Comprehensive treatment and cyclic utilization system for waste of pig farm

Abstract

The invention belongs to the technical field of livestock and poultry breeding waste treatment, and discloses a pig farm waste comprehensive treatment and cyclic utilization system which comprises the steps of obtaining various wastes of a pig farm, collecting multi-dimensional data, matching an optimal working condition mode in combination with three-dimensional situation evaluation, and generating a global collaborative instruction set; performing cross-unit predictive collaboration through a causal-parameter chain, performing waste grading treatment and resource preliminary conversion to obtain a treatment intermediate product and a resource primary product, and generating operation feedback data; performing efficiency-income two-dimensional evaluation, when the efficiency does not reach the standard, calling a historical recovery file to generate an adaptive recovery strategy, and generating an update state report; scheduling operation is executed through resource-load dynamic matching logic, reverse traceability optimization processing parameters are carried out, resource products are obtained, and a circulation benefit report is generated; and fusing the multi-cycle cyclic benefit report and the operation feedback data, generating a three-dimensional optimization suggestion, updating the core rule base, and forming a closed-loop link.

Description

Technical Field

[0001] This invention relates to the field of livestock and poultry breeding waste treatment technology, and more specifically, to a comprehensive treatment and recycling system for pig farm waste. Background Technology

[0002] With the rapid development of large-scale pig farming, the amount of waste generated by pig farms, such as high organic load manure, wastewater from the treatment of dead pigs containing toxins, and routine pen flushing water, has surged. The waste is complex and diverse in composition, and direct discharge will cause environmental pollution problems such as soil compaction and eutrophication of water bodies, which does not meet the requirements of relevant environmental protection regulations. Traditional treatment focuses on "achieving standard discharge", which wastes resources such as biomass energy and organic fertilizer raw materials, and cannot adapt to the dynamic changes of waste with the breeding cycle, making it difficult to balance treatment efficiency, environmental compliance and resource benefits.

[0003] To address these issues, existing technologies often employ fragmented processing, single-resource recycling, or fixed-parameter operation, but these approaches still have several drawbacks: Existing processing models are fragmented, lacking coordination between stages; incomplete pretreatment leads to significant load fluctuations in subsequent processing units, impacting overall processing efficiency; resource recycling efficiency is low, failing to form a complete utilization chain, resulting in the waste of substantial recyclable resources; furthermore, they lack dynamic adaptability, with existing systems often operating with fixed parameters, making it difficult to cope with changes in waste and relying on manual adjustments based on experience; finally, anomaly recovery relies on manual trial and error, resulting in long cycles and a high risk of secondary pollution. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution: a comprehensive treatment and recycling system for pig farm waste, comprising: Instruction generation module: acquires various types of waste from pig farms, collects and verifies multi-dimensional data, combines three-dimensional situation assessment to match the optimal working mode, and generates a global collaborative instruction set that includes processing efficiency targets and resource recovery targets; Resource initial conversion module: Based on the global collaborative instruction set, it performs cross-unit predictive collaboration through causal-parameter chain, and performs waste classification treatment and initial resource conversion to obtain intermediate products and primary resources, and generates operation feedback data simultaneously; System recovery module: Based on operational feedback data, it performs a two-dimensional evaluation of performance and benefits. When performance fails to meet the standards, it calls historical recovery files to generate an appropriate recovery strategy and generates an updated status report after performing the recovery operation. Parameter optimization module: Based on the updated status report, the module executes the scheduling of biogas, wastewater reuse and differentiated organic fertilizer preparation through resource-load dynamic matching logic to obtain resource-based products, and generates a circular benefit report by reverse tracing and optimizing the parameters. Closed-loop optimization module: Integrates multi-cycle benefit reports and operational feedback data to generate three-dimensional optimization suggestions, update the core rule base, and form a closed-loop link.

[0005] Furthermore, the generation method of the global cooperative instruction set includes: We collected high-organic-load waste, toxic waste, and conventional waste from pig farms, and imported them into dedicated temporary storage pools to obtain multi-dimensional data. The three dimensions of the three-dimensional situation assessment are defined as the operational status dimension, the impact characteristic dimension, and the resource potential dimension. Based on the combination of the three dimensions and the preset operating conditions that include processing efficiency targets and resource utilization benefit targets, an operating condition matching matrix is ​​constructed. The three-dimensional situation assessment quantifies and classifies multi-dimensional data, and then the optimal working condition is obtained by matching the working condition matching matrix, generating a global collaborative instruction set.

[0006] Furthermore, the method of performing cross-unit predictive coordination through causal-parameter chains includes: The global collaborative instruction set is broken down into the operation parameters of three treatment units: pretreatment, biological treatment, and sludge treatment. Based on the logic of the preset cause-effect-parameter chain, when the operating parameters of the upstream processing unit deviate from the preset target of the working condition, cross-unit predictive coordination is triggered. Each processing unit performs the adaptation preparation operation according to the corresponding logic and then reports the preparation status after completion.

[0007] Furthermore, the method for obtaining the intermediate products and primary resources includes: After collaborative preparation is completed, the pretreatment unit performs differentiated treatment on the three types of waste; The biological treatment unit, based on the anaerobic-aerobic linkage mode and the differentiated water quality after treatment, degrades organic matter and completes the initial resource conversion to obtain crude biogas and activated sludge. The sludge treatment unit conditioned and filtered the activated sludge to produce wet sludge, which was then temporarily stored according to its resource potential to obtain intermediate products and primary resource products. Data on the processing efficiency and initial resource quality of each processing unit are collected synchronously to form structured operational feedback data.

[0008] Furthermore, the method for conducting the performance-benefit dual-dimensional evaluation includes: Based on operational feedback data, in terms of performance evaluation, pollutant removal and equipment compatibility are quantitatively verified for indicators within different treatment units, and compliance is determined based on treatment performance targets. In terms of revenue assessment, the revenue from resource recovery is calculated based on the initial product of resources, and the achievement of the target is judged based on the revenue from resource recovery. The evaluation results of integrated performance and benefits are used to generate a performance-benefit dual-dimensional evaluation report that includes both compliant and non-compliant results.

[0009] Furthermore, the method of generating an appropriate recovery strategy by invoking historical recovery files when performance is insufficient includes: Based on the non-compliance results in the assessment report, the root causes were traced by linking operational feedback data and operating parameters. Call the built-in historical recovery archive of related shock types, non-compliance indicators, and successful strategies, match the shock type according to the source tracing results, and filter the most matching recovery strategy; Based on the call-based recovery strategy, the feasibility is verified by combining the current equipment load and resource reserves. Once the verification is successful, the optimal recovery strategy, which includes target indicators and unit operation steps, is generated.

[0010] Furthermore, the method for generating the update status report includes: The optimal recovery strategy is broken down into sub-tasks, and each processing unit performs recovery operations in sequence. The system collects monitoring values ​​of indicators that are not met by each processing unit during the recovery operation in real time. If the monitoring value does not reach the current stage target, the system triggers dynamic parameter adjustment of the optimal recovery strategy. When a previously non-compliant indicator meets the target for multiple consecutive stages, an updated status report is generated, which includes the recovery time, indicator changes, and current status.

[0011] Furthermore, the method of obtaining resource-based products by executing the scheduling of biogas, wastewater reuse, and differentiated organic fertilizer preparation through resource-load dynamic matching logic includes: Based on the compliance report and the updated status report, extract real-time equipment load-related data and resource status data of biogas, wastewater, and sludge; Resource scheduling priorities are assigned according to preset load levels, and then differentiated scheduling is executed through dynamic resource-load matching logic: biogas is matched to the purpose according to equipment load, wastewater is matched to the reuse scenario according to water quality, and sludge is matched to the organic fertilizer treatment process according to quality.

[0012] Furthermore, the method for generating the circular benefit report includes: Based on real-time data from differentiated resource scheduling, the conversion effects of biogas, wastewater, and organic fertilizer are evaluated, and items that fail to meet expectations in multiple consecutive evaluations are marked as items to be optimized. By linking the entire process data through the reverse link of resource indicators → conversion units → operation parameters, the root causes of items to be optimized can be traced. Based on the root cause matching processing unit, the processing parameters are optimized according to different scenarios; the data of the whole process is integrated to calculate the resource output and utilization rate and generate a cycle benefit report containing the optimization results.

[0013] Furthermore, the methods for generating 3D optimization suggestions, updating the core rule base, and forming a closed-loop chain include: Based on the cyclic benefit report and operation feedback data, the optimization experience of single processing parameters is transformed into universal rules, forming three-dimensional optimization suggestions, and iterating the three core rule bases of working condition matching matrix, causal-parameter chain and resource scheduling priority. Deploy the updated core rule base to the central control system; establish a long-term tracking mechanism to regularly monitor processing efficiency and resource utilization, and trigger secondary optimization when the effect decays or new operating conditions occur, forming a closed-loop optimization link.

[0014] The technical effects and advantages of the comprehensive treatment and recycling system for pig farm waste of the present invention are as follows: This invention achieves a comprehensive solution to the shortcomings of existing technologies through a step-by-step design. First, by accurately classifying various types of waste from pig farms and matching the optimal working mode with a three-dimensional situation assessment, a global collaborative instruction set containing both processing efficiency targets and resource recovery targets is generated, effectively avoiding the problem of low processing efficiency caused by working mode mismatch. Secondly, based on the global collaborative instruction set, cross-unit predictive collaboration is achieved by leveraging the causal-parameter chain, ensuring the coordinated operation of units such as pretreatment, biological treatment, and sludge treatment. At the same time, different types of waste are graded and treated and the initial resource conversion is completed. Simultaneously, operational feedback data is generated, which not only improves the targeting and effectiveness of waste treatment, but also achieves the initial recycling of resources. Then, a dual-dimensional evaluation of efficiency and benefit is carried out to fully understand the system's processing effect and resource benefits. When the efficiency is not up to standard, historical recovery files are called to generate an appropriate recovery strategy and execute it. Finally, an updated status report is generated, which realizes real-time monitoring and timely error correction of the system's operating status, effectively improving the stability of the system processing and reducing environmental risks. Next, based on the resource-load dynamic matching logic, resources such as biogas, wastewater, and sludge are scheduled in a differentiated manner. At the same time, the processing parameters are optimized by reverse tracing, and finally, resource-based products are obtained and a recycling benefit report is generated. This achieves efficient utilization of resources and maximizes their value, further improving the system's processing efficiency, reducing processing costs, and enhancing the system's economic efficiency. Finally, by integrating multi-cycle benefit reports and operational feedback data, three-dimensional optimization suggestions are generated and the core rule base is updated to form a closed-loop optimization link. This transforms the single optimization experience during system operation into universal rules, ensuring that the system can continuously adapt to the dynamic changes in waste, achieve long-term improvements in processing efficiency, resource utilization, and economic benefits, and enhance the robustness and sustainability of the system. Attached Figure Description

[0015] Figure 1This is a schematic diagram of a comprehensive treatment and recycling system for pig farm waste according to the present invention; Figure 2 This is a schematic diagram of the pretreatment process for high organic load waste in a comprehensive treatment and recycling system for pig farm waste according to the present invention; Figure 3 This is a schematic diagram of a method for comprehensive treatment and recycling of pig farm waste according to the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Example 1 Please see Figure 1 and Figure 2 As shown in this embodiment, a comprehensive treatment and recycling system for pig farm waste includes: Instruction generation module: acquires various types of waste from pig farms, collects and verifies multi-dimensional data, combines three-dimensional situation assessment to match the optimal working mode, and generates a global collaborative instruction set that includes processing efficiency targets and resource recovery targets; Resource initial conversion module: Based on the global collaborative instruction set, it performs cross-unit predictive collaboration through causal-parameter chain, and performs waste classification treatment and initial resource conversion to obtain intermediate products and primary resources, and generates operation feedback data simultaneously; System recovery module: Based on operational feedback data, it performs a two-dimensional evaluation of performance and benefits. When performance fails to meet the standards, it calls historical recovery files to generate an appropriate recovery strategy and generates an updated status report after performing the recovery operation. Parameter optimization module: Based on the updated status report, the module executes the scheduling of biogas, wastewater reuse and differentiated organic fertilizer preparation through resource-load dynamic matching logic to obtain resource-based products, and generates a circular benefit report by reverse tracing and optimizing the parameters. Closed-loop optimization module: Integrates multi-cycle benefit reports and operational feedback data to generate three-dimensional optimization suggestions, update the core rule base, and form a closed-loop link.

[0018] The methods for generating a global cooperative instruction set include: Acquire various types of waste from pig farms, including waste with high organic load, toxic waste, and conventional waste; Among them, high organic load waste mainly consists of manure and wastewater from the cleaning period and centralized flushing wastewater. It can be measured on-site using a portable COD rapid detector. If there is a high concentration of COD (chemical oxygen demand) or SS (suspended solids), it is classified as such. For example, COD ≥ 5000 mg / L or SS ≥ 3000 mg / L. Toxic waste: mainly wastewater from the dissection and treatment of dead pigs and waste liquid from the veterinary room. It can be screened by rapid antibiotic test cards (e.g., test cards that detect more than 10 common antibiotics such as tetracyclines and sulfonamides). If the concentration of any antibiotic is too high (e.g., ≥0.1mg / L), it is determined that there are toxic substances and it is classified into this category. Conventional waste: mainly consists of daily pen flushing water and low-concentration fecal waste. The COD and SS concentrations do not meet the requirements for high organic load waste and no toxic substances are detected, so it is classified as conventional waste. The three types of waste are respectively introduced into dedicated temporary storage tanks (corresponding to high organic shock temporary storage tank, toxic accident isolation tank, and conventional conditioning tank), and the water inlet pipes of the three types of temporary storage tanks are independent; The collected multi-dimensional data includes water quality parameters, biological activity parameters, and resource potential parameters; Specifically, online sensors are used to collect real-time data on COD, antibiotic concentration, SS, pH value, and ammonia nitrogen (NH3-N) concentration of the three types of waste as water quality parameters; pre-embedded sensors are used to collect real-time biogas production rate in the anaerobic pond and SOUR value (i.e., activated sludge respiration rate) in the aerobic pond as biological activity parameters; and near-infrared detectors are used to rapidly determine the organic matter content of sludge in the sludge storage area and the carbon-nitrogen ratio (C / N) of carbon-rich solids in the high organic shock storage pond as resource potential parameters. Data cleaning is performed on the collected multi-dimensional data (using the 3σ principle (i.e., data deviation from the mean exceeds 3 times the standard deviation) combined with human experience thresholds (e.g., COD single jump > 50%), dual-condition triggering of anomaly marking, automatic removal, and filling with the average of the previous few (e.g., 3) valid values) and cross-validation (for key parameters such as COD and antibiotic concentration, data are compared using multiple similar sensors (e.g., 3 COD detectors are set up in a triangle in the high organic pool). If the deviation is small (e.g., ≤ 5%), the average is taken. When the deviation is large (e.g., > 5%), the calibration procedure is started (calibrated with standard solution, and the calibration is considered qualified if the error meets the expectations (e.g., ≤ 2%)). The three dimensions of the three-dimensional situation assessment are defined as the operational status dimension, the impact characteristics dimension, and the resource potential dimension. The three-dimensional situation assessment is used to quantify and classify multi-dimensional data, and then the optimal working condition is obtained by matching the working condition matching matrix. The quantization and grading rules for the three dimensions are as follows: Operational status dimension: The equipment load rate (i.e., the ratio of the current processing capacity of the waste treatment equipment to the designed processing capacity) is used as the indicator; different load levels are divided into different levels by threshold segmentation method, including low load (e.g., <0.6), medium load (e.g., 0.6-0.8) and high load (e.g., >0.8), which can be calculated by calling the pump group and fan operation data of the PLC system in real time; Impact characteristics are defined using the organic impact index (the ratio of the difference between the current COD value and the historical average COD to the historical average COD) and the toxic impact index (the ratio of the measured antibiotic concentration to the preset antibiotic safety concentration threshold). Threshold segmentation is used to classify impacts into different levels based on these two indicators. An example threshold segmentation method is as follows: an organic impact index ≤ 0.2 or a toxic impact index ≤ 0.5 is classified as no impact; an organic impact index between 0.2 and 0.5 is classified as low organic impact; a toxic impact index between 0.5 and 1 is classified as low toxic impact; an organic impact index > 0.5 is classified as high organic impact; and a toxic impact index > 1 is classified as high toxic impact. Resource potential dimension: The biogas production potential index (i.e., the ratio of the product of the carbon-rich solids content and the theoretical biogas production rate (usually taken as 0.3 m³ / kg COD when C / N=25) to the designed biogas production) and the fertilizer production potential index (i.e., the ratio of the sludge organic matter content to the preset target value) are used as indicators; they are also divided into different levels of potential. An example threshold classification method is as follows: biogas production potential index or fertilizer production potential index > 1.2 is high biogas production potential or high fertilizer production potential; biogas production potential index or fertilizer production potential index in the range of 0.8-1.2 is medium biogas production potential or medium fertilizer production potential; biogas production potential index or fertilizer production potential index < 0.8 is low biogas production potential or low fertilizer production potential. Based on the three-dimensional combination of corresponding indicators and the preset working conditions including processing efficiency targets and resource utilization benefit targets, a working condition matching matrix is ​​constructed with operating status, impact characteristics, resource potential and preset working conditions as rows. Among them, the preset exemplary processing efficiency targets and resource utilization benefit targets (by task) are as follows: Treatment efficiency targets: Under impact resource-based treatment conditions, COD removal rate ≥92%, SS removal rate ≥95%, and effluent antibiotics <0.01mg / L; Under steady-state treatment conditions, COD removal rate ≥85%, SS removal rate ≥90%, and sludge moisture content ≤80%. Resource utilization benefit targets: Under the impact of resource utilization, biogas daily production ≥60m³ (purity ≥65%), carbon-rich solid conversion rate ≥90%; Under the detoxification-fertilizer co-production conditions: organic fertilizer organic matter ≥55% (premium ≥15%), wastewater utilization rate ≥80%; Example operating condition matching matrix: [Operating status, impact characteristics, resource potential, matching operating condition]; [High load, high organic impact, high biogas production potential, impact resource processing condition]; [Medium load, low toxic impact, medium fertilizer production potential, detoxification-fertilizer production synergistic operating condition]; [Low load, no impact, medium potential (fertilizer production or biogas production), steady-state treatment condition]; Based on the matched processing efficiency target and resource utilization benefit target, the working conditions are integrated into a global collaborative instruction set (including working condition ID (unique identifier), target threshold within the working condition and associated execution time (e.g., complete preparation within 2 hours)).

[0019] Methods for performing cross-unit predictive collaboration through causal-parameter chains include: Based on the decomposition logic of the pre-set working condition matching results - unit core responsibilities - parameter association mapping, the global collaborative instruction set can be split into exclusive operation parameters for three processing units: pretreatment, biological treatment and sludge treatment, through existing structured data parsing tools (such as Python-grako or Python object mapping libraries (such as JsonObject)) or customized parsing algorithms (for example, algorithms developed based on Python, which parse the JSON instructions of all fields in the global collaborative instruction set and map the instructions to specific operation parameters of each processing unit). For example, taking the impact resource utilization condition as an example, when the condition is impact resource utilization, the threshold of the treatment target in the condition is COD removal rate ≥92%, SS ≥95%, and the threshold of the benefit target is biogas production ≥60m³ per day. Examples of specific operating parameters for each treatment unit: Pretreatment unit parameters (starting the shock buffer tank, PAC (flocculator) dosage 0.2-0.3 kg / t; stirring speed 60-80 rpm, settling time 30 min; influent flow rate 8-10 m³ / h), biological treatment unit parameters (anaerobic tank temperature 35±0.5℃, HRT=20 days; aerobic tank DO (dissolved oxygen) target 2.5 mg / L; anaerobic tank stirring speed 15 rpm), sludge treatment unit parameters (filter press standby, flocculant type PAM+PAC; preset dewatering pressure 1.5 MPa; sludge feed flow rate matched to pretreatment effluent). Verify the reasonable range and unit adaptability of the operation parameters of the global collaborative instruction set. For example, if the amount of PAC added during preprocessing exceeds the preset safety range, it is immediately marked as an abnormal parameter, and the historical best parameter is temporarily replaced. At the same time, a calibration reminder is pushed to the engineer. Pre-defined causal-parameter chain: It adopts a chain structure of cause (upstream processing unit parameter deviation) → effect (intermediate risk prediction) → coordinated action (downstream processing unit response). Based on historical operation data, multiple causal chains covering typical scenarios such as high load and toxic impact are pre-defined; where the symbol → is the sequence pointer. Example causal chain: Cause-and-effect chain in high organic load scenario: COD > 5000 mg / L is identified in the pretreatment unit and judged as high organic load waste (cause) → VFA (volatile organic acid) accumulation in the anaerobic tank of the biological treatment unit (intermediate risk prediction) → increased dewatering difficulty in the sludge treatment unit (chain effect) → triggering the synergistic effect of "pretreatment enhanced sedimentation + sludge premixed flocculant + circulating alkali preparation"; Real-time collection of relevant parameters from each processing unit. When the operating parameters of the upstream processing unit deviate from the preset target of the working condition for a certain period of time (e.g., 1 minute, to avoid accidental triggering due to instantaneous fluctuations), cross-unit predictive coordination is triggered, and coordination instructions are immediately sent to the associated processing unit through the PLC system; the cause-effect chain trigger log (including triggering reason, associated processing unit, and expected action) is recorded synchronously. Each processing unit executes preset adaptation preparation operations according to its corresponding logic. After completing the collaborative preparation, each processing unit uploads a preparation completion signal to the central control system to report its preparation status. The feedback content is a structured message (example: sludge treatment unit: "Flocculant premixing completed, filter press temperature 50℃, pressure 1.2MPa, standby ready"). If feedback is not provided in time, a tiered alarm is triggered based on the time (e.g., no feedback for 10 minutes: audible and visual alarm; no feedback for 20 minutes: engineer telephone notification). If a treatment unit experiences an operational anomaly (such as the filter press stopping after preheating to 45°C), an immediate load adjustment is required. This involves further reducing the pretreatment influent flow rate and increasing the stirring speed of the anaerobic tank in the biological treatment unit. By reducing the overall treatment load, time can be gained for the abnormal treatment unit. After the anomaly is resolved, the parameters should be restored according to the principle of gradual recovery (such as increasing the influent flow rate by 1 m³ / h every 5 minutes) to avoid a sudden increase in load that could cause new problems.

[0020] Methods for obtaining intermediate products and primary resources include: After the collaborative preparation is completed, the pretreatment unit uses the operating parameters of the disassembled treatment unit as a benchmark and combines the collaborative preparation status to perform differentiated and targeted pretreatment on the three types of waste to ensure that the subsequent treatment units can be adapted to the load. For the treatment of high organic load waste (corresponding to shock resource chemical conditions): feed regulation is carried out in sequence (the feed flow rate of the high organic shock storage tank is controlled by an electromagnetic flow meter, and the pH value in the tank is monitored simultaneously (the target is a neutral solution (i.e., between 6.5 and 7.5)), enhanced sedimentation (PAC flocculant is added according to preset parameters, the solution agitator is started, and carbon-rich solids with a solid content of a certain proportion (e.g., 20%) are separated and transported to the feed inlet of the anaerobic tank of the biological treatment unit), and supernatant transportation (after the solution settles in the tank, a high organic supernatant is obtained and filtered, and then transported to the anaerobic tank of the biological treatment unit). For the treatment of toxic waste (corresponding to the detoxification-fertilizer co-processing): Sequential isolation feeding (independent feeding into the toxic accident isolation tank, with real-time monitoring of antibiotic concentrations in the influent using an online antibiotic detector) and targeted detoxification (triggering corresponding solutions based on pollutant type; adding ozone for antibiotics and chelating agents (EDTA) for heavy metals); after detoxification, wastewater is sampled and tested (to detect antibiotic or heavy metal concentrations). If the standards are met, it is transferred to the recycling unit (requiring independent pipelines to prevent mixing with other wastewater); if the standards are not met, it is returned to the toxic accident isolation tank for secondary treatment. For the treatment of conventional waste (corresponding to steady-state treatment conditions): impurity removal is carried out in sequence (wastewater is removed by mechanical bar screen to remove large particles such as weeds and manure, and the impurities are temporarily stored in solid waste bins after collection) and water quality homogenization (the wastewater after impurity removal enters the conventional equalization tank, the submersible mixer is turned on to homogenize the water quality, the water quality fluctuation is monitored by online COD meter, and after homogenization, it is transported to the aerobic tank of the biological treatment unit). Based on the pretreated water quality, the biological treatment unit, according to the anaerobic-aerobic linkage mode, degrades organic matter and completes the initial resource conversion based on the differentiated treated water quality through anaerobic fermentation to produce biogas and aerobic denitrification and purification operations, to obtain crude biogas and activated sludge. Among them, anaerobic fermentation for biogas production (for carbon-rich solids + high organic supernatant): carbon-rich solids and high organic supernatant are mixed and fed at the optimal carbon-nitrogen ratio (e.g., C / N=25); at the same time, the anaerobic tank is maintained at the most suitable temperature (e.g., 35±0.5℃), and the VFA concentration in the tank is monitored in real time (the concentration is controlled below the preset safety concentration, such as ≤3000mg / L, and the feed rate can be reduced if it exceeds the standard); the crude biogas produced by anaerobic fermentation (i.e., biogas with a purity of 60%-65%) is dehydrated in a water seal tank and then enters a buffer gas holder, and the hourly gas production is recorded simultaneously; Aerobic denitrification and purification (for conventional wastewater + anaerobic effluent): Maintain the DO concentration in the aerobic tank within the operating parameters (e.g., 2.2-2.5 mg / L); control the sludge age by monitoring the sludge settling ratio (SVI); when the SVI > the preset settling ratio threshold (e.g., 150 mL / g), discharge the excess sludge and transport it to the sludge treatment unit; temporarily store the effluent after aerobic treatment to prepare for subsequent recycling or advanced treatment; The activated sludge is treated by a sludge treatment unit through sludge conditioning (the remaining sludge is put into a dedicated conditioning tank and a premixed flocculant (such as PAM-PAC composite flocculant) is added to form dense flocs) and filter press dewatering (the preheated filter press is started to dewater the dense flocs and produce wet sludge (i.e., activated sludge with a moisture content ≤95%)). The wet sludge is then temporarily stored according to its resource potential. Among them, the graded temporary storage: the organic matter content of wet sludge is quickly determined by a near-infrared detector. High-quality sludge that meets the quality requirements (such as >60%) is transported to the organic fertilizer preparation temporary storage bin, while basic sludge that does not meet the quality requirements is transported to the ordinary temporary storage bin. The storage volume and organic matter data are recorded respectively. After tiered treatment and initial resource conversion in each treatment unit, intermediate products (including pretreated water from the pretreatment unit and activated sludge from the biological treatment unit) and primary resource products (including coarse biogas from the biological treatment unit and wet sludge from the sludge treatment unit) are obtained. Simultaneously collect data on the treatment efficiency and primary resources of each treatment unit, including the pretreatment unit (SS removal rate, COD degradation rate), biological unit (anaerobic biogas production rate, aerobic DO value), and sludge unit (dewatering moisture content, organic matter content). Use the operating condition ID + timestamp + unit number as a unique index to form structured operation feedback data, such as "Impact Resource Chemical Operation Condition - 20241101 - 10:00 - Pretreatment Unit - SS Removal Rate 95%".

[0021] Methods for conducting performance-benefit dual-dimensional evaluation include: Verify the completeness and consistency of the operational feedback data; Completeness verification: Check whether each indicator is missing (such as "SS removal rate" in the pretreatment unit and "biogas yield" in the biological unit). If missing, trigger data re-collection. If re-collection is not possible, use the historical average under the same working conditions to complete the data. Consistency verification: Compare the data logic of upstream and downstream units (e.g., pretreatment COD removal rate + biological unit COD removal rate ≤ total removal rate). If the deviation is greater than expected (e.g., >5%), mark it as "logic abnormal". The original operation logs of the associated equipment (e.g., flocculant dosing records) are used for traceability. Data from different formats that passed verification will be uniformly converted into a structured format of "indicator name-measured value-unit" to facilitate subsequent comparison; In the performance evaluation dimension, compliance verification is handled by quantitatively verifying pollutant removal and equipment compatibility for indicators within different treatment units, and determining compliance based on treatment performance targets. Specifically, the evaluation indicators for each unit need to be set with differentiated judgment standards according to different operating conditions. For the pretreatment unit, the evaluation indicators are SS removal rate and COD degradation rate; for the biological treatment unit, the evaluation indicators are VFA concentration in the anaerobic tank and DO value in the aerobic tank; for the sludge treatment unit, the evaluation indicator is the moisture content of wet sludge. For the current operating conditions, the indicators meeting the target thresholds are considered qualified. For example, the target thresholds for the pretreatment unit under the shock resource chemical condition are SS removal rate ≥95% and COD degradation rate ≥40%. If both indicators meet the standards, the pretreatment unit is qualified. Assign a value to each treatment unit to indicate its compliance status: 1 for compliance and 0 for non-compliance. Then, weighted summation of the three compliance values ​​yields the overall compliance rate. If the overall compliance rate is greater than the preset overall compliance rate threshold (e.g., 0.9), it is determined that the overall efficiency meets the standard; otherwise, it is marked as non-compliant, and the non-compliant unit and its indicator are located (e.g., the VFA concentration in the anaerobic tank of the biological treatment unit exceeds the standard of 3500 mg / L). In terms of benefit assessment, the resource utilization benefits of the two primary resource products, crude biogas and wet sludge, are quantitatively calculated, and the compliance is judged based on the resource utilization benefit target. Specifically, one method for calculating the revenue from crude biogas is as follows: The hourly biogas production data of the biological treatment unit is read and converted to obtain the average daily biogas production. This average daily production is then compared with a preset target biogas production value to obtain the biogas production compliance rate. Biogas data collected by a biogas composition analyzer is used to confirm the purity of the crude biogas (e.g., 62%). This is compared with a preset purity target value. If the biogas purity does not meet the target, the revenue is adjusted according to a purity discount factor (set based on expert experience and actual conditions; for example, a 1% decrease in purity results in a 1.2% decrease in revenue). The daily biogas revenue is calculated by multiplying the average daily biogas production, the purity discount factor, and the local unit electricity price (e.g., 0.6 yuan / kWh, 1 m³ biogas ≈ 1.8 kWh). A method for calculating the revenue of wet sludge: Read the daily output and organic matter content (e.g., 62%) of wet sludge from the sludge treatment unit, calculate the revenue according to the quality grading pricing method, with higher prices for high-quality sludge and lower prices for basic sludge; then multiply the daily output of wet sludge by the corresponding quality price to obtain the daily revenue of wet sludge. The sum of the daily revenue from the two types of resources is compared with the preset daily revenue target to obtain the revenue achievement rate. If the value is greater than 1, the target is achieved and marked as revenue achievement; otherwise, it is marked as non-achievement and the reason is identified (such as insufficient biogas purity or low sludge quality). The evaluation results of integrated performance and benefits are used to generate a dual-dimensional performance-benefit evaluation report. The core contents include: operating condition ID, the duration of an evaluation cycle, the overall performance compliance rate, the benefit compliance rate, details of non-compliance indicators (such as biogas purity 62% < 65%), and the operational parameters associated with the non-compliance indicators (such as insufficient biogas purity due to anaerobic pond pH = 6.8 < 7.0).

[0022] When performance is insufficient, the methods for generating a suitable recovery strategy by referencing historical recovery files include: Based on the non-compliance results in the performance-benefit dual-dimensional evaluation report, and by linking operational feedback data and operating parameters, the root cause is traced through the three-dimensional correlation logic of indicators, parameters, and operating conditions. Specifically, the logic for tracing the source of substandard performance (using "the concentration of VFA in the biological treatment unit = 3500 mg / L > the threshold of 3000 mg / L" as an example): The direct indicator was correlated with VFA exceeding the standard, which led to inhibited methanogenic bacteria activity (corresponding to sludge SOUR value being less than the preset normal SOUR threshold). By retrospectively analyzing upstream parameters and retrieving data from the pretreatment unit, abnormal data was found, such as "high organic supernatant COD = 3800 mg / L > threshold 3500 mg / L" (pretreatment unit COD degradation rate < corresponding degradation rate threshold), resulting in excessive anaerobic tank load. Operating condition matching verification was performed; the current operating condition was shock resource chemical condition, but the pretreatment unit PAC dosage was less than the value that should be executed according to the disassembled parameters, confirming that "pretreatment unit parameter execution deviation" was the root cause. Logic for identifying reasons why revenue targets are not met (taking "daily biogas revenue < expected daily biogas revenue" as an example): The quality-yield analysis is performed to pinpoint the direct causes of non-compliance (such as substandard biogas purity or insufficient biogas production). Then, environmental parameters are correlated, anaerobic digester data is retrieved, and abnormal data is identified. For example, "pH=6.7<7.0" and "stirring speed<preset standard stirring speed threshold" are found, indicating incomplete fermentation. Finally, resource potential is traced back; for example, the detected carbon-rich solids C / N ratio is found to be <theoretical optimal ratio threshold, confirming that "substrate imbalance + environmental parameter deviation" jointly lead to substandard yield. It calls the built-in historical recovery archive of related shock types, non-compliance indicators, and success strategies. The historical recovery archive is stored in categories according to shock type. Example of historical recovery archive content: Impact type: Organic load impact; Typical non-compliance indicators: VFA concentration in anaerobic tank does not meet the standard, COD removal rate does not meet the standard; Preset initial recovery strategy or historically successful strategy (example): In the pretreatment unit, the PAC dosage is increased by a certain percentage and the settling time is extended by a certain time; in the biological treatment unit, a certain amount of glucose is added to activate the bacterial community. The impact type is matched according to the source tracing results, and the recovery strategy that best matches the current non-compliant indicators is selected. Based on the invoked recovery strategy, a feasibility verification is conducted in conjunction with the current equipment load and resource reserves. This includes verifying the parameter safety range (checking whether the adjusted parameters are within the equipment's tolerance range (e.g., PAC dosage 0.28 kg / t ≤ safety limit 0.3 kg / t)), verifying resource matching (confirming that the reserves of flocculants, seed sludge, and other materials can meet the strategy requirements), and verifying the timing rationality (ensuring that there are no conflicts in the operation steps of each treatment unit (e.g., the pretreatment unit (4 hours) and the sludge treatment unit (10 hours) can be executed in parallel, with a total time ≤ 10 hours)). If the verification fails, an attempt is made to re-match the recovery strategy and the staff is notified to determine whether the specific parameters of the recovery strategy need to be fine-tuned. After successful validation, an optimal recovery strategy is generated, which includes target indicators (such as restoring the COD removal rate to ≥92% within 12 hours) and unit operation steps (such as "adding 0.28 kg / t of PAC and stirring at 80 rpm in the pretreatment unit for 0-2 hours").

[0023] The methods for generating update status reports include: The optimal recovery strategy is broken down into sub-tasks in the order of pretreatment → biological treatment → sludge treatment, so as to clarify the execution time window, parameter threshold and responsible person for each treatment unit operation; For example, consider the preprocessing unit of the "organic load shock recovery strategy": Processing Unit: Pretreatment Unit; Execution Steps: Increase PAC dosage to 0.28 kg / t, extend settling time to 40 minutes; Time Window: 0-2 hours; Key Parameter Threshold: Stirring speed 80 rpm; Responsible Person: Pretreatment Operator Based on the decomposed sub-tasks, each processing unit performs recovery operations in sequence. The system collects monitoring values ​​of indicators that fail to meet the standards in each processing unit during the recovery operation in real time. If the monitoring value does not reach the current preset stage target (for example, the VFA concentration should drop below 3000 mg / L 4 hours after the start of the recovery operation, but the actual detection value is 3200 mg / L > 3000 mg / L), the system triggers dynamic parameter adjustment of the optimal recovery strategy. Example parameter adjustments: Under organic load shock: increase the pretreatment PAC dosage by a certain percentage (e.g., 10%), and add a certain percentage (e.g., 2%) of seed sludge to the biological unit; Under toxic shock: increase the activated carbon dosage by a certain amount (e.g., 0.1 kg / t), and extend the reaction time by a certain time (e.g., 20 minutes). Once the non-compliant indicators meet the preset stage targets, continuous monitoring continues. For example, a stage target is preset every hour, and a judgment is made every hour. If multiple stage targets are met consecutively, the recovery is considered complete, and an updated status report is generated, which includes the recovery time (strategy execution time (e.g., 8 hours), number of adjustments (e.g., 1 time), consumed resources (e.g., PAC, glucose, etc.), indicator changes (showing changes in indicators such as VFA and SOUR values ​​from "exceeding the standard" to "meeting the standard" on a time axis (e.g., VFA from 3500→3200→2800mg / L)), and current status (treatment efficiency of each treatment unit (e.g., COD removal rate 93%), equipment load (e.g., aeration fan frequency), and resource output (e.g., biogas production rate)).

[0024] The methods for scheduling biogas, reusing wastewater, and preparing differentiated organic fertilizers through resource-load dynamic matching logic include: Based on the compliance report and the updated status report, extract real-time equipment load-related data (including equipment load rate, current processing capacity and design processing capacity, as well as equipment operating conditions (such as aeration blower load and filter press standby status)) and resource status data of biogas, wastewater, and sludge (including biogas inventory data (such as buffer gas tank storage capacity and design volume), wastewater quality data (such as COD concentration of wastewater that has not reached high value after detoxification (such as COD≤50mg / L and >30mg / L) and COD concentration of wastewater that has reached high value after treatment (such as COD≤30mg / L)), and sludge inventory and quality (including weight data of high-quality sludge and basic sludge)). Resource scheduling priorities are divided according to preset load levels. The division logic is as follows: load levels are divided according to equipment load rate using a threshold segmentation method. For example, high load (>80%), medium load (60%-80%), and low load (<60%). Then, based on different levels of equipment load, different core resource scheduling objectives are formulated: High load: Prioritize energy consumption of processing units (e.g., heating of biological units); Medium load: Balance processing demand with resource recovery output (e.g., partial biogas power generation); Low load: Maximize resource export benefits (e.g., full biogas power generation and grid connection). Then, differentiated scheduling is performed through dynamic resource-load matching logic: Biogas is matched to the equipment load for the purpose: For high-load scenarios, priority is given to ensuring the treatment needs. The anaerobic tank of the biological treatment unit needs to maintain the optimal temperature. Biogas is converted into heat energy through a biogas boiler. After heat exchange, the hot water heats the anaerobic tank. The tank temperature is monitored in real time. When the tank temperature reaches the optimal temperature, the biogas supply is reduced. Unused biogas is transported to the buffer gas tank. For low-to-medium load scenarios: prioritize power generation and grid connection. After biogas is purified by the desulfurization tower, it is sent to the biogas generator. The power generation strategy is adjusted according to the local peak and off-peak electricity. During peak hours, all power is generated and connected to the grid. During off-peak hours, part of the power is generated and stored in the battery. The remaining biogas is fed into the boiler for sludge drying and preheating. Wastewater reuse scenarios based on water quality matching: For wastewater that has not reached high value after detoxification, it is used for equipment cooling. The wastewater is transported to the cooling system of equipment such as aeration blowers and generators, and the heat is removed from the equipment through plate heat exchangers. The heated wastewater is then returned to the conventional equalization tank. For wastewater that has been treated to achieve high value, it is prioritized for rinsing the pens, and the remaining portion is used for dissolving flocculants in the pretreatment unit (to reduce tap water consumption). Sludge is processed according to quality to match organic fertilizer treatment process: For high-quality sludge, it is transported to the relevant sludge drying workshop, dried by biogas waste heat, and then crushed and temporarily stored in the organic fertilizer raw material warehouse, waiting for subsequent fertilizer production. For basic sludge, low-cost treatment is carried out by directly transporting it to a simple dewatering workshop for simple dewatering (such as reducing the moisture content to 80%), and then temporarily storing it in a solid waste transfer warehouse before sending it to a compliant organic fertilizer plant for co-processing.

[0025] Methods for obtaining resource-based products and generating circular benefit reports include: Based on real-time data of differentiated resource scheduling, the conversion effect of biogas, wastewater and organic fertilizer is evaluated using the evaluation logic of yield-quality-utilization rate. Specifically, for biogas, based on the preset conversion target, the purity, daily gas production and desulfurization efficiency are evaluated; for wastewater, the reuse rate and the COD concentration of wastewater that reaches high value after treatment are evaluated; for organic fertilizer (the result of wet sludge conversion), the organic matter content, total NPK (nitrogen, phosphorus and potassium) and moisture content are evaluated. If any evaluation item fails to meet the expected conversion target multiple times (i.e., multiple evaluation cycles, for example, each cycle is 12 hours, and the target is not met for 3 consecutive evaluation cycles), it is marked as an item to be optimized (e.g., biogas purity is 62% < 65% for 3 consecutive cycles). By linking the entire process data through the reverse link of resource indicators → conversion units → operation parameters, we can locate and trace the root causes of items that need optimization. An exemplary logic for tracing the source of a typical scenario: If the biogas purity does not meet the standard, trace its source: Step 1: Identify the conversion unit. Biogas is produced from the anaerobic digester of the biological treatment unit, and its purity is directly related to the anaerobic fermentation environment. Step 2: Check the operating parameters, retrieve the anaerobic tank data, and check for abnormal values. Step 3: Identify the root cause; For example: When retrieving data from the anaerobic digester, abnormal data was found, including "pH=6.8<7.0" and "VFA=2800mg / L>2500mg / L" (excessive acid production inhibits methanogenic bacteria), and the carbon-rich solids C / N ratio was 22<25 (substrate imbalance). The root cause was determined to be "substrate C / N ratio imbalance → anaerobic digester acidification → decreased biogas purity". If the total NPK content of organic fertilizer does not meet the standard, traceability is required: Step 1: Identify the conversion unit. NPK comes from the nitrification and denitrification processes in the aerobic tank of the biological treatment unit and is directly related to sludge activity. Step 2: Check the operating parameters, retrieve the aerobic tank data, and check for abnormal values. For example, if "DO=1.8mg / L<2.0mg / L" is found (nitrification reaction is insufficient), after performing the recovery operation, the sludge SOUR value is 0.16kgO2 / kgMLSS・d (close to the threshold of 0.15, the activity is low). Step 3: Identify the root cause as "insufficient DO in the aerobic tank → low nitrification efficiency → insufficient nitrogen accumulation in sludge → insufficient NPK total in organic fertilizer"; During the source tracing process, parameters of each process step in the system database are called in real time, such as parameters of each processing unit, parameters of recovery operation (such as seed sludge addition amount), parameters of resource scheduling (such as anaerobic tank stirring speed), etc., to form a "parameter-indicator" correlation chain to ensure that the root cause identified during the source tracing process can be verified. Based on the root cause localization results, and according to the predefined scenario-based optimization scheme, the scenario-based optimization parameters of the matching processing unit are matched. The optimization plan was developed based on the following logic: unit-level parameter adjustment + small-scale testing + full-process rollout. Exemplary scenario-based optimization solutions: If the biogas purity does not meet the standard, optimize the biogas purity: The core logic of the optimization plan is to adjust the substrate ratio in the pretreatment unit. In the anaerobic fermentation and biogas production operation in the biological treatment unit according to the anaerobic-aerobic linkage mode, when mixing carbon-rich solids and high-organic supernatant, adjust the substrate ratio (such as adding rice husks) to adjust the C / N ratio to the target ratio (e.g., from 22 to 25). Add alkali to the anaerobic tank of the biological treatment unit to adjust the pH value to the target ratio (e.g., add NaOH solution to adjust the pH from 6.8 to 7.2). Then, conduct small-scale tests for several scheduling cycles (e.g., 3 cycles), and monitor pH, VFA, and biogas purity regularly. If the purity recovers and stabilizes, the entire process can be promoted; if it does not meet the standard, additional equipment adjustments (e.g., the stirring speed of the anaerobic tank) can be made to ensure that the final standard is met. If the total NPK of organic fertilizer does not meet the standard, optimize the NPK of organic fertilizer: The core logic of the optimization plan is to adjust the parameters of the aerobic tank of the biological treatment unit (such as adjusting the frequency of the aeration blower from 45Hz to 50Hz; increasing the sludge return ratio from 100% to 120%) to make the DO meet the standard; conduct small-scale tests for several scheduling cycles to detect the nitrogen content of sludge, and after meeting the standard, simultaneously adjust the drying time of the sludge treatment unit (such as extending it from 2 hours to 2.5 hours) to enhance nitrogen retention; After the matching optimization parameters are reviewed by the engineer, they are updated to the execution layer of the central control system, replacing the original operation parameters, and the parameter adjustment log is recorded synchronously (including adjustment time, reason, and executor). Integrate data from the entire process to calculate resource output (statistics on monthly total biogas production, average purity, wastewater reuse, and organic fertilizer production), utilization rate (i.e., biogas and wastewater utilization rate; biogas utilization rate = (power generation + heating gas consumption) ÷ total gas production; wastewater utilization rate = reuse ÷ total treatment volume), and economic benefits (calculated at market prices, deducting energy costs (such as PAC and electricity fees) to calculate net profit). Generate a circular benefit report, including the calculation period, operating condition type, benefit data (detailed resource output, utilization rate indicators, and income and expenditure details), and optimization results (comparison of resource assessment items before and after optimization (e.g., biogas purity increased from 62% to 66%).

[0026] The methods for generating 3D optimization suggestions, updating the core rule base, and forming a closed-loop chain include: Based on the cyclic benefit report and operation feedback data, the optimization experience of single processing parameters is transformed into universal rules, forming three-dimensional optimization suggestions, and iterating the three core rule bases of working condition matching matrix, causal-parameter chain and resource scheduling priority. Specifically, the three-dimensional optimization suggestions include universally applicable rules for optimizing the three core rule bases: working condition matching matrix, causal-parameter chain, and resource scheduling priority. The optimization logic of the working condition matching matrix is ​​as follows: extract the correlation between working condition, parameter, and effect from the cycle benefit report (e.g., "under the impact resource utilization condition, the biogas purity compliance rate increases by 20% when the substrate C / N=25"), and correct the corresponding working condition parameters in the original working condition matching matrix (e.g., the substrate C / N of the original working condition matching matrix (impact resource utilization condition) is 20-22, and the biogas purity target is ≥65%; the updated working condition matching matrix (impact resource utilization) has a substrate C / N of 24-26 and a biogas purity target of ≥68%). Check whether the updated parameters conflict with other operating condition rules (e.g., steady-state operating condition C / N=22, which does not conflict with shock operating condition). If there is a conflict, it is resolved by "priority sorting" (shock operating condition has higher priority than steady-state). The optimization logic of the cause-effect chain is as follows: Based on the root cause analysis of the item to be optimized, add or modify the cause-effect chain. For example, if the cause-effect chain is "Pretreatment SS > 2000 mg / L → sludge dewatering is difficult", add "and anaerobic tank VFA > 3000 mg / L → substrate C / N needs to be adjusted simultaneously". The optimization logic for scheduling priority is as follows: Adjust the revenue weight based on economic benefit data. For example, if "the revenue from biogas grid connection is 15% higher than the revenue from heating at low load", the scheduling priority for low load will be adjusted from "heating priority" to "grid connection priority". Supplement abnormal response rules: For example, add an emergency scheduling rule that "when biogas purity is <60%, temporarily shut down grid-connected power generation and prioritize its use for anaerobic pond heating" to ensure coverage of abnormal scenarios not covered by the original rules. Deploy the updated core rule base to the central control system, replace the original rules, and set the effective time to the next processing cycle (to avoid mid-process switching affecting operation). Establish a long-term tracking mechanism to regularly (e.g., monthly) monitor processing efficiency, resource utilization, and economic benefits, and compare them with the values ​​before optimization. If a certain item shows a continuous decline compared to the value before optimization, it is judged as an effect decay. However, when there is an effect decay or a new working condition occurs, root cause tracing is triggered to investigate whether it is caused by a decrease in rule adaptability. Then, the next round of core rule base optimization process is initiated for secondary optimization, forming a closed-loop optimization link to achieve continuous improvement in the long-term operating efficiency and benefits of the system.

[0027] Example 2 Please see Figure 3 As shown, the parts not described in detail in this embodiment are described in Embodiment 1. A method for comprehensive treatment and recycling of pig farm waste is provided, including: S1: Acquire various types of waste from pig farms, collect and verify multi-dimensional data, combine three-dimensional situation assessment to match the optimal working mode, and generate a global collaborative instruction set that includes processing efficiency targets and resource recovery targets; S2: Based on the global collaborative instruction set, cross-unit predictive collaboration is performed through causal-parameter chain, and waste classification and preliminary resource conversion are carried out to obtain intermediate products and primary resources, and operation feedback data is generated simultaneously. S3: Based on operational feedback data, a dual-dimensional evaluation of performance and benefits is performed. When performance fails to meet the standards, historical recovery files are called to generate an appropriate recovery strategy. After the recovery operation is performed, an updated status report is generated. S4: Based on the updated status report, the operation of scheduling biogas, reusing wastewater and preparing differentiated organic fertilizer is executed through resource-load dynamic matching logic to obtain resource-based products, and the parameters are optimized by reverse tracing to generate a circular benefit report; S5: Integrates multi-cycle benefit reports and operational feedback data to generate three-dimensional optimization suggestions, update the core rule base, and form a closed-loop chain.

[0028] Example 3 This embodiment discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the operation mode of the comprehensive treatment and recycling system for pig farm waste described above.

[0029] Since the electronic device described in this embodiment is used to implement the method for comprehensive treatment and recycling of pig farm waste in this application embodiment, those skilled in the art can understand the specific implementation and various variations of the electronic device in this embodiment based on the method for comprehensive treatment and recycling of pig farm waste described in this application embodiment. Therefore, how the electronic device implements the method in this application embodiment will not be described in detail here. Any electronic device used by those skilled in the art to implement the method for comprehensive treatment and recycling of pig farm waste in this application embodiment falls within the scope of protection of this application.

[0030] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0031] The above description is merely a preferred embodiment of the present invention, and the scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for users of ordinary technical skills, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A comprehensive treatment and recycling system for pig farm waste, characterized in that, include: Instruction generation module: acquires various types of waste from pig farms, collects and verifies multi-dimensional data, combines three-dimensional situation assessment to match the optimal working mode, and generates a global collaborative instruction set that includes processing efficiency targets and resource recovery targets; Resource initial conversion module: Based on the global collaborative instruction set, it performs cross-unit predictive collaboration through causal-parameter chain, and performs waste classification treatment and initial resource conversion to obtain intermediate products and primary resources, and generates operation feedback data simultaneously; System recovery module: Based on operational feedback data, it performs a two-dimensional evaluation of performance and benefits. When performance fails to meet the standards, it calls historical recovery files to generate an appropriate recovery strategy and generates an updated status report after performing the recovery operation. Parameter optimization module: Based on the updated status report, the module executes the scheduling of biogas, wastewater reuse and differentiated organic fertilizer preparation through resource-load dynamic matching logic to obtain resource-based products, and generates a circular benefit report by reverse tracing and optimizing the parameters. Closed-loop optimization module: Integrates multi-cycle benefit reports and operational feedback data to generate three-dimensional optimization suggestions, update the core rule base, and form a closed-loop link.

2. The comprehensive treatment and recycling system for pig farm waste according to claim 1, characterized in that, The generation methods of the global cooperative instruction set include: We collected high-organic-load waste, toxic waste, and conventional waste from pig farms, and imported them into dedicated temporary storage pools to obtain multi-dimensional data. The three dimensions of the three-dimensional situation assessment are defined as the operational status dimension, the impact characteristic dimension, and the resource potential dimension. Based on the combination of the three dimensions and the preset operating conditions that include processing efficiency targets and resource utilization benefit targets, an operating condition matching matrix is ​​constructed. The three-dimensional situation assessment quantifies and classifies multi-dimensional data, and then the optimal working condition is obtained by matching the working condition matching matrix, generating a global collaborative instruction set.

3. The comprehensive treatment and recycling system for pig farm waste according to claim 2, characterized in that, The method of performing cross-unit predictive collaboration through causal-parameter chains includes: The global collaborative instruction set is broken down into the operation parameters of three treatment units: pretreatment, biological treatment, and sludge treatment. Based on the logic of the preset cause-effect-parameter chain, when the operating parameters of the upstream processing unit deviate from the preset target of the working condition, cross-unit predictive coordination is triggered. Each processing unit performs the adaptation preparation operation according to the corresponding logic and then reports the preparation status after completion.

4. The comprehensive treatment and recycling system for pig farm waste according to claim 3, characterized in that, The methods for obtaining intermediate products and primary resources include: After collaborative preparation is completed, the pretreatment unit performs differentiated treatment on the three types of waste; The biological treatment unit, based on the anaerobic-aerobic linkage mode and the differentiated water quality after treatment, degrades organic matter and completes the initial resource conversion to obtain crude biogas and activated sludge. The sludge treatment unit conditioned and filtered the activated sludge to produce wet sludge, which was then temporarily stored according to its resource potential to obtain intermediate products and primary resource products. Data on the processing efficiency and initial resource quality of each processing unit are collected synchronously to form structured operational feedback data.

5. The comprehensive treatment and recycling system for pig farm waste according to claim 4, characterized in that, The methods for conducting a two-dimensional performance-benefit evaluation include: Based on operational feedback data, in terms of performance evaluation, pollutant removal and equipment compatibility are quantitatively verified for indicators within different treatment units, and compliance is determined based on treatment performance targets. In terms of revenue assessment, the revenue from resource recovery is calculated based on the initial product of resources, and the achievement of the target is judged based on the revenue from resource recovery. The evaluation results of integrated performance and benefits are used to generate a performance-benefit dual-dimensional evaluation report that includes both compliant and non-compliant results.

6. A comprehensive treatment and recycling system for pig farm waste according to claim 5, characterized in that, The methods for generating an appropriate recovery strategy by calling historical recovery files when performance is insufficient include: Based on the non-compliance results in the assessment report, the root causes were traced by linking operational feedback data and operating parameters. Call the built-in historical recovery archive of related shock types, non-compliance indicators, and successful strategies, match the shock type according to the source tracing results, and filter the most matching recovery strategy; Based on the call-based recovery strategy, the feasibility is verified by combining the current equipment load and resource reserves. Once the verification is successful, the optimal recovery strategy, which includes target indicators and unit operation steps, is generated.

7. A comprehensive treatment and recycling system for pig farm waste according to claim 6, characterized in that, The methods for generating the update status report include: The optimal recovery strategy is broken down into sub-tasks, and each processing unit performs recovery operations in sequence. The system collects monitoring values ​​of indicators that are not met by each processing unit during the recovery operation in real time. If the monitoring value does not reach the current stage target, it triggers dynamic parameter adjustment of the optimal recovery strategy. When a previously non-compliant indicator meets the target for multiple consecutive stages, an updated status report is generated, which includes the recovery time, indicator changes, and current status.

8. A comprehensive treatment and recycling system for pig farm waste according to claim 7, characterized in that, The method of obtaining resource-based products by executing the operations of biogas, wastewater reuse, and differentiated organic fertilizer preparation through resource-load dynamic matching logic includes: Based on the compliance report and the updated status report, extract real-time equipment load-related data and resource status data of biogas, wastewater, and sludge; Resource scheduling priorities are assigned according to preset load levels, and then differentiated scheduling is executed through dynamic resource-load matching logic: biogas is matched to the purpose according to equipment load, wastewater is matched to the reuse scenario according to water quality, and sludge is matched to the organic fertilizer treatment process according to quality.

9. A comprehensive treatment and recycling system for pig farm waste according to claim 8, characterized in that, The methods for generating the circular benefit report include: Based on real-time data from differentiated resource scheduling, the conversion effects of biogas, wastewater, and organic fertilizer are evaluated, and items that fail to meet expectations in multiple consecutive evaluations are marked as items to be optimized. By linking the entire process data through the reverse link of resource indicators → conversion units → operation parameters, the root causes of items to be optimized can be traced. Based on the root cause matching processing unit, the processing parameters are optimized according to different scenarios; the data of the whole process is integrated to calculate the resource output and utilization rate and generate a cycle benefit report containing the optimization results.

10. A comprehensive treatment and recycling system for pig farm waste according to claim 9, characterized in that, The methods for generating 3D optimization suggestions, updating the core rule base, and forming a closed-loop chain include: Based on the cyclic benefit report and operation feedback data, the optimization experience of single processing parameters is transformed into universal rules, forming three-dimensional optimization suggestions, and iterating the three core rule bases of working condition matching matrix, causal-parameter chain and resource scheduling priority. Deploy the updated core rule base to the central control system; establish a long-term tracking mechanism to regularly monitor processing efficiency and resource utilization, and trigger secondary optimization when the effect decays or new operating conditions occur, forming a closed-loop optimization link.

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