Power station ecological flow intelligent monitoring and adaptive release monitoring system

Abstract

This invention discloses a power plant ecological flow intelligent monitoring and adaptive release supervision system, including an adaptive operating condition identification module, a multi-source compliance monitoring module, an intelligent flow calculation module, a dual-channel redundant transmission module, an early warning linkage release module, a full-cycle operation and maintenance module, and a data platform. This invention avoids data anomalies in non-power generation conditions and improves calculation accuracy through adaptive operating condition identification and flow calculation based on different operating conditions; it employs dual-channel redundant transmission to ensure stable and compliant data transmission, meeting industry communication protocol requirements; three-level monitoring points and high-precision sensors achieve full-scenario compliant monitoring, and phased flow calculation conforms to ecological protection standards; a monitoring-early warning-release linkage mechanism ensures priority release of ecological flow, achieving a high compliance rate and supporting automatic reporting of special operating conditions; intelligent operation and maintenance and blockchain archive storage achieve full-process compliant management; the system interoperates with multi-level supervision platforms, fully meeting multiple industry standards and improving the efficiency and reliability of ecological flow supervision.

Description

Technical Field

[0001] This invention belongs to the field of regulatory system technology, specifically relating to a power plant ecological flow intelligent monitoring and adaptive release regulatory system. Background Technology

[0002] In the operation of water conservancy and hydropower projects, ensuring ecological flow is crucial for maintaining the ecological balance of the basin. The state has issued a series of standards, including NB / T 10385-2020, SL 651-2014, SL / T 819-2023, and SL / T 820-2023, which impose strict requirements on the accuracy of ecological flow monitoring, data transmission, flow calculation, discharge compliance, and operation and maintenance supervision. Existing power plant ecological flow monitoring systems generally suffer from poor adaptability to operating conditions, are prone to flow calculation anomalies under non-power generation conditions, use a single-channel, non-redundant communication design with insufficient transmission reliability, fail to perform flow calculations according to ecological base flow and sensitive periods, have weak linkage between monitoring and discharge early warning, rely on manual labor for operation and maintenance calibration and cross-section verification, have low levels of intelligence, are prone to tampering with compliance files, and cannot efficiently collaborate with regulatory platforms, making it difficult to meet the needs of multiple industry standards and full-cycle closed-loop supervision. Summary of the Invention

[0003] In view of this, the main objective of the present invention is to provide a power plant ecological flow intelligent monitoring and adaptive release supervision system.

[0004] To achieve the above objectives, the technical solution of the present invention is implemented as follows: Embodiment 1 of this invention provides a power plant ecological flow intelligent monitoring and adaptive discharge supervision system, including an adaptive operating condition identification module, a multi-source compliance monitoring module, an intelligent flow calculation module, a dual-channel redundant transmission module, an early warning linkage discharge module, a full-cycle operation and maintenance module, and a data platform. Each module communicates with the data platform via an industrial bus. The data platform interacts bidirectionally with the power plant PLC system, the provincial supervision platform, and the municipal supervision platform, forming a closed-loop management system encompassing monitoring, calculation, transmission, early warning, operation and maintenance, and supervision. The adaptive operating condition identification module collects the gate opening degree e, the upstream liquid level H, and the power generation signal in real time, and determines the operating condition according to the following rules: When e / H ≤ 0.65 and the power generation is ≥ 5% of the power plant's rated power, it is determined to be in power generation condition; When e / H > 0.65 or power generation = 0, it is determined to be a non-power generation condition; The intelligent flow calculation module switches the flow calculation model according to operating conditions. The formula used for power generation operating conditions is: Qᵢ=μbe√(2gh) Wherein, μ=0.60−0.176e / h; the historical correlation model of liquid level and flow rate is used to output flow rate under non-power generation conditions; the dual-channel redundant transmission module adopts 4G / 5G main channel + backup channel ultra-shortwave parallel communication, and automatically switches to backup channel when the main channel fails; when the monitoring flow rate of the early warning linkage release module is lower than the ecological base flow or the flow rate threshold during sensitive periods, it sends a gate adjustment command to the power station PLC, and the ecological release command has higher priority than the power generation command; the full-cycle operation and maintenance module automatically generates calibration and cross-section verification reminders according to the hydrological year, and the compliance file is stored using blockchain.

[0005] In the above scheme, the multi-source compliance monitoring module is deployed with three levels of monitoring points: the first-level monitoring point is located 5m downstream of the outlet of the ecological discharge facility; the second-level monitoring point is located 10m downstream of the confluence of the power generation tailwater and the discharge point; and the third-level monitoring point is located 50m upstream of the downstream sensitive target. The monitoring sensors include a gate position sensor with an accuracy of ≤±0.1cm, a water level sensor with an accuracy of ≤±3cm, and an acoustic Doppler flow velocity sensor with an accuracy of ±1%.

[0006] In the above scheme, the intelligent flow calculation module performs phased flow calculation: the ecological base flow adopts the Qp method, taking the average flow of the driest month with a 90% guarantee rate; the sensitive period flow adopts the ecological hydraulic method, satisfying an average water depth ≥ 0.3m and an average flow velocity ≥ 0.3m / s; when 17e > 60h, the system marks the data as abnormal and triggers a level two alarm.

[0007] In the above scheme, the intelligent traffic calculation module adopts a unified operating condition adaptation formula: Q=(μ×b×min(e,h)×√g×h^(4 / 3)) / (n×√L) Where: μ is 0.60−0.176e / h when e≤h, and 1.0 when e>h; b is the gate width, g=9.81m / s², n is the channel roughness, and L is the distance from the gate to the downstream uniform flow section.

[0008] In the above scheme, the switching rule of the dual-channel redundant transmission module is: main channel bit error rate > 1×10⁻ 6 Automatic switching occurs when the backup channel margin is ≤10dB, with a switching response time ≤10s. The data format conforms to the SL651-2014 protocol and uses CRC16 checksum. The uplink message includes the station code, time, opening degree, water level, flow rate, operating condition, and data status.

[0009] In the above scheme, the response delay of the early warning linkage release module is ≤30 seconds; special working conditions such as maintenance and flood control automatically generate reporting documents and push them to the supervision platform, and the ecological flow release satisfaction rate is ≥95%.

[0010] In the above scheme, the full-cycle operation and maintenance module uses RTK positioning for cross-section verification, with a measurement error of ≤±0.1m; calibration reminders are pushed 7 working days in advance, and the calibration timeliness rate is 100%.

[0011] In the above scheme, the historical correlation model of liquid level and flow rate under non-power generation conditions is a BP neural network model, LSTM or SVM model, with a training sample size of ≥1000 groups and a prediction error of ≤±3%.

[0012] In the above scheme, the dual channels are either primary channel satellite communication or backup channel LoRa communication; each sensor can be replaced with a radar level gauge, an electromagnetic velocity gauge, or a magnetostrictive displacement gauge.

[0013] In the above scheme, the instruction and regulatory data transmission is encrypted using AES-256 or RSA-2048.

[0014] Compared with existing technologies, this invention avoids data anomalies in non-power generation conditions and improves calculation accuracy through adaptive identification of operating conditions and flow calculation based on different operating conditions; it adopts dual-channel redundant transmission to ensure stable and compliant data transmission, meeting industry communication protocol requirements; three-level monitoring points and high-precision sensors achieve full-scenario compliant monitoring, and phased flow calculation conforms to ecological protection standards; the monitoring-early warning-release linkage mechanism ensures priority release of ecological flow, with a high compliance rate and supports automatic reporting of special operating conditions; intelligent operation and maintenance and blockchain archive storage achieve full-process compliant management, and the system is interconnected with multi-level regulatory platforms, fully meeting multiple industry standards and improving the efficiency and reliability of ecological flow supervision. Attached Figure Description

[0015] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and, together with their descriptions, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings: Figure 1 This is a flowchart illustrating the adaptive calculation of operating conditions as described in an embodiment of the present invention; Figure 2 This is a diagram showing the layout of monitoring points according to an embodiment of the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0017] In the accompanying drawings of this embodiment, the same or similar reference numerals correspond to the same or similar components. In the description of this invention, it should be understood that the terms "upper," "lower," "left," "right," "inner," "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting this patent. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0018] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, article, or apparatus that includes that element.

[0019] like Figure 1 and Figure 2 As shown, Embodiment 1 of the present invention provides a power plant ecological flow intelligent monitoring and adaptive discharge supervision system, including an adaptive operating condition identification module, a multi-source compliance monitoring module, an intelligent flow calculation module, a dual-channel redundant transmission module, an early warning linkage discharge module, a full-cycle operation and maintenance module, and a data platform. Each module communicates with the data platform through an industrial bus. The data platform interacts bidirectionally with the power plant PLC system, the provincial supervision platform, and the municipal supervision platform, forming a closed-loop management system of monitoring, calculation, transmission, early warning, operation and maintenance, and supervision. The adaptive operating condition identification module collects the gate opening degree e, the upstream liquid level H, and the power generation signal in real time, and determines the operating condition according to the following rules: When e / H ≤ 0.65 and the power generation is ≥ 5% of the power plant's rated power, it is determined to be in power generation condition; When e / H > 0.65 or power generation = 0, it is determined to be a non-power generation condition; The intelligent flow calculation module switches the flow calculation model according to operating conditions. The formula used for power generation operating conditions is: Qᵢ=μbe√(2gh) Wherein, μ=0.60−0.176e / h; the historical correlation model of liquid level and flow rate is used to output flow rate under non-power generation conditions; the dual-channel redundant transmission module adopts 4G / 5G main channel + backup channel ultra-shortwave parallel communication, and automatically switches to backup channel when the main channel fails; when the monitoring flow rate of the early warning linkage release module is lower than the ecological base flow or the flow rate threshold during sensitive periods, it sends a gate adjustment command to the power station PLC, and the ecological release command has higher priority than the power generation command; the full-cycle operation and maintenance module automatically generates calibration and cross-section verification reminders according to the hydrological year, and the compliance file is stored using blockchain.

[0020] In the above scheme, the multi-source compliance monitoring module is deployed with three levels of monitoring points: the first-level monitoring point is located 5m downstream of the outlet of the ecological discharge facility; the second-level monitoring point is located 10m downstream of the confluence of the power generation tailwater and the discharge point; and the third-level monitoring point is located 50m upstream of the downstream sensitive target. The monitoring sensors include a gate position sensor with an accuracy of ≤±0.1cm, a water level sensor with an accuracy of ≤±3cm, and an acoustic Doppler flow velocity sensor with an accuracy of ±1%.

[0021] In the above scheme, the intelligent flow calculation module performs phased flow calculation: the ecological base flow adopts the Qp method, taking the average flow of the driest month with a 90% guarantee rate; the sensitive period flow adopts the ecological hydraulic method, satisfying an average water depth ≥ 0.3m and an average flow velocity ≥ 0.3m / s; when 17e > 60h, the system marks the data as abnormal and triggers a level two alarm.

[0022] In the above scheme, the intelligent traffic calculation module adopts a unified operating condition adaptation formula: Q=(μ×b×min(e,h)×√g×h^(4 / 3)) / (n×√L) Where: μ is 0.60−0.176e / h when e≤h, and 1.0 when e>h; b is the gate width, g=9.81m / s², n is the channel roughness, and L is the distance from the gate to the downstream uniform flow section.

[0023] In the above scheme, the switching rule of the dual-channel redundant transmission module is: main channel bit error rate > 1×10⁻ 6 Automatic switching occurs when the backup channel margin is ≤10dB, with a switching response time ≤10s. The data format conforms to the SL651-2014 protocol and uses CRC16 checksum. The uplink message includes the station code, time, opening degree, water level, flow rate, operating condition, and data status.

[0024] In the above scheme, the response delay of the early warning linkage release module is ≤30 seconds; special working conditions such as maintenance and flood control automatically generate reporting documents and push them to the supervision platform, and the ecological flow release satisfaction rate is ≥95%.

[0025] In the above scheme, the full-cycle operation and maintenance module uses RTK positioning for cross-section verification, with a measurement error of ≤±0.1m; calibration reminders are pushed 7 working days in advance, and the calibration timeliness rate is 100%.

[0026] In the above scheme, the historical correlation model of liquid level and flow rate under non-power generation conditions is a BP neural network model, LSTM or SVM model, with a training sample size of ≥1000 groups and a prediction error of ≤±3%.

[0027] In the above scheme, the dual channels are either primary channel satellite communication or backup channel LoRa communication; each sensor can be replaced with a radar level gauge, an electromagnetic velocity gauge, or a magnetostrictive displacement gauge.

[0028] In the above scheme, the instruction and regulatory data transmission is encrypted using AES-256 or RSA-2048.

[0029] The working principle of this invention is as follows: like Figure 1 and Figure 2As shown, after the system is powered on and initialized, the multi-source compliance monitoring module collects data such as gate opening e, upstream liquid level H, water level, and flow velocity through three monitoring points: the primary ecological discharge outlet, the secondary tailwater confluence, and the upstream boundary of the tertiary sensitive target. This data is collected using gate position sensors, water level sensors, and acoustic Doppler flow velocity sensors. The module automatically adjusts the collection frequency according to the operating conditions and sensitive periods and uploads the data to the data platform. The operating condition adaptive identification module receives e, H, and power generation signals in real time. After passing through a moving average filter, it determines whether the operating condition is power generation or non-power generation according to the discrimination rules and pushes the results to the intelligent flow calculation module. The intelligent flow calculation module automatically switches the calculation model according to the operating conditions. For power generation, it uses Qᵢ=μbe√(2gh) (μ=0.60−0.176e / h) and the unified operating condition adaptation formula Q=(μ×b×min (e,h)×√g×h^(4 / 3)) / (n×√L) to calculate the flow rate. For non-power generation, it uses a BP neural network, LSTM, or SVM. The historical correlation model of liquid level and flow rate outputs the flow rate, and simultaneously calculates the ecological base flow using the Qp method and the sensitive period flow rate using the ecohydraulic method. When 17e > 60h, the data is marked as abnormal and a level 2 alarm is triggered. The dual-channel redundant transmission module uses 4G / 5G as the primary channel and UHF as the backup channel, and monitors the bit error rate and channel margin in real time. It automatically switches when the switching conditions are met. The data is encapsulated according to the SL651-2014 protocol and checked with CRC16. It is encrypted with AES-256 or RSA-2048 and uploaded to the data platform and various levels of supervision platforms. The early warning linkage release module compares the monitored flow rate with the ecological base flow and the sensitive period flow rate threshold in real time. When the flow rate is insufficient, it immediately sends a gate adjustment command to the power plant PLC system. The ecological release command has higher priority than the power generation command, and the response delay does not exceed 30 seconds. Within seconds, the system automatically generates compliant reporting documents for special operating conditions such as maintenance and flood control, and pushes them to the regulatory platform to ensure that the ecological flow release meets at least 95% of the requirements. The full-cycle operation and maintenance module automatically generates equipment calibration and cross-section verification plans according to the hydrological year, and pushes reminders 7 working days in advance. It uses RTK positioning technology to complete cross-section verification, with a measurement error of no more than ±0.1m. All calibration, inspection, and maintenance records are stored in blockchain technology to form an immutable compliance archive, ensuring a 100% timeliness of calibration. Each module interacts in real time with the data platform through the industrial bus. The data platform communicates bidirectionally with the power plant PLC system and provincial and municipal regulatory platforms to achieve closed-loop collaborative operation of the entire process of monitoring, calculation, transmission, early warning, release, operation and maintenance, and supervision, fully meeting the requirements of industry standards such as NB / T 10385-2020, SL651-2014, SL / T 819-2023, and SL / T 820-2023.

[0030] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention.

Claims

1. A power plant ecological flow intelligent monitoring and adaptive discharge supervision system, characterized in that, The system includes an adaptive operating condition identification module, a multi-source compliance monitoring module, an intelligent flow calculation module, a dual-channel redundant transmission module, an early warning linkage and release module, a full-cycle operation and maintenance module, and a data platform. Each module communicates with the data platform via an industrial bus. The data platform interacts bidirectionally with the power plant PLC system, the provincial monitoring platform, and the municipal monitoring platform, forming a closed-loop management system encompassing monitoring, calculation, transmission, early warning, operation and maintenance, and supervision. The adaptive operating condition identification module collects real-time data on the gate opening degree e, the upstream liquid level H, and the power generation signal, and determines the operating condition according to the following rules: When e / H ≤ 0.65 and the power generation is ≥ 5% of the power plant's rated power, it is determined to be in power generation condition; When e / H > 0.65 or power generation = 0, it is determined to be a non-power generation condition; The intelligent flow calculation module switches the flow calculation model according to operating conditions. The formula used for power generation operating conditions is: Qᵢ=μbe√(2gh) Wherein, μ=0.60−0.176e / h; the historical correlation model of liquid level and flow rate is used to output flow rate under non-power generation conditions; the dual-channel redundant transmission module adopts 4G / 5G main channel + backup channel ultra-shortwave parallel communication, and automatically switches to backup channel when the main channel fails; when the monitoring flow rate of the early warning linkage release module is lower than the ecological base flow or the flow rate threshold during sensitive periods, it sends a gate adjustment command to the power station PLC, and the ecological release command has higher priority than the power generation command; the full-cycle operation and maintenance module automatically generates calibration and cross-section verification reminders according to the hydrological year, and the compliance file is stored using blockchain.

2. The intelligent monitoring and adaptive discharge supervision system for power plant ecological flow according to claim 1, characterized in that, The multi-source compliance monitoring module is deployed with three levels of monitoring points: the first-level monitoring point is located 5m downstream of the outlet of the ecological discharge facility; the second-level monitoring point is located 10m downstream of the confluence of the power generation tailwater and the discharge point; and the third-level monitoring point is located 50m upstream of the downstream sensitive target. The monitoring sensors include a gate position sensor with an accuracy of ≤±0.1cm, a water level sensor with an accuracy of ≤±3cm, and an acoustic Doppler flow velocity sensor with an accuracy of ±1%.

3. The intelligent monitoring and adaptive discharge supervision system for power plant ecological flow according to claim 1, characterized in that, The intelligent flow calculation module performs phased flow calculations: the ecological base flow adopts the Qp method, taking the average flow of the driest month with a 90% guarantee rate; the sensitive period flow adopts the ecological hydraulic method, satisfying an average water depth ≥ 0.3m and an average flow velocity ≥ 0.3m / s; when 17e > 60h, the system marks the data as abnormal and triggers a level two alarm.

4. The intelligent monitoring and adaptive discharge supervision system for power plant ecological flow according to claim 1, characterized in that, The intelligent flow calculation module adopts a unified operating condition adaptation formula: Q=(μ×b×min(e,h)×√g×h^(4 / 3)) / (n×√L) Where: μ is 0.60−0.176e / h when e≤h, and 1.0 when e>h; b is the gate width, g=9.81m / s², n is the channel roughness, and L is the distance from the gate to the downstream uniform flow section.

5. The intelligent monitoring and adaptive discharge supervision system for power plant ecological flow according to claim 1, characterized in that, The switching rule for the dual-channel redundant transmission module is: main channel bit error rate > 1×10⁻ 6 Automatic switching occurs when the backup channel margin is ≤10dB, with a switching response time ≤10s. The data format conforms to the SL651-2014 protocol and uses CRC16 checksum. The uplink message includes the station code, time, opening degree, water level, flow rate, operating condition, and data status.

6. The intelligent monitoring and adaptive discharge supervision system for power plant ecological flow according to claim 1, characterized in that, The early warning linkage release module has a response delay of ≤30 seconds; under special working conditions such as maintenance and flood control, it automatically generates reporting documents and pushes them to the monitoring platform, and the ecological flow release satisfaction rate is ≥95%.

7. The intelligent monitoring and adaptive discharge supervision system for power plant ecological flow according to claim 1, characterized in that, The full-cycle operation and maintenance module uses RTK positioning for cross-section verification, with a measurement error of ≤±0.1m; calibration reminders are pushed 7 working days in advance, and the calibration timeliness rate is 100%.

8. The intelligent monitoring and adaptive discharge supervision system for power plant ecological flow according to claim 1, characterized in that, The historical correlation model of liquid level and flow rate under non-power generation conditions is a BP neural network model, LSTM or SVM model, with a training sample size of ≥1000 groups and a prediction error of ≤±3%.

9. The intelligent monitoring and adaptive discharge supervision system for power plant ecological flow according to claim 1, characterized in that, The dual-channel configuration is either primary channel satellite communication or backup channel LoRa communication; each sensor can be replaced with a radar level gauge, electromagnetic velocity gauge, or magnetostrictive displacement gauge.

10. The intelligent monitoring and adaptive discharge supervision system for power plant ecological flow according to claim 1, characterized in that, The instruction and regulatory data transmission is encrypted using AES-256 or RSA-2048.

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