency converter.

[0201] Step S8: Statistically analyze the end-to-end discrimination time and feedback verification failure cases within a preset period. Package the cluster features of the anonymized failure cases that meet the offline calibration conditions into a quarterly maintenance calibration package. This package is used to update the discrimination logic and mapping relationships embedded in the device firmware, completing the periodic self-optimization of the lightweight feedback closed-loop scheduling method. Specifically, this includes: It should be noted that the offline calibration condition refers to the cluster analysis converging to a preset threshold and the discrimination accuracy after weight correction improving to the preset convergence threshold.

[0202] Obtain the raw log of end-to-end discrimination time consumption recorded by the embedded edge discriminator within a preset statistical period, as well as the raw data packets of deviation exceeding the threshold failure cases captured by the dual-channel feedback verification mechanism. Parse the microsecond-level high-precision timestamp field in the raw log of end-to-end discrimination time consumption to extract the trigger time T of each discrimination action. start With the completion time T of instruction issuance end Calculate the net time for a single judgment: Duration = T end -T start This forms a set of time-consuming sequences with absolute time indices. The original data packets of dual-channel feedback verification failure cases are parsed to extract the time T of the failure occurrence. fault The three types of low-dimensional dynamic feature vector snapshots, initial discrete action labels, and quantized state deviation values ​​are used to construct an abnormal event sequence set.

[0203] A linear interpolation algorithm is used to calibrate the time base of the time-consuming sequence and the abnormal event sequence, eliminating minor timestamp offsets with the system master clock as the reference. Based on the calibrated time index, the time T of each abnormal event is... fault The time-consuming data within the preset time window before and after the abnormal event is logically bound to it. If there is no directly corresponding time-consuming record in the window, the nearest valid time-consuming data is selected and the interpolation confidence level is marked. The bound time-consuming data, abnormal event feature vector, action label, and deviation value are encapsulated into a structured data object, and context labels such as device ID, tank partition attribute, and operating mode identifier are added. The data is then sorted and aggregated in chronological order to generate a comprehensive dataset of scheduling operation status.

[0204] The original data of failed cases marked as exceeding the threshold anomaly in the comprehensive dataset of scheduling operation status is obtained, and sensitive biochemical response signal sequences containing the slope of volatile fatty acid concentration change, standard deviation of redox potential, and proportion of low-frequency dissolved oxygen energy are extracted. Numerical perturbation processing based on differential privacy mechanism is performed on the sensitive signals, and a random noise vector inversely proportional to the signal sensitivity is generated according to the Laplace distribution. The original signal values ​​and the noise vector are then linearly superimposed. The desensitized signal value after adding noise is calculated using the following formula: in, The signal value after desensitization. The original sensitive biochemical response signal value. Denotes the Laplace distribution sampling function. This is the global sensitivity coefficient. For privacy budget parameters.

[0205] A generalized mapping is performed on the perturbed signal sequence to discretize the continuous biochemical parameter values ​​into several generalized level labels. A hash salting algorithm is used to unidirectionally encrypt the timestamps and spatial location labels to generate irreversible anonymized index identifiers. The integrated data fields are then reconstructed into a feature set of desensitization failure cases that do not contain the original plaintext information.

[0206] The system receives a feature set of failed desensitization cases, performs Z-Score standardization on the 3D feature vectors, and constructs a standard normal distribution feature space with a mean of 0 and a variance of 1. Based on Euclidean distance, it calculates the geometric distance between any two sample points in the standardized feature space, generating a distance matrix. The K-Means++ initialization algorithm is used to select initial cluster centers, and cluster assignment and center update operations are performed iteratively. Each sample is assigned to the cluster to which the nearest cluster center belongs, and the mean vector of all sample points within each cluster is recalculated to update the center coordinates. A convergence threshold is set where the magnitude of the cluster center displacement vector is less than a preset small value in two consecutive iterations. The iteration terminates when this condition is met, and the centroid coordinate vector of each final cluster (the average of all failed cases within each cluster across three low-dimensional dynamic feature dimensions) is extracted to form the error cluster center vector. Semantic annotation is then performed (e.g., "metabolic inhibition type misjudgment" or "interface migration lag type misjudgment"), generating a cluster feature package for failed cases.

[0207] The algorithm obtains the error cluster center vectors and corresponding initial discrete action labels from the clustering feature package of failed cases, and analyzes the statistical distribution boundary of the desensitized biochemical response feature vectors within each error cluster. A weight correction model based on inverse logic is constructed, calculating the Euclidean distance deviation between the feature vector of each type of failed action label and the center of the corresponding feature cluster of the correct action label. The gradient descent method is used to iteratively update the mapping weight parameters to minimize the cross-entropy loss function between the discrimination output and the ideal action label. The gradient of the loss function with respect to the weight matrix is ​​calculated through backpropagation, and the weights are updated in the reverse direction of the gradient with a preset learning rate. When the discrimination accuracy reaches a preset convergence threshold, the current weights are locked as the corrected action label mapping weights. A difference comparison is performed with the original firmware mapping table, and only the changed weight increment data is extracted. This data is then serialized, encoded, and compressed to generate a quarterly maintenance calibration data package containing version identifiers, checksums, and parameter offsets. Finally, the quarterly maintenance calibration data package is written to the non-volatile storage area of ​​the embedded edge discriminator via a secure firmware update protocol, replacing the old weight parameters and mapping relationships.

[0208] For example, a statistical period of 7 days is set. Failed verification cases are timestamped to generate a comprehensive data entry containing a timestamp, a time consumption of 85ms, a feature vector [0.12, 0.05, 0.33], an action tag "Switch_Anaerobic", and a bias value of 0.45. A privacy budget of ε=0.5 and Δf=0.1 are set. Laplacian noise of 0.02 is added to the original VFA slope of 0.05 to obtain a desensitization value of 0.07. The ORP standard deviation of 15mV is mapped to a "medium" level. The timestamp is hashed using SHA-256 to generate an anonymous ID. K-Means clustering (K=4) is performed on 500 failed desensitization cases, converging after 15 iterations. Four error cluster center vectors are extracted, with a volume of only 2KB. Fifty features were extracted from the failed clusters that were switched to the backup anaerobic tank. The average Euclidean distance from the center of the correct cluster was 0.85. The learning rate was 0.01 and the iterations were performed 100 times. The loss function was reduced from 0.45 to 0.12, and the discrimination accuracy was improved from 75% to 92%. A 2KB calibration data packet was generated and encapsulated after CRC16 verification to complete the firmware update.

[0209] This application also provides a system for the intelligent treatment and utilization management of livestock and poultry breeding waste. This system corresponds to the aforementioned method and can realize all the technical processes described in the method section. The system includes: a data acquisition module, a feature extraction module, a reasoning and decision module, an instruction generation module, and an execution driving module. Specifically: The acquisition module is used to deploy a sensor array at a depth within the anaerobic digester. It collects time-series signals of volatile fatty acid component concentration, redox potential, and dissolved oxygen fluctuations at different depths within the anaerobic digester, generating raw time-series signals containing spatial location tags. The feature extraction module is used to perform sliding window truncation and frequency domain filtering preprocessing on the original time series signal, extract the slope of the concentration change of volatile fatty acid components, the standard deviation of redox potential within a preset time window, and the energy proportion of specific frequency bands in the dissolved oxygen fluctuation spectrum, and generate three types of low-dimensional dynamic feature vectors characterizing the sudden change in microbial metabolic activity, the intensity of system stability disturbance, and the migration trend of aerobic and hypoxic interfaces. The reasoning and decision module is used to perform discrete mapping of three types of low-dimensional dynamic feature vectors using a pre-trained temporal convolutional neural network. Through preset weight parameters, continuous features are mapped into discrete state space, generating a set of four types of discrete action labels that indicate the current operating status of the processing unit. The instruction generation module is used to parse the action tags into standardized electrical control pulse sequences based on the specific instruction types in the four types of discrete action tag sets, combined with a preset protocol conversion rule library, and generate a path switching control instruction stream. The execution drive module is used to send path switching control command streams to valve controllers or aeration fan drivers to perform physical actions such as maintenance, switching, aeration compensation, or sludge return adjustment, thereby completing the real-time dynamic scheduling of the livestock and poultry breeding waste treatment process.

[0210] For those skilled in the art, various other corresponding changes and modifications can be made based on the technical solutions and concepts described above, and all such changes and modifications should fall within the protection scope of the claims of this invention.

[0211] Unless otherwise defined, the technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this application pertains. The terms “first,” “second,” “third,” and similar terms used in this patent application specification and claims do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms “an” or “a” and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. The terms “comprising” or “including” and similar terms mean that the elements or objects preceding “comprising” or “including” encompass the elements or objects listed following “comprising” or “including” and their equivalents, and do not exclude other elements or objects. The “multiple” mentioned in the embodiments of this application refers to two or more. A and / or B indicate three possibilities: A; B; and A and B.

[0212] The above description is merely an exemplary embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and such modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A management method for intelligent treatment and utilization of livestock and poultry breeding waste, characterized in that, include: A sensor array is deployed deep within the anaerobic fermenter to collect time-series signals of volatile fatty acid component concentration, redox potential, and dissolved oxygen fluctuations in reaction zones at different depths inside the anaerobic fermenter, generating raw time-series signals containing spatial location tags. The original time-series signal is preprocessed by sliding window truncation and frequency domain filtering to extract the slope of the concentration change of volatile fatty acid components, the standard deviation of redox potential within a preset time window, and the energy proportion of a specific frequency band in the dissolved oxygen fluctuation spectrum. Three types of low-dimensional dynamic feature vectors are generated to characterize the sudden change in microbial metabolic activity, the intensity of system stability disturbance, and the migration trend of aerobic and hypoxic interfaces. The three types of low-dimensional dynamic feature vectors are discretely mapped using a pre-trained temporal convolutional neural network. The continuous features are mapped into a discrete state space through preset weight parameters, generating a set of four types of discrete action labels that indicate the current operating status of the processing unit. Based on the specific instruction type in the four types of discrete action tag sets, and combined with the preset protocol conversion rule library, the action tags are parsed into standardized electrical control pulse sequences to generate path switching control instruction streams; The path switching control command stream is sent to the valve controller or aeration fan driver to perform physical actions such as maintenance, switching, aeration compensation or sludge return adjustment, thereby completing the real-time dynamic scheduling of the livestock and poultry breeding waste treatment process.

2. The management method for intelligent treatment and utilization of livestock and poultry breeding waste according to claim 1, characterized in that, Also includes: Within the preset feedback time window after the physical action is performed, the sensor array is called again to resample the signal at the same process point, calculate the deviation between the discrimination result and the actual biochemical state, and generate dual-channel feedback verification data for verifying the scheduling accuracy. If the deviation value in the dual-channel feedback verification data exceeds a preset threshold, and if it is determined to be an abnormal state exceeding the threshold, the alternative mapping relationship polling mechanism in the local rule base is automatically triggered to generate a corrected redundant control instruction to replace the failed initial scheduling strategy.

3. The management method for intelligent treatment and utilization of livestock and poultry breeding waste according to claim 2, characterized in that, After generating the corrected redundant control instructions to replace the failed initial scheduling strategy, the method further includes: The system statistically analyzes the end-to-end discrimination time and feedback verification failure cases within a preset period. It then packages the cluster features of the desensitized failure cases that meet the offline calibration conditions into a quarterly maintenance calibration package. This package is used to update the discrimination logic and mapping relationship embedded in the device firmware, thus completing the periodic self-optimization of the lightweight feedback closed-loop scheduling method.

4. The management method for intelligent treatment and utilization of livestock and poultry breeding waste according to claim 1, characterized in that, The preprocessing of the original time-series signal by sliding window truncation and frequency domain filtering specifically includes: The original time-series signal is preprocessed by timestamp alignment, missing value interpolation, sliding window truncation, and frequency domain filtering. Based on the preprocessed data, the slope of the change in the concentration of volatile fatty acid components, the standard deviation of the redox potential, and the energy proportion of dissolved oxygen fluctuations in a specific frequency band are extracted to generate the three types of low-dimensional dynamic feature vectors.

5. The management method for intelligent treatment and utilization of livestock and poultry breeding waste according to claim 1, characterized in that, The method utilizes a pre-trained temporal convolutional neural network to perform discrete mapping on the three types of low-dimensional dynamic feature vectors. Through preset weight parameters, continuous features are mapped into a discrete state space, generating a set of four discrete action labels indicating the current operating state of the processing unit. Specifically, this includes: The three types of low-dimensional dynamic feature vectors are obtained: the slope of the concentration change of volatile fatty acid components, the standard deviation of redox potential within a preset time window, and the energy proportion of a specific frequency band in the dissolved oxygen fluctuation spectrum. The three types of low-dimensional dynamic feature vectors are then subjected to tensor recombination and normalization to generate a standardized feature tensor sequence that meets the input dimension requirements of the embedded edge discriminator. Based on a lightweight temporal convolutional neural network architecture, using the standardized feature tensor sequence as the input data stream, multi-layer causal dilated convolution operations and nonlinear activation function mapping are performed to extract deep spatiotemporal hidden layer feature maps that characterize the mutation trend of microbial metabolic activity, the intensity of system stability perturbation, and the temporal dependence of aerobic and hypoxic interface migration. The pre-trained weight parameter matrix embedded in the device firmware is invoked to perform fully connected layer weighted summation and bias correction processing on the deep spatiotemporal hidden layer feature map, so as to project the high-dimensional continuous feature space onto the low-dimensional discrete decision boundary and generate the original logits output vector containing the probability distribution of four types of operating states. Based on the preset ArgMax maximum value selection strategy, the original logits output vector is subjected to non-probabilistic discrete decision processing to remove redundant probability confidence information and lock the unique optimal solution, generating a set of four types of discrete action labels indicating maintaining the current path, switching to the backup anaerobic tank, starting short-term aeration compensation, or triggering sludge return adjustment.

6. The management method for intelligent treatment and utilization of livestock and poultry breeding waste according to claim 1, characterized in that, The generation of the original time-series signal containing spatial location labels specifically includes: The sensor array is deployed inside the anaerobic fermenter to collect raw time-series signals of volatile fatty acid component concentration, redox potential, and dissolved oxygen fluctuations at various depth points. The original time-series signal is subjected to analog-to-digital conversion, noise suppression, and spatial correlation mapping to generate the original time-series signal containing spatial location labels.

7. The management method for intelligent treatment and utilization of livestock and poultry breeding waste according to claim 1, characterized in that, The step of parsing the four types of discrete action tag sets into standardized electrical control pulse sequences and generating a path switching control command stream based on the four types of discrete action tag sets and a preset protocol conversion rule base specifically includes: Obtain the four types of discrete action label sets, and generate a standardized electrical control pulse sequence based on the protocol conversion rule base; The standardized electrical control pulse sequence is subjected to signal morphology conversion to generate the path switching control command stream.

8. The management method for intelligent treatment and utilization of livestock and poultry breeding waste according to claim 1, characterized in that, The step of sending the path switching control command stream to the valve controller or aeration blower driver to execute physical actions and complete real-time dynamic scheduling specifically includes: The path switching control command stream is sent to the valve controller or aeration blower driver to generate the underlying electrical signal that drives the actuator. The system executes physical actions driven by the underlying electrical signals to maintain the current processing path, switch to the backup anaerobic tank, initiate short-term aeration compensation, or trigger sludge return adjustment, thereby completing the real-time dynamic scheduling of the livestock and poultry breeding waste treatment process.

9. A management method for intelligent treatment and utilization of livestock and poultry breeding waste according to claim 1, characterized in that, The physical actions involve spatial displacement or flow parameter transformation via mechanical transmission components or airflow regulating dampers.

10. A system for the intelligent treatment and utilization management method of livestock and poultry breeding waste according to any one of claims 1-9, characterized in that, include: The acquisition module is used to deploy a sensor array at a depth within the anaerobic digester. It collects time-series signals of volatile fatty acid component concentration, redox potential, and dissolved oxygen fluctuations at different depths within the anaerobic digester, generating raw time-series signals containing spatial location tags. The feature extraction module is used to perform sliding window truncation and frequency domain filtering preprocessing on the original time series signal, extract the slope of the concentration change of volatile fatty acid components, the standard deviation of redox potential within a preset time window, and the energy proportion of a specific frequency band in the dissolved oxygen fluctuation spectrum, and generate three types of low-dimensional dynamic feature vectors characterizing the mutation of microbial metabolic activity, the intensity of system stability disturbance, and the migration trend of aerobic and hypoxic interfaces. The reasoning and decision module is used to perform discrete mapping on the three types of low-dimensional dynamic feature vectors using a pre-trained temporal convolutional neural network. By using weight parameters fixed in the device firmware, continuous features are mapped into discrete state space, and a set of four types of discrete action labels indicating the current operating status of the processing unit is generated. The instruction generation module is used to parse the action tags into standardized electrical control pulse sequences based on the specific instruction types in the four types of discrete action tag sets and in combination with a preset protocol conversion rule library, and generate a path switching control instruction stream. The execution drive module is used to send the path switching control command stream to the valve controller or aeration fan driver to perform physical actions such as maintenance, switching, aeration compensation or sludge return adjustment, and complete the real-time dynamic scheduling of the livestock and poultry breeding waste treatment process.

Images