Agricultural carbon sequestration and emission reduction coordinated regulation and control method and system based on straw cyclic utilization

Abstract

The invention discloses an agricultural carbon sequestration and emission reduction coordinated regulation and control method and system based on straw recycling, and particularly relates to the technical field of carbon sequestration and emission reduction regulation and control. Straw is efficiently decomposed through the aerobic fermentation device to generate carbon dioxide, and the carbon dioxide is used as a greenhouse gas resource after being preliminarily purified; a carbon dioxide concentration sensor is arranged to monitor the concentration change of each area in a greenhouse in real time, and a central control unit is utilized to automatically adjust the opening and closing frequency and flow of a conveying device according to the preset optimal carbon dioxide concentration range of plant growth; meanwhile, distribution of high-concentration gas is optimized through dilution treatment and a distributed flow dividing device, carbon dioxide concentration data and plant growth state data are stored to a cloud platform, a regulation and control strategy is dynamically optimized through a fuzzy logic algorithm, and the carbon dioxide utilization efficiency and the greenhouse environment stability are effectively improved; and collaborative optimization of agricultural carbon sequestration and emission reduction and resource utilization is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon fixation and emission reduction regulation and control, and in particular to a method and system for coordinated regulation and control of agricultural carbon fixation and emission reduction based on straw recycling. Background Art

[0002] The coordinated regulation of agricultural carbon sequestration and emission reduction based on straw recycling refers to promoting the efficient circulation of carbon resources in agricultural ecosystems while reducing greenhouse gas (such as methane) emissions by optimizing the treatment and utilization of crop straw. This process involves transforming straw from single waste management to resource utilization, and achieving the synergistic effect of carbon sequestration (fixing carbon dioxide) and emission reduction (reducing greenhouse gas emissions) through technical means. This approach not only reduces the impact of agricultural production on the environment, but also improves the sustainability of agricultural production.

[0003] Aerobic fermentation of straw (such as composting) avoids the large-scale production of methane in an anaerobic environment. In this process, the carbon dioxide released by the decomposition of straw can be transported to the greenhouse through a pipeline system as a carbon source for plant photosynthesis, thereby increasing the yield of greenhouse vegetables. For example, a study showed that directly using the carbon dioxide produced by fermentation for tomato cultivation in a greenhouse can significantly increase the growth rate of the plant and the quality of the fruit. In addition, returning the straw to the field after treatment with efficient fermentation technology can significantly reduce the amount of methane released during the process of returning to the field, while improving soil fertility.

[0004] The prior art has the following deficiencies: If the concentration of carbon dioxide delivered to the greenhouse is not properly controlled, it may lead to excessive local carbon dioxide concentration, inhibit the normal metabolic process of plants, and even cause acute physiological damage (such as yellowing and falling leaves). High concentrations of carbon dioxide may also reduce the efficiency of photosynthesis and induce environmental acidification. In addition, high concentrations of carbon dioxide cause plant metabolism inhibition and reduced photosynthesis efficiency, which directly leads to a decrease in carbon fixation capacity, making it impossible to effectively realize the resource utilization of carbon dioxide, and even further increase carbon dioxide emissions, violating the emission reduction goals. Summary of the invention

[0005] The purpose of the present invention is to provide a coordinated regulation method and system for agricultural carbon fixation and emission reduction based on straw recycling to address the deficiencies in the background technology.

[0006] In order to achieve the above object, the present invention provides the following technical solution: a coordinated regulation method for agricultural carbon sequestration and emission reduction based on straw recycling, comprising the following steps: S1: Place the straw in an aerobic fermentation device and control the temperature, humidity and oxygen supply conditions to efficiently decompose the straw and release carbon dioxide. The generated carbon dioxide gas is initially purified and stored as a greenhouse gas resource for future use; S2: Arrange multiple carbon dioxide concentration sensors in the greenhouse to monitor the carbon dioxide concentration in different areas in real time, and transmit the monitored carbon dioxide concentration data to the central control unit; S3: The optimal carbon dioxide concentration range for plant growth is preset in the central control unit. Based on the monitored carbon dioxide concentration data, the central control unit performs analysis and calculation. When the carbon dioxide concentration in a certain area of ​​the greenhouse exceeds the preset range, the opening and closing frequency and flow rate of the carbon dioxide delivery device are automatically adjusted; S4: Dilute the high concentration of carbon dioxide and transport the excess gas to the low concentration area through the distributed diversion device. When the carbon dioxide concentration is maintained within the optimal growth range of plants, the photosynthesis efficiency of plants is enhanced and the carbon fixation capacity is improved; S5: The carbon dioxide concentration data and plant growth status data in the greenhouse during the control process are stored in the cloud data management platform, and the control strategy of carbon dioxide delivery is optimized through fuzzy logic algorithm.

[0007] Preferably, in S2, when multiple carbon dioxide concentration sensors are arranged in the greenhouse, different areas are divided according to the spatial structure of the greenhouse, plant distribution characteristics and air flow characteristics.

[0008] Preferably, in S3, the received real-time concentration data of each area is compared with the preset range one by one to determine whether it exceeds the target range, and the setting To monitor the carbon dioxide concentration in real time, is the preset lower limit of carbon dioxide concentration, indicating the minimum concentration required for plant growth. is the preset upper limit of carbon dioxide concentration, indicating the maximum concentration value allowed for plant growth, and S is the state identifier, which is used to indicate the current concentration state; The state flag S is defined as: For each region i, ,Record = low concentration; if ,Record = normal concentration; if ,Record = high concentration; according to the degree of deviation from the range, calculate the opening and closing frequency and flow rate of carbon dioxide that need to be adjusted; Dynamically adjust output parameters: The adjustment formula is: ;in, is the flow adjustment amount, K is the proportional coefficient, and They are the target concentration and the current concentration respectively; the analysis results are converted into control signals to directly drive the actuators of the carbon dioxide delivery device.

[0009] Preferably, the air flow rate required for dilution is calculated based on the target concentration and the current concentration, and the formula is: ;in, is the dilution air flow rate, and are the current concentration and the target concentration, respectively. It is the current carbon dioxide delivery flow rate; it is equipped with a fan or air mixing device, which adjusts the air intake to mix the external air with the high-concentration gas and then release it to the target area. The diluted gas is transported to the low-concentration area in the greenhouse through a pipeline or directly released to the plant canopy in the target area.

[0010] Preferably, in S5, the obtained carbon dioxide concentration value change is analyzed to generate a carbon dioxide concentration fluctuation index, and the method for obtaining the carbon dioxide concentration fluctuation index is: Collect the time series data of carbon dioxide concentration in the greenhouse, recorded as: ;in, It's at the time The measured carbon dioxide concentration value, N is the total number of sampling points; define the sliding window size k, which represents the number of sampling points used to calculate the moving average. For the i-th data point, the moving average of the carbon dioxide concentration data The calculation formula is: Deviation value is the actual concentration value With its moving average The absolute difference is: ; The carbon dioxide concentration fluctuation index is the average of all deviation values, expressed as: ; MF is the carbon dioxide concentration fluctuation index.

[0011] Preferably, the abnormal change of leaf area in the acquired plant growth status data is analyzed to generate a leaf area abnormality index, and the leaf area abnormality index is obtained by: collecting time series data of leaf area: ;in, represents the leaf area at the Mth time point, where M is the total number of sampled data; sets the first quantile Q1, which is the upper limit of the 25% smaller value in the leaf area data; sets the third quantile Q3, which is the lower limit of the 25% larger value in the leaf area data; sets the interquartile range IQR, which represents the middle range of the data distribution, and the calculation formula is: IQR=Q3−Q1; Nether: =Q1−1.5×IQR; upper bound: =Q3+1.5×IQR; if < or > ,but For each data point :Define the outlier set A: ; Calculate the leaf area anomaly index, expressed as the proportion of abnormal points in the total data: ; Where LAAI is the leaf area abnormality index, is the number of outliers.

[0012] Preferably, the control strategy for optimizing carbon dioxide delivery by fuzzy logic algorithm specifically includes: The CO2 concentration fluctuation index MF and leaf area abnormality index LAAI are used as the input items of fuzzy logic, and the control strategy of CO2 delivery is used as the output item of fuzzy logic, including adjusting the flow rate, opening and closing frequency and operation time to optimize the distribution of CO2 concentration in the greenhouse; Define input and output fuzzy sets, and define membership functions for each fuzzy set; Formulate rules between input items and output items, map the specific values ​​of input items MF and LAAI into fuzzy sets, and calculate their membership; According to the fuzzy rule base, match all rules related to the membership of the current input item; Aggregate the results of all rules to obtain the fuzzy membership of the output item CS; Convert the fuzzy output into a specific value: Calculate the output value based on the centroid position of the membership function. The expression is: ; In the formula, is the membership function of the output variable x, indicating the degree to which x belongs to the fuzzy set; x is the actual value of the output variable, indicating the specific carbon dioxide control strategy, The value is directly used in the control system, including adjusting the CO2 delivery flow or frequency; According to the fuzzy logic calculation results, the working parameters of the carbon dioxide delivery device are adjusted, including: increasing or decreasing the gas input, extending or shortening the delivery time cycle, and giving priority to delivering carbon dioxide to areas with abnormally large blade areas.

[0013] The present invention also provides an agricultural carbon sequestration and emission reduction coordinated control system based on straw recycling, including a carbon dioxide generation and storage module, a monitoring and data acquisition module, a central control and control module, a carbon dioxide dilution and diversion module, and an optimization module; Carbon dioxide generation and storage module: The straw is placed in an aerobic fermentation device, and the temperature, humidity and oxygen supply conditions are controlled to efficiently decompose the straw and release carbon dioxide. The generated carbon dioxide gas is initially purified and stored as a greenhouse gas resource for backup; Monitoring and data acquisition module: multiple carbon dioxide concentration sensors are arranged in the greenhouse to monitor the carbon dioxide concentration in different areas in real time, and transmit the monitored carbon dioxide concentration data to the central control unit; Central control and regulation module: The optimal carbon dioxide concentration range for plant growth is preset in the central control unit. Based on the monitored carbon dioxide concentration data, the central control unit performs analysis and calculation. When the carbon dioxide concentration in a certain area of ​​the greenhouse exceeds the preset range, the opening and closing frequency and flow rate of the carbon dioxide delivery device are automatically adjusted; Carbon dioxide dilution and diversion module: dilutes high-concentration carbon dioxide and transports excess gas to low-concentration areas through distributed diversion devices. When the carbon dioxide concentration is maintained within the optimal growth range of plants, the photosynthesis efficiency of plants is enhanced and the carbon fixation capacity is improved; Optimization module: Store the carbon dioxide concentration data and plant growth status data in the greenhouse during the control process to the cloud data management platform, and optimize the carbon dioxide delivery control strategy through fuzzy logic algorithm.

[0014] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. The present invention provides a coordinated control method and system for agricultural carbon fixation and emission reduction based on straw recycling, which effectively solves the problems of excessive local concentration, plant metabolism inhibition, reduced photosynthesis efficiency and attenuated carbon fixation capacity caused by improper control of carbon dioxide concentration in the prior art. Carbon dioxide is efficiently generated by a straw aerobic fermentation device and stored after purification. The carbon dioxide concentration in different areas of the greenhouse is monitored in real time in combination with multiple sensors, and the data is dynamically analyzed using a central control unit to accurately adjust the carbon dioxide delivery flow and opening and closing frequency to ensure that the concentration is always maintained within the optimal range for plant growth. Dilution and diversion technology is further used to optimize carbon dioxide distribution, eliminate the risk of excessive local concentration, and enhance plant photosynthesis efficiency and carbon fixation capacity.

[0015] 2. The present invention stores and analyzes carbon dioxide concentration data and plant growth status data through a cloud data management platform, combines the carbon dioxide concentration fluctuation index and the leaf area abnormality index, and uses a fuzzy logic algorithm to dynamically optimize the delivery strategy, thereby achieving efficient utilization of carbon dioxide resources and intelligent regulation of the greenhouse environment. The present invention not only significantly improves the resource utilization efficiency of carbon dioxide and reduces emissions, but also provides an intelligent and sustainable solution for carbon sequestration and emission reduction in agricultural production, which is of great significance to the realization of greenhouse gas emission reduction targets and the green development of agriculture. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0017] Figure 1 The present invention is a flow chart of the method.

[0018] Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Example 1, please refer to Figure 1 As shown, the coordinated regulation method for agricultural carbon sequestration and emission reduction based on straw recycling described in this embodiment includes the following steps: S1: Place the straw in an aerobic fermentation device and control the temperature, humidity and oxygen supply conditions to efficiently decompose the straw and release carbon dioxide. The generated carbon dioxide gas is initially purified and stored as a greenhouse gas resource for future use; S2: Arrange multiple carbon dioxide concentration sensors in the greenhouse to monitor the carbon dioxide concentration in different areas in real time, and transmit the monitored carbon dioxide concentration data to the central control unit; S3: The optimal carbon dioxide concentration range for plant growth is preset in the central control unit. Based on the monitored carbon dioxide concentration data, the central control unit performs analysis and calculation. When the carbon dioxide concentration in a certain area of ​​the greenhouse exceeds the preset range, the opening and closing frequency and flow rate of the carbon dioxide delivery device are automatically adjusted; S4: Dilute the high concentration of carbon dioxide and transport the excess gas to the low concentration area through the distributed diversion device. When the carbon dioxide concentration is maintained within the optimal growth range of plants, the photosynthesis efficiency of plants is enhanced and the carbon fixation capacity is improved; S5: The carbon dioxide concentration data and plant growth status data in the greenhouse during the control process are stored in the cloud data management platform, and the control strategy of carbon dioxide delivery is optimized through fuzzy logic algorithm.

[0021] In S1, pretreatment of straw: To improve fermentation efficiency, the straw is crushed or cut to increase the surface area for microbial attachment and decomposition. The straw particle size is controlled to 5-20 mm using a straw crusher, and the moisture content is adjusted to 50%-70% as needed.

[0022] Use a heating system (such as electric heating or hot water circulation) to keep the temperature in the fermentation device between 35℃-55℃. Medium-temperature fermentation (about 35℃) is suitable for most bacterial activities, and high-temperature fermentation (about 55℃) can improve degradation efficiency and inhibit pathogens. Equipped with a spray system or humidity sensing device to automatically adjust the humidity of the fermentation environment to prevent the straw from being too dry or too wet. Install an aeration device (such as a blower or air distribution system) to prevent the formation of anaerobic zones through uniform oxygen supply and ensure that the oxygen concentration is maintained at 20%-30%.

[0023] Introduction of multi-component microbial flora: Cellulolytic bacteria: mainly degrade cellulose, hemicellulose and lignin in straw, such as using white rot fungi, brown rot fungi or Streptomyces to increase the decomposition rate of organic matter in straw. Lactic acid bacteria: adjust the pH value of the fermentation system to the range of 4.0-6.0, while inhibiting the growth of pathogens and improving the metabolic environment of microorganisms. Commonly used strains include Lactococcus lactis. Yeast: provides the ability to secrete a variety of enzymes (such as glucose oxidase) to further decompose complex organic matter, while promoting the activity of other strains by releasing small molecules through metabolism. For example, the commonly used strain is Saccharomyces cerevisiae.

[0024] Microbial metabolism: Under aerobic conditions, cellulolytic bacteria decompose cellulose and hemicellulose in the straw to produce carbon dioxide and water, while releasing soluble sugars for further metabolism by lactic acid bacteria and yeast, further improving the efficiency of carbon dioxide production.

[0025] Remove impurity gases that may be produced during the fermentation process, such as hydrogen sulfide, ammonia, etc., to ensure the purity of carbon dioxide resources. Purification method: Adsorption method: Use activated carbon filtration device to remove acidic gases such as hydrogen sulfide. Washing method: Use alkaline solution (such as sodium hydroxide solution) to absorb acidic impurities and improve the purity of carbon dioxide gas.

[0026] The purified carbon dioxide can be liquefied by a compression pump and stored in a high-pressure gas cylinder, or it can be directly transported to the greenhouse as a resource. The pipeline transportation system needs to be equipped with flow control valves and pressure monitoring devices to ensure safe and uniform supply. The activity of cellulose decomposing bacteria significantly increases the decomposition rate of straw and shortens the fermentation cycle (usually reduced to 7-10 days). The synergistic effect of microorganisms further metabolizes the sugars that are not completely decomposed during the fermentation process, greatly improving the efficiency of carbon dioxide generation (the generation rate can be increased by 30%-50%). The fermentation process avoids the formation of anaerobic conditions, fundamentally reduces the amount of methane generated, and reduces greenhouse gas emissions.

[0027] Agricultural greenhouse gas management, straw resource utilization, greenhouse vegetable production, ecological agriculture and other fields. Realize high-value utilization of straw resources and promote the development of agricultural circular economy; improve the carbon fixation capacity of greenhouse production and reduce the carbon footprint of agricultural production; use natural microbial resources and reduce dependence on chemical additives, with good sustainability and economy.

[0028] S2: When multiple carbon dioxide concentration sensors are arranged in a greenhouse, in order to achieve accurate monitoring and scientific regulation, different areas can be divided according to the spatial structure of the greenhouse, plant distribution characteristics and air flow characteristics. The specific division and data transmission methods are as follows: Horizontal division (plane division): spatial layout and gas distribution characteristics within the greenhouse.

[0029] Division method: Planting area: Sensors are placed close to the crop growing area to monitor the carbon dioxide concentration near the plant leaves to directly reflect the absorption status of the crops.

[0030] Ventilation area: Sensors are placed near the air inlet and exhaust vents in the greenhouse to monitor changes in the concentration of carbon dioxide input and emission and determine the air exchange effect.

[0031] Central area: Sensors are placed in the center of the greenhouse space to obtain the average state of the overall carbon dioxide distribution.

[0032] Vertical layer division (height division): Carbon dioxide has a higher density than air and tends to accumulate at the bottom. Division method: Bottom layer (0-50 cm above the ground): Detect the concentration of carbon dioxide close to the ground and monitor the risk of gas deposition. Middle layer (50-150 cm): Detect the concentration near the height of crop leaves to ensure that the photosynthesis needs of plants are met. Top layer (above 150 cm): Detect the concentration of carbon dioxide in the upper layer of the greenhouse and monitor gas diffusion.

[0033] Functional zone division: gas utilization requirements of different functional areas in the greenhouse. Division method: Working area: detect the carbon dioxide concentration in the manual operation area in the greenhouse to ensure safety. Cultivation area: more densely deploy sensors for high-value or special crop planting areas to ensure accurate monitoring. Edge area: deploy sensors in areas close to the greenhouse wall to monitor the possibility of gas leakage or environmental interference.

[0034] Use carbon dioxide concentration sensors that support wireless communication (such as infrared carbon dioxide sensors), each of which has independent data collection and data transmission functions. Set the sampling interval to 10-60 seconds to ensure a balance between real-time and data processing efficiency.

[0035] Data transmission methods include: Wired transmission: Use optical fiber or Ethernet lines to connect sensors and central control units, which is suitable for scenarios that are sensitive to interference signals or have a small greenhouse area. Wireless transmission: Use Wi-Fi, Zigbee or LoRa technology to establish a sensor network and transmit monitoring data to the central control unit in real time, which is suitable for large or complex greenhouse environments. Hybrid method: Combine wired and wireless technologies, aggregate wireless sensor data through gateway devices (such as LoRa gateways), and transmit it to the central control unit via optical fiber.

[0036] Central control unit: equipped with a microprocessor and cloud computing interface, after receiving sensor data, it performs the following processing: Data cleaning: remove erroneous or abnormal data points. Unify the monitoring data from different areas and heights to generate a distribution map of carbon dioxide concentration in the greenhouse. Automatically control the carbon dioxide delivery system and dynamically adjust the supply according to the concentration. Upload the processed data to the cloud, allowing managers to view and adjust strategies in real time through mobile devices or computers.

[0037] S3: Preset the optimal CO2 concentration range for plant growth: According to scientific research and actual planting needs, set the optimal CO2 concentration range for the target crop, for example, 400 ppm to 800 ppm. Input the concentration range into the parameter setting module of the central control unit as a reference value for determining whether the CO2 supply needs to be adjusted. Establish a parameter interface in the control unit software to allow users to enter or modify the concentration range and adjust the target value for different crops and growth stages.

[0038] The sensor transmits the real-time carbon dioxide concentration data to the central control unit via wireless or wired network. The control unit stores this data and binds it with the area number (sensor location) and timestamp. An embedded system (such as Raspberry Pi) is used to connect the sensor network and use local storage (such as SD card) or database (such as SQLite) to record the monitoring data.

[0039] Detect whether there are abnormal values ​​(such as concentration data outside the physical range or constant data caused by sensor failure). If a sensor returns abnormal values ​​many times in a row, an alarm will be triggered or the data source will be temporarily removed to avoid interfering with control decisions. Use Python or C language to write anomaly detection algorithms, for example, by setting physical thresholds (such as carbon dioxide concentration must be between 200 ppm and 2000 ppm).

[0040] The real-time concentration data of each area received is compared with the preset range one by one to determine whether it exceeds the target range. The analysis results are recorded as "low concentration", "normal concentration" or "high concentration" status identification and displayed in real time on the control interface. The logic judgment module is embedded in the control unit, and the conditional statements (such as IF-ELSE) are used to process the data one by one and output the status identification.

[0041] set up It is the real-time monitored carbon dioxide concentration (unit: ppm), provided by the sensor. It is the preset lower limit of carbon dioxide concentration, indicating the minimum concentration value required for plant growth. is the preset upper limit of carbon dioxide concentration, indicating the highest concentration value allowed for plant growth. S is the status indicator, used to indicate the current concentration status.

[0042] The state flag S is defined as: For each region i, the real-time carbon dioxide concentration is input into the above rules for judgment; if ,Record = low concentration; if ,Record = normal concentration; if ,Record =High concentration.

[0043] According to the degree of deviation from the range, calculate the parameters of the carbon dioxide delivery device that need to be adjusted, including: opening and closing frequency (such as the number of openings per minute). Flow rate (such as carbon dioxide input volume L / min). For example: if the current concentration is lower than 400ppm, increase the flow rate by 10%; if it is higher than 800ppm, reduce the flow rate by 10% or suspend input.

[0044] Combine sensor feedback values ​​and preset algorithms (such as linear proportional adjustment formulas) to dynamically adjust output parameters: Adjustment formula example: ;in, is the flow adjustment amount, K is the proportional coefficient, and are the target concentration and current concentration, respectively.

[0045] The analysis results are converted into control signals to directly drive the actuators of the CO2 delivery device (such as solenoid valves or gas flow regulators). If the concentrations in multiple areas are significantly different, a partition control method can be used to adjust the local gas supply according to the needs of each area. The opening and closing frequency and valve opening of the flow regulator are controlled by PWM (pulse width modulation) signals or analog voltage signals.

[0046] After the adjustment is completed, the CO2 concentration in each area is continuously monitored, and the change value before and after the adjustment is recorded to evaluate the control effect. If the concentration does not return to the target range, increase or decrease the adjustment intensity to ensure final stability. Add a feedback loop in the control system, combined with the PID (proportional-integral-differential) control algorithm, and gradually optimize the adjustment process.

[0047] For example, multiple sets of sensors are arranged in a greenhouse area (e.g., planting area 1, vents, and the middle layer of the crop canopy) to monitor the carbon dioxide concentration in real time. When the concentration in planting area 1 drops to 350 ppm, the central control unit analyzes and concludes that the flow rate of the conveying device needs to be increased by 20% (e.g., from 10 L / min to 12 L / min), which is executed by controlling the gas valve. After adjustment, sampling and monitoring continue, and after confirming that the concentration has risen above 400 ppm, the flow rate is gradually restored to the standard value.

[0048] S4: High-concentration carbon dioxide dilution treatment includes: monitoring the gas state in high-concentration areas, arranging multiple carbon dioxide concentration sensors in the greenhouse, real-time monitoring of gas concentration distribution, and identifying high-concentration areas (such as more than 800 ppm). The central control unit summarizes the monitoring data, generates a carbon dioxide concentration distribution map in the greenhouse, and marks hot spots with abnormal concentrations.

[0049] Introduce outside air (low-concentration carbon dioxide air, about 400 ppm) to mix with high-concentration gas to reduce local gas concentration. Use diversion technology to transport excess carbon dioxide to low-concentration areas to achieve dynamic balance of concentration.

[0050] The air flow required for dilution is calculated based on the target concentration and the current concentration. The formula is: ;in, is the dilution air flow rate, and are the current concentration and the target concentration, respectively. The current carbon dioxide delivery flow.

[0051] Equipped with a fan or air mixing device, by adjusting the air intake, the external air is mixed with the high-concentration gas and then released to the target area. The diluted gas is transported to the low-concentration area in the greenhouse through a pipeline or directly released to the plant canopy in the target area. Use valves and flow regulators to dynamically adjust the dilution ratio and total flow of the external air to ensure a stable concentration.

[0052] Arrange multiple diversion points (such as top pipe openings, vents, etc.) in the greenhouse and connect them to the high-concentration gas delivery pipeline. According to sensor feedback, the central control unit selectively opens or closes the diversion points to accurately deliver high-concentration gas to low-concentration areas. Prioritize delivery to areas with low concentration or active plant photosynthesis to avoid wasting gas resources.

[0053] The gas flow is adjusted by electric valves, and the excess gas is distributed to each diversion point in proportion according to the concentration requirements of the target area. Each diversion point is equipped with a separate adjustment device (such as a gas flow meter and a control valve) to achieve precise control of different areas.

[0054] System operation and plant photosynthesis enhancement include: the concentration is maintained within the optimal range, and the carbon dioxide concentration in the greenhouse is diluted or diverted to maintain the optimal concentration range for plant photosynthesis (400 ppm to 800 ppm). At this time, the photosynthesis rate of the plant reaches its peak, absorbing more carbon dioxide and converting it into organic matter. As a raw material for photosynthesis, the appropriate supply of carbon dioxide can improve the carbon fixation efficiency of chloroplasts and enhance the growth rate and biomass accumulation of crops. As plant photosynthesis is enhanced, more carbon dioxide is fixed as carbon compounds in the plant body, reducing free carbon dioxide in the greenhouse and indirectly achieving the goal of agricultural carbon fixation. Efficiently utilize carbon dioxide resources, reduce emissions, and improve the effect of greenhouse gas emission reduction. Long-term improvement of crop yield and quality lays the foundation for achieving sustainable agricultural development.

[0055] S5: Store the greenhouse carbon dioxide concentration data and plant growth status data during the control process to the cloud data management platform. Specifically, the carbon dioxide sensors arranged in the greenhouse monitor the gas concentration in each area in real time, and the collected data include concentration value, sampling time and location number. Use high-definition cameras or multi-spectral imaging equipment to obtain plant image data, and combine image recognition technology to extract growth parameters such as leaf area and color health index (NDVI).

[0056] Preprocess the collected carbon dioxide concentration data and plant growth status data, including: unified labeling of timestamps and area numbers. Detect and clean outliers (such as removing concentration values ​​or image noise that exceed the physical range of the sensor). Format the data into structured records (such as tables) for storage and analysis.

[0057] Sensors and cameras inside the greenhouse are connected to a local gateway device via a wireless network (such as Wi-Fi or LoRa) to collect and aggregate data. The local gateway uploads the data to the cloud platform via the Internet to ensure real-time transmission and remote storage. A secure and efficient communication protocol (such as MQTT, HTTP / HTTPS) is used to upload the data, ensuring data integrity and low latency. The data is encrypted during transmission (such as using the TLS / SSL protocol) to prevent the leakage of sensitive information.

[0058] The cloud data management platform stores data using a relational database or a distributed file system: carbon dioxide concentration data is stored in a time series format for easy trend analysis. Plant growth status data is stored as image files or structured records of analysis results. A scalable cloud storage service (such as Amazon AWS S3 or Google Cloud Storage) is used to ensure the ability to support long-term storage of large-scale data.

[0059] Indexes are established according to time, area number, and data type (such as gas concentration, image data) for easy and quick retrieval and analysis. The data is regularly backed up to off-site servers to ensure quick recovery even in case of unexpected power outages or hardware failures.

[0060] After analyzing the change situation of the obtained carbon dioxide concentration values, a carbon dioxide concentration fluctuation index is generated. The method for obtaining the carbon dioxide concentration fluctuation index is as follows: Collect the carbon dioxide concentration time series data in the greenhouse, denoted as: ; where is the carbon dioxide concentration value measured at time point , and N is the total number of sampling points.

[0061] Define a sliding window size k, which represents the number of sampling points used to calculate the moving average. The window size k should be selected according to the characteristics of the time series data and the requirements of fluctuation analysis. For example, if the data sampling interval is 1 minute, choosing k = 10 represents a 10-minute moving average.

[0062] For the i-th data point, the moving average of the carbon dioxide concentration data is calculated as follows: ; when i < k, the window is not fully covered, and it can be calculated starting from the first point: .

[0063] The deviation value is the absolute difference between the actual concentration value and its moving average : ; The larger the value of , the greater the degree of deviation of the carbon dioxide concentration at the i-th moment from the moving average, and the more obvious the fluctuation. The deviation values ​​of multiple observations are used for subsequent fluctuation index calculations.

[0064] The carbon dioxide concentration fluctuation index is the average of all deviation values, expressed as: ; MF is the carbon dioxide concentration fluctuation index.

[0065] The leaf area abnormality index is generated by analyzing the abnormal changes in the leaf area in the acquired plant growth status data. The method for obtaining the leaf area abnormality index is as follows: Collect time series data on leaf area: ;in, represents the leaf area at the Mth time point, where M is the total number of sampling data.

[0066] The first quantile Q1 is set, Q1 is the upper limit of the smaller 25% value in the leaf area data, and the third quantile Q3 is set, Q3 is the lower limit of the larger 25% value in the leaf area data; the interquartile range IQR is set to represent the middle range of the data distribution, and the calculation formula is: IQR=Q3−Q1.

[0067] Nether: =Q1−1.5×IQR; upper bound: =Q3+1.5×IQR; if < or > ,but is an outlier. For each data point :if , then it is marked as an outlier and the outlier set A is defined as: ; Calculate the leaf area anomaly index, expressed as the proportion of abnormal points in the total data: ; Where LAAI is the leaf area abnormality index, is the number of outliers.

[0068] The control strategy for optimizing carbon dioxide delivery is optimized through fuzzy logic algorithms, including: The CO2 concentration fluctuation index MF and leaf area abnormality index LAAI are used as the input items of fuzzy logic, and the control strategy of CO2 delivery is used as the output item of fuzzy logic, including adjusting the flow rate, opening and closing frequency and operation time to optimize the distribution of CO2 concentration in the greenhouse; Input fuzzy set: MF is defined as “low volatility”, “medium volatility” and “high volatility”. LAAI is defined as “normal”, “slightly abnormal” and “severely abnormal”.

[0069] Output item fuzzy set: CS is defined as “reduce delivery”, “maintain delivery” and “increase delivery”.

[0070] Define membership functions for each fuzzy set, for example, use triangular or trapezoidal functions to define fuzzy ranges such as "low volatility", "medium volatility", "high volatility", etc.

[0071] The value range of MF may be [0,1], where 0 indicates no fluctuation and 1 indicates severe fluctuation. The value range of LAAI may be [0,1], where 0 indicates completely normal and 1 indicates extremely abnormal.

[0072] Define rules between input items and output items based on experience or experimental data. For example: If MF is “high fluctuation” and LAAI is “severely abnormal”, CS is “reduced delivery”.

[0073] If the MF is “moderate fluctuation” and the LAAI is “mildly abnormal”, the CS is “maintain delivery”.

[0074] If MF is "Low Fluctuation" and LAAI is "Normal", CS is "Increase Delivery".

[0075] Map the specific values ​​of the input items MF and LAAI to the fuzzy set and calculate their membership. For example, if MF=0.6, the membership to "medium fluctuation" is 0.8, and the membership to "high fluctuation" is 0.2. If LAAI=0.3, the membership to "slightly abnormal" is 0.7, and the membership to "normal" is 0.3.

[0076] According to the fuzzy rule base, match all rules related to the membership of the current input item. For example: Rule 1: If MF is "medium fluctuation" (0.8) and LAAI is "slightly abnormal" (0.7), then CS is "maintain delivery". Rule 2: If MF is "high fluctuation" (0.2) and LAAI is "slightly abnormal" (0.7), then CS is "reduce delivery".

[0077] Use the "minimum rule" (MIN) or "product rule" in fuzzy logic to calculate the weight of the rule: for rule 1, the weight is min(0.8,0.7)=0.7. For rule 2, the weight is min(0.2,0.7)=0.2.

[0078] Aggregate the results of all rules to get the fuzzy membership of the output item CS. For example, the membership of "maintaining delivery" is 0.7. The membership of "reducing delivery" is 0.2.

[0079] The commonly used methods for converting fuzzy output into specific values ​​(defuzzification) include: Centroid Method: Calculate the output value according to the centroid position of the membership function, the expression is: ; In the formula, is the membership function of the output variable x, indicating the degree to which x belongs to the fuzzy set (such as "decrease", "remain stable" or "increase"); x is the actual value of the output variable, indicating the specific carbon dioxide control strategy. For example, x can be the percentage of adjustment (such as increase by 10% or decrease by 10%), or the increment of the delivery flow (such as ±5 L / min). is the value range of the output variable x, that is, the range of all possible control strategies. The value is directly used in the control system, including adjusting the CO2 delivery flow rate or frequency.

[0080] According to the fuzzy logic calculation results, adjust the working parameters of the carbon dioxide delivery device, including: Delivery flow: increase or decrease the gas input (such as adjusting from 5 L / min to 7 L / min). Delivery time: extend or shorten the delivery time cycle (such as adjusting from 30 minutes / time to 15 minutes / time). Regional delivery: give priority to delivering carbon dioxide to areas with abnormally large leaf areas.

[0081] Continuously monitor the changes of MF and LAAI, compare the control results with the target values, and optimize the system performance by learning and adjusting fuzzy rules or membership functions.

[0082] In this embodiment, by placing the straw in an aerobic fermentation device, controlling the temperature, humidity and oxygen supply conditions, the straw is efficiently decomposed and releases carbon dioxide, and the generated carbon dioxide is stored as a greenhouse gas resource after preliminary purification. A plurality of carbon dioxide concentration sensors are arranged in the greenhouse to monitor the carbon dioxide concentration in different areas in real time and transmit the data to the central control unit. The central control unit analyzes the monitoring data in real time according to the preset optimal carbon dioxide concentration range of the plant, and automatically adjusts the opening and closing frequency and flow rate of the conveying device when the carbon dioxide concentration in a certain area exceeds the preset range. For high-concentration carbon dioxide, the excess gas is transported to the low-concentration area through dilution treatment and distributed diversion devices to achieve a dynamic balance of carbon dioxide concentration in the greenhouse, enhance the efficiency of plant photosynthesis and improve carbon fixation capacity. At the same time, the carbon dioxide concentration data and plant growth status data during the regulation process are stored in the cloud data management platform, and the regulation strategy of carbon dioxide delivery is further optimized through the fuzzy logic algorithm.

[0083] Example 2, please refer to Figure 2As shown, the agricultural carbon sequestration and emission reduction coordinated control system based on straw recycling described in this embodiment includes a carbon dioxide generation and storage module, a monitoring and data acquisition module, a central control and control module, a carbon dioxide dilution and diversion module, and an optimization module; Carbon dioxide generation and storage module: The straw is placed in an aerobic fermentation device, and the temperature, humidity and oxygen supply conditions are controlled to efficiently decompose the straw and release carbon dioxide. The generated carbon dioxide gas is initially purified and stored as a greenhouse gas resource for backup; Monitoring and data acquisition module: multiple carbon dioxide concentration sensors are arranged in the greenhouse to monitor the carbon dioxide concentration in different areas in real time, and transmit the monitored carbon dioxide concentration data to the central control unit; Central control and regulation module: The optimal carbon dioxide concentration range for plant growth is preset in the central control unit. Based on the monitored carbon dioxide concentration data, the central control unit performs analysis and calculation. When the carbon dioxide concentration in a certain area of ​​the greenhouse exceeds the preset range, the opening and closing frequency and flow rate of the carbon dioxide delivery device are automatically adjusted; Carbon dioxide dilution and diversion module: dilutes high-concentration carbon dioxide and transports excess gas to low-concentration areas through distributed diversion devices. When the carbon dioxide concentration is maintained within the optimal growth range of plants, the photosynthesis efficiency of plants is enhanced and the carbon fixation capacity is improved; Optimization module: Store the carbon dioxide concentration data and plant growth status data in the greenhouse during the control process to the cloud data management platform, and optimize the carbon dioxide delivery control strategy through fuzzy logic algorithm.

[0084] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0085] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0086] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0087] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be covered by the protection scope of the present application.

Claims

1. A coordinated regulation method for agricultural carbon sequestration and emission reduction based on straw recycling, characterized in that: The following steps are involved: S1: Place the straw in an aerobic fermentation device and control the temperature, humidity and oxygen supply conditions to efficiently decompose the straw and release carbon dioxide. The generated carbon dioxide gas is initially purified and stored as a greenhouse gas resource for future use; S2: Arrange multiple carbon dioxide concentration sensors in the greenhouse to monitor the carbon dioxide concentration in different areas in real time, and transmit the monitored carbon dioxide concentration data to the central control unit; S3: The optimal carbon dioxide concentration range for plant growth is preset in the central control unit. Based on the monitored carbon dioxide concentration data, the central control unit performs analysis and calculation. When the carbon dioxide concentration in a certain area of ​​the greenhouse exceeds the preset range, the opening and closing frequency and flow rate of the carbon dioxide delivery device are automatically adjusted; S4: Dilute the high concentration of carbon dioxide and transport the excess gas to the low concentration area through the distributed diversion device. When the carbon dioxide concentration is maintained within the optimal growth range of plants, the photosynthesis efficiency of plants is enhanced and the carbon fixation capacity is improved; S5: The carbon dioxide concentration data and plant growth status data in the greenhouse during the control process are stored in the cloud data management platform, and the control strategy of carbon dioxide delivery is optimized through fuzzy logic algorithm.

2. The method for coordinated regulation of agricultural carbon sequestration and emission reduction based on straw recycling according to claim 1, characterized in that: In S2, when multiple carbon dioxide concentration sensors are arranged in the greenhouse, different areas are divided according to the spatial structure of the greenhouse, plant distribution characteristics, and air flow characteristics.

3. The method for coordinated regulation of agricultural carbon sequestration and emission reduction based on straw recycling according to claim 2, characterized in that: In S3, the real-time concentration data of each area received is compared with the preset range one by one to determine whether it exceeds the target range and set To monitor the carbon dioxide concentration in real time, is the preset lower limit of carbon dioxide concentration, indicating the minimum concentration required for plant growth. is the preset upper limit of carbon dioxide concentration, indicating the maximum concentration value allowed for plant growth, and S is the state identifier, which is used to indicate the current concentration state; The state flag S is defined as: For each region i, if ,Record = low concentration; if ,Record = normal concentration; if ,Record = high concentration; according to the degree of deviation from the range, calculate the opening and closing frequency and flow rate of carbon dioxide that need to be adjusted; Dynamically adjust output parameters: The adjustment formula is: ;in, is the flow adjustment amount, K is the proportional coefficient, and They are the target concentration and the current concentration respectively; the analysis results are converted into control signals to directly drive the actuators of the carbon dioxide delivery device.

4. The method for coordinated regulation of agricultural carbon sequestration and emission reduction based on straw recycling according to claim 3, characterized in that: The air flow required for dilution is calculated based on the target concentration and the current concentration. The formula is: ;in, is the dilution air flow rate, and are the current concentration and the target concentration, respectively. It is the current carbon dioxide delivery flow rate; it is equipped with a fan or air mixing device, which adjusts the air intake to mix the external air with the high-concentration gas and then release it to the target area. The diluted gas is transported to the low-concentration area in the greenhouse through a pipeline or directly released to the plant canopy in the target area.

5. The method for coordinated regulation of agricultural carbon sequestration and emission reduction based on straw recycling according to claim 1, characterized in that: In S5, the obtained carbon dioxide concentration value change is analyzed to generate a carbon dioxide concentration fluctuation index. The carbon dioxide concentration fluctuation index is obtained as follows: Collect the time series data of carbon dioxide concentration in the greenhouse, recorded as: ;in, It's at the time The measured carbon dioxide concentration value, N is the total number of sampling points; define the sliding window size k, which represents the number of sampling points used to calculate the moving average. For the i-th data point, the moving average of the carbon dioxide concentration data The calculation formula is: Deviation value is the actual concentration value With its moving average The absolute difference is: ; The carbon dioxide concentration fluctuation index is the average of all deviation values, expressed as: ; MF is the carbon dioxide concentration fluctuation index.

6. The method for coordinated regulation of agricultural carbon sequestration and emission reduction based on straw recycling according to claim 5, characterized in that: The leaf area abnormality index is generated by analyzing the abnormal changes in the leaf area in the acquired plant growth status data. The method for obtaining the leaf area abnormality index is as follows: Collect time series data on leaf area: ;in, represents the leaf area at the Mth time point, where M is the total number of sampled data; sets the first quantile Q1, which is the upper limit of the 25% smaller value in the leaf area data; sets the third quantile Q3, which is the lower limit of the 25% larger value in the leaf area data; sets the interquartile range IQR, which represents the middle range of the data distribution, and the calculation formula is: IQR=Q3−Q1; Nether: =Q1−1.5×IQR; upper bound: =Q3+1.5×IQR; if < or > ,but For each data point :Define the outlier set A: ; Calculate the leaf area anomaly index, expressed as the proportion of abnormal points in the total data: ; where LAAI is the leaf area abnormality index, is the number of outliers.

7. The method for coordinated regulation of agricultural carbon sequestration and emission reduction based on straw recycling according to claim 6, characterized in that: The control strategy for optimizing carbon dioxide delivery is optimized through fuzzy logic algorithms, including: The CO2 concentration fluctuation index MF and leaf area abnormality index LAAI are used as the input of fuzzy logic, and the control strategy of CO2 delivery is used as the output of fuzzy logic, including adjusting the flow rate, opening and closing frequency and operation time to optimize the distribution of CO2 concentration in the greenhouse. Define input and output fuzzy sets, and define membership functions for each fuzzy set; Formulate rules between input items and output items, map the specific values ​​of input items MF and LAAI into fuzzy sets, and calculate their membership; According to the fuzzy rule base, match all rules related to the membership of the current input item; Aggregate the results of all rules to obtain the fuzzy membership of the output item CS; Convert the fuzzy output into a specific value: Calculate the output value based on the centroid position of the membership function. The expression is: ; In the formula, is the membership function of the output variable x, indicating the degree to which x belongs to the fuzzy set; x is the actual value of the output variable, indicating the specific carbon dioxide control strategy, The value is directly used in the control system, including adjusting the CO2 delivery flow or frequency; According to the fuzzy logic calculation results, the working parameters of the carbon dioxide delivery device are adjusted, including: increasing or decreasing the gas input, extending or shortening the delivery time cycle, and giving priority to delivering carbon dioxide to areas with abnormally large blade areas.

8. A coordinated control system for agricultural carbon sequestration and emission reduction based on straw recycling, used to implement a coordinated control method for agricultural carbon sequestration and emission reduction based on straw recycling as described in any one of claims 1 to 7, characterized in that: It includes a carbon dioxide generation and storage module, a monitoring and data acquisition module, a central control and regulation module, a carbon dioxide dilution and diversion module, and an optimization module; Carbon dioxide generation and storage module: The straw is placed in an aerobic fermentation device, and the temperature, humidity and oxygen supply conditions are controlled to efficiently decompose the straw and release carbon dioxide. The generated carbon dioxide gas is initially purified and stored as a greenhouse gas resource for backup; Monitoring and data acquisition module: multiple carbon dioxide concentration sensors are arranged in the greenhouse to monitor the carbon dioxide concentration in different areas in real time, and transmit the monitored carbon dioxide concentration data to the central control unit; Central control and regulation module: The optimal carbon dioxide concentration range for plant growth is preset in the central control unit. Based on the monitored carbon dioxide concentration data, the central control unit performs analysis and calculation. When the carbon dioxide concentration in a certain area of ​​the greenhouse exceeds the preset range, the opening and closing frequency and flow rate of the carbon dioxide delivery device are automatically adjusted; Carbon dioxide dilution and diversion module: dilutes high-concentration carbon dioxide and transports excess gas to low-concentration areas through distributed diversion devices. When the carbon dioxide concentration is maintained within the optimal growth range of plants, the photosynthesis efficiency of plants is enhanced and the carbon fixation capacity is improved; Optimization module: Store the carbon dioxide concentration data and plant growth status data in the greenhouse during the control process to the cloud data management platform, and optimize the carbon dioxide delivery control strategy through fuzzy logic algorithm.

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