Reservoir area water regimen risk dynamic evaluation and supervision system based on multi-source perception

Abstract

The invention relates to the technical field of water conservancy administrative management and supervision, and discloses a reservoir area water regimen risk dynamic evaluation and supervision system based on multi-source perception, and the system comprises a data monitoring integration unit which is used for receiving multi-source monitoring data; the supervision instruction generation unit is used for calculating a water level change rate and outputting a primary supervision instruction; the response feedback verification unit is used for extracting feedback duration data; the management load arbitration unit is used for recognizing the business backlog state of the administrative terminal according to the feedback duration data and generating priority adjustment parameters, the management saturation is recognized through the feedback duration, the risk judgment benchmark is dynamically adjusted so as to suppress low-risk information, the processing task is focused to a high instruction under the working condition that the administrative resources are limited, and the management efficiency is improved. And reservoir area risk assessment and supervision load endogenous hedging are realized.

Description

Technical Field

[0001] This invention relates to a dynamic assessment and monitoring system for reservoir water situation risks based on multi-source sensing, belonging to the field of water conservancy administration and supervision technology. Background Technology

[0002] Currently, reservoir safety supervision and management typically employs a combination of technologies, including water level monitoring, rainfall monitoring, and dam condition monitoring. Such monitoring systems set absolute thresholds for water level or rainfall and collect sensor data based on a fixed periodic polling logic. In the early stages of a risk event, the system compares real-time data with preset thresholds to generate corresponding early warning signals and sends them to administrative terminals. This regulatory paradigm, based on static thresholds and timed data collection, provides standardized data support for administrative decision-making under normal water conditions.

[0003] Under high-risk conditions such as heavy rainfall or sudden increases in reservoir water levels, the evolution of physical risks exhibits a non-linear acceleration characteristic. Administrative supervision processes are constrained by management decision-making bandwidth and the response time of implementing agencies, resulting in inherent response lags. Existing systems, when dealing with such sudden evolutions, suffer from redundancy in low-value information related to normal fluctuations due to fixed sampling frequencies and singular logical weights. This can lead to blind spots in response due to instruction back-end accumulation during high-risk windows. This imbalance in regulatory resource allocation reflects a deep mismatch between administrative response capabilities and the rate of physical risk evolution. Besides hardware sampling limitations, the back-end regulatory logic lacks sufficient matching between the depth of risk assessment and administrative response capabilities. For example, Chinese invention patent CN113792437A discloses a multi-dimensional comprehensive assessment method and system for flood control situations, utilizing flood forecasting, evolution, and inundation water conservancy... The model, based on multi-source data analysis of river flood discharge and reservoir flood control elements, combined with GIS platform display, relies on hydraulic physical model calculations. It focuses on simulating the physical risk evolution process but neglects the efficiency of human-machine coupling in the regulatory process. The existing logic does not establish a dynamic feedback relationship between management resource saturation and risk assessment benchmarks. After identifying risks, the system indiscriminately pushes early warning instructions, leading to early warning fatigue among management personnel with limited bandwidth. Core risk instructions are buried in redundant information, making it difficult to achieve an endogenous offset between regulatory load and administrative response efficiency. Linear optimization paths that increase sampling density or simply lower alarm thresholds are prone to early warning fatigue. When conflicts occur in multi-source sensing data or signal fluctuations are severe, simply relying on increasing hardware coverage or shortening time intervals cannot effectively resolve the contradiction between administrative response resource allocation and physical evolution rate, resulting in impaired supervision efficiency.

[0004] Therefore, how to generate priority adjustment parameters based on the changing characteristics of the rate of change of the perceived information source over time, so as to dynamically correct the risk judgment benchmark in the supervision instruction generation logic and compensate for the logical time delay generated by the administrative response process, is the technical problem to be solved by this invention. Summary of the Invention

[0005] To address the problems mentioned in the background art, the technical solution of this invention is as follows: A dynamic assessment and monitoring system for reservoir water situation risk based on multi-source sensing, the system comprising:

[0006] The data monitoring and integration unit is used to receive multi-source monitoring data uploaded by on-site equipment in the reservoir area. The multi-source monitoring data includes water level data, rainfall data, and dam displacement data.

[0007] The supervision instruction generation unit is connected to the data monitoring and integration unit by signal. It is used to calculate the real-time change rate of water level data and output a first-level supervision instruction to the administrative management terminal when the real-time change rate exceeds the preset risk threshold.

[0008] The response feedback verification unit is used to obtain the feedback confirmation receipt of the administrative management terminal for the first-level supervision instruction, and to extract the feedback duration data from the time the first-level supervision instruction is issued to the time the feedback confirmation receipt is received.

[0009] The management load arbitration unit is logically connected to both the response feedback verification unit and the supervision instruction generation unit. It establishes a logical relationship between feedback duration data and a preset response time threshold to identify the instruction response saturation state of the administrative management terminal and generates priority adjustment parameters to correct the risk assessment benchmark of the supervision instruction generation unit. The management load arbitration unit feeds back the generated priority adjustment parameters to the supervision instruction generation unit to correct its risk assessment benchmark. When the feedback duration data continuously exceeds the preset response time threshold, the management load arbitration unit raises the risk assessment benchmark through the priority adjustment parameters. This allows for the merging or suppression of supervision information with risk assessment results below the preset threshold value based on the instruction response saturation state. Thus, under conditions of limited administrative management resources, the processing tasks of the administrative management terminal are focused on instructions with significant core risks.

[0010] Preferably, the supervision instruction generation unit performs data logic consistency verification. By calculating the time-series correlation index between the real-time change rate of water level data and rainfall data, it identifies sensor data drift caused by external environmental disturbances. When the time-series correlation index is lower than a preset logic threshold, the supervision instruction generation unit generates a weight limit signal for a specific sensor to shield against the mismobilization of administrative resources caused by abnormal fluctuations in single-source monitoring data.

[0011] Preferably, the response feedback verification unit monitors the operation record timestamps of the first-level supervision instructions on the administrative management terminal, calculates the retention time of the first-level supervision instructions on the administrative management terminal, and extracts the distribution variance of the retention time within a preset observation period.

[0012] Preferably, the management load arbitration unit calculates the management load index. The calculation formula is: ,in, To manage the load index, For the first The duration of a Level 1 supervisory instruction, in units of , The preset expected response time for this instruction type, in units of , The total number of instructions within the preset observation period; the management load arbitration unit arbitrates according to the management load index. The numerical dynamic adjustment of the risk assessment benchmark.

[0013] Preferably, the system also includes an anomaly data analysis module, which is used to capture residual data that is not adopted by the management load arbitration unit in the process of generating priority adjustment parameters, and calculate the distribution deviation characteristics of the residual data on the time axis; when the distribution deviation characteristics exceed the preset deviation range, the anomaly data analysis module resets the risk assessment model of the supervision instruction generation unit.

[0014] Preferably, the supervision instruction generation unit performs a cross-dimensional logical comparison with the preset instruction execution expectation model based on the real-time change rate of water level data; when it is found that the administrative response status does not match the water level change trend, the supervision instruction generation unit outputs a secondary compensation supervision instruction to achieve closed-loop audit of the execution status of the supervision instruction.

[0015] Preferably, the data monitoring and integration unit dynamically adjusts the sampling frequency of the on-site hydrological monitoring equipment based on the real-time rate of change of water level data; when the real-time rate of change of water level data exceeds a preset abrupt change threshold, the sampling frequency is switched to a high-frequency sampling mode, and the sampling frequency of the high-frequency sampling mode is not lower than... .

[0016] Preferably, when the management load arbitration unit executes the instruction merging logic, it retrieves redundant response paths for the same management area from the first-level supervision instructions, compresses the features of multiple instructions with a logical correlation higher than a preset correlation threshold, and generates a single, highly obvious composite early warning instruction.

[0017] Preferably, the system adopts a distributed architecture deployment; wherein, the data monitoring and integration unit is deployed at the edge processing node in the warehouse area, and the supervision instruction generation unit, response feedback verification unit, and management load arbitration unit are deployed on the central control server.

[0018] Preferably, the system also includes an effectiveness evaluation module, which continuously records historical data of feedback duration and generates statistical reports characterizing the effectiveness of administrative management response based on the historical data.

[0019] Compared with the prior art, the beneficial effects of the present invention are:

[0020] 1. In the dynamic assessment of reservoir water risk through multi-source sensing, the second-order rate of change of real-time water level data is extracted by the characteristic variability analysis unit, and the supervision weight matrix is ​​retrieved by the supervision priority arbitration unit in combination with the characteristics of environmental rainfall intensity. This realizes the logical switch of administrative supervision mode from timed polling to event-driven mode, and solves the timeliness contradiction between the static supervision cycle and the nonlinear evolution of risk in reservoir supervision. Under this mechanism, the accelerated evolution of physical parameters is directly transformed into the driving force for the reconstruction of supervision weights, so that the generation logic of administrative intervention instructions can cover the explicit outbreak window of water risk in advance, eliminate the supervision blind spot caused by the fixed sampling frequency in the traditional scheme, and improve the accuracy of administrative resources in extreme disaster conditions.

[0021] 2. The administrative instruction generation unit extracts administrative response delay parameters by monitoring the time difference between instruction issuance and receipt acquisition. The supervision priority arbitration unit then adjusts the growth slope of the dynamic risk index based on this, constructing a negative feedback hedging mechanism between management load and risk index. This design treats the decision-making bandwidth of administrative personnel and the response capability of the implementing agency as endogenous variables. When the administrative link experiences response saturation or fatigue, it avoids administrative paralysis caused by high-frequency warning signals through logical threshold fine-tuning and instruction semantic aggregation, ensuring that core supervision instructions still have high execution efficiency even when management resources are limited.

[0022] 3. The instruction response verification module compares the rheological characteristics of the water level data obtained by the multi-source sensing integration unit with the preset instruction execution expectation model across domains to achieve endogenous verification of the administrative instruction execution status. By utilizing the causal relationship between physical quantity changes and administrative actions, without adding physical sensors, it identifies the instruction vacuum state caused by communication link damage or execution mechanism failure through logical mutual printing of data streams. It also drives the administrative instruction generation unit to output a secondary compensation supervision instruction, extending the supervision link from simple instruction issuance to closed-loop status auditing, thereby enhancing the anti-interference capability of the supervision system. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating the closed-loop operation logic of reservoir area water situation risk monitoring in this invention.

[0024] Figure 2 This is a causal analysis diagram illustrating the risk assessment and monitoring effectiveness of this invention.

[0025] Figure 3 This is a diagram showing the distributed deployment architecture and data interaction topology of the system of this invention. Detailed Implementation

[0026] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. This part is intended to explain the present invention and is not intended to limit the scope of protection of the present invention.

[0027] This invention provides a dynamic assessment and monitoring system for reservoir water risk based on multi-source sensing, designed to address the mismatch between the rate of risk evolution in the reservoir area and the administrative response bandwidth. Deployed in a distributed architecture, the system includes a data monitoring and integration unit at edge processing nodes in the reservoir area, and a monitoring instruction generation unit, a response feedback verification unit, and a management load arbitration unit deployed on a central control server. The data monitoring and integration unit receives multi-source monitoring data uploaded from equipment in the reservoir area, including water level data. Rainfall data and dam displacement data The data monitoring and integration unit uses water level data. The real-time rate of change is used to dynamically adjust the sampling frequency of the on-site hydrological monitoring equipment; when the water level data... When the real-time rate of change exceeds the preset mutation threshold, the data monitoring and integration unit will switch the sampling frequency to a high-frequency sampling mode, where the sampling frequency is no less than [a certain value]. The data acquisition process for this physical quantity ensures data accuracy during periods of high risk, providing a foundation for subsequent administrative oversight. The oversight instruction generation unit calculates water level data. The system monitors the real-time rate of change and, when this rate exceeds a preset risk threshold, outputs a Level 1 monitoring instruction to the administrative management terminal. To prevent mis-mobilization of administrative resources caused by sensor data drift, the monitoring instruction generation unit performs data logic consistency verification and calculates water level data. Real-time rate of change and rainfall data The time-series correlation index between the data is used; when the time-series correlation index is lower than the preset logic threshold, the supervision instruction generation unit determines that the water level fluctuation is caused by external environmental disturbances. At this time, a weight limit signal for a specific sensor is generated to shield the impact of abnormal fluctuations in single-source monitoring data on administrative decisions. In addition, the supervision instruction generation unit will use the water level data... The real-time rate of change is compared with the preset instruction execution expectation model through cross-dimensional logic. When a mismatch is found between the administrative response status and the water level change trend, a secondary compensation supervision instruction is output to achieve closed-loop audit of the execution status of the supervision instruction.

[0028] In the closed-loop audit of instruction status, the monitoring instruction generation unit uses a built-in instruction execution expectation model to verify the dynamic response of water level, and stores the expected sequence of the second derivative of water level corresponding to the spillway opening / closing instruction or flood discharge scheduling instruction. The operation procedure includes: after outputting the first-level monitoring command, extracting water level monitoring data with a sampling step size of 60 seconds, and calculating the real-time second-order rate of change of water level data using the second-order difference formula. Compare it with the model Time sequence comparison, if in the preset audit observation window Inside, If the indicator does not show a positive-to-negative inflection point and continues to deviate from the expected negative gradient range, it is determined to be an administrative execution deviation, and a secondary compensation supervision instruction is output. and The quantification is based on the reservoir capacity curve of the target reservoir area and the discharge flow formula of the flood discharge outlet. The theoretical water level drop acceleration under different discharge flows at each water level segment is calculated, and the acceleration value of 0.8 times is set as the benchmark criterion for identifying execution blockage. The response feedback verification unit is used to obtain the feedback confirmation receipt of the administrative management terminal for the first-level supervision instruction, and extract the feedback duration data from the time the first-level supervision instruction is issued to the time the feedback confirmation receipt is received. The response feedback verification unit monitors the operation record timestamp of the administrative management terminal for the first-level supervision instruction, calculates the retention time of the first-level supervision instruction on the administrative management terminal, and extracts the distribution variance of the retention time within the preset observation period. The processing of this data stream transforms the decision-making reaction time of the administrative management personnel into a load characteristic that the system can identify.

[0029] The management load arbitration unit establishes a logical relationship between feedback duration data and preset response time thresholds, identifies the saturation state of the administrative management terminal's command response, and generates priority adjustment parameters; the management load arbitration unit calculates the management load index. The calculation formula is as follows: ,in, To manage the load index, For the first The duration of a Level 1 supervisory instruction, in units of , The preset expected response time for this instruction type, in units of , The total number of instructions within the preset observation period; in the management load index When the response time limit is continuously exceeded, the management load arbitration unit raises the risk judgment benchmark by adjusting the priority parameter, and merges or suppresses the supervision information whose risk judgment result is lower than the preset threshold value according to the instruction response saturation state. When executing instruction merging, the management load arbitration unit retrieves redundant response paths for the same management area in the first-level supervision instructions, compresses the features of multiple instructions with a logical correlation higher than the preset correlation threshold, and generates a single highly obvious composite early warning instruction.

[0030] When the management load arbitration unit executes command merging, it uses a Euclidean distance-based clustering algorithm to process redundant response paths and establishes a system containing geospatial coordinates. and risk weight coefficient The logical association matrix execution procedure is as follows: calculate the spatial straight-line distance between different points in the sequence of first-level supervisory instructions to be processed. ,exist Less than the preset association threshold Furthermore, if the risk category identifiers are consistent, multiple sub-instructions belonging to the same administrative jurisdiction will have their features aggregated, and the highest risk weight of the instruction cluster will be extracted. It also generates a composite early warning message, which includes the identifiers of the sub-points involved in the merging process, their geographical distribution range, and the associated threshold. The established procedures include: offline analysis of the path overlap in historical inspection records of the warehouse area; statistical analysis of the average overlapping range of movement trajectories of administrative personnel performing multi-point inspection tasks; and setting the overlap range at 1.5 times. The benchmark value guides administrative resources to focus on core operational areas when the management bandwidth is saturated. The system also includes an anomaly data analysis module, which captures residual data that was not adopted by the management load arbitration unit in the process of generating priority adjustment parameters, and calculates the distribution deviation characteristics of the residual data on the time axis. When the distribution deviation characteristics exceed the preset deviation range, the anomaly data analysis module resets the risk assessment model of the supervision instruction generation unit. In addition, the performance evaluation module continuously records the historical records of feedback duration data and generates statistical reports that characterize the performance of administrative management response based on the historical records.

[0031] Example 1: Under reservoir monitoring conditions that include continuous heavy rainfall and dam deformation risk monitoring, the data monitoring and integration unit collects water level data uploaded by on-site sensors in the reservoir area. Rainfall data and dam displacement data Due to the increase in environmental rainfall intensity from normal to Water level data The real-time rate of change is from Growth to The supervision instruction generation unit identifies that the rate of change exceeds a preset risk threshold and issues a series of inspection instructions for different warehouse sections to the administrative management terminal. Upon receiving these high-frequency level-one supervision instructions, the administrative management terminal experiences a delay in the feedback confirmation receipt recorded by the response feedback verification unit due to limitations in on-site human resources and processing bandwidth. The extracted feedback duration data is then... Increase to The management load arbitration unit obtains the feedback duration data output by the response feedback verification unit, and calculates the pre-response time limit by establishing a logical relationship between the feedback duration data and the preset response time limit threshold. Duration of instruction With the preset expected response time The deviation is used to determine the management load index. The calculation formula is as follows: ,in, To manage the load index, For the first The duration of a Level 1 supervisory instruction, in units of , The preset expected response time for this instruction type, in units of , This represents the total number of instructions within the preset observation period.

[0032] In managing load index When the operating condition continuously exceeds the preset response time limit threshold, the management load arbitration unit generates priority adjustment parameters, driving the monitoring instruction generation unit to adjust the risk assessment benchmark for water level rise from... Upgraded to Simultaneously, it retrieves logical characteristics of the same dam section from the primary supervision instructions, and identifies those within the same management area with a logical correlation higher than a preset correlation threshold. The individual early warning commands are compressed and merged to output a single data point on dam displacement to the administrative management terminal. The high-sounding composite early warning command coupled with rising water levels enables the decision-making behavior of administrative management terminals to focus on core risk items. The supervision command generation unit continuously monitors the water level change trend after the issuance of the first-level supervision command and performs cross-dimensional logical comparison with the preset command execution expectation model. When it is found that the rate of water level rise has not shown a reverse inflection point within a preset time, it determines that there is an execution deviation in the administrative response and outputs a second-level compensation supervision command. At this time, the abnormal data analysis module captures the residual data that was not adopted by the management load arbitration unit in the process of generating priority adjustment parameters, calculates the distribution deviation characteristics of the residual data on the time axis, and resets the risk assessment model of the supervision command generation unit when the distribution deviation characteristics exceed the preset deviation range. The system ultimately maintains a stable operating state in which the processing tasks are focused on the high-sounding core risk commands.

[0033] Example 2: In a reservoir water situation monitoring stress test scenario based on a distributed simulation architecture, an experimental dataset consisting of historical flood season operation records and corresponding administrative response logs from a large reservoir is used to simulate the water level evolution law in the reservoir area based on an unsteady fluid dynamics model. To verify the system's operating status under complex disturbance environments, the experimental signal source is used to analyze the water level data. Active superposition amplitude is Gaussian white noise was used to simulate the physical disturbance of the sensor under strong wind and wave conditions, aiming to verify the effectiveness of focusing the risk assessment benchmark on core risk instructions under administrative processing bandwidth-constrained conditions.

[0034] The total number of commands within the preset observation period for setting core experimental parameters At that time, the technical trade-off lies in balancing statistical response agility; if If the value is too small, it is easily affected by the abnormal response time of a single command, which will lead to a decrease in the management load index. If non-physical fluctuations are generated, If the value is too large, it will cause a lag in the system's perception of the instantaneous saturation state of the administrative link; the experiment will Set as This value is based on the historical command response variance at a confidence level of 1. The minimum sample size required for convergence is determined, and the expected response time is preset. Differentiated labeling is applied based on the type of Level 1 monitoring command, with Level 1 monitoring commands for spillway opening and closing set as follows: The first-level supervision instruction for general area inspections is set as follows: This setting corresponds to the time limit requirements for water conservancy administration; the experimental group adopted the complete technical solution, while the control group removed the feedback correction link of the management load arbitration unit, assuming the simulated rainfall intensity remained at a certain level. Under the operating conditions, the test group calculated the management load index through the management load arbitration unit. And generate priority adjustment parameters, see Table 1.

[0035] Table 1: Operational data of the prototype of this invention under different management load levels

[0036] Based on the measurement data in Table 1, when the management load index In to When the range is reached, the risk assessment benchmark remains at the initial value. The frequency of issuing first-level supervision orders increases with the rate of change in water level. Exceed After a performance inflection point appears, the risk assessment benchmark is adjusted upwards according to priority parameters. The system's instruction merging logic compresses the features of low- and medium-risk warnings. Data shows that when... As the instruction generation density approaches the upper limit, it decreases from the initial value. The number of bars decreased per hour to Hourly data on dam displacement The core risk response delay of the mutation was compared to the control group. shortened to Feedback duration data provides management bandwidth awareness for the supervision instruction generation unit, enabling non-linear coupling between physical risk identification and administrative processing efficiency.

[0037] Example 3: This example combines Figures 1 to 3 This section describes the dynamic assessment and monitoring system for reservoir water risk based on multi-source sensing, such as... Figure 1As shown, the operational logic flow begins with the multi-source monitoring data source provided by the on-site equipment in the reservoir area. The data monitoring and integration unit is responsible for receiving the multi-source monitoring data and adjusting the sampling frequency. Subsequently, the processed data is transferred to the supervision instruction generation unit, which calculates the water level change rate based on logic and outputs a first-level supervision instruction. After completing the instruction reception and execution, the administrative management terminal generates a feedback confirmation receipt. The response feedback verification unit then extracts the feedback duration data and monitors the operation record timestamp. The extracted feedback duration data is transmitted to the management load arbitration unit, which identifies the instruction response saturation state and generates priority adjustment parameters. Finally, the priority adjustment parameters are used to correct the risk judgment benchmark of the supervision instruction generation unit, thereby forming a closed-loop control logic with endogenous adjustment capabilities.

[0038] like Figure 2 As shown, the multi-source monitoring and sensing branch covers data acquisition of water level, rainfall, and dam body, supports 10Hz frequency conversion sampling mode, and executes data consistency verification. The supervision command closed-loop branch includes primary supervision command generation, redundant command logic merging, and secondary compensation auditing functions. The management load arbitration branch involves priority parameter adjustment, management load index L calculation, and feedback duration data extraction. The risk dynamic judgment branch integrates cross-dimensional logical comparison, residual data anomaly analysis, and dynamic correction mechanism for judgment benchmarks. All branches aim at the core objective of improving the effectiveness of reservoir area water risk assessment and supervision. Figure 3 As shown, the system architecture is divided into two main parts: the reservoir area edge processing node and the central control server. The reservoir area edge processing node is equipped with a data monitoring and integration unit, which is responsible for aggregating signals from water level monitoring equipment, rainfall sensors, and dam displacement gauges, and uploading multi-source data through a dedicated communication network. The central control server is equipped with a supervision instruction generation unit, a management load arbitration unit, a response feedback verification unit, and a historical record database. The server issues supervision instructions to administrative management terminals, including PCs / mobile devices. The feedback receipts generated by the management personnel's operation terminals are transmitted back to the server via the network, forming a complete physical interaction topology.

[0039] Example 4: Under the administrative supervision of the reservoir area, which includes flood discharge scheduling and coordinated monitoring of dam deformation, the supervision instruction generation unit receives the water level data output by the data monitoring and integration unit. and rainfall data It is used to identify instantaneous data drift caused by environmental electromagnetic interference to the sensor; the supervision instruction generation unit executes the data logic consistency verification algorithm by constructing a real-time water level change rate sequence. With rainfall sequence The sliding sampling window is used to calculate the hysteresis step between the two sequences. Cross-correlation coefficients at time ;in, As a time-series correlation indicator, for Real-time rate of change of water level at any given moment. for Rainfall sequence at any given time, The timing lag step size; where a preset logic threshold is defined. The determination procedure is as follows: during the system initialization phase, continuous data collection is performed. Calculate the residual distribution based on the historical running residuals. The upper boundary of the confidence interval is set as follows: In actual measurement Below At that time, the supervision instruction generation unit generates a weight limit signal for a specific sensor.

[0040] The supervision instruction generation unit performs closed-loop auditing of the administrative response status based on the instruction execution expectation model. The instruction execution expectation model includes a preset physical state gradient matrix, which stores the second derivative of the expected water level change rate corresponding to different types of administrative instructions. ; For water level data The second derivative with respect to time is used to characterize the acceleration feature of the evolution of physical risk; after the system outputs a first-level supervision command to the administrative management terminal to open the spillway, the supervision command generation unit... Extracting water level data for sampling step size The second-order gradient features are obtained and compared with the expected negative gradient values ​​stored in the matrix; if the preset time limit is set after the first-level supervision instruction is issued... Internal water level data The second derivative did not show an inflection point characteristic of turning from positive to negative. The supervision instruction generation unit determined that the administrative execution link had a logical blockage and output a secondary compensation supervision instruction to the administrative management terminal. To preset the response time limit threshold, the value of which is determined by the safe response time window specified in the reservoir flood control dispatching regulations, the management load arbitration unit initiates the feature compression and logical merging process for the first-level supervision instructions after receiving a signal that the feedback duration data continuously exceeds the preset response time limit threshold. The management load arbitration unit constructs a logical correlation evaluation matrix, the input of which is the reservoir area geographical coordinate labels corresponding to multiple first-level supervision instructions. Weights based on risk type The management load arbitration unit calculates the Euclidean distance between different commands, and determines the appropriate command if the distance is less than a preset correlation threshold. At that time, those belonging to the same administrative jurisdiction and whose physical risk causes are related will be included. The inspection instructions are clustered to extract the maximum risk feature value and generate a highly significant composite early warning instruction. For the first The geospatial coordinates corresponding to each instruction This is the risk weighting coefficient for the instruction. Preset association threshold; Preset association threshold The calibration procedure is as follows: Execute in offline mode. A group of simulated stress tests were conducted, recording the operational accuracy of administrative personnel under different command densities. The inflection point of the command interval where the accuracy curve began to decline was selected as the baseline. The value of this parameter is determined based on the need to guide administrative resources when the bandwidth is saturated.

[0041] The abnormal data analysis module performs real-time redundancy verification on the priority adjustment parameters generated by the management load arbitration unit, captures residual data that has not entered the feedback loop, and performs distribution entropy analysis; when the calculated distribution entropy value deviates from the preset physical evolution path... At that time, the abnormal data analysis module resets the risk assessment model parameters of the supervision instruction generation unit through a hard trigger instruction, and the system returns to the initial perception state driven by real-time physical variability. Through the closed loop of the above algorithm path and parameter calibration procedure, the system achieves the coupling of physical risk perception and management saturation identification in the scenario of limited administrative resources, and finally maintains a stable operating state in which the processing tasks focus on high core risk instructions.

[0042] Example 5: Under the calibration conditions of the newly deployed reservoir area risk dynamic assessment system, the management load arbitration unit executes the baseline calibration procedure for administrative response effectiveness to determine the preset expected response time. The quantitative benchmark, specifically the process includes: the data monitoring and integration unit generating simulated water level data. And trigger an early warning message, the response feedback verification unit records the administrative management terminal's actions during this period. The system generates feedback confirmation receipts for standardized sequence instructions, calculates the average confirmation time for administrative personnel to receive instructions across different business dimensions, and determines the response stability coefficient of the administrative link based on the standard deviation of the feedback duration data. In order to target the Preset expected response time for each instruction type, in units of This baseline data is used to set the management load index. The calculation starts from the beginning of subsequent formal operation; when the administrative management resources of the reservoir area change or the monitoring equipment is deployed in clusters, the management load arbitration unit starts the parameter fine-tuning program, calculates the coefficient of variation of the feedback duration data on the sliding time axis to update the preset response time limit threshold, performs regression analysis on the business status of the administrative terminal based on the operation timestamp in the historical response log, generates a set of initial priority adjustment parameters for correcting the risk judgment benchmark, and generates adjustment signals based on the deviation between the measured feedback duration and the baseline response capability, so that the risk assessment model parameters are matched with the real-time saturation state of administrative management resources, and the system ultimately maintains an operating state in which the risk assessment results and administrative response effectiveness are offset.

[0043] Under high-altitude reservoir deployment and commissioning conditions, the data monitoring and integration unit executes a standardized calibration procedure for the sensing benchmark to determine preset risk thresholds. The system operates continuously without rainfall interference and with a stable dam structure. During the silent observation period, with Water level data were collected at the baseline sampling rate. The background fluctuation envelope is extracted, and the background rate of change baseline under this specific geographical condition is obtained by calculating the first-order central difference of the water level sequence. This baseline is then... The value obtained by superimposing the standard deviation to the historical average rise rate is set as the preset risk threshold; the calibrated water level change noise power is used as the initial bias input parameter for subsequent data logic consistency verification, ensuring that the triggering logic of the first-level supervision command is anchored to the physical background of this specific hydrological environment; when the system faces the objective condition of cross-regional administrative management system integration, the management load arbitration unit executes the initial filling procedure of the logical correlation evaluation matrix, constructs a logical mapping model by traversing the three-year history of level three and above early warning events and their administrative response logs in the reservoir area, and calculates the logical coupling coefficient between different physical risk feature vectors and administrative response paths; in the pre-deployment stage, the decision damping coefficient of the administrative link is calculated based on the initial interaction delay distribution collected by the response feedback verification unit. And inject it as an adaptive correction factor into the management load index. In the computation function, the system ultimately maintains an operating state in which the characteristics of the sensing source and the execution link are aligned.

[0044] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0045] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A dynamic assessment and monitoring system for reservoir water situation risk based on multi-source sensing, characterized in that the system... include: The data monitoring and integration unit is used to receive multi-source monitoring data uploaded by on-site equipment in the reservoir area. The multi-source monitoring data includes water level data, rainfall data, and dam displacement data. The supervision instruction generation unit is connected to the data monitoring and integration unit by signal. It is used to calculate the real-time change rate of water level data and output a first-level supervision instruction to the administrative management terminal when the real-time change rate exceeds the preset risk threshold. The response feedback verification unit is used to obtain the feedback confirmation receipt of the administrative management terminal for the first-level supervision instruction, and to extract the feedback duration data from the time the first-level supervision instruction is issued to the time the feedback confirmation receipt is received. The management load arbitration unit is logically connected to the response feedback verification unit and the supervision instruction generation unit, respectively. It is used to establish a logical relationship between the feedback duration data and the preset response time limit threshold, so as to identify the instruction response saturation state of the administrative management terminal and generate priority adjustment parameters to correct the risk judgment benchmark of the supervision instruction generation unit. The management load arbitration unit feeds back the generated priority adjustment parameters to the supervision instruction generation unit to correct its risk judgment benchmark. When the feedback time data continuously exceeds the preset response time limit threshold, the management load arbitration unit raises the risk judgment benchmark by adjusting the priority adjustment parameter. Based on the instruction response saturation state, it merges or suppresses the supervision information with risk judgment results below the preset threshold value. Thus, under the condition of limited administrative management resources, the processing tasks of the administrative management terminal are focused on instructions with high and obvious core risks.

2. The reservoir area water situation risk dynamic assessment and monitoring system based on multi-source sensing according to claim 1, characterized in that, The supervision instruction generation unit performs data logic consistency verification. By calculating the time-series correlation index between the real-time change rate of water level data and rainfall data, it identifies sensor data drift caused by external environmental disturbances. When the time-series correlation index is lower than the preset logic threshold, the supervision instruction generation unit generates a weight limit signal for a specific sensor to shield against the mismobilization of administrative resources caused by abnormal fluctuations in single-source monitoring data.

3. The reservoir area water situation risk dynamic assessment and monitoring system based on multi-source sensing according to claim 1, characterized in that, The response feedback verification unit monitors the operation record timestamps of the first-level supervision instructions on the administrative management terminal, calculates the dwell time of the first-level supervision instructions on the administrative management terminal, and extracts the distribution variance of the dwell time within the preset observation period.

4. The reservoir area water situation risk dynamic assessment and monitoring system based on multi-source sensing according to claim 3, characterized in that, The management load arbitration unit calculates the management load index. The calculation formula is: ,in, To manage the load index, For the first The duration of a Level 1 supervisory instruction, in units of , The preset expected response time for this instruction type, in units of , The total number of instructions within the preset observation period; the management load arbitration unit arbitrates according to the management load index. The numerical dynamic adjustment of the risk assessment benchmark.

5. The reservoir area water situation risk dynamic assessment and monitoring system based on multi-source sensing according to claim 1, characterized in that, The system also includes an anomaly data analysis module, which is used to capture residual data that was not adopted by the management load arbitration unit in the process of generating priority adjustment parameters, and calculate the distribution deviation characteristics of the residual data on the time axis; when the distribution deviation characteristics exceed the preset deviation range, the anomaly data analysis module resets the risk assessment model of the supervision instruction generation unit.

6. The reservoir area hydrological risk dynamic assessment and monitoring system based on multi-source sensing according to claim 1, characterized in that, The supervision instruction generation unit performs a cross-dimensional logical comparison between the real-time change rate of water level data and the preset instruction execution expectation model; when it identifies a mismatch between the administrative response status and the water level change trend, the supervision instruction generation unit outputs a secondary compensation supervision instruction.

7. The reservoir area water situation risk dynamic assessment and monitoring system based on multi-source sensing according to claim 1, characterized in that, The data monitoring and integration unit dynamically adjusts the sampling frequency of the on-site hydrological monitoring equipment based on the real-time rate of change of water level data. When the real-time rate of change of water level data exceeds a preset abrupt change threshold, the sampling frequency is switched to a high-frequency sampling mode, with the sampling frequency in the high-frequency sampling mode not lower than [a certain value]. .

8. A reservoir area hydrological risk dynamic assessment and monitoring system based on multi-source sensing according to claim 1, characterized in that, When the management load arbitration unit executes the instruction merging logic, it retrieves redundant response paths for the same management area from the first-level supervision instructions, compresses the features of multiple instructions with a logical correlation higher than the preset correlation threshold, and generates a single, highly obvious composite early warning instruction.

9. A reservoir area hydrological risk dynamic assessment and monitoring system based on multi-source sensing according to claim 1, characterized in that, The system adopts a distributed architecture; the data monitoring and integration unit is deployed at the edge processing node in the warehouse area, while the supervision instruction generation unit, response feedback verification unit, and management load arbitration unit are deployed on the central control server.

10. A reservoir area hydrological risk dynamic assessment and monitoring system based on multi-source sensing according to claim 1, characterized in that, The system also includes a performance evaluation module, which continuously records historical data on feedback duration and generates statistical reports based on the historical data to characterize the performance of administrative management response.

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