A method and system for intelligent monitoring and regulation of reclaimed water irrigation

Abstract

This invention provides a method and system for intelligent monitoring and regulation of reclaimed water irrigation, relating to the field of water resource utilization technology. The method includes: deploying a sensor network, which includes water quality sensors, flow sensors, and pressure sensors; the sensor network monitors irrigation system parameters in real time and sends the parameters to an intelligent controller; the intelligent controller uploads the parameters to a cloud server; the cloud server analyzes the parameters through an intelligent control algorithm and dynamically adjusts water resource allocation; and the intelligent controller receives regulation instructions from the cloud server and automatically adjusts the irrigation system parameters.

Description

Technical Field

[0001] This invention belongs to the field of water resource utilization technology, specifically relating to a method and system for intelligent monitoring and regulation of reclaimed water irrigation. Background Technology

[0002] Against the backdrop of increasing global water scarcity, the utilization of reclaimed water has become particularly important. Traditional irrigation methods typically rely on manual adjustment and fixed irrigation times, lacking targeted and dynamic adjustment capabilities, leading to water waste. Meanwhile, modern agriculture has increasingly stringent requirements for irrigation parameters such as water quality, flow rate, and pressure. How to efficiently utilize reclaimed water and maximize water resource utilization while ensuring healthy crop growth has become a crucial issue in the agricultural sector.

[0003] Existing irrigation systems mostly employ a single parameter monitoring model, lacking real-time data analysis and automatic adjustment capabilities, and are unable to dynamically adjust irrigation according to environmental changes and the actual needs of crops. Therefore, there is an urgent need to develop an intelligent and automated reclaimed water irrigation method. Summary of the Invention

[0004] The summary section introduces a series of simplified concepts, which will be further explained in detail in the detailed description section. This summary section is not intended to limit the key and essential technical features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0005] The first aspect of this application proposes a method for intelligent monitoring and regulation of reclaimed water irrigation, comprising:

[0006] Deploy a sensor network, which includes water quality sensors, flow sensors, and pressure sensors;

[0007] The sensor network monitors irrigation system parameters in real time and sends the parameters to the smart controller;

[0008] The intelligent controller uploads the parameters to the cloud server;

[0009] The cloud server analyzes parameters through intelligent control algorithms and dynamically adjusts water resource allocation;

[0010] The intelligent controller receives adjustment instructions from the cloud server and automatically adjusts the irrigation system parameters.

[0011] In one feasible implementation, the above-described deployment of the sensor network includes:

[0012] The sensor node set is S = (X1, Y1), (X2, Y2), ..., (X... N Y N), where N is the number of sensors, (X i Y i Let represent the coordinates of the i-th sensor. The optimization objective is to minimize the total distance between the sensor and the central controller, while ensuring that each important area is covered by at least one sensor.

[0013] The objective function to be optimized is:

[0014]

[0015] In the formula, λ is the Lagrange multiplier; A is the number of critical regions; δ a Whether each key area has been covered; w i Let be the weight of the i-th sensor;

[0016] d(X i Y i Let be the distance function between the sensor and the control center, expressed as:

[0017]

[0018] In the formula, (X c Y c () represents the coordinates of the i-th sensor;

[0019] Cover all critical areas, i.e., each critical area R a At least one sensor node S i satisfy:

[0020] d(S i R a )≤d max

[0021] In the formula, d max This refers to the effective coverage radius of the sensor.

[0022] In one feasible implementation, the weight w of the aforementioned sensor i Determined based on the following formula:

[0023]

[0024] In the formula, Let I(S) be the initial weight of the i-th sensor; i S j ) is sensor S i and S j The mutual information between them; α is the adjustment coefficient;

[0025] Mutual Information Quantity I(S) i S j The formula for calculating ) is:

[0026]

[0027] In the formula, p(x) i x j ) is sensor S i and S j Joint probability distribution of the collected data; p(x i ) and p(x j ) are their respective marginal probability distributions.

[0028] In one feasible implementation, the aforementioned intelligent controller performs data fusion operations before uploading the parameters to the cloud server, including:

[0029] Set each sensor S i The value collected at time t is S i (t), the data fusion function is expressed as:

[0030]

[0031] In the formula, S i (t) represents the sensor S i The data collected at time t; β and ε are weighting coefficients, controlling the influence of direct fusion and spatiotemporal correlation terms, respectively; γ ij For sensor S i and S j The spatiotemporal correlation between them; Δt is the time delay, representing the impact of the data from the previous time step on the current data;

[0032] Spatiotemporal correlation γ ij Represented as:

[0033]

[0034] In the formula, H(S) i ) and H(S j (S) are sensors S i and sensor S j The entropy.

[0035] In one feasible implementation, the aforementioned cloud server analyzes parameters using an intelligent control algorithm to dynamically adjust water resource allocation, including:

[0036] Using a recursive neural network to analyze the future state X t+1 The formula for prediction is:

[0037]

[0038] In the formula, The next state X predicted based on historical time series data. t+1;f(X) t ) represents the state transition function; W represents the number of time steps; W step The weights for each time step; g(X) t-step X represents the state at historical time t-step; t This represents the overall state of the irrigation system at time t.

[0039] The Kalman filter is used to correct the predictions of an RNN. The Kalman filter formula is expressed as:

[0040]

[0041] In the formula, The initial prediction results given by the RNN model; X t Y is the system state vector; t K is the sensor data vector; t H is the adaptive gain matrix; H is the observation matrix;

[0042] To optimize water resource allocation, the optimal operation O is solved. opt Maximize the efficiency of water resource utilization. water (t) and minimize the operation adjustment cost C adjust (O o The objective function is:

[0043]

[0044] In the formula, O o This is an optional operation scheduling scheme; U water (t) is the benefit function of water resource utilization, which depends on time t; C adjust (O o The cost of operation adjustment represents the cost of performing a specific operation. o The required resource consumption.

[0045] In one feasible implementation, the aforementioned adaptive gain matrix K t Determined based on the following formula:

[0046] K t =P t ·H T ·(H·P t ·H T +R t ) -1

[0047] In the formula, P t R is the covariance matrix of the system state; t This is the covariance matrix for measuring noise.

[0048] In one feasible implementation, the aforementioned intelligent controller receives adjustment instructions from a cloud server and automatically adjusts irrigation system parameters, including:

[0049] Construct a nonlinear adaptive control model, described by the following equation:

[0050]

[0051] In the formula, u(t) is the output control quantity of the controller at time t; y(t) is the current state variable of the system; y set (t) represents the target value set; K is the adaptive gain matrix;

[0052] The adjustment rule for the adaptive gain matrix K is determined based on the following formula:

[0053]

[0054] In the formula, K0 is the initial gain value; γ is the adaptive adjustment coefficient; Sensitivity is the sensitivity parameter; |y(t)-y set (t) represents the deviation between the current state and the set value.

[0055] Secondly, this application proposes an intelligent monitoring and regulation reclaimed water irrigation device, comprising:

[0056] Deployment unit, used to deploy sensor networks, including water quality sensors, flow sensors, and pressure sensors;

[0057] The monitoring unit is used by the sensor network to monitor irrigation system parameters in real time and send the parameters to the intelligent controller.

[0058] The transmission unit is used by the intelligent controller to upload parameters to the cloud server;

[0059] The analysis unit is used by the cloud server to analyze parameters through intelligent control algorithms and dynamically adjust water resource allocation;

[0060] The receiving unit is used by the intelligent controller to receive adjustment instructions from the cloud server and automatically adjust the irrigation system parameters.

[0061] Thirdly, an electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program stored in the memory to implement the steps of the intelligent monitoring and regulation method for reclaimed water irrigation as described in any one of claims 1-7.

[0062] Fourthly, this application also proposes a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the intelligent monitoring and regulation method for reclaimed water irrigation as described in any one of claims 1-7.

[0063] In summary, this invention, by deploying a sensor network including water quality sensors, flow sensors, and pressure sensors, can monitor key parameters in the irrigation system in real time, ensuring that each irrigation stage is in optimal condition. The intelligent controller can analyze the data collected by the sensors through intelligent control algorithms on a cloud server, adjusting the allocation of water resources in real time, avoiding the manual adjustment errors of traditional irrigation and significantly improving irrigation efficiency. The optimized sensor layout and adaptive weight adjustment model can reduce the transmission of redundant data while ensuring monitoring coverage, effectively reducing system operating costs and energy consumption, thus achieving dual savings in water resources and energy. The system can automatically adjust irrigation parameters according to adjustment instructions issued by the cloud server. When abnormal situations are detected (such as water quality exceeding standards, abnormal flow, etc.), the system can promptly activate an emergency response mechanism to ensure the safe and stable operation of the system and reduce resource waste caused by abnormal situations. Based on intelligent algorithms using recurrent neural networks (RNN) and Kalman filtering, the system can accurately predict future irrigation needs and optimize the scheduling and allocation of water resources according to actual conditions, further improving the efficiency of water resource utilization.

[0064] The tunnel advanced geological prediction and monitoring method proposed in this invention, other advantages, objectives and features of this invention will be partly apparent from the following description, and partly understood by those skilled in the art through research and practice of this invention. Attached Figure Description

[0065] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit this specification. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0066] Figure 1 This is a schematic diagram of a method for intelligent monitoring and regulation of reclaimed water irrigation according to this application;

[0067] Figure 2 This is a schematic diagram of a reclaimed water irrigation structure with intelligent monitoring and regulation according to this application;

[0068] Figure 3 This is a schematic diagram of an electronic device for intelligent monitoring and regulation of reclaimed water irrigation according to this application. Detailed Implementation

[0069] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus. The technical solutions of the embodiments of this application will now be clearly and completely described in conjunction with the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them.

[0070] Please see Figure 1 This is a schematic flowchart of a tunnel advanced geological prediction and monitoring method provided in an embodiment of this application, which may specifically include:

[0071] S110. Deploy a sensor network, which includes water quality sensors, flow sensors, and pressure sensors;

[0072] For example, an efficient sensor network and intelligent control system can be built to ensure accurate monitoring of key parameters in the irrigation system (such as water quality, flow rate, pressure, etc.), and to reduce latency and improve data acquisition efficiency through optimized sensor layout and data transmission paths.

[0073] First, the sensor network is optimized for optimal layout. Sensor types include:

[0074] Water quality sensor: detects water's pH value, salinity, pollutant concentration, etc.

[0075] Flow sensor: monitors water flow velocity and volume.

[0076] Pressure sensor: Monitors water pressure to ensure the normal operation of water pumps and piping systems.

[0077] The system employs a distributed sensor deployment model, which, through mathematical optimization, ensures dense sensor placement in key areas to maximize data coverage and accuracy. The core of the deployment optimization problem is how to effectively deploy sensors in a non-uniform environment to maximize coverage while minimizing data transmission costs.

[0078] The sensor network optimization problem can be reduced to a multi-objective optimization problem. Let the sensor node set be S = (X1, Y1), (X2, Y2), ..., (X...N Y N ), where N is the number of sensors, (X i Y i Let represent the coordinates of the i-th sensor. The optimization goal is to minimize the total distance between the sensors and the central controller, while ensuring that each critical area is covered by at least one sensor.

[0079] The objective function to be optimized is:

[0080]

[0081] In the formula, λ is the Lagrange multiplier used to balance the target and constraints; A is the number of critical regions, i.e., the total number of important regions that need to be covered by sensors; δ a Check if each key area is covered; if covered, then δ a =1, otherwise 0; w i The weight of the i-th sensor represents its importance (e.g., sensors in the water source area have a greater weight).

[0082] d(X i Y i Let be the distance function between the sensor and the control center, defined as the Euclidean distance, expressed as:

[0083]

[0084] In the formula, (X c Y c ) represents the coordinates of the i-th sensor.

[0085] Constraints are set to ensure coverage of all critical regions, i.e., each critical region R... a Each has at least one sensor node S i satisfy:

[0086] d(S i ,R a )≤d max

[0087] In the formula, d max This refers to the effective coverage radius of the sensor.

[0088] The sensor weights are adaptively adjusted. Based on actual monitoring needs, the weights can be dynamically adjusted over time. The initial weights can be set according to environmental risks and importance, and then gradually optimized through a feedback mechanism. This application introduces an adaptive adjustment formula for sensor weights. The weights are not only affected by the environment, but also related to the mutual information between sensors. That is, if the information collected by two sensors is highly redundant, the weight of one of them needs to be reduced.

[0089] Adaptive weight adjustment formula:

[0090]

[0091] In the formula, Let I(S) be the initial weight of the i-th sensor; i S j ) is sensor S i and S j The mutual information between the two sensors represents the similarity of the information collected by the two sensors. A larger mutual information means higher data redundancy. α is an adjustment coefficient used to balance the initial weights and information redundancy.

[0092] Mutual Information Quantity I(S) i S j The formula for calculating ) is:

[0093]

[0094] In the formula, p(x) i x j ) is sensor S i and S j Joint probability distribution of the collected data; p(x i ) and p(x j ) are their respective marginal probability distributions.

[0095] S120: Sensor network monitors irrigation system parameters in real time and sends the parameters to the intelligent controller;

[0096] For example, to improve data transmission efficiency, the data transmission path from the sensor to the central controller needs to be optimized. Let all possible transmission paths P = {P1, P2, ..., P...} M We need to choose an optimal path P. optimal This minimizes the total transmission cost and latency.

[0097] Optimal path selection formula:

[0098]

[0099] In the formula, P route For a certain candidate path; P k P represents the k-th transmission path; optimal The optimal transmission path; D lat (P k The latency (C) is the data transmission delay, which is affected by path length and network bandwidth. Longer paths and lower bandwidth will increase latency. trans (P k ) is through path P k The cost of data transmission, taking into account transmission energy consumption and data volume, is expressed as:

[0100]

[0101] In the formula, E l L represents the energy consumption of node l on the path; l The amount of data transmitted via the path;

[0102] For example, the sensor placement optimization formula reduces the distance to the central controller by adjusting sensor positions, while also adjusting sensor density according to the needs of different areas. The data transmission path optimization formula selects the path with the lowest transmission energy consumption and least latency, ensuring fast and efficient data transmission. The mutual information in the weighting formula is used to determine the redundancy of different sensors; if two sensors collect similar information, the weights are automatically adjusted to reduce the transmission of redundant data.

[0103] S130, the intelligent controller uploads parameters to the cloud server;

[0104] For example, irrigation system data collected is uploaded to a cloud server in real time via a smart controller and an IoT platform, enabling remote monitoring. The smart controller aggregates data collected by various sensors and uploads it to the cloud through the IoT platform. To improve data transmission stability, a dynamic distributed data fusion model is used to minimize transmission latency between network nodes and ensure data accuracy.

[0105] Multidimensional Spatiotemporal Data Fusion Model: Sensor-collected data often exhibits spatiotemporal correlation; therefore, data fusion and preprocessing are necessary before transmission. Let S be the data from each sensor. i The value collected at time point is S i (t), then the total data fusion function can be expressed as:

[0106]

[0107] In the formula, S i (t) represents the sensor S i The data collected at time t; β and ε are weighting coefficients, controlling the influence of direct fusion and spatiotemporal correlation terms, respectively; γ ij For sensor S i and S j The spatiotemporal correlation between the two sensors reflects the correlation between the values ​​collected at different times; Δt is the time delay, which represents the influence of the data from the previous time step on the current data.

[0108] Spatiotemporal correlation γ ij Represented as:

[0109]

[0110] In the formula, H(S) i ) and H(S j (S) are sensors S i and S j Entropy represents the uncertainty of data.

[0111] S140, the cloud server analyzes parameters through intelligent control algorithms and dynamically adjusts water resource allocation;

[0112] For example, data received from a cloud server is analyzed and predicted using intelligent control algorithms to identify potential problems in the system and propose optimization decisions. The aim is to ensure the efficient use and rational allocation of water resources, avoid waste, and achieve automatic regulation during the monitoring of reclaimed water irrigation.

[0113] The system acquires data (such as water quality, flow rate, and pressure parameters) through a sensor network. After processing by a cloud server, the data is analyzed and predicted using an algorithm combining adaptive control and deep learning. This system combines multidimensional recurrent neural networks (RNNs) with adaptive Kalman filtering (AKF) technology to detect and predict potential problems in the irrigation system in real time, and optimize scheduling based on the prediction results to provide the best irrigation solution.

[0114] It relies on a multidimensional prediction and adaptive filtering model and an optimal scheduling model: the multidimensional prediction and adaptive filtering model is used to predict the future system state and continuously correct the prediction error based on sensor data. The optimal scheduling model is used to generate the optimal irrigation plan, achieving global optimization by maximizing water resource utilization efficiency and minimizing operational adjustment costs.

[0115] Multidimensional prediction and adaptive filtering models, including:

[0116] System Status X t This represents the overall state of the irrigation system at time t, including multiple dimensions such as water quality, flow rate, and pressure. A recurrent neural network (RNN) combined with an adaptive Kalman filter is used to analyze the future state X. t+1 For prediction, the formula for the prediction stage (prediction based on historical data) of a recurrent neural network is:

[0117]

[0118] In the formula, The next state X predicted based on historical time series data. t+1 ;f(X) tThe state transition function f is the prediction of the system state at the current time t. Assuming the system state changes over time, the state transition function f can describe the internal dynamics of the system; for example, in water resource management, it might represent the natural trend of water flow. W is the number of time steps. step The weight for each time step reflects the importance of historical data to the current prediction. More recent data may have a greater weight than more distant data; g(X) t-step The state at a historical moment t-step reflects the changes in the system state at a certain point in the past and provides background support for the current state.

[0119] The Kalman filter correction phase (combining real-time observation data for correction) involves correcting the predictions from the RNN using the Kalman filter, combined with the current observation data Y. t To improve prediction accuracy, the Kalman filter formula can be expressed as:

[0120]

[0121] In the formula, This is a preliminary prediction given by the RNN model. This prediction may contain errors because it is based solely on historical data; X t Y is the system state vector, representing the current system state; t Let K be the sensor data vector, representing the observation data collected by the sensor at time t, and the sensor observation data at the current time t. Sensor observations provide a true reflection of the current system state and are used to correct the prediction results of the RNN; t The Kalman gain is an adaptive gain matrix, dynamically adjusted through Kalman filtering to correct prediction errors. It determines how the prediction results are adjusted to more closely approximate the actual observations. The gain matrix K... t The size of is determined by the uncertainty of the system prediction and the observation noise. By calculating the Kalman gain, the uncertainty of the system can be adapted. H is the observation matrix, which represents the mapping from the state to the sensor observation data.

[0122] The core of Kalman filtering is to use sensor data to correct the system state and dynamically adjust the gain matrix K. t To minimize the prediction error, the state update formula for Kalman filtering is:

[0123] K t =P t ·H T ·(H·P t ·H T +R t ) -1

[0124] In the formula, P t Let H be the covariance matrix of the system state, representing the state uncertainty; H is the observation matrix, mapping the system state to sensor data; R t This is the covariance matrix for measuring noise.

[0125] Optimize the scheduling model, including:

[0126] To optimize water resource allocation, the system solves for the optimal operation O. opt Maximize the efficiency of water resource utilization. water (t) and minimize the operation adjustment cost C adjust (O o The objective function is:

[0127]

[0128] In the formula, O o This is an optional operation scheduling scheme; U water (t) is the benefit function of water resource utilization, which depends on time t; C adjust (O o The cost of operation adjustment represents the cost of performing a specific operation. o The required resource consumption.

[0129] For example, the RNN model recursively predicts future states using historical data, utilizing historical water quality, flow rate, pressure, and other parameters to predict future irrigation needs. Adaptive Kalman filtering corrects system prediction errors using real-time sensor data, ensuring prediction accuracy. The optimized scheduling model generates an optimal irrigation plan based on the current system state, satisfying irrigation needs while maximizing water resource utilization efficiency and reducing unnecessary waste.

[0130] The S150 intelligent controller receives adjustment instructions from the cloud server and automatically adjusts the irrigation system parameters.

[0131] For example, through instructions issued by the cloud server, the intelligent controller automatically adjusts various parameters of the irrigation system to ensure that the system's operating status remains stable under various changing environments, thereby achieving efficient utilization of irrigation resources.

[0132] The intelligent controller dynamically adjusts the operating parameters of the irrigation system based on the adjustment instructions generated by the cloud server after data analysis and decision-making (S140). This includes valve opening, pump start-up and shutdown times, and flow control. During this process, a nonlinear adaptive control algorithm is employed to adapt to changes in various environmental parameters within the system (e.g., water pressure, flow rate, humidity), ensuring that the irrigation system operates efficiently and stably even under changing conditions.

[0133] Nonlinear adaptive control model: The control quantity u(t) in the system (e.g., valve opening or pump start / stop command) is adjusted in real time according to the current system state. The nonlinear adaptive control model can be described by the following formula:

[0134]

[0135] In the formula, u(t) is the output control quantity of the controller at time t; y(t) is the current state variable of the system, such as the water flow or pressure in the current irrigation system; set (t) is the set target value, such as the water flow or pressure set value that the system needs to achieve; K is the adaptive gain matrix, which is used to dynamically adjust the controller's response to state deviations.

[0136] State error control: The first term of the above formula directly responds to the deviation between the system state and the setpoint. If the current system state deviates from the target setpoint, the controller will correct this deviation by adjusting the valve opening or the pump state. The magnitude of the gain matrix K determines the speed and strength of the controller's response to this deviation.

[0137] The integral term, the second term in the above formula, is the integral part of the controller and is used to eliminate steady-state errors in the system. This term acts as an error accumulator in the control system, ensuring that the system can accurately track the setpoint during long-term operation.

[0138] Nonlinear adaptive gain matrix adjustment: To improve the robustness and adaptability of the control system, the gain matrix K can be dynamically adjusted based on the real-time feedback signal. The basic rules for gain matrix adjustment can be described by the following formula:

[0139]

[0140] In the formula, K0 is the initial gain value; γ is the adaptive adjustment coefficient; Sensitivity is the sensitivity parameter, which determines the speed and magnitude of gain adjustment; |y(t)-y set (t) represents the deviation between the current state and the set value.

[0141] This formula shows that when the deviation between the system state and the target value is large, the gain matrix increases rapidly, and the controller response becomes more aggressive in order to correct the system state as quickly as possible. When the deviation is small, the gain matrix tends to stabilize, ensuring that the system maintains smooth operation and avoiding unnecessary over-adjustment.

[0142] By combining the aforementioned nonlinear adaptive control model and real-time feedback mechanism, the irrigation system can achieve highly intelligent automatic adjustment, ensuring optimal water resource utilization.

[0143] For example, when the system detects an abnormal situation (such as water quality parameters exceeding the standard, abnormal flow, etc.), the intelligent controller will immediately activate the emergency response mechanism to ensure the safe and stable operation of the irrigation system and avoid resource waste or system failure.

[0144] The system utilizes a combination of dynamic fault detection algorithms and fuzzy logic control to identify real-time operational status, particularly anomalies in water quality and flow parameters. Anomaly detection relies on time-series analysis, combined with data from the sensor network, to ensure problem identification in the shortest possible time and prompt implementation of appropriate measures through an emergency response control mechanism. During emergency response, the intelligent controller stabilizes the system and restores it to normal operation according to preset rules (such as closing valves and activating backup pumps).

[0145] Dynamic Fault Detection Model: This model is used to detect abnormal states in a system, such as excessive water quality or excessive flow. By calculating the rate of change of the system state and the differences between sensors, it quickly locates the source of the anomaly, represented as:

[0146]

[0147] In the formula, R(t) is the fault diagnosis function, used to evaluate the degree of anomaly of the system at time t; S i (t) represents the measurement value (e.g., water quality, flow rate, etc.) of the i-th sensor at time t; W i The weight of the i-th sensor reflects its importance to the system. For example, sensors in critical areas would be assigned higher weights. The parameters are adjusted to balance the effects of the rate of change of state and the differences between sensors; Let S be the rate of change of the measurement value of the i-th sensor, representing the dynamic change of the system state; (S i (t)-S j (t)) 2 The difference between the measured values ​​of the i-th and j-th sensors represents the abnormal difference between the two sensors. If the difference is too large, it indicates that there is an anomaly in the system.

[0148] Sensor status change: The first item assesses the system's operating status through the rate of change of sensor measurements. If parameters such as water quality or flow rate change too rapidly, the system will consider an anomaly to exist. Sensor weight W i Ensure that sensors in critical areas can react more quickly.

[0149] Sensor Difference Analysis: The second item compares the measured values ​​from different sensors. When multiple sensors in the system measure the same parameter, if the measured values ​​from some sensors differ significantly from those from other sensors, it may indicate an anomaly in that area.

[0150] Fuzzy logic emergency response control model: Upon detecting an anomaly, the system generates an emergency response strategy based on the fuzzy logic control model. This model responds based on the membership function of the sensor state and the system's preset emergency rules.

[0151] Emergency response control rules can be expressed by the following formula:

[0152] E resp =min(μ A (A i ),μ B (B j ))

[0153] In the formula, E resp For emergency response control outputs, representing the current intensity of emergency handling; μ A (A i ) is an abnormal state A i The membership function represents the severity of the current abnormal state; μ B (B j ) is Emergency Response Rule B j The membership function represents the execution conditions of the corresponding emergency response measures.

[0154] Membership function: Membership function μ A and μ B It is a core concept in fuzzy logic, used to define the abnormal states of a system and the degree to which emergency response measures are implemented. For example, when water quality detects excessive pollutants, the system will use μ... A To determine the severity of pollution exceeding standards, and through μ B Match the most suitable emergency response measures, such as shutting off valves or activating the filtration system.

[0155] Minimization strategy: The min operation in the model indicates that the strength of the emergency response depends on the degree of matching between the abnormal state and the response rules. The system will only execute corresponding mandatory measures when the abnormal state reaches a certain severity.

[0156] By combining dynamic fault detection and fuzzy logic emergency response, the system can quickly and accurately handle anomalies in complex environments, ensuring the stable and efficient operation of the irrigation system.

[0157] Please see Figure 2 The diagram below illustrates the structure of an intelligent monitoring and regulation reclaimed water irrigation device according to an embodiment of this application, comprising:

[0158] Deployment unit 21 is used to deploy a sensor network, which includes a water quality sensor, a flow sensor, and a pressure sensor.

[0159] Monitoring unit 22 is used for real-time monitoring of irrigation system parameters by sensor network and sending the parameters to intelligent controller;

[0160] Transmission unit 23 is used by the intelligent controller to upload parameters to the cloud server;

[0161] Analysis unit 24 is used by the cloud server to analyze parameters through intelligent control algorithms and dynamically adjust water resource allocation;

[0162] The receiving unit 25 is used by the intelligent controller to receive adjustment instructions from the cloud server and automatically adjust the irrigation system parameters.

[0163] Please see Figure 3 This application also provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor. When the processor 320 executes the computer program 311, it implements the steps of any of the above-mentioned intelligent monitoring and regulation methods for reclaimed water irrigation.

[0164] Since the electronic device described in this embodiment is the device used to implement the intelligent monitoring and regulation reclaimed water irrigation device in the embodiment of this application, those skilled in the art can understand the specific implementation method and its various variations of the electronic device in this embodiment based on the method described in the embodiment of this application. Therefore, how the electronic device implements the method in the embodiment of this application will not be described in detail here. Any device used by those skilled in the art to implement the method in the embodiment of this application is within the scope of protection of this application.

[0165] In practice, when the computer program 311 is executed by the processor, it can implement any of the embodiments corresponding to the first aspect.

[0166] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0167] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-readable program code.

[0168] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0169] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0170] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0171] This application also provides a computer program product, which includes computer software instructions that, when executed on a processing device, cause the processing device to perform... Figure 1 The process flow of the intelligent monitoring and regulation reclaimed water irrigation method in the corresponding embodiment.

[0172] A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0173] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0174] In the several embodiments provided in this application, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms.

[0175] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0176] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0177] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0178] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

[0179] Although preferred embodiments have been described in this specification, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this specification.

[0180] Obviously, those skilled in the art can make various modifications and variations to this specification without departing from its spirit and scope. Therefore, if such modifications and variations fall within the scope of the claims and their equivalents, this specification is also intended to include such modifications and variations.

Claims

1. A method of smart monitoring and regulated reclaimed water irrigation, characterized in that, The method comprises: deploying a sensor network, the sensor network comprising water quality sensors, flow sensors, pressure sensors; the sensor network monitoring irrigation system parameters in real time and sending the parameters to an intelligent controller; the intelligent controller uploading the parameters to a cloud server; the cloud server analyzing the parameters through intelligent control algorithms and dynamically adjusting water resource allocation, including: Using a recurrent neural network to analyze future states The formula for prediction is: ； wherein is the predicted next time state based on historical time series data ; is the state transition function; is the number of time steps; is the weight of the time step; is the state at historical time ; is the overall state of the irrigation system at time ; correcting the prediction of the RNN through a Kalman filter, and the Kalman filter formula is represented as: ； wherein is a preliminary prediction result given by the RNN model; is a system state vector; is a sensor data vector; is an adaptive gain matrix; is an observation matrix; To optimize water resource allocation, an optimal operation is solved to maximize the benefit of water resource utilization and minimize the cost of operation regulation The objective function is optimized as follows: ； wherein is an optional operational dispatch scheme; is a benefit function for water resource utilization, dependent on time ; is an operational regulation cost, representing the resource consumption required to implement the optional operational dispatch scheme ; the intelligent controller receiving the adjustment instructions from the cloud server and automatically adjusting the irrigation system parameters.

2. The method of claim 1, wherein, The deployment of the sensor network comprises: The set of sensor nodes is wherein is the number of sensors, denotes the coordinates of the th sensor, and the goal of the optimization is to minimize the total distance between the sensors and the central controller while each important region is covered by at least one sensor; The optimization objective function is: ； wherein is a Lagrange multiplier; is the number of critical regions; is whether each critical region has been covered; is the weight of the th sensor; The distance function for the sensor to the control center is represented as: ； In the formula, represents the coordinates of the control center; cover all critical areas, i.e. each critical area at least one sensor node satisfies: ； In the formula, is the effective coverage radius of the sensor.

3. The intelligently monitored and regulated reclaimed water irrigation method of claim 2, wherein, weights of the sensors is determined based on the following equation: ； wherein is the initial weight of the th sensor; is the mutual information between the th sensor and the th sensor; is the adjustment coefficient; Mutual information The formula for calculating mutual information is: ； wherein is a sensor and joint probability distribution of the collected data; and are the respective marginal probability distributions.

4. The smart monitored and regulated reclaimed water irrigation method according to claim 1, wherein, Before the intelligent controller uploads the parameters to the cloud server, a data fusion operation is performed, including: Setting each sensor At the moment the values collected are The data fusion function is expressed as: ； wherein, is a sensor at time the collected value; and is a weight coefficient, respectively controlling the influence of direct fusion and spatio-temporal correlation term; is a sensor and spatio-temporal correlation degree between and is a time delay, representing the influence of the data of the previous time step on the current data; spatiotemporal correlation degree is expressed as: ； wherein and are the entropy of the sensor and the sensor respectively.

5. The smart monitored and regulated reclaimed water irrigation method according to claim 1, wherein, The adaptive gain matrix is determined based on the following equation: ； wherein is the covariance matrix of the system states; is the covariance matrix of the measurement noise.

6. The intelligently monitored and regulated reclaimed water irrigation method of claim 1, wherein, The intelligent controller receives the adjustment instructions from the cloud server and automatically adjusts the irrigation system parameters, including: A nonlinear adaptive control model is constructed, which is described by the following formula: ； wherein is the output control variable of the controller at time ; is the state variable of the current system; is the setpoint value; is the adaptive gain matrix; Adaptive gain matrix The adjustment rule is determined based on the following equation: ； wherein is an initial gain value; is an adaptive adjustment coefficient; is a sensitivity parameter; is a deviation between the current state and the set value.

7. A smart monitored and regulated reclaimed water irrigation device, characterized in that, The device applies the intelligent monitoring and adjustment method of reclaimed water irrigation according to any one of claims 1-6, and the device comprises: a deployment unit for deploying a sensor network, the sensor network comprising water quality sensors, flow sensors, pressure sensors; a monitoring unit for the sensor network to monitor irrigation system parameters in real time and send the parameters to an intelligent controller; a transmission unit for the intelligent controller to upload the parameters to a cloud server; an analysis unit for the cloud server to analyze the parameters through intelligent control algorithms and dynamically adjust water resource allocation; a receiving unit for the intelligent controller to receive the adjustment instructions from the cloud server and automatically adjust the irrigation system parameters.

8. An electronic device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to execute the computer program stored in the memory to implement the steps of the intelligent monitoring and adjustment method of reclaimed water irrigation according to any one of claims 1-6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to implement the intelligent monitoring and adjustment method of reclaimed water irrigation according to any one of claims 1-6.

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