Smart irrigation district management method and system based on the Internet of Things

Abstract

The present application provides a smart irrigation district management method and system based on the Internet of Things, wherein the method includes: obtaining real-time irrigation water volume and water pump energy consumption data for each water transmission channel in the target irrigation district; generating a dynamic water resource supply and demand relationship map based on the real-time irrigation water volume and water pump energy consumption data; determining the target water transmission channels corresponding to the water resource transaction requests of each irrigation district water terminal from the target irrigation district; if any target water transmission channel has a water pump overload or a siltation diffusion coefficient value greater than a preset diffusion coefficient threshold, then generating a water resource transaction price for the target water transmission channel based on dynamic water price adjustment rules and the dynamic water resource supply and demand relationship map; generating target water transmission optimization parameters for each target water transmission channel based on the water resource transaction prices of all target water transmission channels and the node status in the dynamic water resource supply and demand relationship map. The present application improves the water resource allocation efficiency of the irrigation district and the operational stability of the water transmission system.

Description

Technical Field

[0001] The present application relates to the field of Internet of Things technology, and in particular to a smart irrigation district management method and system based on the Internet of Things. Background Art

[0002] Modern large-scale irrigation districts face challenges such as inefficient water resource allocation, high water delivery energy consumption, and lagging supply-demand matching. Consequently, precise water resource management based on real-time monitoring is urgently needed. In particular, in water rights trading scenarios, dynamic assessment of channel siltation, pump energy consumption, and other status data is required to optimize water resource allocation through intelligent pricing mechanisms while ensuring the stable operation of water delivery systems.

[0003] Current solutions rely on data acquisition and monitoring control systems to manage irrigation district water resources. These methods deploy flow meters and pressure sensors in key channels, collect water flow data, and upload it to a central control platform. Based on historical water usage patterns, these systems generate warning rules with fixed thresholds. Detecting abnormal flow or pressure triggers manual intervention. Some systems also incorporate static water pricing models, setting tiered pricing based on seasonal and regional water usage.

[0004] The shortcomings of this solution include reliance on fixed threshold warnings, a water price model that is disconnected from the real-time supply and demand status, a lack of a pricing feedback mechanism for data such as energy consumption and siltation, and a centralized architecture that leads to data silos, making it difficult to support water rights transaction verification involving multiple parties. Summary of the Invention

[0005] The present application provides a smart irrigation district management method and system based on the Internet of Things to solve the problems of low water resource allocation efficiency and poor operational stability of the water delivery system in the existing technology.

[0006] In a first aspect, the present application provides a smart irrigation district management method based on the Internet of Things, comprising:

[0007] Obtain real-time irrigation water volume and pump energy consumption data for each water delivery channel within the target irrigation area;

[0008] Generating a dynamic water resource supply and demand relationship map based on the real-time irrigation water volume and water pump energy consumption data;

[0009] determining target water transfer channels corresponding to the water resource transaction requests of the water use terminals in each irrigation area from the target irrigation area, and generating a water resource transaction price for the target water transfer channel based on a dynamic water price adjustment rule and the dynamic water resource supply and demand relationship map if a water pump is overloaded or a siltation diffusion coefficient value is greater than a preset diffusion coefficient threshold in any of the target water transfer channels;

[0010] According to the water resource transaction prices of all the target water transmission channels and the node status in the dynamic water resource supply and demand relationship map, the target water transmission optimization parameters of each target water transmission channel are generated.

[0011] Optionally, determining target water transfer channels corresponding to the water resource transaction requests of the water use terminals in each irrigation area from the target irrigation area, and if any of the target water transfer channels has a water pump overload or a siltation diffusion coefficient value greater than a preset diffusion coefficient threshold, generating a water resource transaction price based on a dynamic water price adjustment rule and the dynamic water resource supply and demand relationship map, includes:

[0012] determining, according to the geographic coordinate range in each of the water resource transaction requests, a target water transmission channel corresponding to each of the water resource transaction requests;

[0013] Calculate the siltation diffusion coefficient value of the target water transfer channel based on the historical extreme values ​​of the channel siltation diffusion coefficient recorded in the dynamic water resource supply and demand relationship map, combined with the real-time monitored channel water flow rate and sediment content; and determine that the target water transfer channel has a siltation risk when the siltation diffusion coefficient value exceeds a preset diffusion coefficient threshold;

[0014] Based on the real-time energy consumption curve of the water pump in the target water transmission channel, the peak energy consumption fluctuation rate within a preset time period in the future is predicted. If the peak energy consumption fluctuation rate exceeds the critical slope corresponding to the water pump energy consumption threshold, it is determined that there is a risk of water pump overload in the target water transmission channel;

[0015] Extracting the water pump energy consumption deviation from the dynamic water resource supply and demand relationship map;

[0016] When there is a risk of siltation or water pump overload in any of the target water transmission channels, the water resource transaction price of the target water transmission channel is calculated based on the initial pricing gradient value associated with the target water transmission channel in the dynamic water price adjustment rule, and the compensation factor generated by the nonlinear relationship between the siltation diffusion coefficient value and the water pump energy consumption deviation is superimposed.

[0017] Optionally, the calculating of the water resource transaction price of the target water transmission channel based on the initial pricing gradient value associated with the target water transmission channel in the dynamic water price adjustment rule and superimposing a compensation factor generated by a nonlinear relationship between the sedimentation diffusion coefficient value and the water pump energy consumption deviation degree includes:

[0018] Extracting the real-time demand fluctuation of the target water transmission channel from the dynamic water resource supply and demand relationship map;

[0019] extracting an initial pricing gradient value associated with the target water transmission channel from the dynamic water price adjustment rule according to the real-time demand fluctuation amount;

[0020] Determine the coefficient corresponding to the priority level of the real-time demand fluctuation as the pricing coefficient;

[0021] Determine the product of the initial pricing gradient value and the pricing coefficient as the target pricing gradient value;

[0022] Based on the target pricing gradient value and the compensation factor, the water resource transaction price of the target water transmission channel is calculated.

[0023] Optionally, the step of calculating the water resource transaction price of the target water transfer channel based on the target pricing gradient value and adding a compensation factor includes:

[0024] The first weight factor and the second weight factor are coupled and calculated using a pre-constructed nonlinear compensation function to generate a compensation factor, wherein the pre-constructed nonlinear compensation function is constructed based on a nonlinear relationship between the siltation diffusion coefficient value and the water pump energy consumption deviation, the first weight factor is a ratio of the water pump energy consumption deviation to a preset energy consumption deviation threshold, and the second weight factor is a ratio of the current water transfer channel siltation diffusion coefficient value to a preset diffusion coefficient change rate threshold;

[0025] Superimposing the target pricing gradient value and the compensation factor according to a preset ratio to obtain a superposition result;

[0026] According to the superposition result, combined with the water transfer weights of adjacent water transfer channels recorded in the dynamic water resource supply and demand relationship map, the water resource transaction price of the target water transfer channel is calculated.

[0027] Optionally, the calculating of the water resource transaction price of the target water transmission channel based on the superposition result and the water transmission weights of adjacent water transmission channels recorded in the dynamic water resource supply and demand relationship map includes:

[0028] Based on the water transmission weights of the adjacent water transmission channels recorded in the dynamic water resource supply and demand relationship map, the average water transmission weights of the adjacent water transmission channels directly connected to the current water transmission channel are calculated, and the average water transmission weights of the adjacent water transmission channels are used as the adjustment coefficient for allocating the target water transmission channel;

[0029] The target pricing gradient value and the adjustment coefficient are weighted and fused according to the superposition result to generate a water resource transaction price.

[0030] Optionally, generating target water transfer optimization parameters for each target water transfer channel based on the water resource transaction prices of all the target water transfer channels and the node states in the dynamic water resource supply and demand relationship map includes:

[0031] The water resource write-off rate of each water transmission channel in the private blockchain network is used as an attenuation factor of the initial pricing gradient value associated with each water transmission channel in the dynamic water price adjustment rule;

[0032] For each target water transmission channel, the water transmission resource weights of adjacent water transmission channels are adjusted according to the real-time energy consumption curve of the water pumps in the target water transmission channel to generate a water transmission resource weight increment;

[0033] generating initial water transfer optimization parameters of a target water transfer channel based on the sedimentation diffusion coefficient value and the water transfer resource weight increment;

[0034] According to the water resource transaction price and the node status of the target water transmission channel in the dynamic water resource supply and demand relationship map, the initial water transmission optimization parameters are adjusted to generate target water transmission optimization parameters.

[0035] Optionally, adjusting the initial water transfer optimization parameters to generate target water transfer optimization parameters based on the water resource transaction price and the node status of the target water transfer channel in the dynamic water resource supply and demand relationship map includes:

[0036] Generate a dynamic correction coefficient for the initial water transfer optimization parameter based on the node status of the target water transfer channel in the dynamic water resource supply and demand relationship map and the water transfer weight of the target water transfer channel, combined with the energy consumption compensation factor embedded in the water resource transaction price;

[0037] Calculating the sedimentation impact coefficient of the target water transfer channel based on the spatial coupling relationship between the channel sedimentation diffusion coefficient value recorded in the dynamic water resource supply and demand relationship map and the water transfer weights of adjacent water transfer channels;

[0038] generating parameter adjustment constraints based on the siltation impact coefficient and the water pump energy consumption deviation;

[0039] Dynamically scaling the initial water delivery optimization parameter according to the dynamic correction coefficient and the parameter adjustment constraint to obtain an intermediate water delivery optimization parameter;

[0040] The intermediate water transfer optimization parameter is boundary-matched with the maximum water carrying capacity threshold of the target water transfer channel recorded in the dynamic water resource supply and demand relationship map to generate the target water transfer optimization parameter, wherein the maximum water carrying capacity threshold is calculated based on the water transfer capacity corresponding to the water pump energy consumption threshold in the target water transfer channel.

[0041] In a second aspect, the present application provides a smart irrigation district management system based on the Internet of Things, comprising:

[0042] The acquisition module is used to obtain the real-time irrigation water volume and water pump energy consumption data of each water delivery channel in the target irrigation area;

[0043] A first generating module is used to generate a dynamic water resource supply and demand relationship map based on the real-time irrigation water volume and water pump energy consumption data;

[0044] a determination module, configured to determine, from the target irrigation districts, target water transfer channels corresponding to the water resource transaction requests of the water use terminals in each irrigation district; and, if any of the target water transfer channels has a water pump overload or a siltation diffusion coefficient value greater than a preset diffusion coefficient threshold, generate a water resource transaction price for the target water transfer channel based on a dynamic water price adjustment rule and the dynamic water resource supply and demand relationship map;

[0045] The second generating module is used to generate target water transfer optimization parameters for each target water transfer channel according to the water resource transaction prices of all the target water transfer channels and the node status in the dynamic water resource supply and demand relationship map.

[0046] In a third aspect, the present application provides a computing device comprising a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute any one of the IoT-based smart irrigation district management methods described in the first aspect.

[0047] In a fourth aspect, the present application provides a computer storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, implement an IoT-based smart irrigation district management method as described in any one of the first aspects.

[0048] In the present application, a smart irrigation district management method based on the Internet of Things is provided, which includes: obtaining real-time irrigation water volume and water pump energy consumption data of each water transmission channel in the target irrigation district; generating a dynamic water resource supply and demand relationship map based on the real-time irrigation water volume and water pump energy consumption data; determining the target water transmission channels corresponding to the water resource trading requests of each irrigation district water use terminal from the target irrigation district, and if any of the target water transmission channels has a water pump overload or a siltation diffusion coefficient value greater than a preset diffusion coefficient threshold, generating a water resource trading price for the target water transmission channel based on dynamic water price adjustment rules and the dynamic water resource supply and demand relationship map; generating target water transmission optimization parameters for each target water transmission channel based on the water resource trading prices of all the target water transmission channels and the node status in the dynamic water resource supply and demand relationship map.

[0049] The technical solution of this application has the following beneficial effects:

[0050] This application uses an IoT sensor network to capture water volume and pump energy consumption data for water transfer channels, digitally mapping the operational status of irrigation districts and providing a data foundation for dynamic decision-making. Real-time data is integrated with historical patterns to generate a dynamic supply-demand relationship map, visualizing the water supply capacity and demand hotspots of each channel and improving the efficiency of water resource allocation in the irrigation district. Water prices are dynamically adjusted based on the risks of channel siltation and pump overload. Transaction prices and node status are combined to generate optimized water transfer parameters, achieving a closed-loop "monitoring-pricing-control" management system and improving the operational stability of the water transfer system.

[0051] Furthermore, the embodiment of the present application also determines transaction-related channels through geographic coordinate matching, calculates the congestion diffusion coefficient and energy consumption volatility in real time to perform risk assessment, and when there is a risk of congestion or overload in the channel, a dynamic compensation factor generated by the degree of congestion and energy consumption deviation is superimposed on the basic pricing gradient, and finally outputs a differentiated transaction price that reflects the health status of the channel.

[0052] In addition, this plan breaks through the traditional static pricing model and directly links the physical state of the channel with the water resource transaction price, so that the water price accurately reflects the infrastructure operating costs, encourages water users to avoid high-risk areas, and achieves the dual goals of protecting irrigation area infrastructure and efficiently allocating water resources.

[0053] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0055] Figure 1 A flowchart of a smart irrigation district management method based on the Internet of Things provided in an embodiment of the present application;

[0056] Figure 2 A schematic diagram of the structure of an IoT-based smart irrigation district management system provided in an embodiment of the present application;

[0057] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0058] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0059] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0060] Researchers have found that current irrigation district water resource management has problems such as delayed data collection, extensive supply and demand matching, and disconnection between pricing mechanisms and infrastructure status, resulting in inefficient water resource allocation and serious losses in the water transmission system. Based on this, the embodiment of the present application provides an IoT-based smart irrigation district management method. This method constructs a dynamic supply and demand map by collecting irrigation water volume and water pump energy consumption data from each water transmission channel in real time, realizes differentiated pricing based on channel siltation and pump station overload risks, and generates optimization parameters based on full channel status data, forming a "monitoring, pricing, and regulation" closed-loop management. The technical solution of this application can be applied to scenarios that require refined management, such as water rights trading in large irrigation districts and cross-regional water resource allocation. Specifically, a dynamic water resource supply and demand relationship map is constructed by acquiring real-time irrigation water volume and water pump energy consumption data of each water transmission channel in the target irrigation area. Based on the map, water terminal transaction requests are matched with target water transmission channels. When a pump overload or siltation risk is detected in a channel, differentiated transaction prices are generated in combination with dynamic water price adjustment rules. Optimization parameters are generated by combining the transaction prices of each channel and node status data, realizing closed-loop management from data collection to intelligent decision-making.

[0061] The entire R&D process reflects the direct linking of the physical infrastructure status with the water resource transaction price, so that the water price accurately reflects the channel operation status, effectively improves the water resource allocation efficiency and the stability of the water transmission system, while reducing energy loss and providing an intelligent solution for irrigation area water resource management.

[0062] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0063] Figure 1 The present application provides a flowchart of a smart irrigation district management method based on the Internet of Things, such as Figure 1 As shown, the method includes:

[0064] Step 101: Acquire real-time irrigation water volume and water pump energy consumption data of each water delivery channel in the target irrigation area.

[0065] In this step, real-time irrigation water volume refers to instantaneous irrigation water volume data collected by channel flow meters, measured in m³ / s. Pump energy consumption data, including real-time pump power, efficiency, and accumulated power consumption, is collected via smart meters. The target irrigation area refers to the specific irrigation region requiring water resource management, encompassing main canals, branch canals, and other water transmission channels. Water transmission channels can be represented in the form of Kxx+xxx. The relationship between pumps and irrigation area water transmission channels: As the core power equipment in water transmission channels, pumps' energy consumption directly reflects the intensity of water transmission operations. Each kWh of energy consumed by a pump lifts approximately 3.6 m³ of water (based on a head of 10 m). Energy consumption data effectively quantifies the work performed by the channel in water transmission. Dedicated pumps are deployed at each key node in the water transmission channel (such as diversion gates and bends), forming a "one pump per channel" or "multiple pumps per channel" topology. By spatially matching the coordinates of the pump positioning system with the channel's geographic information system, energy consumption data can be accurately mapped to specific channel sections. For example, the coordinates (X, Y) of a pumping station correspond to the channel section K12+300~K14+800.

[0066] In an embodiment of the present application, ultrasonic flow meters and smart meters are deployed at key nodes of each water transmission channel in the target irrigation area. The flow meter collects the water flow velocity and water level of the channel cross section every 5 minutes, and obtains the real-time irrigation water volume through integral calculation; the smart meter records the three-phase voltage, current and power factor of the water pump at intervals of 1 minute. The edge computing gateway filters the raw data and removes outliers, and then uploads it to the cloud platform through the 5G network, which is finally integrated into the water pump energy consumption data.

[0067] For example, taking the K12+500 section of the main canal in a certain irrigation area as an example, a certain type of ultrasonic flow meter and a certain type of smart meter are installed there. The ultrasonic flow meter measures the average flow velocity v=1.2m / s in the cross section of the canal through the Doppler frequency shift method, and the water surface height h=1.8m is measured by the pressure sensor. The canal has a trapezoidal cross section, a bottom width b=1m, a designed slope coefficient m=1.5, and a water flow area A=(b+m×h)×h=(1 +1.5×1.8)×1.8=4.86m², and the flow rate Q=A×v=4.86×1.2=5.832m³ / s. After calibration with a cross-sectional correction factor of 0.37 (calibrated value), the actual irrigation water flow is Q=5.832×0.37≈2.16m³ / s. The smart meter directly measures the pump shaft power of 55kW, the rated power of 70kW, and the current load factor = 55 / 70 = 78.6%. The data is pre-processed by the edge gateway, marked with a timestamp and the pump positioning system coordinates, and then uploaded to the central database by the edge gateway.

[0068] Step 102: Generate a dynamic water resource supply and demand relationship map based on the real-time irrigation water volume and water pump energy consumption data.

[0069] In this step, the dynamic water resource supply and demand relationship map represents a topological network model, where nodes represent channel diversion gates, pumping stations, channel hubs, etc., and edges represent channel sections. Node attributes include real-time water volume, demand gap, etc., and edge attributes include water flow direction, transportation efficiency, etc.

[0070] In the embodiment of the present application, a graph structure is constructed with channel hubs as nodes and channel segments as edges. The node weights are dynamically calculated based on the difference between the declared water demand of the water user terminal and the actual water supply, and the edge weights are set according to the channel water transmission efficiency (real-time flow / design flow). The optimal water transmission path is calculated using the Dijkstra algorithm, and the long-short-term memory network model is trained in combination with historical transaction data to predict the demand fluctuations within a preset time for each node, and finally a dynamic water resource supply and demand relationship map including time and space dimensions is generated.

[0071] For example, the K12+500 section of the main channel is used as a graph edge, the designed branch channel flow is 2.5 m³ / s, and the water transmission efficiency = actual irrigation water volume / branch channel flow = 2.16 / 2.5 = 0.864≈0.86; based on the data of smart water meters of water users in a village, it is known that there are three types of user demands: (1) agricultural irrigation demand is 1.2 m³ / s, which is calculated based on the reference crop evapotranspiration (ET0) value of 500 mu of crops; (2) industrial water demand is 0.4 m³ / s, which can be known from the park water supply contract; (3) domestic water demand is 0.2 m³ / s, which can be roughly calculated based on population × per capita quota. Therefore, the water terminal reported demand is 1.2+0.4+0.2=1.8 m³ / s, forming a node attribute with a surplus capacity of 2.16-1.8=0.36 m³ / s. Forecast model input parameters: Meteorological data: temperature rise 3°C / h, humidity decrease 10% / h, water consumption history: water consumption growth slope 0.15 m³ / (s·h) over the same period, real-time data: current water load rate 85%, long-short-term memory network model prediction process: input the water consumption sequence of the past 24 hours (sampling interval 15 minutes), consider the meteorological mutation index ΔW = 0.8, output demand increment after 1 hour: 0.2 m³ / s, final prediction value = current 1.8 + increment 0.2 = 2.0 m³ / s. The system predicts that demand in this area will increase to 2.0 m³ / s in 1 hour. Warning threshold setting: 85% of the design flow rate of 2.5 m³ / s = 2.125 m³ / s. Forecast demand 2.0 > 2.125 × 0.95 (buffer coefficient) = 2.02 m³ / s. Warning level classification: 2.0-2.125 m³ / s: yellow warning, 2.125 m³ / s: red warning, so the supply and demand warning is triggered.

[0072] Step 103: Determine the target water transfer channels corresponding to the water resource trading requests of the water use terminals in each irrigation area from the target irrigation area. If any of the target water transfer channels has a water pump overload or a siltation diffusion coefficient value greater than a preset diffusion coefficient threshold, generate a water resource trading price for the target water transfer channel based on the dynamic water price adjustment rules and the dynamic water resource supply and demand relationship map.

[0073] In this step, pump overload refers to a situation where real-time power consistently exceeds rated power. The siltation diffusion coefficient is an indicator of channel siltation levels calculated from water flow rate and sediment content. The dynamic water pricing adjustment rules consist of three components: a base price, a supply-demand adjustment coefficient, and a risk compensation coefficient. Water resource transaction requests submitted by irrigation district water users (such as agricultural cooperatives, industrial enterprises, and village water supply stations) through the intelligent water rights trading platform contain structured data such as geographic coordinates (GPS boundaries of the requested water area), declared water volume (water demand per unit time, e.g., m³ / s), water use time period (start and end times), and a price sensitivity coefficient (acceptable premium). These data serve as the core inputs for triggering dynamic pricing. The preset diffusion coefficient threshold is a critical safety value determined based on channel design specifications and historical siltation accident data. When the real-time calculated siltation diffusion coefficient (water flow rate × sediment content) exceeds this value, it indicates that the channel's water delivery capacity has decreased due to siltation, triggering a price adjustment mechanism. The preset diffusion coefficient threshold is dynamically updated through machine learning, taking into account channel material, age, and seasonal factors. The water resource transaction price refers to a dynamic unit price (yuan / m³) generated based on real-time supply and demand relationships and the health status of the channel. It includes the basic water price, energy consumption surcharge (reflecting the loss of water pump efficiency) and siltation compensation fee (channel maintenance cost), and is automatically executed through blockchain smart contracts.

[0074] In the embodiment of the present application, the transaction request is first matched with the target channel through the geographic information system, and the real-time water flow rate and sediment content are obtained by using an acoustic Doppler flowmeter and a turbidity meter to calculate the siltation diffusion coefficient; the volatility of the water pump energy consumption curve (sliding window standard deviation) is simultaneously analyzed. When any indicator exceeds the threshold, the corresponding price adjustment strategy is selected according to the rule base: the siltation risk increases the base price according to the linear model, and the energy consumption overload increases according to the exponential model, and finally the water resource transaction price is generated by superposition.

[0075] For example, in the K12+500 section, the acoustic Doppler current profiler measures in real time the velocity distribution of different depth layers on the vertical profile of the water flow, which is 1.2 m / s for the surface layer, 1.05 m / s for the middle layer, and 0.85 m / s for the bottom layer. The weighted average of the three layers is 1.0 m / s, and the water flow velocity is reduced to 1.0 m / s. The turbidity meter is combined with the sampling laboratory calibration: the online turbidity meter measures NTU=280, and the laboratory establishes a local calibration curve: NTU=156×sand content (R²=0.98), which is converted to: 280 / 156≈1.8 kg / m³, the sediment content is 1.8 kg / m³, and the siltation coefficient is 1.8, which exceeds the threshold of 1.5; the base price of 0.5 yuan / m³ is added with the siltation surcharge of 0.1 yuan and the energy consumption surcharge of 0.08 yuan, and the final water resource transaction price is 0.68 yuan / m³.

[0076] Step 104: generating target water transfer optimization parameters for each target water transfer channel based on the water resource transaction prices of all the target water transfer channels and the node states in the dynamic water resource supply and demand relationship map.

[0077] In this step, the node status of the target water channel refers to the operating status of each monitoring point or device related to water pump energy consumption in the area. These nodes may include but are not limited to sensors, controllers, the pump itself, and the connected pipe system. For example, the node status generation process involves defining key facilities such as channel intersections, pumping stations, and diversion gates as network nodes based on the irrigation district's water channel topology. IoT sensors deployed at these nodes collect real-time physical parameters such as water level, flow, pump power, and gate opening. A state assessment model for each node is then established based on historical operating data. Pump node status calculates an energy consumption anomaly index based on the deviation between the real-time energy consumption curve and the standard operating condition curve. Channel node status generates a siltation risk coefficient based on the ratio of real-time water flow rate to design flow rate and sediment content data. Diversion gate node status calculates a regulation efficiency value based on the matching degree between gate opening and flow monitoring values. Finally, the energy consumption anomaly index, siltation risk coefficient, and regulation efficiency value for each node are normalized to a node status value ranging from 0 to 1 and annotated in a dynamic water resource supply and demand relationship map. Node status data is then stored in a distributed ledger along with the corresponding geographic coordinates and timestamp via a blockchain network. Target water delivery optimization parameters include control variables such as recommended flow adjustment amplitude, pump frequency conversion instructions, and gate opening.

[0078] In an embodiment of the present application, a multi-objective optimization model is established based on the water resource transaction prices of all the target water transfer channels and the node states in the dynamic water resource supply and demand relationship map: the channel balanced load is used as the objective function, the constraints include maximum carrying flow, minimum service pressure, etc., and a non-dominated sorting genetic algorithm is used to solve the Pareto optimal solution set, and the target water transfer optimization parameters of each channel are output.

[0079] For example, the K12+500 section, due to its high pricing and 30% weighting on equipment operating conditions, has pump efficiency (currently 78%, benchmark 85%), resulting in a score of 78 / 85 (≈ 0.92). The gate opening degree matching is currently 90%, resulting in a score of 0.90. Water quality indicators (20% weighting): turbidity (1.8 kg / m³, threshold 2.0), resulting in a score of 1-1.8 / 2.0 = 0.90. Dissolved oxygen (6.5 mg / L, standard ≥ 5), resulting in a score of 1.0. Sedimentation risk (30% weighting): Sedimentation diffusion coefficient (1.8, threshold 1.5), resulting in a score of 1-1.8 / (2 × 1.5) = 0.40 (penalty). Historical stability (20% weighting): Flow volatility over the past seven days (8%, threshold 10%), resulting in a score of 1-8 / 10 = 0.80. The overall score (a score of 0.7 is considered "good", 0.8 and above is excellent, 0.6-0.8 is good, and below 0.6 requires a warning) is:

[0080] 0.3×(0.92×0.5+0.90×0.5)+0.2×(0.90×0.5+1.0×0.5)+0.3×0.40+0.2×0.80=0.70, and the node status score is 0.7 (good). The optimization model recommends increasing its flow rate from 2.16 m³ / s to 2.3 m³ / s, reducing the adjacent K10+200 section from 1.5 m³ / s to 1.4 m³ / s, and adjusting the water pump speed from 1450 rpm to 1500 rpm using the inverter.

[0081] This solution uses the IoT perception layer to capture the operating status of channels and water pumps in real time, constructing a dynamic supply and demand map to achieve visual scheduling of water resources. It innovatively converts the status of physical facilities (siltation, energy consumption) into economic signals (dynamic pricing), and then uses optimization algorithms to reversely control the physical system to form a "perception-decision-control" closed loop, improving the accuracy of water resource allocation in irrigation areas and the energy efficiency of water transmission systems, while providing a transparent and scientific pricing basis for the water rights trading market.

[0082] In order to solve the problem of disconnection between pricing and channel health status in irrigation district water resource transactions and further improve the accuracy and fairness of water resource allocation, in some embodiments, step 103: determining target water transfer channels corresponding to water resource transaction requests from water use terminals in each irrigation district from the target irrigation district, and if any of the target water transfer channels has a water pump overload or a siltation diffusion coefficient value greater than a preset diffusion coefficient threshold, generating a water resource transaction price based on dynamic water price adjustment rules and the dynamic water resource supply and demand relationship map, includes:

[0083] Step 201: Determine the target water transmission channel corresponding to each water resource transaction request according to the geographic coordinate range in each water resource transaction request.

[0084] In step 201, the geographic coordinate range represents the boundary coordinates of the water demand area declared by the water user terminal.

[0085] In this embodiment, the spatial analysis function of a geographic information system (GIS) is used to overlay the coordinate range of the water user terminal with the irrigation canal network, and a nearest neighbor algorithm is used to match water supply relationships. For example, if the coordinate range reported by a village overlaps 90% with the water supply service area of ​​the main canal section K12+500, the canal is determined to be the target water transmission canal. The matching result must ensure that the channel's designed water supply capacity covers the reported water volume.

[0086] Step 202: Based on the historical extreme values ​​of the channel siltation diffusion coefficient recorded in the dynamic water resource supply and demand relationship map, combined with the real-time monitored channel water flow rate and sediment content, the siltation diffusion coefficient value of the target water transfer channel is calculated. When the siltation diffusion coefficient value exceeds a preset diffusion coefficient threshold, it is determined that the target water transfer channel has a siltation risk.

[0087] In step 202, the historical extreme value of the channel siltation diffusion coefficient represents the maximum safe value of the channel's siltation coefficient over the past year. Channel water velocity refers to the average speed of water flowing through a channel cross-section per unit time (unit: m / s) and is a core indicator of water delivery efficiency. Sediment content refers to the mass concentration of suspended sediment per unit volume of water (unit: kg / m³) and is a key parameter for assessing channel siltation risk.

[0088] In this embodiment, an acoustic Doppler current meter measures the average flow velocity of the target channel in real time, while a turbidity sensor measures the sediment content. This real-time data is substituted into the formula (flow velocity × sediment content) to calculate the current sediment diffusion coefficient and compare it with historical extreme values. If the current value exceeds a threshold, the channel is flagged as being at risk of sedimentation.

[0089] Step 203: Based on the real-time energy consumption curve of the water pump in the target water transmission channel, the peak energy consumption fluctuation rate within a preset time period in the future is predicted. If the peak energy consumption fluctuation rate exceeds the critical slope corresponding to the water pump energy consumption threshold, it is determined that there is a risk of water pump overload in the target water transmission channel.

[0090] In step 203, the real-time energy consumption curve for the pumps in the target water transmission channel is generated as follows: Smart meters and triaxial vibration sensors are deployed at each pump unit in the target water transmission channel. Voltage, current, power factor, and vibration spectrum data are continuously collected with a one-minute sampling period. The raw data is processed using an edge computing gateway to remove outliers and perform a sliding average filter. The processed power data is stored in a time series as 15-minute energy consumption segments. Different operating conditions are marked using pump operating status signals (start / stop, variable frequency). By comparing the current power data with a baseline curve in the pump standard operating condition database, a real-time energy consumption curve is generated, including instantaneous power, cumulative energy consumption, and energy efficiency deviation. The peak energy consumption fluctuation rate represents the magnitude of the pump power change within a preset time period. The critical slope represents the maximum power change rate allowed for safe pump operation. The pump energy consumption threshold is the critical maximum energy consumption change rate allowed for safe pump operation.

[0091] In this embodiment, minute-by-minute pump power data is obtained from a smart meter. A sliding window approach is used to calculate the power fluctuation rate for the next 15 minutes. If the fluctuation rate exceeds a critical slope, the pump's rated power is used to determine whether it is overloaded. For example, if a pump's rated power is 70kW and its real-time power fluctuates to 80kW and continues to rise, an overload warning is triggered.

[0092] Step 204: extracting the water pump energy consumption deviation from the dynamic water resource supply and demand relationship map.

[0093] In step 204 , the energy consumption deviation represents the percentage of deviation between the real-time power and the standard operating power.

[0094] In this embodiment, the pump's standard power curve (e.g., 55 kW under design conditions) is read from the dynamic supply and demand graph and compared with the actual power (e.g., 62 kW). The deviation is calculated ((62 - 55) / 55 ≈ 12.7%). This deviation is used to quantify the degree of energy efficiency loss.

[0095] Step 205: When there is a risk of siltation or water pump overload in any of the target water transmission channels, the water resource transaction price of the target water transmission channel is calculated based on the initial pricing gradient value associated with the target water transmission channel in the dynamic water price adjustment rule and the compensation factor generated by the nonlinear relationship between the siltation diffusion coefficient value and the water pump energy consumption deviation.

[0096] In step 205, the initial pricing gradient value represents the product of the basic water price and the regional supply and demand coefficient. The compensation factor represents the price adjustment coefficient generated by the sedimentation coefficient and the energy consumption deviation through a nonlinear function.

[0097] In this example, if channel K12+500 is at risk of siltation (coefficient 1.8) and the pump deviation is 12%, the compensation function calculates additional fees: siltation compensation fee of 0.1 yuan / m³ (20% over-threshold × 0.5 yuan), and energy consumption surcharge of 0.06 yuan / m³ (12% deviation × 0.5 yuan). The final price = base price of 0.5 yuan + compensation fee of 0.16 yuan = 0.66 yuan / m³.

[0098] Here's a specific example:

[0099] An irrigation district received a water request from Farm A (coordinate range X, water demand 1.8 m³ / s). The system matched the main canal to section K12+500 and detected a current flow rate of 1.0 m / s, 1.8 kg / m³ of sediment (a sedimentation coefficient of 1.8 > a threshold of 1.5), and a 15% pump power fluctuation (overload risk). Based on dynamic water pricing rules, a base price of 0.5 yuan was added, along with a 0.1 yuan sedimentation compensation and a 0.08 yuan energy consumption compensation, for a final price of 0.68 yuan / m³.

[0100] In this application, by matching transaction requests with channel status in real time and dynamically overlaying siltation and energy consumption compensation prices, water prices accurately reflect infrastructure operation risks while balancing supply and demand through optimized instructions. Ultimately, this achieves efficient allocation of water resources in irrigation areas and proactive protection of water delivery facilities.

[0101] To address the issue of insufficient dynamic price response in irrigation district water resource transactions and further improve the sensitivity of water prices to real-time supply and demand changes and facility status, in some embodiments, step 205: calculating the water resource transaction price of the target water transfer channel based on the initial pricing gradient value associated with the target water transfer channel in the dynamic water price adjustment rule and adding a compensation factor generated by the nonlinear relationship between the sedimentation diffusion coefficient value and the pump energy consumption deviation, includes:

[0102] Step 301: extracting the real-time demand fluctuation of the target water transmission channel from the dynamic water resource supply and demand relationship map.

[0103] In step 301, the real-time demand fluctuation represents the percentage difference between the water volume declared by the water user terminal in the current period and the designed water supply capacity of the channel.

[0104] In this embodiment, the real-time declared water volume (e.g., 2.0 m³ / s) and the designed water supply capacity (1.8 m³ / s) of the target water channel (e.g., section K12+500) are read from the dynamic water resource supply and demand relationship map. The fluctuation is calculated as: (2.0 - 1.8) / 1.8 ≈ 11.1%. This data is updated every 5 minutes and synchronized to the pricing module via the blockchain.

[0105] Step 302: extracting an initial pricing gradient value associated with the target water transmission channel from the dynamic water price adjustment rule according to the real-time demand fluctuation.

[0106] In step 302, the dynamic water price adjustment rule includes a mapping relationship between the initial pricing gradient value, the demand fluctuation range, and the priority corresponding to each water transmission channel in the target tank farm.

[0107] In this example, the dynamic water price adjustment rule is queried based on a real-time demand fluctuation of 11.1%. If a fluctuation of 10-15% corresponds to a "Level 2 Tight" level, the initial gradient value is extracted = base price 0.5 yuan x 1.2 = 0.6 yuan / m³. The rule base uses machine learning to update the supply and demand level thresholds quarterly.

[0108] Step 303: Determine the coefficient corresponding to the priority level of the real-time demand fluctuation as the pricing coefficient.

[0109] In step 303 , the coefficient corresponding to the priority represents the adjustment weight set according to the water terminal type (agriculture / industry / civilian).

[0110] In the embodiment of the present application, the target channel water supply object is identified as the industrial park (priority coefficient 1.1), and according to the fluctuation range and correction coefficient comparison table preset in the rule base: the fluctuation range (5%~10%, 10%~15%, 15%~20%) corresponds to the correction coefficient (1.02, 1.05, 1.08) respectively, and the interval (10-15%) where the real-time fluctuation amount is 11.1% is determined, and the final pricing coefficient = 1.1×1.05 (fluctuation correction) = 1.155 is obtained from the rule base matching.

[0111] Step 304: Determine the target pricing gradient value as the product of the initial pricing gradient value and the pricing coefficient.

[0112] In step 304 , the target pricing gradient value represents the base price after superimposing supply and demand fluctuations and priority.

[0113] In this example, the initial gradient value of 0.6 yuan is multiplied by the pricing coefficient of 1.155 to obtain a target gradient value of 0.693 yuan / m³. The system automatically rounds to two decimal places and outputs 0.69 yuan / m³ as the price adjustment benchmark.

[0114] Step 305: Based on the target pricing gradient value and the compensation factor, the water resource transaction price of the target water transmission channel is calculated.

[0115] In the embodiment of the present application, the siltation coefficient of 1.8 and the energy consumption deviation of 12% are input into the compensation model: siltation surcharge = 0.5×(1.8-1.5) / 1.5=0.1 yuan, energy consumption surcharge = 0.5×12%×0.8=0.048 yuan, and the final price = 0.69+0.1+0.048≈0.84 yuan / m³ (rounded to 0.8 yuan).

[0116] Here's a specific example:

[0117] An industrial park applied to draw 2.0 m³ / s of water from the K12+500 section (design capacity 1.8 m³ / s, fluctuation +11.1%). The system identified this as industrial water use (coefficient 1.1), with an initial gradient value of 0.6 yuan and a target gradient value of 0.69 yuan. Adding a siltation compensation of 0.1 yuan (coefficient 1.8) and an energy consumption compensation of 0.05 yuan (12% deviation), the final price was 0.8 yuan / m³.

[0118] In this application, the embodiment uses hierarchical extraction of supply and demand fluctuations, priority coefficients, and facility status compensation to dynamically link water prices with real-time operational risks. This not only ensures high-priority water demand, but also guides resource optimization through price leverage, while also compensating for infrastructure maintenance costs.

[0119] To further improve the dynamic responsiveness of water resource transaction prices to channel health and energy efficiency, in some embodiments, step 305: calculating the water resource transaction price of the target water transmission channel based on the target pricing gradient value and adding a compensation factor, includes:

[0120] Step 401: The first weight factor and the second weight factor are coupled and calculated using a pre-constructed nonlinear compensation function to generate a compensation factor, wherein the pre-constructed nonlinear compensation function is constructed through a nonlinear relationship between the siltation diffusion coefficient value and the water pump energy consumption deviation, the first weight factor is the ratio of the water pump energy consumption deviation to a preset energy consumption deviation threshold, and the second weight factor is the ratio of the current water channel siltation diffusion coefficient value to a preset diffusion coefficient change rate threshold.

[0121] In the embodiment of the present application, the first weight factor is calculated: real-time energy consumption deviation 12% / threshold 10%=1.2; the second weight factor is calculated: sedimentation coefficient 1.8 / threshold 1.5=1.2; the double weight factors are input into the nonlinear function: function form: f(x,y)=0.2×[1 / (1+e^-(x+y-2))] (x,y are weight factors), and the output result: f(1.2,1.2)=0.15 yuan / m³, which is used as a compensation factor.

[0122] Step 402: Superimpose the target pricing gradient value and the compensation factor according to a preset ratio to obtain a superposition result.

[0123] In step 402 , a predetermined ratio represents the weight distribution of the base price and the compensation factor.

[0124] In the embodiment of the present application, the target pricing gradient value is: 0.69 yuan / m³; the compensation factor is: 0.15 yuan / m³; the proportional superposition is: 0.69×70%+0.15×30%=0.55 yuan (basic part) + 0.05 yuan (compensation part) = 0.60 yuan / m³.

[0125] Step 403: Calculate the water resource transaction price of the target water transmission channel based on the superposition result and the water transmission weights of the adjacent water transmission channels recorded in the dynamic water resource supply and demand relationship map.

[0126] In step 403, the water delivery weight of the water delivery channel represents the adjustment coefficient calculated based on the flow distribution ratio of the upstream and downstream channels.

[0127] ;

[0128] It represents the water transfer weight, which is a dynamic parameter that quantifies the relative importance of adjacent channels in the water resource allocation of the target channel, and its value range is 0~1. Indicates the available water volume of the adjacent channel (m³ / h), Indicates the deviation of energy consumption of adjacent channel pumps (%), Indicates the maximum allowable energy consumption deviation threshold (usually 15%).

[0129] In the embodiment of the present application, the water transfer weight (currently 0.95) of the adjacent channel K10+200 is obtained from the dynamic water resource supply and demand relationship map; the final price = the superimposed result 0.60 yuan × weight 0.95 ≈ 0.57 yuan / m³; the rounding rule description, the rounding basis: 0.05 yuan is the minimum pricing unit (similar to the "five-cent rounding method" in the currency system); the calculation rule: if the unadjusted price is P, then: calculate the remainder R of P ÷ 0.05; if R ≥ 0.025, round up to the nearest multiple of 0.05; if R < 0.025, round down to the nearest multiple of 0.05. Calculate the remainder: 0.57 ÷ 0.05 = 11.4, take the integer part 11, and the remainder 0.4 × 0.05 = 0.02 yuan; determine the rounding direction: if the remainder 0.02 yuan is less than 0.025 yuan, round it down to 11 × 0.05 = 0.55 yuan; the final output: the adjusted price is 0.55 yuan / m³ (instead of 0.60 yuan).

[0130] Here's a specific example:

[0131] Section K12+500 of the main canal in an irrigation district: Input: target gradient value 0.69 yuan, compensation factor 0.15 yuan, adjacent weight 0.95; Calculation: 0.69 × 70% + 0.15 × 30% = 0.60 yuan, 0.60 × 0.95 = 0.57 yuan, rounded to 0.55 yuan; Output: final transaction price 0.55 yuan / m³.

[0132] In this embodiment, a nonlinear compensation function is used to quantify the impact of facility status, combined with weight adjustments for adjacent channels, to achieve a dual price response to local risk and global equilibrium. This ultimately forms a dynamic pricing mechanism that both incentivizes water conservation and ensures facility stability.

[0133] To further improve the responsiveness of water resource transaction prices to regional supply and demand balances, in some embodiments, step 403: calculating the water resource transaction price of a target water transmission channel based on the superposition result and the water transmission weights of adjacent water transmission channels recorded in the dynamic water resource supply and demand relationship map, includes:

[0134] Step 501: Based on the water transmission weights of the adjacent water transmission channels recorded in the dynamic water resource supply and demand relationship map, calculate the average water transmission weights of the adjacent water transmission channels directly connected to the current water transmission channel, and use the average water transmission weights of the adjacent water transmission channels as the adjustment coefficient for allocating the target water transmission channel.

[0135] In step 501, the mean water transmission weight of adjacent water transmission channels represents the arithmetic mean of the weights of upstream and downstream channels directly connected to the target channel, and is used to smooth local fluctuations.

[0136] In the embodiment of the present application, the adjacent channel data of the target channel K12+500 are extracted from the dynamic water resource supply and demand relationship map: the upstream K10+200 weight is 1.1 (industrial water use is prioritized), the downstream K15+000 weight is 0.9 (agricultural water use is second priority), and the average is calculated: (1.1+0.9) / 2=1.0, which is used as the adjustment coefficient.

[0137] Step 502: The target pricing gradient value and the adjustment coefficient are weighted and fused according to the superposition result to generate a water resource transaction price.

[0138] In step 502, weighted fusion means dynamically allocating weights to the target pricing gradient value and the adjustment coefficient according to the degree of supply and demand tension.

[0139] In the embodiment of the present application, the input parameters are: target pricing gradient value: 0.69 yuan / m³, adjustment coefficient: 1.0, and the weight ratio of the superimposed result is assumed to be: base price 70% + adjustment 30%; the calculation process is: basic part: 0.69×70%=0.483 yuan, adjustment part: 1.0×30%×0.69=0.207 yuan, total: 0.483+0.207=0.69 yuan, and the price is rounded off: rounded to 0.70 yuan / m³ in units of 0.05 yuan (because the remainder of 0.69, 0.04>0.025).

[0140] Here's a specific example:

[0141] The K12+500 section of the main canal in a certain irrigation district needs to be priced: Input data: weights of adjacent channels: upstream 1.1 (industry), downstream 0.9 (agriculture), adjustment coefficient 1.0; target gradient value: 0.69 yuan (including base price and compensation factor); price calculation: weighted according to the ratio of 7:3: 0.69 × 70% + 1.0 × 30% × 0.69 = 0.70 yuan; output result: final transaction price: 0.70 yuan / m³.

[0142] In this embodiment, by dynamically adjusting the average weights of adjacent channels, the price reflects both the independent status of the target channel and the overall balance of the regional water supply network. Ultimately, this achieves the unification of local precision pricing and global coordinated water resource scheduling.

[0143] To further improve the dynamic optimization capability of the irrigation district water delivery system, in some embodiments, step 104: generating target water delivery optimization parameters for each target water delivery channel based on the water resource transaction prices of all target water delivery channels and the node status in the dynamic water resource supply and demand relationship map, includes:

[0144] Step 601: The water resource write-off rate of each water transmission channel in the private blockchain network is used as an attenuation factor of the initial pricing gradient value associated with each water transmission channel in the dynamic water price adjustment rule.

[0145] In step 601, the water resource redemption rate represents the ratio of actual water usage to declared water usage per unit time, reflecting the user's performance. The decay factor adjusts the coefficient of the initial pricing gradient; the lower the redemption rate, the greater the decay. The private blockchain network is deployed by the irrigation district management agency.

[0146] In this example, we retrieved the water rights transaction records for the target channel K12+500 over the past 24 hours from a private blockchain: the declared total volume was 1000 m³, the actual write-off was 900 m³, and the write-off rate was 900 / 1000 = 0.9. According to the rules, a write-off rate of 0.9 corresponds to a decay factor of 0.95. With an initial pricing gradient of 0.69 yuan / m³, the decay factor is 0.69 × 0.95 ≈ 0.66 yuan / m³.

[0147] Step 602: For each target water transmission channel, adjust the water transmission resource weights of adjacent water transmission channels according to the real-time energy consumption curve of the water pump in the target water transmission channel to generate a water transmission resource weight increment.

[0148] In step 602, the water transfer resource weight increment represents the adjustment range of the flow distribution of adjacent channels, which is determined by the fluctuation characteristics of the water pump energy consumption curve.

[0149] In the embodiment of the present application, the real-time power curve of the water pump of the target channel K12+500 is analyzed (sampling interval is 1 minute); it is detected that the power fluctuation rate increases from 10% to 18% during the period of 15:00-15:30; based on the fluctuation rate increment of 8%, the weight adjustment rule is queried: fluctuation rate 5-10%: weight increment +0.05, 10-15%: +0.10, 15%: +0.15; the weight increment of the adjacent channel K10+200 is output = +0.15.

[0150] Step 603: Based on the sedimentation diffusion coefficient value and the water transfer resource weight increment, generate initial water transfer optimization parameters of the target water transfer channel.

[0151] In step 603, the initial water delivery optimization parameters include an initial plan of control instructions such as recommended flow rate, gate opening, and pump speed.

[0152] In the embodiment of the present application, the input parameters are: siltation diffusion coefficient 1.8, weight increment +0.15; through the optimization model calculation: the target channel K12+500 flow recommendation: 2.16m³ / s increased to 2.3m³ / s (+6.5%), the adjacent channel K10+200 flow recommendation: 1.5m³ / s decreased to 1.4m³ / s (-6.7%), and the initial water delivery optimization parameters are output (channel section: K12+500, flow adjustment: +6.5%, gate opening: 70% to 75%, water pump speed: 1450 to 1500rpm) and (channel section: K10+200, flow adjustment: -6.7%, gate opening: 65% to 60%, water pump speed: maintained at 1400rpm).

[0153] Step 604: According to the water resource transaction price and the node status of the target water transmission channel in the dynamic water resource supply and demand relationship map, the initial water transmission optimization parameters are adjusted to generate target water transmission optimization parameters.

[0154] In the embodiment of the present application, the K12+500 node status score is obtained as 0.7 (good); according to the premium margin of the transaction price of 0.70 yuan / m³ (base price 0.5 yuan), the initial parameters are allowed to float up by 10%. For example, the floating ratio is calculated by coupling the price premium rate with the node status score: premium margin calculation: base price: 0.5 yuan / m³, transaction price: 0.7 yuan / m³, premium rate = (0.7-0.5) / 0.5 = 40%; node status score impact: score 0.7 (good level) corresponds to an allowable floating coefficient of 0.25 (excellent 0.3, good 0.25, average 0.2); actual floating ratio: premium rate 40% × score coefficient 0.25 = 10%; adjusted target parameters: K12+500 flow: 2.3 m³ / s becomes 2.25 m³ / s (compromise solution), for example, the initial optimization value: 2.3 m³ / s (+6 0.5%)); 10% upward constraint: maximum allowable value = original flow 2.16m³ / s×(1+6.5%+10%)=2.52m³ / s; but the following restrictions must be met: the channel design flow upper limit is 2.5m³ / s, and the adjacent channel minimum guarantee demand (K10+200 must be ≥1.45m³ / s); compromise decision: the middle value between the initial value 2.3m³ / s and the constraint upper limit 2.5m³ / s becomes 2.25m³ / s; K10+200 flow: 1.4m³ / s becomes 1.45m³ / s (guaranteed minimum demand), for example, minimum guarantee demand: this channel is responsible for people's livelihood water supply, and the designed minimum flow is 1.45m³ / s (agreed in the contract); initial optimization value: 1.4m³ / s (-6.7%) violates the minimum demand; automatic correction: forced adjustment to 1.45m³ / s (meeting the minimum requirement), while proportionally reducing the flow of other non-priority channels.

[0155] In this application, by dynamically coupling credit write-offs, energy consumption fluctuations, and node status, we generate optimized parameters that both meet economic leverage and ensure facility safety. This enables closed-loop control from water rights trading to physical scheduling, improving the overall operational efficiency of the irrigation district.

[0156] To further improve the accuracy of the dynamic response of water delivery optimization parameters to the real-time channel status and transaction price, in some embodiments, step 604: adjusting the initial water delivery optimization parameters based on the water resource transaction price and the node status of the target water delivery channel in the dynamic water resource supply and demand relationship map to generate target water delivery optimization parameters, includes:

[0157] Step 701: Generate a dynamic correction coefficient for the initial water transmission optimization parameter based on the node status of the target water transmission channel in the dynamic water resource supply and demand relationship map and the water transmission weight of the target water transmission channel, combined with the energy consumption compensation factor embedded in the water resource transaction price.

[0158] In step 701 , the energy consumption compensation factor represents the portion of the transaction price used to compensate for the energy efficiency loss of the water pump.

[0159] In the embodiment of the present application, the node status of K12+500 is 0.7 and the water transmission weight is 1.1; the energy consumption compensation factor of 0.08 yuan in the transaction price of 0.7 yuan is analyzed and converted into an impact coefficient of 1.08. For example, the calculation is based on: the ratio of the real-time water pump energy consumption deviation (such as 12%) to the preset threshold (such as 10%); the energy consumption impact weight of the basic water price (0.5 yuan / m³) (such as 0.5×12%×0.8=0.048 yuan); after correction by the nonlinear compensation function, the energy consumption compensation factor of 0.08 yuan / m³ is finally generated. Calculate the proportion of energy consumption compensation: energy consumption compensation factor ÷ basic water price = 0.08 / 0.5 = 0.16 (i.e. 16%); weighted correction: proportion × weight ratio = 0.16 × 0.5 = 0.08; generate impact coefficient: 1 + weighted result = 1 + 0.08 = 1.08; calculate dynamic correction coefficient = node status × water transmission weight × energy consumption coefficient = 0.7 × 1.1 × 1.08 ≈ 0.76.

[0160] Step 702: Calculate the sedimentation impact coefficient of the target water transfer channel based on the spatial coupling relationship between the channel sedimentation diffusion coefficient value recorded in the dynamic water resource supply and demand relationship map and the water transfer weights of adjacent water transfer channels.

[0161] In step 702 , the spatial coupling relationship represents the product relationship between the adjacent channel weights and the siltation diffusion coefficient of the target channel.

[0162] In the embodiment of the present application, the siltation coefficient of K12+500 is 1.8, the weight of the adjacent channel K10+200 is 0.9, and the weight of K15+000 is 1.1; the calculated mean weight = (0.9+1.1) / 2=1.0; the siltation influence coefficient = siltation coefficient × mean weight = 1.8×1.0=1.8.

[0163] Step 703: Generate parameter adjustment constraints based on the sedimentation impact coefficient and the water pump energy consumption deviation.

[0164] In step 703, the parameter adjustment constraint conditions represent a set of inequalities that limit the flow adjustment range.

[0165] In the embodiment of the present application, the input sedimentation impact coefficient is 1.8 (>1.5 threshold), and the water pump energy consumption deviation is 12%; the constraint model generates: maximum flow increase = 5% (siltation penalty), minimum flow decrease = 8% (energy consumption compensation), and the output constraint condition is: -8%≤ΔQ≤+5%.

[0166] Step 704: Dynamically scale the initial water delivery optimization parameters according to the dynamic correction coefficient and the parameter adjustment constraint to obtain intermediate water delivery optimization parameters.

[0167] In step 704, the intermediate water delivery optimization parameters represent a preliminary optimization solution that satisfies the constraints.

[0168] In the embodiment of the present application, the initial optimized recommended flow rate value for the K12+500 section (an increase of 6.5% compared to the original flow rate of 2.16 m³ / s) is: K12+500 = 2.3 m³ / s (+6.5%); the initial optimized recommended flow rate value for the K10+200 section (a decrease of 6.7% compared to the original flow rate of 1.5 m³ / s) is: K10+200 = 1.4 m³ / s (-6.7%); application constraints (constraint rule: when the sedimentation influence coefficient is greater than 0.8, the maximum flow rate increase is allowed to be ≤5%; original flow rate: 2. 16m³ / s): K12+500 allows a maximum of +5%: the maximum allowable flow rate = 2.16×(1+5%)=2.27m³ / s; K10+200 must be at least -3% (constraint rule: when the energy consumption deviation is greater than 10%, the adjacent channel must be reduced by at least 3%; original flow rate: 1.5m³ / s): the minimum allowable flow rate = 1.5×(1-3%)=1.46m³ / s; output intermediate parameters: K12+500=2.27m³ / s, K10+200=1.46m³ / s.

[0169] Step 705: Boundary matching is performed between the intermediate water transfer optimization parameter and the maximum water carrying capacity threshold of the target water transfer channel recorded in the dynamic water resource supply and demand relationship map to generate target water transfer optimization parameters, wherein the maximum water carrying capacity threshold is calculated based on the water transfer capacity corresponding to the water pump energy consumption threshold in the target water transfer channel.

[0170] In step 705, the maximum water carrying capacity threshold represents the safe water delivery capacity of the channel under the design energy consumption threshold.

[0171] In the embodiment of the present application, the maximum threshold value of K12+500 is 2.5m³ / s (corresponding to the water pump energy consumption threshold of 55kW); the intermediate parameter 2.27m³ / s<2.5m³ / s is directly adopted; the final target parameters are: (channel section: K12+500, flow adjustment: 2.27m³ / s, gate opening: 73%) and (channel section: K10+200, flow adjustment: 1.46m³ / s, gate opening: 61%).

[0172] Here's a specific example:

[0173] Optimization process of the main canal system in an irrigation district: Input: K12+500 node score 0.7, transaction price 0.7 yuan (energy consumption compensation 0.08 yuan); sedimentation coefficient 1.8, adjacent weight 0.9, energy consumption deviation 12%. Calculation: Correction coefficient 0.76 and constraints (+5%, -3%); intermediate parameters: K12+500=2.27m³ / s, K10+200=1.46m³ / s; Output: Final target parameters: (channel section: K12+500, flow adjustment: 2.27m³ / s, gate opening: 73%) and (channel section: K10+200, flow adjustment: 1.46m³ / s, gate opening: 61%). For example, the original opening: The current gate opening of the K12+500 channel is 65% (collected in real time through IoT sensors); Flow adjustment ratio: The target flow increases from 2.16m³ / s to 2.27m³ / s (an increase of +5.1%); Opening-flow relationship model: According to the historical operation data of the channel, for every 1% increase in flow, the gate opening needs to increase by approximately 1.57% (calibrated by the hydraulic model). Opening increment = 5.1% × 1.57 ≈ 8%; new gate opening = 65% + 8% = 73%, boundary check: the designed maximum opening is 80% (anti-scour safety limit); 73% < 80%, so 73% is finally adopted. The current status of the adjacent channel K10 + 200: original flow: 1.5m³ / s (corresponding to the original gate opening of 65%, read in real time through the channel); target flow: 1.46m³ / s (intermediate parameter output in step 704, 2.7% less than the original flow). Opening-flow relationship: According to the historical hydraulic model of the channel, for every 1% decrease in flow, the gate opening needs to be reduced by about 1.48% (calibrated by least squares fitting). Flow reduction = 1.51.5 1.46 × 100% ≈ 2.67%; gate opening reduction = 2.67% × 1.48 ≈ 4%; new gate opening = 65% − 4% = 61%. Boundary check: The design minimum opening is 60% (to prevent siltation caused by complete gate closure). 61% > 60%, so 61% is ultimately adopted.

[0174] In the embodiments of this application, through multi-dimensional parameter coupling and dynamic constraint matching, the generated optimization parameters not only meet the physical facility limits but also respond to changes in market supply and demand, achieving coordinated optimization of the safety and economy of the irrigation district water delivery system.

[0175] Figure 2 The present application provides a schematic diagram of a smart irrigation district management system based on the Internet of Things. Figure 2 As shown, the system includes:

[0176] The acquisition module 21 is used to obtain the real-time irrigation water volume and water pump energy consumption data of each water delivery channel in the target irrigation area.

[0177] The first generating module 22 is configured to generate a dynamic water resource supply and demand relationship map based on the real-time irrigation water volume and water pump energy consumption data.

[0178] The determination module 23 is used to determine the target water transfer channels corresponding to the water resource trading requests of the water use terminals in each irrigation area from the target irrigation area. If any of the target water transfer channels has a water pump overload or a siltation diffusion coefficient value greater than a preset diffusion coefficient threshold, a water resource trading price for the target water transfer channel is generated based on the dynamic water price adjustment rules and the dynamic water resource supply and demand relationship map.

[0179] The second generating module 24 is configured to generate target water transfer optimization parameters for each target water transfer channel according to the water resource transaction prices of all the target water transfer channels and the node states in the dynamic water resource supply and demand relationship map.

[0180] Figure 2 The smart irrigation district management system based on the Internet of Things can be implemented Figure 1 The implementation principles and technical effects of the IoT-based smart irrigation district management method described in the illustrated embodiment are not further elaborated. The specific manner in which each module and unit performs operations in the IoT-based smart irrigation district management system described in the aforementioned embodiment has been described in detail in the related embodiments and will not be further elaborated here.

[0181] In one possible design, Figure 2 The embodiment shown is a smart irrigation district management system based on the Internet of Things that can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0182] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0183] The processing component 32 is as follows Figure 1 The embodiment provides a smart irrigation district management method based on the Internet of Things.

[0184] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above method.

[0185] The storage component 31 is configured to store various types of data to support operations in the terminal. The storage component can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random-access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0186] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0187] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0188] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0189] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0190] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The embodiment shown is a smart irrigation district management method based on the Internet of Things.

[0191] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0192] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0193] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0194] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A smart irrigation district management method based on the Internet of Things, characterized in that: include: Obtain real-time irrigation water volume and pump energy consumption data for each water delivery channel within the target irrigation area; Generating a dynamic water resource supply and demand relationship map based on the real-time irrigation water volume and water pump energy consumption data; determining target water transfer channels corresponding to the water resource transaction requests of the water use terminals in each irrigation area from the target irrigation area, and generating a water resource transaction price for the target water transfer channel based on a dynamic water price adjustment rule and the dynamic water resource supply and demand relationship map if a water pump is overloaded or a siltation diffusion coefficient value is greater than a preset diffusion coefficient threshold in any of the target water transfer channels; generating target water transfer optimization parameters for each target water transfer channel based on the water resource transaction prices of all the target water transfer channels and the node states in the dynamic water resource supply and demand relationship map; Generating target water transfer optimization parameters for each target water transfer channel based on the water resource transaction prices of all the target water transfer channels and the node states in the dynamic water resource supply and demand relationship map includes: The water resource write-off rate of each water transmission channel in the private blockchain network is used as an attenuation factor of the initial pricing gradient value associated with each water transmission channel in the dynamic water price adjustment rule; For each target water transmission channel, the water transmission resource weights of adjacent water transmission channels are adjusted according to the real-time energy consumption curve of the water pumps in the target water transmission channel to generate a water transmission resource weight increment; generating initial water transfer optimization parameters of a target water transfer channel based on the sedimentation diffusion coefficient value and the water transfer resource weight increment; According to the water resource transaction price and the node status of the target water transmission channel in the dynamic water resource supply and demand relationship map, the initial water transmission optimization parameters are adjusted to generate target water transmission optimization parameters.

2. The method according to claim 1, characterized in that The target water transfer channels corresponding to the water resource transaction requests of the water use terminals in each irrigation area are determined from the target irrigation areas. If any of the target water transfer channels has a water pump overload or a siltation diffusion coefficient value greater than a preset diffusion coefficient threshold, a water resource transaction price is generated based on a dynamic water price adjustment rule and the dynamic water resource supply and demand relationship map, including: determining, according to the geographic coordinate range in each of the water resource transaction requests, a target water transfer channel corresponding to each of the water resource transaction requests; Calculate the siltation diffusion coefficient value of the target water transfer channel based on the historical extreme values ​​of the channel siltation diffusion coefficient recorded in the dynamic water resource supply and demand relationship map, combined with the real-time monitored channel water flow rate and sediment content; and determine that the target water transfer channel has a siltation risk when the siltation diffusion coefficient value exceeds a preset diffusion coefficient threshold; Based on the real-time energy consumption curve of the water pump in the target water transmission channel, the peak energy consumption fluctuation rate within a preset time period in the future is predicted. If the peak energy consumption fluctuation rate exceeds the critical slope corresponding to the water pump energy consumption threshold, it is determined that there is a risk of water pump overload in the target water transmission channel; Extracting the water pump energy consumption deviation from the dynamic water resource supply and demand relationship map; When there is a risk of siltation or water pump overload in any of the target water transmission channels, the water resource transaction price of the target water transmission channel is calculated based on the initial pricing gradient value associated with the target water transmission channel in the dynamic water price adjustment rule, and the compensation factor generated by the nonlinear relationship between the siltation diffusion coefficient value and the water pump energy consumption deviation is superimposed.

3. The method according to claim 2, characterized in that The water resource transaction price of the target water transmission channel is calculated based on the initial pricing gradient value associated with the target water transmission channel in the dynamic water price adjustment rule and a compensation factor generated by the nonlinear relationship between the sedimentation diffusion coefficient value and the water pump energy consumption deviation, including: Extracting the real-time demand fluctuation of the target water transmission channel from the dynamic water resource supply and demand relationship map; extracting an initial pricing gradient value associated with the target water transmission channel from the dynamic water price adjustment rule according to the real-time demand fluctuation amount; Determine the coefficient corresponding to the priority level of the real-time demand fluctuation as the pricing coefficient; Determine the product of the initial pricing gradient value and the pricing coefficient as the target pricing gradient value; Based on the target pricing gradient value and the compensation factor, the water resource transaction price of the target water transmission channel is calculated.

4. The method according to claim 3, characterized in that The water resource transaction price of the target water transmission channel is calculated based on the target pricing gradient value and the compensation factor, including: The first weight factor and the second weight factor are coupled and calculated using a pre-constructed nonlinear compensation function to generate a compensation factor, wherein the pre-constructed nonlinear compensation function is constructed based on a nonlinear relationship between the siltation diffusion coefficient value and the water pump energy consumption deviation, the first weight factor is a ratio of the water pump energy consumption deviation to a preset energy consumption deviation threshold, and the second weight factor is a ratio of the current water transfer channel siltation diffusion coefficient value to a preset diffusion coefficient change rate threshold; Superimposing the target pricing gradient value and the compensation factor according to a preset ratio to obtain a superposition result; According to the superposition result, combined with the water transfer weights of adjacent water transfer channels recorded in the dynamic water resource supply and demand relationship map, the water resource transaction price of the target water transfer channel is calculated.

5. The method according to claim 4, characterized in that The water resource transaction price of the target water transmission channel is calculated based on the superposition result and in combination with the water transmission weights of the adjacent water transmission channels recorded in the dynamic water resource supply and demand relationship map, including: Based on the water transmission weights of the adjacent water transmission channels recorded in the dynamic water resource supply and demand relationship map, the average water transmission weights of the adjacent water transmission channels directly connected to the current water transmission channel are calculated, and the average water transmission weights of the adjacent water transmission channels are used as the adjustment coefficient for allocating the target water transmission channel; The target pricing gradient value and the adjustment coefficient are weighted and fused according to the superposition result to generate a water resource transaction price.

6. The method according to claim 1, characterized in that The step of adjusting the initial water transfer optimization parameters to generate target water transfer optimization parameters based on the water resource transaction price and the node status of the target water transfer channel in the dynamic water resource supply and demand relationship map includes: Generate a dynamic correction coefficient for the initial water transfer optimization parameter based on the node status of the target water transfer channel in the dynamic water resource supply and demand relationship map and the water transfer weight of the target water transfer channel, combined with the energy consumption compensation factor embedded in the water resource transaction price; Calculating the sedimentation impact coefficient of the target water transfer channel based on the spatial coupling relationship between the channel sedimentation diffusion coefficient value recorded in the dynamic water resource supply and demand relationship map and the water transfer weights of adjacent water transfer channels; generating parameter adjustment constraints based on the siltation impact coefficient and the pump energy consumption deviation; Dynamically scaling the initial water delivery optimization parameter according to the dynamic correction coefficient and the parameter adjustment constraint to obtain an intermediate water delivery optimization parameter; The intermediate water transfer optimization parameter is boundary-matched with the maximum water carrying capacity threshold of the target water transfer channel recorded in the dynamic water resource supply and demand relationship map to generate the target water transfer optimization parameter, wherein the maximum water carrying capacity threshold is calculated based on the water transfer capacity corresponding to the water pump energy consumption threshold in the target water transfer channel.

7. A smart irrigation district management system based on the Internet of Things, characterized by: include: The acquisition module is used to obtain the real-time irrigation water volume and water pump energy consumption data of each water delivery channel in the target irrigation area; A first generating module is used to generate a dynamic water resource supply and demand relationship map based on the real-time irrigation water volume and water pump energy consumption data; a determination module, configured to determine, from the target irrigation districts, target water transfer channels corresponding to the water resource transaction requests of the water use terminals in each irrigation district; and, if any of the target water transfer channels has a water pump overload or a siltation diffusion coefficient value greater than a preset diffusion coefficient threshold, generate a water resource transaction price for the target water transfer channel based on a dynamic water price adjustment rule and the dynamic water resource supply and demand relationship map; A second generating module is configured to generate target water transfer optimization parameters for each target water transfer channel based on the water resource transaction prices of all the target water transfer channels and the node states in the dynamic water resource supply and demand relationship map; Generating target water transfer optimization parameters for each target water transfer channel based on the water resource transaction prices of all the target water transfer channels and the node states in the dynamic water resource supply and demand relationship map includes: The water resource write-off rate of each water transmission channel in the private blockchain network is used as an attenuation factor of the initial pricing gradient value associated with each water transmission channel in the dynamic water price adjustment rule; For each target water transmission channel, the water transmission resource weights of adjacent water transmission channels are adjusted according to the real-time energy consumption curve of the water pumps in the target water transmission channel to generate a water transmission resource weight increment; generating initial water transfer optimization parameters of a target water transfer channel based on the sedimentation diffusion coefficient value and the water transfer resource weight increment; According to the water resource transaction price and the node status of the target water transmission channel in the dynamic water resource supply and demand relationship map, the initial water transmission optimization parameters are adjusted to generate target water transmission optimization parameters.

8. A computing device, characterized in that It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a smart irrigation district management method based on the Internet of Things as described in any one of claims 1 to 6.

9. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, the method for smart irrigation district management based on the Internet of Things as described in any one of claims 1 to 6 is implemented.

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