Water consumption process-based water quality prediction method for shrimp-rice co-culture system

Abstract

The invention discloses a water consumption process-based water quality prediction method for a shrimp-rice co-culture system, which belongs to the technical field of water quality prediction and comprises the following steps: acquiring basic data of the shrimp-rice co-culture system and initializing a state quantity; identifying the production stage of the shrimp-rice co-culture system, and determining stage parameters; calculating the water volume change of the shrimp-rice co-culture system in the current time step based on a water volume balance principle, and updating the water volume; and calculating an external pollution input load and a pollutant output load based on the basic data, the stage parameters and the updated water body volume, and performing water quality prediction. According to the water quality prediction method based on the shrimp-rice co-culture water consumption rule, a shrimp-rice co-culture system is taken as a research object, and the characteristics that a water body exists for a long time, the water quantity is frequently regulated and controlled and the water quality is jointly influenced by multiple factors in the shrimp-rice co-culture production process are considered; according to the technical thought of production stage recognition, water balance calculation, external load quantification and water quality state updating, the water quality change process of the prawn and rice co-culture system is predicted.

Description

Technical Field

[0001] This invention belongs to the technical field of water quality prediction for rice-shrimp co-cultivation systems, specifically relating to a method for predicting water quality in rice-shrimp co-cultivation systems based on water consumption processes. Background Technology

[0002] Rice-crayfish co-cultivation is an integrated farming model that combines rice cultivation with crayfish farming. In recent years, it has been widely promoted and applied in the middle and lower reaches of the Yangtze River in my country. This model, by setting up crayfish ditches within rice paddies, allows rice production and aquaculture to be carried out synergistically within the same field, offering significant advantages in improving land use efficiency, increasing farmers' income, and improving the ecological environment.

[0003] Compared to traditional monoculture rice cultivation, the rice-crayfish co-cultivation model significantly alters field structure and water usage. On one hand, the presence of shrimp ditches increases the volume and surface area of ​​water in the field; on the other hand, to meet the growth needs of crayfish, the rice-crayfish co-cultivation system requires maintaining a relatively deep water layer for most of the year, and employs differentiated water layer control methods at different production stages. These changes make the hydrological processes of the rice-crayfish co-cultivation system significantly different from ordinary paddy fields, with its water consumption process exhibiting strong phased characteristics, high continuity, and significant influence from human intervention.

[0004] Existing research mainly focuses on determining irrigation quotas, water balance analysis, and water management methods in rice-shrimp co-cultivation systems. For example, by dividing the rice-shrimp co-cultivation production process into the rice non-growing period, the rice-shrimp separate rearing stage during the rice growing season, and the rice-shrimp co-rearing stage during the rice growing season, systematic studies have been conducted on the water consumption patterns and irrigation quotas of rice-shrimp co-cultivation systems based on the principle of water balance. This type of research provides important theoretical basis for water management in rice-shrimp co-cultivation systems, but its focus is primarily on the water quantity level, with less attention paid to water quality changes.

[0005] In actual production, the water quality of the rice-crayfish co-cultivation system directly affects the survival rate of crayfish, the growth status of rice, and overall economic benefits. Water quality indicators such as ammonia nitrogen, total nitrogen, total phosphorus, chemical oxygen demand, and dissolved oxygen are influenced by a variety of factors, including changes in water volume, meteorological conditions, and agricultural activities such as feeding and fertilization. However, current water quality management in rice-crayfish co-cultivation systems still relies mainly on experience-based judgment or periodic monitoring, lacking technical means to predict dynamic changes in the production process.

[0006] Existing water quality prediction methods are mostly applied to water bodies such as lakes, reservoirs, or rivers, typically focusing on fitting and extrapolating historical water quality monitoring data based on statistical analysis or machine learning. These methods often treat the water body as a relatively stable system, making it difficult to reflect the rapid water quality changes caused by frequent water volume adjustments, significant production stage transitions, and intense agricultural disturbances in rice-shrimp co-cultivation systems. Furthermore, existing methods generally fail to consider the unique water consumption patterns of rice-shrimp co-cultivation as a crucial constraint for water quality prediction, resulting in insufficient reliability of the prediction results in practical applications.

[0007] During the rice-shrimp co-cultivation process, agricultural activities such as feeding and fertilization continuously introduce external pollution loads into the water body. Meteorological conditions such as temperature, rainfall, and sunshine also significantly affect the rate of water quality change. However, existing technologies lack a water quality prediction method that can comprehensively consider water consumption patterns, agricultural activities, and meteorological factors, making it difficult to reflect the true process of water quality changes in the rice-shrimp co-cultivation system. Summary of the Invention

[0008] The purpose of this invention is to address the aforementioned shortcomings in the prior art by providing a water quality prediction method for shrimp-rice co-cultivation systems based on water consumption processes. This method aims to solve the problem that the prior art lacks a water quality prediction method that can comprehensively consider water consumption patterns, agricultural activities, and meteorological factors, making it difficult to reflect the true process of water quality changes in shrimp-rice co-cultivation systems.

[0009] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A water quality prediction method for a rice-shrimp co-cultivation system based on water consumption processes includes the following steps: S1. Obtain the basic data of the rice-shrimp co-cultivation system and initialize the state variables; S2. Identify the current production stage of the rice-shrimp co-cultivation system based on the basic data, and determine the stage parameters corresponding to this production stage. S3. Under the constraints of the production stage and its corresponding stage parameters, calculate the water volume change of the shrimp-rice co-cultivation system in the current time step based on the water balance principle, and update the water volume. S4. Based on the basic data, the stage parameters, and the updated water volume, calculate the external pollution input load and the pollutant output load, and perform water quality prediction.

[0010] Furthermore, step S1 includes the following sub-steps: S11. Obtain basic data of the rice-shrimp co-cultivation system at the current moment from the data acquisition device or data interface; the basic data includes meteorological data, agricultural activity data, field structure parameters and production stage related information; S12. Perform unified processing on the time scale and unit of the acquired basic data; S13. Initialize or read the water quantity status and water quality status output from the previous time step; The water state parameters include water volume and water depth; The water quality parameters are expressed in concentration form, including ammonia nitrogen concentration. Total nitrogen concentration Total phosphorus concentration Chemical oxygen demand concentration and dissolved oxygen concentration .

[0011] Furthermore, in S11, the meteorological data includes: rainfall. Temperature Wind speed and sunshine duration or radiation information ; The agricultural activity data includes: feeding amount Fertilizer application amount Dosage of drugs or disinfectants and irrigation and drainage operation records; the irrigation and drainage operation records are recorded using irrigation volume records. and drainage volume record Or, irrigation water depth in the form of equivalent water depth. and drainage depth ; The field structure parameters include: paddy field area shrimp ditch area and water surface area ; The relevant information for the production stage includes: rice growth period indicators, shrimp-rice separate or co-culture management indicators, and transplanting and harvesting times.

[0012] Furthermore, in S12, if the irrigation or drainage operation record is given in the form of equivalent water depth, it is converted into volume form according to the following relationship: .

[0013] Furthermore, step S2 includes the following sub-steps: S21. Based on the production stage information, identify the current production stage of the rice-shrimp co-cultivation system, and then obtain the stage identifier. ; The production stages include the non-growing period of rice, the rice-shrimp separate farming stage during the rice growing period, and the rice-shrimp co-farming stage during the rice growing period. S22. Determine the stage identifier. Corresponding stage parameter set ; The set of stage parameters include: Water quantity calculation parameters, including crop coefficient Leakage coefficient Lower limit of water layer control in stages Upper limit of water layer control in stages and the depth of the phased target ; Water quality reaction parameters, including the rate of organic matter decay at 20°C. ammonia nitrogen nitrification rate Total nitrogen denitrification rate and total phosphorus sedimentation or adsorption rate ; Temperature correction parameters, including temperature coefficients used to correct water quality reaction rates, and temperature correction coefficients for organic matter decay. Ammonia nitrification temperature correction factor Total nitrogen denitrification temperature correction factor Total phosphorus sedimentation / adsorption temperature correction factor .

[0014] Furthermore, step S3 includes the following sub-steps: S31. Calculate the rainfall recharge volume in the rice-shrimp co-cultivation system: In the formula, The effective rainfall coefficient; Volume supplied by rainfall; S32. Calculate the evapotranspiration volume in the rice-shrimp co-cultivation system: in, In the formula, To reduce the volume consumed by evaporation, This represents the actual evaporation rate. This refers to the crop coefficient for a given stage; S33. Calculate the seepage loss volume in the rice-shrimp co-cultivation system: In the formula, For the volume of leakage loss, The stage leakage coefficient, This is the current water depth; S34. Determine the irrigation and drainage volumes in the rice-shrimp co-cultivation system; S35. Based on the rainfall replenishment volume, evapotranspiration loss volume, seepage loss volume, and irrigation and drainage volume, the water volume and water depth in the shrimp-rice co-cultivation system are updated using the water balance principle.

[0015] Furthermore, S34 specifically includes: If irrigation volume or drainage operation records exist, then the irrigation volume record shall be used. and drainage volume record If no record is made, the determination is made according to the stage water layer control rules, which are as follows: when At that time, water replenishment volume ; when At that time, drainage volume ; when When the water level is within the water layer control threshold range, no irrigation or drainage operations are performed.

[0016] Furthermore, in S35, the water volume update in the shrimp-rice co-cultivation system is represented as follows: In the formula, The volume of water at the next moment. The volume of water; The water depth update at the next moment is represented as: In the formula, For the water depth at the next moment, This represents the water surface area at the next moment.

[0017] Furthermore, step S4 includes the following sub-steps: S41. Quantify the external pollution input load based on the aforementioned agricultural activity data: The feeding input quality is as follows: In the formula, To input nitrogen mass for feeding, To input phosphorus mass for feeding, Input COD quality to feed. For feed nitrogen content, For the phosphorus content of feed, This is the COD equivalent coefficient. Nitrogen influent coefficient, The phosphorus influent coefficient, COD influent coefficient; The fertilizer input quality is: In the formula, To input nitrogen quality for fertilization, To ensure the quality of phosphorus input for fertilization, For fertilizer nitrogen content, For fertilizer phosphorus content, This refers to the fertilizer nitrogen influent coefficient. This refers to the phosphorus influent coefficient of fertilizer; When irrigation water is present, the input mass carried by the irrigation water is: In the formula, To ensure that irrigation water carries X-index quality, Background concentration of irrigation water; S42. Calculate the pollutant output load based on the drainage volume: In the formula, For the quality of drainage output X index. The concentration of water quality indicator X; S43. Dynamically adjust water quality reaction rate parameters based on air temperature data: In the formula, This represents the temperature-corrected water quality reaction rate. This represents the basic reaction rate corresponding to production stage s(t) at a standard temperature of 20℃; This is a temperature correction factor, including the temperature correction factor for organic matter decay. Ammonia nitrification temperature correction factor Total nitrogen denitrification temperature correction factor Total phosphorus sedimentation / adsorption temperature correction factor ; S44. Calculate the reaction items in the water body by combining the water quality reaction rate parameters and meteorological data, including one or more of organic matter decay, ammonia nitrogen nitrification, total nitrogen denitrification and total phosphorus sedimentation and adsorption. S45. Based on the current pollutant stock, according to the external pollution input load, pollutant output load and reaction items, and based on the water volume at the next time in S3, calculate the total mass of pollutants at the next time, and convert the total mass of pollutants at the next time into a concentration index as the water quality prediction result for the next time. S46. Dissolved oxygen renewal includes the amount of reoxygenation and oxygen consumption in the water body.

[0018] The water quality prediction method for shrimp-rice co-cultivation systems based on water consumption processes provided by this invention has the following beneficial effects: 1. This invention, by combining the water consumption characteristics of different production stages of rice-shrimp co-cultivation and comprehensively considering the impact of meteorological conditions and agricultural activities on water quality, predicts the water quality change trend of the rice-shrimp co-cultivation system, providing technical support for water quality risk early warning and production management, thereby improving the scientificity and effectiveness of water quality management in the rice-shrimp co-cultivation system. 2. This invention is a water quality prediction method based on the water consumption law of shrimp-rice co-cultivation. Taking the shrimp-rice co-cultivation system as the research object, it focuses on the characteristics of long-term water body existence, frequent water quantity regulation and the influence of multiple factors on water quality during the shrimp-rice co-cultivation production process. Following the technical approach of "production stage identification - water balance calculation - external load quantification - water quality status update", it predicts the water quality change process of the shrimp-rice co-cultivation system.

[0019] 3. This invention addresses the characteristics of rice-shrimp co-cultivation systems, including long-term water body presence, frequent water volume regulation, and significant impact from agricultural activities. By identifying the production stage of the rice-shrimp co-cultivation system, it establishes a phased water balance relationship based on the water consumption patterns of different production stages. Furthermore, it quantifies agricultural activities such as feeding and fertilization as external pollution loads and introduces a water quality reaction kinetic model to update the water quality status. This ensures that the water quality prediction process is simultaneously constrained by water volume changes, production management practices, and meteorological conditions, thereby accurately reflecting the dilution and concentration effects of water volume changes on water quality concentration. This enables continuous and dynamic prediction of water quality in rice-shrimp co-cultivation systems. Compared to existing water quality prediction methods that rely solely on empirical judgment or historical data fitting, this invention has a clearer prediction mechanism, stronger applicability, and provides reliable technical support for water quality management and risk warning in rice-shrimp co-cultivation systems. Attached Figure Description

[0020] Figure 1 This is a flowchart of the water quality prediction method for the rice-shrimp co-cultivation system based on the water consumption process in this embodiment.

[0021] Figure 2 This is a schematic diagram of the shrimp-rice co-cultivation system in this embodiment.

[0022] Figure 3 This is the water quality prediction technology process for the shrimp-rice co-cultivation system in this embodiment. Detailed Implementation

[0023] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0024] This embodiment of the water quality prediction method for a rice-shrimp co-cultivation system based on water consumption processes first identifies the production stage of the system based on rice growth period and production management information. Then, based on the water consumption patterns at different production stages, a phased water balance relationship is established, and the water volume is dynamically updated. On this basis, agricultural activities such as feeding and fertilization are quantified as external pollution load inputs, and the water quality change process is corrected in conjunction with meteorological conditions. Finally, a water quality status update model is constructed to predict key water quality indicators of the rice-shrimp co-cultivation system. Figure 1 and Figure 3 Specifically, it includes the following: S1. Obtain the basic data of the rice-shrimp co-cultivation system and initialize the state variables, which specifically includes the following sub-steps: S11. Obtain basic data of the rice-shrimp co-cultivation system at the current moment from the data acquisition equipment or data interface; the basic data includes meteorological data, agricultural activity data, field structure parameters and production stage related information; Meteorological data is used to characterize the impact of external meteorological conditions on changes in water quantity and water quality processes, and includes: rainfall. Temperature Wind speed and sunshine duration or radiation information ; Agricultural activity data is used to quantify external pollution load inputs, including: feed intake. Fertilizer application amount Dosage of drugs or disinfectants And irrigation and drainage operation records; irrigation and drainage operation records are recorded using irrigation volume records. and drainage volume record Or, irrigation water depth in the form of equivalent water depth. and drainage depth ; refer to Figure 2 The field structure parameters are used to determine the spatial scale and volume conversion relationship of water bodies, including: paddy field area. shrimp ditch area and water surface area In some implementations, the water surface area Desirable and The sum; in other embodiments, the water surface area The water level-area relationship function can be updated as the water depth changes through a preset function. Production stage-related information is used to characterize the production status of the rice-shrimp co-culture system, including: rice growth period indicators, rice-shrimp separate or co-culture management indicators, transplanting and harvesting times, etc.

[0025] S12. Perform unified processing on the time scale and units of the acquired basic data. If the irrigation or drainage operation records are given in the form of equivalent water depth, then convert them into volume form according to the following relationship: S13. Initialize or read the water quantity status and water quality status output from the previous time step; Water state quantities include water volume and water depth The two satisfy the following relationship: Water quality parameters are expressed in concentration form, including ammonia nitrogen concentration. Total nitrogen concentration Total phosphorus concentration Chemical oxygen demand concentration and dissolved oxygen concentration The aforementioned water quality parameters can be obtained from online monitoring equipment, manual sampling and testing results, or initial values ​​set by the system.

[0026] In this embodiment, the output of step S1 is the current basic data set Dt and the state set St, which serve as the input for subsequent steps S2, S3 and S4.

[0027] S2. Identify the current production stage of the rice-shrimp co-cultivation system based on basic data, and determine the corresponding stage parameters. This includes the following sub-steps: S21. Based on relevant information about the production stage, identify the current production stage of the rice-shrimp co-cultivation system, and thus obtain the stage identifier. ; The production stages include the non-growing period of rice, the rice-shrimp separate farming stage during the rice growing period, and the rice-shrimp co-farming stage during the rice growing period. Production stage identification can be achieved using a rule-based approach based on the rice growth calendar and farmer management identifiers, thereby obtaining stage identifiers. ; S22. Determination and Stage Identification Corresponding stage parameter set ; After completing the production stage identification, call the stage identifier. Corresponding stage parameter set Stage parameter set include: Water quantity calculation parameters, including crop coefficient Leakage coefficient Lower limit of water layer control in stages Upper limit of water layer control in stages and the depth of the phased target ; Water quality reaction parameters, including the rate of organic matter decay at 20°C. ammonia nitrogen nitrification rate Total nitrogen denitrification rate and total phosphorus sedimentation or adsorption rate ; Temperature correction parameters, including temperature coefficients used to correct water quality reaction rates, and temperature correction coefficients for organic matter decay. Ammonia nitrification temperature correction factor Total nitrogen denitrification temperature correction factor Total phosphorus sedimentation / adsorption temperature correction factor .

[0028] The aforementioned stage parameters can be obtained by fitting historical monitoring data, measuring on-site tests, or preset in a parameter library and then corrected during application.

[0029] The output of step S2 in this embodiment is a stage identifier. and the corresponding set of stage parameters This is used in subsequent steps S3 and S4.

[0030] S3. Under the constraints of the production stage and its corresponding stage parameters, calculate the water volume change of the shrimp-rice co-cultivation system in the current time step based on the water balance principle, and update the water volume. Taking into account rainfall replenishment, evaporation or evapotranspiration, seepage loss, and human intervention factors such as irrigation and drainage, a water balance relationship is established, and the change in water volume from the current moment to the next moment is calculated. Preferably, if irrigation or drainage records exist, the recorded data is used; if irrigation or drainage records are lacking, it is determined whether to replenish or drain water based on the stage water layer control threshold and the target water depth, specifically including the following sub-steps: S31. Calculate the rainfall recharge volume in the rice-shrimp co-cultivation system: In the formula, The effective rainfall coefficient is used to characterize the proportion of effective water replenishment formed by rainfall; Volume supplied by rainfall; S32. Calculate the evapotranspiration volume in the rice-shrimp co-cultivation system: First, obtain the reference evaporation. Then, the actual evapotranspiration is calculated based on the crop stage coefficient: Then, calculate the evapotranspiration volume in the rice-shrimp co-cultivation system: In the formula, To reduce the volume consumed by evaporation, This represents the actual evaporation rate. This refers to the crop coefficient for a given stage; S33. Calculate the seepage loss volume in the rice-shrimp co-cultivation system: In the formula, For the volume of leakage loss, The stage leakage coefficient, This is the current water depth; S34. Determine the irrigation and drainage volumes in the rice-shrimp co-cultivation system, including: If irrigation volume or drainage operation records exist, then the irrigation volume record shall be used. and drainage volume record If no record is made, the determination is made according to the stage water layer control rules, which are as follows: when At that time, water replenishment volume ; when At that time, drainage volume ; when When the water level is within the water layer control threshold range, no irrigation or drainage operations are performed; S35. Based on rainfall replenishment volume, evapotranspiration loss volume, seepage loss volume, and irrigation and drainage volume, the water volume and water depth in the rice-shrimp co-cultivation system are updated using the water balance principle. In the rice-shrimp co-cultivation system, the water volume update is represented as follows: In the formula, The volume of water at the next moment. The volume of water; The water depth update at the next moment is represented as: In the formula, For the water depth at the next moment, This represents the water surface area at the next moment. The output of step S3 in this embodiment is the water volume at the next moment (the updated water volume). The water depth at the next moment and irrigation volume and drainage volume This serves as the input for step S4.

[0031] S4. Based on basic data, stage parameters, and updated water volume, calculate the input load of external pollution sources and the output load of pollutants, and make water quality predictions. This embodiment quantifies the external pollution load based on the agricultural activity data obtained in S1, the stage parameters determined in S2, and the updated water volume in S3, and constructs a water quality status update model to predict water quality indicators. Specifically, agricultural activities such as feeding and fertilization are converted into input loads of pollutants such as nitrogen, phosphorus, and organic matter; simultaneously, based on the drainage volume obtained in S3, the amount of pollutants carried away by the drainage is calculated. Based on this, the water quality reaction process is corrected by incorporating meteorological conditions such as air temperature, and a water quality status update equation is constructed. The concentrations of water quality indicators such as ammonia nitrogen, total nitrogen, total phosphorus, and chemical oxygen demand are updated according to the mass conservation relationship. For dissolved oxygen, the update calculation is performed under the combined effects of organic matter oxygen consumption, biological respiration oxygen consumption, and water reoxygenation, thereby obtaining the water quality prediction result for the next time step. This includes the following sub-steps: S41. Quantifying the input load of external pollution sources based on agricultural activity data: The feeding input quality is as follows: In the formula, To input nitrogen mass for feeding, To input phosphorus mass for feeding, Input COD quality to feed. For feed nitrogen content, For the phosphorus content of feed, This is the COD equivalent coefficient. Nitrogen influent coefficient, The phosphorus influent coefficient, COD influent coefficient; The fertilizer input quality is: In the formula, To input nitrogen quality for fertilization, To ensure the quality of phosphorus input for fertilization, For fertilizer nitrogen content, For fertilizer phosphorus content, This refers to the fertilizer nitrogen influent coefficient. This refers to the phosphorus influent coefficient of fertilizer; When irrigation water is present, the input mass carried by the irrigation water is: In the formula, To ensure that irrigation water carries X-index quality, Background concentration of irrigation water; S42. Calculate the pollutant output load based on the drainage volume: In the formula, For the quality of drainage output X index. The concentration of water quality indicator X; S43. Dynamically adjust water quality reaction rate parameters based on air temperature data: In the formula, This represents the temperature-corrected water quality reaction rate. This represents the basic reaction rate corresponding to production stage s(t) at a standard temperature of 20℃; This is a temperature correction factor, including the temperature correction factor for organic matter decay. Ammonia nitrification temperature correction factor Total nitrogen denitrification temperature correction factor Total phosphorus sedimentation / adsorption temperature correction factor ; S44. Combining water quality reaction rate parameters, meteorological data and current pollutant stock in the water body, calculate the reaction items in the water body. The reaction items include one or more of organic matter decay, ammonia nitrogen nitrification, total nitrogen denitrification and total phosphorus sedimentation and adsorption. That is, according to the first-order reaction kinetics principle, calculate the mass of pollutants that are reduced or transformed due to biodegradation, nitrification, denitrification or adsorption and sedimentation within the current time step. S45. Calculate the total mass of pollutants at the next moment based on the principle of mass conservation. Add the total input of external pollutants to the current pollutant stock, subtract the mass of pollutants carried away by drainage, and subtract or add the pollutant reaction mass (reaction term). Using the updated water volume at the next moment from step S3, convert the calculated total mass of pollutants at the next moment into a concentration index, which serves as the water quality prediction result for the next moment. The concentration calculation reflects the physical dilution or concentration effect of water volume changes on water quality. S46. Dissolved oxygen renewal, including the amount of reoxygenation and oxygen consumption in the water body; Specifically, the amount of reoxygenation can be determined based on the current temperature, wind speed, and the difference between the current and saturated dissolved oxygen levels; the amount of oxygen consumption can be determined based on the oxygen consumption of organic matter degradation and the oxygen consumption of aquatic organism respiration; by combining the amounts of reoxygenation and oxygen consumption, the current dissolved oxygen concentration is corrected to obtain the dissolved oxygen concentration at the next moment.

[0032] The output of step S4 in this embodiment is the water quality prediction result for the next time step, and it is used as the initial water quality state input for the next time step S1, thereby realizing the rolling update of water quality prediction.

[0033] Although specific embodiments of the invention have been described in detail with reference to the accompanying drawings, this should not be construed as limiting the scope of protection of this patent. Various modifications and variations that can be made by a person skilled in the art without inventive effort within the scope described in the claims still fall within the scope of protection of this patent.

Claims

1. A method for predicting water quality in a rice-shrimp co-cultivation system based on water consumption processes, characterized in that, Includes the following steps: S1. Obtain the basic data of the rice-shrimp co-cultivation system and initialize the state variables; S2. Identify the current production stage of the rice-shrimp co-cultivation system based on the basic data, and determine the stage parameters corresponding to this production stage. S3. Under the constraints of the production stage and its corresponding stage parameters, calculate the water volume change of the shrimp-rice co-cultivation system in the current time step based on the water balance principle, and update the water volume. S4. Based on the basic data, the stage parameters, and the updated water volume, calculate the external pollution input load and the pollutant output load, and perform water quality prediction.

2. The water quality prediction method for a rice-shrimp co-cultivation system based on water consumption processes according to claim 1, characterized in that, S1 includes the following sub-steps: S11. Obtain basic data of the rice-shrimp co-cultivation system at the current moment from the data acquisition device or data interface; the basic data includes meteorological data, agricultural activity data, field structure parameters and production stage related information; S12. Perform unified processing on the time scale and unit of the acquired basic data; S13. Initialize or read the water quantity status and water quality status output from the previous time step; The water state parameters include water volume and water depth; The water quality parameters are expressed in concentration form, including ammonia nitrogen concentration. Total nitrogen concentration Total phosphorus concentration Chemical oxygen demand concentration and dissolved oxygen concentration .

3. The water quality prediction method for a rice-shrimp co-cultivation system based on water consumption processes according to claim 2, characterized in that, In S11, the meteorological data includes: rainfall. Temperature Wind speed and sunshine duration or radiation information ; The agricultural activity data includes: feeding amount Fertilizer application amount Dosage of drugs or disinfectants and irrigation and drainage operation records; the irrigation and drainage operation records are recorded using irrigation volume records. and drainage volume record Or, irrigation water depth in the form of equivalent water depth. and drainage depth ; The field structure parameters include: paddy field area shrimp ditch area and water surface area ; The relevant information for the production stage includes: rice growth period indicators, shrimp-rice separate or co-culture management indicators, and transplanting and harvesting times.

4. The water quality prediction method for a rice-shrimp co-cultivation system based on water consumption processes according to claim 3, characterized in that, In step S12, if the irrigation or drainage operation record is given in the form of equivalent water depth, it shall be converted into volume form according to the following relationship: 。 5. The water quality prediction method for a rice-shrimp co-cultivation system based on water consumption processes according to claim 1, characterized in that, S2 includes the following sub-steps: S21. Based on the production stage information, identify the current production stage of the rice-shrimp co-cultivation system, and then obtain the stage identifier. ; The production stages include the non-growing period of rice, the rice-shrimp separate farming stage during the rice growing period, and the rice-shrimp co-farming stage during the rice growing period. S22. Determine the stage identifier. Corresponding stage parameter set ; The set of stage parameters include: Water quantity calculation parameters, including crop coefficient Leakage coefficient Lower limit of water layer control in stages Upper limit of water layer control in stages and the depth of the phased target ; Water quality reaction parameters, including the rate of organic matter decay at 20°C. ammonia nitrogen nitrification rate Total nitrogen denitrification rate and total phosphorus sedimentation or adsorption rate ; Temperature correction parameters, including temperature coefficients used to correct water quality reaction rates, and temperature correction coefficients for organic matter decay. Ammonia nitrification temperature correction factor Total nitrogen denitrification temperature correction factor Total phosphorus sedimentation / adsorption temperature correction factor .

6. The water quality prediction method for a rice-shrimp co-cultivation system based on water consumption processes according to claim 3, characterized in that, S3 includes the following sub-steps: S31. Calculate the rainfall recharge volume in the rice-shrimp co-cultivation system: In the formula, The effective rainfall coefficient; Volume supplied by rainfall; S32. Calculate the evapotranspiration volume in the rice-shrimp co-cultivation system: in, In the formula, To reduce the volume consumed by evaporation, This represents the actual evaporation rate. This refers to the crop coefficient for a given stage; S33. Calculate the seepage loss volume in the rice-shrimp co-cultivation system: In the formula, For the volume of leakage loss, The stage leakage coefficient, This is the current water depth; S34. Determine the irrigation and drainage volumes in the rice-shrimp co-cultivation system; S35. Based on the rainfall replenishment volume, evapotranspiration loss volume, seepage loss volume, and irrigation and drainage volume, the water volume and water depth in the shrimp-rice co-cultivation system are updated using the water balance principle.

7. The water quality prediction method for a rice-shrimp co-cultivation system based on water consumption processes according to claim 6, characterized in that, S34 specifically includes: If irrigation volume or drainage operation records exist, then the irrigation volume record shall be used. and drainage volume record If no record is made, the determination is made according to the stage water layer control rules, which are as follows: when At that time, water replenishment volume ; when At that time, drainage volume ; when When the water level is within the water layer control threshold range, no irrigation or drainage operations are performed.

8. The water quality prediction method for a rice-shrimp co-cultivation system based on water consumption processes according to claim 6, characterized in that, In S35, the water volume update in the rice-shrimp co-cultivation system is represented as follows: In the formula, The volume of water at the next moment. The volume of water; The water depth update at the next moment is represented as: In the formula, For the water depth at the next moment, This represents the water surface area at the next moment.

9. The water quality prediction method for a rice-shrimp co-cultivation system based on water consumption processes according to claim 7, characterized in that, S4 includes the following sub-steps: S41. Quantify the external pollution input load based on the aforementioned agricultural activity data: The feeding input quality is as follows: In the formula, To input nitrogen mass for feeding, To input phosphorus mass for feeding, Input COD quality to feed. For feed nitrogen content, For the phosphorus content of feed, This is the COD equivalent coefficient. Nitrogen influent coefficient, The phosphorus influent coefficient, COD influent coefficient; The fertilizer input quality is: In the formula, To input nitrogen quality for fertilization, To ensure the quality of phosphorus input for fertilization, For fertilizer nitrogen content, For fertilizer phosphorus content, This refers to the fertilizer nitrogen influent coefficient. This refers to the phosphorus influent coefficient of fertilizer; When irrigation water is present, the input mass carried by the irrigation water is: In the formula, To ensure that irrigation water carries X-index quality, Background concentration of irrigation water; S42. Calculate the pollutant output load based on the drainage volume: In the formula, For the quality of drainage output X index. The concentration of water quality indicator X; S43. Dynamically adjust water quality reaction rate parameters based on air temperature data: In the formula, This represents the temperature-corrected water quality reaction rate. This represents the basic reaction rate corresponding to production stage s(t) at a standard temperature of 20℃; This is a temperature correction factor, including the temperature correction factor for organic matter decay. Ammonia nitrification temperature correction factor Total nitrogen denitrification temperature correction factor Total phosphorus sedimentation / adsorption temperature correction factor ; S44. Calculate the reaction items in the water body by combining the water quality reaction rate parameters and meteorological data, including one or more of organic matter decay, ammonia nitrogen nitrification, total nitrogen denitrification and total phosphorus sedimentation and adsorption. S45. Based on the current pollutant stock, according to the external pollution input load, pollutant output load and reaction items, and based on the water volume at the next time in S3, calculate the total mass of pollutants at the next time, and convert the total mass of pollutants at the next time into a concentration index as the water quality prediction result for the next time. S46. Dissolved oxygen renewal includes the amount of reoxygenation and oxygen consumption in the water body.

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