Intelligent regulation method for ecological flow of reservoir

Abstract

This invention provides an intelligent method for regulating ecological flow in reservoirs. The method includes acquiring upstream water level, river flow, ecological discharge flow, and optical imaging data from a multi-spectral water imaging sensor via a multi-source data acquisition module to generate ecological status assessment parameters. A control module calculates flow regulation commands based on these parameters and reservoir scheduling instructions using an adaptive optimization algorithm. Upon receiving the commands, an execution module drives a gate opening controller to regulate the ecological flow, with the regulation accuracy meeting a preset threshold. The optical imaging data is used to monitor the ecological characteristics of the water body, and the multi-spectral data includes reflectance in the visible, near-infrared, and ultraviolet bands. The method also involves turbidity compensation factor correction, dual-mode communication for transmitting control signals, anomaly alarms, and backup power switching. It optimizes the ecological flow threshold through a multi-source data fusion algorithm and rolling time-domain control, and finally uses PID control to achieve precise gate opening regulation. This invention can improve water resource utilization efficiency.

Description

Technical Field

[0001] This invention relates to the field of water conservancy engineering and ecological protection technology, and more specifically, to a method for intelligent regulation of ecological flow in reservoirs. Background Technology

[0002] In the field of reservoir management and ecological flow regulation, with the increasing scarcity of water resources and the growing awareness of ecological environmental protection, ensuring the health and stability of river ecosystems while meeting the basic functions of reservoirs such as flood control, power generation, and irrigation has become an urgent problem to be solved. Traditional methods of reservoir ecological flow regulation mainly rely on empirical formulas or simple hydrological models. While these methods can achieve preliminary regulation of ecological flow to a certain extent, they have many limitations. For example, they often cannot reflect the complex needs of river ecosystems in real time and accurately, and are difficult to adapt to dynamic changes in hydrological conditions caused by factors such as climate change and human activities. In addition, traditional methods are usually limited to basic information such as water level and flow in data collection, lacking comprehensive monitoring of the ecological characteristics of water bodies, making it difficult to effectively evaluate the actual effect of ecological flow regulation. At the execution level, the precision of traditional gate control systems is limited, making it difficult to meet the requirements of refined ecological flow regulation.

[0003] In implementing the embodiments of the present invention, the prior art has at least the following problems or defects: First, the data collection and monitoring methods are limited and cannot comprehensively and accurately reflect the ecological characteristics of the water body, resulting in a lack of scientific basis for ecological flow regulation; second, the ecological flow regulation strategy lacks adaptability and is difficult to dynamically adjust according to real-time ecological needs and hydrological conditions; third, the gate control system lacks precision and cannot achieve precise regulation of ecological flow; and fourth, there is a lack of an effective early warning mechanism for potential risks in the process of ecological flow regulation, such as timely response to ecological problems such as abnormal algal proliferation. Summary of the Invention

[0004] This invention provides a method for intelligent regulation of ecological flow in reservoirs, comprising: In response to acquiring upstream water level data, river flow data, ecological discharge flow data and optical imaging data through a multi-source data acquisition module, ecological status assessment parameters are generated. The optical imaging data is acquired by a multispectral water body imaging sensor and is used to monitor the ecological characteristics of the water body. The control module generates a flow regulation instruction based on the ecological state assessment parameters and the reservoir scheduling instructions, wherein the flow regulation instruction is calculated through an adaptive optimization algorithm; In response to the execution module receiving the flow regulation command, the gate opening controller is driven to regulate the ecological flow, wherein the regulation accuracy of the gate opening controller meets a preset threshold.

[0005] Furthermore, before generating ecological status assessment parameters in response to acquiring upstream water level data, river flow data, ecological discharge flow data, and optical imaging data through the multi-source data acquisition module, the method further includes: The optical imaging data is processed by an optical compensation algorithm to generate a turbidity compensation factor, wherein the turbidity compensation factor is calculated using the following formula:

[0006] in: Turbidity compensation factor; This indicates the measured value of turbidity in the water. Indicates the optical attenuation coefficient; The turbidity compensation factor is used to correct the ecological state assessment parameters.

[0007] Furthermore, after the control module generates a flow regulation command based on the ecological state assessment parameters and the reservoir scheduling command, the method further includes: Based on the flow regulation command and real-time flow data, a gate control signal is generated; The gate control signal is transmitted to the execution module via a dual-mode communication terminal, wherein the dual-mode communication terminal supports both satellite communication and mobile communication.

[0008] Furthermore, before the execution module receives the flow regulation command and drives the gate opening controller to regulate the ecological flow, the method further includes: In response to the detection that the ecological flow deviation exceeds the first threshold, the audible and visual alarm device is activated. In response to the detection that the algal biomass index exceeds the second threshold, the redundant gate system is activated.

[0009] Furthermore, the method also includes: In response to a power supply anomaly detected by the photovoltaic energy storage system, the system switches to a backup power mode, wherein the photovoltaic energy storage system includes a photovoltaic conversion unit and an energy storage unit; The backup power mode satisfies the power supply constraints:

[0010] in: This refers to the output power. Photovoltaic output power; For load power; This is the conversion efficiency coefficient.

[0011] Furthermore, the generated ecological state assessment parameters include: The upstream water level data, river flow data, and optical imaging data are processed using a multi-source data fusion algorithm, wherein the multi-source data fusion algorithm is based on an improved D-S evidence theory. The multi-source data fusion algorithm satisfies the optimization objective:

[0012] in: This is the measured value of the ecological discharge flow. For dynamic ecological flow thresholds; To control energy consumption costs; This is the weighting coefficient for the difference between the measured value of ecological discharge flow and the dynamic ecological flow threshold. The weighting coefficient is used to regulate energy consumption costs.

[0013] Furthermore, the method also includes: Periodically updated via rolling time-domain control algorithm The update cycle is a fixed time interval; The rolling time-domain control algorithm satisfies the following constraints:

[0014] in: This represents the upper limit of the ecological flow threshold. Design traffic; This is the proportionality coefficient between the measured value of ecological discharge flow and the upper limit of the ecological flow threshold. This is the proportional coefficient relating the rate of change of ecological discharge flow to the design flow.

[0015] Furthermore, the drive gate opening controller includes: Calculate the gate opening adjustment amount:

[0016] in: This is the adjustment amount for the gate opening; For target traffic; This is a proportional control parameter; These are integral control parameters; This is the measured value of the ecological discharge flow. Drive actuator according to Adjust the gate opening.

[0017] Furthermore, the optical imaging data includes: Reflectance data in the visible light band; Near-infrared reflectance data; Ultraviolet reflectance data.

[0018] Furthermore, the method also includes: In response to the detection of abnormal algal proliferation characteristics by the multispectral water imaging sensor, an algal biomass index is generated. ; Calculate the algal biomass index:

[0019] in: Algal biomass index; For band Reflectivity; Turbidity compensation factor; The number of bands involved in the calculation; The dynamic ecological flow threshold is adjusted based on the algal biomass index.

[0020] The embodiments of the present invention have at least the following beneficial effects: 1. By integrating water level, flow rate and multispectral optical imaging data through a multi-source data acquisition module, and combining it with a turbidity compensation algorithm to dynamically correct ecological assessment parameters, the problem of single monitoring methods and inaccurate ecological status assessment in traditional methods is solved, thereby improving the accuracy of water body ecological feature identification and environmental adaptability. 2. By adopting an adaptive optimization algorithm combined with a rolling time-domain control strategy, the ecological flow threshold is dynamically adjusted and the gate opening control is optimized. This overcomes the problem that the fixed release mode is difficult to adapt to hydrological changes, and realizes the coordinated optimization of ecological water demand and reservoir scheduling. This improves the efficiency of water resource regulation while ensuring the ecological health of the downstream area. 3. Through multiple protection mechanisms such as dual-mode communication, redundant gate system and photovoltaic energy storage backup power, the risks of unstable equipment communication, sudden abnormal working conditions and power supply interruption in remote areas are solved, the reliability and anti-interference ability of the system are enhanced, and the continuous and stable operation of ecological flow regulation is ensured. Attached Figure Description

[0021] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings. Several embodiments of the invention are illustrated in the drawings by way of example and not limitation, wherein: Figure 1 This is a flowchart illustrating a method for intelligent regulation of reservoir ecological flow according to an embodiment of the present invention. Detailed Implementation

[0022] The principles and spirit of the invention will now be described with reference to several exemplary embodiments. It should be understood that these embodiments are provided merely to enable those skilled in the art to better understand and implement the invention, and are not intended to limit the scope of the invention in any way. Rather, these embodiments are provided to make the invention more thorough and complete, and to fully convey the scope of the invention to those skilled in the art.

[0023] Those skilled in the art will recognize that embodiments of the present invention can be implemented as a system, apparatus, device, method, or computer program product. Therefore, the present invention can be specifically implemented in the following forms: entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0024] It should be noted that the number of any elements in the accompanying drawings is for illustrative purposes only and not as a limitation, and any naming is for distinction only and has no limiting meaning.

[0025] The following is for reference. Figure 1 , Figure 1 This is a schematic flowchart of a smart regulation method for reservoir ecological flow provided in an embodiment of the present invention. Figure 1 As shown, a method for intelligent regulation of reservoir ecological flow includes: S1. In response to acquiring upstream water level data, river flow data, ecological discharge flow data and optical imaging data through a multi-source data acquisition module, ecological status assessment parameters are generated, wherein the optical imaging data is acquired by a multispectral water body imaging sensor and is used to monitor the ecological characteristics of the water body. S2. The control module generates a flow regulation instruction based on the ecological state assessment parameters and the reservoir scheduling instruction, wherein the flow regulation instruction is calculated by an adaptive optimization algorithm; S3. In response to the execution module receiving the flow adjustment command, drive the gate opening controller to adjust the ecological flow, wherein the adjustment accuracy of the gate opening controller meets a preset threshold.

[0026] It should be noted that this invention proposes an intelligent regulation method for reservoir ecological flow. This method acquires upstream water level data, river flow data, ecological discharge flow data, and optical imaging data through a multi-source data acquisition module to generate ecological status assessment parameters. Specifically, the optical imaging data is acquired using a multispectral water imaging sensor to monitor the ecological characteristics of the water body. The multi-source data acquisition module is a device integrating multiple sensors, capable of acquiring various data related to reservoir ecological flow in real time. The acquisition of optical imaging data is accomplished through a multispectral water imaging sensor, which can capture reflectance data in different wavelength bands, thus providing rich information for monitoring the ecological characteristics of the water body. Through comprehensive analysis of these data, the ecological status of the reservoir can be more comprehensively assessed, providing a scientific basis for the intelligent regulation of ecological flow.

[0027] Specifically, the upstream water level data in the multi-source data acquisition module refers to the water level height upstream of the reservoir, which is obtained in real time through water level sensors; the river flow data refers to the flow rate of water in the river channel, which is obtained through flow meters; and the ecological release flow data refers to the flow rate released from the reservoir to maintain ecological balance, which is obtained through flow sensors. Optical imaging data includes visible light band reflectance data, near-infrared band reflectance data, and ultraviolet band reflectance data, which reflect ecological characteristics such as algae and suspended solids in the water body. A multispectral water imaging sensor is a device capable of simultaneously measuring reflectance in multiple bands. By analyzing this reflectance data, the types and quantities of algae in the water body, as well as the concentration of suspended solids, can be identified. Ecological status assessment parameters are obtained through comprehensive analysis of these data, reflecting the current ecological status of the reservoir and providing a basis for the generation of subsequent flow regulation commands.

[0028] Preferably, the process of generating ecological state assessment parameters can employ a multi-source data fusion algorithm based on an improved DS evidence theory. DS evidence theory is a mathematical theory for handling uncertain information; by fusing data from different sources, the accuracy of ecological state assessment can be improved. In this invention, the optimization objective of the multi-source data fusion algorithm is to minimize the difference between the measured ecological discharge flow and the dynamic ecological flow threshold, as well as the weighted sum of regulation energy costs. The measured ecological discharge flow refers to the flow data actually measured by sensors; the dynamic ecological flow threshold is the flow target value dynamically adjusted according to ecological needs; and the regulation energy cost refers to the energy cost consumed in regulating the ecological flow. By optimizing the weighting coefficients of these parameters, a balance between the economic efficiency and ecological benefits of ecological flow regulation can be achieved.

[0029] In some embodiments, before generating ecological status assessment parameters in response to acquiring upstream water level data, river flow data, ecological discharge flow data, and optical imaging data through a multi-source data acquisition module, the method further includes: The optical imaging data is processed by an optical compensation algorithm to generate a turbidity compensation factor, wherein the turbidity compensation factor is calculated using the following formula:

[0030] in: Turbidity compensation factor; This indicates the measured value of turbidity in the water. Indicates the optical attenuation coefficient; The turbidity compensation factor is used to correct the ecological state assessment parameters.

[0031] It should be noted that this invention employs an optical compensation algorithm when processing optical imaging data. The purpose of this algorithm is to generate a turbidity compensation factor to correct ecological state assessment parameters. The turbidity compensation factor is calculated based on the water's turbidity measurement and the optical attenuation coefficient. Here, the turbidity measurement refers to the numerical value of the water's turbidity obtained through a turbidity sensor, while the optical attenuation coefficient is a parameter describing the energy loss of light as it propagates through water. This compensation algorithm effectively reduces the impact of water turbidity on optical imaging data, thereby improving the accuracy of ecological state assessment.

[0032] Specifically, the core of the optical compensation algorithm is to calculate the turbidity compensation factor based on the water's turbidity measurement and optical attenuation coefficient. The turbidity measurement is acquired in real-time by turbidity sensors installed in the reservoir, reflecting the concentration of suspended particles in the water. The optical attenuation coefficient is a parameter related to water characteristics, which can be determined experimentally or derived from empirical formulas. The calculation of the turbidity compensation factor involves substituting the turbidity measurement and the optical attenuation coefficient into a specific formula, which is an exponential decay model. This model quantifies the impact of water turbidity on optical imaging data. The calculated turbidity compensation factor is used to correct ecological status assessment parameters, ensuring that the assessment results more accurately reflect the ecological condition of the water body. This correction process is crucial for improving the scientific rigor and accuracy of ecological flow regulation.

[0033] Preferably, to further refine the implementation process of the optical compensation algorithm, the following steps can be taken. First, a high-precision turbidity sensor is installed in the reservoir to monitor the turbidity changes of the water body in real time and transmit the data to the data processing unit. Second, based on the characteristics of the water body, the specific value of the optical attenuation coefficient is determined through experiments or empirical formulas. In practical applications, the optical attenuation coefficient can be obtained by analyzing the light attenuation characteristics under different water quality conditions. Then, the real-time monitored turbidity measurement value and the predetermined optical attenuation coefficient are substituted into the calculation formula of the turbidity compensation factor to obtain the turbidity compensation factor. Finally, this compensation factor is used to correct the optical imaging data, and the corrected data will be used to generate ecological state assessment parameters. In addition, to ensure the accuracy and reliability of the compensation factor, the optical attenuation coefficient can be calibrated periodically to adapt to changes in water body characteristics. This dynamic compensation mechanism can effectively improve the adaptability and reliability of the ecological flow control system, ensuring its accurate operation under different water body conditions.

[0034] In some embodiments, after the control module generates a flow regulation command based on the ecological state assessment parameters and the reservoir scheduling command, the method further includes: Based on the flow regulation command and real-time flow data, a gate control signal is generated; The gate control signal is transmitted to the execution module via a dual-mode communication terminal, wherein the dual-mode communication terminal supports both satellite communication and mobile communication.

[0035] It should be noted that after generating the flow regulation command, this invention generates a gate control signal based on the command and real-time flow data, and transmits the signal to the execution module via a dual-mode communication terminal. This dual-mode communication terminal is a device that supports both satellite and mobile communication, ensuring stable signal transmission in different communication environments. Satellite communication is suitable for remote areas or areas with insufficient signal coverage, while mobile communication is suitable for regular areas with good signal. This combination of dual-mode communication ensures that the gate control signal reaches the execution module in a timely and accurate manner, thereby achieving precise regulation of ecological flow.

[0036] Specifically, the flow regulation command is calculated by the control module based on ecological status assessment parameters and reservoir scheduling instructions. It is a command used to guide the adjustment of the gate opening. Real-time flow data refers to the river flow data monitored in real time during the regulation process. This data is acquired through flow sensors and compared with the flow regulation command to generate a more accurate gate control signal. The gate control signal is a signal used to drive the gate opening controller, containing specific adjustment instructions for the gate opening. The dual-mode communication terminal is a device that integrates a satellite communication module and a mobile communication module. It can automatically switch communication modes according to the communication environment. The satellite communication module transmits data via satellite signals, suitable for areas without terrestrial communication network coverage; the mobile communication module transmits data via mobile networks, suitable for areas with network coverage. In this way, the reliability and stability of signal transmission are ensured, enabling precise control of the gate regardless of the environment.

[0037] Preferably, the process of generating the gate control signal can be further refined. First, the control module calculates the adjustment amount of the gate opening based on the flow regulation command and real-time flow data. This adjustment amount is calculated based on the difference between the current flow and the target flow, ensuring that the gate opening can be accurately adjusted to the target flow. Then, the control module converts the adjustment amount into a specific gate control signal, which contains the specific command for the gate opening. Next, the dual-mode communication terminal selects the most suitable communication method based on the current communication environment. If it is in an area with good mobile network coverage, it will select the mobile communication module for signal transmission; if it is in a remote area or an area with weak mobile network signal, it will automatically switch to the satellite communication module. During signal transmission, the dual-mode communication terminal monitors the signal quality in real time and switches when necessary to ensure the continuity and stability of signal transmission. In addition, the dual-mode communication terminal can also be prioritized, for example, prioritizing the use of the mobile communication module and only switching to the satellite communication module when mobile communication is unavailable, to save communication costs and improve communication efficiency.

[0038] In some embodiments, before the execution module receives the flow regulation instruction and drives the gate opening controller to regulate the ecological flow, the method further includes: In response to the detection that the ecological flow deviation exceeds the first threshold, the audible and visual alarm device is activated. In response to the detection that the algal biomass index exceeds the second threshold, the redundant gate system is activated.

[0039] It should be noted that before performing ecological flow regulation, this invention detects whether the ecological flow deviation and algal biomass index exceed set thresholds. If the ecological flow deviation exceeds the first threshold, an audible and visual alarm device is activated to alert the operator; if the algal biomass index exceeds the second threshold, a redundant gate system is activated. Here, ecological flow deviation refers to the difference between the actual ecological flow and the target ecological flow, while the algal biomass index is calculated from optical imaging data and is used to assess algal growth. The audible and visual alarm device is a device that emits sound and light signals to alert the operator to potential system problems. The redundant gate system refers to the ability to automatically switch to a backup gate system when the main gate system malfunctions, ensuring continuous and stable regulation of the ecological flow.

[0040] Specifically, ecological flow deviation refers to the difference between the actual measured ecological discharge flow and the target flow. The first threshold is a pre-set value used to determine whether the ecological flow is within the normal range. When the deviation between the actual flow and the target flow exceeds this threshold, it indicates that the ecological flow may not meet ecological needs, requiring timely measures. The algal biomass index is calculated by analyzing optical imaging data collected by multispectral water imaging sensors; it reflects the growth status of algae in the water. The second threshold is a standard used to determine whether algal biomass is abnormally proliferating. The audible and visual alarm device includes a sound alarm and a light alarm. When the ecological flow deviation is detected to exceed the first threshold, the sound alarm will sound, and the light alarm will flash a warning light, reminding operators to handle the situation promptly. The redundant gate system is a backup gate control mechanism. When the main gate system fails due to a malfunction or other reasons, the redundant gate system will automatically activate to ensure that the regulation of ecological flow is not affected.

[0041] Preferably, the detection of ecological flow deviation can be achieved by real-time monitoring of ecological discharge flow and comparing it with the target flow. The target flow is calculated based on ecological needs and reservoir scheduling instructions, while the actual flow is measured in real-time by flow sensors. When the difference between the two exceeds a first threshold, the system automatically triggers an audible and visual alarm. The settings of the audible and visual alarm can be adjusted according to the actual application scenario, such as setting the frequency of the alarm sound and the flashing frequency of the lights. The calculation of the algal biomass index can be accomplished by analyzing the reflectivity information in optical imaging data. Specifically, the calculation process combines reflectivity data of different bands with a turbidity compensation factor to obtain a quantitative index of algal biomass. When the algal biomass index exceeds a second threshold, the system automatically activates the redundant gate system. The activation process of the redundant gate system includes detecting the fault signal of the main gate system, then switching to the backup gate, and continuing to regulate the ecological flow through the backup gate control system. This mechanism can effectively improve the reliability and stability of the system and ensure the continuity of ecological flow regulation.

[0042] In some embodiments, the method further includes: In response to a power supply anomaly detected by the photovoltaic energy storage system, the system switches to a backup power mode, wherein the photovoltaic energy storage system includes a photovoltaic conversion unit and an energy storage unit; The backup power mode satisfies the power supply constraints:

[0043] in: This refers to the output power. Photovoltaic output power; For load power; This is the conversion efficiency coefficient.

[0044] It should be noted that the intelligent regulation method for reservoir ecological flow in this invention specifically introduces a backup power switching mechanism in case of power supply anomalies. When the photovoltaic energy storage system detects a power supply anomaly, the system automatically switches to backup power mode to ensure the continuous operation of the regulation system. A photovoltaic energy storage system is a device that combines photovoltaic power generation and energy storage functions. It converts solar energy into electrical energy through a photovoltaic conversion unit and stores it through an energy storage unit for system use. The backup power mode is a power supply method activated when the main power supply fails. It continues to supply power to the system through a backup power source, ensuring the normal operation of the regulation system. This design can effectively cope with sudden power supply problems and ensure the stability and reliability of the ecological flow regulation system.

[0045] Specifically, a photovoltaic energy storage system consists of a photovoltaic conversion unit and an energy storage unit. The photovoltaic conversion unit is responsible for converting solar energy into electrical energy, typically composed of solar panels, whose output power depends on the light intensity and the conversion efficiency of the panels. The energy storage unit stores electrical energy, generally using battery packs, and can provide power to the system when there is insufficient or no sunlight. The power supply constraint of the backup power mode is described by a formula: output power equals the conversion efficiency coefficient multiplied by the photovoltaic output power minus the load power. Here, the conversion efficiency coefficient is a value between 0 and 1, representing the unavoidable energy loss during energy conversion; the load power is the power required for the system to operate normally. In this way, the backup power mode can continue to supply power to the system using the electrical energy stored in the energy storage unit when the main power source fails, ensuring the continuous operation of the ecological flow regulation system.

[0046] Preferably, the implementation of the photovoltaic energy storage system can be further refined. First, the photovoltaic conversion unit should be installed in an area with ample sunlight and no obstructions to maximize solar energy acquisition. Second, the capacity of the energy storage unit should be designed based on the system's maximum load power and the expected duration of power supply. For example, if the system's maximum load power under no-sunlight conditions is 1000 watts and requires continuous power supply for 4 hours, the energy storage unit's capacity should be at least 4000 watt-hours. The switching mechanism for the backup power mode can be implemented by monitoring the output power of the photovoltaic conversion unit. When the output power falls below a set threshold, the system automatically switches to backup power mode. At this time, the energy storage unit converts DC power to AC power via an inverter to supply power to the system. Simultaneously, the system monitors the remaining power of the energy storage unit in real time, and when the power falls below a set safety threshold, an alarm will be issued to remind operators to handle the situation promptly. Furthermore, to improve system reliability, a priority mechanism can be set in the backup power mode to prioritize power supply to critical equipment, such as gate control systems, to ensure that the core functions of ecological flow regulation are not affected.

[0047] In some embodiments, the generated ecological state assessment parameters include: The upstream water level data, river flow data, and optical imaging data are processed using a multi-source data fusion algorithm, wherein the multi-source data fusion algorithm is based on an improved D-S evidence theory. The multi-source data fusion algorithm satisfies the optimization objective:

[0048] in: This is the measured value of the ecological discharge flow. For dynamic ecological flow thresholds; To control energy consumption costs; This is the weighting coefficient for the difference between the measured value of ecological discharge flow and the dynamic ecological flow threshold. The weighting coefficient is used to regulate energy consumption costs.

[0049] It should be noted that in the intelligent regulation method for reservoir ecological flow of this invention, the process of generating ecological state assessment parameters employs a multi-source data fusion algorithm, which is based on an improved DS evidence theory. The core of the multi-source data fusion algorithm lies in integrating data from different sensors, including upstream water level data, river flow data, and optical imaging data, to more comprehensively reflect the ecological state of the reservoir. DS evidence theory is a mathematical method for handling uncertainty and fuzzy information; by fusing data from different sources, it can improve the accuracy and reliability of ecological state assessment. Furthermore, the algorithm sets an optimization objective: minimizing the difference between the measured ecological discharge flow and the dynamic ecological flow threshold, as well as the weighted sum of regulation energy costs, thereby reducing energy costs while meeting ecological needs.

[0050] Specifically, the multi-source data fusion algorithm involves upstream water level data, which refers to the real-time water level height upstream of the reservoir monitored by water level sensors; river flow data, which refers to the flow rate of water in the river measured by flow meters; and optical imaging data, which is collected by multispectral water imaging sensors, including reflectance data in the visible, near-infrared, and ultraviolet bands, used to monitor ecological characteristics such as algae and suspended solids in the water. The measured value of ecological discharge flow refers to the actual measured discharge flow from the reservoir, the dynamic ecological flow threshold is the target flow value dynamically adjusted according to ecological needs, and the regulation energy cost refers to the energy cost consumed in regulating ecological flow. The weighting coefficients in the optimization objective are used to balance the relationship between ecological flow and energy cost, ensuring that energy consumption is minimized while meeting ecological needs. Through this multi-source data fusion algorithm, the ecological status of the reservoir can be assessed more accurately, providing a scientific basis for intelligent regulation of ecological flow.

[0051] Preferably, the construction process of the multi-source data fusion algorithm can be further refined. First, upstream water level data, river flow data, and optical imaging data are collected in real time using sensors installed at different locations in the reservoir. The acquisition of optical imaging data requires a multispectral water imaging sensor, which can capture reflectance data in different wavelength bands for analyzing ecological characteristics such as algae and suspended matter in the water. Then, this data is input into a multi-source data fusion algorithm based on an improved DS evidence theory. This algorithm adjusts the weighting coefficients by setting an optimization objective: minimizing the difference between the measured ecological discharge flow and the dynamic ecological flow threshold, and the weighted sum of control energy costs. The weighting coefficients can be set according to actual needs. For example, if ecological needs are more important, the weight of the difference between the measured ecological discharge flow and the dynamic ecological flow threshold can be increased; if energy costs need to be controlled, the weight of energy costs can be increased. Through this optimization process, the algorithm can dynamically adjust the weighting coefficients, thereby reducing energy costs while meeting ecological needs. Furthermore, the algorithm can dynamically update the ecological flow threshold based on real-time data to adapt to changes in hydrological conditions.

[0052] In some embodiments, the method further includes: Periodically updated via rolling time-domain control algorithm The update cycle is a fixed time interval; The rolling time-domain control algorithm satisfies the following constraints:

[0053] in: This represents the upper limit of the ecological flow threshold. Design traffic; This is the proportionality coefficient between the measured value of ecological discharge flow and the upper limit of the ecological flow threshold. This is the proportional coefficient relating the rate of change of ecological discharge flow to the design flow.

[0054] It should be noted that in the intelligent regulation method for reservoir ecological flow of the present invention, the updating of the dynamic ecological flow threshold adopts a rolling time-domain control algorithm. This algorithm, by periodically updating the dynamic ecological flow threshold, can dynamically adjust the ecological flow according to real-time hydrological conditions and ecological needs, thereby better adapting to environmental changes. The rolling time-domain control algorithm is a time-series-based optimization method that ensures the flexibility and adaptability of ecological flow regulation by updating the ecological flow threshold at fixed time intervals. The constraints of this algorithm include the relationship between the measured value of ecological discharge flow and the upper limit of the ecological flow threshold, and the relationship between the rate of change of ecological discharge flow and the design flow, thereby ensuring that the regulation of ecological flow meets both ecological needs and the safety requirements of engineering design.

[0055] Specifically, the rolling time-domain control algorithm updates at fixed time intervals, meaning the system periodically recalculates the ecological flow threshold based on the latest hydrological and ecological data. (Ecological flow threshold upper limit) It is the maximum allowable flow value set according to ecological needs to ensure the health of the ecosystem. Design flow This is the flow rate determined during the reservoir's design phase to meet functional requirements such as flood control and power generation. The measured value of the ecological release flow rate is also included. It is the flow data obtained in real time through sensors, and the ratio between the upper limit of the ecological flow threshold and the measured value is the coefficient. Used to ensure that the actual flow does not exceed the safe range required by the ecosystem. The proportionality coefficient between the rate of change of ecological release flow and the design flow. The rate of change in flow is controlled to prevent rapid changes from impacting the ecosystem. By setting these parameters, the rolling time-domain control algorithm can ensure the safety and stability of reservoir operation while meeting ecological needs.

[0056] Preferably, the construction and implementation of the rolling time-domain control algorithm can be further refined. First, the algorithm's input parameters include real-time measured values ​​of ecological discharge flow, the upper limit of the ecological flow threshold, the design flow, and related proportional coefficients. These parameters can be acquired in real-time by sensors or calculated using empirical formulas. The algorithm's output is an updated dynamic ecological flow threshold, which will guide subsequent ecological flow regulation. During the algorithm's implementation, a fixed time interval can be set, such as every hour or every half-day. At the end of each time interval, the algorithm recalculates the ecological flow threshold based on the latest input data. The specific calculation process includes: checking whether the current measured value of the ecological discharge flow meets the constraint condition of the upper limit of the ecological flow threshold, i.e., whether the measured value is greater than or equal to a certain proportion of the upper limit, and whether the flow change rate is within the allowable range. If the conditions are met, the dynamic ecological flow threshold is updated; if not, an alarm is issued and corresponding adjustment measures are taken. Furthermore, to improve the algorithm's adaptability and flexibility, an adaptive mechanism can be introduced to dynamically adjust the proportional coefficients based on historical data and real-time feedback, thereby better addressing complex hydrological and ecological conditions.

[0057] In some embodiments, the drive gate opening controller includes: Calculate the gate opening adjustment amount:

[0058] in: This is the adjustment amount for the gate opening; For target traffic; This is a proportional control parameter; These are integral control parameters; This is the measured value of the ecological discharge flow. Drive actuator according to Adjust the gate opening.

[0059] It should be noted that in the intelligent regulation method for reservoir ecological flow of this invention, the gate opening controller employs a proportional-integral (PI) control algorithm. This method drives the actuator by calculating the gate opening adjustment, thereby achieving precise regulation of ecological flow. The proportional-integral (PI) control algorithm is a classic automatic control algorithm widely used in applications requiring precise control. It adjusts the control quantity through proportional and integral control parameters to ensure rapid system response and stability. This algorithm can dynamically adjust the gate opening based on the deviation between the target flow and the actual flow, thus achieving precise regulation of ecological flow.

[0060] Specifically, the gate opening adjustment is calculated using a proportional-integral (PI) control algorithm. The target flow rate refers to the desired flow rate value set according to ecological needs and reservoir scheduling instructions; the measured ecological discharge flow rate is the current flow rate value obtained in real time through flow sensors. The proportional and integral control parameters are the core parameters of the algorithm, used to adjust the magnitude and response speed of the control quantity. The proportional control parameter determines the direct response strength of the control quantity to deviations, while the integral control parameter is used to eliminate the cumulative effect of deviations, ensuring the long-term stability of the system. By substituting the difference between the target flow rate and the measured flow rate into the algorithm, the gate opening adjustment can be calculated, thereby driving the actuator to adjust the gate opening. This control method can quickly respond to flow deviations and eliminate the accumulation of deviations through integral action, thus achieving precise regulation of ecological flow.

[0061] Preferably, the implementation of the proportional-integral (PI) control algorithm can be further refined. First, a suitable target flow rate is set based on the specific conditions of the reservoir and ecological needs. Then, the measured values ​​of the ecological discharge flow rate are collected in real time by flow sensors installed in the river channel. The proportional and integral control parameters need to be adjusted according to the dynamic characteristics of the system. For example, if the system requires a rapid response, the proportional control parameter can be increased; if it is necessary to eliminate the accumulation of deviations, the integral control parameter can be appropriately increased. In practical applications, the optimal values ​​of these parameters can be determined through experiments or empirical formulas. The process of calculating the gate opening adjustment is to substitute the difference between the target flow rate and the measured flow rate into the PPI control algorithm to obtain an adjustment amount. Then, the actuator is driven to adjust the gate opening based on this adjustment amount. To improve the stability and reliability of the system, a feedback mechanism can also be introduced to monitor the adjusted flow rate in real time and further adjust the control parameters based on the feedback results. In addition, to cope with complex hydrological conditions, an adaptive mechanism can be introduced into the algorithm to dynamically adjust the control parameters based on real-time data, thereby better achieving precise regulation of ecological flow.

[0062] In some embodiments, the optical imaging data includes: Reflectance data in the visible light band; Near-infrared reflectance data; Ultraviolet reflectance data.

[0063] It should be noted that in the intelligent regulation method for reservoir ecological flow of this invention, the acquisition and analysis of optical imaging data is a crucial step in achieving precise ecological flow regulation. Optical imaging data includes reflectance data in the visible light band, near-infrared band, and ultraviolet band. This data is acquired through a multispectral water imaging sensor and reflects ecological characteristics such as algae and suspended matter in the water body. By analyzing this reflectance data, an algal biomass index can be generated, thereby adjusting the dynamic ecological flow threshold to adapt to changes in the water body's ecological conditions. This method not only enables real-time monitoring of water body ecological characteristics but also provides a scientific basis for the intelligent regulation of ecological flow.

[0064] Specifically, optical imaging data is acquired using a multispectral water imaging sensor. This sensor can capture reflectance data across different wavelengths, including the visible, near-infrared, and ultraviolet bands. Visible light reflectance data is primarily used to monitor the concentration of algae and suspended matter in the water; near-infrared reflectance data can be used to analyze vegetation cover; and ultraviolet reflectance data helps detect the organic matter content in the water. The algal biomass index is calculated by analyzing this reflectance data; it is a quantitative indicator used to assess the growth status of algae in the water. The calculation of this index involves reflectance data from multiple wavelengths and is corrected using a turbidity compensation factor to ensure the accuracy of the results. In this way, a more comprehensive understanding of the ecological status of the water body can be obtained, and the ecological flow threshold can be adjusted accordingly to meet ecological needs.

[0065] Preferably, the processing of optical imaging data and the calculation of the algal biomass index can be further refined. First, the data collected by the multispectral water imaging sensor needs to be preprocessed, including noise removal and sensor bias correction. Then, by analyzing the reflectance data of different bands and combining it with pre-set thresholds, characteristics of abnormal algal proliferation are identified. For example, if the reflectance of a certain band is significantly higher than the normal range, it indicates that algae are proliferating in large quantities. The algal biomass index can be calculated using a weighted summation method, where the reflectance data of each band is multiplied by a corresponding weighting coefficient, which can be adjusted according to the reflectance characteristics of algae in different bands. The calculated algal biomass index can be used to dynamically adjust the ecological flow threshold. For example, when the algal biomass index exceeds the preset threshold, the ecological flow can be appropriately increased to improve the ecological condition of the water body. In addition, to improve the accuracy of monitoring, the multispectral water imaging sensor can be calibrated periodically and combined with other monitoring data, such as water quality parameters, for comprehensive analysis.

[0066] In some embodiments, the method further includes: In response to the detection of abnormal algal proliferation characteristics by the multispectral water imaging sensor, an algal biomass index is generated. ; Calculate the algal biomass index:

[0067] in: Algal biomass index; For band Reflectivity; Turbidity compensation factor; The number of bands involved in the calculation; The dynamic ecological flow threshold is adjusted based on the algal biomass index.

[0068] It should be noted that monitoring and regulating abnormal algal proliferation is a crucial step in the intelligent reservoir ecological flow regulation method of this invention. After detecting abnormal algal proliferation characteristics using a multispectral water imaging sensor, an algal biomass index Ia is generated. This index is calculated based on reflectance data from different wavelengths and is used to quantify the algal growth status. By adjusting the dynamic ecological flow threshold, the ecological risks posed by abnormal algal proliferation can be effectively addressed. This method not only enables real-time monitoring of algal growth but also improves the aquatic ecological environment through ecological flow regulation, thereby achieving effective control over algal growth.

[0069] Specifically, the algal biomass index Ia is calculated by analyzing reflectance data collected by a multispectral water imaging sensor. This sensor can capture reflectance data across multiple wavelengths, including the visible, near-infrared, and ultraviolet bands. The reflectance of each band... It reflects the optical properties of algae and other substances in the water. Turbidity compensation factor This index is used to correct for the influence of water turbidity on reflectance data, thereby improving the accuracy of the algal biomass index. The algal biomass index is calculated by multiplying the reflectance data for each band by the corresponding weighting coefficient and then summing the results. The weighting coefficients can be adjusted according to the reflectance characteristics of algae in different bands to ensure that the calculation results accurately reflect algal biomass. In this way, the growth status of algae can be quantified, and the ecological flow threshold can be adjusted accordingly to address the ecological risks caused by abnormal algal proliferation.

[0070] Preferably, the calculation and application of the algal biomass index can be further refined. First, the reflectance data collected by the multispectral water imaging sensor needs to be preprocessed, including noise removal and sensor bias correction. Then, based on the reflectance characteristics of algae in different bands, a weighting coefficient is assigned to the reflectance data for each band. For example, if the reflectance of algae changes most significantly in a certain band, the weighting coefficient for that band can be set higher. When calculating the algal biomass index, the reflectance data for each band is multiplied by the corresponding weighting coefficient and summed, and then corrected using a turbidity compensation factor. The calculated algal biomass index can be used to dynamically adjust the ecological flow threshold. For example, when the algal biomass index exceeds a preset threshold, it indicates that algae may be proliferating abnormally, and the ecological flow can be appropriately increased to improve the ecological condition of the water body. In addition, to improve the accuracy and reliability of monitoring, other monitoring data, such as water quality parameters and water temperature, can be combined for comprehensive analysis, and the multispectral water imaging sensor can be calibrated regularly to ensure its long-term stable operation.

[0071] The above embodiments of the present invention have the following beneficial effects: 1. By integrating water level, flow rate and multispectral optical imaging data through a multi-source data acquisition module, and combining it with a turbidity compensation algorithm to dynamically correct ecological assessment parameters, the problem of single monitoring methods and inaccurate ecological status assessment in traditional methods is solved, thereby improving the accuracy of water body ecological feature identification and environmental adaptability. 2. By adopting an adaptive optimization algorithm combined with a rolling time-domain control strategy, the ecological flow threshold is dynamically adjusted and the gate opening control is optimized. This overcomes the problem that the fixed release mode is difficult to adapt to hydrological changes, and realizes the coordinated optimization of ecological water demand and reservoir scheduling. This improves the efficiency of water resource regulation while ensuring the ecological health of the downstream area. 3. Through multiple protection mechanisms such as dual-mode communication, redundant gate system and photovoltaic energy storage backup power, the risks of unstable equipment communication, sudden abnormal working conditions and power supply interruption in remote areas are solved, the reliability and anti-interference ability of the system are enhanced, and the continuous and stable operation of ecological flow regulation is ensured.

[0072] Furthermore, the storage medium in the embodiments of this application stores program instructions capable of implementing all the above methods. These program instructions can be stored in the storage medium in the form of a software product, including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in 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, or terminal devices such as computers, servers, mobile phones, and tablets.

[0073] The above description is merely an explanation of some preferred embodiments of the present invention and the technical principles employed. Those skilled in the art should understand that the scope of the invention as described in the embodiments of the present invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention.

Claims

1. A method for intelligent regulation of ecological flow in a reservoir, characterized in that, include: In response to acquiring upstream water level data, river flow data, ecological discharge flow data and optical imaging data through a multi-source data acquisition module, ecological status assessment parameters are generated. The optical imaging data is acquired by a multispectral water body imaging sensor and is used to monitor the ecological characteristics of the water body. The control module generates a flow regulation instruction based on the ecological state assessment parameters and the reservoir scheduling instructions, wherein the flow regulation instruction is calculated through an adaptive optimization algorithm; In response to the execution module receiving the flow adjustment command, the gate opening controller is driven to adjust the ecological flow, wherein the adjustment accuracy of the gate opening controller meets a preset threshold. The generated ecological state assessment parameters include: The upstream water level data, river flow data, and optical imaging data are processed using a multi-source data fusion algorithm, wherein the multi-source data fusion algorithm is based on an improved D-S evidence theory. The multi-source data fusion algorithm satisfies the optimization objective: in, This is the measured value of the ecological discharge flow. For dynamic ecological flow thresholds; To control energy consumption costs; This is the weighting coefficient for the difference between the measured value of ecological discharge flow and the dynamic ecological flow threshold. Weighting coefficients for adjusting energy consumption costs; The method further includes: Periodically updated via rolling time-domain control algorithm The update cycle is a fixed time interval; The rolling time-domain control algorithm satisfies the following constraints: in, This represents the upper limit of the ecological flow threshold. Design traffic; This is the proportionality coefficient between the measured value of ecological discharge flow and the upper limit of the ecological flow threshold. This is the proportionality coefficient between the rate of change of ecological discharge flow and the design flow. The drive gate opening controller includes: Calculate the gate opening adjustment amount: in, This is the adjustment amount for the gate opening; For target traffic; This is a proportional control parameter; These are integral control parameters; Drive actuator according to Adjust the gate opening.

2. The method according to claim 1, characterized in that, Before generating ecological status assessment parameters in response to acquiring upstream water level data, river flow data, ecological discharge flow data, and optical imaging data through a multi-source data acquisition module, the method further includes: The optical imaging data is processed by an optical compensation algorithm to generate a turbidity compensation factor, wherein the turbidity compensation factor is calculated using the following formula: in, Turbidity compensation factor; This indicates the measured value of turbidity in the water. Indicates the optical attenuation coefficient; The turbidity compensation factor is used to correct the ecological state assessment parameters.

3. The method according to claim 1, characterized in that, After the control module generates a flow regulation command based on the ecological state assessment parameters and the reservoir scheduling command, the method further includes: Based on the flow regulation command and real-time flow data, a gate control signal is generated; The gate control signal is transmitted to the execution module via a dual-mode communication terminal, wherein the dual-mode communication terminal supports both satellite communication and mobile communication.

4. The method according to claim 1, characterized in that, Before the execution module receives the flow regulation command and drives the gate opening controller to regulate the ecological flow, the method further includes: In response to the detection that the ecological flow deviation exceeds the first threshold, the audible and visual alarm device is activated. In response to the detection that the algal biomass index exceeds the second threshold, the redundant gate system is activated.

5. The method according to claim 1, characterized in that, The method further includes: In response to a power supply anomaly detected by the photovoltaic energy storage system, the system switches to a backup power mode, wherein the photovoltaic energy storage system includes a photovoltaic conversion unit and an energy storage unit; The backup power mode satisfies the power supply constraints: in, This refers to the output power. Photovoltaic output power; For load power; This is the conversion efficiency coefficient.

6. The method according to any one of claims 1-5, characterized in that, The optical imaging data includes: Reflectance data in the visible light band; Near-infrared reflectance data; Ultraviolet reflectance data.

7. The method according to claim 6, characterized in that, The method further includes: In response to the detection of abnormal algal proliferation characteristics by the multispectral water imaging sensor, an algal biomass index is generated. ; Calculate the algal biomass index: in, Algal biomass index; For band Reflectivity; Turbidity compensation factor; The number of bands involved in the calculation; The dynamic ecological flow threshold is adjusted based on the algal biomass index.

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