Agricultural wastewater carbon thermal cooperative treatment and intelligent regulation and control system

Abstract

The invention relates to the technical field of intelligent regulation and control, in particular to an agricultural wastewater carbon heat cooperative treatment and intelligent regulation and control system which comprises a water inlet regulation and control module, a reaction state recognition module, an aeration regulation module, a carbon heat recovery module and a carbon heat distribution module. According to the method, a parameter set is constructed based on real-time water inlet data, a water inlet abnormal area is identified, zoning response is triggered, a reaction state judgment mechanism is constructed in combination with multiple load index deviations in a reaction tank, the current oxygen supply rate and an aerobic load deviation value are linked, targeted aeration frequency adjustment is implemented, and the aeration efficiency is improved. Carbon heat capture conditions are cooperatively judged through gas monitoring data and heat pump control parameters, gas heat energy resources are dynamically distributed in combination with the gas heat state of the greenhouse demand side, multi-dimensional linkage control from water inlet detection, reaction state recognition and oxygen supply adjustment to energy recovery is integrally achieved, the problems of insufficient processing precision, resource waste and response lag are effectively solved, and the service life of a greenhouse is prolonged. The overall operation stability and the treatment efficiency are improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology, and in particular to an intelligent control system for the synergistic treatment of carbon and heat in agricultural wastewater. Background Technology

[0002] The field of intelligent control technology primarily involves the application of control algorithms, sensor technology, data analysis, and decision support systems to achieve precise control of various systems or processes, aiming to optimize performance, improve efficiency, and reduce resource consumption. This field is widely used in various industries, including industrial production, agriculture, energy, and environmental protection. The core of intelligent control systems includes technologies such as real-time data acquisition, data processing and analysis, feedback control, and optimization decision-making. These systems typically utilize sensors to collect real-time data from the environment or processes, process and analyze the data, and output corresponding control signals to adjust the system's operating state in response to dynamically changing demands and conditions.

[0003] The traditional agricultural wastewater carbon-thermal synergistic treatment and intelligent control system refers to the efficient treatment of agricultural wastewater through a combination of carbon-thermal synergistic treatment technology and intelligent control methods. Agricultural wastewater treatment primarily removes harmful substances from wastewater through physical, chemical, and biological methods; however, traditional methods often suffer from unstable treatment effects, high resource consumption, and significant environmental impact. Carbon-thermal synergistic treatment technology combines the adsorption characteristics of carbon materials with the catalytic effect of thermal energy, effectively decomposing pollutants in wastewater and reducing the emission of harmful substances. Simultaneously, the intelligent control system utilizes real-time monitoring, data analysis, and feedback adjustment to dynamically adjust process parameters based on wastewater characteristics and treatment requirements, improving treatment efficiency and reducing energy consumption. This system typically includes real-time monitoring and control of parameters such as wastewater flow rate, temperature, and concentration, and optimizes the treatment process through algorithms. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and propose an agricultural wastewater carbon-thermal synergistic treatment and intelligent control system.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: an agricultural wastewater carbon-thermal synergistic treatment and intelligent control system, the system comprising: The influent control module acquires real-time monitoring data from agricultural wastewater inlets, extracts the instantaneous flow rate, pH value, and total suspended solids concentration set within each zone, compares the parameters within each set with the baseline values, filters out the zone numbers that do not meet any baseline range, records the influent zones that need adjustment, and generates a pretreatment adjustment zone list. Based on the pre-processing adjustment zoning list, the reaction status identification module extracts the combined values ​​of chemical oxygen demand, total nitrogen, total phosphorus, and temperature in each reaction tank area. According to the preset reaction suitability range, it identifies the reaction tank number with abnormal load and obtains the reaction load abnormality block set. The aeration adjustment module calls the reaction load abnormal block set, calculates the difference between the oxygen demand value under the unit chemical oxygen demand load and the oxygen supply rate under the current blower frequency, determines whether the difference exceeds the set dissolved oxygen supply and demand offset threshold, adjusts the blower frequency, and generates a dynamic aeration control instruction set. The carbon heat recovery module obtains real-time monitoring data from the CO2 concentration sensor, flue gas thermometer, and gas flow meter in the gas channel above the corresponding reaction tank according to the dynamic aeration control instruction set. It calls the CO2 adsorption temperature range and heat pump start / stop threshold to determine whether the carbon heat co-capture conditions are met, records the gas collection numbers that meet the conditions, and generates a carbon heat synchronous collection number set. The carbon heat distribution module, based on the carbon heat synchronous acquisition number set, calls the current values ​​of the plate heat exchanger outlet temperature and the greenhouse air CO2 concentration in the greenhouse area to construct a heat distribution status data set. It compares the deviation between the data set and the target set value indicated by the acquisition number, adjusts the target control parameters, and generates a carbon heat control execution form.

[0006] As a further embodiment of the present invention, the pretreatment adjustment partition list includes adjustment number information, adjustment type classification and parameter anomaly identifier; the reaction load anomaly block set includes reaction number identifier, load anomaly index value and over-limit ratio parameter; the dynamic aeration control command set includes frequency change command, response area code and adjustment amplitude level; the carbon-thermal synchronous acquisition number set includes gas source code, synchronous acquisition timestamp and acquisition status label; and the carbon-thermal regulation execution form includes regulation target number, ratio adjustment item and execution priority order.

[0007] As a further aspect of the present invention, the water inlet control module includes: The zonal data acquisition submodule acquires real-time monitoring data from agricultural wastewater inlets, including real-time data collected by flow sensors, pH sensors, and suspended solids concentration sensors. It collects the instantaneous flow rate, pH value, and total suspended solids concentration at each monitoring point, classifies and assigns the collected data according to the region to which the inlet pipeline belongs, and constructs a set of regional water quality parameters bound to the zonal number. The benchmark deviation judgment submodule calls the instantaneous flow rate, pH value and total suspended solids concentration data in the set of regional water quality parameters, and calculates the difference with the set influent flow rate control range, pH adjustment target range and total suspended solids concentration limit threshold respectively. Based on whether it exceeds any benchmark range, it identifies whether the zoning status is abnormal, extracts the zoning number information that exceeds the benchmark range, and obtains the water quality exceeding limit zoning number set. The pretreatment identifier generation submodule, based on the partition number in the set of water quality exceeding limits partition numbers, matches the flow deviation direction, pH offset range and total suspended solids concentration anomaly type of the corresponding partition, combines them to construct the treatment item classification results of the partition, and converts each record into an executable tag according to the predefined treatment type mapping table to establish a pretreatment adjustment partition list.

[0008] As a further aspect of the present invention, the reaction state recognition module includes: The reaction data acquisition submodule, based on the processing partition identifier in the preprocessing adjustment partition list, calls the real-time monitoring output data of the temperature sensor, chemical oxygen demand sensor, total nitrogen sensor and total phosphorus sensor in each partition reaction tank, synchronously records the temperature value, chemical oxygen demand value, total nitrogen value and total phosphorus value according to the region number, establishes the dataset mapping relationship under the corresponding number, and generates the regional reaction monitoring dataset. The load parameter construction submodule compares the chemical oxygen demand, total nitrogen, total phosphorus and temperature values ​​in the regional reaction monitoring dataset with the set organic load reference range, nitrogen and phosphorus ratio threshold and temperature tolerance range, calculates the offset amplitude value of each data group, integrates the corresponding partition number and offset data, and obtains the regional load offset index set. The load anomaly determination submodule calls the offset amplitude value and corresponding index type in the regional load offset index set. Based on the load anomaly determination logic structure, it filters the regional numbers whose offset amplitude is greater than the tolerance limit value, identifies the reaction pool status corresponding to the number as a load anomaly state, summarizes the abnormal regional numbers and records the corresponding offset items to obtain the reaction load anomaly block set.

[0009] As a further aspect of the present invention, the process of setting the tolerance limit value specifically involves: experimentally monitoring the reaction performance of agricultural wastewater under various influent load conditions, recording the fluctuation range of chemical oxygen demand, total nitrogen, total phosphorus, and temperature within a continuous operating cycle, extracting the maximum allowable fluctuation range of each indicator when no abnormal fluctuations occur in the effluent water quality, and using the maximum allowable fluctuation range as the tolerance limit value.

[0010] As a further aspect of the present invention, the aeration adjustment module includes: The oxygen demand parameter extraction submodule, based on the abnormal area number identified by the abnormal reaction load block, calls the dissolved oxygen sensor data, temperature sensor data and blower frequency operation data in the corresponding reaction tank to extract the current dissolved oxygen value, reaction temperature value and blower frequency value of each area, and uniformly constructs the parameter combination corresponding to the area number to generate the aeration control input parameter set. The supply-demand difference calculation submodule calculates the oxygen demand value of the corresponding unit chemical oxygen demand load in the region based on the dissolved oxygen value and temperature value in the aeration control input parameter set, combined with the set oxygen demand conversion coefficient under the unit chemical oxygen demand load. It calculates the current oxygen supply rate by comparing the region blower frequency value with the oxygen supply efficiency table, and calculates the difference between the two values ​​to obtain the oxygen supply-demand offset value per unit load. The aeration frequency adjustment submodule identifies the region number of data items whose offset value in the unit load oxygen supply and demand offset value is greater than the set dissolved oxygen supply and demand offset threshold, calculates the required frequency correction range, matches the blower operating frequency adjustment command according to the frequency adjustment granularity level, summarizes the adjustment results and number mapping relationship of each region, and establishes a dynamic aeration control command set.

[0011] As a further aspect of the present invention, the process of setting the dissolved oxygen supply and demand offset threshold specifically involves collecting dissolved oxygen values, influent COD concentration, temperature, and blower frequency data over multiple cycles while the reaction is in a stable operating state. The difference sequence between the oxygen input and supply corresponding to the removal of a unit of chemical oxygen demand within each cycle is calculated. The range and standard deviation of the difference sequence are statistically analyzed. Based on the reactor's ability to maintain dissolved oxygen balance under multiple loads, the maximum difference within the 95% confidence interval is used as the dissolved oxygen supply and demand offset threshold.

[0012] As a further aspect of the present invention, the carbon heat recovery module includes: The exhaust gas parameter acquisition submodule collects real-time data from the CO2 concentration sensor, flue gas thermometer and gas flow meter set at the gas channel above the reaction tank based on the blower adjustment command and mapping number of the corresponding area in the dynamic aeration control command set. It constructs a multi-parameter data combination of CO2 volume fraction, flue gas emission temperature and flow rate under the corresponding number to generate the regional exhaust gas state parameter group. The collaborative capture judgment submodule calls the corresponding CO2 volume fraction, flue gas emission temperature and flow rate values ​​in the regional exhaust gas state parameter group. Based on the preset CO2 adsorption temperature range setting value and heat pump start-stop threshold setting value, it judges whether the data combination simultaneously meets the adsorption temperature requirement and heat exchange triggering condition, filters the data numbers that meet the capture standard, and obtains a list of capture condition satisfying numbers. The gas numbering and identification submodule retrieves the corresponding data recording time, equipment channel number, and exhaust process segment code for the data number in the number list that meets the capture conditions, constructs a traceable numbering and identification system, and associates it with the gas capture command number under the current time window to establish a carbon-thermal synchronous acquisition number set.

[0013] As a further aspect of the present invention, the method for setting the CO2 adsorption temperature range setting value is as follows: based on the combined data of CO2 volume fraction and flue gas temperature monitored by the non-dispersive infrared sensor, the temperature ranges corresponding to the stable fluctuation state of CO2 concentration under multiple temperature ranges are statistically analyzed. Combined with conventional CO2 condensation and physical adsorption characteristic parameters, the temperature range that can still have the target collection capability under the condition of not forming saturated steam condensation is selected as the basis for setting the CO2 adsorption temperature range setting value. The method for setting the heat pump start-stop threshold is as follows: obtain the temperature difference value between the heat exchanger inlet temperature and the greenhouse water circulation return temperature over a continuous time period, extract the temperature difference baseline when the heat pump is in a stable operating state, identify the temperature difference node corresponding to when the actual output efficiency of the heat pump enters the nonlinear response region, and use the temperature difference node as the basis for setting the heat pump start-stop threshold.

[0014] As a further aspect of the present invention, the carbon heat distribution module includes: The deviation extraction submodule is based on the acquisition number identified in the carbon-thermal synchronous acquisition number set. It calls the real-time monitoring values ​​of the plate heat exchanger outlet temperature and the CO2 concentration in the greenhouse air within the greenhouse area, constructs a comparison set of outlet temperature value and gas concentration value corresponding to each number, retrieves the target heating temperature set value and target CO2 concentration set value associated with the acquisition number, compares the difference between the two with the current measured value, obtains the temperature difference value and concentration difference value under the corresponding number, and obtains the gas-thermal target deviation matrix. The control parameter calculation submodule superimposes the gas concentration difference and temperature difference values ​​according to the number of each group of temperature difference and concentration difference values ​​in the gas-heat target deviation matrix, calculates and obtains the control intensity value corresponding to the acquisition number, matches the equipment operating level according to the control intensity value, obtains the control level number of each number, and generates a linear control response parameter set. The instruction set generation submodule calls the control intensity value corresponding to each group number in the linear control response parameter set, locates the corresponding equipment execution status instruction according to the control classification table of gas and heat equipment, extracts the number-execution instruction pair, summarizes all number and control instruction mapping combinations, and establishes a carbon heat control execution form.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, a parameter set is constructed based on real-time influent data to identify abnormal influent areas and trigger zoned responses. A reaction status determination mechanism is built by combining deviations of multiple load indicators in the reaction tank. The current oxygen supply rate and oxygen demand load offset are linked to implement targeted aeration frequency adjustments. Carbon capture conditions are judged collaboratively by gas monitoring data and heat pump control parameters. Gas thermal energy resources are dynamically allocated based on the demand-side gas and heat status of the greenhouse. The whole system realizes multi-dimensional linkage control from influent detection, reaction status identification, oxygen supply adjustment to energy recovery. Based on multiple monitoring data, differentiated control paths are formed for different treatment stages, effectively solving the problems of insufficient treatment accuracy, resource waste and response lag, and improving overall operational stability and treatment efficiency. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a system flowchart of the present invention; Figure 2 This is a schematic diagram of the system framework of the present invention; Figure 3 This is a flowchart of the water inlet control module of the present invention; Figure 4 This is a flowchart of the reaction state recognition module of the present invention; Figure 5 This is a flowchart of the aeration adjustment module of the present invention; Figure 6 This is a flowchart of the carbon heat recovery module of the present invention; Figure 7 This is a flowchart of the carbon heat distribution module of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0019] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0020] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0021] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0022] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0023] Please see Figure 1 An agricultural wastewater carbon-heat synergistic treatment and intelligent control system, the system includes an influent control module, a reaction state identification module, an aeration adjustment module, a carbon-heat recovery module and a carbon-heat distribution module; The influent control module acquires real-time monitoring data from agricultural wastewater inlets, including real-time data collected by flow sensors, pH sensors, and suspended solids concentration sensors. It constructs a set of instantaneous flow values, pH values, and total suspended solids concentrations for each zone, calls the set influent treatment baseline range, judges the parameters in each set against the baseline values, filters out the zone numbers that do not meet any baseline range, records the influent zones that need adjustment, and generates a list of pretreatment adjustment zones. The reaction status identification module, based on the pre-processed adjustment zoning list, calls the temperature sensor, chemical oxygen demand sensor, total nitrogen sensor, and total phosphorus sensor set in the reaction tank of each zone to collect data, constructs the current combination value of chemical oxygen demand, total nitrogen, total phosphorus, and temperature in the reaction tank of each zone, compares the deviation between the current combination and the set value according to the preset reaction suitable value range, identifies the reaction tank number with abnormal load, and obtains the reaction load abnormal block set; The aeration adjustment module calls the dissolved oxygen sensor values, aeration blower frequency data and temperature sensor data of the corresponding area in the reaction load abnormality block set, calculates the difference between the oxygen demand value under unit chemical oxygen demand load and the oxygen supply rate under the current blower frequency, determines whether the difference exceeds the set dissolved oxygen supply and demand offset threshold, and if it exceeds, adjusts the blower frequency according to the difference, constructs the reaction zone aeration parameter adjustment record, and generates a dynamic aeration control instruction set. The carbon heat recovery module obtains real-time monitoring data from CO2 concentration sensors, flue gas thermometers, and gas flow meters in the gas channel above the corresponding reaction tank based on the changes in aeration frequency affected by the instructions in the dynamic aeration control instruction set. It constructs a combination value of flue gas CO2 volume fraction, exhaust temperature, and flow rate, calls the CO2 adsorption temperature range and heat pump start / stop threshold, determines whether the carbon heat co-capture conditions are met, records the gas collection numbers that meet the conditions, and generates a carbon heat synchronous collection number set. The carbon heat distribution module is based on the carbon heat synchronous acquisition number set. It calls the current values ​​of the plate heat exchanger outlet temperature and the greenhouse air CO2 concentration in the greenhouse area to construct a heat distribution status data set. It compares the deviation between the data set and the target set value indicated by the acquisition number, filters the number that needs to be controlled, adjusts the target control parameters, and generates a carbon heat control execution form. The pretreatment adjustment zone list includes adjustment number information, adjustment type classification, and parameter anomaly identifier; the reaction load anomaly block set includes reaction number identifier, load anomaly index value, and over-limit ratio parameter; the dynamic aeration control command set includes frequency change command, response area code, and adjustment amplitude level; the carbon and heat synchronous acquisition number set includes gas source code, synchronous acquisition timestamp, and acquisition status label; and the carbon and heat regulation execution form includes regulation target number, ratio adjustment item, and execution priority order.

[0024] Please see Figure 2 and Figure 3 The water inlet control module includes: The zonal data acquisition submodule acquires real-time monitoring data from agricultural wastewater inlets, including real-time data collected by flow sensors, pH sensors, and suspended solids concentration sensors. It collects the instantaneous flow rate, pH value, and total suspended solids concentration at each monitoring point, classifies and assigns the collected data according to the region to which the inlet pipeline belongs, and constructs a set of regional water quality parameters bound to the zonal number. Industrial-grade electromagnetic flowmeters, online glass electrode pH meters, and light-scattering suspended solids concentration sensors were used to synchronously monitor four inlet pipe areas (A, B, C, and D) within the agricultural park. The PLC controller polled the register values ​​of each sensor via the Modbus RTU protocol, extracting the instantaneous flow rate of the inlet pipe in area A as 45.2. The pH value is 6.8, and the total suspended solids concentration (TSS) is 185. Meanwhile, the instantaneous flow rate of region B was read as 52.1. pH value is 5.5, TSS value is 245. The system adds timestamps and area identifiers (such as Area_ZN_01 and Area_ZN_02) to the collected physical quantity values. Based on the physical connection relationship in the pipeline topology database, the data packets are written into the corresponding area data storage blocks to complete the conversion of raw signals into structured data and construct a set of regional water quality parameters bound to the partition number.

[0025] The benchmark deviation judgment submodule calls the instantaneous flow rate, pH value and total suspended solids concentration data in the regional water quality parameter set, and calculates the difference with the set influent flow rate control range, pH adjustment target range and total suspended solids concentration limit threshold respectively. Based on whether it exceeds any benchmark range, it identifies whether the zoning status is abnormal, extracts the zoning number information that exceeds the benchmark range, and obtains the water quality exceeding limit zoning number set. Retrieve preset operating parameter database: Inlet flow rate control range set to 40-60 (Based on the hydraulic retention time design standard of the sedimentation tank), the target pH adjustment range is set at 6.0-9.0 (based on the tolerance range of microorganisms in subsequent biochemical reactions), and the total suspended solids concentration limit threshold is set at 200. (Based on the maximum load settings of the bar screen and primary sedimentation tank); Taking data from area B as an example, perform the difference calculation: flow deviation. (Not exceeding limits), pH difference (Below the lower limit), TSS difference (Exceeding the upper limit), the logic judge identifies that both the pH value and the TSS value exceed the allowable range, triggers state position 1, marks area B (Area_ZN_02) as "abnormal state", and writes the number to the abnormal queue to obtain the water quality exceeding limit zone number set.

[0026] The pretreatment identifier generation submodule is based on the partition number in the water quality exceedance partition number set. It matches the flow deviation direction, pH offset range and total suspended solids concentration anomaly type of the corresponding partition, combines them to construct the treatment item classification results of the partition, and converts each record into an executable label according to the predefined treatment type mapping table to establish a pretreatment adjustment partition list. The abnormal details of Area_ZN_02 are read, and the pH deviation direction is identified as "negative shift" (i.e., excessive acidity) and the TSS abnormality type is "positive exceedance" (i.e., excessive particulate matter). The logic controller matches the pre-set processing strategy matrix: for the negative pH shift, the "alkali metering pump dosing" strategy is matched, and for the positive TSS exceedance, the "enhanced flocculant dosing and extended sedimentation time" strategy is matched. The two physical processing requirements are combined and encoded to generate a composite processing label "PRE_ALK_DOSING_AND_COAGULATION". This label is associated with the partition number Area_ZN_02, stored in the task queue to be executed, and a pre-processing adjustment partition list is established.

[0027] Please see Figure 2 and Figure 4 The reaction state recognition module includes: The reaction data acquisition submodule, based on the processing partition identifier in the preprocessing adjustment partition list, calls the real-time monitoring output data of the temperature sensor, chemical oxygen demand sensor, total nitrogen sensor and total phosphorus sensor in each partition reaction tank, synchronously records the temperature value, chemical oxygen demand value, total nitrogen value and total phosphorus value according to the area number, establishes the dataset mapping relationship under the corresponding number, and generates the regional reaction monitoring dataset. Addressing to the biochemical reaction tank (number RCT_02) corresponding to Area_ZN_02, the real-time readings of the submersible multi-parameter water quality analyzer in this reaction tank are read via industrial Ethernet, and the current reaction temperature is simultaneously collected. Chemical oxygen demand (COD) is 1250. Total nitrogen (TN) was 55. Total phosphorus (TP) was 8.5%. The specific data records are shown in Table 1. The system aligns these four sets of asynchronous data from the same source according to the acquisition time, encapsulates them into a "reaction pool operation status package", and writes them into the corresponding partition form of the real-time database to generate a regional reaction monitoring dataset.

[0028] Table 1 Real-time monitoring data of the regional reaction tank The load parameter construction submodule compares the chemical oxygen demand, total nitrogen, total phosphorus and temperature values ​​in the regional reaction monitoring dataset with the set organic load reference range, nitrogen and phosphorus ratio threshold and temperature tolerance range, calculates the offset amplitude value of each data group, integrates the corresponding partition number and offset data, and obtains the regional load offset index set. Referencing biochemical system design parameters: Organic load reference range set to 1000-1100. (Based on the maximum degradation rate of activated sludge), the nitrogen-phosphorus ratio threshold is set between TN:TP = 5:1 and 10:1, and the allowable temperature range is [not specified]. ;Calculate the COD offset for RCT_02 data. TN / TP ratio (Within the range), temperature offset (Within the range), the system will calculate a positive COD offset value of 150. It is bound to the partition number RCT_02 as the main load pressure indicator to obtain the regional load offset indicator set.

[0029] The load anomaly determination submodule calls the offset amplitude value and corresponding index type in the regional load offset index set. Based on the load anomaly determination logic structure, it filters the regional numbers whose offset amplitude is greater than the tolerance limit value, identifies the reaction pool status corresponding to the number as a load anomaly state, summarizes the abnormal regional numbers and records the corresponding offset items to obtain the reaction load anomaly block set. The process of setting tolerance limits involves experimentally monitoring the reaction performance of agricultural wastewater under various influent load conditions, recording the fluctuation range of chemical oxygen demand, total nitrogen, total phosphorus, and temperature within a continuous operating cycle, extracting the maximum allowable fluctuation range for each indicator when no abnormal fluctuations occur in the effluent water quality, and using the maximum allowable fluctuation range as the tolerance limit value.

[0030] COD offset value 150 for RCT_02 The tolerance limit value was compared with the standard tolerance limit value. The process for setting the tolerance limit value was as follows: During the experimental phase, the system underwent a 30-day continuous pressure test, and the influent COD concentration was gradually increased. When the influent COD fluctuation range was within 60... When the water quality is within the specified range, it remains at Grade I standard without significant fluctuations; when the fluctuation exceeds 60... The water quality began to deteriorate, therefore the COD tolerance limit was set at 60. ; Decision logic execution comparison: If the result is true, RCT_02 is determined to be in a "high organic load shock state", and the anomaly type is recorded as "COD_OVERLOAD". By summarizing these numbers, the set of reaction load anomaly blocks is obtained.

[0031] Please see Figure 2 and Figure 5 The aeration adjustment module includes: The oxygen demand parameter extraction submodule is based on the abnormal area number identified by the abnormal reaction load block. It calls the dissolved oxygen sensor data, temperature sensor data and blower frequency operation data in the corresponding reaction tank to extract the current dissolved oxygen value, reaction temperature value and blower frequency value of each area. It then constructs a parameter combination corresponding to the area number to generate an aeration control input parameter set. For the RCT_02 reactor, the dissolved oxygen (DO) sensor reading, obtained via the SCADA system, is 1.2. Reconfirm the current water temperature The current operating frequency of the blower is read from the inverter feedback signal as 35.0. These three parameters (DO=1.2, T=18.5, Freq=35.0) are packaged into a state vector and used as the basic input for calculating the aeration supply and demand balance, thus generating a set of aeration control input parameters.

[0032] The supply-demand difference calculation submodule calculates the oxygen demand value per unit chemical oxygen demand load in the corresponding area based on the dissolved oxygen value and temperature value in the aeration control input parameter set, combined with the set oxygen demand conversion coefficient per unit chemical oxygen demand load. It calculates the current oxygen supply rate by comparing the area blower frequency value with the oxygen supply efficiency table, and calculates the difference between the two values ​​to obtain the oxygen supply-demand offset value per unit load. Calculate theoretical oxygen demand: Set the oxygen demand coefficient per unit of COD. It is 1.2 (Based on stoichiometry and cellular synthesis yield calculations), the current excess COD load is 150. This corresponds to the increased oxygen demand per unit volume; combined with the current blower frequency of 35... Consult the blower performance curve table (35) Corresponding standard oxygen transfer rate 25 ), with temperature correction factor After adjusting for pressure, calculate the current actual oxygen supply rate. It is 18.5 Simultaneously, based on the real-time influent flow rate and total COD concentration, the total oxygen demand was calculated to be 22.1. The system performs difference calculation: This negative value indicates that there is currently 3.6. The oxygen deficit is determined, and the oxygen supply and demand offset per unit load is obtained.

[0033] The aeration frequency adjustment submodule identifies the region number of data items whose offset value in the unit load oxygen supply and demand offset value is greater than the set dissolved oxygen supply and demand offset threshold, calculates the required frequency correction range, matches the blower operating frequency adjustment command according to the frequency adjustment granularity level, summarizes the adjustment results and number mapping relationship of each region, and establishes a dynamic aeration control command set. The specific process for setting the dissolved oxygen supply and demand offset threshold is as follows: when the reaction is in a stable operating state, data on dissolved oxygen value, influent COD concentration, temperature and blower frequency are collected over multiple cycles. The difference sequence between the oxygen input and supply corresponding to the removal of a unit of chemical oxygen demand in each cycle is calculated. The range and standard deviation of the difference sequence are statistically analyzed. Based on the reactor's ability to maintain dissolved oxygen balance under multiple loads, the maximum difference within the 95% confidence interval is taken as the dissolved oxygen supply and demand offset threshold. The dissolved oxygen supply-demand offset threshold is set at 1.5. The threshold was set based on the following: during the stable operation phase of the reactor, the oxygen supply and demand difference was recorded every hour for 72 consecutive hours, and the standard deviation of the difference sequence was calculated. Take the upper limit of the 95% confidence interval. This represents the fluctuation range that the system's own adjustment capability can cover; the current absolute value of the offset. The system determines that external intervention is needed; it then uses the PID algorithm to reverse-calculate and fill in the missing 3.6. The required frequency increment for the gap is 4.2. According to 0.5 The adjustment granularity is rounded down, resulting in "frequency increased by 4.5". The command "" sets the target frequency of the RCT_02 blower to 39.5 GHz. Establish a dynamic aeration control instruction set.

[0034] Please see Figure 2 and Figure 6 The carbon heat recovery module includes: The exhaust gas parameter acquisition submodule collects real-time data from the CO2 concentration sensor, flue gas thermometer and gas flow meter set at the gas channel above the reaction tank based on the blower adjustment command and mapping number of the corresponding area in the dynamic aeration control command set. It constructs a multi-parameter data combination of CO2 volume fraction, flue gas emission temperature and flow rate under the corresponding number to generate the regional exhaust gas state parameter group. For area RCT_02, the monitoring component located at the outlet of the gas collection hood at the top of the aeration tank was activated to collect data at a CO2 volume fraction of 12.5% ​​and a flue gas emission temperature of [missing data]. The gas flow rate is 4.8. The data acquisition module synchronously samples these three parameters and timestamps them (Timestamp_T0) to form a vector describing the thermodynamic and component characteristics of the exhaust gas, generating a set of regional exhaust gas state parameters.

[0035] The collaborative capture judgment submodule calls the corresponding CO2 volume fraction, flue gas emission temperature and flow rate values ​​in the regional exhaust gas state parameter group. Based on the preset CO2 adsorption temperature range setting value and heat pump start-stop threshold setting value, it judges whether the data combination simultaneously meets the adsorption temperature requirement and heat exchange triggering condition, filters the data numbers that meet the capture standard, and obtains a list of capture condition met numbers. The method for setting the CO2 adsorption temperature range is as follows: based on the combined data of CO2 volume fraction and flue gas temperature monitored by the non-dispersive infrared sensor, the temperature ranges corresponding to the stable fluctuation of CO2 concentration under multiple temperature ranges are statistically analyzed. Combined with the conventional CO2 condensation and physical adsorption characteristic parameters, the temperature range that can still have the target collection capability under the condition of not forming saturated steam condensation is selected as the basis for setting the CO2 adsorption temperature range. The method for setting the heat pump start-stop threshold is to obtain the temperature difference value between the heat exchanger inlet temperature and the greenhouse water circulation return temperature over a continuous time period, extract the temperature difference baseline when the heat pump is in a stable operating state, identify the temperature difference node corresponding to when the actual output efficiency of the heat pump enters the nonlinear response region, and use the temperature difference node as the basis for setting the heat pump start-stop threshold. The preset CO2 adsorption temperature range is: The range was set based on thermogravimetric analysis (TGA) of the modified amine adsorbent used, and the data showed that... Within the specified range, the adsorption capacity reaches over 90% of its maximum value without significant desorption; the preset heat pump start-stop temperature difference threshold is... The setting is based on: monitoring the coefficient of performance (COP) of the water source heat pump under different inlet temperature differences; when the temperature difference between the heat exchanger inlet and return is less than... At that time, the COP drops sharply below 2.5, entering the nonlinear inefficient region; the temperature in the current data Falling within the range of [45, 65], and the heat exchanger inlet temperature ( ) and greenhouse recirculation temperature ( Temperature difference is The system determines that the exhaust gas has both heat recovery value and carbon capture conditions, and obtains a list of numbers that meet the capture conditions.

[0036] The gas numbering and identification submodule retrieves the corresponding data record time, equipment channel number and exhaust process segment code for data numbers that meet the capture conditions in the number list, constructs a traceable numbering and identification system, and associates it with the gas capture command number under the current time window to establish a carbon-thermal synchronous acquisition number set. For data numbers in the list that meet the capture conditions, extract the current time window 2025-12-09 10:30:00 for RCT_02, combine it with the physical port number of the exhaust pipe Port_02, generate a unique tracking code "CAP_20251209_1030_RCT02", and associate it with the action command "open the three-way valve V-02 to guide the flow to the greenhouse", and establish a carbon-thermal synchronous acquisition number set.

[0037] Please see Figure 2 and Figure 7 The carbon heat distribution module includes: The deviation extraction submodule is based on the acquisition number identified in the carbon-thermal synchronous acquisition number set. It calls the real-time monitoring values ​​of the outlet temperature of the plate heat exchanger and the CO2 concentration of the greenhouse air in the greenhouse area, constructs a comparison set of outlet temperature value and gas concentration value corresponding to each number, retrieves the target heating temperature set value and target CO2 concentration set value associated with the acquisition number, compares the difference between the two with the current measurement value, obtains the temperature difference value and concentration difference value under the corresponding number, and obtains the gas-thermal target deviation matrix. The measured outlet temperature of the greenhouse side plate heat exchanger was read as follows. The measured CO2 concentration inside the greenhouse was 450. Read the current greenhouse management system's set target: heating temperature setpoint is [value missing]. The CO2 concentration is set at 1000. The system calculates the temperature difference. Concentration difference A deviation matrix describing the supply-demand gap is constructed to obtain the gas and heat target deviation matrix.

[0038] The control parameter calculation submodule, based on each group of temperature and concentration differences in the gas-heat target deviation matrix, superimposes the gas concentration and temperature values ​​according to their assigned numbers, using the following formula: ; The calculation obtains the control intensity value corresponding to the acquisition number, matches the equipment operating level based on the control intensity value, obtains the control level number for each number, and generates a linear control response parameter set; in, For the first The control intensity value of the gas heat channel. For the first The normalized value of the outlet temperature of the plate heat exchanger in the channel group is obtained by subtracting the average heat exchange temperature of the system from the actual temperature value and then dividing by the maximum temperature difference. For the first The normalized value of the temperature setpoint for the group channel, the normalization method is the same as... This originates from a system-defined threshold. For the first The normalized value of CO2 concentration in the group channel is obtained by dividing the CO2 concentration by the system's maximum set concentration value. For the first The normalized value of the target CO2 concentration setpoint for the group channel, and the normalization method is the same as... Consistent, For the first The normalized gas velocity value of the group channel is obtained by dividing the instantaneous gas velocity by the upper limit of the system gas velocity. The index for the acquisition number represents the index value of the control channel number. The total number of data collection numbers is a positive integer, representing the total number of data collection numbers in this round of regulation, used for normalization coefficient adjustment; the regulation intensity value. After generation, the value is entered into the equipment control strategy mapping table for matching, and the corresponding gas valve opening degree and heat exchanger flow control ratio are found. Then, the CO2 injection frequency and heat flow rate setting of the target channel are updated in a linked manner to complete the dynamic adjustment of equipment parameters. The gas concentration difference and temperature difference values ​​were superimposed according to their respective numbers, using the following formula: ; The advantage of this formula is that the molecular part passes through the linear temperature difference term. Responding to heat demand, and through This geometric mean term incorporates the mass flux momentum factor, i.e., it increases the control weight at high concentrations and high flow rates to prevent misjudgments of transport efficiency caused by simply high concentrations but low flow rates; the denominator... A normalization factor for the global concentration bias was introduced, when other channels in the system... When the total concentration deviation is large, the current channel will be automatically reduced. The sensitivity of the control plays a role in global damping and load balancing, preventing system oscillation caused by local over-adjustment. Parameter quantification and setting: (Temperature normalized value): Measured outlet temperature Average heat exchange of the system Maximum temperature difference .calculate: . (Set temperature normalized value): Set value .calculate: . (Concentration normalized value): The measured CO2 concentration in the exhaust gas is 12.5% ​​(i.e., 125,000 ppm), and the maximum concentration limit is set. (20%). Calculation: . (Normalized velocity): Measured velocity 4.8 Maximum 10 .calculate: The summation term in the denominator: assuming only one channel needs adjustment at present (this round). ,index (Referring to the current channel itself), and taking into account the normalized concentration setpoint deviation. Greenhouse target 1000 (0.1%), normalized value .but .

[0039] Substitute into the formula calculation: Calculate the absolute value of the temperature difference term: ; Calculate the concentration-flow rate coupling term: ; Calculate the numerator: ; Calculate the denominator: ; Calculate the control intensity value : ; The calculated control intensity value of 0.4615 is a dimensionless control gain coefficient. This value is located in the middle of the 0-1 interval, indicating that the current operating condition requires a moderate level of intervention. The corresponding equipment execution level will be matched based on this value by the linear control response parameter set.

[0040] The instruction set generation submodule calls the control intensity value corresponding to each group number in the linear control response parameter set, locates the corresponding equipment execution status instruction according to the control classification table of gas and heat equipment, extracts the number-execution instruction pair, summarizes all number and control instruction mapping combinations, and establishes a carbon heat control execution form. For the calculated Query the "Gas and Heat Equipment Control Classification Table": the range [0.0-0.3] corresponds to low level (valve opening 30%), the range [0.3-0.6] corresponds to medium level (valve opening 60%, variable frequency pump frequency 40Hz), and the range [0.6-1.0] corresponds to high level (valve fully open, variable frequency pump full load); the system matches the medium level command, extracts the control code "CMD_VALVE_60_PUMP_40", binds it with the number CAP_20251209_1030_RCT02, and sends this command to the greenhouse gas distribution manifold and circulating water pump controller to complete the action mapping of physical equipment and establish a carbon and heat control execution form.

[0041] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0042] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0043] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0044] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0045] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0046] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0047] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0048] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0049] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0050] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A system for the synergistic treatment and intelligent control of carbon and heat in agricultural wastewater, characterized in that, The system includes: The influent control module acquires real-time monitoring data from agricultural wastewater inlets, extracts the instantaneous flow rate, pH value, and total suspended solids concentration set within each zone, compares the parameters within each set with the baseline values, filters out the zone numbers that do not meet any baseline range, records the influent zones that need adjustment, and generates a pretreatment adjustment zone list. Based on the pre-processing adjustment zoning list, the reaction status identification module extracts the combined values ​​of chemical oxygen demand, total nitrogen, total phosphorus, and temperature in each reaction tank area. According to the preset reaction suitability range, it identifies the reaction tank number with abnormal load and obtains the reaction load abnormality block set. The aeration adjustment module calls the reaction load abnormal block set, calculates the difference between the oxygen demand value under the unit chemical oxygen demand load and the oxygen supply rate under the current blower frequency, determines whether the difference exceeds the set dissolved oxygen supply and demand offset threshold, adjusts the blower frequency, and generates a dynamic aeration control instruction set. The carbon heat recovery module obtains real-time monitoring data from the CO2 concentration sensor, flue gas thermometer, and gas flow meter in the gas channel above the corresponding reaction tank according to the dynamic aeration control instruction set. It calls the CO2 adsorption temperature range and heat pump start / stop threshold to determine whether the carbon heat co-capture conditions are met, records the gas collection numbers that meet the conditions, and generates a carbon heat synchronous collection number set. The carbon heat distribution module, based on the carbon heat synchronous acquisition number set, calls the current values ​​of the plate heat exchanger outlet temperature and the greenhouse air CO2 concentration in the greenhouse area to construct a heat distribution status data set. It compares the deviation between the data set and the target set value indicated by the acquisition number, adjusts the target control parameters, and generates a carbon heat control execution form.

2. The agricultural wastewater carbon-thermal synergistic treatment and intelligent control system according to claim 1, characterized in that, The pretreatment adjustment zone list includes adjustment number information, adjustment type classification, and parameter anomaly identifier; the reaction load anomaly block set includes reaction number identifier, load anomaly index value, and over-limit ratio parameter; the dynamic aeration control command set includes frequency change command, response area code, and adjustment amplitude level; the carbon-thermal synchronous acquisition number set includes gas source code, synchronous acquisition timestamp, and acquisition status label; and the carbon-thermal regulation execution form includes regulation target number, ratio adjustment item, and execution priority order.

3. The agricultural wastewater carbon-thermal synergistic treatment and intelligent control system according to claim 2, characterized in that, The water inlet control module includes: The zonal data acquisition submodule acquires real-time monitoring data from agricultural wastewater inlets, including real-time data collected by flow sensors, pH sensors, and suspended solids concentration sensors. It collects the instantaneous flow rate, pH value, and total suspended solids concentration at each monitoring point, classifies and assigns the collected data according to the region to which the inlet pipeline belongs, and constructs a set of regional water quality parameters bound to the zonal number. The benchmark deviation judgment submodule calls the instantaneous flow rate, pH value and total suspended solids concentration data in the set of regional water quality parameters, and calculates the difference with the set influent flow rate control range, pH adjustment target range and total suspended solids concentration limit threshold respectively. Based on whether it exceeds any benchmark range, it identifies whether the zoning status is abnormal, extracts the zoning number information that exceeds the benchmark range, and obtains the water quality exceeding limit zoning number set. The pretreatment identifier generation submodule, based on the partition number in the set of water quality exceeding limits partition numbers, matches the flow deviation direction, pH offset range and total suspended solids concentration anomaly type of the corresponding partition, combines them to construct the treatment item classification results of the partition, and converts each record into an executable tag according to the predefined treatment type mapping table to establish a pretreatment adjustment partition list.

4. The agricultural wastewater carbon-thermal synergistic treatment and intelligent control system according to claim 3, characterized in that, The reaction state identification module includes: The reaction data acquisition submodule, based on the processing partition identifier in the preprocessing adjustment partition list, calls the real-time monitoring output data of the temperature sensor, chemical oxygen demand sensor, total nitrogen sensor and total phosphorus sensor in each partition reaction tank, synchronously records the temperature value, chemical oxygen demand value, total nitrogen value and total phosphorus value according to the region number, establishes the dataset mapping relationship under the corresponding number, and generates the regional reaction monitoring dataset. The load parameter construction submodule compares the chemical oxygen demand, total nitrogen, total phosphorus and temperature values ​​in the regional reaction monitoring dataset with the set organic load reference range, nitrogen and phosphorus ratio threshold and temperature tolerance range, calculates the offset amplitude value of each data group, integrates the corresponding partition number and offset data, and obtains the regional load offset index set. The load anomaly determination submodule calls the offset amplitude value and corresponding index type in the regional load offset index set. Based on the load anomaly determination logic structure, it filters the regional numbers whose offset amplitude is greater than the tolerance limit value, identifies the reaction pool status corresponding to the number as a load anomaly state, summarizes the abnormal regional numbers and records the corresponding offset items to obtain the reaction load anomaly block set.

5. The agricultural wastewater carbon-thermal synergistic treatment and intelligent control system according to claim 4, characterized in that, The process of setting the tolerance limit value is as follows: by experimentally monitoring the reaction performance of agricultural wastewater under various influent load conditions, recording the fluctuation range of chemical oxygen demand, total nitrogen, total phosphorus and temperature in a continuous operating cycle, extracting the maximum allowable fluctuation range of each indicator when there is no abnormal fluctuation in the effluent water quality, and using the maximum allowable fluctuation range as the tolerance limit value.

6. The agricultural wastewater carbon-thermal synergistic treatment and intelligent control system according to claim 5, characterized in that, The aeration adjustment module includes: The oxygen demand parameter extraction submodule, based on the abnormal area number identified by the abnormal reaction load block, calls the dissolved oxygen sensor data, temperature sensor data and blower frequency operation data in the corresponding reaction tank to extract the current dissolved oxygen value, reaction temperature value and blower frequency value of each area, and uniformly constructs the parameter combination corresponding to the area number to generate the aeration control input parameter set. The supply-demand difference calculation submodule calculates the oxygen demand value of the corresponding unit chemical oxygen demand load in the region based on the dissolved oxygen value and temperature value in the aeration control input parameter set, combined with the set oxygen demand conversion coefficient under the unit chemical oxygen demand load. It calculates the current oxygen supply rate by comparing the region blower frequency value with the oxygen supply efficiency table, and calculates the difference between the two values ​​to obtain the oxygen supply-demand offset value per unit load. The aeration frequency adjustment submodule identifies the region number of data items whose offset value in the unit load oxygen supply and demand offset value is greater than the set dissolved oxygen supply and demand offset threshold, calculates the required frequency correction range, matches the blower operating frequency adjustment command according to the frequency adjustment granularity level, summarizes the adjustment results and number mapping relationship of each region, and establishes a dynamic aeration control command set.

7. The agricultural wastewater carbon-thermal synergistic treatment and intelligent control system according to claim 6, characterized in that, The process of setting the dissolved oxygen supply and demand offset threshold is as follows: when the reaction is in a stable operating state, data on dissolved oxygen value, influent COD concentration, temperature and blower frequency are collected over multiple cycles. The difference sequence between the oxygen input and supply corresponding to the removal of a unit of chemical oxygen demand in each cycle is calculated. The range and standard deviation of the difference sequence are statistically analyzed. Based on the reactor's ability to maintain dissolved oxygen balance under multiple loads, the maximum difference within the 95% confidence interval is taken as the dissolved oxygen supply and demand offset threshold.

8. The agricultural wastewater carbon-thermal synergistic treatment and intelligent control system according to claim 7, characterized in that, The carbon heat recovery module includes: The exhaust gas parameter acquisition submodule collects real-time data from the CO2 concentration sensor, flue gas thermometer and gas flow meter set at the gas channel above the reaction tank based on the blower adjustment command and mapping number of the corresponding area in the dynamic aeration control command set. It constructs a multi-parameter data combination of CO2 volume fraction, flue gas emission temperature and flow rate under the corresponding number to generate the regional exhaust gas state parameter group. The collaborative capture judgment submodule calls the corresponding CO2 volume fraction, flue gas emission temperature and flow rate values ​​in the regional exhaust gas state parameter group. Based on the preset CO2 adsorption temperature range setting value and heat pump start-stop threshold setting value, it judges whether the data combination simultaneously meets the adsorption temperature requirement and heat exchange triggering condition, filters the data numbers that meet the capture standard, and obtains a list of capture condition satisfying numbers. The gas numbering and identification submodule retrieves the corresponding data recording time, equipment channel number, and exhaust process segment code for the data number in the number list that meets the capture conditions, constructs a traceable numbering and identification system, and associates it with the gas capture command number under the current time window to establish a carbon-thermal synchronous acquisition number set.

9. The agricultural wastewater carbon-thermal synergistic treatment and intelligent control system according to claim 8, characterized in that, The method for setting the CO2 adsorption temperature range is as follows: based on the combined data of CO2 volume fraction and flue gas temperature monitored by the non-dispersive infrared sensor, the temperature ranges corresponding to the stable fluctuation of CO2 concentration under multiple temperature ranges are statistically analyzed. Combined with the conventional CO2 condensation and physical adsorption characteristic parameters, the temperature range that can still have the target collection capability under the condition of not forming saturated steam condensation is selected as the basis for setting the CO2 adsorption temperature range. The method for setting the heat pump start-stop threshold is as follows: obtain the temperature difference value between the heat exchanger inlet temperature and the greenhouse water circulation return temperature over a continuous time period, extract the temperature difference baseline when the heat pump is in a stable operating state, identify the temperature difference node corresponding to when the actual output efficiency of the heat pump enters the nonlinear response region, and use the temperature difference node as the basis for setting the heat pump start-stop threshold.

10. The agricultural wastewater carbon-thermal synergistic treatment and intelligent control system according to claim 9, characterized in that, The carbon heat distribution module includes: The deviation extraction submodule is based on the acquisition number identified in the carbon-thermal synchronous acquisition number set. It calls the real-time monitoring values ​​of the plate heat exchanger outlet temperature and the CO2 concentration in the greenhouse air within the greenhouse area, constructs a comparison set of outlet temperature value and gas concentration value corresponding to each number, retrieves the target heating temperature set value and target CO2 concentration set value associated with the acquisition number, compares the difference between the two with the current measured value, obtains the temperature difference value and concentration difference value under the corresponding number, and obtains the gas-thermal target deviation matrix. The control parameter calculation submodule superimposes the gas concentration difference and temperature difference values ​​according to the number of each group of temperature difference and concentration difference values ​​in the gas-heat target deviation matrix, calculates and obtains the control intensity value corresponding to the acquisition number, matches the equipment operating level according to the control intensity value, obtains the control level number of each number, and generates a linear control response parameter set. The instruction set generation submodule calls the control intensity value corresponding to each group number in the linear control response parameter set, locates the corresponding equipment execution status instruction according to the control classification table of gas and heat equipment, extracts the number-execution instruction pair, summarizes all number and control instruction mapping combinations, and establishes a carbon heat control execution form.

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