Intelligent irrigation district management method and system based on Internet of Things

Abstract

The invention provides an intelligent irrigation district management method and system based on the Internet of Things, and the method comprises the steps: obtaining the real-time irrigation water amount and water pump energy consumption data of each water conveyance channel in a target irrigation district; according to the real-time irrigation water amount and the water pump energy consumption data, generating a dynamic water resource supply and demand relation graph; determining a target water delivery channel corresponding to the water resource transaction request of the water consumption terminal of each irrigation area from a target irrigation area, and if any target water delivery channel has a condition that a water pump is overloaded or a deposition diffusion coefficient value is greater than a preset diffusion coefficient threshold value, determining the water resource transaction request of the water consumption terminal of each irrigation area based on a dynamic water price adjustment rule and a dynamic water resource supply and demand relationship graph, generating a water resource transaction price of the target water conveyance channel; and according to the water resource transaction prices of all the target water conveyance channels and the node states in the dynamic water resource supply and demand relationship graph, generating target water conveyance optimization parameters of each target water conveyance channel. The water resource allocation efficiency of the irrigation area and the operation stability of the water conveying system are improved.

Description

Technical Field

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

[0002] Modern large-scale irrigation districts face problems such as low water resource allocation efficiency, high water conveyance energy consumption, and lag in supply-demand matching. There is an urgent need to achieve precise water resource management based on real-time monitoring. Especially in the scenario of water rights trading, it is necessary to dynamically evaluate status data such as channel siltation and pump energy consumption, optimize water resource allocation through an intelligent pricing mechanism, and ensure the stable operation of the water conveyance system at the same time.

[0003] The current existing solution is an irrigation district water resource scheduling method based on a data acquisition and monitoring control system. By deploying flow meters and water pressure sensors at key channels, water conveyance data is collected and uploaded to a central control platform, and an early warning rule with a fixed threshold is generated in combination with historical water use patterns. When abnormal flow or pressure is detected, manual intervention is triggered for adjustment. Some systems also introduce a static water price model and set ladder prices according to seasonal and regional water consumption differences.

[0004] The deficiencies of this solution are as follows: relying on fixed-threshold early warning, the water price model is disconnected from the real-time supply-demand status, lacking a pricing feedback mechanism for data such as energy consumption and siltation, and the centralized architecture leads to data islands, making it difficult to support the verification of water rights trading involving multiple parties. Summary of the Invention

[0005] The present application provides a method and system for intelligent irrigation district management based on the Internet of Things to solve the problems of low water resource allocation efficiency and poor operation stability of the water conveyance system in the prior art.

[0006] In a first aspect, the present application provides a method for intelligent irrigation district management based on the Internet of Things, including: Obtaining real-time irrigation water volume and pump energy consumption data of each water conveyance channel in a target irrigation district; Generating a dynamic water resource supply-demand relationship map according to the real-time irrigation water volume and pump energy consumption data; Determining, from the target irrigation district, target water conveyance channels corresponding to water resource trading requests of each irrigation district water use terminal respectively. If there is a situation where any of the target water conveyance channels has a pump overload or a siltation diffusion coefficient value greater than a preset diffusion coefficient threshold, then based on a dynamic water price adjustment rule and the dynamic water resource supply-demand relationship map, generating a water resource trading price for the target water conveyance channel; Generating target water conveyance optimization parameters for each target water conveyance channel according to the water resource trading prices of all the target water conveyance channels and the node states in the dynamic water resource supply-demand relationship map.

[0007] Optionally, determine the target water conveyance channels corresponding to the water resource trading requests of each water use terminal in the target irrigation area respectively. If any of the target water conveyance channels has a situation where the pump is overloaded or the silt diffusion coefficient value is greater than the preset diffusion coefficient threshold, generate a water resource trading price based on the dynamic water price adjustment rule and the dynamic water resource supply-demand relationship map, including: Determine the target water conveyance channels corresponding to each of the water resource trading requests according to the geographical coordinate ranges in each of the water resource trading requests; According to the historical extreme values of the channel silt diffusion coefficient recorded in the dynamic water resource supply-demand relationship map, combined with the real-time monitored channel water flow velocity and sediment content, calculate the silt diffusion coefficient value of the target water conveyance channel. When the silt diffusion coefficient value exceeds the preset diffusion coefficient threshold, determine that the target water conveyance channel has a siltation risk; Based on the real-time energy consumption curve of the pump in the target water conveyance channel, predict the energy consumption peak volatility in a future preset time period. If the energy consumption peak volatility exceeds the critical slope corresponding to the pump energy consumption threshold, determine that the target water conveyance channel has a pump overload risk; Extract the pump energy consumption deviation degree from the dynamic water resource supply-demand relationship map; When any of the target water conveyance channels has a siltation risk or a pump overload risk, according to the initial pricing gradient value associated with the target water conveyance channel in the dynamic water price adjustment rule, superimpose the compensation factor generated by the non-linear relationship between the silt diffusion coefficient value and the pump energy consumption deviation degree, and calculate the water resource trading price of the target water conveyance channel.

[0008] Optionally, the calculating the water resource trading price of the target water conveyance channel by superimposing the compensation factor on the initial pricing gradient value associated with the target water conveyance channel in the dynamic water price adjustment rule includes: Extract the real-time demand fluctuation amount of the target water conveyance channel from the dynamic water resource supply-demand relationship map; According to the real-time demand fluctuation amount, extract the initial pricing gradient value associated with the target water conveyance channel from the dynamic water price adjustment rule; Determine the coefficient corresponding to the priority level to which the real-time demand fluctuation amount belongs as the pricing coefficient; Determine the product result of the initial pricing gradient value and the pricing coefficient as the target pricing gradient value; Based on the target pricing gradient value, superimpose the compensation factor, and calculate the water resource trading price of the target water conveyance channel.

[0009] Optionally, the calculating the water resource trading price of the target water conveyance channel by superimposing the compensation factor on the target pricing gradient value includes: The first weight factor and the second weight factor are coupled and calculated through a pre-constructed non-linear compensation function to generate a compensation factor. The pre-constructed non-linear compensation function is constructed based on the non-linear relationship between the sediment deposition diffusion coefficient value and the deviation degree of the pump energy consumption. The first weight factor is the ratio of the deviation degree of the pump energy consumption to the preset energy consumption deviation threshold, and the second weight factor is the ratio of the current sediment deposition diffusion coefficient value of the water conveyance channel to the preset diffusion coefficient change rate threshold; The target pricing gradient value and the compensation factor are superimposed according to a preset ratio to obtain a superimposed result; According to the superimposed result, combined with the water conveyance weights of adjacent water conveyance channels recorded in the dynamic water resource supply-demand relationship map, the water resource trading price of the target water conveyance channel is calculated.

[0010] Optionally, the calculating the water resource trading price of the target water conveyance channel according to the superimposed result, combined with the water conveyance weights of adjacent water conveyance channels recorded in the dynamic water resource supply-demand relationship map, includes: Based on the water conveyance weights of adjacent water conveyance channels recorded in the dynamic water resource supply-demand relationship map, the average value of the water conveyance weights of adjacent water conveyance channels directly connected to the current water conveyance channel is calculated, and the average value of the water conveyance weights of adjacent water conveyance channels is used as the adjustment coefficient for allocating the target water conveyance channel; The target pricing gradient value and the allocation adjustment coefficient are weighted and fused according to the superimposed result to generate the water resource trading price.

[0011] Optionally, the generating the target water conveyance optimization parameters of each target water conveyance channel according to the water resource trading prices of all the target water conveyance channels and the node states in the dynamic water resource supply-demand relationship map includes: The water resource cancellation rates of each water conveyance channel in the private blockchain network are respectively used as the attenuation factors of the initial pricing gradient values associated with each water conveyance channel in the dynamic water price adjustment rule; For each target water conveyance channel, according to the real-time energy consumption curve of the pump in the target water conveyance channel, the water conveyance weights of adjacent water conveyance channels are adjusted to generate an increment of the water conveyance weights; Based on the sediment deposition diffusion coefficient value and the increment of the water conveyance weights, the initial water conveyance optimization parameters of the target water conveyance channel are generated; According to the water resource trading price and the node state of the target water conveyance channel in the dynamic water resource supply-demand relationship map, the initial water conveyance optimization parameters are adjusted to generate the target water conveyance optimization parameters.

[0012] Optionally, the adjusting the initial water conveyance optimization parameters according to the water resource trading price and the node state of the target water conveyance channel in the dynamic water resource supply-demand relationship map to generate the target water conveyance optimization parameters includes: Generate a dynamic correction coefficient for the initial water conveyance optimization parameters based on the node status of the target water conveyance channel in the dynamic water resource supply-demand relationship map and the water conveyance weight of the target water conveyance channel, in combination with the energy consumption compensation factor embedded in the water resource trading price; Calculate the siltation influence coefficient of the target water conveyance channel based on the spatial coupling relationship between the recorded channel siltation diffusion coefficient value and the water conveyance weight of adjacent water conveyance channels in the dynamic water resource supply-demand relationship map; Generate parameter adjustment constraint conditions based on the siltation influence coefficient and the pump energy consumption deviation degree; Dynamically scale the initial water conveyance optimization parameters according to the dynamic correction coefficient and the parameter adjustment constraint conditions to obtain intermediate water conveyance optimization parameters; Perform boundary matching on the intermediate water conveyance optimization parameters and the maximum water carrying capacity threshold of the target water conveyance channel recorded in the dynamic water resource supply-demand relationship map to generate target water conveyance optimization parameters, where the maximum water carrying capacity threshold is calculated based on the water conveyance capacity corresponding to the pump energy consumption threshold in the target water conveyance channel.

[0013] In a second aspect, the present application provides an Internet of Things-based intelligent irrigation district management system, including: An acquisition module for acquiring real-time irrigation water volume and pump energy consumption data of each water conveyance channel in the target irrigation district; A first generation module for generating a dynamic water resource supply-demand relationship map according to the real-time irrigation water volume and pump energy consumption data; A determination module for determining, from the target irrigation district, target water conveyance channels corresponding to the water resource trading requests of each irrigation district water use terminal respectively. If there is a situation where any of the target water conveyance channels has pump overload or the siltation diffusion coefficient value is greater than a preset diffusion coefficient threshold, then based on the dynamic water price adjustment rule and the dynamic water resource supply-demand relationship map, generate the water resource trading price of the target water conveyance channel; A second generation module for generating target water conveyance optimization parameters for each target water conveyance channel according to the water resource trading prices of all the target water conveyance channels and the node status in the dynamic water resource supply-demand relationship map.

[0014] In a third aspect, the present application provides a computing device, including a processor and a memory, where a computer program is stored in the memory, and the processor is configured to run the computer program to execute any one of the methods of an Internet of Things-based intelligent irrigation district management method in the first aspect.

[0015] In a fourth aspect, the present application provides a computer storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, an Internet of Things-based intelligent irrigation district management method described in any one of the first aspects is implemented.

[0016] In this application, a smart irrigation district management method based on the Internet of Things is provided. The method includes: obtaining real-time irrigation water volume and pump energy consumption data of each water conveyance channel in the target irrigation district; generating a dynamic water resource supply-demand relationship map according to the real-time irrigation water volume and pump energy consumption data; determining target water conveyance channels corresponding to the water resource trading requests of each irrigation district water use terminal from the target irrigation district. If there is a situation where any of the target water conveyance channels has a pump overload or the siltation diffusion coefficient value is greater than the preset diffusion coefficient threshold, then based on the dynamic water price adjustment rule and the dynamic water resource supply-demand relationship map, generating the water resource trading price of the target water conveyance channel; generating target water conveyance optimization parameters for each target water conveyance channel according to the water resource trading prices of all the target water conveyance channels and the node states in the dynamic water resource supply-demand relationship map.

[0017] The technical solution of this application has the following beneficial effects: This application obtains the water volume of the water conveyance channel and the pump energy consumption data through the Internet of Things sensing network, realizes the digital mapping of the operation state of the irrigation district, and provides a data basis for dynamic decision-making. Integrate real-time data with historical laws to generate a dynamic supply-demand relationship map, visually present the water resource supply capacity and demand hotspots of each channel, and improve the water resource allocation efficiency of the irrigation district. Dynamically adjust the water price based on the risk of channel siltation and pump overload. Combine the trading price and the node state to generate water conveyance optimization parameters, realize the closed-loop management of "monitoring - pricing - regulation", and improve the operation stability of the water conveyance system.

[0018] Furthermore, the embodiment of this application also determines the transaction-related channels through geographic coordinate matching, calculates the siltation diffusion coefficient and energy consumption volatility in real time for risk determination. When there is a risk of channel siltation or overload, a dynamic compensation factor generated by the siltation degree and energy consumption deviation is superimposed on the basic pricing gradient, and finally a differentiated trading price reflecting the channel health state is output.

[0019] Moreover, this solution breaks through the traditional static pricing model, directly associates the physical state of the channel with the water resource trading price, enables the water price to accurately reflect the operation cost of the infrastructure, encourages water users to avoid high-risk areas, and realizes the dual goals of protecting the irrigation district infrastructure and efficiently allocating water resources.

[0020] These aspects or other aspects of this application will be more clearly understood in the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0022] Figure 1 It is a flowchart of a smart irrigation area management method based on the Internet of Things provided by an embodiment of the present application; Figure 2 It is a schematic structural diagram of a smart irrigation area management system based on the Internet of Things provided by an embodiment of the present application; Figure 3 It is a schematic structural diagram of a computing device provided by an embodiment of the present application. Detailed implementation manners

[0023] In order to enable those skilled in the art of this technology to better understand the solutions of the present application, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application.

[0024] In some processes described in the specification, claims and the above-mentioned drawings of the present application, there are multiple operations that appear in a specific order. However, 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 operation numbers such as 101 and 102 are only used to distinguish different operations, and the numbers themselves do not represent any execution order. 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 such as "first" and "second" in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, nor do they limit that "first" and "second" are of different types.

[0025] Researchers found that there are problems in the current water resources management of irrigation areas, such as lagging data collection, extensive supply-demand matching, and the disconnection between the pricing mechanism and the infrastructure status, resulting in low efficiency of water resources allocation and serious losses in the water conveyance system. Based on this, the embodiments of this application provide an Internet of Things-based intelligent irrigation area management method. This method constructs a dynamic supply-demand map by collecting the irrigation water volume and pump energy consumption data of each water conveyance channel in real time, realizes differential pricing in combination with the risk of channel siltation and pump station overload, and generates optimization parameters based on the full-channel status data to form a closed-loop management of "monitoring, pricing, and regulation". The technical solution of this application is applicable to scenarios that require refined management, such as water rights trading in large irrigation areas and cross-regional water resources allocation. Specifically, by obtaining the irrigation water volume and pump energy consumption data of each water conveyance channel in the target irrigation area in real time, a dynamic water resources supply-demand relationship map is constructed, and the water use terminal transaction request is matched with the target water conveyance channel based on the map. When it is detected that there is a risk of pump overload or siltation in the channel, a differential transaction price is generated in combination with the dynamic water price adjustment rule, and optimization parameters are generated by integrating the transaction prices of each channel and the node status data, realizing a closed-loop management from data collection to intelligent decision-making.

[0026] The entire R & D process reflects that by directly associating the physical infrastructure status with the water resources transaction price, the water price can accurately reflect the channel operation status, effectively improve the water resources allocation efficiency and the stability of the water conveyance system, while reducing energy consumption, providing an intelligent solution for the water resources management of irrigation areas.

[0027] Next, the technical solutions in the embodiments of this application will be described clearly and completely with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of this application.

[0028] Figure 1 The following is a flowchart of an Internet of Things-based intelligent irrigation area management method provided by the embodiments of this application. As Figure 1 shown, this method includes: Step 101: Obtain the real-time irrigation water volume and pump energy consumption data of each water conveyance channel in the target irrigation area.

[0029] In this step, the real-time irrigation water volume refers to the instantaneous irrigation water consumption data collected by the channel flowmeter, with the unit of m³ / s. The pump energy consumption data includes the real-time power, efficiency, and cumulative power consumption of the pump, which are collected by the smart meter. The target irrigation area refers to a specific irrigation area that requires water resources management, including water conveyance channels such as main canals and branch canals. The water conveyance channels can be represented in the form of Kxx+xxx. The correlation between the pump and the water conveyance channel of the irrigation area: The pump is the core power equipment of the water conveyance channel, and its energy consumption directly reflects the intensity of the channel water conveyance operation. For every 1 kWh of electrical energy consumed by the pump, about 3.6 m³ of water can be lifted (calculated based on a head of 10 m). The energy consumption data actually quantifies the work done by the channel water conveyance. Special pumps are configured at each key node (such as a sluice gate, bend) of the water conveyance channel to form a topological structure of "one pump for one channel" or "multiple pumps for one channel". Through the spatial matching of the coordinates of the pump positioning system and the geographical information system of the channel, the energy consumption data can be accurately mapped to a specific channel section. For example, the coordinates (X, Y) of a certain pump station correspond to the section of the channel from K12+300 to K14+800.

[0030] In the embodiment of the present application, ultrasonic flowmeters and smart meters are deployed at each key node of the water conveyance channels in the target irrigation area. The flowmeter collects the water flow velocity and water level height of the channel cross-section every 5 minutes, and the real-time irrigation water volume is obtained through integral calculation; the smart meter records the three-phase voltage, current, and power factor of the pump at 1-minute intervals. After the edge computing gateway filters and removes outliers from the original data, it uploads the data to the cloud platform through the 5G network, and finally integrates it into the pump energy consumption data.

[0031] For example, taking the section of the main canal K12+500 in a certain irrigation area as an example, an ultrasonic flowmeter of a certain model and a smart meter of a certain model are installed at this location. The ultrasonic flowmeter measures the average flow velocity v = 1.2 m / s of the channel cross-section through the Doppler frequency shift method, and measures the water surface height h = 1.8 m through the pressure sensor. The channel is a trapezoidal section, with a bottom width b = 1 m and a designed slope coefficient m = 1.5. The cross-sectional area A = (b + m×h)×h = (1 + 1.5×1.8)×1.8 = 4.86 m², and the flow rate Q = A×v = 4.86×1.2 = 5.832 m³ / s. After calibration by the section correction coefficient of 0.37 (calibrated value), the actual irrigation water volume is Q = 5.832×0.37 ≈ 2.16 m³ / s; the shaft power of the pump is directly measured by the smart meter as 55 kW, the rated power of the pump is 70 kW, and the current load rate = 55 / 70 = 78.6%. After the data is preprocessed by the edge gateway with a time stamp and the coordinates of the pump positioning system marked, it is uploaded to the central database by the edge gateway.

[0032] Step 102: Generate a dynamic water resources supply-demand relationship map according to the real-time irrigation water volume and the pump energy consumption data.

[0033] In this step, the dynamic water resource supply-demand relationship map represents a topological network model, where nodes represent canal water diversion gates, pumping stations, canal hubs, etc., edges represent canal sections, node attributes include real-time water volume, demand gap, etc., and edge attributes include water flow direction, transportation efficiency, etc.

[0034] In the embodiment of the present application, a graph structure with canal hubs as nodes and canal sections as edges is constructed. The node weights are dynamically calculated based on the difference between the declared demand of water use terminals and the actual water supply, and the edge weights are set according to the canal water conveyance efficiency (real-time flow / designed flow); the optimal water conveyance path is calculated through the Dijkstra algorithm, and the long short-term memory network model is trained in combination with historical transaction data, and then the demand fluctuations within a preset time for each node are predicted, and finally a dynamic water resource supply-demand relationship map including spatio-temporal dimensions is generated.

[0035] For example, the main canal section at K12+500 is used as an edge in the map. The designed branch canal flow is 2.5 m³ / s, and the water conveyance efficiency = actual irrigation water volume / branch canal flow = 2.16 / 2.5 = 0.864 ≈ 0.86; according to the summary of the smart water meter data of water users in a certain village, it is known that there are three types of user demands: (1) the agricultural irrigation demand is 1.2 m³ / s, and the agricultural irrigation demand is calculated based on the reference crop evapotranspiration (ET0) value of 500 mu of crops, (2) the industrial water demand is 0.4 m³ / s, and this industrial water demand can be obtained according to the park water supply contract, (3) the domestic water demand is 0.2 m³ / s. Exemplarily, it can be roughly calculated by population × per capita quota. Therefore, the declared demand of the water use terminal is 1.2 + 0.4 + 0.2 = 1.8 m³ / s, forming a node attribute with a remaining capacity of 2.16 - 1.8 = 0.36 m³ / s. Input parameters of the prediction model: meteorological data: the temperature rises by 3 °C / h, the humidity drops by 10% / h, water use history: the same-period water use growth slope is 0.15 m³ / (s·h), real-time data: the current water use load rate is 85%. The prediction process of the long short-term memory network model: input the water use sequence in the past 24 hours (sampling interval of 15 minutes), considering the meteorological mutation index ΔW = 0.8, and output the demand increment after 1 hour: 0.2 m³ / s. The final predicted value = the current 1.8 + the increment 0.2 = 2.0 m³ / s. The system predicts that the demand in this area will increase to 2.0 m³ / s after 1 hour. Early warning threshold setting: 85% of the designed flow of 2.5 m³ / s = 2.125 m³ / s, the predicted demand 2.0 > 2.125 × 0.95 (buffer coefficient) = 2.02 m³ / s. Early warning level classification: 2.0 - 2.125 m³ / s: yellow warning, 2.125 m³ / s: red warning. Therefore, a supply-demand early warning is triggered.

[0036] Step 103: Determine the target water conveyance channels corresponding to the water resource trading requests of each water use terminal in the target irrigation area respectively. If there is a situation where any of the target water conveyance channels has a pump overload or the sedimentation diffusion coefficient value is greater than the preset diffusion coefficient threshold, then based on the dynamic water price adjustment rule and the dynamic water resource supply and demand relationship map, generate the water resource trading price of the target water conveyance channel.

[0037] In this step, pump overload means that the real-time power continuously exceeds the rated power. The sedimentation diffusion coefficient is an index of the channel sedimentation degree calculated through the water flow velocity and sediment content. The dynamic water price adjustment rule consists of three parts: the basic price, the supply and demand adjustment coefficient, and the risk compensation coefficient. The water resource trading requests submitted by each water use terminal in the irrigation area (such as agricultural cooperatives, industrial enterprises, village and town water supply stations, etc.) through the intelligent water rights trading platform include structured data such as the geographical coordinate range (the GPS boundary of the water application area), the declared water volume (the water demand per unit time, such as m³ / s), the water use period (start and end times), and the price sensitivity coefficient (the acceptable premium range), which are the core inputs triggering dynamic pricing. The preset diffusion coefficient threshold is a critical safety value determined according to the channel design specifications and historical sedimentation accident data. When the real-time calculated sedimentation diffusion coefficient (water flow velocity × sediment content) exceeds this value, it indicates that the channel water conveyance capacity has decreased due to sedimentation, and the price adjustment mechanism needs to be triggered. The preset diffusion coefficient threshold is dynamically updated through machine learning, comprehensively considering factors such as channel material, service life, and season. The water resource trading price refers to the dynamic unit price (yuan / m³) generated based on the real-time supply and demand relationship and the channel health status, including the basic water price, the energy consumption surcharge (reflecting the pump efficiency loss), and the sedimentation compensation fee (channel maintenance cost), which is automatically executed through the blockchain smart contract.

[0038] In the embodiment of the present application, first, the trading request is matched with the target channel through the geographic information system, and the acoustic Doppler velocimeter and turbidimeter are used to obtain the real-time water flow velocity and sediment content, and calculate the sedimentation diffusion coefficient; at the same time, the volatility (sliding window standard deviation) of the pump energy consumption curve is analyzed. When any index exceeds the threshold, the corresponding price adjustment strategy is selected according to the rule base: the sedimentation risk increases the basic price according to the linear model, and the energy consumption overload increases exponentially, and finally the water resource trading price is generated by superposition.

[0039] For example, at the K12+500 section, the flow velocity distribution at different depth layers in the vertical water flow profile is measured in real time by an acoustic Doppler current profiler as 1.2 m / s at the surface layer, 1.05 m / s at the middle layer, and 0.85 m / s at the bottom layer. Taking the weighted average of the three layers as 1.0 m / s, the water flow velocity drops to 1.0 m / s. The combination of a turbidimeter and sampling laboratory calibration is adopted: the online turbidimeter measures NTU = 280, and the local calibration curve is established in the laboratory: NTU = 156×sediment concentration (R² = 0.98). Through conversion, it is obtained that: 280 / 156 ≈ 1.8 kg / m³, the sediment content is 1.8 kg / m³, and the siltation coefficient of 1.8 exceeds the threshold of 1.5. Then, the base price of 0.5 yuan / m³ is superimposed with the siltation surcharge of 0.1 yuan and the energy consumption surcharge of 0.08 yuan, and the final water resource trading price is 0.68 yuan / m³.

[0040] Step 104: Generate the target water conveyance optimization parameters for each target water conveyance channel according to the water resource trading price of all the target water conveyance channels and the node states in the dynamic water resource supply and demand relationship map.

[0041] In this step, the node state of the target water conveyance channel refers to the working states of various monitoring points or devices related to pump energy consumption in this area. These nodes may include but are not limited to parts such as sensors, controllers, the pump itself, and the pipeline system connected thereto. Exemplarily, the specific generation process of the node state is to define key facilities such as channel junctions, pump stations, and sluice gates as network nodes based on the topological structure of the irrigation area water conveyance channel, and to collect physical parameters such as water level, flow rate, pump power, and gate opening in real time through Internet of Things sensors deployed at the nodes. Combining historical operation data to establish a state evaluation model for each node. Among them, the pump node state calculates the energy consumption anomaly index through the deviation degree between the real-time energy consumption curve and the standard working condition curve, the channel node state generates the siltation risk coefficient by superimposing the sediment content data according to the ratio of the real-time water flow velocity to the designed water flow velocity, and the sluice gate node state calculates the regulation efficiency value based on the matching degree between the gate opening and the flow rate monitoring value. Finally, the energy consumption anomaly index, siltation risk coefficient, and regulation efficiency value of each node are normalized to the node state value of 0-1 and marked in the dynamic water resource supply and demand relationship map. At the same time, the node state data is stored in the distributed ledger together with the corresponding geographical coordinates and time stamps through the blockchain network. The target water conveyance optimization parameters include control variables such as the recommended flow rate adjustment range, pump frequency conversion command, and gate opening.

[0042] In the embodiment of the present application, based on the water resource trading price of all the target water conveyance channels and the node states in the dynamic water resource supply and demand relationship map, a multi-objective optimization model is established: taking the channel balanced load as the objective function, and the constraint conditions include the maximum carrying flow rate, the minimum service pressure, etc. The non-dominated sorting genetic algorithm is used to solve the Pareto optimal solution set, and the target water conveyance optimization parameters of each channel are output.

[0043] For example, in the section of K12+500, due to the relatively high pricing and a weight of 30% for the equipment operating conditions: pump efficiency (currently 78%, benchmark 85%), the score is 78 / 85≈0.92, and the current gate opening matching degree is 90%, with a score of 0.90. Water quality index (weight 20%): turbidity (1.8 kg / m³, threshold 2.0), the score is 1 - 1.8 / 2.0 = 0.90, dissolved oxygen (6.5 mg / L, standard ≥5), the score is 1.0. Siltation risk (weight 30%): siltation diffusion coefficient (1.8, threshold 1.5), the score is 1 - 1.8 / (2×1.5)=0.40 (penalty term). Historical stability (weight 20%): flow rate volatility in the past 7 days (8%, threshold 10%), the score is 1 - 8 / 10 = 0.80. The comprehensive score (a score of 0.7 belongs to the "good" level, above 0.8 is excellent, 0.6 - 0.8 is good, and below 0.6 requires early warning) is as follows: 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. The node status score is 0.7 (good). The optimization model suggests increasing its flow rate from 2.16 m³ / s to 2.3 m³ / s, while reducing the flow rate of the adjacent section of K10+200 from 1.5 m³ / s to 1.4 m³ / s, and adjusting the pump speed from 1450 rpm to 1500 rpm through a frequency converter.

[0044] This solution captures the operation status of the channel - pump in real time through the Internet of Things perception layer, constructs a dynamic supply - demand map to achieve visual scheduling of water resources, innovatively converts the physical facility status (siltation, energy consumption) into economic signals (dynamic pricing), and then uses an optimization algorithm to reverse - control the physical system, forming a "perception - decision - control" closed - loop, improving the accuracy of water resource allocation in the irrigation area and the energy efficiency of the water conveyance system, and at the same time providing a transparent and scientific pricing basis for the water rights trading market.

[0045] To solve the problem of the disconnection between pricing and the health status of the channel in the water resource trading of the irrigation area and further improve the accuracy and fairness of water resource allocation, in some embodiments, step 103: determining the target water conveyance channels corresponding to the water resource trading requests of each irrigation area water use terminal from the target irrigation area. If there is a situation where any of the target water conveyance channels has a pump overload or the value of the siltation diffusion coefficient is greater than the preset diffusion coefficient threshold, then based on the dynamic water price adjustment rule and the dynamic water resource supply - demand relationship map, generating the water resource trading price includes: Step 201: determining the target water conveyance channels corresponding to each of the water resource trading requests according to the geographical coordinate ranges in each of the water resource trading requests.

[0046] In step 201, the geographical coordinate range represents the boundary coordinates of the water - demand area declared by the water use terminal.

[0047] In the embodiments of the present application, through the spatial analysis function of the geographic information system, the coordinate range of the water-using terminal is superimposed and analyzed with the irrigation district channel network, and the nearest neighbor algorithm is used to match the water supply relationship. For example, if the overlap degree between the declared coordinate range of a certain village and the water supply service area of the main canal section K12+500 reaches 90%, then it is determined that this channel is the target water conveyance channel. The matching result needs to satisfy that the designed water supply capacity of the channel covers the declared water volume.

[0048] Step 202: According to the historical extreme value 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 velocity and sediment content, calculate the siltation diffusion coefficient value of the target water conveyance channel. When the siltation diffusion coefficient value exceeds the preset diffusion coefficient threshold, it is determined that the target water conveyance channel has a siltation risk.

[0049] In step 202, the historical extreme value of the channel siltation diffusion coefficient represents the maximum safety value of the siltation coefficient of this channel in the past year. The channel water flow velocity refers to the average flow velocity of the water body passing through the cross-section of the channel per unit time (unit: m / s), which is the core index reflecting the water conveyance efficiency. The sediment content refers to the mass concentration of suspended sediment in the water body per unit volume (unit: kg / m³), which is the key parameter for evaluating the channel siltation risk.

[0050] In the embodiments of the present application, the cross-sectional average flow velocity of the target channel is measured in real time by an acoustic Doppler velocimeter, and the sediment content is obtained by a turbidity sensor at the same time. The real-time data is substituted into the formula (flow velocity × sediment content) to calculate the current siltation diffusion coefficient, and it is compared with the historical extreme value. If the current value exceeds the threshold, it is marked that this channel has a siltation risk.

[0051] Step 203: Based on the real-time energy consumption curve of the water pump in the target water conveyance channel, predict the energy consumption peak volatility in the future preset time period. If the energy consumption peak volatility exceeds the critical slope corresponding to the water pump energy consumption threshold, it is determined that the target water conveyance channel has a water pump overload risk.

[0052] In step 203, the generation process of the real-time energy consumption curve of the water pumps in the target water conveyance channel is as follows: Install smart electricity meters and triaxial vibration sensors on each pump unit in the target water conveyance channel, continuously collect voltage, current, power factor, and vibration spectrum data with a sampling period of 1 minute. The edge computing gateway performs outlier rejection and moving average filtering on the original data, stores the processed power data as energy consumption segments with a 15-minute granularity in time series, marks different operating condition segments in combination with the pump operation status signals (start / stop, frequency conversion frequency), and generates a real-time energy consumption curve including instantaneous power, cumulative energy consumption, and energy efficiency deviation degree by comparing the current power data with the reference curve in the pump standard operating condition database. The energy consumption peak volatility represents the change range of the pump power within a preset time period. The critical slope represents the maximum power change rate allowed for the safe operation of the pump. The pump energy consumption threshold refers to the critical value of the maximum energy consumption change rate allowed for the pump in a safe operating state.

[0053] In the embodiment of the present application, the minute-level power data of the pump is obtained from the smart electricity meter, and the power volatility in the next 15 minutes is calculated using a sliding window method. If the volatility exceeds the critical slope, it is combined with the rated power of the pump to determine whether there is overload. For example, for a certain pump with a rated power of 70 kW, if the real-time power fluctuates to 80 kW and continues to rise, an overload warning is triggered.

[0054] Step 204: Extract the pump energy consumption deviation degree from the dynamic water resource supply and demand relationship map.

[0055] In step 204, the energy consumption deviation degree represents the deviation percentage between the real-time power and the standard operating condition power.

[0056] In the embodiment of the present application, the standard power curve of the pump (such as 55 kW under the design operating condition) is read from the dynamic supply and demand map, compared with the real-time power (such as 62 kW), and the deviation degree is calculated ((62 - 55) / 55 ≈ 12.7%). The deviation degree is used to quantify the degree of energy efficiency loss.

[0057] Step 205: When there is a risk of siltation or pump overload in any of the target water conveyance channels, according to the initial pricing gradient value associated with the target water conveyance channel in the dynamic water price adjustment rule, superimpose the compensation factor generated by the non-linear relationship between the siltation diffusion coefficient value and the pump energy consumption deviation degree, and calculate the water resource trading price of the target water conveyance channel.

[0058] 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 siltation coefficient and the energy consumption deviation degree through a non-linear function.

[0059] In the embodiments of the present application, if there is a risk of siltation (coefficient 1.8) in channel K12+500 and the deviation of the water pump is 12%, the additional cost is calculated through a compensation function: the siltation compensation fee is 0.1 yuan / m³ (exceeding the threshold by 20% × 0.5 yuan), and the energy consumption surcharge is 0.06 yuan / m³ (deviation of 12% × 0.5 yuan). The final price = the basic price of 0.5 yuan + the compensation fee of 0.16 yuan = 0.66 yuan / m³.

[0060] The following is a specific example: An irrigation area receives a water use request from Farm A (coordinate range X, water demand 1.8 m³ / s). The system matches it to section K12+500 of the main canal. It is detected that the current flow velocity of this channel is 1.0 m / s and the sediment is 1.8 kg / m³ (siltation coefficient 1.8 > threshold 1.5), and the power fluctuation rate of the water pump is 15% (overload risk). According to the dynamic water price rule, the basic price of 0.5 yuan is superimposed with the siltation compensation of 0.1 yuan and the energy consumption compensation of 0.08 yuan, and the final price is 0.68 yuan / m³.

[0061] In the embodiments of the present application, by real-time matching the transaction request with the channel status, dynamically superimposing the siltation and energy consumption compensation prices, the water price can accurately reflect the operation risk of the infrastructure, and at the same time, the supply and demand are balanced through optimizing the instructions. Finally, the efficient allocation of water resources in the irrigation area and the active protection of the water conveyance facilities are realized.

[0062] To solve the problem of insufficient dynamic response of the price in the water resources transaction in the irrigation area and further improve the sensitivity of the water price to the real-time supply and demand changes and the facility status, in some embodiments, step 205: adjusting the initial pricing gradient value associated with the target water conveyance channel in the dynamic water price adjustment rule, and superimposing a compensation factor generated by the non-linear relationship between the siltation diffusion coefficient value and the deviation of the water pump energy consumption to calculate the water resources transaction price of the target water conveyance channel, including: Step 301: Extract the real-time demand fluctuation amount of the target water conveyance channel from the dynamic water resources supply and demand relationship map.

[0063] In step 301, the real-time demand fluctuation amount represents the difference percentage between the declared water volume of the water use terminal in the current period and the designed water supply capacity of the channel.

[0064] In the embodiments of the present application, the real-time declared water volume (such as 2.0 m³ / s) and the designed water supply capacity (1.8 m³ / s) of the target water conveyance channel (such as section K12+500) are read from the dynamic water resources supply and demand relationship map, and the fluctuation amount is calculated: (2.0 - 1.8) / 1.8 ≈ 11.1%. This data is updated every 5 minutes and synchronized to the pricing module through the blockchain.

[0065] Step 302: According to the real-time demand fluctuation amount, extract the initial pricing gradient value associated with the target water conveyance channel from the dynamic water price adjustment rule.

[0066] In step 302, the dynamic water price adjustment rule includes the mapping relationship between the initial pricing gradient value, the demand fluctuation range, and the priority corresponding to each water conveyance channel in the target tank area.

[0067] In the embodiment of the present application, query the dynamic water price adjustment rule according to the real-time demand fluctuation of 11.1%: If the fluctuation range of 10 - 15% corresponds to the "secondary tension" level, then extract the initial gradient value = basic price of 0.5 yuan × 1.2 = 0.6 yuan / m³. The rule base updates the supply and demand level threshold values quarterly through machine learning.

[0068] Step 303: Determine the coefficient corresponding to the priority of the real-time demand fluctuation as the pricing coefficient.

[0069] In step 303, the coefficient corresponding to the priority represents the adjustment weight set according to the type of water use terminal (agriculture / industry / livelihood).

[0070] In the embodiment of the present application, it is identified that the water supply object of the target channel is an industrial park (priority coefficient 1.1). According to the comparison table of the fluctuation range and the correction coefficient preset in the rule base: the fluctuation ranges (5% - 10%, 10% - 15%, 15% - 20%) respectively correspond to the correction coefficients (1.02, 1.05, 1.08). Determine the interval (10 - 15%) where the real-time fluctuation of 11.1% is located, and match from the rule base to obtain the final pricing coefficient = 1.1 × 1.05 (fluctuation correction) = 1.155.

[0071] Step 304: Determine the product result of the initial pricing gradient value and the pricing coefficient as the target pricing gradient value.

[0072] In step 304, the target pricing gradient value represents the benchmark price after superimposing the supply and demand fluctuations and the priority.

[0073] In the embodiment of the present application, multiply the initial gradient value of 0.6 yuan by the pricing coefficient of 1.155 to obtain the target gradient value of 0.693 yuan / m³. The system automatically retains two decimal places and outputs 0.69 yuan / m³ as the price adjustment benchmark.

[0074] Step 305: Based on the target pricing gradient value, superimpose the compensation factor to calculate the water resource trading price of the target water conveyance channel.

[0075] In the embodiment of the present application, input the siltation coefficient of 1.8 and the energy consumption deviation of 12% 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).

[0076] The following is a specific example: An industrial park applies to withdraw 2.0 m³ / s of water from section K12+500 (designed capacity 1.8 m³ / s, fluctuation +11.1%). The system determines it as industrial water (coefficient 1.1), with an initial gradient value of 0.6 yuan and a target gradient value of 0.69 yuan. Adding a sedimentation compensation of 0.1 yuan (coefficient 1.8) and an energy consumption compensation of 0.05 yuan (deviation 12%), the final pricing is 0.8 yuan / m³.

[0077] In the embodiment of the present application, by hierarchically extracting supply-demand fluctuations, priority coefficients, and facility status compensations, the dynamic binding of water price and real-time operation risks is achieved. It not only ensures the high-priority water demand but also guides the optimal allocation of resources through the price lever while compensating for the infrastructure maintenance costs.

[0078] To further improve the dynamic response ability of the water resource trading price to the channel health status and energy consumption efficiency, in some embodiments, step 305: calculating the water resource trading price of the target water conveyance channel based on the target pricing gradient value and superimposing a compensation factor, includes: Step 401: Coupling and calculating the first weight factor and the second weight factor through a pre-constructed non-linear compensation function to generate a compensation factor, where the pre-constructed non-linear compensation function is constructed through the non-linear relationship between the sediment diffusion coefficient value and the deviation of the pump energy consumption. The first weight factor is the ratio of the deviation of the pump energy consumption to the preset energy consumption deviation threshold, and the second weight factor is the ratio of the current sediment diffusion coefficient value of the water conveyance channel to the preset diffusion coefficient change rate threshold.

[0079] In the embodiment of the present application, calculate the first weight factor: real-time energy consumption deviation of 12% / threshold of 10% = 1.2; calculate the second weight factor: sedimentation coefficient of 1.8 / threshold of 1.5 = 1.2; input the double weight factors into the non-linear function: function form: f(x,y)=0.2×[1 / (1+e^-(x+y-2))] (x,y are weight factors), output result: f(1.2,1.2)=0.15 yuan / m³, as the compensation factor.

[0080] Step 402: Superimpose the target pricing gradient value and the compensation factor according to a preset ratio to obtain a superimposed result.

[0081] In step 402, the preset ratio represents the weight distribution between the base price and the compensation factor.

[0082] 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.

[0083] Step 403: According to the superposition result, combined with the water conveyance weights of adjacent water conveyance channels recorded in the dynamic water resource supply and demand relationship map, calculate the water resource trading price of the target water conveyance channel.

[0084] In step 403, the water conveyance weight of the water conveyance channel represents an adjustment coefficient calculated according to the flow distribution ratio of the upstream and downstream channels. Exemplarily, ; represents the water conveyance weight, which is a dynamic parameter quantifying the relative importance of adjacent channels in the water resource allocation of the target channel, and its value range is 0 to 1. represents the available water volume (m³ / h) of the adjacent channel. represents the deviation degree of the pump energy consumption of the adjacent channel (%). represents the maximum allowable energy consumption deviation threshold (usually 15%).

[0085] In the embodiment of the present application, obtain the water conveyance weight of the adjacent channel K10+200 from the dynamic water resource supply and demand relationship map (currently 0.95); the final price = the superposition result of 0.60 yuan × the weight of 0.95 ≈ 0.57 yuan / m³; the rounding rule description: the rounding benchmark is 0.05 yuan as the minimum pricing unit (similar to the "five-cent rounding method" in the currency cent 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 0.05 multiple; if R<0.025, round down to the nearest 0.05 multiple. Calculate the remainder: 0.57÷0.05 = 11.4, take the integer part 11, and the remainder 0.4×0.05 = 0.02 yuan; judge the rounding direction: the remainder 0.02 yuan <0.025 yuan, round down to 11×0.05 = 0.55 yuan; the final output: the adjusted price is 0.55 yuan / m³ (instead of 0.60 yuan).

[0086] The following is a specific example: For the main canal section K12+500 of a certain irrigation area: Input: the target gradient value of 0.69 yuan, the compensation factor of 0.15 yuan, and the adjacent weight of 0.95; calculation: 0.69×70% + 0.15×30% = 0.60 yuan, 0.60×0.95 = 0.57 yuan, rounding to 0.55 yuan; output: the final trading price is 0.55 yuan / m³.

[0087] In the embodiments of the present application, the impact of facility status is quantified through a non-linear compensation function, and combined with the adjustment of adjacent channel weights, a dual response of price to local risk and global equilibrium is achieved. Finally, a dynamic pricing mechanism that can both stimulate water conservation and ensure the stability of facilities is formed.

[0088] In order to further improve the response ability of the water resource trading price to the regional supply-demand balance, in some embodiments, step 403: calculating the water resource trading price of the target water conveyance channel according to the superposition result and in combination with the water conveyance weights of adjacent water conveyance channels recorded in the dynamic water resource supply-demand relationship map includes: Step 501: Based on the water conveyance weights of adjacent water conveyance channels recorded in the dynamic water resource supply-demand relationship map, calculate the average value of the water conveyance weights of adjacent water conveyance channels directly connected to the current water conveyance channel, and use the average value of the water conveyance weights of adjacent water conveyance channels as the adjustment coefficient for allocating the target water conveyance channel.

[0089] In step 501, the average value of the water conveyance weights of adjacent water conveyance channels represents the arithmetic average of the weights of the upstream and downstream channels directly connected to the target channel, and is used to smooth local fluctuations.

[0090] In the embodiments of the present application, extract the adjacent channel data of the target channel K12+500 from the dynamic water resource supply-demand relationship map: the weight of the upstream K10+200: 1.1 (industrial water is given priority), the weight of the downstream K15+000: 0.9 (agricultural water is given secondary priority), calculate the average value: (1.1 + 0.9) / 2 = 1.0, and use it as the allocation adjustment coefficient.

[0091] Step 502: Perform weighted fusion on the target pricing gradient value and the allocation adjustment coefficient according to the superposition result to generate the water resource trading price.

[0092] 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-demand tension.

[0093] In the embodiments of the present application, input parameters: target pricing gradient value: 0.69 yuan / m³, allocation adjustment coefficient: 1.0, assume the weight ratio of the superposition result: basic price 70% + adjustment 30%; calculation process: 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, price rounding: round to 0.70 yuan / m³ in units of 0.05 yuan (because the remainder of 0.69 is 0.04 > 0.025).

[0094] The following is a specific example: The section of the main canal at K12+500 in a certain irrigation district needs to be priced: Input data: Weights of adjacent canals: 1.1 (industry) upstream and 0.9 (agriculture) downstream, adjustment coefficient 1.0; Target gradient value: 0.69 yuan (including the base price and compensation factor); Price calculation: Weighted according to a 7:3 ratio: 0.69×70% + 1.0×30%×0.69 = 0.70 yuan; Output result: Final transaction price: 0.70 yuan / m³.

[0095] In the embodiments of the present application, through the dynamic mean adjustment of the weights of adjacent canals, the price not only reflects the independent state of the target canal but also takes into account the overall balance of the regional water supply network. Finally, the unity of local precise pricing and global water resources collaborative scheduling is achieved.

[0096] In order to further improve the dynamic optimization ability of the water conveyance system in the irrigation district, in some embodiments, step 104: Generating the target water conveyance optimization parameters for each target water conveyance canal according to the water resources trading prices of all the target water conveyance canals and the node states in the dynamic water resources supply and demand relationship map, including: Step 601: Taking the water resources cancellation rates of each water conveyance canal in the private blockchain network as the attenuation factors of the initial pricing gradient values associated with each water conveyance canal in the dynamic water price adjustment rule.

[0097] In step 601, the water resources cancellation rate represents the ratio of the actual water consumption in the water rights transaction to the declared water volume per unit time, reflecting the compliance credit of the water use terminal. The attenuation factor is used to adjust the coefficient of the initial pricing gradient. The lower the cancellation rate, the greater the attenuation. The private blockchain network is deployed based on the irrigation district management agency.

[0098] In the embodiments of the present application, obtaining the water rights transaction records of the target canal K12+500 in the past 24 hours from the private blockchain: The declared total volume is 1000 m³, and the actual cancellation is 900 m³. The cancellation rate = 900 / 1000 = 0.9. According to the rule: The cancellation rate of 0.9 corresponds to an attenuation factor of 0.95, and the initial pricing gradient value is 0.69 yuan / m³, and the attenuation is 0.69×0.95≈0.66 yuan / m³.

[0099] Step 602: For each target water conveyance canal, adjusting the water resources weights of adjacent water conveyance canals according to the real-time energy consumption curve of the water pump in the target water conveyance canal to generate the water resources weight increment.

[0100] In step 602, the water resources weight increment represents the adjustment range of the flow distribution of adjacent canals, which is determined by the fluctuation characteristics of the water pump energy consumption curve.

[0101] In the embodiment of the present application, the real-time power curve of the water pump at the target channel K12+500 is analyzed (sampling interval: 1 minute); it is detected that the power volatility increases from 10% to 18% during the period from 15:00 to 15:30; according to the volatility increment of 8%, the weight adjustment rule is queried: volatility 5-10%: weight increment +0.05, 10-15%: +0.10, 15%: +0.15; the weight increment of the adjacent channel K10+200 is output as = +0.15.

[0102] Step 603: Generate the initial water conveyance optimization parameters of the target water conveyance channel based on the sediment deposition diffusion coefficient value and the water conveyance resource weight increment.

[0103] In step 603, the initial water conveyance optimization parameters include the initial schemes of control instructions such as the recommended flow rate, gate opening, and pump speed.

[0104] In the embodiment of the present application, the input parameters are: sediment deposition diffusion coefficient 1.8, weight increment +0.15; through the optimization model calculation: the recommended flow rate of the target channel K12+500 increases from 2.16 m³ / s to 2.3 m³ / s (+6.5%), and the recommended flow rate of the adjacent channel K10+200 decreases from 1.5 m³ / s to 1.4 m³ / s (-6.7%); the initial water conveyance optimization parameters are output as (channel section: K12+500, flow rate adjustment: +6.5%, gate opening: changes from 70% to 75%, pump speed: changes from 1450 to 1500 rpm) and (channel section: K10+200, flow rate adjustment: -6.7%, gate opening: changes from 65% to 60%, pump speed: remains 1400 rpm).

[0105] Step 604: Adjust the initial water conveyance optimization parameters according to the water resource trading price and the node state of the target water conveyance channel in the dynamic water resource supply and demand relationship map to generate the target water conveyance optimization parameters.

[0106] In the embodiments of the present application, the node status score of K12+500 is obtained as 0.7 (good); according to the premium rate of the transaction price of 0.70 yuan / m³ (basic price of 0.5 yuan), the initial parameters are allowed to float by 10%. Exemplarily, this floating ratio is obtained through the coupled calculation of the price premium rate and the node status score: Premium rate calculation: Basic price: 0.5 yuan / m³, Transaction price: 0.7 yuan / m³, Premium rate = (0.7 - 0.5) / 0.5 = 40%; Influence of node status score: The score of 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 of 40% × score coefficient of 0.25 = 10%; Adjusted target parameter: The flow rate of K12+500: 2.3 m³ / s becomes 2.25 m³ / s (compromise solution). Exemplarily, the initial optimization value: 2.3 m³ / s (+6.5%); Floating 10% constraint: The maximum allowable value = original flow rate of 2.16 m³ / s × (1 + 6.5% + 10%) = 2.52 m³ / s; However, the following restrictions need to be met: The upper limit of the channel design flow rate is 2.5 m³ / s, and the minimum guarantee demand of the adjacent channel (K10+200 needs to be ≥1.45 m³ / s); Compromise decision: Take the intermediate value between the initial value of 2.3 m³ / s and the constraint upper limit of 2.5 m³ / s and change it to 2.25 m³ / s; The flow rate of K10+200: 1.4 m³ / s becomes 1.45 m³ / s (guarantee the minimum demand). Exemplarily, the minimum guarantee demand: This channel undertakes the livelihood water supply, and the designed minimum flow rate is 1.45 m³ / s (agreed in the contract); The initial optimization value: 1.4 m³ / s (-6.7%) violates the minimum demand; Automatic correction: Forcefully adjust it to 1.45 m³ / s (meet the minimum requirements), and at the same time reduce the flow rates of other non-priority channels in equal proportion.

[0107] In the embodiments of the present application, through the dynamic coupling of credit cancellation, energy consumption fluctuation and node status, optimized parameters that conform to the economic leverage effect and ensure the safety of facilities are generated. The closed-loop control from water rights trading to physical scheduling is realized, and the overall operation efficiency of the irrigation area is improved.

[0108] In order to further improve the dynamic response accuracy of the water conveyance optimization parameters to the real-time status of the channel and the transaction price, in some embodiments, step 604: The adjusting the initial water conveyance optimization parameters according to the water resources transaction price and the node status of the target water conveyance channel in the dynamic water resources supply and demand relationship map to generate the target water conveyance optimization parameters includes: Step 701: Generate a dynamic correction coefficient of the initial water conveyance optimization parameters according to the node status of the target water conveyance channel in the dynamic water resources supply and demand relationship map and the water conveyance weight of the target water conveyance channel, in combination with the energy consumption compensation factor embedded in the water resources transaction price.

[0109] In step 701, the energy consumption compensation factor represents the part of the transaction price used to compensate for the energy efficiency loss of the water pump.

[0110] In the embodiment of the present application, the node state of K12+500 is obtained as 0.7, and the water conveyance 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 influence coefficient of 1.08. Exemplarily, the calculation basis is: the ratio of the real-time water pump energy consumption deviation (such as 12%) to the preset threshold (such as 10%); the energy consumption influence weight of the basic water price (0.5 yuan / m³) (such as 0.5×12%×0.8 = 0.048 yuan); after being corrected by the non-linear compensation function, the final energy consumption compensation factor of 0.08 yuan / m³ is 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 the influence coefficient: 1 + weighted result = 1 + 0.08 = 1.08; calculate the dynamic correction coefficient = node state × water conveyance weight × energy consumption coefficient = 0.7×1.1×1.08≈0.76.

[0111] Step 702: Calculate the siltation influence coefficient of the target water conveyance channel based on the spatial coupling relationship between the channel siltation diffusion coefficient value recorded in the dynamic water resource supply and demand relationship map and the water conveyance weight of the adjacent water conveyance channel.

[0112] In step 702, the spatial coupling relationship represents the product relationship between the weights of adjacent channels and the siltation diffusion coefficient of the target channel.

[0113] In the embodiment of the present application, the siltation coefficient of K12+500 is obtained as 1.8, the weight of the adjacent channel K10+200 is 0.9, and the weight of K15+000 is 1.1; calculate the average weight = (0.9 + 1.1) / 2 = 1.0; the siltation influence coefficient = siltation coefficient × average weight = 1.8×1.0 = 1.8.

[0114] Step 703: Generate parameter adjustment constraint conditions based on the siltation influence coefficient and the water pump energy consumption deviation.

[0115] In step 703, the parameter adjustment constraint conditions represent an inequality group that restricts the flow adjustment range.

[0116] In the embodiment of the present application, the input siltation influence coefficient is 1.8 (>1.5 threshold), and the water pump energy consumption deviation is 12%; through the constraint model, the maximum flow increase = 5% (siltation penalty), and the minimum flow decrease = 8% (energy consumption compensation) are generated, and the output constraint condition is: -8%≤ΔQ≤+5%.

[0117] Step 704: Dynamically scale the initial water conveyance optimization parameters according to the dynamic correction coefficient and the parameter adjustment constraint conditions to obtain intermediate water conveyance optimization parameters.

[0118] In step 704, the intermediate water conveyance optimization parameters represent a preliminary optimization plan that meets the constraint conditions.

[0119] In the embodiment of the present application, the initial optimized recommended flow values for the K12+500 section (an increase of 6.5% compared to the original flow of 2.16 m³ / s): K12+500 = 2.3 m³ / s (+6.5%); the initial optimized recommended flow values for the K10+200 section (a decrease of 6.7% compared to the original flow of 1.5 m³ / s): K10+200 = 1.4 m³ / s (-6.7%); applying the constraint (constraint rule: when the sedimentation influence coefficient > 0.8, the maximum allowable flow increase ≤ 5%; original flow: 2.16 m³ / s): the maximum allowable increase for K12+500 is +5%: the maximum allowable flow = 2.16×(1 + 5%) = 2.27 m³ / s; for K10+200, it is required to be at least -3% (constraint rule: when the energy consumption deviation > 10%, the adjacent channels need to be reduced by at least 3%; original flow: 1.5 m³ / s): the minimum allowable flow = 1.5×(1 - 3%) = 1.46 m³ / s; output intermediate parameters: K12+500 = 2.27 m³ / s, K10+200 = 1.46 m³ / s.

[0120] Step 705: Perform boundary matching between the intermediate water conveyance optimization parameters and the maximum water carrying capacity threshold of the target water conveyance channel recorded in the dynamic water resource supply and demand relationship map to generate target water conveyance optimization parameters, where the maximum water carrying capacity threshold is calculated based on the water conveyance capacity corresponding to the pump energy consumption threshold in the target water conveyance channel.

[0121] In step 705, the maximum water carrying capacity threshold represents the safe water conveyance capacity of the channel under the designed energy consumption threshold.

[0122] In the embodiment of the present application, obtain the maximum threshold of 2.5 m³ / s for K12+500 (corresponding to the pump energy consumption threshold of 55 kW); the intermediate parameter 2.27 m³ / s < 2.5 m³ / s, directly adopted; the final target parameters: (channel section: K12+500, flow adjustment: 2.27 m³ / s, gate opening: 73%) and (channel section: K10+200, flow adjustment: 1.46 m³ / s, gate opening: 61%).

[0123] The following is a specific example: Optimization process of the main canal system in a certain irrigation area: Input: The score of node K12+500 is 0.7, and the transaction price is 0.7 yuan (energy consumption compensation is 0.08 yuan); the siltation coefficient is 1.8, the adjacent weight is 0.9, and the energy consumption deviation is 12%. Calculation: The correction coefficient is 0.76 and the constraint conditions are (+5%, -3%); Intermediate parameters: K12+500 = 2.27 m³ / s, K10+200 = 1.46 m³ / s; Output: Final target parameters: (Channel section: K12+500, Flow adjustment: 2.27 m³ / s, Gate opening: 73%) and (Channel section: K10+200, Flow adjustment: 1.46 m³ / s, Gate opening: 61%). Exemplarily, the original opening: The current gate opening of the K12+500 channel is 65% (real-time collected by the Internet of Things sensor); Flow adjustment ratio: The target flow increases from 2.16 m³ / s to 2.27 m³ / s (increase of +5.1%); Opening-flow relationship model: According to the historical operation data of this channel, for every 1% increase in flow, the gate opening needs to increase by about 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-scouring safety limit); 73% < 80%, so 73% is finally adopted. Current state of the adjacent channel K10+200: Original flow: 1.5 m³ / s (corresponding to the original gate opening of 65%, read in real time through the channel); Target flow: 1.46 m³ / s (intermediate parameter output in step 704, a decrease of 2.7% compared to the original flow). Opening-flow relationship: According to the historical hydraulic model of this channel, for every 1% decrease in flow, the gate opening needs to be reduced by about 1.48% (calibrated by the least squares fitting). Flow reduction = (1.5 - 1.46) / 1.5 × 100% ≈ 2.67%; Opening reduction = 2.67% × 1.48 ≈ 4%; New gate opening = 65% - 4% = 61%. Boundary check: The designed minimum opening is 60% (to prevent siltation caused by the complete closure of the gate); 61% > 60%, so 61% is finally adopted.

[0124] In the embodiment of the present 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 market supply and demand changes, achieving the collaborative optimization of the safety and economy of the irrigation area water conveyance system.

[0125] Figure 2 The structure diagram of a smart irrigation area management system based on the Internet of Things is provided for the embodiment of the present application, as Figure 2 shown. The system includes: An acquisition module 21, configured to acquire the real-time irrigation water volume and pump energy consumption data of each water conveyance channel in the target irrigation area.

[0126] The first generation module 22 is configured to generate a dynamic water resource supply-demand relationship map according to the real-time irrigation water volume and pump energy consumption data.

[0127] The determination module 23 is configured to determine, from the target irrigation area, the target water conveyance channels respectively corresponding to the water resource trading requests of each irrigation area water use terminal. If there is a situation where any of the target water conveyance channels has a pump overload or the sediment diffusion coefficient value is greater than a preset diffusion coefficient threshold, then based on the dynamic water price adjustment rule and the dynamic water resource supply-demand relationship map, generate the water resource trading price of the target water conveyance channel.

[0128] The second generation module 24 is configured to generate the target water conveyance optimization parameters of each target water conveyance channel according to the water resource trading prices of all the target water conveyance channels and the node states in the dynamic water resource supply-demand relationship map.

[0129] Figure 2 The described intelligent irrigation area management system based on the Internet of Things can execute Figure 1 The described intelligent irrigation area management method based on the Internet of Things in the illustrated embodiment, and its implementation principle and technical effects will not be elaborated further. For each module and unit in the intelligent irrigation area management system based on the Internet of Things in the above embodiment, the specific manner of performing operations has been described in detail in the embodiments related to the method, and will not be elaborated here.

[0130] In a possible design, Figure 2 The intelligent irrigation area management system based on the Internet of Things in the illustrated embodiment can be implemented as a computing device, such as Figure 3 shown, the computing device may include a storage component 31 and a processing component 32; 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.

[0131] The processing component 32 is the above Figure 1 The intelligent irrigation area management method based on the Internet of Things in the illustrated embodiment.

[0132] Among them, 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 by 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, and is used to execute the above method.

[0133] The storage component 31 is configured to store various types of data to support the operation of the terminal. The storage component can be implemented by any type of volatile or non-volatile storage 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.

[0134] Of course, the computing device may also necessarily include other components, such as input / output interfaces, display components, communication components, etc.

[0135] The input / output interface provides an interface between the processing component and the peripheral interface module, and the above peripheral interface module may be an output device, an input device, etc.

[0136] The communication component is configured to facilitate wired or wireless communication between the computing device and other devices, etc.

[0137] Among them, the computing device may be a physical device or an elastic computing host provided by a cloud computing platform, etc. At this time, the computing device may refer to a cloud server, and the above processing component, storage component, etc. may be basic server resources leased or purchased from a cloud computing platform.

[0138] The embodiments of the present application also provide a computer storage medium storing a computer program, and when the computer program is executed by a computer, it can implement the above-mentioned Figure 1 a method for intelligent irrigation area management based on the Internet of Things shown in the embodiments.

[0139] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0140] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0141] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0142] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment of the present application.

Claims

1. A smart irrigation district management method based on the Internet of Things, characterized in that, Including: Obtaining the real-time irrigation water volume and pump energy consumption data of each water conveyance channel in the target irrigation area; Generating a dynamic water resource supply-demand relationship map according to the real-time irrigation water volume and pump energy consumption data; Determining the target water conveyance channels corresponding to the water resource trading requests of each irrigation water terminal in the target irrigation area respectively. If there is a situation where any of the target water conveyance channels has a pump overload or the sedimentation diffusion coefficient value is greater than the preset diffusion coefficient threshold, then based on the dynamic water price adjustment rule and the dynamic water resource supply-demand relationship map, generating the water resource trading price of the target water conveyance channel; Generating the target water conveyance optimization parameters of each target water conveyance channel according to the water resource trading prices of all the target water conveyance channels and the node states in the dynamic water resource supply-demand relationship map.

2. The method according to claim 1, wherein The determining the target water conveyance channels corresponding to the water resource trading requests of each irrigation water terminal in the target irrigation area respectively. If there is a situation where any of the target water conveyance channels has a pump overload or the sedimentation diffusion coefficient value is greater than the preset diffusion coefficient threshold, then based on the dynamic water price adjustment rule and the dynamic water resource supply-demand relationship map, generating the water resource trading price includes: Determining the target water conveyance channels corresponding to each of the water resource trading requests according to the geographical coordinate ranges in each of the water resource trading requests; Calculating the sedimentation diffusion coefficient value of the target water conveyance channel according to the historical extreme values of the channel sedimentation diffusion coefficient recorded in the dynamic water resource supply-demand relationship map, in combination with the real-time monitored channel water flow velocity and sediment content. When the sedimentation diffusion coefficient value exceeds the preset diffusion coefficient threshold, determining that the target water conveyance channel has a sedimentation risk; Based on the real-time energy consumption curve of the pump in the target water conveyance channel, predicting the energy consumption peak volatility in a future preset time period. If the energy consumption peak volatility exceeds the critical slope corresponding to the pump energy consumption threshold, determining that the target water conveyance channel has a pump overload risk; Extracting the pump energy consumption deviation degree from the dynamic water resource supply-demand relationship map; When any of the target water conveyance channels has a sedimentation risk or a pump overload risk, according to the initial pricing gradient value associated with the target water conveyance channel in the dynamic water price adjustment rule, superimposing the compensation factor generated by the non-linear relationship between the sedimentation diffusion coefficient value and the pump energy consumption deviation degree, and calculating the water resource trading price of the target water conveyance channel.

3. The method according to claim 2, wherein The calculating the water resource trading price of the target water conveyance channel according to the initial pricing gradient value associated with the target water conveyance channel in the dynamic water price adjustment rule, superimposing the compensation factor generated by the non-linear relationship between the sedimentation diffusion coefficient value and the pump energy consumption deviation degree includes: Extracting the real-time demand fluctuation amount of the target water conveyance channel from the dynamic water resource supply-demand relationship map; Extracting the initial pricing gradient value associated with the target water conveyance channel from the dynamic water price adjustment rule according to the real-time demand fluctuation amount; Determining the coefficient corresponding to the priority level to which the real-time demand fluctuation amount belongs as the pricing coefficient; Determining the product result of the initial pricing gradient value and the pricing coefficient as the target pricing gradient value; Based on the target pricing gradient value, superimposing the compensation factor, and calculating the water resource trading price of the target water conveyance channel.

4. The method according to claim 3, wherein Calculating the water resource trading price of the target water conveyance channel based on the target pricing gradient value and superimposing a compensation factor, including: Coupling and calculating the first weight factor and the second weight factor through a pre-constructed non-linear compensation function to generate a compensation factor, wherein the pre-constructed non-linear compensation function is constructed based on the non-linear relationship between the sediment deposition diffusion coefficient value and the deviation degree of the pump energy consumption. The first weight factor is the ratio of the deviation degree of the pump energy consumption to the preset energy consumption deviation degree threshold, and the second weight factor is the ratio of the current sediment deposition diffusion coefficient value of the water conveyance channel to the preset diffusion coefficient change rate threshold; Superimposing the target pricing gradient value and the compensation factor according to a preset ratio to obtain a superimposed result; According to the superimposed result, and in combination with the water conveyance weights of adjacent water conveyance channels recorded in the dynamic water resource supply and demand relationship map, calculating the water resource trading price of the target water conveyance channel.

5. The method according to claim 4, characterized in that The calculating the water resource trading price of the target water conveyance channel according to the superimposed result and in combination with the water conveyance weights of adjacent water conveyance channels recorded in the dynamic water resource supply and demand relationship map includes: Based on the water conveyance weights of adjacent water conveyance channels recorded in the dynamic water resource supply and demand relationship map, calculating the average value of the water conveyance weights of adjacent water conveyance channels directly connected to the current water conveyance channel, and using the average value of the water conveyance weights of adjacent water conveyance channels as the adjustment coefficient for allocating the target water conveyance channel; Weightedly fusing the target pricing gradient value and the allocation adjustment coefficient according to the superimposed result to generate a water resource trading price.

6. The method according to claim 1, characterized in that, Generating the target water conveyance optimization parameters for each target water conveyance channel according to the water resource trading prices of all the target water conveyance channels and the node states in the dynamic water resource supply and demand relationship map, including: Taking the water resource cancellation rates of each water conveyance channel in the private blockchain network as the attenuation factors of the initial pricing gradient values associated with each water conveyance channel in the dynamic water price adjustment rule; For each target water conveyance channel, adjusting the water resource weights of adjacent water conveyance channels according to the real-time energy consumption curve of the pump in the target water conveyance channel to generate an increment of the water resource weight; Based on the sediment deposition diffusion coefficient value and the increment of the water resource weight, generating the initial water conveyance optimization parameters of the target water conveyance channel; According to the water resource trading price and the node state of the target water conveyance channel in the dynamic water resource supply and demand relationship map, adjusting the initial water conveyance optimization parameters to generate the target water conveyance optimization parameters.

7. The method according to claim 6, wherein The adjusting the initial water conveyance optimization parameters according to the water resource trading price and the node state of the target water conveyance channel in the dynamic water resource supply and demand relationship map to generate the target water conveyance optimization parameters includes: According to the node state of the target water conveyance channel in the dynamic water resource supply and demand relationship map and the water conveyance weight of the target water conveyance channel, and in combination with the energy consumption compensation factor embedded in the water resource trading price, generating a dynamic correction coefficient of the initial water conveyance optimization parameters; Based on the spatial coupling relationship between the sediment deposition diffusion coefficient value recorded in the dynamic water resource supply and demand relationship map and the water conveyance weights of adjacent water conveyance channels, calculating the sediment deposition influence coefficient of the target water conveyance channel; Generate parameter adjustment constraint conditions based on the sedimentation influence coefficient and the pump energy consumption deviation degree; Dynamically scale the initial water conveyance optimization parameters according to the dynamic correction coefficient and the parameter adjustment constraint conditions to obtain intermediate water conveyance optimization parameters; Perform boundary matching between the intermediate water conveyance optimization parameters and the maximum water carrying capacity threshold of the target water conveyance channel recorded in the dynamic water resource supply-demand relationship map to generate target water conveyance optimization parameters, where the maximum water carrying capacity threshold is calculated based on the water conveyance capacity corresponding to the pump energy consumption threshold in the target water conveyance channel.

8. An Internet of Things-based intelligent irrigation district management system, characterized in that, Include: An acquisition module for acquiring the real-time irrigation water volume and pump energy consumption data of each water conveyance channel in the target irrigation area; A first generation module for generating a dynamic water resource supply-demand relationship map according to the real-time irrigation water volume and pump energy consumption data; A determination module for determining the target water conveyance channels corresponding to the water resource trading requests of each irrigation water use terminal in the target irrigation area respectively. If there is a situation where any of the target water conveyance channels has pump overload or the sedimentation diffusion coefficient value is greater than the preset diffusion coefficient threshold, generate the water resource trading price of the target water conveyance channel based on the dynamic water price adjustment rule and the dynamic water resource supply-demand relationship map; A second generation module for generating the target water conveyance optimization parameters of each target water conveyance channel according to the water resource trading prices of all the target water conveyance channels and the node states in the dynamic water resource supply-demand relationship map.

9. 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 area management method based on the Internet of Things as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, Stores a computer program, and when the computer program is executed by the computer, it implements a smart irrigation area management method based on the Internet of Things as described in any one of claims 1 to 7.

Images