Water conservancy project intelligent maintenance system based on Internet of Things
By collecting and processing water level data in real time through Internet of Things (IoT) technology, dynamically calculating early warning assessments, and triggering intelligent flood discharge operations, the problem of low efficiency in traditional dam flood discharge regulation has been solved, achieving precise flood discharge control and flood safety assurance.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-30
- Publication Date
- 2026-04-14
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Traditional dam flood discharge control methods rely on human experience or fixed thresholds, resulting in low control efficiency and slow response speed. They are difficult to achieve accurate and reliable flood discharge control under complex and ever-changing hydrological conditions, thus affecting flood control safety.
The water conservancy project adopts an intelligent maintenance system based on the Internet of Things, which includes a data acquisition module, an early warning assessment module, and a flood discharge control module. It collects water level data in real time, determines the early warning starting point through the early warning assessment module and triggers flood discharge operation, and adjusts the flood discharge intensity according to water level changes to ensure flood discharge effect and downstream safety.
It has achieved adaptive and refined flood discharge regulation, improved the flood discharge response speed and regulation accuracy, coordinated the reservoir with downstream flood control targets, and enhanced the intelligence level and flood control safety guarantee capability of water conservancy projects.
Smart Images
Figure CN121860211A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of reservoir flood discharge regulation technology, specifically to an intelligent maintenance system for water conservancy projects based on the Internet of Things. Background Technology
[0002] Water conservancy projects, as an important part of national infrastructure, play a crucial role in flood control and disaster reduction, and water resource allocation. The safe operation of dams is directly related to the safety of life and property in upstream and downstream areas. With the rapid development of Internet of Things (IoT) technology, the application of intelligent monitoring systems in the water conservancy field is gradually deepening, providing new technical approaches for realizing real-time perception and intelligent decision-making of dam status.
[0003] Traditional dam flood discharge control methods mainly rely on manual experience or automated control based on fixed thresholds. These methods monitor reservoir water levels and execute water release operations by referring to historical data or simple rules, lacking comprehensive analysis of multi-source data and dynamic response mechanisms. However, these existing technologies suffer from low control efficiency, slow response speed, and poor adaptability, making it difficult to achieve precise and reliable flood discharge control under complex and variable hydrological conditions, thus affecting the overall flood control safety and operational efficiency. Summary of the Invention
[0004] To address the current technical challenge of achieving intelligent and adaptive optimization of dam flood discharge control processes to improve control accuracy and response efficiency, this invention aims to provide an intelligent maintenance system for water conservancy projects based on the Internet of Things (IoT). The specific technical solution adopted is as follows: In a first aspect, the present invention provides an intelligent maintenance system for water conservancy projects based on the Internet of Things, comprising: a data acquisition module, an early warning assessment module, and a flood discharge control module; the data acquisition module is used to acquire water level summary data; wherein, the water level summary data includes reservoir water level data and water level data at multiple downstream monitoring locations; the early warning assessment module is used to determine the early warning assessment for each monitoring moment based on the reservoir water level data, and when the early warning assessment shows an upward trend and exceeds a preset threshold, the monitoring moment corresponding to the early warning assessment is determined as the early warning start moment; wherein, the early warning assessment is used to characterize the degree to which the reservoir water level approaches the safe water level and the upward trend per unit time; the flood discharge control module is used to execute standard flood discharge operations starting from the early warning start moment, and determine the flood discharge result based on the changes in the reservoir water level; wherein, the flood discharge result is used to characterize the actual change effect of the reservoir water level after the standard flood discharge operation; the flood discharge control module is also used to control the flood discharge intensity based on the flood discharge result; the flood discharge control module is also used to determine the end of flood discharge when the early warning assessment at the current monitoring moment is lower than the preset threshold, and the water levels at all downstream monitoring locations are lower than the preset safe water level.
[0005] In one possible implementation, when the early warning assessment module determines the early warning evaluation for each monitoring moment based on the reservoir's water level data, it specifically performs the following steps: The early warning assessment module is also used to determine the rate of water level rise based on the reservoir water level data at the current monitoring moment and the reservoir water level data at the previous monitoring moment; The early warning assessment module is also used to determine the early warning evaluation for each monitoring moment based on the difference between the current water level and the safe water level, as well as the rate of water level rise.
[0006] In one possible implementation, the flood discharge control module includes: a standard flood discharge execution submodule, a flood discharge intensity adjustment submodule, and a flood discharge termination determination submodule 133; the standard flood discharge execution submodule is used to execute standard flood discharge operations from the warning start time and determine the flood discharge result based on the reservoir water level change; the flood discharge intensity adjustment submodule is used to adjust the flood discharge intensity based on the flood discharge result; the flood discharge termination determination submodule 133 is used to determine the termination of flood discharge when the warning evaluation at the current monitoring time is lower than the preset threshold and the water level at all downstream monitoring locations is lower than the preset safe water level.
[0007] In one possible implementation, when determining the flood discharge result based on the reservoir water level change, the standard flood discharge execution submodule specifically performs the following steps: The standard flood discharge execution submodule is also used to determine the water level change value after performing the standard flood discharge operation based on the water level data at the warning start time and the water level data at the current monitoring time; The standard flood discharge execution submodule is also used to determine the flood discharge result based on the water level change value and the warning evaluation at the current monitoring time.
[0008] In one possible implementation, when the flood discharge intensity regulation submodule adjusts the flood discharge intensity based on the flood discharge results, it specifically performs the following steps: The flood discharge intensity regulation submodule is also used to increase the flood discharge intensity when the flood discharge results indicate that the reservoir water level is rising, based on the ratio of the warning evaluation at the current monitoring time to the historical maximum warning evaluation within the warning stage; wherein, the warning stage is the time period from the start of the warning to the end of the flood discharge.
[0009] In one possible implementation, when the flood discharge intensity regulation submodule adjusts the flood discharge intensity based on the flood discharge results, it specifically performs the following steps: The flood discharge intensity regulation submodule is also used to reduce the flood discharge intensity based on the downstream safety factor generated by the flood discharge when the flood discharge results indicate a drop in water level; wherein, the downstream safety factor is used to characterize the water level safety and stability at each monitoring location downstream under the current flood discharge intensity.
[0010] In one possible implementation, when the flood discharge intensity regulation submodule adjusts the flood discharge intensity based on the flood discharge results, it specifically performs the following steps: The flood discharge intensity regulation submodule is also used to determine the downstream safety factor generated by the flood discharge for each downstream monitoring location based on the distance between the downstream monitoring location and the reservoir, the preset safe water level of the downstream monitoring location, and the water level data at the current monitoring time.
[0011] In one possible implementation, before determining the downstream safety factor generated by the flood discharge, the flood discharge intensity regulation submodule also performs the following steps: the flood discharge intensity regulation submodule is also used to determine the water flow propagation time from the reservoir to each downstream monitoring location based on the river geographical distance between the reservoir and each downstream monitoring location, and the average water flow velocity between the reservoir and each downstream monitoring location.
[0012] In one possible implementation, the IoT-based intelligent maintenance system for water conservancy projects also includes: a flood discharge post-treatment module; the flood discharge post-treatment module is used to close and lock the flood discharge gate after the flood discharge is completed, and to record the water level data and flood discharge operation data during the flood discharge period.
[0013] In one possible implementation, the data acquisition module includes: a data acquisition submodule and a data preprocessing submodule; the data acquisition submodule is used to acquire initial water level data through water level sensors deployed in the reservoir and downstream river channel; wherein, the initial water level data includes initial water level data of the reservoir and initial water level data of multiple monitoring locations downstream; the data preprocessing submodule is used to perform preprocessing operations on the initial water level data to obtain summary water level data; wherein, the preprocessing includes removing outliers and synchronizing the water level data of the reservoir and downstream monitoring locations in time.
[0014] Secondly, this invention provides an intelligent maintenance method for water conservancy projects based on the Internet of Things, comprising: acquiring aggregated water level data; wherein the aggregated water level data includes reservoir water level data and water level data at multiple downstream monitoring locations; determining an early warning assessment for each monitoring moment based on the reservoir water level data, and determining the monitoring moment corresponding to the early warning assessment as the early warning start moment when the early warning assessment shows an upward trend and exceeds a preset threshold; wherein the early warning assessment is used to characterize the degree to which the reservoir water level approaches the safe water level and the upward trend per unit time; executing standard flood discharge operations from the early warning start moment, and determining the flood discharge result based on changes in the reservoir water level; wherein the flood discharge result is used to characterize the actual change effect of the reservoir water level after the standard flood discharge operation; adjusting the flood discharge intensity based on the flood discharge result; and determining the end of flood discharge when the early warning assessment at the current monitoring moment is lower than the preset threshold and the water levels at all downstream monitoring locations are lower than the preset safe water level.
[0015] Thirdly, the present invention provides an electronic device, comprising: a processor and a memory; wherein the memory is used to store one or more programs, the one or more programs including computer-executable instructions, and when the electronic device is running, the processor executes the computer-executable instructions stored in the memory to cause the electronic device to perform the Internet of Things-based intelligent maintenance method for water conservancy projects as described in the first aspect and any possible implementation thereof.
[0016] Fourthly, the present invention provides a computer-readable storage medium storing one or more programs, the one or more programs including instructions that, when executed by an electronic device of the present invention, cause the electronic device to perform the Internet of Things-based intelligent maintenance method for water conservancy projects as described in the first aspect and any possible implementation thereof.
[0017] Fifthly, the present invention provides a computer program product containing instructions that, when executed on a computer, cause the electronic device of the present invention to perform the Internet of Things-based intelligent maintenance method for water conservancy projects as described in the first aspect and any possible implementation thereof.
[0018] Sixthly, the present invention provides a chip system applied to an IoT-based intelligent maintenance device for water conservancy projects; the chip system includes one or more interface circuits and one or more processors. The interface circuits and the processors are interconnected via lines; the interface circuits are used to receive signals from the memory of the IoT-based intelligent maintenance device for water conservancy projects and to send the signals to the processors, the signals including computer instructions stored in the memory. When the processor executes the computer instructions, the IoT-based intelligent maintenance device for water conservancy projects performs the IoT-based intelligent maintenance method for water conservancy projects as described in the first aspect and any possible design embodiment.
[0019] This invention has the following beneficial effects: By collecting and processing reservoir and downstream water level data in real time, and accurately triggering flood discharge operations based on dynamic calculation and early warning evaluation, an intelligent flood discharge system capable of adaptive and refined regulation based on reservoir water level change trends and downstream safety conditions is constructed. This significantly improves the flood discharge response speed and regulation accuracy, while effectively coordinating reservoir safety and downstream flood control objectives. It solves the core problems of traditional methods, such as delayed response, extensive regulation, and difficulty in balancing upstream and downstream safety, and comprehensively enhances the intelligent level of water conservancy project operation and flood control safety assurance capabilities. Attached Figure Description
[0020] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0021] Figure 1 This is a schematic diagram of the architecture of an intelligent maintenance system for water conservancy projects based on the Internet of Things, provided as an embodiment of the present invention. Figure 2 This is a schematic diagram of the architecture of a data acquisition module provided in one embodiment of the present invention; Figure 3 This is a schematic diagram of the architecture of a flood discharge control module provided in one embodiment of the present invention; Figure 4 A flowchart illustrating an Internet of Things-based intelligent maintenance method for water conservancy projects, provided as an embodiment of the present invention; Figure 5 This is a flowchart illustrating another IoT-based intelligent maintenance method for water conservancy projects, provided as an embodiment of the present invention. Detailed Implementation
[0022] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the specific implementation methods, structures, features, and effects of the present invention will be described in detail below with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.
[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.
[0024] The following description, in conjunction with the accompanying drawings, details a specific solution for an IoT-based intelligent maintenance system for water conservancy projects provided by this invention.
[0025] For example, such as Figure 1 The diagram shown is an architectural schematic of an Internet of Things-based intelligent maintenance system for water conservancy projects (hereinafter referred to as the intelligent maintenance system) according to an embodiment of the present invention. The intelligent maintenance system 10 includes: a data acquisition module 11, an early warning and assessment module 12, a flood discharge control module 13, and a flood discharge post-processing module 14. The modules are described below in sequence: (1) Data acquisition module 11.
[0026] The data acquisition module 11 is responsible for collecting water level data from the reservoir and downstream areas, performing standardized processing, and outputting water level summary data that can be directly used for analysis in subsequent modules.
[0027] For example, such as Figure 2 As shown, the data acquisition module 11 may include two sub-modules: a data acquisition sub-module 111 and a data preprocessing sub-module 112. These two sub-modules are described below: (1.1) Data acquisition submodule 111.
[0028] Optionally, the data acquisition submodule 111 is used to acquire initial water level data through water level sensors deployed in the reservoir and downstream river channel. The initial water level data includes initial water level data from the reservoir and initial water level data from multiple monitoring locations downstream.
[0029] Specifically, the water level sensors are installed according to the principles of covering key monitoring points and avoiding interference. They are installed at multiple monitoring locations in the reservoir area (monitoring the reservoir water level) and the downstream river channel (monitoring the downstream water level). All sensors must be deployed on seepage paths at different depths of the dam body to ensure that the water flows evenly through the sensor monitoring area and avoid the impact of turbulence and dead zones on measurement accuracy.
[0030] Depending on the type of data collected, the data acquisition submodule 111 collects initial water level data of the reservoir (reflecting the reservoir's water storage status) and initial water level data from multiple monitoring locations downstream (reflecting the downstream flood-bearing capacity) in real time. The acquisition frequency is synchronized with the analysis frequency of subsequent modules to ensure data timeliness. Furthermore, the data acquisition submodule 111 transmits the collected initial data to the data preprocessing submodule 112 in real time via wireless communication technology to avoid analysis bias caused by data delay.
[0031] (1.2) Data preprocessing submodule 112.
[0032] Optionally, the data preprocessing submodule 112 is used to preprocess the initial water level data to obtain summarized water level data. The preprocessing includes removing outliers and synchronizing the water level data between the reservoir and downstream monitoring locations.
[0033] Specifically, the data preprocessing submodule 112 removes outliers in the data. It can use statistical filtering algorithms (such as the moving average method) to identify and remove outliers (such as jump data caused by sensor failure) and noise (such as small errors caused by water flow fluctuations) in the initial data to ensure data accuracy.
[0034] In addition, the data preprocessing submodule 112 performs time synchronization, specifically by calibrating the timestamps of the initial water level data of the reservoir and the initial water level data of the downstream area, to ensure that the monitoring times of the two types of data are completely consistent, forming a dataset (i.e., water level summary data) that can be directly compared and analyzed.
[0035] Furthermore, the water level summary data will be continuously transmitted to the early warning assessment module 12 for calculating the early warning evaluation and the flood discharge control module 13 for judging the flood discharge effect and the downstream safety status.
[0036] (2) Early warning assessment module 12.
[0037] The early warning assessment module 12 is responsible for calculating the early warning assessment for each monitoring moment based on the water level summary data 1121 output by the data acquisition module 11, and determining the early warning start time. The early warning start time will serve as the trigger signal for the flood discharge control module 13 to initiate standard flood discharge operations, directly determining the timing of the flood discharge control initiation.
[0038] Specifically, the early warning assessment module 12 determines the rate of water level rise based on the reservoir water level data at the current monitoring time and the reservoir water level data at the previous monitoring time; then, based on the difference between the current water level and the safe water level, and the rate of water level rise, it determines the early warning assessment for each monitoring time.
[0039] Furthermore, the early warning assessment module 12 presets an early warning evaluation threshold; when the early warning assessment module 12 detects that the early warning evaluation shows a continuous upward trend and the value at a certain monitoring time exceeds 0.7, the early warning assessment module 12 determines that time as the early warning start time and transmits the signal at that time to the flood discharge control module 13 in real time to trigger subsequent flood discharge operations.
[0040] It should be noted that the empirical value for the early warning evaluation threshold is 0.7. This value is determined based on historical hydrological data and through statistical analysis over N flood events, balancing the false alarm rate and the missed alarm rate. Those skilled in the art can adjust it within the range of 0.6 to 0.8 according to the specific characteristics of the reservoir and flood control standards.
[0041] (3) Flood discharge control module 13.
[0042] The flood discharge control module 13 is responsible for controlling the entire process of standard flood discharge, effect judgment, intensity adjustment, and termination judgment based on the warning start time of the early warning assessment module 12 and the water level summary data of the data acquisition module 11, and finally outputs the flood discharge termination signal.
[0043] Optionally, the flood discharge control module 13 is used to execute standard flood discharge operations from the warning start time and determine the flood discharge result based on the reservoir water level change; wherein, the flood discharge result is used to characterize the actual change effect of the reservoir water level after the standard flood discharge operation.
[0044] Optionally, the flood discharge control module 13 is also used to control the flood discharge intensity based on the flood discharge results; and to determine the end of flood discharge when the early warning evaluation at the current monitoring time is lower than the preset threshold and the water level at all downstream monitoring locations is lower than the preset safe water level.
[0045] For example, such as Figure 3 As shown, the flood discharge control module 13 may include a standard flood discharge execution submodule 131, a flood discharge intensity adjustment submodule 132, and a flood discharge termination determination submodule 133. These four submodules are described below: (3.1) Standard flood discharge execution submodule 131.
[0046] Optionally, the standard flood discharge execution submodule 131 is used to execute standard flood discharge operations starting from the warning start time.
[0047] Specifically, after receiving the warning start time transmitted by the warning assessment module 12, the standard flood discharge execution submodule 131 immediately executes the standard flood discharge operation. The flood discharge intensity of this operation is the "standard flood discharge intensity" (a safe flood discharge volume preset based on historical flood control experience, which can initially alleviate the pressure on the reservoir water level without causing excessive impact on the downstream area).
[0048] Optionally, the standard flood discharge execution submodule 131 is also used to determine the flood discharge result based on changes in the reservoir water level.
[0049] Specifically, the standard flood discharge execution submodule 131 determines the water level change value after performing the standard flood discharge operation based on the water level data at the warning start time and the water level data at the current monitoring time. Then, the standard flood discharge execution submodule 131 determines the flood discharge result based on the water level change value and the warning evaluation at the current monitoring time. It should be noted that the specific process by which the standard flood discharge execution submodule 131 performs the aforementioned steps to determine the flood discharge result is described in S403 below and will not be repeated here.
[0050] (3.2) Flood discharge intensity adjustment submodule 132.
[0051] Optionally, the flood discharge intensity adjustment submodule 132 is used to adjust the flood discharge intensity based on the flood discharge results.
[0052] Specifically, the flood discharge intensity adjustment submodule 132 receives the flood discharge results from the standard flood discharge execution submodule 131, and combines them with the water level summary data from the data acquisition module 11 to adjust the flood discharge intensity in two scenarios to ensure that the pressure on the reservoir is relieved without causing downstream flooding: Scenario 1: The flood discharge results indicate that the water level has risen, so the flood discharge intensity is increased.
[0053] At this time, the flood discharge intensity adjustment submodule 132 increases the flood discharge intensity based on the ratio of the warning evaluation at the current monitoring time to the historical maximum warning evaluation during the warning period; wherein, the warning period is the time period from the start of the warning to the end of the flood discharge.
[0054] Scenario 2: The flood discharge results indicate a drop in water level, so the flood discharge intensity is reduced.
[0055] At this time, the flood discharge intensity adjustment submodule 132 reduces the flood discharge intensity based on the downstream safety factor generated by the flood discharge; wherein, the downstream safety factor is used to characterize the water level safety and stability at each monitoring location downstream under the current flood discharge intensity.
[0056] It should be noted that the specific process for regulating the flood discharge intensity in the two scenarios mentioned above is described in S501-S502 below, and will not be repeated here.
[0057] (3.3) Flood discharge end determination submodule 133.
[0058] Optionally, the flood discharge end determination submodule 133 is used to determine the end of flood discharge when the early warning evaluation at the current monitoring time is lower than a preset threshold and the water level at all downstream monitoring locations is lower than a preset safe water level.
[0059] (4) Post-discharge treatment module 14.
[0060] The post-discharge processing module 14 is responsible for receiving the discharge termination signal from the discharge control module 13, performing follow-up operations to ensure the long-term safety of the reservoir dam, and accumulating experience for the subsequent operation of the intelligent maintenance system 10. Specifically, this may include: closing and locking the floodgates after the discharge is completed, and recording the water level data and discharge operation data during the discharge period.
[0061] Therefore, maintenance personnel can perform equipment maintenance and report writing based on the water level data and flood discharge operation data recorded by the flood discharge post-processing module 14 during the flood discharge period.
[0062] The above provides an introduction to the intelligent maintenance system 10 and its included modules.
[0063] For example, such as Figure 4 The diagram shown is a flowchart illustrating an intelligent maintenance method for water conservancy projects based on the Internet of Things, according to an embodiment of the present invention, which includes the following steps: S401. Obtain summary water level data. This summary water level data includes reservoir water level data and water level data from multiple monitoring locations downstream.
[0064] For example, this step can be performed by the data acquisition module 11 in the intelligent maintenance system 10 described above, and specifically includes the following steps: (1) Initial water level data are collected by water level sensors deployed in the reservoir and downstream river channel. The initial water level data includes the initial water level data of the reservoir and the initial water level data of multiple monitoring locations downstream.
[0065] Optionally, this sub-step is performed by the data acquisition submodule 111 in the data acquisition module 11. Specifically, the water level sensors are installed according to the principles of covering key monitoring points and avoiding interference, and are installed at multiple monitoring locations in the reservoir area (monitoring the reservoir water level) and the downstream river channel (monitoring the downstream water level). All sensors need to be deployed on seepage paths at different depths of the dam body to ensure that the water flow passes through the sensor monitoring area evenly and to avoid the impact of turbulence and dead zones on measurement accuracy.
[0066] Depending on the type of data collected, the data acquisition submodule 111 collects initial water level data of the reservoir (reflecting the reservoir's water storage status) and initial water level data from multiple monitoring locations downstream (reflecting the downstream flood-bearing capacity) in real time. The acquisition frequency is synchronized with the analysis frequency of subsequent modules to ensure data timeliness. Furthermore, the data acquisition submodule 111 transmits the collected initial data to the data preprocessing submodule 112 in real time via wireless communication technology to avoid analysis bias caused by data delay.
[0067] (2) Data preprocessing submodule, used to preprocess the initial water level data to obtain the water level summary data. The preprocessing includes removing outliers and synchronizing the water level data of the reservoir with the downstream monitoring location.
[0068] For example, this sub-step can be performed by the data preprocessing submodule 112 in the data acquisition module 11. Specifically, the data preprocessing submodule 112 removes outliers from the data. It can use statistical filtering algorithms (such as the moving average method) to identify and remove outliers (such as jump data caused by sensor failure) and noise (such as small errors caused by water flow fluctuations) in the initial data to ensure data accuracy.
[0069] In addition, the data preprocessing submodule 112 performs time synchronization, specifically by calibrating the timestamps of the initial water level data of the reservoir and the initial water level data of the downstream area, to ensure that the monitoring times of the two types of data are completely consistent, forming a dataset (i.e., water level summary data) that can be directly compared and analyzed.
[0070] Furthermore, the water level summary data will be continuously transmitted to the early warning assessment module 12 for calculating the early warning evaluation and the flood discharge control module 13 for judging the flood discharge effect and the downstream safety status.
[0071] S402. Based on the reservoir water level data, determine the early warning assessment for each monitoring moment. If the early warning assessment shows an upward trend and exceeds a preset threshold, determine the monitoring moment corresponding to the early warning assessment as the early warning start time. The early warning assessment characterizes the degree to which the reservoir water level approaches the safe water level and its upward trend per unit time.
[0072] For example, this step can be performed by the early warning assessment module 12 in the intelligent maintenance system 10, and specifically includes the following steps: (1) Determine the rate of water level rise based on the reservoir water level data at the current monitoring time and the reservoir water level data at the previous monitoring time.
[0073] In this step, the early warning assessment module 12 first receives the water level summary data output by the data acquisition module 11, and extracts the reservoir water level data at the continuous monitoring time from the water level summary data 1121. Specifically, this includes the reservoir water level data at the current monitoring time, denoted as... ; and the reservoir water level data of the previous monitoring time adjacent to the current monitoring time, denoted as Simultaneously, the time interval between the current monitoring time and the previous monitoring time is determined and denoted as Δt.
[0074] Δt is a fixed monitoring cycle preset by the intelligent maintenance system 10, such as 5 minutes or 10 minutes. The specific value can be set according to the sensitivity requirements of the water conservancy project to water level changes, so as to ensure that water level fluctuations can be captured in real time.
[0075] Furthermore, the early warning assessment module 12 calculates the rate of water level rise. = ( ) / Δt.
[0076] (2) Determine the early warning assessment for each monitoring moment based on the difference between the current water level and the safe water level, as well as the rate of water level rise.
[0077] For example, the early warning assessment module 12 determines the early warning evaluation for each monitoring time based on the following two scenarios: Scenario 1: The current water level of the reservoir (i.e., the subsequent...) The water level is less than the safe water level (i.e., the subsequent water level). ).
[0078] At this point, the early warning assessment for each monitoring moment is calculated using the following formula: In the above formula, The warning evaluation at the current monitoring time (i.e., the i-th monitoring time) is a dimensionless value after normalization of the maximum and minimum values, and its value range is 0-1. The larger the value, the closer the reservoir water level is to the safe water level and the stronger the urgency of the rise, and the higher the flood risk level. The smaller the value, the safer the reservoir operation, and the less need to initiate flood discharge control. This indicates the safe water level data of the reservoir; Indicates the first Reservoir water level data at each monitoring time; Indicates the first The rate of water level rise at each monitoring moment; This represents the maximum-minimum value normalization function. This represents the difference between the safe water level and the current water level. For parameter tuning coefficients, if If it is 0, then set it to 0.01. If it is not 0, then set it to 0.
[0079] Scenario 2: The current water level of the reservoir is higher than the safe water level.
[0080] It should be noted that, in Greater than In this case, The value is fixed at 1.
[0081] (3) When the early warning evaluation shows an upward trend and exceeds the preset threshold, the monitoring time corresponding to the early warning evaluation shall be determined as the early warning start time.
[0082] In this step, the early warning assessment module 12 presets an early warning evaluation threshold; when the early warning assessment module 12 detects that the early warning evaluation shows a continuous upward trend (i.e., Warning assessment greater than the previous monitoring time If the value of a certain monitoring moment exceeds 0.7, that moment will be determined as the warning start moment, and the signal at that moment will be transmitted to the flood discharge control module 13 in real time to trigger subsequent flood discharge operations.
[0083] It should be noted that the empirical value for the early warning evaluation threshold is 0.7. This value is determined based on historical hydrological data and through statistical analysis over N flood events, balancing the false alarm rate and the missed alarm rate. Those skilled in the art can adjust this value within the range of 0.6 to 0.8 according to the specific characteristics of the reservoir and flood control standards.
[0084] S403. Standard flood discharge operations shall be executed from the start time of the warning, and the flood discharge results shall be determined based on the changes in reservoir water level. The flood discharge results shall be used to characterize the actual effect of the standard flood discharge operations on the changes in reservoir water level.
[0085] For example, this step can be executed by the flood discharge control module 13, specifically by its sub-module—the standard flood discharge execution sub-module 131, which includes the following steps: (1) Determine the water level change value after the standard flood discharge operation is performed based on the water level data at the start of the warning and the water level data at the current monitoring time.
[0086] In this step, the standard flood discharge execution submodule 131 executes the standard flood discharge operation based on the standard flood discharge force. Specifically, after receiving the warning start time 121 transmitted by the warning assessment module 12, the standard flood discharge execution submodule 131 immediately initiates the standard flood discharge operation using that time as the trigger signal. The initial flood discharge force used in the standard flood discharge operation is the standard flood discharge force W0, with an exemplary value of 100 cubic meters per second. The value is determined by a preset rule that considers the dam structure's bearing capacity, the downstream river channel's flood carrying capacity threshold, and historical safe flood discharge data.
[0087] During execution, the standard flood discharge execution submodule 131 sends IoT control commands to drive the dam's flood discharge gates to open to a degree matching the standard flood discharge capacity W0. Furthermore, during execution, this submodule continuously extracts real-time reservoir water level data and real-time water level data from downstream monitoring locations from the water level summary data provided by the data acquisition module 11, ensuring that this real-time data is synchronously transmitted to the standard flood discharge execution submodule 131 for subsequent calculations and analysis.
[0088] Furthermore, the standard flood discharge execution submodule 131 determines the water level change value after executing the standard flood discharge operation based on the difference between the reservoir water level data at the current monitoring time and the warning start time, which is the value in the following steps. .
[0089] (2) Determine the flood discharge result based on the water level change value and the early warning evaluation at the current monitoring time.
[0090] For example, the standard flood discharge execution submodule 131 calculates the flood discharge result using the following formula: In the above formula, The first phase of the warning stage. The results of flood discharge from the reservoir at each monitoring point; Indicates the first Early warning assessment at each monitoring point; Indicates the first Water level data at each monitoring time; This indicates the water level data at the start of the warning period; It should be noted that, Indicates the first The difference between the water level data at each monitoring time and the warning start time (i.e., the water level change value), when the difference is negative. A negative number indicates that the current water release is effective, the water level has begun to drop, and... The smaller the value, the more significant the decrease; when the difference is positive, A positive number indicates that the current water release has not caused the water level to drop, but rather has caused it to rise. The larger the value, the more significant the rise, requiring increased flood discharge to effectively lower the reservoir water level.
[0091] S404. Adjust the flood discharge intensity based on the flood discharge results.
[0092] For example, this step can be executed by the flood discharge control module 13 mentioned above. Specifically, it includes: combining the water level summary data 1121 from the data acquisition module 11 and the early warning evaluation data from the early warning assessment module 12, adjusting the flood discharge intensity according to two scenarios: insufficient flood discharge intensity and effective flood discharge intensity, to ensure that both the reservoir water level pressure is alleviated and downstream flooding is avoided. It should be noted that the specific process of the flood discharge control module 13 adjusting the flood discharge intensity according to the aforementioned steps is described in S501-S502 below, and will not be repeated here.
[0093] Therefore, the flood discharge control module 13 has achieved a leap from static threshold control to dynamic adaptive optimization by introducing a dual control mechanism that dynamically enhances the flood discharge intensity based on the early warning evaluation ratio and accurately reduces the flood discharge intensity by combining the downstream impact and downstream safety factor. This significantly improves the response speed and accuracy of flood discharge control while ensuring the safety of the reservoir dam, and effectively takes into account the flood control safety of the downstream area.
[0094] S405. When the early warning assessment at the current monitoring time is lower than the preset threshold, and the water level at all downstream monitoring locations is lower than the preset safe water level, the flood discharge is determined to end.
[0095] In this step, after flood discharge regulation according to the above steps, the flood discharge end determination submodule 133 traverses all downstream monitoring locations. If all downstream monitoring locations are below the safe water level, and the current monitoring time's warning assessment is below the preset threshold for entering the warning stage, then the effective drainage of the reservoir and downstream safety are ensured. The flood discharge end determination submodule 133 determines the flood discharge has ended until the following two conditions are met: In the formula, Indicates the current monitoring time (i.e., the [number]th monitoring point). Early warning assessment (at each monitoring time point); Indicates the first Safe water level data at each downstream monitoring location; Indicates the first The monitoring time at the first monitoring moment Current water level data at each downstream monitoring location.
[0096] In one possible implementation, after the flood discharge ends, the flood discharge post-processing module 14, upon receiving the flood discharge end signal from the flood discharge control module 13, performs follow-up operations—controlling relevant IoT devices to clean sediment from the flood discharge channel, checking the status of the flood discharge equipment, and ensuring the long-term safety of the reservoir dam. Additionally, it closes and locks the flood discharge gates after the flood discharge ends and records the water level data and flood discharge operation data during the discharge period. Thus, maintenance personnel can perform equipment maintenance and evaluate the flood discharge effect based on the water level data and flood discharge operation data recorded by the flood discharge post-processing module 14.
[0097] Based on the above technical solutions, this invention constructs an intelligent flood discharge system that can adaptively and precisely regulate the water level of the reservoir and downstream areas by collecting and processing reservoir and downstream water level data in real time and accurately triggering flood discharge operations based on dynamic calculation and early warning evaluation. This significantly improves the flood discharge response speed and regulation accuracy while effectively coordinating reservoir safety and downstream flood control objectives. It solves the core problems of traditional methods, such as delayed response, extensive regulation, and difficulty in balancing upstream and downstream safety, and comprehensively enhances the intelligent level of water conservancy project operation and flood control safety guarantee capabilities.
[0098] For example, in combination Figure 4 ,like Figure 5 The diagram shown is a flowchart illustrating another intelligent maintenance method for water conservancy projects based on the Internet of Things (IoT) according to an embodiment of the present invention. In this method, the flood discharge intensity is adjusted based on the flood discharge results, specifically including the following steps: S501. When the flood discharge results indicate that the reservoir water level is rising, increase the flood discharge intensity based on the ratio of the current monitoring time's early warning assessment to the historical maximum early warning assessment during the early warning phase.
[0099] The warning phase is the period from the start of the warning to the end of the flood discharge.
[0100] It is understandable that S501 corresponds to the scenario one described above, that is, the flood discharge result indicates that the water level is rising, so the flood discharge intensity is increased. For example, this step is performed by the flood discharge capacity adjustment submodule 132, and the increased flood discharge capacity is calculated according to the following formula: In the above formula, Indicates the current monitoring time (i.e., the [number]th monitoring point). (At each monitoring point) the increased flood discharge intensity; Indicates the standard flood discharge capacity; , Indicates the first The early warning assessment at each monitoring time and the maximum value of the early warning assessment during the early warning phase.
[0101] S502. When the flood discharge results indicate a drop in water level, reduce the flood discharge intensity based on the downstream safety factor generated by the flood discharge.
[0102] Among them, the downstream safety factor is used to characterize the safety and stability of water levels at various monitoring locations downstream under the current flood discharge intensity.
[0103] It is understandable that S502 corresponds to scenario two described above, which is that the flood discharge result indicates a drop in water level, thus reducing the flood discharge intensity. This indicates that the initial flood discharge is effective. The discharge intensity needs to be reduced based on the downstream safety situation. The specific steps are as follows: (1) Determine the water flow propagation time from the reservoir to each downstream monitoring location based on the river geographical distance between the reservoir and each downstream monitoring location, and the average water flow velocity between the reservoir and each downstream monitoring location.
[0104] In this step, the flood discharge capacity adjustment submodule 132 calculates the water flow propagation time using the following formula: In the above formula, Indicates the first Water flow propagation time at each downstream monitoring location; Indicates the reservoir and the first The geographical distance of each downstream monitoring location from the river; Representing the reservoir and the first The water flow velocity at each downstream monitoring location at the same time.
[0105] It should be noted that the reservoir and the first Average water flow velocity at each downstream monitoring location It is considered as the flow velocity of water in this section of the river, and the ratio of distance to velocity is used to represent the water flow propagation time, thus balancing the velocity loss during water transport.
[0106] (2) For each downstream monitoring location, the downstream safety factor caused by the flood discharge is determined based on the distance between the downstream monitoring location and the reservoir and the preset safe water level of the downstream monitoring location.
[0107] For example, the flood discharge intensity regulation submodule 132 calculates the downstream safety factor for each downstream monitoring location using the following formula: In the above formula, Indicates the current monitoring time (i.e., the [number]th monitoring point). The downstream safety factor generated by the flood discharge at each monitoring time is processed using the maximum-minimum normalization method to ensure that its value range is within the [0,1] interval; This indicates the number of all monitoring locations downstream; This represents the distance from the reservoir and will not be zero in practical applications; Indicates the first The downstream monitoring location, with reference time (i.e., the first downstream monitoring location). The monitoring time is superimposed with the water flow propagation time to that monitoring location. The variance of all water level data within a time window (e.g., a length of Δt, which can be 15 minutes) is used to characterize the stability of downstream water level fluctuations within this time window. In specific calculations, it can be calculated using the sample variance formula in statistics based on multiple water level values collected at equal time intervals within this window. Indicates the first Safe water level data at each downstream monitoring location; Indicates the first The monitoring time at the first monitoring moment Current water level data at each downstream monitoring location; This indicates the standard flood discharge capacity.
[0108] It should be noted that, for each downstream monitoring location j, the safe water level difference is calculated. (The larger the difference, the safer it is), divided by the distance. To reflect spatial weight (the closer the distance, the greater the influence), it is then multiplied by a penalty factor. In the penalty factor, variance A larger variance indicates more drastic water level fluctuations and a correspondingly smaller safety contribution; a smaller variance means the penalty factor approaches 1, and the safety contribution is mainly determined by the safe water level difference. The downstream safety factor is obtained by summing and normalizing the contribution values from all monitoring locations. Its range is [0,1], and the larger the value, the safer the downstream.
[0109] (3) Reduce the intensity of flood discharge based on the downstream safety factor generated by the flood discharge.
[0110] For example, the flood discharge capacity adjustment submodule 132 calculates the reduced flood discharge capacity according to the following formula: In the above formula, Indicates the current monitoring time (i.e., the [number]th monitoring point). (at each monitoring time point) the reduced flood discharge intensity; Indicates the standard flood discharge capacity; Indicates the first The downstream safety factor resulting from flood discharge at each monitoring point. The larger the value, the safer the downstream area, and the greater the discharge capacity can be maintained to accelerate the decline of the reservoir water level; downstream safety factor The smaller the value, the less safe the downstream area is; the discharge capacity should be reduced to protect downstream safety. Adjusted discharge capacity. The value range is [0, To ensure that the discharge capacity does not exceed the standard discharge capacity, the control objective of reducing the discharge capacity is achieved.
[0111] Based on the above technical solutions, this embodiment of the invention constructs a complete closed-loop control mechanism. During the early warning stage, it not only dynamically adjusts the flood discharge intensity based on real-time feedback of reservoir water level changes—increasing the flood discharge intensity according to the early warning evaluation ratio to quickly reduce pressure when the water level rises, but also precisely weakens the flood discharge intensity by combining the water level safety and stability status of downstream monitoring locations after synchronization of water flow propagation time when the water level falls. This achieves dynamic adaptation and collaborative optimization of reservoir flood discharge and downstream risk-bearing capacity, ultimately ensuring the structural safety of the reservoir dam while significantly improving the flood control safety level and the intelligence level of the control process of the entire basin.
[0112] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
[0113] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.
Claims
1. An Internet of Things based intelligent maintenance system for water works, characterized in that, The IoT-based intelligent maintenance system for water conservancy projects includes: a data acquisition module, an early warning and assessment module, and a flood discharge control module; The data acquisition module is used to acquire water level summary data; wherein, the water level summary data includes reservoir water level data and water level data from multiple monitoring locations downstream; The early warning assessment module is used to determine the early warning assessment for each monitoring moment based on the reservoir water level data, and when the early warning assessment shows an upward trend and exceeds a preset threshold, the monitoring moment corresponding to the early warning assessment is determined as the early warning start moment; wherein, the early warning assessment is used to characterize the degree to which the reservoir water level approaches the safe water level and the upward trend per unit time. The flood discharge control module is used to execute standard flood discharge operations starting from the warning start time and determine the flood discharge result based on the reservoir water level change; wherein, the flood discharge result is used to characterize the actual change effect of the reservoir water level after the standard flood discharge operation; The flood discharge control module is also used to control the flood discharge intensity based on the flood discharge results; The flood discharge control module is also used to determine the end of flood discharge when the early warning evaluation at the current monitoring time is lower than the preset threshold and the water level at all downstream monitoring locations is lower than the preset safe water level.
2. The Internet of Things based intelligent maintenance system for water works as claimed in claim 1 wherein, When determining the early warning assessment based on the reservoir's water level data for each monitoring moment, the early warning assessment module specifically performs the following steps: The early warning assessment module is also used to determine the rate of water level rise based on the reservoir water level data at the current monitoring time and the reservoir water level data at the previous monitoring time. The early warning assessment module is also used to determine the early warning assessment for each monitoring moment based on the difference between the current water level and the safe water level, as well as the rate of water level rise.
3. The Internet of Things based intelligent maintenance system for water works as claimed in claim 1 wherein, The flood discharge control module includes: a standard flood discharge execution submodule, a flood discharge intensity adjustment submodule, and a flood discharge end determination submodule; The standard flood discharge execution submodule is used to execute the standard flood discharge operation from the warning start time and determine the flood discharge result based on the reservoir water level change; The flood discharge intensity adjustment submodule is used to adjust the flood discharge intensity according to the flood discharge result; The flood discharge end determination submodule is used to determine the end of flood discharge when the early warning evaluation at the current monitoring time is lower than the preset threshold and the water level at all downstream monitoring locations is lower than the preset safe water level.
4. The Internet of Things based intelligent maintenance system for water works as claimed in claim 3 wherein, When determining the flood discharge result based on the reservoir water level change, the standard flood discharge execution submodule specifically performs the following steps: The standard flood discharge execution submodule is also used to determine the water level change value after executing the standard flood discharge operation based on the water level data at the warning start time and the water level data at the current monitoring time. The standard flood discharge execution submodule is also used to determine the flood discharge result based on the water level change value and the early warning evaluation at the current monitoring time.
5. The Internet of Things based intelligent maintenance system for water works as claimed in claim 3 wherein, When the flood discharge intensity adjustment submodule adjusts the flood discharge intensity based on the flood discharge results, it specifically performs the following steps: The flood discharge intensity adjustment submodule is further configured to increase the flood discharge intensity when the flood discharge result indicates that the reservoir water level is rising, based on the ratio of the warning evaluation at the current monitoring time to the historical maximum warning evaluation within the warning period; wherein, the warning period is the time period from the start time of the warning to the end time of the flood discharge.
6. The Internet of Things based intelligent maintenance system for water works as claimed in claim 3 wherein, When the flood discharge intensity adjustment submodule adjusts the flood discharge intensity based on the flood discharge results, it specifically performs the following steps: The flood discharge intensity adjustment submodule is also used to reduce the flood discharge intensity when the flood discharge result indicates a drop in water level, based on the downstream safety factor generated by the flood discharge downstream; wherein, the downstream safety factor is used to characterize the water level safety and stability at each monitoring location downstream under the current flood discharge intensity.
7. The Internet of Things based intelligent maintenance system for water works as claimed in claim 6 wherein, When the flood discharge intensity adjustment submodule adjusts the flood discharge intensity based on the flood discharge results, it specifically performs the following steps: The flood discharge intensity adjustment submodule is also used to determine the downstream safety factor of the flood discharge for each downstream monitoring location based on the distance between the downstream monitoring location and the reservoir, the preset safe water level of the downstream monitoring location, and the water level data at the current monitoring time.
8. The Internet of Things based intelligent maintenance system for water works as claimed in claim 7 wherein, Before determining the downstream safety factor caused by the flood discharge, the flood discharge capacity adjustment submodule also performs the following steps: The flood discharge intensity adjustment submodule is also used to determine the water flow propagation time from the reservoir to each downstream monitoring location based on the river geographical distance between the reservoir and each downstream monitoring location, and the average water flow velocity between the reservoir and each downstream monitoring location.
9. The Internet of Things based intelligent maintenance system for water works as claimed in claim 1 wherein, The IoT-based intelligent maintenance system for water conservancy projects also includes: a flood discharge post-treatment module; The flood discharge post-processing module is used to close and lock the flood discharge gate after the flood discharge is completed, and to record the water level data and flood discharge operation data during the flood discharge period.
10. The Internet of Things based intelligent maintenance system for water works as claimed in any one of the claims 1 to 9 wherein, The data acquisition module includes: a data acquisition submodule and a data preprocessing submodule; The data acquisition submodule is used to collect initial water level data through water level sensors deployed in the reservoir and downstream river channel; wherein, the initial water level data includes initial water level data of the reservoir and initial water level data of multiple monitoring locations downstream; The data preprocessing submodule is used to preprocess the initial water level data to obtain the water level summary data; wherein, the preprocessing includes removing outliers and synchronizing the water level data of the reservoir with the downstream monitoring location.