Method for water-soil conservation, drought resistance and disaster reduction of navel orange garden rainwater runoff resources

Abstract

The application discloses a navel orange garden rainwater runoff resource water and soil conservation drought resistance and disaster reduction method, relates to the technical field of intelligent agricultural water resource management, and is used for solving the problems of water and fertilizer waste and water and soil loss. Through construction of a closed-loop dynamic regulation and control mechanism of sensing, decision-making, execution and verification, a thing networking monitoring network is deployed to collect multi-source environmental data, and then an edge computing gateway generates control instructions in real time according to a rule base, so that valves, water pumps and other facilities are linked to execute precise irrigation and runoff regulation. Meanwhile, key benefit indexes such as rainwater storage capacity, irrigation water saving rate and soil loss reduction amount are calculated based on execution feedback data, and the hash values of the indexes are stored in a trusted manner. Finally, when the benefits do not meet the standards or the data is abnormal, root cause analysis is performed according to the stored historical data, and the optimal repair work order is generated based on the cost priority rules, so that the system is self-optimized. The application significantly improves the precision, system robustness and long-term benefits of water resource management while reducing manual intervention.

Description

Technical Field

[0001] This invention relates to the field of smart agricultural water resource management technology, and more specifically, to a method for soil and water conservation, drought resistance and disaster reduction using rainwater runoff resources in navel orange orchards. Background Technology

[0002] In the field of smart agriculture water and soil resource management, water cycle regulation and drought resistance in specific economic fruit orchards, such as navel orange orchards, have formed a clear technological direction. Current technologies typically rely on deploying sensor networks such as soil moisture and weather stations, and automating the control of irrigation and water storage facilities based on threshold rules. The core of these technologies lies in achieving on-demand allocation of water resources through data collection and single-point response.

[0003] The fundamental contradiction in existing technologies lies in the unbridgeable gap between the discrete decision-making commands issued by the central system and the continuous and nonlinear dynamic evolution of the field environment. Specifically, the upload cycle of sensor data, the generation and issuance of control commands, and the mechanical response of physical equipment such as valves and pumping stations all involve uncertain and non-uniform time delays. This multi-level delay in the "perception-decision-execution" chain, coupled with the inherent performance differences of different execution units, causes the system response to lag significantly behind the actual development of drought conditions and the instantaneous changes in rainwater runoff. Therefore, multi-source data and the final control actions cannot be precisely aligned and coordinated in time and space, causing soil and water conservation measures to often miss the optimal intervention time and making it difficult to achieve precise regulation based on real dynamics. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a method for soil and water conservation and drought relief in navel orange orchards to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] The method for protecting soil and water resources and mitigating drought in navel orange orchards by utilizing rainwater runoff is characterized by:

[0007] S1. Within the navel orange orchard area, deploy an Internet of Things (IoT) monitoring network consisting of a weather station, soil moisture sensor, water level sensor, water quality sensor, and runoff monitoring sensor. The IoT monitoring network collects meteorological data, soil volumetric water content data, reservoir water level data, water turbidity data, and surface flow velocity and flow data, which are then aggregated to an edge computing gateway to form a real-time data collection set based on the edge computing gateway.

[0008] S2. Based on the real-time collected data set, the rules base pre-built in the edge computing gateway is used for comparison and judgment. When the real-time collected data set meets the triggering conditions of the corresponding threshold parameter rule in the rule base, control commands and early warning information for electric valves, water pumps and guide vanes are generated.

[0009] S3. In response to control commands, the edge computing gateway sends control commands to the corresponding electric valves, water pumps or deflectors to drive the electric valves, water pumps or deflectors to perform the corresponding control operations.

[0010] S4. Based on the data collected by the Internet of Things monitoring network during the control operation of the controlled hydraulic facilities, the rainwater retention capacity is calculated using the water balance algorithm, the irrigation water saving rate is calculated using the comparative analysis method, and the soil loss reduction is calculated based on the surface flow velocity and flow data. The rainwater retention capacity, irrigation water saving rate and soil loss reduction are merged as key benefit indicators, and the corresponding hash values ​​are uploaded to the distributed evidence storage network node for evidence storage.

[0011] S5. When the amount of rainwater retention, irrigation water saving rate, or reduction in soil loss does not reach the preset target, or when the sensor data in the IoT monitoring network is continuously abnormal, a repair work order is generated based on the set of reliable historical data stored in the distributed evidence storage network nodes and the scheme with the lowest comprehensive cost is selected according to the preset cost priority rules.

[0012] In a preferred embodiment, within the navel orange orchard area, an Internet of Things (IoT) monitoring network is deployed, consisting of a weather station, soil moisture sensors, water level sensors, water quality sensors, and runoff monitoring sensors. The collected meteorological data, soil volumetric water content data, reservoir water level data, water turbidity data, and surface flow velocity and flow rate data are aggregated through the IoT monitoring network and sent to an edge computing gateway. Specific steps include:

[0013] Based on the topographic slope and furrow distribution of the navel orange orchard, weather stations are deployed at key runoff points, water level sensors are deployed in reservoirs, water quality sensors are deployed at the inlets of irrigation branch pipes, soil moisture sensors are deployed in the root zone of fruit trees, and runoff monitoring sensors are deployed downstream of key runoff points or in furrows to form an Internet of Things monitoring network.

[0014] Through the Internet of Things (IoT) monitoring network, meteorological data, soil volumetric water content data, reservoir water level data, water turbidity data, and surface flow velocity and flow data collected by weather stations, water level sensors, water quality sensors, soil moisture sensors, and runoff monitoring sensors at a set sampling frequency are transmitted in real time to the edge computing gateway.

[0015] In a preferred embodiment, a real-time data collection set is formed based on an edge computing gateway, and the specific steps include:

[0016] Within the edge computing gateway, the received data from various sensors are aligned, denoised, and formatted according to timestamps and device identifiers.

[0017] The processed data is integrated into structured data records and organized according to time series to form a real-time data collection set.

[0018] In a preferred embodiment, based on the real-time collected data set, a pre-built rule base within the edge computing gateway performs comparison and judgment. When the real-time collected data set meets the triggering conditions of the corresponding threshold parameter rule in the rule base, control commands and early warning information for the electric valve, water pump, and deflector are generated. The specific steps include:

[0019] Based on the real-time data collection, real-time values ​​of meteorological data, soil volumetric water content data, reservoir water level data, water turbidity data, and surface velocity and flow rate data are extracted from the real-time data collection.

[0020] The real-time values ​​are compared item by item with the threshold parameter rules in the pre-built rule base within the edge computing gateway to obtain the comparison results. The threshold parameter rules are associated with the corresponding control targets and warning levels.

[0021] Based on the comparison results, when the real-time value of one or more items meets the triggering conditions defined by the threshold parameter rule, the event response rule in the rule base is called and executed to generate control instructions and early warning information for electric valves, water pumps, and guide vanes.

[0022] In a preferred embodiment, in response to a control command, a control command is sent to the corresponding electric valve, water pump, or baffle plate via an edge computing gateway to drive the electric valve, water pump, or baffle plate to perform the corresponding control operation. Specific steps include:

[0023] The edge computing gateway receives the generated control commands and parses the controlled hydraulic facility identifier, execution action type, and execution parameters contained in the control commands;

[0024] Based on the identification of the controlled hydraulic facilities, determine the electric valve, water pump or baffle corresponding to the control command, and encapsulate the type of action and execution parameters into a drive command that the equipment can recognize;

[0025] Drive commands are sent to the corresponding electric valves, water pumps, or deflectors through the control links in the Internet of Things monitoring network.

[0026] Electric valves respond to drive commands to open or close; water pumps respond to drive commands to start, stop, or adjust speed; and baffles respond to drive commands to adjust their angle.

[0027] In a preferred embodiment, based on data collected from controlled hydraulic facilities during control operations via an IoT monitoring network, a water balance algorithm is used to calculate rainwater retention capacity, a comparative analysis method is used to calculate irrigation water-saving rate, and surface velocity and flow rate data are used to calculate soil erosion reduction. Specific steps include:

[0028] Based on the Internet of Things monitoring network, meteorological data, reservoir water level data, and surface velocity and flow data are collected during the control operation of the controlled hydraulic facilities.

[0029] Based on meteorological data and reservoir water level data, the rainwater retention capacity is calculated using a water balance algorithm.

[0030] Based on soil volumetric water content data, and compared with preset irrigation thresholds, the irrigation water-saving rate is calculated using a comparative analysis method.

[0031] Based on surface velocity and flow data, the reduction in soil loss is calculated.

[0032] In a preferred embodiment, rainwater retention capacity, irrigation water saving rate, and soil erosion reduction are used as key benefit indicator datasets, and the corresponding hash values ​​are uploaded to a distributed evidence storage network node for evidence storage. Specific steps include:

[0033] The amount of rainwater retention, irrigation water saving rate and soil loss reduction are integrated into a set of key benefit indicators;

[0034] A digital digest of the key benefit indicator data set is generated as its hash value, and the hash value is uploaded to the distributed evidence storage network node for evidence storage.

[0035] In a preferred embodiment, when the rainwater retention capacity, irrigation water saving rate, or reduction in soil erosion fails to meet preset targets, or when sensor data in the IoT monitoring network remains abnormal, the specific steps include: Based on the trusted historical data set stored in the distributed evidence storage network nodes, the following steps are taken:

[0036] When the amount of rainwater retention, irrigation water saving rate, or reduction in soil erosion fails to meet the preset targets, or when sensor data in the IoT monitoring network continues to be abnormal, a problem trigger event is generated.

[0037] In response to a problem-triggered event, the system queries and retrieves the key benefit indicator data set and corresponding historical sensor data from the distributed evidence storage network nodes, which serve as a trusted historical data set.

[0038] In a preferred embodiment, a repair work order is generated by selecting the solution with the lowest overall cost according to a preset cost priority rule. Specific steps include:

[0039] Based on reliable historical data sets and real-time collected data sets, data comparison and root cause analysis are conducted to identify key influencing factors and associated controlled hydraulic facilities that lead to non-compliance or abnormalities.

[0040] Based on key influencing factors and associated controlled hydraulic facilities, and combined with a predefined remediation strategy knowledge base, a set of candidate remediation solutions is generated, including equipment overhaul, parameter adjustment, component replacement, or network maintenance actions.

[0041] For each candidate repair scheme in the candidate repair scheme set, based on the preset cost priority rules, the resource consumption cost, operation time cost and expected benefit cost of the candidate repair scheme are evaluated, and the comprehensive cost value is calculated.

[0042] Select the candidate repair solution with the lowest overall cost from the set of candidate repair solutions, and generate a repair work order that includes specific execution actions, resource list, time schedule and acceptance criteria.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1. By integrating real-time multi-source heterogeneous data from the edge and implementing closed-loop dynamic control based on physical rules, this invention achieves collaborative optimization management of multi-dimensional agricultural hydraulic parameters such as soil moisture, runoff path, water quality, and water storage capacity. Based on high-frequency, multi-type sensor time-series data and a pre-built expert rule base, it can accurately identify complex critical states such as soil moisture deficit, runoff overload, and water quality exceeding standards. Furthermore, by generating control commands for valves, pumps, and deflectors that are strictly matched to these states, it enables precise triggering and coordinated execution of irrigation, water retention, and drainage operations. Compared to traditional timed and quantitative or single-factor threshold control methods, this invention constructs a complete closed loop of "perception-decision-execution-verification," dynamically coupling discrete engineering facilities with continuous environmental conditions. This significantly improves the spatiotemporal accuracy and response speed of water resource allocation, maximizing drought resistance, water conservation, and soil and water conservation benefits while reducing human intervention.

[0045] 2. By establishing reliable evidence for key benefit indicators and employing cost-priority-based intelligent diagnosis and repair, a self-optimizing and self-protecting mechanism for the long-term stable operation of the system has been constructed. Based on blockchain evidence storage technology, core performance indicators such as rainwater retention, irrigation water saving rate, and soil erosion reduction are solidified and tamper-proofed, forming a traceable and verifiable "reliable historical data set." When system performance declines or data anomalies occur, in-depth root cause analysis can be performed based on this reliable data baseline to accurately locate "key influencing factors" such as sensor drift, equipment performance degradation, or localized soil seepage. Furthermore, by quantitatively evaluating the resource consumption, operation time, and expected benefits of each candidate repair scheme, the repair strategy with the optimal comprehensive cost is automatically selected and a work order is generated based on preset priority rules. This mechanism transforms reactive post-event maintenance into data-driven predictive maintenance and global optimization decision-making, effectively avoiding system performance degradation caused by single equipment failures or local parameter inaccuracies, and significantly improving the robustness, reliability, and long-term return on investment of the entire water resource management system. Attached Figure Description

[0046] Figure 1 This is a schematic diagram of the structure of the method for soil and water conservation, drought resistance and disaster reduction of rainwater runoff resources in navel orange orchards according to the present invention. Detailed Implementation

[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] Example 1: Figure 1 A schematic diagram of the method for soil and water conservation and drought mitigation in navel orange orchards according to the present invention is provided, which includes the following steps:

[0049] S1. Within the navel orange orchard area, deploy an Internet of Things (IoT) monitoring network consisting of a weather station, soil moisture sensor, water level sensor, water quality sensor, and runoff monitoring sensor. The IoT monitoring network collects meteorological data, soil volumetric water content data, reservoir water level data, water turbidity data, and surface flow velocity and flow data, which are then aggregated to an edge computing gateway to form a real-time data collection set based on the edge computing gateway.

[0050] S2. Based on the real-time collected data set, the rules base pre-built in the edge computing gateway is used for comparison and judgment. When the real-time collected data set meets the triggering conditions of the corresponding threshold parameter rule in the rule base, control commands and early warning information for electric valves, water pumps and guide vanes are generated.

[0051] S3. In response to control commands, the edge computing gateway sends control commands to the corresponding electric valves, water pumps or deflectors to drive the electric valves, water pumps or deflectors to perform the corresponding control operations.

[0052] S4. Based on the data collected by the Internet of Things monitoring network during the control operation of the controlled hydraulic facilities, the rainwater retention capacity is calculated using the water balance algorithm, the irrigation water saving rate is calculated using the comparative analysis method, and the soil loss reduction is calculated based on the surface flow velocity and flow data. The rainwater retention capacity, irrigation water saving rate and soil loss reduction are merged as key benefit indicators, and the corresponding hash values ​​are uploaded to the distributed evidence storage network node for evidence storage.

[0053] S5. When the amount of rainwater retention, irrigation water saving rate, or reduction in soil loss does not reach the preset target, or when the sensor data in the IoT monitoring network is continuously abnormal, a repair work order is generated based on the set of reliable historical data stored in the distributed evidence storage network nodes and the scheme with the lowest comprehensive cost is selected according to the preset cost priority rules.

[0054] Within the navel orange orchard area, an Internet of Things (IoT) monitoring network is deployed, consisting of weather stations, soil moisture sensors, water level sensors, water quality sensors, and runoff monitoring sensors. This IoT network collects meteorological data, soil volumetric water content data, reservoir water level data, water turbidity data, and surface flow velocity and flow rate data, which are then aggregated at an edge computing gateway. The specific implementation is as follows:

[0055] Based on the topographic slope and furrow distribution of the navel orange orchard, key runoff points, reservoirs, irrigation branch pipe inlets, fruit tree root zones, and downstream of key runoff points or furrows were identified. Weather stations were deployed at key runoff points to monitor rainfall, wind speed, temperature, and humidity. Water level sensors were installed inside reservoirs to monitor changes in water level. Water quality sensors were installed at irrigation branch pipe inlets to monitor water turbidity. Soil moisture sensors were buried in the soil of the fruit tree root zone to monitor soil volumetric moisture content. Runoff monitoring sensors were installed downstream of key runoff points or in furrows to monitor surface water velocity and flow rate. All sensors and weather stations were connected via wired or wireless communication modules to form an Internet of Things (IoT) monitoring network covering the navel orange orchard.

[0056] Each device in the IoT monitoring network works continuously according to the preset sampling frequency. For example, meteorological data is collected once per minute, soil volumetric moisture content data is collected once every 10 minutes, water level data in reservoirs is collected once every 5 minutes, water turbidity data is collected once every 30 minutes, and surface flow velocity and flow rate data are collected in a triggered manner when water flow is detected.

[0057] Meteorological data collected by weather stations, water level data collected by water level sensors, turbidity data collected by water quality sensors, soil volumetric water content data collected by soil moisture sensors, and surface velocity and flow rate data collected by runoff monitoring sensors will be transmitted in real time via IoT communication protocols and ultimately all converged to the edge computing gateway.

[0058] The real-time data collection is formed based on the edge computing gateway, and the specific implementation is as follows:

[0059] The edge computing gateway parses raw data packets from weather stations, water level sensors, water quality sensors, soil moisture sensors, and runoff monitoring sensors. It extracts device identifiers such as unique sensor numbers and the precise time of data acquisition, i.e., timestamps, from each raw data packet. The edge computing gateway arranges continuous data from the same device identifier in chronological order based on the timestamps and aligns the data from all different sensors according to the same time base.

[0060] Subsequently, the edge computing gateway calls its built-in denoising algorithm to clean the data. Here, denoising refers to identifying and removing outliers that are clearly outside the physically reasonable range, such as soil volumetric water content being greater than field capacity or less than the wilting coefficient, water level in reservoirs exceeding their physical depth, and surface flow velocity reaching an unrealistically high rate. The judgment criteria are based on the pre-stored range thresholds of various sensors and a physical common sense database of the navel orange orchard environment. After denoising is completed, the edge computing gateway uniformly formats all data into a preset standard structure. This standard structure is required to contain several specific fields, including device identifier, timestamp, sensor type, data value, data unit, and data status identifier.

[0061] The edge computing gateway sets a fixed time alignment reference unit, such as 1 minute. At the start of any time alignment reference unit, the edge computing gateway will collect the data from all sensors received during this period. For sensor data with a sampling frequency higher than this reference, the last valid reading in this time period will be taken. For sensors with a sampling frequency lower than this reference, the previous valid reading will be used until a new valid reading arrives. Through this rule, the edge computing gateway generates a structured data record for each time alignment reference unit.

[0062] This data record is implemented as a row in a data table. Its fields encompass the data values ​​corresponding to all on-network sensors at that moment, including a timestamp field, as well as specific data fields such as rainfall, wind speed, temperature, humidity, water level, turbidity, multiple soil moisture content values, and multiple flow velocity and flow rate values. Subsequently, the edge computing gateway continuously appends each structured data record generated to a time-series database stored locally on the edge computing gateway, according to the order of its timestamp. This time-series database is organized using time series as an index, thereby constructing and maintaining a real-time collected data set.

[0063] Based on the real-time collected data set, a pre-built rule base within the edge computing gateway performs comparisons and judgments. When the real-time collected data set meets the triggering conditions of the corresponding threshold parameter rule in the rule base, control commands and early warning information for electric valves, water pumps, and deflectors are generated. Specifically, the implementation is as follows:

[0064] The edge computing gateway extracts the latest structured data records from the time-series database based on a preset judgment period, such as every 5 minutes. The judgment period is set in accordance with the time reference unit for data alignment. From these data records, the edge computing gateway extracts meteorological data including rainfall, wind speed, temperature, and humidity, soil volumetric water content data of each fruit tree root zone, water level data of each reservoir, turbidity data of water at the inlet of irrigation branch pipes, and surface velocity and flow rate data of each key confluence point downstream or furrow. These extracted latest values ​​are the real-time values.

[0065] The edge computing gateway compares the threshold defined by each threshold parameter rule in its internal pre-built rule base with the extracted corresponding real-time values ​​to obtain the comparison results. The threshold parameter rules include: soil moisture thresholds for different depths in the root zone of fruit trees. These thresholds are set by agronomic experience based on the water requirements of navel oranges at different growth stages, as a specific percentage value between field capacity and wilting coefficient. Field capacity and wilting coefficient are obtained by selecting representative soil samples from the navel orange orchard, measuring field capacity using an indoor ring cutter method, and measuring the wilting coefficient using a pot method. Based on this, for example, the irrigation trigger threshold can be set to 65% of the measured field capacity. The thresholds also include water level thresholds for reservoirs of different capacities. These water level thresholds are set according to the physical depth and designed safety capacity of the reservoir, including measures to prevent overflow. The system includes high water level alarm thresholds and low water level early warning thresholds to ensure water supply safety. It also includes turbidity thresholds for the water at the inlet of irrigation branch pipes, set according to the requirements for suspended solids content in irrigation water quality standards, specifically referring to the water quality requirements for general crop irrigation water in the People's Republic of China National Standard GB5084-2021, and incorporating engineering experience in preventing blockages in drip irrigation systems. For example, the turbidity threshold triggering filtration or stopping water intake can be set to 50. Additionally, it includes velocity and flow rate thresholds for downstream of key confluence points or furrows, set according to the flow capacity of ditches and soil and water conservation requirements. For instance, for unlined loam drainage ditches in the park, to effectively prevent erosion, the critical flow velocity that may cause erosion can be set to 0.8 based on soil and water conservation engineering experience. Each threshold parameter rule clearly links the control target to the early warning level.

[0066] Based on the comparison results, when the real-time value of one or more items meets the triggering condition defined by any threshold parameter rule, the event response rule corresponding to that threshold parameter rule in the rule base is called and executed, thereby generating specific control instructions and warning information. The event response rule defines the specific triggering logic and response actions.

[0067] For example, an irrigation event response rule for a specific irrigation block is triggered when the real-time value of the soil volumetric moisture content data of the irrigation block is lower than the soil moisture threshold set for the block, and the real-time value of the water level data of the associated reservoir supplying water to the block is higher than its low water level warning threshold, and the real-time value of the turbidity data of the water body on the water supply path is lower than the turbidity threshold. Then, the triggering condition is determined to be met, the edge computing gateway executes the rule, generates a warning message containing three levels of warnings, and sends an opening control command to the associated specific numbered electric valve and water pump.

[0068] A drainage event response rule for preventing gully erosion is triggered when the real-time value of the surface velocity and flow rate data downstream of a key confluence point exceeds the velocity and flow rate threshold set for that point, and the real-time value of the turbidity data of the water body at that point also exceeds the turbidity threshold. In this case, the triggering condition is met, and the edge computing gateway generates an early warning message containing a three-level warning and sends an angle adjustment control command to the associated specific numbered guide vane. For cases where only an early warning is needed, such as when the real-time value of the water level data of a reservoir exceeds its high water level alarm threshold, the corresponding rule is triggered to generate an early warning message containing a two-level warning without generating a control command.

[0069] In response to control commands, the edge computing gateway sends control commands to the corresponding electric valves, pumps, or baffles, driving them to perform corresponding control operations. Specifically, this is implemented as follows:

[0070] The edge computing gateway receives control commands, which are structured digital instruction packages. The edge computing gateway parses these commands and extracts key information fields, including: a controlled hydraulic facility identifier, which uniquely identifies a specific device deployed in the navel orange orchard, such as the electric valve at the outlet of reservoir A or the main irrigation pump B; an action type, which clarifies the fundamental operation category the device needs to perform, such as opening or closing for an electric valve, starting, stopping, or speed adjustment for a pump, and angle adjustment for a baffle plate; and execution parameters, which quantify the specific requirements of the action, such as no parameters or an opening percentage for an opening action, a target speed or power percentage for a speed adjustment action, and a target angle value for an angle adjustment action.

[0071] The edge computing gateway queries a pre-stored device address mapping table based on the controlled hydraulic facility's identifier. This table, pre-configured and stored within the gateway, defines the mapping relationship between logical identifiers and physical network addresses. It contains two corresponding fields: the controlled hydraulic facility identifier and the device's network address. By querying the device address mapping table, the edge computing gateway resolves the controlled hydraulic facility identifier into the corresponding device's unique addressable information within the IoT monitoring network, determining the physical device to which the control command needs to be sent—the corresponding electric valve, water pump, or baffle plate.

[0072] Subsequently, the edge computing gateway encapsulates the action type and execution parameters into drive instructions specified by the device manufacturer, which can be directly recognized and executed by the device controller, based on the device model and communication protocol. This conversion is based on the protocol driver library pre-installed by the edge computing gateway, which contains mapping rules for the instruction sets of different device models.

[0073] The drive command is sent to the network address of the corresponding electric valve, water pump, or deflector via a pre-established wired or wireless control link in the IoT monitoring network. The control link is a dedicated communication path that is independent of or logically isolated from the data acquisition communication channel. After the drive command is sent, the edge computing gateway starts an acknowledgment timer and waits to receive an instruction reception acknowledgment signal from the device controller. The instruction reception acknowledgment signal is an acknowledgment message returned by the device controller after correctly receiving and verifying the drive command, which includes its own device identifier and a successful reception status code.

[0074] If no acknowledgment signal is received within the preset timeout period, such as 5 seconds, the edge computing gateway will retransmit according to the preset retransmission policy. The parameters of the retransmission policy, such as the retransmission interval and the maximum number of retries, are pre-configured based on the network communication quality and device response characteristics. For example, the retransmission interval can be set to 3 seconds and the maximum number of retries can be set to 2. If it still fails, a command issuance failure event containing the controlled hydraulic facility identifier and the failure timestamp will be recorded, and corresponding device communication failure warning information will be generated.

[0075] When an electric valve, water pump, or deflector receives a drive command from an edge computing gateway, its built-in controller first verifies the command. If the verification passes, it immediately returns an acknowledgment signal to the edge computing gateway. Subsequently, the controller parses the drive command and drives the specific action: For electric valves, the controller drives the motor to rotate, causing the valve core to move until it reaches the fully open, fully closed, or specific opening percentage position required by the drive command. It is usually equipped with a position sensor to feed back the actual valve position to the controller. For water pumps, the controller drives a contactor or frequency converter to perform start and stop operations, or adjusts the output frequency of the frequency converter according to the speed control command to change the motor speed. The operating status, such as current and frequency, can be monitored by the controller. For deflectors, the controller drives a servo motor or stepper motor to push the deflector to rotate around the axis until the actual angle value fed back by the angle sensor matches the target angle value of the drive command. After all devices have completed their execution, the controller can transmit the final execution result status, such as open, stopped, or fault code, back to the edge computing gateway through the control link.

[0076] Based on data collected from controlled hydraulic structures via an IoT monitoring network, rainwater retention is calculated using a water balance algorithm, irrigation water-saving rate is calculated using a comparative analysis method, and soil erosion reduction is calculated based on surface velocity and flow data. The specific implementation is as follows:

[0077] Based on the Internet of Things monitoring network, during the time period when electric valves, water pumps, or deflectors perform control operations, the edge computing gateway extracts meteorological data such as rainfall, wind speed, temperature, and humidity collected by meteorological stations at key confluence points, water level data collected by water level sensors in water storage tanks, and surface velocity and flow data collected by runoff monitoring sensors downstream of key confluence points or in furrows from the time-series database.

[0078] Rainwater retention capacity The difference between the actual water volume retained in the reservoir during the control operation and the theoretical water volume expected to flow in under the same natural runoff conditions is calculated using a water balance algorithm. The formula is as follows: .

[0079] in: This refers to rainwater retention capacity, measured in cubic meters. It represents the additional amount of water retained through active control of runoff paths. The actual change in water volume in the reservoir during the controlled operation period, i.e., the actual retained water volume, is expressed in cubic meters. It is calculated using the following formula: Formula, These are the start and end times of the control operation, respectively, and the water level data of the reservoir collected by the water level sensor, in meters. This represents the known bottom area of ​​the reservoir, in square meters.

[0080] The theoretical volume of water expected to flow into the reservoir under natural runoff conditions during the same time period, without such control measures, is expressed in cubic meters. It is calculated using the following formula: [Formula omitted] ,in This refers to the total rainfall data, in millimeters, collected by meteorological stations deployed at key confluence points during the control operation period. This is an effective rainfall coefficient, pre-determined based on the underlying surface conditions of the navel orange orchard, including soil type, vegetation cover, and initial soil moisture before rainfall. Its value ranges from 0 to 1. The effective rainfall coefficient represents the catchment area of ​​the reservoir, expressed in square meters. This refers to the proportion of rainfall that forms surface runoff and flows into the catchment area. Its specific value is obtained by regression analysis of the measured rainfall P from multiple typical rainfall events in the navel orange orchard and the total runoff of the corresponding catchment area observed by runoff monitoring sensors under natural conditions without human intervention.

[0081] First, the soil volumetric water content data corresponding to the root zone of the fruit trees, collected by the associated soil moisture sensor at the trigger time of generating the irrigation control command, is extracted from the time-series database. This soil volumetric water content data is used as the actual value of the soil volumetric water content data before irrigation. The preset irrigation threshold is the soil moisture threshold set by the irrigation event response rules in the edge computing gateway rule base. This threshold is preset based on the field water holding capacity and wilting coefficient measured by representative soil samples in the navel orange orchard, and combined with the water requirement characteristics of navel oranges at different growth stages, for example, it is set to 65% of the measured field water holding capacity.

[0082] The water-saving effect is evaluated by comparing the total irrigation water consumption of an automatic irrigation mode triggered by real-time data collection and rule base within a complete irrigation cycle under the same climatic conditions and crop growth stage with the total irrigation water consumption of a traditional timed and quantitative irrigation mode during the same period. A complete irrigation cycle can be defined as a complete crop growth stage or a fixed statistical period. The total irrigation water consumption under the automatic precision irrigation mode is obtained by accumulating all irrigation events issued and successfully executed by the edge computing gateway within the cycle, and calculating the single irrigation water volume based on the changes in the water level sensor data of the associated reservoir and the known bottom area of ​​the reservoir. The calculation process of the single irrigation water volume is as follows: the water level sensor value of the reservoir is read and recorded as the initial water level before the start of each irrigation control process, and the water level sensor value at the stable moment after the end of the irrigation control process and confirmation that the water pump and valve are closed is recorded as the final water level. The difference between the initial water level and the final water level is calculated as the water level drop height. This water level drop height is multiplied by the known constant bottom area of ​​the reservoir, and the product is the single irrigation water volume corresponding to that irrigation control process. The irrigation control process refers to a complete control operation in which, when real-time data collection meets the triggering conditions of the irrigation event response rules, the edge computing gateway generates and sends opening control commands to specific electric valves and water pumps, which are then successfully executed.

[0083] The total irrigation water consumption under the traditional timed and quantitative irrigation mode is determined based on the total water consumption of fixed-duration and fixed-flow irrigation schemes used for fruit trees of similar age in the same season under similar meteorological conditions, according to the historical management records of the navel orange orchard. The final irrigation water-saving rate is calculated by the following relationship: the irrigation water-saving rate is equal to the total irrigation water consumption of the traditional timed and quantitative irrigation mode minus the total irrigation water consumption of the automatic precision irrigation mode, and the difference is then divided by the total irrigation water consumption of the traditional timed and quantitative irrigation mode.

[0084] The edge computing gateway extracts continuous surface velocity and flow data collected by runoff monitoring sensors from the local time-series database within a selected evaluation period. The selected evaluation period refers to a period of time after the deflector angle adjustment control operation is performed to change the runoff path, or a complete independent rainfall event process from the start of rainfall to the complete cessation of surface runoff.

[0085] The reduction in soil loss is calculated based on a soil erosion coefficient pre-calibrated through field tests. This coefficient characterizes the amount of soil that can be carried away per unit runoff at a specific flow velocity. A bare test slope or ditch section without additional soil and water conservation measures is selected within the navel orange orchard. This test slope or ditch section serves as a standard monitoring section. Runoff monitoring sensors and specialized soil loss collection and measurement devices are installed at this location. After one or more typical rainfall events, runoff velocity and flow data measured by the runoff monitoring sensors are recorded synchronously. Based on the runoff velocity and flow data, the total runoff passing through the monitoring section during that rainfall event is calculated, along with the total soil loss in the test area during the corresponding time period measured by the soil loss collection and measurement device. Finally, regression analysis is performed on the total soil loss and corresponding total runoff data obtained from multiple synchronous observations to calculate the proportional relationship between the total runoff and total soil loss under the underlying surface conditions of the navel orange orchard. This proportion is the soil erosion coefficient. The total runoff refers to the total volume of runoff passing through the monitoring section within the selected assessment time period.

[0086] When calculating the reduction in soil loss, the edge computing gateway calculates the total runoff through the monitoring section during the selected assessment period based on each piece of surface velocity and flow data collected during that period. Then, it multiplies this total runoff by a pre-calibrated soil erosion coefficient to obtain the actual amount of soil erosion that occurred during that period under the current control measures.

[0087] As a basis for comparison, the theoretical soil erosion under no control measures needs to be calculated. The edge computing gateway queries the time series database for surface velocity and flow data recorded by runoff monitoring sensors at the same monitoring point during historical periods with similar meteorological conditions to the current assessment period and without any runoff control operations. Based on this historical data, the corresponding historical total runoff is calculated and then multiplied by the same soil erosion coefficient. The result is the theoretical soil erosion under natural conditions. The final reduction in soil loss is the difference between the theoretical soil erosion and the actual soil erosion.

[0088] The datasets of rainwater retention, irrigation water saving rate, and soil erosion reduction are used as key benefit indicators. These datasets are then merged, and the corresponding hash values ​​are uploaded to a distributed evidence storage network node for storage. The specific implementation is as follows:

[0089] The edge computing gateway associates and packages the rainwater retention capacity, irrigation water saving rate, and soil erosion reduction with the corresponding selected assessment time period and the relevant controlled hydraulic facility identifiers. The edge computing gateway organizes this information according to a preset standard data format, which includes the data record generation timestamp, indicator type field, indicator value field, start and end timestamps of the selected assessment time period corresponding to the indicator calculation, and the associated controlled hydraulic facility identifier field, forming a structured set of key benefit indicator data.

[0090] The edge computing gateway calls its built-in cryptographic hash function, such as the SHA-256 algorithm, to perform calculations on the complete data content of the key benefit indicator data set, generating a fixed-length string that uniquely corresponds to the current content of the data set. This string is the hash value of the data set. After generating the hash value, the edge computing gateway encapsulates the hash value along with the generation timestamp of the key benefit indicator data set into a storage request message conforming to a preset format through its network interface, and sends it to the pre-configured distributed storage network node.

[0091] When the rainwater retention capacity, irrigation water saving rate, or reduction in soil erosion fails to meet the preset targets, or when sensor data in the IoT monitoring network continues to be abnormal, the specific implementation is based on the trusted historical data set stored in the distributed evidence storage network nodes:

[0092] The edge computing gateway compares the preset targets defined by the threshold parameter rules for rainwater retention, irrigation water saving rate, and soil erosion reduction in its internal pre-built rule base with each of the following: rainwater retention, irrigation water saving rate, and soil erosion reduction. The preset targets are the minimum retention volume target value for rainwater retention, the minimum water saving percentage target value for irrigation water saving rate, and the minimum reduction mass target value for soil erosion reduction. These target values ​​are pre-set and configured in the rule base by the management personnel according to the water resource management plan and soil and water conservation goals of the navel orange orchard.

[0093] When the amount of rainwater retention, irrigation water saving rate, and reduction in soil erosion are lower than their corresponding preset targets, it is determined that the preset targets have not been met. The edge computing gateway continuously monitors the data status of each sensor in the IoT monitoring network. When its built-in pre-stored sensor range threshold and physical common sense library identify abnormal values ​​from the same sensor that are outside the physical reasonable range for three consecutive data alignment cycles, or when the sensor communication link is continuously interrupted for more than a preset time, such as more than 15 minutes, it is determined that the sensor data is continuously abnormal. Once the preset target is not met or the data is continuously abnormal, the edge computing gateway generates a problem trigger event. The data alignment cycle is a preset time interval used to coordinate and synchronize data from different sensors. Its length is preset according to the sensor type and data update frequency.

[0094] The edge computing gateway determines the selected evaluation time period to be verified based on the event type and associated identifier recorded in the problem-triggered event. Then, it sends a query request to the distributed evidence storage network node. After receiving the query request, it retrieves the corresponding evidence storage record from the distributed ledger based on the hash value and returns the complete set of key benefit indicator data corresponding to these evidence storage records.

[0095] After receiving the returned set of key benefit indicators, the edge computing gateway recalculates its hash value using the same cryptographic hash function, such as the SHA-256 algorithm, and compares it with the hash value obtained from the evidence storage network node. If they match, it verifies that the set of data has not been tampered with since it was stored. At the same time, the edge computing gateway extracts historical sensor data from the local time series database, including meteorological data, soil volumetric water content data, reservoir water level data, water turbidity data, and surface flow velocity and flow data collected by relevant sensors in the IoT monitoring network within the same selected evaluation period. The verified set of key benefit indicators and the corresponding historical sensor data are associated and merged by timestamp to form a trusted historical data set.

[0096] The repair work order is generated based on the preset cost priority rules, selecting the solution with the lowest overall cost. The specific implementation is as follows:

[0097] The edge computing gateway performs time-series alignment and item-by-item comparison between historical sensor data and corresponding key benefit indicator data sets from a trusted historical data set containing similar meteorological conditions during a selected evaluation period, and the corresponding data for the current period in the real-time data set. For example, if the irrigation water-saving rate does not meet the standard, it compares the change curve of soil volumetric water content data with irrigation events during the historical normal irrigation cycle (i.e., the historical normal water content curve) with the curve of the current cycle, and calculates the normal range threshold of the soil water content rise rate based on the historical normal curve. At the same time, it performs correlation analysis on the rate of decline of water level data in reservoirs, whether the water turbidity data is abnormal, and the execution record of irrigation control commands. The analysis is automatically executed by the edge computing gateway calling its internal pre-built root cause analysis rule base, which contains multiple clearly defined symptom-factor mapping rules.

[0098] Each symptom-factor mapping rule consists of specific triggering conditions, corresponding conclusions (i.e., key influencing factors), and associated controlled hydraulic facilities identifiers. For example, a specific rule for failure to meet irrigation water-saving standards is defined as follows: If, during a single irrigation control process, the increase in soil volumetric water content per unit time after irrigation begins is calculated through linear fitting, and this increase is lower than the historical normal threshold, and the actual irrigation water consumption calculated based on the drop in reservoir water level and the reservoir bottom area exceeds 10% of the theoretical water demand estimated based on the difference between the soil characteristics and soil volumetric water content data of the irrigation block, then the key influencing factor of abnormally high soil seepage rate in the irrigation block is triggered. This is then associated with the soil moisture sensor and water supply electric valve corresponding to the irrigation block. The edge computing gateway can identify one or more core parameters or links most likely to cause the problem, i.e., key influencing factors, and determine the controlled hydraulic facilities directly related to these key influencing factors.

[0099] Based on the identified key influencing factors and associated controlled hydraulic facilities, the edge computing gateway queries its internal pre-built repair strategy knowledge base to generate a set of candidate repair solutions containing specific maintenance actions. The repair strategy knowledge base is a predefined rule database that stores standardized repair process templates for various common equipment failures, performance degradation, or abnormal parameter scenarios.

[0100] Each remediation strategy entry is explicitly associated with specific key influencing factors and controlled hydraulic structures, and defines a detailed sequence of remediation actions. For example, if the key influencing factor, soil moisture sensor data, is continuously abnormal, the associated controlled hydraulic structure is the sensor itself. The remediation strategy knowledge base may provide candidate remediation actions, including on-site sensor calibration, cleaning sensor probes, inspecting and repairing sensor communication lines, replacing sensor components, or replacing the entire sensor. The edge computing gateway takes the key influencing factors and the associated controlled hydraulic structure identifiers as input, matches all applicable remediation strategy templates in the remediation strategy knowledge base, and instantiates and generates multiple specific candidate remediation schemes. The candidate remediation schemes include equipment overhaul, parameter adjustment, component replacement, or network maintenance actions.

[0101] For each candidate repair scheme in the set of candidate repair schemes, the edge computing gateway calls its internally pre-defined cost priority rule for evaluation. This cost priority rule defines the quantitative evaluation method for three cost dimensions and their weight coefficients in the comprehensive decision-making.

[0102] First, the resource consumption cost is assessed. This assessment refers to the quantitative value of the economic costs, such as manpower, materials, and external services, required to implement the candidate repair plan. This is obtained by querying a pre-stored resource price list, multiplying the required man-hours, component models and quantities, and the market benchmark unit price of any special services that may be involved in the candidate repair plan, and then summing them up. The resource price list is pre-entered and stored by the management personnel based on local market prices and maintenance contracts.

[0103] Secondly, the time cost of the operation is evaluated. This time cost refers to the estimated total time required from the start of preparation work to the completion and verification of the repair. It is determined by adding up the standard operation time of each network maintenance action in the candidate repair scheme, and additionally taking into account the process connection, on-site conditions and necessary waiting time for static or testing. The standard operation time of each maintenance action comes from the pre-stored equipment maintenance knowledge base. The basic data of this knowledge base is imported from the standard maintenance time manual provided by the equipment manufacturer.

[0104] The expected benefit-cost assessment is a reverse indicator, calculated based on the key influencing factors corresponding to the candidate repair scheme and predicted in conjunction with the normal performance baseline of the equipment recorded in the historical reliable data set. Specifically, for a sensor calibration scheme, its expected benefit is quantified by a benefit evaluation value. The calculation process for this benefit evaluation value is as follows: determine the confidence percentage that the data accuracy is expected to recover to the normal error range after calibration. This confidence percentage is obtained from the statistical analysis of the historical successful calibration records of this model of sensor. For example, the proportion of events in the past when the data recovered to normal in the next acquisition cycle after completing the same calibration process. At the same time, determine the expected effective time, that is, the number of hours required from the completion of the calibration operation to the recovery of the data to normal. Then, the edge computing gateway queries its internal preset timeliness coefficient mapping table. This timeliness coefficient mapping table defines the timeliness coefficients corresponding to different effective time intervals. Multiply the confidence percentage by the timeliness coefficient obtained from the query to obtain the benefit evaluation value of the candidate repair scheme. The expected benefit-cost of the scheme is equal to the preset maximum benefit evaluation value minus this benefit evaluation value.

[0105] The edge computing gateway then multiplies the resource consumption cost, operation time cost, and expected benefit cost of each candidate repair solution by a pre-configured weighting coefficient and adds them together to calculate a comprehensive cost. The weighting coefficients are pre-set and configured in the rule base by the administrator based on the priority of cost control, downtime tolerance, and system reliability requirements in the actual operation and maintenance of the navel orange orchard.

[0106] The candidate repair scheme with the lowest overall cost is selected from the candidate repair scheme set as the final selected execution scheme, and a structured repair work order is generated based on this scheme. The repair work order includes the following specific contents: a specific sequence of execution actions, that is, detailed operation steps from the start of preparation work to the final verification completion. This sequence of execution actions comes from the predefined repair action sequence corresponding to the candidate repair scheme in the repair strategy knowledge base; a list of required resources, including the estimated number of man-hours, the model and quantity of parts to be replaced, special tools or external services. This list of resources is generated by parsing the repair action sequence, querying the pre-stored equipment maintenance knowledge base to obtain standard material and man-hour requirements, and querying the resource price list to obtain the current unit price; a time schedule, including the suggested start time, the estimated duration of each step based on the operation time cost, and the total completion time; and acceptance criteria, that is, quantifiable indicators used to judge whether the repair is successful. For example, after the repair is completed, the relevant sensor data should be continuously within its physical reasonable range for a continuous preset number of data alignment periods and the data reporting success rate should be higher than a preset threshold, or the associated controlled hydraulic facilities can respond correctly and achieve the expected control effect after receiving the test command.

[0107] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0108] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, and a combination of electronic hardware and software. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0109] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0110] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0111] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for water and soil conservation, drought resistance, and disaster reduction in navel orange orchards based on rainwater runoff resources, characterized by: S1. Within the navel orange orchard area, deploy an Internet of Things (IoT) monitoring network consisting of a weather station, soil moisture sensor, water level sensor, water quality sensor, and runoff monitoring sensor. The IoT monitoring network collects meteorological data, soil volumetric water content data, reservoir water level data, water turbidity data, and surface flow velocity and flow data, which are then aggregated to an edge computing gateway to form a real-time data collection set based on the edge computing gateway. S2. Based on the real-time collected data set, the rules base pre-built in the edge computing gateway is used for comparison and judgment. When the real-time collected data set meets the triggering conditions of the corresponding threshold parameter rule in the rule base, control commands and early warning information for electric valves, water pumps and guide vanes are generated. S3. In response to control commands, the edge computing gateway sends control commands to the corresponding electric valves, water pumps or deflectors to drive the electric valves, water pumps or deflectors to perform the corresponding control operations. S4. Based on the data collected by the Internet of Things monitoring network during the control operation of the controlled hydraulic facilities, the rainwater retention capacity is calculated using the water balance algorithm, the irrigation water saving rate is calculated using the comparative analysis method, and the soil loss reduction is calculated based on the surface flow velocity and flow data. The rainwater retention capacity, irrigation water saving rate and soil loss reduction are merged as key benefit indicators, and the corresponding hash values ​​are uploaded to the distributed evidence storage network node for evidence storage. S5. When the amount of rainwater retention, irrigation water saving rate, or reduction in soil loss does not reach the preset target, or when the sensor data in the IoT monitoring network is continuously abnormal, a repair work order is generated based on the set of reliable historical data stored in the distributed evidence storage network nodes and the scheme with the lowest comprehensive cost is selected according to the preset cost priority rules.

2. The method for water and soil conservation, drought resistance, and disaster reduction in navel orange orchards based on rainwater runoff resources according to claim 1, characterized in that: Within the navel orange orchard area, an Internet of Things (IoT) monitoring network is deployed, consisting of weather stations, soil moisture sensors, water level sensors, water quality sensors, and runoff monitoring sensors. This IoT network collects meteorological data, soil volumetric water content data, reservoir water level data, water turbidity data, and surface flow velocity and flow rate data, which are then aggregated at an edge computing gateway. Specific steps include: Based on the topographic slope and furrow distribution of the navel orange orchard, weather stations are deployed at key runoff points, water level sensors are deployed in reservoirs, water quality sensors are deployed at the inlets of irrigation branch pipes, soil moisture sensors are deployed in the root zone of fruit trees, and runoff monitoring sensors are deployed downstream of key runoff points or in furrows to form an Internet of Things monitoring network. Through the Internet of Things (IoT) monitoring network, meteorological data, soil volumetric water content data, reservoir water level data, water turbidity data, and surface flow velocity and flow data collected by weather stations, water level sensors, water quality sensors, soil moisture sensors, and runoff monitoring sensors at a set sampling frequency are transmitted in real time to the edge computing gateway.

3. The method for water and soil conservation, drought resistance, and disaster reduction in navel orange orchards based on rainwater runoff resources according to claim 2, characterized in that: The process of forming a real-time data collection set based on an edge computing gateway includes the following steps: Within the edge computing gateway, the received data from various sensors are aligned, denoised, and formatted according to timestamps and device identifiers. The processed data is integrated into structured data records and organized according to time series to form a real-time data collection set.

4. The method for water and soil conservation, drought resistance, and disaster reduction in navel orange orchard rainwater runoff resources according to claim 1, characterized in that: Based on the real-time collected data set, a pre-built rule base within the edge computing gateway performs comparisons and judgments. When the real-time collected data set meets the triggering conditions of the corresponding threshold parameter rule in the rule base, control commands and early warning information for electric valves, water pumps, and deflectors are generated. The specific steps include: Based on the real-time data collection, real-time values ​​of meteorological data, soil volumetric water content data, reservoir water level data, water turbidity data, and surface velocity and flow rate data are extracted from the real-time data collection. The real-time values ​​are compared item by item with the threshold parameter rules in the pre-built rule base within the edge computing gateway to obtain the comparison results. The threshold parameter rules are associated with the corresponding control targets and warning levels. Based on the comparison results, when the real-time value of one or more items meets the triggering conditions defined by the threshold parameter rule, the event response rule in the rule base is called and executed to generate control instructions and early warning information for electric valves, water pumps, and guide vanes.

5. The method for water and soil conservation, drought resistance, and disaster reduction in navel orange orchards based on rainwater runoff resources according to claim 1, characterized in that: In response to control commands, the edge computing gateway sends control commands to the corresponding electric valves, pumps, or baffles to drive them to perform corresponding control operations. Specific steps include: The edge computing gateway receives the generated control commands and parses the controlled hydraulic facility identifier, execution action type, and execution parameters contained in the control commands; Based on the identification of the controlled hydraulic facilities, determine the electric valve, water pump or baffle corresponding to the control command, and encapsulate the type of action and execution parameters into a drive command that the equipment can recognize; Drive commands are sent to the corresponding electric valves, water pumps, or deflectors through the control links in the Internet of Things monitoring network. Electric valves respond to drive commands to open or close; water pumps respond to drive commands to start, stop, or adjust speed; and baffles respond to drive commands to adjust their angle.

6. The method for water and soil conservation, drought resistance, and disaster reduction in navel orange orchards based on rainwater runoff resources according to claim 1, characterized in that: Based on data collected from controlled hydraulic structures via an IoT monitoring network, the rainwater retention capacity is calculated using a water balance algorithm, the irrigation water-saving rate is calculated using a comparative analysis method, and the reduction in soil erosion is calculated based on surface velocity and flow data. Specific steps include: Based on the Internet of Things monitoring network, meteorological data, reservoir water level data, and surface velocity and flow data are collected during the control operation of the controlled hydraulic facilities. Based on meteorological data and reservoir water level data, the rainwater retention capacity is calculated using a water balance algorithm. Based on soil volumetric water content data, and compared with preset irrigation thresholds, the irrigation water-saving rate is calculated using a comparative analysis method. Based on surface velocity and flow data, the reduction in soil loss is calculated.

7. The method for water and soil conservation, drought resistance, and disaster reduction in navel orange orchard rainwater runoff resources according to claim 6, characterized in that: The datasets of rainwater retention, irrigation water saving rate, and soil erosion reduction are used as key benefit indicators. The corresponding hash values ​​are then uploaded to a distributed evidence storage network node for storage. Specific steps include: The amount of rainwater retention, irrigation water saving rate and soil loss reduction are integrated into a set of key benefit indicators; A digital digest of the key benefit indicator data set is generated as its hash value, and the hash value is uploaded to the distributed evidence storage network node for evidence storage.

8. The method for water and soil conservation, drought resistance, and disaster reduction in navel orange orchard rainwater runoff resources according to claim 1, characterized in that: When rainwater retention, irrigation water saving rate, or reduction in soil erosion fails to meet preset targets, or when sensor data in the IoT monitoring network remains abnormal, the specific steps include: Based on the trusted historical data set stored in the distributed evidence storage network nodes, the following steps are taken: When the amount of rainwater retention, irrigation water saving rate, or reduction in soil erosion fails to meet the preset targets, or when sensor data in the IoT monitoring network continues to be abnormal, a problem trigger event is generated. In response to a problem-triggered event, the system queries and retrieves the key benefit indicator data set and corresponding historical sensor data from the distributed evidence storage network nodes, which serve as a trusted historical data set.

9. The method for water and soil conservation, drought resistance, and disaster reduction in navel orange orchard rainwater runoff resources according to claim 8, characterized in that: A repair work order is generated by selecting the solution with the lowest overall cost according to the preset cost priority rules. The specific steps include: Based on reliable historical data sets and real-time collected data sets, data comparison and root cause analysis are conducted to identify key influencing factors and associated controlled hydraulic facilities that lead to non-compliance or abnormalities. Based on key influencing factors and associated controlled hydraulic facilities, and combined with a predefined remediation strategy knowledge base, a set of candidate remediation solutions is generated, including equipment overhaul, parameter adjustment, component replacement, or network maintenance actions. For each candidate repair scheme in the candidate repair scheme set, based on the preset cost priority rules, the resource consumption cost, operation time cost and expected benefit cost of the candidate repair scheme are evaluated, and the comprehensive cost value is calculated. Select the candidate repair solution with the lowest overall cost from the set of candidate repair solutions, and generate a repair work order that includes specific execution actions, resource list, time schedule and acceptance criteria.

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