Ecological pond water distribution method based on Internet of Things and regulation and control system

Abstract

The invention relates to the technical field of water distribution regulation and control, and discloses an ecological pond water distribution method based on the Internet of Things and a regulation and control system. The method comprises the following steps: collecting water quality parameters of different depths of an ecological pond through a vertical layered sensor to form a depth gradient data set; quantitatively analyzing the layering state of the water body by using a thermocline recognition algorithm to obtain thermocline position, dissolved oxygen vertical gradient and nutritive salt layering state dynamic variables; comparing the dynamic variable with a preset threshold value, and generating a layering abnormal trigger signal and a target water distribution depth; cooperatively scheduling the multi-depth water distribution equipment, and generating a water distribution execution instruction; executing directional water distribution treatment to obtain water quality improvement effect data and updating the parameter optimization database. The problems that in ecological pond water quality management, the water layering state cannot be accurately recognized, targeted water distribution control is lacked, and the water distribution effect is difficult to quantify and optimize are solved.

Description

Technical Field

[0001] The present application relates to the technical field of water distribution and control, and in particular to an ecological pond water distribution method and control system based on the Internet of Things. Background Art

[0002] Existing eco-pond water quality management technologies mainly use regular manual testing and uniform water distribution to regulate water quality. Water conditioners or oxygenation equipment are placed on the surface or at a fixed depth of the eco-pond to improve the overall water quality. Some advanced water quality management equipment can realize automated water quality monitoring and basic water quality regulation functions, but these technologies still have obvious limitations in water stratification identification, precise water distribution control and effect evaluation.

[0003] The main deficiencies in existing technologies include: the inability to accurately identify and quantify the vertical stratification state of water bodies, especially the dynamic changes in the position of the thermocline and the vertical gradient distribution of dissolved oxygen; the lack of precise water distribution control capabilities for water quality anomalies at different depths, and the inability to adjust differentiated water distribution strategies according to the type of stratification anomaly; the lack of real-time monitoring and quantitative evaluation mechanisms for water distribution effects, and the inability to establish a correlation between water distribution parameters and improvement effects, resulting in blindness and inefficiency in water distribution operations.

[0004] Since existing technologies are unable to accurately identify and quantitatively analyze the stratification status of water bodies, and thus are unable to formulate corresponding water distribution strategies based on different types of stratification anomalies, and there is a lack of real-time effect monitoring and parameter optimization mechanisms during the water distribution process, progressive technical problems such as insufficient water distribution accuracy, unpredictable effects, and unreasonable parameter configuration have emerged in the water quality management of ecological ponds. These problems are interrelated and deepen layer by layer, seriously affecting the scientific nature and effectiveness of water quality management of ecological ponds. Summary of the Invention

[0005] This application provides an ecological pond water distribution method and control system based on the Internet of Things, which is used to solve the problems of inability to accurately identify water stratification status, lack of targeted water distribution control, and difficulty in quantifying and optimizing water distribution effects in ecological pond water quality management.

[0006] In a first aspect, the present application provides an eco-pond water distribution method based on the Internet of Things, the eco-pond water distribution method based on the Internet of Things comprising: collecting and processing water quality parameters at different depths of the eco-pond using vertical layered sensors to obtain a depth gradient data set including temperature, dissolved oxygen, pH value, and turbidity; According to the depth gradient data set, the water body stratification state is quantitatively analyzed and processed by the thermocline identification algorithm to obtain the dynamic variables of the thermocline position, the dynamic variables of the dissolved oxygen vertical gradient and the dynamic variables of the nutrient stratification state; the dynamic variables of the thermocline position, the dynamic variables of the dissolved oxygen vertical gradient and the dynamic variables of the nutrient stratification state are respectively compared and judged with the corresponding preset thresholds to obtain a stratification abnormality trigger signal and a corresponding target water distribution depth; according to the stratification abnormality trigger signal, the multi-depth water distribution equipment is coordinated and scheduled to obtain a water distribution execution instruction including the water distribution depth, flow rate and duration; the water body of the target water distribution depth is subjected to a directional water distribution process through the water distribution execution instruction to obtain water quality improvement effect data and update the water distribution parameter optimization database.

[0007] In a second aspect, the present application provides an Internet of Things-based ecological pond water distribution control system, the Internet of Things-based ecological pond water distribution control system comprising: The acquisition module is used to collect and process water quality parameters at different depths of the ecological pond through vertical layered sensors to obtain a depth gradient data set including temperature, dissolved oxygen, pH value and turbidity; a quantification module for performing quantitative analysis and processing on the water body stratification state by using a thermocline identification algorithm according to the depth gradient data set, to obtain dynamic variables of the thermocline position, dynamic variables of the vertical gradient of dissolved oxygen, and dynamic variables of the nutrient stratification state; a judgment module, configured to compare and perform judgment on the dynamic variable of the thermocline position, the dynamic variable of the dissolved oxygen vertical gradient, and the dynamic variable of the nutrient stratification state with corresponding preset thresholds, to obtain a stratification abnormality trigger signal and a corresponding target water distribution depth; a scheduling module, configured to perform coordinated scheduling processing on the multi-depth water distribution equipment according to the layered abnormality trigger signal, and obtain a water distribution execution instruction including the water distribution depth, flow rate and duration; The water distribution module is used to perform directional water distribution processing on the water body at the target water distribution depth through the water distribution execution instruction, obtain water quality improvement effect data and update the water distribution parameter optimization database.

[0008] In a third aspect, an ecological pond water distribution device based on the Internet of Things is provided, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the ecological pond water distribution device based on the Internet of Things executes the above-mentioned ecological pond water distribution method based on the Internet of Things.

[0009] In a fourth aspect, a computer-readable storage medium is provided, wherein instructions are stored in the computer-readable storage medium, which, when executed on a computer, enables the computer to execute the above-mentioned ecological pond water distribution method based on the Internet of Things.

[0010] In the technical solution provided in this application, the technical feature of collecting and processing water quality parameters at different depths of the ecological pond through vertical stratified sensors realizes comprehensive perception of the vertical distribution state of the water body. Compared with traditional single-point or surface monitoring methods, it can obtain a depth gradient data set containing temperature, dissolved oxygen, pH value and turbidity, providing a complete data basis for subsequent precise analysis. At the same time, the thermocline identification algorithm is a core technical feature. By quantitatively analyzing and processing the depth gradient data set, it can accurately identify the stratification state of the water body and extract the dynamic variables of the thermocline position, the dynamic variables of the vertical gradient of dissolved oxygen and the dynamic variables of the nutrient stratification state. The algorithm adopts the method of comparing the mean of three consecutive temperature gradients and identifying gradient mutation points, which significantly improves the accuracy and stability of thermocline positioning, and solves the technical problem of inaccurate thermocline identification in traditional methods. The technical feature of comparing the dynamic variables with the preset threshold for judgment and processing establishes a quantitative abnormality judgment standard, avoids the subjectivity and inconsistency of manual judgment, and can timely and accurately identify stratification anomalies and determine the target water distribution depth.

[0011] The technical features of collaborative scheduling and processing of multi-depth water distribution equipment realize differentiated water distribution strategies for different types of layered anomalies through refined control links such as equipment matching, depth positioning, flow allocation and timing coordination. Compared with the traditional unified water distribution method, it can formulate precise water distribution execution instructions including water distribution depth, flow and duration according to the specific anomaly type and location, significantly improving the targetedness and efficiency of water distribution operations. The technical features of targeted water distribution treatment combined with real-time water quality monitoring not only achieve precise treatment of water bodies at the target depth, but also can track the water distribution effect in real time. Through data processing methods such as difference calculation, improvement degree quantification and water distribution efficiency calculation, a quantitative relationship between water distribution parameters and improvement effects is established. The application of machine learning models further enhances the system's self-optimization capability. Through parameter optimization and strategy update processing, the water distribution parameter optimization database is continuously improved, forming a closed-loop intelligent water quality management system, which fundamentally solves the technical problems of traditional ecological pond management such as strong blindness of water distribution, unpredictable effects and unreasonable parameter configuration, and realizes precise, intelligent and efficient ecological pond water quality management. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0013] Figure 1 This is a schematic diagram of an embodiment of an ecological pond water distribution method based on the Internet of Things in an embodiment of the present application; Figure 2 This is a schematic diagram of an embodiment of an ecological pond water distribution control system based on the Internet of Things in an embodiment of the present application; Figure 3 It is a structural schematic block diagram of an ecological pond water distribution device based on the Internet of Things in an embodiment of the present invention. DETAILED DESCRIPTION

[0014] The embodiments of the present application provide an ecological pond water distribution method and control system based on the Internet of Things. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or that are inherent to these processes, methods, products or devices.

[0015] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiment of the present application, an embodiment of the ecological pond water distribution method based on the Internet of Things includes: Step S101: collecting and processing water quality parameters at different depths of the ecological pond using vertical layered sensors to obtain a depth gradient data set including temperature, dissolved oxygen, pH value, and turbidity; Step S102: Quantitatively analyzing and processing the water stratification state using a thermocline identification algorithm based on the depth gradient data set to obtain dynamic variables of the thermocline position, the dissolved oxygen vertical gradient, and the nutrient stratification state; Step S103: Compare and judge the dynamic variables of the thermocline position, the dissolved oxygen vertical gradient, and the nutrient stratification state with corresponding preset thresholds to obtain a stratification anomaly trigger signal and a corresponding target water distribution depth; Step S104: Coordinated scheduling of the multi-depth water distribution equipment is performed according to the layered abnormality trigger signal to obtain a water distribution execution instruction including the water distribution depth, flow rate, and duration; Step S105: Perform directional water distribution processing on the water body at the target water distribution depth through the water distribution execution instruction, obtain water quality improvement effect data and update the water distribution parameter optimization database.

[0016] It is understandable that the execution subject of this application can be an ecological pond water distribution control system based on the Internet of Things, or a terminal or a server, which is not limited here. The embodiment of this application is described by taking the server as the execution subject as an example.

[0017] Specifically, when the ecological pond water distribution method based on the Internet of Things collects and processes water quality parameters at different depths of the ecological pond through vertically layered sensors, sensor nodes including temperature sensors, dissolved oxygen sensors, pH sensors and turbidity sensors are first deployed in the water body of the ecological pond at a depth interval of 0.5 meters. The raw data collected by each sensor node is time-calibrated through a clock synchronization protocol to ensure that data at different depths remain consistent in the time dimension. Then, depth identification and data association processing are performed based on the synchronized water quality data, and a mapping relationship is established between the water quality parameters of each depth layer and its corresponding depth coordinates to form a water quality parameter matrix arranged in depth layers. Then, the water quality parameter matrix is ​​subjected to data quality inspection and outlier elimination processing. The valid water quality data is screened out through the set data quality standards, and the valid water quality data is classified and integrated according to the four dimensions of temperature, dissolved oxygen, pH value and turbidity to form a depth gradient data set.

[0018] In the process of quantitative analysis and processing of water body stratification state through thermocline identification algorithm based on depth gradient data set, thermocline identification algorithm is a water body stratification identification method based on temperature gradient change characteristics. The algorithm first arranges the temperature data in the depth gradient data set according to the depth sequence from shallow to deep to form a temperature depth distribution sequence, and then calculates the temperature difference layer by layer and analyzes the gradient change rate of the temperature depth distribution sequence. The temperature gradient distribution data between each depth layer is determined by calculating the temperature difference between adjacent depth layers. Then, the mean value of the temperature gradient of three consecutive layers is compared and the gradient mutation point is identified based on the temperature gradient distribution data. The process groups the temperature gradient distribution data into three consecutive layers in depth order, calculates the arithmetic mean of the three gradient values ​​in each group, and obtains the three-layer gradient mean at each depth position. By performing difference calculation and change rate analysis on the three-layer gradient means at adjacent depth positions, the gradient mutation depth range is identified. The dynamic variables of the thermocline position are determined by precise positioning and depth interpolation calculation within the temperature gradient mutation depth range. At the same time, the dissolved oxygen data in the depth gradient data set are subjected to inter-layer difference accumulation calculation processing, and the pH value and turbidity data are subjected to distribution variance statistical processing to obtain the dynamic variables of the dissolved oxygen vertical gradient and the dynamic variables of the nutrient stratification state.

[0019] The dynamic variables of the thermocline position, the dissolved oxygen vertical gradient, and the nutrient stratification status are compared with corresponding preset thresholds for judgment. The preset thresholds are critical values ​​determined based on ecological pond water quality management standards and historical data statistics. The comparison and judgment process first compares the dynamic variables of the thermocline position with the thermocline position threshold. When the dynamic variables of the thermocline position exceed the preset range, a thermocline anomaly judgment result and a thermocline trigger flag are generated. At the same time, the dynamic variables of the dissolved oxygen vertical gradient are compared with the dissolved oxygen gradient threshold. When the dissolved oxygen vertical gradient exceeds the normal range, a hypoxia stratification judgment result and a hypoxia trigger flag are generated. The dynamic variables of the nutrient stratification status are compared with the nutrient stratification threshold. When the nutrient stratification status is abnormal, a nutrient anomaly judgment result and a nutrient trigger flag are generated. Then, the thermocline trigger flag, hypoxia trigger flag, and nutrient trigger flag are logically combined and prioritized. The stratification anomaly trigger signal is determined based on the severity and urgency of different anomaly types. Based on the trigger type of the stratification anomaly trigger signal, the corresponding depth layer position is queried and the depth coordinates are extracted to determine the target water distribution depth.

[0020] In the process of collaborative scheduling of multi-depth water distribution equipment based on layered abnormality trigger signals, the multi-depth water distribution equipment includes a variable-depth water distributor and a flow control valve group. The collaborative scheduling process first matches the trigger type in the layered abnormality trigger signal to the device and selects the water distribution mode. The corresponding water distribution operation mode is selected and the equipment call list is determined according to different abnormality types. The variable-depth water distributor is depth-located and position-adjusted based on the target water distribution depth. The water distributor is adjusted to the specified depth position through a precise depth control mechanism to obtain precise depth coordinates and equipment placement confirmation signals. The flow control valve group is opened and the flow is distributed according to the water distribution operation mode. The valve opening is determined by calculating the water distribution flow required for each depth layer, and the water distribution flow parameters and valve control instructions for each depth layer are obtained. The equipment placement confirmation signals and valve control instructions are time-coordinated and started synchronously to ensure that multiple devices work together in the correct time sequence. The water distribution duration is calculated and the stop conditions are set based on the multi-device collaborative operation schedule to form a water distribution execution instruction containing the water distribution depth, flow, and duration.

[0021] Directed water distribution, when executed through water distribution execution instructions, is the process of precisely delivering treated water to a specific depth. This process involves targeted water distribution and real-time water quality monitoring at the target depth based on the water distribution execution instructions. During the distribution process, changes in water quality parameters are simultaneously monitored to obtain water quality change data. This data is then compared with the pre-distribution water quality baseline and the degree of improvement is quantified. Improvement quantification is calculated by comparing changes in temperature, dissolved oxygen, pH, and turbidity before and after distribution. Distribution efficiency is calculated and parameter correlation analysis is performed on these improvement quantifications. The correlation between distribution depth, flow rate, duration, and water quality improvement is analyzed. This correlation data is then fed into a machine learning model for parameter optimization and strategy update. The machine learning model optimizes the water distribution strategy by analyzing historical distribution and effect data, obtaining water quality improvement results. This water quality improvement data is then written to the water distribution parameter optimization database and model parameters are updated, continuously improving the water distribution strategy and parameter configuration.

[0022] In a specific embodiment, the process of executing step S101 may specifically include the following steps: Vertically layered sensors were deployed in the ecological pond water body at a depth of 0.5 meters, and sensor nodes including temperature sensors, dissolved oxygen sensors, pH sensors, and turbidity sensors were obtained; The raw data collected by the sensor nodes are time-calibrated through a clock synchronization protocol to obtain synchronized water quality data; Based on the synchronized water quality data, depth identification and data association processing are performed to obtain a water quality parameter matrix arranged in layers according to depth; Perform data quality inspection and outlier removal on the water quality parameter matrix to obtain valid water quality data; The effective water quality data were classified and integrated according to the four dimensions of temperature, dissolved oxygen, pH value and turbidity to obtain a depth gradient data set.

[0023] Specifically, when vertically layered sensors are deployed in the water body of the ecological pond at a depth of 0.5 meters, the vertically layered sensors refer to a multi-layer sensor array installed in the vertical direction of the water body. By deploying sensor nodes at different depths, comprehensive monitoring of the stratification status of the water body is achieved. Each sensor node integrates four water quality detection devices: temperature sensor, dissolved oxygen sensor, pH sensor and turbidity sensor. Among them, the temperature sensor is used to detect changes in water temperature, the dissolved oxygen sensor measures the oxygen content in the water, the pH sensor monitors the acidity and alkalinity of the water, and the turbidity sensor detects the turbidity of the water. The sensor nodes are arranged in a fixed spacing of 0.5 meters. The sensor nodes are installed from the surface of the ecological pond downward to ensure the water quality parameter collection coverage and data continuity of each depth layer.

[0024] The raw data collected by the sensor nodes are time-calibrated through the clock synchronization protocol. The clock synchronization protocol is a communication protocol that ensures that the time of each node in the distributed sensor network remains consistent. This protocol eliminates the time deviation between different sensor nodes by setting a unified time base in the sensor network. The raw data contains the temperature, dissolved oxygen, pH value and turbidity values ​​collected by each sensor node at different times. The time calibration process establishes the correspondence between the data and the collection time by adding an accurate timestamp to each data point. Synchronized water quality data refers to a set of water quality parameter data with a unified time base after time calibration. This data set ensures that the data collected by sensor nodes at different depths at the same time have time consistency.

[0025] When depth identification and data association processing are performed based on synchronized water quality data, depth identification refers to marking the corresponding depth coordinate information for each water quality data point. Data association processing is the process of establishing a mapping relationship between water quality parameters and their spatial position and time information. This processing process first reads the sensor node position information in the synchronized water quality data, and then assigns a corresponding depth identification to each data point based on the installation depth of the sensor node. Then, an association relationship between the data point and the depth coordinate is established to form a multidimensional data structure containing depth, time, temperature, dissolved oxygen, pH value and turbidity information. The water quality parameter matrix arranged in layers by depth arranges the associated data from shallow to deep in order of depth, forming a two-dimensional data matrix structure with depth as rows and water quality parameters as columns.

[0026] In the process of data quality inspection and outlier removal of water quality parameter matrix, data quality inspection is the process of evaluating the accuracy, completeness and rationality of data through set quality standards. The process includes three aspects: data range inspection, data continuity inspection and data consistency inspection. Data range inspection identifies obviously erroneous data by judging whether the water quality parameter values ​​are within a reasonable physical range. Data continuity inspection discovers abnormal jumping data points by analyzing the change amplitude of data at adjacent time points or adjacent depth layers. Data consistency inspection identifies logically contradictory data by comparing the correlation between different parameters at the same depth. Outlier removal processing uses statistical methods to calculate the standard deviation and mean of each parameter, and marks data points that exceed the range of three times the standard deviation as outliers and removes them from the data set. Valid water quality data refers to the reliable data set retained after quality inspection and outlier removal.

[0027] When the effective water quality data are classified and integrated according to the four dimensions of temperature, dissolved oxygen, pH value and turbidity, the classification and integration processing refers to the process of reorganizing and classifying the mixed water quality parameter data according to the parameter type. The processing process first extracts the temperature data from the effective water quality data, arranges it in depth order to form a temperature depth distribution array, then extracts the dissolved oxygen data, arranges it in depth order to form a dissolved oxygen depth distribution array, then extracts the pH data, arranges it in depth order to form a pH depth distribution array, and finally extracts the turbidity data, arranges it in depth order to form a turbidity depth distribution array. The depth gradient data set is to integrate the four depth distribution arrays according to a unified depth coordinate to form a comprehensive data structure containing the depth change information of all water quality parameters. The data structure is indexed by depth, and each depth position corresponds to a complete set of water quality parameter values.

[0028] In a specific embodiment, the process of executing step S102 may specifically include the following steps: Arrange the temperature data in the depth gradient dataset according to the depth sequence from shallow to deep to obtain the temperature depth distribution sequence; The temperature depth distribution sequence is processed by layer-by-layer temperature difference calculation and gradient change rate analysis to obtain the temperature gradient distribution data between layers at each depth; Based on the temperature gradient distribution data, the mean value of three consecutive temperature gradients is compared and the gradient mutation point is identified to obtain the temperature gradient mutation depth range; The maximum gradient point within the temperature gradient mutation depth range is precisely located and depth interpolated to obtain the dynamic variables of the thermocline position. The dissolved oxygen data in the depth gradient data set were processed by inter-layer difference accumulation calculation and the pH value and turbidity data were processed by distribution variance statistics to obtain the dynamic variables of the vertical gradient of dissolved oxygen and the dynamic variables of the nutrient stratification state.

[0029] Specifically, when the temperature data in the depth gradient dataset are arranged in a depth sequence from shallow to deep, the temperature depth distribution sequence refers to an ordered data sequence formed by reorganizing the temperature values ​​of each depth layer in the order of depth coordinates from small to large. The sequence uses depth as the index and temperature as the value to form a one-dimensional array structure that reflects the vertical distribution characteristics of water body temperature. The arrangement process first reads all temperature data points in the depth gradient dataset, extracts the depth coordinates and temperature values ​​corresponding to each data point, and then reorders the temperature data in the order of depth coordinates from shallow to deep to form a sequence of corresponding relationships between depth and temperature.

[0030] When performing layer-by-layer temperature difference calculation and gradient change rate analysis on the temperature depth distribution sequence, layer-by-layer temperature difference calculation refers to the process of calculating the temperature value difference between adjacent depth layers. This calculation process reflects the temperature change amplitude by reading the temperature values ​​of two adjacent depth positions in the temperature depth distribution sequence and calculating the difference between the two. Gradient change rate analysis refers to the process of calculating the temperature gradient by dividing the temperature difference by the corresponding depth spacing. The temperature gradient represents the rate at which temperature changes with depth. The calculation formula is that the temperature gradient is equal to the temperature difference divided by the depth spacing. The temperature gradient distribution data between each depth layer is a data set containing the temperature gradient values ​​between all adjacent depth layers. This data set reflects the distribution law of the intensity of water temperature changes in the vertical direction.

[0031] When performing three-layer continuous temperature gradient mean comparison and gradient mutation point identification processing based on temperature gradient distribution data, the three-layer continuous temperature gradient mean comparison refers to grouping the temperature gradient distribution data into three consecutive layers in depth order, calculating the arithmetic mean of the three gradient values ​​of each group, and obtaining the three-layer gradient mean of each depth position, and then performing difference calculation and change rate analysis on the three-layer gradient mean of adjacent depth positions to form a gradient mean change sequence. The gradient mutation point identification processing sets a gradient change threshold, performs threshold comparison and mutation point marking on the gradient mean change sequence, and when the gradient mean change exceeds the preset threshold, it is marked as a gradient mutation point, and the corresponding depth coordinates are recorded. The starting depth and ending depth are determined based on the continuity analysis of the gradient mutation flag. The temperature gradient mutation depth range refers to the continuous depth interval containing the gradient mutation point.

[0032] When accurately locating the maximum gradient point within the temperature gradient mutation depth range and performing depth interpolation calculations, accurate positioning refers to finding the depth position with the maximum gradient value within the temperature gradient mutation depth range. This process determines the maximum gradient point by traversing all temperature gradient values ​​within the mutation depth range and comparing the sizes of each gradient value. The depth interpolation calculation uses a linear interpolation method to perform refined calculations near the maximum gradient point. A more accurate thermocline depth position is determined by analyzing the temperature change trend before and after the maximum gradient point. The dynamic variable of the thermocline position is a numerical parameter representing the specific depth position of the thermocline in the water body. This parameter reflects the core position characteristics of the water body temperature stratification.

[0033] When performing inter-layer difference accumulation calculation on the dissolved oxygen data in the depth gradient dataset, the inter-layer difference accumulation calculation refers to the process of calculating the difference between the dissolved oxygen value of each depth layer and the dissolved oxygen value of the adjacent depth layer, and then accumulating and summing these differences. The calculation process first extracts the dissolved oxygen values ​​of all depth layers in the depth gradient dataset, calculates the dissolved oxygen difference between adjacent layers in depth order, and then accumulates and sums all the inter-layer differences to obtain a cumulative index reflecting the vertical change intensity of dissolved oxygen. The dynamic variable of the dissolved oxygen vertical gradient is a quantitative parameter that represents the distribution change characteristics of dissolved oxygen in the vertical direction of the water body.

[0034] When performing distribution variance statistics on pH and turbidity data, distribution variance statistics refers to a statistical analysis method that calculates the degree of dispersion of pH and turbidity data at each depth layer. The processing process first extracts the pH data and turbidity data of all depth layers in the depth gradient data set, calculates the mean and variance of the pH data, and calculates the mean and variance of the turbidity data. Then, the pH variance and turbidity variance are comprehensively processed to form a comprehensive indicator reflecting the nutrient stratification state. The dynamic variable of the nutrient stratification state is a quantitative parameter that represents the vertical distribution state of nutrients in the water body. This parameter indirectly reflects the nutrient stratification characteristics through the distribution variance of pH and turbidity.

[0035] In a specific embodiment, the process of performing the step of comparing the mean values ​​of the temperature gradients of three consecutive layers and identifying the gradient mutation points based on the temperature gradient distribution data may specifically include the following steps: The temperature gradient distribution data are grouped into three consecutive layers in order of depth to obtain three consecutive layers of temperature gradient combination data; The three gradient values ​​in each group of the continuous three-layer temperature gradient combination data are calculated by arithmetic mean to obtain the three-layer gradient mean at each depth position; The gradient mean values ​​at adjacent depths are subjected to difference calculation and change rate analysis to obtain a gradient mean change sequence. Perform threshold comparison and mutation point marking on the gradient mean change sequence to obtain the gradient mutation flag and corresponding depth coordinate; The starting depth and ending depth are determined based on the continuity analysis of the gradient mutation flag, and the temperature gradient mutation depth range is obtained.

[0036] Specifically, when the temperature gradient distribution data is processed by continuous three-layer grouping in depth order, the continuous three-layer grouping refers to a processing method in which the gradient values ​​in the temperature gradient distribution data are grouped into a data group for every three adjacent gradient values ​​in depth order. This processing method adopts a sliding window method, starting from the first gradient value of the temperature gradient distribution data, and successively selecting three consecutive gradient values ​​as a group, and then moving down one position, and then selecting the next group of three consecutive gradient values. This process is repeated until all gradient data are traversed. The continuous three-layer temperature gradient combination data refers to a collection of multiple three-element gradient data groups obtained by grouping processing, each data group contains the temperature gradient values ​​of three adjacent depth layers. This grouping method can capture the changing characteristics of the temperature gradient within the local depth range. When calculating the arithmetic mean of the three gradient values ​​in each group of three consecutive layers of temperature gradient combination data, the arithmetic mean calculation refers to the mathematical operation process of adding the three gradient values ​​and then dividing by three. This calculation process first reads the three temperature gradient values ​​in each data group, sums these three values, and then divides the sum by three to obtain the average value of the group of data. The three-layer gradient mean at each depth position refers to the arithmetic mean corresponding to each consecutive three-layer data group. This mean reflects the average level of temperature gradient within the local depth range. The average value calculation can reduce the impact of accidental fluctuations of a single gradient value on the overall trend judgment.

[0037] When performing difference calculation and change rate analysis on the three-layer gradient means at adjacent depth positions, the difference calculation refers to the process of calculating the numerical difference between the three-layer gradient means at two adjacent depth positions. The calculation process calculates the difference between each two adjacent means by reading the three-layer gradient mean sequence arranged in depth order. The change rate analysis refers to the process of calculating the gradient mean change rate by dividing the gradient mean difference by the corresponding depth spacing. The gradient mean change sequence is a data sequence containing the gradient mean change rates of all adjacent depth positions. The sequence reflects the dynamic change characteristics of the temperature gradient in the vertical direction.

[0038] When performing threshold comparison and mutation point marking processing on the gradient mean change sequence, threshold comparison refers to the process of comparing the size of each change rate value in the gradient mean change sequence with the preset change rate threshold. The preset threshold is a critical value determined based on the normal water body temperature gradient change law. When the change rate value exceeds the preset threshold, it indicates that there is an abnormal gradient change at the depth position. Mutation point marking processing refers to the process of marking the depth position exceeding the threshold and recording its depth coordinates. The gradient mutation flag is a Boolean flag indicating whether there is a gradient mutation. The corresponding depth coordinate is the specific depth position of the mutation point in the water body. Through threshold comparison and marking processing, the depth position where the temperature gradient changes significantly can be accurately identified.

[0039] When determining the starting depth and ending depth based on the continuity analysis of the gradient mutation flag, continuity analysis refers to a processing method for checking the continuous distribution characteristics of the gradient mutation flag in the depth sequence. This analysis method traverses all gradient mutation flags to find a sequence of continuously true flags. The starting depth determination processing refers to finding the depth coordinate corresponding to the first true flag in the continuous mutation flag sequence. The ending depth determination processing refers to finding the depth coordinate corresponding to the last true flag in the continuous mutation flag sequence. The temperature gradient mutation depth range refers to the continuous depth interval from the starting depth to the ending depth, which includes all depth positions where temperature gradient mutations occur.

[0040] For example, during the summer stratification period of a certain ecological pond, the temperature gradient distribution data showed that the temperature gradient values ​​of each depth layer were unevenly distributed. The continuous three-layer grouping processing organized the gradient data into multiple three-element data groups in a sliding window manner. The arithmetic mean calculation processing calculated the average value of each group of data to obtain a smooth three-layer gradient mean sequence. The difference calculation found that the difference between adjacent gradient means within a certain depth range increased significantly. The change rate analysis showed that the gradient mean change rate of this depth range was much higher than that of other depth positions. The threshold comparison processing compared the change rate with the preset threshold and found that the change rate of this depth range exceeded the threshold. The mutation point marking processing marked these depth positions as gradient mutation points. The continuity analysis found that these mutation points were continuously distributed in depth. The starting depth was determined as the first depth position of the mutation point sequence, and the ending depth was determined as the last depth position of the mutation point sequence, thereby determining the temperature gradient mutation depth range, which accurately reflects the distribution area of ​​the thermocline in the ecological pond.

[0041] In a specific embodiment, the process of executing step S103 may specifically include the following steps: The thermocline position dynamic variables are numerically compared with the thermocline position threshold to obtain the thermocline anomaly judgment result and thermocline trigger mark; The dynamic variable of dissolved oxygen vertical gradient is numerically compared with the dissolved oxygen gradient threshold to obtain the hypoxia stratification judgment result and hypoxia triggering mark; Numerical comparison is performed on the dynamic variables of the nutrient salt stratification state and the nutrient salt stratification threshold value to obtain the nutrient salt abnormality judgment result and the nutrient salt trigger mark; Based on the thermocline triggering sign, hypoxia triggering sign and nutrient triggering sign, logical combination and priority sorting are performed to obtain the layered abnormality triggering signal; According to the trigger type of the layered abnormality trigger signal, the corresponding depth layer position is queried and the depth coordinates are extracted to obtain the target water distribution depth.

[0042] Specifically, when the thermocline position dynamic variable is numerically compared with the thermocline position threshold, the thermocline position threshold is the critical value of the thermocline depth determined according to the normal water stratification state of the ecological pond. The numerical comparison processing reads the numerical value of the thermocline position dynamic variable and judges the size relationship between it and the preset thermocline position threshold. When the thermocline position dynamic variable exceeds the normal depth range, it indicates that the thermocline position is abnormal. The thermocline abnormality judgment result is the logical output of the comparison processing, including three states: normal, shallow, and deep. The thermocline trigger flag is a Boolean mark indicating whether water distribution processing is required for the thermocline abnormality. When the judgment result is abnormal, the trigger flag is set to true.

[0043] In the process of numerically comparing the dynamic variable of the dissolved oxygen vertical gradient with the dissolved oxygen gradient threshold, the dissolved oxygen gradient threshold is the vertical gradient critical value determined based on the normal oxygen distribution characteristics of the water body. This threshold reflects the normal vertical variation range of dissolved oxygen in the ecological pond. The numerical comparison processing reads the numerical value of the dynamic variable of the dissolved oxygen vertical gradient and compares the numerical value with the dissolved oxygen gradient threshold. When the dissolved oxygen vertical gradient exceeds the normal range, it indicates that there is anoxic stratification phenomenon in the water body. The hypoxic stratification judgment result is the output state of the comparison processing, including three stratification states: normal distribution, mild hypoxia, and severe hypoxia. The hypoxia trigger flag is a mark indicating whether aeration water distribution treatment is required. When the judgment result shows hypoxia, the trigger flag is set to true.

[0044] When the dynamic variable of the nutrient stratification state is numerically compared with the nutrient stratification threshold, the nutrient stratification threshold is the critical value of the stratification state determined according to the normal distribution law of nutrients in the water body. The threshold is determined comprehensively through historical data statistics and water quality management standards. The numerical comparison processing reads the value of the dynamic variable of the nutrient stratification state and compares it with the nutrient stratification threshold. When the nutrient stratification state exceeds the normal range, it indicates that the nutrient distribution in the water body is abnormal. The abnormal nutrient judgment results include three states: uniform distribution, mild stratification, and severe stratification. The nutrient trigger flag is a mark indicating whether nutrient adjustment and water distribution treatment is required. When the judgment result is abnormal stratification, the trigger flag is set to true.

[0045] In the process of logical combination and priority sorting based on the thermocline trigger mark, hypoxia trigger mark and nutrient trigger mark, logical combination refers to a processing method of combining the three trigger marks according to preset logical rules. The processing method first reads the status values ​​of the three trigger marks, and then determines the priority weight of each mark according to the order of importance of ecological pond water quality management. The priority sorting process sorts the triggered anomaly types according to severity and urgency. Hypoxia stratification has the highest priority because it directly affects the survival of aquatic organisms. Thermocline anomaly has a medium priority because it affects water mixing. Nutrient stratification has a lower priority because the impact is relatively slow. The stratification anomaly trigger signal is the output result of the logical combination and priority sorting process. The signal contains the anomaly type identifier and the corresponding priority information.

[0046] When querying the corresponding depth layer position and extracting the depth coordinates according to the trigger type of the stratified abnormality trigger signal, the trigger type refers to the specific abnormality type identified in the stratified abnormality trigger signal, including three types: thermocline abnormality, hypoxic stratification abnormality, and nutrient stratification abnormality. The query processing reads the trigger type information and searches for the depth position where the corresponding abnormality type occurs in the depth gradient dataset. The depth coordinate extraction processing extracts the specific depth value from the query result. The target water distribution depth is the water distribution operation depth coordinate determined according to the abnormality type and occurrence location. The depth coordinate points to the specific water layer location where water quality adjustment is required.

[0047] In a specific embodiment, the process of executing step S104 may specifically include the following steps: The trigger type in the layered abnormal trigger signal is processed for equipment matching and water distribution mode selection to obtain the corresponding water distribution operation mode and equipment call list; Based on the target water distribution depth, the variable depth water distributor is depth-located and position-adjusted to obtain accurate depth coordinates and equipment placement confirmation signals; According to the water distribution operation mode, the flow control valve group is opened and the flow distribution is processed to obtain the water distribution flow parameters and valve control instructions for each depth layer; Coordinate the equipment in-place confirmation signal and valve control instruction in time sequence and start synchronously to obtain a multi-equipment collaborative operation schedule; The water distribution duration is calculated and the stop condition is set based on the multi-device collaborative operation schedule to obtain the water distribution execution instruction.

[0048] Specifically, when the trigger type in the layered abnormality trigger signal is processed for equipment matching and water distribution mode selection, equipment matching refers to the process of selecting corresponding water distribution equipment according to different abnormality types. The processing process first reads the trigger type information in the layered abnormality trigger signal, and then determines the corresponding water distribution equipment type according to the preset equipment configuration table. The water distribution mode selection process determines the corresponding water distribution strategy by analyzing the severity and impact range of the abnormality type. The water distribution operation mode includes three working modes: single-point directional water distribution, multi-point collaborative water distribution, and continuous layered water distribution. The equipment call list is a specific equipment usage plan determined according to the water distribution operation mode. The list includes the number of variable depth water distributors that need to be enabled, the flow regulating valve group configuration and the corresponding control parameters.

[0049] During the depth positioning and position adjustment process of the variable depth water distributor based on the target water distribution depth, the variable depth water distributor is a water distribution device that can adjust its vertical position in the water body. The device achieves precise depth control through motor drive and depth sensor feedback. The depth positioning process reads the target water distribution depth value, converts it into a device control instruction and sends it to the variable depth water distributor. The position adjustment process controls the water distributor to move to the specified depth position in the water body, and confirms whether the device has reached the target position by real-time monitoring of the device depth sensor feedback. The precise depth coordinate is the actual depth value after the water distributor reaches the target position. The equipment in place confirmation signal is a status mark indicating that the water distributor has reached the specified position and is ready for water distribution operations.

[0050] When the flow regulating valve group is calculated for opening and flow distribution according to the water distribution operation mode, the flow regulating valve group is a flow control system composed of multiple adjustable flow valves. The system can independently control the water distribution flow at different depth layers. The opening calculation process calculates the opening degree of each valve according to the flow demand of the water distribution operation mode. The calculation process takes into account the severity of the abnormality type, the water quality of the target depth layer and the water distribution effect requirements. The flow distribution process distributes the total water distribution flow to valves at different depth layers according to a preset ratio. The water distribution flow parameters of each depth layer are the specific flow values ​​corresponding to each depth position. The valve control instructions include the opening angle and flow setting value of each valve.

[0051] In the process of timing coordination and synchronous start-up of the equipment in place confirmation signal and valve control instruction, timing coordination refers to a processing method of arranging different equipment to perform operations in a reasonable time sequence. This processing method first confirms that all variable-depth water distributors have issued equipment in place confirmation signals, and then sends valve control instructions according to the preset start-up sequence. The synchronous start-up process ensures that the water distribution equipment at each depth layer starts operation at the same time, avoiding uneven water distribution effects due to differences in start-up time. The multi-device collaborative operation schedule is a detailed time schedule that includes the start time, operation duration and stop time of each equipment. This schedule coordinates the working rhythm of each equipment.

[0052] When calculating the water distribution duration and setting the stop conditions based on the multi-device collaborative operation schedule, the water distribution duration calculation determines the duration of the water distribution operation based on the severity of the abnormality type, the water distribution flow rate and the expected improvement effect. The calculation process takes into account factors such as the change rate of water quality parameters and the water distribution efficiency. The stop condition setting process establishes the termination judgment criteria for the water distribution operation, including stop conditions such as time reaching the preset value, water quality parameters reaching the target range, and equipment failure. The water distribution execution instruction is a comprehensive control instruction set that integrates the water distribution depth, flow, duration and stop conditions. This instruction set guides the execution process of the entire water distribution operation.

[0053] In a specific embodiment, the process of executing step S105 may specifically include the following steps: Based on the water distribution execution instruction, directional water distribution operation and real-time water quality monitoring and processing are carried out on the water body at the target water distribution depth to obtain water quality change data during the water distribution process; The difference between the water quality change data during the water distribution process and the water quality baseline value before water distribution is calculated and the degree of improvement is quantified to obtain quantitative improvement indicators of temperature, dissolved oxygen, pH value and turbidity; Calculate the water distribution efficiency and conduct parameter correlation analysis on the improvement quantitative indicators to obtain the correlation data of water distribution depth, flow rate, duration and water quality improvement degree; The associated data is input into the machine learning model for parameter optimization and strategy update processing to obtain water quality improvement effect data; Based on the water quality improvement effect data, data is written into the water distribution parameter optimization database and model parameters are updated to obtain an updated water distribution parameter optimization database.

[0054] Specifically, when directional water distribution operation and real-time water quality monitoring and processing are performed on the water body at the target water distribution depth based on the water distribution execution instruction, the directional water distribution operation refers to the process of injecting treated water into a specific depth layer according to the depth, flow and duration parameters in the water distribution execution instruction. This operation releases the treated water at the target depth position through a variable depth water distributor, and at the same time, the flow regulating valve group controls the injection rate according to the preset flow parameters. The real-time water quality monitoring and processing continuously collects the changes in water quality parameters of each depth layer during the water distribution process through vertical layered sensors. The monitoring data includes real-time values ​​of four dimensions: temperature, dissolved oxygen, pH value and turbidity. The water quality change data during the water distribution process is the time series data of water quality parameters continuously collected during the water distribution operation. The data reflects the dynamic impact of the water distribution operation on the water quality.

[0055] In the process of performing difference calculation and improvement degree quantification between the water quality change data during the water distribution process and the water quality benchmark value before water distribution, the water quality benchmark value before water distribution refers to the water quality parameter value of each depth layer collected before the start of the water distribution operation, which is used as a comparison benchmark for evaluating the water distribution effect. The difference calculation process reads the value of each parameter in the water quality change data during the water distribution process, and performs subtraction operation with the corresponding water quality benchmark value before water distribution to obtain the change value of each water quality parameter. The improvement degree quantification process converts the change value of water quality parameters into a standardized improvement degree index. This process takes into account the normal range and improvement direction of each parameter. The improvement quantitative indexes of temperature, dissolved oxygen, pH value and turbidity are standardized values ​​indicating the improvement degree of each water quality parameter. These indicators are uniformly expressed in the form of improvement percentage.

[0056] When calculating the water distribution efficiency and performing parameter correlation analysis on the improvement quantitative indicators, the water distribution efficiency calculation refers to the calculation process of evaluating the contribution of unit water distribution volume to water quality improvement. This calculation process divides the improvement quantitative indicator by the product of the water distribution flow and duration to obtain the efficiency value. The parameter correlation analysis uses statistical methods to analyze the quantitative relationship between water distribution depth, flow, duration and the degree of water quality improvement. This analysis process determines the correlation strength by calculating the correlation coefficient between each parameter and the improvement effect. The correlation data of water distribution depth, flow, duration and water quality improvement degree is a data set containing the correlation between each water distribution parameter and the improvement effect. This data set reveals the influence of different water distribution strategies on water quality improvement.

[0057] In the process of inputting the associated data into the machine learning model for parameter optimization and strategy update processing, the machine learning model is an artificial intelligence algorithm trained based on historical water distribution data. The model can identify the complex nonlinear relationship between water distribution parameters and improvement effects. The parameter optimization process adjusts the weight coefficients and threshold parameters within the model by analyzing the parameter-effect relationship in the associated data. The strategy update process corrects the selection logic and parameter setting rules of the water distribution strategy based on the new associated data. The water quality improvement effect data is the comprehensive evaluation result output by the machine learning model. The data includes the effect score and improvement suggestions of this water distribution operation.

[0058] When writing data and updating model parameters to the water distribution parameter optimization database based on water quality improvement effect data, the water distribution parameter optimization database is a data warehouse that stores historical water distribution operation parameters and effect data. The database is classified and stored according to water distribution conditions, parameter configurations and improvement effects. The data writing process stores the water quality improvement effect data in the corresponding table of the database in a predetermined format, and records the time, conditions and parameter information of the water distribution operation. The model parameter update process adjusts the parameter configuration of the machine learning model according to the newly added effect data, including key parameters such as learning rate, weight coefficient and decision threshold. The updated water distribution parameter optimization database contains new water distribution experience and optimization strategies. The database supports strategy selection and parameter configuration for subsequent water distribution operations.

[0059] For example, when an anoxic stratified water distribution operation is carried out in an ecological pond, the water distribution execution instruction is set to inject oxygen-rich water at the bottom layer, and the directional water distribution operation injects the treated water into the anoxic layer through the variable depth water distributor. The real-time monitoring and processing of water quality show that the dissolved oxygen concentration in the bottom layer gradually increases during the water distribution process, and the temperature and pH value also change accordingly. The water quality change data of the water distribution process records the time change curve of each parameter. The difference calculation process compares the dissolved oxygen concentration after water distribution with the baseline value before water distribution, and it is found that the dissolved oxygen concentration is significantly improved. The quantitative processing of the improvement degree converts the increase in dissolved oxygen concentration into an improvement percentage indicator. The water distribution efficiency calculation finds that the oxygen-rich water body per unit flow can significantly improve the bottom oxygen condition. The parameter correlation analysis process The analysis revealed a positive correlation between water distribution flow and the degree of dissolved oxygen improvement. The correlation data showed that a larger water distribution flow corresponds to a more significant improvement effect. The machine learning model adjusted the parameter setting strategy of anoxic stratified water distribution based on these correlation data. The parameter optimization processing updated the coefficients in the flow calculation formula. The strategy update processing corrected the judgment standard of the anoxic stratified water distribution duration. The water quality improvement effect data comprehensively evaluated the success of this water distribution operation. The data writing processing stored the parameter configuration and improvement effect of this water distribution in the database. The model parameter update processing adjusted the decision weight of the machine learning model according to the new data. The updated water distribution parameter optimization database includes new experience in anoxic stratified water distribution.

[0060] The above describes the ecological pond water distribution method based on the Internet of Things in the embodiment of the present application. The following describes the ecological pond water distribution control system based on the Internet of Things in the embodiment of the present application. Figure 2 In the embodiment of the present application, an embodiment of the ecological pond water distribution control system based on the Internet of Things includes: The acquisition module is used to collect and process water quality parameters at different depths of the ecological pond through vertical layered sensors to obtain a depth gradient data set including temperature, dissolved oxygen, pH value and turbidity; a quantification module for performing quantitative analysis and processing on the water body stratification state by using a thermocline identification algorithm according to the depth gradient data set, to obtain dynamic variables of the thermocline position, dynamic variables of the vertical gradient of dissolved oxygen, and dynamic variables of the nutrient stratification state; a judgment module, configured to compare and perform judgment on the dynamic variable of the thermocline position, the dynamic variable of the dissolved oxygen vertical gradient, and the dynamic variable of the nutrient stratification state with corresponding preset thresholds, to obtain a stratification abnormality trigger signal and a corresponding target water distribution depth; a scheduling module, configured to perform coordinated scheduling processing on the multi-depth water distribution equipment according to the layered abnormality trigger signal, and obtain a water distribution execution instruction including the water distribution depth, flow rate and duration; The water distribution module is used to perform directional water distribution processing on the water body at the target water distribution depth through the water distribution execution instruction, obtain water quality improvement effect data and update the water distribution parameter optimization database.

[0061] above Figure 2 The ecological pond water distribution control system based on the Internet of Things in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The ecological pond water distribution equipment based on the Internet of Things in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0062] Reference Figure 3 In the embodiment of the present invention, an ecological pond water distribution device based on the Internet of Things is also provided. The ecological pond water distribution device based on the Internet of Things can be a server, and its internal structure can be as follows Figure 3As shown. The ecological pond water distribution equipment based on the Internet of Things includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. Among them, the computer-designed processor is used to provide computing and control capabilities. The memory of the ecological pond water distribution equipment based on the Internet of Things includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the ecological pond water distribution equipment based on the Internet of Things is used to store the corresponding data in this embodiment. The network interface of the ecological pond water distribution equipment based on the Internet of Things is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0063] Those skilled in the art will understand that Figure 3 The structure shown in is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the ecological pond water distribution equipment based on the Internet of Things to which the solution of the present invention is applied.

[0064] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions, and when the instructions are run on a computer, the computer executes the steps of the ecological pond water distribution method based on the Internet of Things.

[0065] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0066] 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 the present invention, or the portion 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 for causing an IoT-based ecological pond water distribution device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0067] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. An ecological pond water distribution method based on the Internet of Things, characterized in that: The method comprises: Water quality parameters at different depths in the ecological pond were collected and processed using vertical layered sensors to obtain a depth gradient data set including temperature, dissolved oxygen, pH value, and turbidity. Quantitatively analyzing and processing the water body stratification state using a thermocline identification algorithm based on the depth gradient data set to obtain dynamic variables of the thermocline position, the dissolved oxygen vertical gradient, and the nutrient stratification state; Compare and judge the dynamic variable of the thermocline position, the dynamic variable of the dissolved oxygen vertical gradient, and the dynamic variable of the nutrient stratification state with corresponding preset thresholds, respectively, to obtain a stratification abnormality trigger signal and a corresponding target water distribution depth; Coordinated scheduling of multi-depth water distribution equipment is performed according to the layered abnormality trigger signal to obtain a water distribution execution instruction including water distribution depth, flow rate and duration; Directional water distribution processing is performed on the water body at the target water distribution depth through the water distribution execution instruction to obtain water quality improvement effect data and update the water distribution parameter optimization database.

2. The ecological pond water distribution method based on the Internet of Things according to claim 1, characterized in that: The vertical layered sensors are used to collect and process water quality parameters at different depths of the ecological pond to obtain a depth gradient data set containing temperature, dissolved oxygen, pH value and turbidity, including: Vertically layered sensors were deployed in the ecological pond water body at a depth of 0.5 meters, and sensor nodes including temperature sensors, dissolved oxygen sensors, pH sensors, and turbidity sensors were obtained; The raw data collected by the sensor node is time-calibrated through a clock synchronization protocol to obtain synchronized water quality data; Performing depth identification and data association processing based on the synchronized water quality data to obtain a water quality parameter matrix arranged in layers according to depth; Performing data quality inspection and outlier elimination processing on the water quality parameter matrix to obtain valid water quality data; The effective water quality data are classified and integrated according to four dimensions: temperature, dissolved oxygen, pH value and turbidity to obtain the depth gradient data set.

3. The ecological pond water distribution method based on the Internet of Things according to claim 1, characterized in that: The method of quantitatively analyzing and processing the water body stratification state by using a thermocline identification algorithm according to the depth gradient data set to obtain dynamic variables of thermocline position, dynamic variables of dissolved oxygen vertical gradient, and dynamic variables of nutrient stratification state includes: Arranging the temperature data in the depth gradient data set according to a depth sequence from shallow to deep to obtain a temperature depth distribution sequence; The temperature depth distribution sequence is subjected to layer-by-layer temperature difference calculation and gradient change rate analysis to obtain temperature gradient distribution data between layers at each depth; Based on the temperature gradient distribution data, a comparison of the mean values ​​of the temperature gradients of three consecutive layers and a gradient mutation point identification process are performed to obtain a temperature gradient mutation depth range; Accurately locate the maximum gradient point within the temperature gradient mutation depth range and perform depth interpolation calculation processing to obtain the dynamic variable of the thermocline position; The dissolved oxygen data in the depth gradient data set are respectively subjected to inter-layer difference accumulation calculation processing and the pH value turbidity data are subjected to distribution variance statistics processing to obtain the dissolved oxygen vertical gradient dynamic variable and the nutrient salt stratification state dynamic variable.

4. The ecological pond water distribution method based on the Internet of Things according to claim 3, characterized in that: The temperature gradient distribution data is based on the temperature gradient mean comparison of three layers and the identification of gradient mutation points to obtain the temperature gradient mutation depth range, including: The temperature gradient distribution data is grouped into three consecutive layers in order of depth to obtain three consecutive layers of temperature gradient combination data; Performing arithmetic mean calculation on the three gradient values ​​of each group in the three-layer continuous temperature gradient combination data to obtain the three-layer gradient mean at each depth position; The gradient mean values ​​at adjacent depths are subjected to difference calculation and change rate analysis to obtain a gradient mean change sequence. Performing threshold comparison and mutation point marking processing on the gradient mean change sequence to obtain a gradient mutation flag and a corresponding depth coordinate; The starting depth and the ending depth are determined based on the continuity analysis of the gradient mutation flag to obtain the temperature gradient mutation depth range.

5. The ecological pond water distribution method based on the Internet of Things according to claim 1, characterized in that: The step of comparing the thermocline position dynamic variable, the dissolved oxygen vertical gradient dynamic variable, and the nutrient stratification state dynamic variable with corresponding preset thresholds to obtain a stratification abnormality trigger signal and a corresponding target water distribution depth includes: Numerical comparison processing is performed on the thermocline position dynamic variable and the thermocline position threshold to obtain a thermocline anomaly judgment result and a thermocline trigger mark; Numerical comparison processing is performed on the dynamic variable of the dissolved oxygen vertical gradient and the dissolved oxygen gradient threshold to obtain a hypoxia stratification judgment result and a hypoxia triggering mark; Comparing the dynamic variable of the nutrient salt stratification state with the nutrient salt stratification threshold value to obtain a nutrient salt abnormality judgment result and a nutrient salt trigger flag; Performing logical combination and priority sorting based on the thermocline trigger flag, the hypoxia trigger flag, and the nutrient trigger flag to obtain the stratification abnormality trigger signal; According to the trigger type of the layered abnormality trigger signal, the corresponding depth layer position is queried and the depth coordinate extraction process is performed to obtain the target water distribution depth.

6. The ecological pond water distribution method based on the Internet of Things according to claim 1, characterized in that: The collaborative scheduling process of the multi-depth water distribution equipment according to the layered abnormality trigger signal to obtain a water distribution execution instruction including the water distribution depth, flow rate and duration includes: Performing equipment matching and water distribution mode selection processing on the trigger type in the layered abnormality trigger signal to obtain a corresponding water distribution operation mode and equipment call list; Performing depth positioning and position adjustment processing on the variable depth water distributor based on the target water distribution depth to obtain accurate depth coordinates and a device placement confirmation signal; Calculate the opening of the flow regulating valve group and perform flow distribution processing according to the water distribution operation mode to obtain the water distribution flow parameters and valve control instructions for each depth layer; Performing time sequence coordination and synchronous start processing on the equipment in-place confirmation signal and the valve control instruction to obtain a multi-equipment collaborative operation schedule; The water distribution duration calculation and stop condition setting processing are performed based on the multi-device collaborative operation schedule to obtain the water distribution execution instruction.

7. The method for distributing water in ecological ponds based on the Internet of Things according to claim 1, characterized in that: The step of performing directional water distribution processing on the water body at the target water distribution depth by using the water distribution execution instruction, obtaining water quality improvement effect data and updating the water distribution parameter optimization database includes: Based on the water distribution execution instruction, a directional water distribution operation and real-time water quality monitoring processing are performed on the water body at the target water distribution depth to obtain water quality change data during the water distribution process; The water quality change data during the water distribution process is calculated and the improvement degree is quantified by comparing the water quality baseline value before the water distribution to obtain quantitative improvement indicators of temperature, dissolved oxygen, pH value and turbidity; Calculating the water distribution efficiency and performing parameter correlation analysis on the improvement quantitative indicators to obtain correlation data on the water distribution depth, flow rate, duration and degree of water quality improvement; Inputting the associated data into a machine learning model for parameter optimization and strategy update processing to obtain the water quality improvement effect data; Based on the water quality improvement effect data, data is written into the water distribution parameter optimization database and model parameter update processing is performed to obtain the updated water distribution parameter optimization database.

8. An ecological pond water distribution control system based on the Internet of Things, characterized in that: For realizing the ecological pond water distribution method based on the Internet of Things according to any one of claims 1 to 7, the ecological pond water distribution control system based on the Internet of Things comprises: The acquisition module is used to collect and process water quality parameters at different depths of the ecological pond through vertical layered sensors to obtain a depth gradient data set including temperature, dissolved oxygen, pH value and turbidity; a quantification module for performing quantitative analysis and processing on the water body stratification state by using a thermocline identification algorithm according to the depth gradient data set, to obtain dynamic variables of the thermocline position, dynamic variables of the vertical gradient of dissolved oxygen, and dynamic variables of the nutrient stratification state; a judgment module, configured to compare and perform judgment on the dynamic variable of the thermocline position, the dynamic variable of the dissolved oxygen vertical gradient, and the dynamic variable of the nutrient stratification state with corresponding preset thresholds, to obtain a stratification abnormality trigger signal and a corresponding target water distribution depth; a scheduling module, configured to perform coordinated scheduling processing on the multi-depth water distribution equipment according to the layered abnormality trigger signal, and obtain a water distribution execution instruction including the water distribution depth, flow rate and duration; The water distribution module is used to perform directional water distribution processing on the water body at the target water distribution depth through the water distribution execution instruction, obtain water quality improvement effect data and update the water distribution parameter optimization database.

9. An ecological pond water distribution device based on the Internet of Things, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the ecological pond water distribution method based on the Internet of Things described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the processor executes the ecological pond water distribution method based on the Internet of Things as claimed in any one of claims 1 to 7.

Images