Water conservancy automatic management system based on Internet of Things

Abstract

The invention relates to the technical field of programmed control, and provides a water conservancy automatic management system based on the Internet of Things. Comprising a data acquisition module, a data processing unit and an Internet of Things transmission module, and the data acquisition module is used for acquiring real-time water regimen data of an upstream reservoir and a downstream reservoir and transmitting the real-time water regimen data to the data processing unit through the Internet of Things; the data processing unit is used for calculating the residual water storage of a downstream reservoir and the maximum allowable flood discharge of upstream water; the Internet of Things transmission module is used for transmitting the real-time water level data acquired by the data acquisition module to the data processing unit; and the gate opening parameter calculated by the data processing unit is transmitted to an upstream gate controller. The management efficiency of the water conservancy project is improved to a certain extent, and the risk of dam overflow of the downstream reservoir caused by mismatching of upstream flood discharge and water storage capacity of the downstream reservoir in the multi-stage flood discharge process of the reservoir group in the mountainous area is greatly reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of programmed control, and in particular to a water conservancy automation management system based on the Internet of Things. Background Art

[0002] With the rapid development of information technology and automation control technology, the intelligent management of water conservancy projects has become an important direction for the development of modern water conservancy. Traditional water conservancy project management mainly relies on manual experience for gate adjustment and water volume scheduling. This method is often difficult to achieve precise control and rapid response when facing complex water conditions. In recent years, the introduction of Internet of Things technology has provided new technical support for the intelligent management of water conservancy projects. Through the real-time collection of hydrological data through sensor networks and the combination of remote control systems for gate adjustment, the management efficiency of water conservancy projects has been improved to a certain extent. However, the current water conservancy automation system still has many technical difficulties in the joint scheduling of multi-level reservoirs, especially in the multi-level flood discharge process of mountain reservoirs, the mismatch between the upstream flood discharge and the water storage capacity of the downstream reservoir leads to the risk of overflow of the downstream reservoir. Summary of the invention

[0003] The embodiments of the present invention provide a water conservancy automation management method based on the Internet of Things, which can at least to a certain extent solve the problem of overflowing the downstream reservoir due to the mismatch between the upstream flood discharge and the water storage capacity of the downstream reservoir during the multi-stage flood discharge process of a mountain reservoir group.

[0004] Other features and advantages of the present invention will become apparent from the following detailed description, or may be learned in part by practice of the present invention.

[0005] According to one aspect of the present invention, there is provided a water conservancy automation management system based on the Internet of Things, comprising: A data acquisition module is used to collect real-time water level data of upstream reservoirs and downstream reservoirs, and transmit the real-time water level data to a data processing unit through the Internet of Things; A data processing unit, including a water storage capacity calculation module and a flood discharge capacity calculation module; The water storage capacity calculation module is used to calculate the remaining water storage capacity of the downstream reservoir according to the real-time water level data and storage capacity curve of the downstream reservoir; The flood discharge calculation module is used to calculate the maximum allowable flood discharge of the upstream reservoir based on the remaining water storage calculated by the water storage calculation module and the flood discharge rate of the upstream reservoir, and convert the maximum allowable flood discharge into a gate opening parameter; The Internet of Things transmission module is used to transmit the real-time water level data collected by the data acquisition module to the data processing unit; and transmit the gate opening parameters calculated by the data processing unit to the upstream gate controller.

[0006] In the present invention, based on the above scheme, the reservoir capacity curve is segmentedly fitted based on the reservoir terrain characteristics, including the interval from dead water level to flood limit water level and the interval from flood limit water level to design flood level; The reservoir capacity curve from dead water level to flood limit water level is as follows: ; in: ; The reservoir capacity curve from the flood limit water level to the design flood level is shown as follows: ; in, Reservoir at dead water level Flood limit water level The storage capacity within the interval, Indicates that the reservoir is at the flood limit water level To design flood level The storage capacity within the interval, It is the storage capacity below the dead water level. is the current water level, is the dead water level, The flood limit water level is For the design flood level, is the water surface area at water level x, is the attenuation coefficient, is the scale coefficient, is the basin widening coefficient, is the fitting order.

[0007] In the present invention, based on the above solution, the determination of the attenuation coefficient and the scale coefficient includes: Establish basic characteristic values ​​based on topographic survey data, calculate topographic relief index, intersection angle coefficient of tributaries and main streams, and vegetation cover density; A correction value is calculated according to the topographic relief index, the intersection angle coefficient of the tributary and the main stream, and the vegetation coverage density, and the correction value is added to the initial values ​​of the attenuation coefficient and the scale coefficient to obtain the final attenuation coefficient and the scale coefficient.

[0008] In the present invention, based on the above scheme, the calculation of the remaining water storage capacity is shown as follows: ; in: ; in, is the remaining storable water volume at time t, The maximum storage capacity designed for the reservoir is is the current water storage corresponding to the water level at time t, To dynamically adjust capacity, is the safety margin factor, is the water level change rate, To compensate for capacity, is the seasonal influence coefficient, is the calculation cycle.

[0009] In the present invention, based on the above scheme, the compensation capacity calculation includes: Determine the benchmark compensation capacity and calculate the dynamic correction factor based on real-time monitoring data; Set the seasonal correction factor and dispatch correction factor according to the operating period and operating status of the reservoir; The benchmark compensation capacity is multiplied by a dynamic correction factor, a seasonal correction factor and a dispatch correction factor respectively to obtain the compensation capacity; Determination of the dynamic correction coefficient includes: When the accumulated rainfall in the first preset period upstream exceeds the first preset threshold, the dynamic correction coefficient increases by the first preset correction value for each time it exceeds the first preset increment, and the maximum does not exceed the second preset threshold; when the accumulated rainfall in the rainfall forecast for the second preset period upstream exceeds the third preset threshold, the dynamic correction coefficient increases by the second preset correction value for each time it exceeds the second preset increment, and the maximum does not exceed the fourth preset threshold; when the average inflow flow in the upstream maximum preset statistical period exceeds the preset proportion of the design inflow flow, the dynamic correction coefficient increases by the third preset correction value for each time it increases by the third preset increment, and the maximum does not exceed the fifth preset threshold; the maximum value of the above three correction coefficients is selected as the final dynamic correction coefficient.

[0010] In the present invention, based on the above scheme, the calculation of the maximum allowable flood discharge is shown in the following formula: ; Among them, the safety factor As shown below: ; in, is the maximum allowable flood discharge at time t, The safe discharge volume of the downstream river is is the discharge of the upstream reservoir at time t, is the remaining water storage capacity of the downstream reservoir, is the length of the scheduling period, Design flood discharge for downstream reservoirs, is the downstream interval flow, is the water level change rate influence coefficient (0.8-1.2), is the water level change rate.

[0011] In the present invention, based on the above scheme, the discharge flow of the upstream reservoir is calculated as shown in the following formula: ; in: ; in, is the discharge of the upstream reservoir at time t, is the leakage coefficient, is the basic leakage coefficient, is the gate width, is the gate opening at time t, is the acceleration due to gravity, is the upstream head at time t, is the downstream head, is the head ratio correction coefficient, is the flooding influence coefficient.

[0012] According to one aspect of the present invention, there is provided a water conservancy automation management method based on the Internet of Things, comprising: Collecting real-time water condition data of the upstream reservoir and the downstream reservoir, and transmitting the real-time water level data to the data processing unit through the Internet of Things transmission module; The data processing unit calculates the remaining water storage capacity of the downstream reservoir according to the real-time water level data and storage capacity curve of the downstream reservoir; Based on the remaining water storage capacity and in combination with the flood discharge rate of the upstream reservoir, the maximum allowable flood discharge of the upstream reservoir is calculated; The data processing unit converts the maximum allowable flood discharge into an upstream gate opening parameter, and sends it to the upstream gate controller through the Internet of Things transmission module for automatic adjustment.

[0013] In the technical solution of the present invention, the water level data of the upstream and downstream reservoirs are collected and transmitted in real time through the Internet of Things technology, and the remaining water storage capacity of the downstream reservoir is accurately calculated in combination with the high-precision storage capacity curve and dynamic correction model; the improved discharge flow calculation method is used to significantly improve the calculation accuracy of the maximum allowable flood discharge of the upstream reservoir, ensuring that the flood discharge operation meets the downstream safety requirements; at the same time, the intelligent gate adjustment algorithm is adopted to make the gate control more accurate and stable, reducing the risk of misoperation. In addition, through the dual communication mode and dual power supply design, the continuity and reliability of data collection and transmission are guaranteed, providing efficient and stable technical support for flood control and dispatching during the flood season, comprehensively improving the intelligence and emergency response capabilities of the reservoir dispatching system, and greatly reducing the problem of downstream reservoir overflow caused by the mismatch between upstream flood discharge and downstream reservoir water storage capacity during the multi-level flood discharge process of mountain reservoir groups.

[0014] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings herein are incorporated into and constitute a part of the specification, showing embodiments consistent with the present invention, and together with the specification, are used to explain the principles of the present invention. Obviously, the accompanying drawings described below are only some embodiments of the present invention, and for those of ordinary skill in the art, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0016] Figure 1 The system block diagram of a water conservancy automation management system based on the Internet of Things in one embodiment of the present invention is schematically shown.

[0017] Figure 2 The flowchart of a water conservancy automation management system based on the Internet of Things in one embodiment of the present invention is schematically shown. DETAILED DESCRIPTION

[0018] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the present invention will be more comprehensive and complete and fully convey the concept of the example embodiments to those skilled in the art.

[0019] In addition, the described features, structures or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present invention. However, those skilled in the art will appreciate that the technical solution of the present invention can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring various aspects of the present invention.

[0020] The implementation details of the technical solution of the present invention are described in detail below: Figure 1 FIG. 1 is a flow chart of a water conservancy automation management system based on the Internet of Things according to an embodiment of the present invention. Figure 1 shown.

[0021] According to an embodiment of the present invention, a water conservancy automation management system based on the Internet of Things includes: a data acquisition module, a data processing unit, and an Internet of Things transmission module.

[0022] The data acquisition module collects real-time water conditions data of the upstream reservoir and the downstream reservoir, and transmits the real-time water level data to the data processing unit through the Internet of Things; more specifically, ultrasonic water level sensors with IP67 waterproof grade are installed at measuring points every 200 meters along the spillway inlet section of the upstream reservoir and the downstream reservoir, on both sides of the flood discharge gate, and on the flood control dam body. The ultrasonic water level sensor has a range of 0-30 meters and a measurement accuracy of ±0.2%FS. Real-time water level data is collected every 10 minutes by the ultrasonic water level sensor.

[0023] More specifically, the ultrasonic water level sensor transmits the collected real-time water level data to the nearest Internet of Things data collector through a shielded twisted pair cable and a RS485 bus with a baud rate of 9600. The Internet of Things data collector then performs a 16-bit CRC cyclic redundancy check on the real-time water level data. After the check, the real-time water level data is packaged into JSON format and uploaded to the data processing unit via the 4G wireless communication network using the TCP / IP protocol. When the 4G signal strength is detected to be lower than -95dBm for 30 seconds, the Internet of Things data collector automatically switches to the Beidou communication module to continue transmitting the real-time water level data.

[0024] In addition, the Internet of Things data collector adopts a dual power supply mode. When the main power supply is cut off, it automatically switches to a lithium battery backup power supply that can provide continuous power for 72 hours. If a transmission failure occurs during the upload of the real-time water level data, the real-time water level data will be temporarily stored in the data cache module and attempted to be re-uploaded every 1 minute until the real-time water level data is successfully transmitted to the data processing unit.

[0025] On the other hand, the data processing unit includes a water storage calculation module and a flood discharge calculation module. The water storage calculation module is used to calculate the remaining water storage capacity of the downstream reservoir according to the real-time water level data and storage capacity curve of the downstream reservoir.

[0026] The flood discharge calculation module is used to calculate the maximum allowable flood discharge of the upstream reservoir based on the remaining water storage calculated by the water storage calculation module and the flood discharge rate of the upstream reservoir, and convert the maximum allowable flood discharge into a gate opening parameter.

[0027] After receiving the real-time water level data of the downstream reservoir, the water storage capacity calculation module matches the real-time water level data with the pre-stored segmented storage capacity curve of the downstream reservoir, wherein the segmented storage capacity curve is fitted based on the water storage capacity data corresponding to the water point at each interval of 0.1 meters from the dead water level to the design flood level of the downstream reservoir. Specifically, the segmented storage capacity curve is established by a segmented fitting method based on the terrain characteristics of the reservoir, as follows: For the dead water level to flood limit water level interval ( ): ; in: ; For the interval from flood limit water level to design flood level ( ): ; in, Reservoir at dead water level Flood limit water level The storage capacity within the interval, Indicates that the reservoir is at the flood limit water level To design flood level The storage capacity within the interval, It is the storage capacity below the dead water level. is the current water level, is the dead water level, The flood limit water level is For the design flood level, is the water surface area at water level x, is the attenuation coefficient, is the scale coefficient, is the basin widening coefficient, is the fitting order.

[0028] It should be noted that the determination of the terrain characteristic coefficient includes: firstly, establishing basic characteristic values ​​based on high-precision measurement data, and the basic characteristic values ​​include: the cross-sectional shape factor of the reservoir area is equal to the ratio of the measured cross-sectional area to the product of the maximum water depth and the water surface width of the cross-sectional area; the longitudinal slope change rate of the reservoir area is equal to the ratio of the riverbed elevation difference between adjacent cross-sectional areas to the cross-sectional spacing; the lateral widening coefficient of the reservoir basin is equal to the ratio of the area increment to the elevation increment between adjacent contour lines; and then calculating the landform undulation index, specifically: in a unit grid of 10 meters × 10 meters, the landform undulation index is equal to the height difference between the highest point and the lowest point in the grid divided by the grid side length. When the landform undulation index is greater than 0.3, every increase of 0.1, Increase by 0.05, Reduce by 0.08; then interpret the vegetation coverage density based on remote sensing images. When the vegetation coverage is below 30%, Increase by 0.03, Decrease by 0.04, when the vegetation coverage is between 30% and 70%, and Remain unchanged. When the vegetation coverage exceeds 70%, Decrease by 0.03, Increased by 0.04.

[0029] In addition, the characteristics of tributaries entering the reservoir area were analyzed, and the intersection angle coefficient between the tributaries and the main stream was calculated. When the intersection angle was less than 30°, Increase by 0.06, Reduce by 0.07, when the intersection angle is between 30°-60°, Increase by 0.04, Decrease by 0.05, when the intersection angle is greater than 60°, Increase by 0.02, Decrease by 0.03; at the same time, calculate the cross-sectional mutation coefficient of the tributary, which is equal to the ratio of the cross-sectional area of ​​the tributary confluence to the cross-sectional area of ​​the upstream. When the cross-sectional mutation coefficient is greater than 1.5, increase by 0.2, Increase by 0.04, Reduce by 0.06; finally, based on the cumulative effect of each calculation indicator, determine and The initial value of the dead water level to flood limit water level range The initial value of is 0.8, The initial value of is 0.6, and the flood limit water level to the design flood level range The initial value of ᵢ is 1.2, The initial value of is 0.9, and the calculated correction values ​​are added to the initial value to obtain the final terrain characteristic coefficient.

[0030] When the fitting error between the calculated terrain characteristic coefficient and the measured water level-reservoir capacity relationship exceeds 2%, the parameters need to be adjusted according to the following rules: When the reservoir capacity calculated by the model is too large, reduce the corresponding Increase , otherwise it will increase accordingly Reduce , adjust the step size to 1 / 2 of the above correction value until the fitting error is less than 2%.

[0031] More importantly, to ensure calculation stability, the terrain characteristic coefficients must meet the following constraints: ; ; ; ; Then, based on the measured water level, the current water storage capacity corresponding to the water level is calculated by cubic spline interpolation method, and the difference between the designed storage capacity of the downstream reservoir and the current water storage capacity is calculated to obtain the remaining water storage capacity of the downstream reservoir. As shown in the following formula: ; in: ; in, is the remaining storable water volume at time t, The maximum storage capacity designed for the reservoir is is the current water storage corresponding to the water level at time t, To dynamically adjust capacity, is the safety margin factor, is the water level change rate, To compensate for capacity, is the seasonal influence coefficient, is the calculation period (usually 24 hours).

[0032] It should be noted that the calculation process of compensation capacity is as follows: The calculation process of the compensation capacity of the downstream reservoir first determines the benchmark compensation capacity, which is equal to 30% of the difference between the storage capacity corresponding to the flood limit water level of the downstream reservoir and the storage capacity corresponding to the dead water level; then the dynamic correction coefficient is calculated based on the real-time monitoring data, specifically: when the 48-hour cumulative rainfall in the upstream exceeds 50mm, the dynamic correction coefficient increases by 0.02 for every 10mm exceeding, and the maximum does not exceed 0.3; when the 72-hour rainfall forecast cumulative rainfall in the upstream exceeds 100mm, the dynamic correction coefficient increases by 0.03 for every 20mm exceeding, and the maximum does not exceed 0.25; when the maximum 3-hour average inflow flow in the upstream exceeds 60% of the designed inflow flow, the dynamic correction coefficient increases by 0.04 for every 5% increase, and the maximum does not exceed 0.35; the maximum value of the above three correction coefficients is selected as the final dynamic correction coefficient; then Then, the seasonal correction coefficient is set according to the operating period of the reservoir, where June 1 to September 30 each year is the flood season, and the seasonal correction coefficient is 1.2; December 1 to February 28 of the following year is the dry season, and the seasonal correction coefficient is 0.8; the rest of the time is the normal water period, and the seasonal correction coefficient is 1.0; in addition, the scheduling correction coefficient is set based on the current operating status of the reservoir. When the reservoir is in a flood control scheduling state, that is, when the current water level exceeds the flood limit water level, the scheduling correction coefficient is 1.3; when the reservoir is in a water supply scheduling state, that is, when the current water level is 1.5 meters lower than the flood limit water level, the scheduling correction coefficient is 0.7; in other cases, the scheduling correction coefficient is 1.0; finally, the benchmark compensation capacity is multiplied by the dynamic correction coefficient, the seasonal correction coefficient and the scheduling correction coefficient respectively to obtain the final compensation capacity value.

[0033] Finally, the calculated remaining water storage capacity data is updated to the database in real time and displayed in the form of numerical values ​​and trend graphs through a visual interface. At the same time, the system automatically records the data update time, calculation process and verification results to form a complete data processing log.

[0034] On the other hand, the flood discharge calculation module calculates the maximum allowable flood discharge of the upstream reservoir based on the remaining water storage capacity and the flood discharge rate of the upstream reservoir; more specifically, a discharge model is established based on the current flood discharge and gate opening of the upstream reservoir, wherein the discharge calculation formula is: ; Calculation of the discharge coefficient When the water level of the downstream reservoir is close to the flood limit water level, the traditional discharge coefficient calculation method fails to effectively consider the impact of downstream support, and only uses a fixed flooding coefficient for correction. This results in a large deviation between the discharge calculation result and the actual value under the condition of rapid changes in the downstream water level, affecting the accurate calculation of the maximum allowable flood discharge of the upstream reservoir. Especially during the joint dispatch of upstream and downstream reservoirs during the flood season, this error will cause the actual water storage capacity of the downstream reservoir to exceed expectations, increasing the risk of flood control. Therefore, this embodiment adopts an improved hydraulic correction formula: ; in, is the discharge of the upstream reservoir at time t, is the leakage coefficient, is the basic discharge coefficient (0.65-0.7), is the gate width, is the gate opening at time t, is the acceleration due to gravity, is the upstream head at time t, is the downstream head, is the head ratio correction coefficient (0.1-0.15), is the flooding influence coefficient (0.2-0.3).

[0035] It should be noted that by introducing a secondary correction term based on the downstream relative head , a dynamic response relationship between the discharge coefficient and the downstream water level was established. After verification by measured data, the improved calculation formula improved the discharge flow calculation accuracy by 40% when the downstream water level was close to the flood limit water level, and the maximum error was controlled within 4%.

[0036] The system then calculates the maximum allowable flood discharge based on the safe discharge of the downstream river and the current water transfer capacity, combined with the remaining water storage capacity of the downstream reservoir: ; Safety Factor Consider the influence of water level change rate: ; in, is the maximum allowable flood discharge at time t, The safe discharge volume of the downstream river is is the remaining water storage capacity of the downstream reservoir, is the length of the scheduling period, Design flood discharge for downstream reservoirs, is the downstream interval flow, is the water level change rate influence coefficient (0.8-1.2), is the water level change rate.

[0037] After receiving the maximum allowable flood discharge, the data processing unit first reversely calculates the corresponding target gate opening based on the discharge flow formula. To ensure control accuracy, the system uses an iterative calculation method to obtain an accurate target opening value through multiple approximations. After determining the target opening, the system comprehensively considers the mechanical characteristics, hydraulic characteristics and safety factors of the gate, automatically generates a segmented adjustment curve, and divides the opening adjustment process into three stages: acceleration, uniform speed and deceleration, to ensure that the gate opening and closing process is smooth and controllable.

[0038] It should be noted that when the system obtains the maximum allowable flood discharge, it takes this as the target value and starts to execute the iterative calculation cycle. In each round of iteration, the system first calculates the theoretical flow at the current opening through the discharge flow formula, compares it with the target flood discharge, and obtains the flow deviation. Then, the opening correction amount is calculated based on the deviation, and the current opening is adjusted to obtain the opening value of the next round of iteration.

[0039] When the flow deviation obtained in a round of iterative calculation is less than the preset allowable error, the opening value at this time is the final target opening required. If the flow deviation is still greater than the allowable error, the next round of iterative calculation will be performed. If the flow deviation has not converged after multiple consecutive rounds of iterations, or the number of iterations exceeds the set maximum value, the system will automatically adjust the iteration step size and re-execute the iteration process.

[0040] Subsequently, the data processing unit digitizes and encrypts the calculated opening parameters, and sends the control instructions to the gate controller through the IoT communication gateway. After receiving the instructions, the gate controller first verifies the data integrity and operation authority, and converts the opening parameters into specific motor control signals after verification. During the adjustment process, the system collects operating parameters such as gate opening, opening and closing force, and water level in real time, and monitors them in real time according to the preset safety constraints, including multiple protections such as opening and closing force limit, speed limit, and acceleration limit.

[0041] In the technical solution of the present invention, the water level data of the upstream and downstream reservoirs are collected and transmitted in real time through the Internet of Things technology, and the remaining water storage capacity of the downstream reservoir is accurately calculated in combination with the high-precision storage capacity curve and dynamic correction model; the improved discharge flow calculation method is used to significantly improve the calculation accuracy of the maximum allowable flood discharge of the upstream reservoir, ensuring that the flood discharge operation meets the downstream safety requirements; at the same time, the intelligent gate adjustment algorithm is adopted to make the gate control more accurate and stable, reducing the risk of misoperation. In addition, through the dual communication mode and dual power supply design, the continuity and reliability of data collection and transmission are guaranteed, providing efficient and stable technical support for flood control and dispatching during the flood season, comprehensively improving the intelligence and emergency response capabilities of the reservoir dispatching system, and greatly reducing the problem of downstream reservoir overflow caused by the mismatch between upstream flood discharge and downstream reservoir water storage capacity during the multi-level flood discharge process of mountain reservoir groups.

[0042] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Among them, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0043] The units involved in the embodiments of the present invention may be implemented by software or hardware, and the units described may also be arranged in a processor. The names of these units do not, in some cases, limit the units themselves.

[0044] According to one aspect of the present invention, a computer program product or a computer program is provided, the computer program product or the computer program comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the methods provided in the above-mentioned various optional implementations.

[0045] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the implementation of the present invention.

[0046] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the embodiments disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary technical means in the art that are not disclosed in the present invention.

[0047] It should be understood that the present invention is not limited to the exact construction that has been described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A water conservancy automation management system based on the Internet of Things, characterized in that: include: A data acquisition module is used to collect real-time water level data of upstream reservoirs and downstream reservoirs, and transmit the real-time water level data to a data processing unit through the Internet of Things; A data processing unit, including a water storage capacity calculation module and a flood discharge capacity calculation module; The water storage capacity calculation module is used to calculate the remaining water storage capacity of the downstream reservoir according to the real-time water level data and storage capacity curve of the downstream reservoir; The flood discharge calculation module is used to calculate the maximum allowable flood discharge of the upstream reservoir based on the remaining water storage calculated by the water storage calculation module and the flood discharge rate of the upstream reservoir, and convert the maximum allowable flood discharge into a gate opening parameter; The Internet of Things transmission module is used to transmit the real-time water level data collected by the data acquisition module to the data processing unit; and transmit the gate opening parameters calculated by the data processing unit to the upstream gate controller.

2. The water conservancy automation management system based on the Internet of Things according to claim 1 is characterized in that: The reservoir capacity curve is segmentedly fitted based on the reservoir topographic features, including the interval from dead water level to flood limit water level and the interval from flood limit water level to design flood level; The reservoir capacity curve from dead water level to flood limit water level is as follows: ; in: ; The reservoir capacity curve from the flood limit water level to the design flood level is shown as follows: ; in, Reservoir at dead water level Flood limit water level The storage capacity within the interval, Indicates that the reservoir is at the flood limit water level To design flood level The storage capacity within the interval, It is the storage capacity below the dead water level. is the current water level, is the dead water level, The flood limit water level is For the design flood level, is the water surface area at water level x, is the attenuation coefficient, is the scale coefficient, is the basin widening coefficient, is the fitting order.

3. The water conservancy automation management system based on the Internet of Things according to claim 1 is characterized in that: The determination of the attenuation coefficient and the scale coefficient includes: Establish basic characteristic values ​​based on topographic survey data, calculate topographic relief index, intersection angle coefficient of tributaries and main streams, and vegetation cover density; A correction value is calculated according to the topographic relief index, the intersection angle coefficient of the tributary and the main stream, and the vegetation coverage density, and the correction value is added to the initial values ​​of the attenuation coefficient and the scale coefficient to obtain the final attenuation coefficient and the scale coefficient.

4. The water conservancy automation management system based on the Internet of Things according to claim 1 is characterized in that: The calculation of the remaining water storage capacity is shown in the following formula: ; in: ; in, is the remaining storable water volume at time t, The maximum storage capacity designed for the reservoir is is the current water storage corresponding to the water level at time t, To dynamically adjust capacity, is the safety margin factor, is the water level change rate, To compensate for capacity, is the seasonal influence coefficient, is the calculation cycle.

5. The water conservancy automation management system based on the Internet of Things according to claim 4 is characterized in that: The compensation capacity calculation includes: Determine the benchmark compensation capacity and calculate the dynamic correction factor based on real-time monitoring data; Set the seasonal correction factor and dispatch correction factor according to the operating period and operating status of the reservoir; The benchmark compensation capacity is multiplied by a dynamic correction factor, a seasonal correction factor and a dispatch correction factor respectively to obtain the compensation capacity; Determination of the dynamic correction coefficient includes: When the accumulated rainfall in the first preset period upstream exceeds the first preset threshold, the dynamic correction coefficient increases by the first preset correction value for each time it exceeds the first preset increment, and the maximum does not exceed the second preset threshold; when the accumulated rainfall in the rainfall forecast for the second preset period upstream exceeds the third preset threshold, the dynamic correction coefficient increases by the second preset correction value for each time it exceeds the second preset increment, and the maximum does not exceed the fourth preset threshold; when the average inflow flow in the upstream maximum preset statistical period exceeds the preset proportion of the design inflow flow, the dynamic correction coefficient increases by the third preset correction value for each time it increases by the third preset increment, and the maximum does not exceed the fifth preset threshold; the maximum value of the above three correction coefficients is selected as the final dynamic correction coefficient.

6. The water conservancy automation management system based on the Internet of Things according to claim 1 is characterized in that: The maximum allowable flood discharge is calculated as follows: ; Among them, the safety factor As shown below: ; in, is the maximum allowable flood discharge at time t, The safe discharge volume of the downstream river is is the discharge of the upstream reservoir at time t, is the remaining water storage capacity of the downstream reservoir, is the length of the scheduling period, Design flood discharge for downstream reservoirs, is the downstream interval flow, is the water level change rate influence coefficient (0.8-1.2), is the water level change rate.

7. The water conservancy automation management system based on the Internet of Things according to claim 6 is characterized in that: The discharge flow of the upstream reservoir is calculated as follows: ; in: ; in, is the discharge of the upstream reservoir at time t, is the leakage coefficient, is the basic leakage coefficient, is the gate width, is the gate opening at time t, is the acceleration due to gravity, is the upstream head at time t, is the downstream head, is the head ratio correction coefficient, is the flooding influence coefficient.

8. A water conservancy automation management method based on the Internet of Things, characterized in that: include: Collecting real-time water condition data of the upstream reservoir and the downstream reservoir, and transmitting the real-time water level data to the data processing unit through the Internet of Things transmission module; The data processing unit calculates the remaining water storage capacity of the downstream reservoir according to the real-time water level data and storage capacity curve of the downstream reservoir; Based on the remaining water storage capacity and in combination with the flood discharge rate of the upstream reservoir, the maximum allowable flood discharge of the upstream reservoir is calculated; The data processing unit converts the maximum allowable flood discharge into an upstream gate opening parameter, and sends it to the upstream gate controller through the Internet of Things transmission module for automatic adjustment.

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