Rainwater reuse intelligent irrigation decision-making system based on Internet of Things

Abstract

The invention relates to the technical field of intelligent agricultural irrigation, in particular to a rainwater reuse intelligent irrigation decision-making system based on the Internet of Things, which comprises a rainwater data acquisition module, a multi-source data processing module, a dynamic availability analysis module, an irrigation decision-making generation module and an instruction execution control module, wherein the rainwater data acquisition module is used for acquiring rainwater storage amount data and turbidity data; the multi-source data processing module is used for calculating a dynamic correction memory space; generating an irrigation blockage risk grade; the dynamic availability analysis module is used for outputting actual available water; the irrigation decision generation module is used for matching a water demand curve in a crop growth stage and generating irrigation duration and a valve opening sequence; and the instruction execution control module is used for issuing an instruction to an irrigation actuator. According to the invention, by fusing multi-source environmental data and crop water demand characteristics, a closed-loop control intelligent decision-making mechanism is constructed, and dynamic optimal distribution of rainwater resources and accurate management and control of an irrigation process are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent agricultural irrigation, and in particular to a rainwater recycling intelligent irrigation decision-making system based on the Internet of Things. Background Art

[0002] In modern agricultural irrigation practice, how to efficiently utilize limited water resources, especially rainwater resources that can be collected in cities or industrial parks, has become a key issue in green agriculture and sustainable development. Traditional rainwater irrigation methods mostly rely on manual judgment and experience-based decision-making, and lack systematic analysis of the dynamic changes in rainwater, crop water requirements and irrigation risks. In addition, most conventional irrigation systems only support timed or quantitative water supply, and cannot be precisely controlled based on the actual water requirements of crops and the moisture status of the soil, resulting in low irrigation efficiency and serious water waste.

[0003] Existing technologies haven't yet managed to coordinate rainwater storage dynamics, meteorological trends, soil moisture conditions, differences in crop water requirements during growth stages, and water quality risk factors. This makes it impossible to ensure rational crop water use while effectively mitigating the risk of pipe network blockages caused by excessive turbidity. Therefore, an IoT-based intelligent rainwater reuse irrigation decision-making system is urgently needed to address these issues. Summary of the Invention

[0004] Based on the above objectives, the present invention provides a rainwater reuse intelligent irrigation decision-making system based on the Internet of Things.

[0005] The IoT-based rainwater reuse intelligent irrigation decision-making system includes a rainwater data acquisition module, a multi-source data processing module, a dynamic availability analysis module, an irrigation decision-making module, and an instruction execution control module.

[0006] Rainwater data acquisition module: used to obtain real-time rainwater storage data and turbidity data through the water level sensor and turbidity sensor deployed in the rainwater collection device;

[0007] Multi-source data processing module: This module receives rainwater storage data and calculates the dynamic correction of storage capacity based on the probability of rainfall in the next 24 hours provided by the meteorological forecast platform. It also receives turbidity data and generates an irrigation blockage risk level when the turbidity exceeds a preset threshold.

[0008] Dynamic availability analysis module: This module is used to calculate the difference between the dynamically corrected storage capacity and the soil moisture data of each irrigation area to obtain the theoretical irrigable water volume. It also reduces the theoretical irrigable water volume based on the irrigation blockage risk level and outputs the actual available water volume.

[0009] Irrigation decision generation module: used to match the preset crop growth stage water demand curve based on the actual available water volume, and generate the irrigation duration and valve opening sequence for each irrigation area;

[0010] Instruction execution control module: used to send irrigation duration and valve opening sequence to the irrigation actuators in the corresponding area through the IoT gateway.

[0011] Optionally, the rainwater data collection module includes a water level detection unit, a turbidity detection unit, and a data synchronization uploading unit; wherein:

[0012] Water level detection unit: Deployed at the bottom or middle area of ​​the rainwater collection device, it uses an ultrasonic or pressure-type water level sensor to collect current rainwater height information, combines it with the cross-sectional area parameters of the collection device, calculates the corresponding rainwater storage capacity data, and outputs the rainwater storage capacity data in the form of a digital signal;

[0013] Turbidity detection unit: Set at the same vertical height as the water level detection unit, it is used to obtain the scattered light intensity of the water body through a photoelectric turbidity sensor and convert the light signal into a standard turbidity value based on the built-in turbidity conversion algorithm;

[0014] Data synchronization upload unit: used to timestamp the data collected by the water level detection unit and the turbidity detection unit, and upload them to the multi-source data processing module synchronously at a set frequency.

[0015] Optionally, the multi-source data processing module includes a storage capacity correction unit and a risk level generation unit; wherein:

[0016] Storage capacity correction unit: used to receive the rainwater storage capacity data uploaded by the rainwater data acquisition module, and simultaneously receive the next 24 hours rainfall probability data provided by the external meteorological forecast platform, and dynamically correct the current storage capacity according to the preset meteorological sensitivity coefficient to obtain the dynamic correction storage capacity V r ;

[0017] Risk level generation unit: used to receive rainwater turbidity data provided by the rainwater data acquisition module and compare it with the preset turbidity threshold. When the turbidity value is higher than the threshold, the irrigation blockage risk level is set according to the degree of deviation.

[0018] Optionally, the risk level generating unit includes:

[0019] Turbidity comparison subunit: used to receive the rainwater turbidity value from the rainwater data acquisition module, set as T, and compare it with the preset turbidity safety threshold T0 to determine whether it exceeds the standard; when T>T0, enter the risk classification process; when T≤T0, it is determined to be no risk level;

[0020] Deviation calculation subunit: used to calculate the turbidity deviation ratio D when the turbidity exceeds the standard. The formula is:

[0021] Risk coding subunit: used to match the turbidity deviation ratio D with the risk level correspondence table, generate the corresponding irrigation blockage risk level according to the preset interval, and the output result is represented by hierarchical coding;

[0022] When 0 < D ≤ 0.2, generate a low risk level L1;

[0023] When 0.2 < D ≤ 0.5, generate a medium risk level L2;

[0024] When D > 0.5, generate a high risk level L3.

[0025] Optionally, the dynamic availability analysis module includes a soil moisture content acquisition unit, a difference calculation unit, and a reduction processing unit; where:

[0026] Soil moisture content acquisition unit: used to deploy soil moisture sensors in each irrigation area, obtain the current soil moisture content data of each irrigation area, and classify and store them according to the area number;

[0027] Difference calculation unit: used to receive the dynamically corrected storage volume output by the multi-source data processing module and the current water content data of each area provided by the soil moisture content acquisition unit; calculate the difference between the target water content and the current water content of each irrigation area to obtain the water demand of the corresponding area, and comprehensively estimate the theoretical available irrigation water volume accordingly;

[0028] Reduction processing unit: used to receive the irrigation blockage risk level output by the multi-source data processing module, perform proportional reduction on the theoretical available irrigation water volume according to the preset risk impact coefficient table, and output the final actual available water volume.

[0029] Optionally, the difference calculation unit includes:

[0030] Regional water demand calculation subunit: used to receive the current water content data of each irrigation area provided by the soil moisture content acquisition unit, call the preset target water content value of each area, calculate the difference by area, obtain the water demand per unit volume of soil in each area, and then calculate the total water demand W of each area in combination with the area and the irrigation depth of the soil layer i ;

[0031] Available irrigation volume estimation subunit: used to summarize the total water demand W of all areas i , and then compare it with the dynamically corrected storage volume provided by the multi-source data processing module, and take the minimum value as the theoretical available irrigation water volume; the calculation formula is: Among them, V t represents the theoretical available irrigation water volume, V r represents the dynamically corrected storage volume, and n represents the total number of irrigation areas.

[0032] Optionally, the reduction processing unit includes:

[0033] Level identification subunit: used to receive the irrigation blockage risk level information output by the multi-source data processing module and call the reduction coefficient β corresponding to the level in the preset risk impact coefficient table; the reduction coefficient value range is between 0 and 1;

[0034] Reduction calculation subunit: used to calculate the theoretical irrigable water volume V output by the difference calculation unit according to the reduction coefficient β determined by the level identification subunit t Perform proportional reduction to calculate the final actual available water volume V a ; The formula is: V a =β·V t .

[0035] Optionally, the irrigation decision generation module includes a water demand curve matching unit, an irrigation duration calculation unit, and a valve sequence generation unit; wherein:

[0036] Water demand curve matching unit: used to receive the actual available water output by the dynamic availability analysis module, and call the water demand curve of the corresponding crop growth stage of each irrigation area preset in the system, identify its target water demand intensity value according to the current crop stage, and combine the planting area and water demand intensity of each area to form the expected irrigation water volume

[0037] Irrigation duration calculation unit: based on the expected irrigation water volume for each irrigation area and the unit flow rate F of the corresponding area in the irrigation system i , calculate the original irrigation time T of each area i When the total expected water volume Exceeds the actual available water volume V a When , the irrigation time of each area is scaled proportionally, and the final irrigation time T is output. i ′, to meet the total water resource constraint;

[0038] The specific calculation formula is: Among them, T i represents the original irrigation duration of region i, T i ′ represents the final irrigation duration after scaling adjustment; V a is the actual available water volume;

[0039] Valve sequence generation unit: used to generate the opening sequence, duration and time segment number of multi-zone irrigation valves based on the irrigation priority of each zone and the calculated final irrigation duration, and output it as a structured irrigation control instruction set.

[0040] Optionally, the water demand curve matching unit includes:

[0041] Crop stage identification subunit: used to call the preset crop variety database and determine the crop growth stage number S of each irrigation area in combination with the current date and the start time of crop planting i ;

[0042] Water demand intensity extraction subunit: used to extract water from the identified crop stage number S i The daily water demand intensity per unit area at this stage is extracted from the corresponding water demand curve, and the expected daily irrigation water volume for this area at this growth stage is calculated by allocating it to the planting area of ​​each region. The formula is: in, represents the expected daily irrigation water volume of the ith irrigation area, Q i is the daily water demand intensity per unit area, A i is the planting area;

[0043] Regional total expected water volume generation subunit: used for the total expected water volume of the entire irrigation area Accumulate and form the total expected irrigation water volume for the current cycle

[0044] Optionally, the instruction execution control module includes an instruction parsing unit, a communication encapsulation unit, a gateway transmission unit, and an execution status confirmation unit; wherein:

[0045] Instruction parsing unit: used to receive a structured control instruction set containing the irrigation duration information and valve number opening sequence corresponding to each irrigation area, and parse it into a standardized control instruction format;

[0046] Communication encapsulation unit: used to group the parsed control instructions by area, encapsulate them into a data communication protocol format compatible with the IoT gateway, and generate instruction data packets with duration, area number, and valve sequence fields;

[0047] Gateway transmission unit: used to send the encapsulated instruction data packets to the irrigation actuators in each corresponding area through the deployed IoT gateway in a wireless communication manner;

[0048] Execution status confirmation unit: After the command is issued, it is used to continuously monitor the response status information of each irrigation actuator to confirm whether the valve is opened in the preset order and duration.

[0049] Beneficial effects of the present invention:

[0050] The present invention, by constructing a modular architecture that includes rainwater data collection, multi-source data processing, dynamic availability analysis, irrigation decision generation and instruction execution control, can achieve collaborative analysis of the relationship between rainwater resource status, meteorological trends, soil moisture and crop water demand, automatically match irrigation plans according to the actual available water volume and crop water demand intensity, and significantly improve irrigation accuracy and water source utilization efficiency.

[0051] This invention uses a turbidity risk grading and reduction mechanism to avoid the risk of pipe blockage caused by highly polluted rainwater entering the irrigation system. Combined with the closed-loop control logic between the IoT gateway and the irrigation actuator, it achieves accurate issuance of irrigation control instructions and feedback on execution status, ensuring the system's real-time response capability and operational stability. It is suitable for intelligent rainwater irrigation scenarios in complex farmland and urban green spaces. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0053] Figure 1 Schematic diagram of a rainwater reuse intelligent irrigation decision-making system according to an embodiment of the present invention;

[0054] Figure 2 Schematic diagram of a dynamic availability analysis module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It is also noted that, to provide a more detailed description, the following embodiments are best and preferred embodiments, and those skilled in the art may employ alternative methods for implementing certain known technologies. Furthermore, the accompanying drawings are intended only to provide a more detailed description of the embodiments and are not intended to limit the present invention.

[0056] It should be noted that references in the specification to "one embodiment," "an embodiment," "an exemplary embodiment," "some embodiments," etc. indicate that the described embodiments may include specific features, structures, or characteristics, but not every embodiment necessarily includes such specific features, structures, or characteristics. In addition, when specific features, structures, or characteristics are described in conjunction with an embodiment, it is within the knowledge of persons skilled in the relevant art to implement such features, structures, or characteristics in conjunction with other embodiments (whether or not explicitly described).

[0057] In general, terms can be understood, at least in part, from their use in context. For example, depending at least in part on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending at least in part on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0058] like Figure 1-Figure 2 As shown in FIG, the rainwater reuse intelligent irrigation decision-making system based on the Internet of Things includes a rainwater data acquisition module, a multi-source data processing module, a dynamic availability analysis module, an irrigation decision generation module, and an instruction execution control module; wherein:

[0059] Rainwater data acquisition module: used to obtain real-time rainwater storage data and turbidity data through the water level sensor and turbidity sensor deployed in the rainwater collection device;

[0060] Multi-source data processing module: This module receives rainwater storage data and calculates the dynamic correction of storage capacity based on the probability of rainfall in the next 24 hours provided by the meteorological forecast platform. It also receives turbidity data and generates an irrigation blockage risk level when the turbidity exceeds a preset threshold.

[0061] Dynamic availability analysis module: This module is used to calculate the difference between the dynamically corrected storage capacity and the soil moisture data of each irrigation area to obtain the theoretical irrigable water volume. It also reduces the theoretical irrigable water volume based on the irrigation blockage risk level and outputs the actual available water volume.

[0062] Irrigation decision generation module: used to match the preset crop growth stage water demand curve based on the actual available water volume, and generate the irrigation duration and valve opening sequence for each irrigation area;

[0063] Instruction execution control module: used to send irrigation duration and valve opening sequence to the irrigation actuators in the corresponding area through the IoT gateway.

[0064] The rainwater data acquisition module includes a water level detection unit, a turbidity detection unit, and a data synchronization upload unit; wherein:

[0065] Water level detection unit: Deployed at the bottom or middle area of ​​the rainwater collection device, it uses an ultrasonic or pressure-type water level sensor to collect current rainwater height information, combines it with the cross-sectional area parameters of the collection device, calculates the corresponding rainwater storage capacity data, and outputs the rainwater storage capacity data in the form of a digital signal;

[0066] The calculation formula for rainwater storage data is: V = A × H, where V represents the rainwater storage capacity (unit: L); A represents the effective horizontal cross-sectional area of ​​the rainwater collection device (unit: m 2 ); H represents the detected rain height (unit: m); the calculation result can be converted into liter unit (L) for processing module to call;

[0067] Turbidity detection unit: This is located at the same vertical height as the water level detection unit. It uses a photoelectric turbidity sensor to obtain the scattered light intensity of the water body and converts the light signal into a standard turbidity value based on the built-in turbidity conversion algorithm to reflect the cleanliness of the current rainwater.

[0068] The conversion formula for turbidity value is: Where, T represents the turbidity value (unit: NTU); V out is the analog voltage value output by the sensor (unit: V); V ref is the reference voltage value (unit: V); k is the calibration factor, which is used to convert the voltage ratio into standard turbidity units;

[0069] Data synchronization upload unit: used to timestamp the data collected by the water level detection unit and the turbidity detection unit, and upload them to the multi-source data processing module synchronously at a set frequency to ensure the timeliness and consistency of the data received by the system.

[0070] The multi-source data processing module includes a storage capacity correction unit and a risk level generation unit; wherein:

[0071] Storage capacity correction unit: used to receive the rainwater storage capacity data uploaded by the rainwater data acquisition module, and simultaneously receive the next 24 hours rainfall probability data provided by the external meteorological forecast platform, and dynamically correct the current storage capacity according to the preset meteorological sensitivity coefficient to obtain the dynamic correction storage capacity V r , the expression is: V r =V o +α·P, where V r Indicates the dynamic correction storage capacity (unit: L); V o Indicates the current collected raw rainwater storage capacity (unit: L); P represents the probability of rainfall in the next 24 hours (range: 0-1); α is the set meteorological sensitivity coefficient (unit: L), which is used to reflect the impact of rainfall probability on future water storage trends;

[0072] Risk level generation unit: It is used to receive the rain turbidity data provided by the rain data collection module, compare it with the preset turbidity threshold, and when the turbidity value is higher than the threshold, set the irrigation blockage risk level according to its deviation degree; the above unit can realize the quantitative correction of the future trend of rainwater resources by linking the quantitative rainfall probability provided by the meteorological prediction platform with the current rainwater storage data, improve the foresight and dynamic adaptability of water volume estimation; at the same time, through the hierarchical processing of the over-threshold turbidity, it effectively identifies the risk of pipeline blockage that rainwater may cause, and provides a stable and reliable data basis for the subsequent generation of irrigation strategies.

[0073] The risk level generation unit includes:

[0074] Turbidity comparison sub-unit: It is used to receive the rain turbidity value from the rain data collection module, set it as T, and compare its numerical value with the preset turbidity safety threshold T0 to judge whether there is an over-standard situation; when T>T0 is established, enter the risk classification processing process; when T≤T0, it is determined that there is no risk level;

[0075] Deviation calculation sub-unit: It is used to calculate the turbidity deviation ratio D when the turbidity exceeds the standard to quantify the current degree of turbidity exceeding the standard. The formula is:

[0076] Risk coding sub-unit: It is used to match the turbidity deviation ratio D with the risk level correspondence table, generate the corresponding irrigation blockage risk level according to the preset interval, and the output result is represented by hierarchical coding;

[0077] If 0<D≤0.2, generate a low risk level L1;

[0078] If 0.2<D≤0.5, generate a medium risk level L2;

[0079] If D>0.5, generate a high risk level L3; the above sub-units can realize the continuous identification of rainwater cleanliness and the subdivision of risk levels by introducing the quantification mechanism of the turbidity deviation ratio, avoid misjudgment or missed judgment caused by too absolute threshold judgment, and improve the response accuracy and actual adaptability of the system to irrigation blockage risks.

[0080] The dynamic availability analysis module includes a soil moisture content acquisition unit, a difference calculation unit, and a reduction processing unit; among them:

[0081] Soil moisture content acquisition unit: It is used to deploy soil moisture sensors in each irrigation area to obtain the current soil moisture content data of each irrigation area, classify and store them according to the area number, and provide basic data support for the subsequent regional irrigation water volume analysis;

[0082] The difference calculation unit is used to receive the dynamic correction storage capacity output by the multi-source data processing module and the current moisture content data of each area provided by the soil moisture collection unit; it calculates the difference between the target moisture content and the current moisture content of each irrigation area to obtain the water demand of the corresponding area, and based on this, comprehensively estimates the theoretical irrigable water volume;

[0083] Reduction processing unit: used to receive the irrigation blockage risk level output by the multi-source data processing module, proportionally reduce the theoretical irrigable water volume according to the preset risk impact coefficient table, and output the final actual available water volume; the above unit can reasonably allocate irrigation water sources based on the current soil water shortage by linking the dynamic correction storage volume with the soil moisture status of each region; at the same time, a blockage risk level reduction mechanism is introduced to ensure water allocation while avoiding high-risk irrigation areas, effectively improving the system's adaptability to complex irrigation environments and resource utilization safety.

[0084] The difference calculation unit includes:

[0085] Regional water demand calculation subunit: used to receive the current water content data M of each irrigation area provided by the soil moisture collection unit i , and call the preset target moisture content value for each area The difference calculation is performed by region to obtain the unit volume soil water requirement of each region, and then the total water requirement W of each region is calculated based on the regional area and the irrigation depth of the soil layer. i ;

[0086] The calculation formula for regional water demand is: W i =ΔM i ×A i ×D i , where W i represents the water demand of the ith irrigation area (unit: L); A i Indicates the area of ​​the i-th region (unit: m 2 );D i represents the effective irrigation depth of the i-th soil (unit: m), ΔM i Indicates the difference in water content (unit: m 3 / m 3 ), the formula result has been converted to volume units;

[0087] Irrigable volume estimation subunit: used to summarize the total water demand W of all areas i , and then compare it with the dynamic correction storage volume provided by the multi-source data processing module, and take the minimum value as the theoretical irrigable water volume; the calculation formula is: Among them, V t Indicates the theoretical irrigation water volume (unit: L), V rrepresents the dynamically corrected storage capacity, and n represents the total number of irrigation areas. By establishing a mechanism for calculating and summarizing the difference in regional water demands, the water supply needs of each irrigation area can be quantitatively identified, and the calculation results can be dynamically compared with the available rainwater resources to ensure that water scheduling not only meets regional differences but also does not exceed the current resource limit of the system, significantly improving the scientificity and practicality of irrigation plan generation.

[0088] The reduction processing units include:

[0089] Level identification subunit: used to receive the irrigation blockage risk level information output by the multi-source data processing module and call the reduction coefficient β corresponding to the level in the preset risk impact coefficient table; the reduction coefficient value range is between 0 and 1, where the higher the level, the smaller the corresponding β value;

[0090] Table 1 Examples of risk impact coefficients

[0091]

[0092] Reduction calculation subunit: used to calculate the theoretical irrigable water volume V output by the difference calculation unit according to the reduction coefficient β determined by the level identification subunit t Perform proportional reduction to calculate the final actual available water volume V a ; The formula is: V a =β·V t By introducing a reduction coefficient corresponding to the blockage risk level, the above-mentioned subunits can dynamically regulate the amount of water available for irrigation, ensuring that water resources are not ineffectively utilized in high-risk situations, thereby effectively improving the robustness and water-saving performance of the overall system operation.

[0093] The irrigation decision generation module includes a water demand curve matching unit, an irrigation duration calculation unit, and a valve sequence generation unit; wherein:

[0094] Water demand curve matching unit: used to receive the actual available water output by the dynamic availability analysis module, and call the water demand curve of the corresponding crop growth stage of each irrigation area preset in the system, identify its target water demand intensity value according to the current crop stage, and combine the planting area and water demand intensity of each area to form the expected irrigation water volume

[0095] Irrigation duration calculation unit: based on the expected irrigation water volume for each irrigation area and the unit flow rate F of the corresponding area in the irrigation system i (Unit: L / min), calculate the original irrigation time T of each area i When the total expected water volume Exceeds the actual available water volume V aWhen , the irrigation time of each area is scaled proportionally, and the final irrigation time T is output. i ′, to meet the total water resource constraint;

[0096] The specific calculation formula is: Among them, T i represents the original irrigation duration of area i (unit: min), T i ′ represents the final irrigation duration after scaling adjustment (unit: min); V a is the actual available water volume (unit: L);

[0097] Valve sequence generation unit: used to generate the opening sequence, duration and time period number of multi-area irrigation valves based on the irrigation priority of each area and the calculated final irrigation time, and output it as a structured irrigation control instruction set for the instruction execution control module to call; the above unit matches and analyzes the water demand model of crops at different growth stages with the current available water resources. The system can achieve precise irrigation resource allocation and dynamically adjust the irrigation rhythm in combination with management and control capabilities, effectively improving water resource utilization and the coordination of crop growth.

[0098] The water demand curve matching unit includes:

[0099] Crop stage identification subunit: used to call the preset crop variety database and determine the crop growth stage number S of each irrigation area in combination with the current date and the start time of crop planting i ;

[0100] Table 2 Example of crop variety database

[0101]

[0102] In Table 2 above, the crop number is a unique identifier used to quickly retrieve crop information; the crop name is consistent with the planting variety set in the irrigation area; the growth stage number / name is a stage identifier divided according to agronomic standards and is used for stage identification; the start day / end day is the cumulative number of days from the sowing date, which is used to accurately locate the current stage; the daily water demand intensity per unit area is the stage water demand parameter determined experimentally, which provides a quantitative basis for the subsequent calculation of the expected irrigation water volume.

[0103] Water demand intensity extraction subunit: used to extract water from the identified crop stage number S i The daily water demand intensity per unit area at this stage is extracted from the corresponding water demand curve, and the expected daily irrigation water volume for this area at this growth stage is calculated by allocating it to the planting area of ​​each region. The formula is: in, represents the expected daily irrigation water volume of the ith irrigation area (unit: L), Q iis the daily water demand intensity per unit area, A i is the planting area (unit: m 2 );

[0104] Regional total expected water volume generation subunit: used for the total expected water volume of the entire irrigation area Accumulate and form the total expected irrigation water volume for the current cycle This value is then output to the irrigation duration calculation subunit for subsequent water distribution processing.

[0105] The instruction execution control module includes an instruction parsing unit, a communication encapsulation unit, a gateway transmission unit, and an execution status confirmation unit; wherein:

[0106] Instruction parsing unit: used to receive a structured control instruction set containing the irrigation duration information and valve number opening sequence corresponding to each irrigation area, and parse it into a standardized control instruction format;

[0107] Communication encapsulation unit: used to group the parsed control instructions by area, encapsulate them into a data communication protocol format compatible with the IoT gateway, and generate instruction data packets with duration, area number, and valve sequence fields;

[0108] Gateway transmission unit: used to send encapsulated command data packets to irrigation actuators in corresponding areas via wireless communication through the deployed IoT gateway, ensuring reliable transmission of control commands and triggering execution actions;

[0109] Execution status confirmation unit: After the command is issued, it is used to continuously monitor the response status information of each irrigation actuator to confirm whether the valve is opened in the preset order and duration. The above unit integrates command disassembly, protocol encapsulation, communication issuance and execution into a closed operation process to achieve standardized issuance and precise execution of irrigation control commands under the Internet of Things architecture, ensuring the high reliability and real-time response of irrigation commands in a multi-region and multi-device environment.

[0110] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been described in detail.

[0111] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. The rainwater reuse intelligent irrigation decision-making system based on the Internet of Things is characterized by: It includes a rainwater data collection module, a multi-source data processing module, a dynamic availability analysis module, an irrigation decision generation module, and an instruction execution control module; among which: Rainwater data collection module: It is used to obtain rainwater storage data and turbidity data in real time through a water level sensor and a turbidity sensor deployed in the rainwater collection device. Multi-source data processing module: It is used to receive the rainwater storage data, combine the rainfall probability in the next 24 hours provided by the meteorological prediction platform, and calculate the dynamically corrected storage volume. At the same time, it receives the turbidity data and generates an irrigation blockage risk level when the turbidity exceeds the preset threshold. Dynamic availability analysis module: It is used to calculate the difference between the dynamically corrected storage volume and the soil moisture content data of each irrigation area to obtain the theoretical available irrigation water volume. At the same time, it reduces the theoretical available irrigation water volume according to the irrigation blockage risk level and outputs the actual available water volume. Irrigation decision generation module: It is used to match the preset water demand curve for the crop growth stage according to the actual available water volume, and generate the irrigation duration and valve opening sequence for each irrigation area. Instruction execution control module: It is used to send the irrigation duration and valve opening sequence to the irrigation actuator in the corresponding area through the IoT gateway.

2. The rainwater reuse intelligent irrigation decision-making system based on the Internet of Things according to claim 1 is characterized in that: The rainwater data collection module includes a water level detection unit, a turbidity detection unit, and a data synchronization and upload unit; among which: Water level detection unit: It is deployed at the bottom or middle area of the rainwater collection device, uses an ultrasonic or pressure water level sensor to collect the current rainwater height information, combines the cross-sectional area parameters of the collection device, calculates the corresponding rainwater storage data, and outputs the rainwater storage data in the form of a digital signal. Turbidity detection unit: It is set at the same vertical height as the water level detection unit, and is used to obtain the scattered light intensity of the water body through a photoelectric turbidity sensor, and convert the optical signal into a standard turbidity value according to the built-in turbidity conversion algorithm. Data synchronization and upload unit: It is used to mark the time stamps of the data collected by the water level detection unit and the turbidity detection unit, and synchronously upload them to the multi-source data processing module at a set frequency.

3. The rainwater reuse intelligent irrigation decision-making system based on the Internet of Things according to claim 1 is characterized in that: The multi-source data processing module includes a storage volume correction unit and a risk level generation unit; among which: Storage capacity correction unit: used to receive the rainwater storage capacity data uploaded by the rainwater data acquisition module, and simultaneously receive the next 24 hours rainfall probability data provided by the external meteorological forecast platform, and dynamically correct the current storage capacity according to the preset meteorological sensitivity coefficient to obtain the dynamic correction storage capacity V r ; Risk level generation unit: It is used to receive the rainwater turbidity data provided by the rainwater data collection module, compare it with the preset turbidity threshold, and when the turbidity value is higher than the threshold, set the irrigation blockage risk level according to its deviation degree.

4. The rainwater reuse intelligent irrigation decision-making system based on the Internet of Things according to claim 3 is characterized in that: The risk level generation unit includes: Turbidity comparison sub-unit: It is used to receive the rainwater turbidity value from the rainwater data collection module, set it as T, and compare its numerical value with the preset turbidity safety threshold T0 to judge whether there is an over-standard situation. When T>T0 holds, it enters the risk classification processing process. When T≤T0, it is determined that there is no risk level. Deviation calculation subunit: used to calculate the turbidity deviation ratio D when the turbidity exceeds the standard. The formula is: Risk coding sub-unit: It is used to match the turbidity deviation ratio D with the risk level correspondence table, generate the corresponding irrigation blockage risk level according to the preset interval, and the output result is represented by hierarchical coding. If 0<D≤0.2, generate a low risk level L1; If 0.2<D≤0.5, generate a medium risk level L2; If D>0.5, generate a high risk level L3.

5. The rainwater reuse intelligent irrigation decision-making system based on the Internet of Things according to claim 1 is characterized in that: The dynamic availability analysis module includes a soil moisture collection unit, a difference calculation unit, and a reduction processing unit; wherein: Soil moisture collection unit: used to deploy soil moisture sensors in each irrigation area to obtain the current soil moisture data of each irrigation area and classify and store it according to the area number; The difference calculation unit is used to receive the dynamic correction storage capacity output by the multi-source data processing module and the current moisture content data of each area provided by the soil moisture collection unit; it calculates the difference between the target moisture content and the current moisture content of each irrigation area to obtain the water demand of the corresponding area, and based on this, comprehensively estimates the theoretical irrigable water volume; Reduction processing unit: used to receive the irrigation blockage risk level output by the multi-source data processing module, proportionally reduce the theoretical irrigable water volume according to the preset risk impact coefficient table, and output the final actual available water volume.

6. The rainwater reuse intelligent irrigation decision-making system based on the Internet of Things according to claim 5 is characterized in that: The difference calculation unit includes: Regional water demand calculation subunit: used to receive the current water content data of each irrigation area provided by the soil water content acquisition unit, and call the preset target water content value of each area, perform difference calculation according to the area, and obtain the unit volume soil water demand of each area, and then calculate the total water demand W of each area based on the regional area and soil layer irrigation depth. i ; Irrigable volume estimation subunit: used to summarize the total water demand W of all areas i , and then compare it with the dynamic correction storage volume provided by the multi-source data processing module, and take the minimum value as the theoretical irrigable water volume; the calculation formula is: Among them, V t Indicates the theoretical irrigable water volume, V r represents the dynamically corrected storage capacity, and n represents the total number of irrigation areas.

7. The rainwater reuse intelligent irrigation decision-making system based on the Internet of Things according to claim 6 is characterized in that: The reduction processing unit includes: Level identification subunit: used to receive the irrigation blockage risk level information output by the multi-source data processing module and call the reduction coefficient β corresponding to the level in the preset risk impact coefficient table; the reduction coefficient value range is between 0 and 1; Reduction calculation subunit: used to calculate the theoretical irrigable water volume V output by the difference calculation unit according to the reduction coefficient β determined by the level identification subunit. t Perform proportional reduction to calculate the final actual available water volume V a ; The formula is: V a =β·V t .

8. The rainwater reuse intelligent irrigation decision-making system based on the Internet of Things according to claim 9 is characterized in that: The irrigation decision generation module includes a water demand curve matching unit, an irrigation duration calculation unit, and a valve sequence generation unit; wherein: Water demand curve matching unit: used to receive the actual available water output by the dynamic availability analysis module, and call the water demand curve of the corresponding crop growth stage of each irrigation area preset in the system, identify its target water demand intensity value according to the current crop stage, and combine the planting area and water demand intensity of each area to form the expected irrigation water volume Irrigation duration calculation unit: based on the expected irrigation water volume for each irrigation area and the unit flow rate F of the corresponding area in the irrigation system i , calculate the original irrigation time T of each area i ; When the total expected water volume Exceeds the actual available water volume V a When , the irrigation time of each area is scaled proportionally, and the final irrigation time T is output. i ′, to meet the total water resource constraint; The specific calculation formula is: Among them, T i represents the original irrigation duration of region i, T i ′ represents the final irrigation duration after scaling adjustment; V a is the actual available water volume; Valve sequence generation unit: used to generate the opening sequence, duration and time segment number of multi-zone irrigation valves based on the irrigation priority of each zone and the calculated final irrigation duration, and output it as a structured irrigation control instruction set.

9. The Internet of Things-based rainwater reuse intelligent irrigation decision-making system according to claim 8 is characterized in that: The water demand curve matching unit includes: Crop stage identification subunit: used to call the preset crop variety database and determine the crop growth stage number S of each irrigation area in combination with the current date and the start time of crop planting i ; Water demand intensity extraction subunit: used to extract water from the identified crop stage number S i The daily water demand intensity per unit area at this stage is extracted from the corresponding water demand curve, and the expected daily irrigation water volume for this area at this growth stage is calculated by allocating it to the planting area of ​​each region. The formula is: in, represents the expected daily irrigation water volume of the ith irrigation area, Q i is the daily water demand intensity per unit area, A i is the planting area; Regional total expected water volume generation subunit: used for the total expected water volume of the entire irrigation area Accumulate and form the total expected irrigation water volume for the current cycle 10. The rainwater reuse intelligent irrigation decision-making system based on the Internet of Things according to claim 8 is characterized in that: The instruction execution control module includes an instruction parsing unit, a communication encapsulation unit, a gateway transmission unit, and an execution status confirmation unit; wherein: Instruction parsing unit: used to receive a structured control instruction set containing the irrigation duration information and valve number opening sequence corresponding to each irrigation area, and parse it into a standardized control instruction format; Communication encapsulation unit: used to group the parsed control instructions by area, encapsulate them into a data communication protocol format compatible with the IoT gateway, and generate instruction data packets with duration, area number, and valve sequence fields; Gateway transmission unit: used to send the encapsulated instruction data packets to the irrigation actuators in each corresponding area through the deployed IoT gateway in a wireless communication manner; Execution status confirmation unit: After the command is issued, it is used to continuously monitor the response status information of each irrigation actuator to confirm whether the valve is opened in the preset order and duration.

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