Gate opening degree self-adaptive control method and system based on resonance risk dynamic evaluation

Abstract

The application discloses a gate opening degree self-adaptive control method and system based on resonance risk dynamic evaluation, relates to the technical field of data processing, and comprises the following steps: acquiring a current time point, acquiring a processing time period, acquiring a historical water flow frequency and a historical gate frequency; acquiring the deviation degree corresponding to each to-be-processed time point, acquiring the gradient correction value corresponding to the current time point based on a prediction model and the deviation degree corresponding to a plurality of to-be-processed time points, and acquiring the trend correction value corresponding to the current time point based on a trend model and the deviation degree corresponding to the plurality of to-be-processed time points; acquiring the current water flow frequency and the current gate frequency of the current time point, and acquiring a risk index according to the current water flow frequency and the current gate frequency; acquiring a target opening degree according to the risk index, the gradient correction value and the trend correction value, and controlling the gate to open according to the target opening degree. The application has the advantages of dynamic perception, trend prediction and self-adaptive control.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a gate opening adaptive control method and system based on dynamic evaluation of resonance risk. Background Art

[0002] In water conservancy projects, gates are critical flow control devices, and their operational stability is directly related to the safety and functionality of these facilities. Existing gate control methods primarily assess risk based on static frequency thresholds or single amplitude monitoring. These methods suffer from significant technical limitations. First, the static frequency difference assessment mechanism evaluates only the instantaneous difference between the current flow excitation frequency and the gate's natural frequency, failing to consider the time-varying cumulative effect of vibration energy and lacking a quantitative analysis of the cumulative effect of historical risks. This results in an inability to identify the dynamic impact of the amplitude change rate on resonance risk. For example, when the flow excitation frequency slowly approaches the natural frequency, the amplitude may enter a dangerous growth phase before reaching the preset threshold. However, traditional methods lack monitoring of changes in vibration energy gradients, preventing early warning. Second, existing technologies generally employ responsive control strategies that rely on fixed-period parameter sampling and hysteresis adjustments, making them difficult to respond promptly to sudden flow disturbances or mechanical state changes. For example, in scenarios with heavy rain-induced flow surges, gate opening adjustment lags behind actual changes in flow frequency, easily inducing structural resonance. Finally, existing methods usually adopt linear weight distribution or a single feedback mechanism, which makes it difficult to coordinately handle the nonlinear coupling relationship of multi-source parameters, resulting in insufficient control accuracy and easily causing false operations or missed alarms. Summary of the Invention

[0003] In view of the defects in the prior art, the present invention provides a gate opening adaptive control method and system based on dynamic evaluation of resonance risk.

[0004] A gate opening adaptive control method based on dynamic assessment of resonance risk includes: obtaining the current time point and the processing time period, taking time points separated from the current time point by different numbers of processing time periods as different time points to be processed, and obtaining the historical water flow frequency and historical gate frequency corresponding to each time point to be processed; obtaining the degree of deviation corresponding to each time point to be processed according to the historical water flow frequency and historical gate frequency corresponding to each time point to be processed, and obtaining the gradient correction value corresponding to the current time point based on the prediction model and the degree of deviation corresponding to multiple time points to be processed, and obtaining the trend correction value corresponding to the current time point based on the trend model and the degree of deviation corresponding to multiple time points to be processed; obtaining the current water flow frequency and the current gate frequency at the current time point, and obtaining the risk index based on the current water flow frequency and the current gate frequency; obtaining the target opening according to the risk index, the gradient correction value and the trend correction value, and controlling the gate opening according to the target opening.

[0005] Optionally, obtaining the processing time period includes: obtaining a first adjustment parameter based on current weather conditions, and obtaining a second adjustment parameter based on the historical failure frequency of the gate; obtaining basic monitoring density, and obtaining the processing time period based on the basic monitoring density, the first adjustment parameter and the second adjustment parameter.

[0006] Optionally, the prediction model in which the gradient correction value corresponding to the current time point is obtained based on the prediction model and the deviation degrees corresponding to multiple time points to be processed is expressed as: ;in, is the gradient correction value corresponding to the current time point, is the number of deviations, For the The degree of deviation corresponding to the time point to be processed, For the The degree of deviation corresponding to the time point to be processed.

[0007] Optionally, the trend model in which the trend correction value corresponding to the current time point is obtained based on the trend model and the deviation degrees corresponding to multiple time points to be processed is expressed as: ;in, is the trend correction value corresponding to the current time point, is the number of time points to be processed in the sampling window, is the minimum deviation threshold, The first The degree of deviation corresponding to the time point to be processed, The first The degree of deviation corresponding to the time point to be processed.

[0008] Optionally, the risk index is obtained based on the current water flow frequency and the current gate frequency and is expressed as: ;in, is a risk indicator, is the important coefficient, is the current gate frequency, is the current water flow frequency.

[0009] Optionally, obtaining the target opening based on the risk indicator, gradient correction value and trend correction value includes: obtaining the adjustment index based on the risk indicator, gradient correction value and trend correction value; obtaining multiple opening adjustment amounts corresponding to different numerical ranges, and obtaining the corresponding opening adjustment amounts based on the numerical range in which the adjustment indicator falls; obtaining the target opening based on the current opening and the opening adjustment amount.

[0010] Optionally, the adjustment indicator is obtained based on the risk indicator, gradient correction value, and trend correction value as follows: ;in, To adjust the index, a trend correction value corresponding to the current time point, a gradient correction value corresponding to the current time point, a risk index.

[0011] The application further provides a gate opening adaptive control system based on resonance risk dynamic assessment, which comprises a data acquisition module, a first data processing module, a second data processing module and an adaptive control module.

[0012] Optionally, the data acquisition module is further configured to: acquire a first adjustment parameter according to the current weather condition, and acquire a second adjustment parameter according to the historical gate failure frequency; and acquire a basic monitoring density, and acquire the processing time interval according to the basic monitoring density, the first adjustment parameter and the second adjustment parameter.

[0013] Optionally, the adaptive control module is further configured to: acquire an adjustment index according to the risk index, the gradient correction value and the trend correction value; acquire a plurality of opening adjustment amounts respectively corresponding to different numerical value ranges, and acquire the opening adjustment amount corresponding to the numerical value range in which the adjustment index falls; and acquire the target opening degree according to the current opening degree and the opening adjustment amount.

[0014] The application has the following beneficial effects:

[0015] In the entire adaptive gate opening control method based on dynamic assessment of resonance risk, first, the monitoring density is controlled by dynamically adjusting the length of the processing time period, achieving intelligent matching of data acquisition frequency and operating condition risks. The monitoring frequency is automatically enhanced in high-risk scenarios such as heavy rain or equipment aging, effectively capturing the characteristics of transient water flow disturbances and solving the response lag problem caused by the existing fixed sampling period. Furthermore, the time-attenuated weight distribution mechanism based on historical data, combined with the dual prediction model of gradient correction value and trend correction value, not only quantifies the temporal cumulative effect of vibration energy, but also identifies the risk evolution trend through incremental changes in the degree of deviation. For example, when the natural frequency of the gate slowly shifts due to structural fatigue, potential progressive resonance risks can be warned several hours in advance. Furthermore, the risk indicator achieves accurate risk assessment by amplifying the critical state sensitivity and combining it with the operating condition adaptive adjustment of the dynamic important coefficient. At the same time, the hierarchical discretization control strategy maps the continuous adjustment indicator into a step-by-step opening action, realizing nonlinear optimization of the control response through preset multi-level adjustment intervals. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly describes the drawings required for the specific embodiments or the description of the prior art. Similar elements or parts are generally identified by similar reference numerals throughout the drawings. Elements or parts in the drawings are not necessarily drawn to scale.

[0017] Figure 1 Schematic diagram of the steps of a gate opening adaptive control method based on dynamic evaluation of resonance risk according to the present invention in one embodiment;

[0018] Figure 2 Schematic diagram of some steps S1 in the gate opening adaptive control method based on dynamic evaluation of resonance risk of the present invention;

[0019] Figure 3 Schematic diagram of some steps of S4 in the gate opening adaptive control method based on dynamic evaluation of resonance risk of the present invention. DETAILED DESCRIPTION

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0021] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort are intended to fall within the scope of protection of the present invention.

[0022] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. In addition, the terms "first," "second," etc. are used only to distinguish the descriptions and are not to be understood as indicating or implying relative importance.

[0023] like Figure 1 As shown, a gate opening adaptive control method based on dynamic evaluation of resonance risk is provided, comprising:

[0024] S1. Obtain the current time point and the processing time period, set time points separated from the current time point by different processing time periods as different time points to be processed, and obtain the historical water flow frequency and historical gate frequency corresponding to each time point to be processed;

[0025] S2. Obtain the degree of deviation corresponding to each time point to be processed based on the historical water flow frequency and the historical gate frequency corresponding to each time point to be processed, and obtain the gradient correction value corresponding to the current time point based on the prediction model and the degree of deviation corresponding to the multiple time points to be processed, and obtain the trend correction value corresponding to the current time point based on the trend model and the degree of deviation corresponding to the multiple time points to be processed;

[0026] S3. Obtain the current water flow frequency and the current gate frequency at the current time point, and obtain the risk index based on the current water flow frequency and the current gate frequency;

[0027] S4. Obtain the target opening according to the risk index, gradient correction value and trend correction value, and control the gate opening according to the target opening.

[0028] In this embodiment, it should be noted that in S1, the density and scope of historical data collection are dynamically determined to provide a temporal and spatial correlation basis for subsequent risk accumulation effect analysis. First, the length of the processing time period is dynamically adjusted based on real-time weather conditions (such as rainfall intensity and upstream water volume) and historical mechanical reliability (such as the frequency of wear of gate components): in heavy rain warning or high failure rate scenarios, the processing time period is shortened to increase the monitoring frequency and capture high-frequency water flow disturbance characteristics; while in stable operating conditions or low failure rate scenarios, the processing time period is extended to reduce the computational load. For example, when the weather radar detects that heavy rainfall is about to occur in the basin, the processing time period will be shortened to 30% of the original value, so that the time points to be processed are densely distributed in the area close to the current time, thereby enhancing the ability to capture sudden water flow frequency fluctuations.

[0029] Furthermore, based on the current time point, the system looks back through multiple integer multiples of the processing time period to generate a sequence of time points to be processed with time-decaying weights. Each time point to be processed is associated with the historical data of the water flow excitation frequency and gate vibration frequency at the corresponding moment, forming a data sequence with temporal and spatial correlation. For example, in a scenario where the gate undergoes periodic opening and closing operations, adjacent historical cycle segments are preferentially selected as the time points to be processed. Abnormal trends are identified by comparing the frequency deviation patterns under continuous operating conditions. This mechanism can assign higher weights to recent data through the time-decaying function, thereby quantifying the time-varying cumulative effect of vibration energy.

[0030] In S2, a dual correction value is obtained through the dynamic evolution characteristics of historical data for subsequent correction of resonance risks. First, a gradient analysis is performed on the degree of deviation between the water flow frequency and the gate frequency at each time point to be processed: by calculating the incremental change in the degree of deviation between adjacent time points, a gradient correction value is constructed in combination with the time attenuation coefficient. This value not only reflects the instantaneous increase or decrease in the degree of deviation, but also gives a higher weight to recent changes through an exponential decay function, thereby capturing the nonlinear characteristics of energy accumulation. For example, when the gate's degree of deviation presents a "slow rise and steep fall" pattern due to periodic fluctuations in upstream water, the gradient correction value will highlight the steep rise trend in the most recent time window. Even if the current instantaneous deviation value does not reach the threshold, the potential risk can still be predicted through the amplification acceleration feature.

[0031] Furthermore, the trend correction value dynamically adjusts the direction of risk assessment through a dual criterion: on the one hand, a sign function is used to extract the overall change direction of the deviation degree sequence (positive growth or negative decay); on the other hand, the number of times the deviation degree exceeds the minimum threshold within the sampling window is counted, and the baseline sensitivity of the correction amplitude is ensured by taking the maximum value function. For example, in a continuous rainstorm scenario, if the deviation degree between the water flow frequency and the gate frequency exceeds the minimum threshold for three consecutive cycles and shows a monotonically increasing trend, the trend correction value will automatically strengthen the positive adjustment coefficient, causing the opening control strategy to enter active intervention mode in advance. This mechanism not only retains the trend inertia characteristics of historical data, but also identifies abnormal fluctuations through the frequency of threshold crossings, effectively overcoming the shortcomings of traditional linear weight allocation in handling nonlinear coupling relationships.

[0032] In S3, a real-time risk quantification model is constructed to address the problem of insufficient sensitivity of existing methods to instantaneous frequency deviations. First, a nonlinear risk indicator model is established based on the relative difference between the current water flow excitation frequency and the gate's natural frequency. This model can amplify small frequency deviations under critical conditions and effectively identify potential risks approaching the resonance threshold. For example, in a scenario where the gate's natural frequency slowly decreases due to silt accumulation, when the water flow frequency has not reached the original natural frequency but is close to the current actual frequency, the square operation will significantly increase the numerical sensitivity of the risk indicator, allowing the early warning mechanism to be triggered in the stage of linear growth of the deviation degree, avoiding the response delay caused by the lack of sensitivity of the traditional linear model.

[0033] Furthermore, the risk index introduces an importance coefficient as a dynamic adjustment factor. This coefficient is dynamically adjusted according to the gate structure characteristics, historical operation and maintenance data, and real-time load status to achieve the adaptability of risk assessment to working conditions. For example, for the radial gates of reservoirs that undertake the core function of flood control, the importance coefficient will be automatically increased based on the fatigue monitoring data of their steel structures, so that the risk index increases more significantly under the same frequency deviation degree, ensuring the implementation of stricter safety control strategies on key facilities. This mechanism not only retains the basic physical meaning of the frequency deviation degree, but also realizes the association between risk assessment and equipment health status through dynamic parameter correction, overcoming the adaptability defects of static threshold models under complex working conditions.

[0034] In S4, a collaborative control mechanism for multi-dimensional parameters is established to achieve dynamic and precise adjustment of the gate opening. First, by taking the trend correction value as a directional factor and multiplying it with the linear combination of the gradient correction value and the risk index, a regulation index with direction sensitivity is generated. This nonlinear fusion method can not only inherit the trend inertia characteristics of historical data, but also amplify the regulation weight of sudden risk events. For example, when the water flow frequency is monitored to show an accelerating deviation trend, the positive reinforcement effect of the trend correction value will cause the regulation index to show exponential growth. The drive control will start the gate opening graded adjustment before the traditional threshold is triggered, effectively overcoming the hysteresis defect of fixed-cycle regulation.

[0035] Furthermore, a discrete grading strategy is employed to map the continuous control index to a specific opening adjustment value. This allows for nonlinear adaptation of the control response through pre-set multi-level adjustment intervals. Each adjustment interval corresponds to a different opening variation, which is then combined with the actual current gate opening to calculate the target opening.

[0036] In summary, the entire gate opening adaptive control method based on dynamic assessment of resonance risk, first, controls the monitoring density by dynamically adjusting the length of the processing time period, achieving intelligent matching of data acquisition frequency with operating condition risks. In high-risk scenarios such as heavy rain or equipment aging, the monitoring frequency is automatically enhanced, effectively capturing transient water flow disturbance characteristics and solving the response lag problem caused by the existing fixed sampling period. Furthermore, a time-decay weight distribution mechanism based on historical data, combined with a dual prediction model of gradient correction value and trend correction value, not only quantifies the temporal cumulative effect of vibration energy, but also identifies the risk evolution trend through incremental changes in the degree of deviation. For example, when the gate natural frequency slowly shifts due to structural fatigue, a potential progressive resonance risk can be warned several hours in advance. Furthermore, the risk indicator achieves accurate risk assessment by amplifying the critical state sensitivity and combining it with the operating condition adaptive adjustment of the dynamic important coefficient. At the same time, the hierarchical discretization control strategy maps the continuous adjustment indicator into a step-by-step opening action, achieving nonlinear optimization of the control response through preset multi-level adjustment intervals. In summary, the entire method significantly improves the comprehensive processing capabilities of historical cumulative effects, nonlinear coupling relationships, and sudden disturbance events while retaining the basic physical logic through the closed-loop coordination of spatiotemporal data correlation analysis, trend prediction, dynamic correction, and graded action output. It upgrades gate control from passive response to active prevention, ensuring the safety of water conservancy facilities while extending the service life of equipment.

[0037] like Figure 2 As shown, in one embodiment, obtaining the processing time period in S1 includes:

[0038] S11. Obtain a first adjustment parameter based on current weather conditions, and obtain a second adjustment parameter based on a historical gate failure frequency;

[0039] S12. Obtain a basic monitoring density, and obtain a processing time period according to the basic monitoring density, the first adjustment parameter, and the second adjustment parameter.

[0040] In this embodiment, it should be noted that in S11, a dual perception mechanism for the environment and equipment status is established to achieve intelligent adaptation of monitoring density. A first adjustment parameter is generated by real-time analysis of meteorological warning data (such as rainfall intensity forecasts and basin runoff changes). This parameter directly reflects the potential impact of the external environment on water flow disturbances. For example, during the warning period of continuous heavy rainfall caused by a typhoon, the sensitivity of the first adjustment parameter is dynamically increased by analyzing satellite cloud images and hydrological forecast models, prompting the subsequent processing time period to be shortened to match the upcoming high-frequency water flow fluctuations. At the same time, the gate's full life cycle operation and maintenance records (such as bearing wear times and hydraulic failure intervals) are retrieved, and the second adjustment parameter is generated by the frequency of mechanical abnormal events within the statistical period. For example, for gates with a long service life and frequent recent jamming, the weight of the second adjustment parameter is automatically increased to strengthen the response priority for historical fault characteristics and ensure that the monitoring density is increased during the period of component performance degradation.

[0041] In S12, the dynamic optimization configuration of the processing time period is completed through the collaborative mapping of multi-source parameters. The preset basic monitoring density is used as the benchmark value, and the environmental disturbance coefficient of the first adjustment parameter and the equipment health coefficient of the second adjustment parameter are combined to generate the final processing time period using a nonlinear superposition algorithm. For example, in the scenario of spring snowmelt and equipment maintenance, the basic monitoring density is set to a normal value according to seasonal characteristics. The first adjustment parameter remains low due to the stable snowmelt runoff, and the second adjustment parameter is significantly reduced because the equipment has just completed maintenance. The three work together to automatically extend the processing time period, ensuring the effectiveness of monitoring while reducing data transmission energy consumption. This mechanism can not only avoid the risk of overfitting caused by single parameter regulation, but also achieve optimal resource allocation through dynamic checks and balances between parameters. For example, in the case of sudden local rainstorms but the gate is in good health, the processing time period is shortened based on the first adjustment parameter, without being overly restricted by the historical inertia of the equipment status parameters.

[0042] In one embodiment, the prediction model in S2 is expressed as follows:

[0043] ;in,

[0044] is the gradient correction value corresponding to the current time point, is the number of deviations, For the The degree of deviation corresponding to the time point to be processed, For the The degree of deviation corresponding to the time point to be processed.

[0045] In this embodiment, it should be noted that, in the entire expression, is the difference between adjacent deviations, which quantifies the rate of change of the deviation between adjacent time points. If the difference is positive, it means that the deviation is decreasing, which means that the water flow frequency is getting closer to the gate frequency, which is more likely to cause resonance, resulting in a sharp increase in the gate amplitude or even structural damage. If it is negative, it means that the risk is easing. By accumulating these differences, the model can capture the overall trend of historical data. is the time attenuation coefficient, which realizes dynamic weight distribution. When k increases (that is, the time point is closer to the current moment), The value of is close to 1, giving more weight to recent changes; the weight of early changes (smaller k) increases with Increases and decays; ensures smooth weight changes and avoids the lack of sensitivity of linear decay to recent data. For example, when k=1, the weight is , and when k=4, the weight is , significantly increasing the contribution of recent changes.

[0046] In summary, the model captures time-varying cumulative effects. By weighting and accumulating historical rates of change, it not only reflects the current degree of deviation but also quantifies the accumulation of risk energy. For example, when slowly approaching the natural frequency, even if the instantaneous difference does not reach the threshold, the combination of a sustained positive difference and a high weight will trigger an early warning. Furthermore, dynamic trend perception is achieved. The exponential decay coefficient enhances the sensitivity of recent data, enabling the identification of nonlinear patterns such as "slow rise and steep fall." Traditional static threshold methods may miss such gradual risks.

[0047] For example, assume that the deviation degree sequence of the time points to be processed is: =14, =13, =11, =12, =14 (unit: Hz), a total of n = 5 data points. Substitute into the expression to calculate the gradient correction value .

[0048] In summary, the gradient correction value , which reflects that a certain amount of risk reduction is obtained from multiple deviation degrees, which will not make a positive contribution to the risk increase for the time being, but the gradient correction value has approached a positive number, suggesting that there may be risk accumulation in the future; further, although the deviation degree from the first to-be-processed time point to the third to-be-processed time point shows a downward trend, the deviation degree from the third to-be-processed time point to the fifth to-be-processed time point shows an upward trend, and the recent changes are given a higher weight through the time attenuation coefficient. Therefore, the result accurately reflects the growth of the deviation degree, providing a basis for subsequent precise control.

[0049] In one embodiment, the trend model in S2, in which the trend correction value corresponding to the current time point is obtained based on the trend model and the deviation degrees corresponding to the multiple time points to be processed, is expressed as:

[0050] ;in,

[0051] is the trend correction value corresponding to the current time point, is the number of time points to be processed in the sampling window, is the minimum deviation threshold, The first The degree of deviation corresponding to the time point to be processed, The first The degree of deviation corresponding to the time point to be processed.

[0052] In this embodiment, it should be noted that It is the direction judgment item, or the direction function part, which is mainly used to judge the overall change direction of the deviation degree; if the sum of adjacent differences is positive ( ), indicating that the degree of deviation is slowing down or attenuating, the output is +1, making a positive contribution to improving the risk index; if the sum is negative ( ), indicating that the degree of deviation is accelerating, output -1, making a positive contribution to reducing the risk index; if the sum is zero ( ), indicating that the trend is stable, the output is 0; the direction function part encodes the direction information as -1, 0 or +1 by accumulating the difference.

[0053] Furthermore, It is the over-limit statistical item, or the maximum function part, which is used to count the deviations below the minimum threshold. The frequency and the lower limit of the amplitude are forced to be 2, which means that the over-limit statistics are only used to reflect the frequency below the minimum threshold. When the deviation degree exceeds two, it means that further enhancement adjustment is triggered only when it falls below the threshold at least twice; In, when (abnormal deviation), output +1; when (The deviation is normal), and the output is -1. Traditional linear weighting cannot distinguish between occasional and persistent violations. By counting the number of anomalies and taking the maximum value, we can identify persistent risks (such as multiple violations) while avoiding false triggering of control by brief interference.

[0054] Furthermore, the direction judgment item result (-1, 0 or +1) is independent of the limit statistical item result (≥2); after adding the two together, when the direction is attenuation (+1) and the amplitude criterion exceeds 2, When the value is greater than 3, it indicates that the adjustment is emphasized; when the direction is increasing (-1) and the amplitude criterion is 2, =1, means no adjustment correction; no matter how many times the limit is exceeded, the minimum amplitude criterion is 2, ensuring 1 (when the direction criterion is 1) to avoid completely suppressing the regulation action.

[0055] For example, the sampling window is: m=5 (the 5 time points to be processed before the current time point), =4, deviation degree sequence: =3, =2, =5, =1, =2. Substitute into the expression to calculate the trend correction value =4.

[0056] In summary, the overall deviation degree in the sampling window shows a decaying trend, and the resonance risk increases. At the same time, there are a large number (4) of time points to be processed that are less than the minimum deviation degree threshold, which further increases the resonance risk. Therefore, a stronger trend correction value (4) is finally obtained.

[0057] In summary, the trend model separates the directional judgment of risk trends from the frequency statistics of exceeding standards through an additive structure that decouples direction and amplitude, thus solving the control lag problem caused by direction insensitivity and linear weight distribution in traditional methods. The entire trend correction value clearly adjusts the direction, upgrading the gate control from passive threshold triggering to active prevention with coordinated direction and amplitude.

[0058] In one embodiment, the risk index obtained in S3 according to the current water flow frequency and the current gate frequency is expressed as:

[0059] ;in,

[0060] is a risk indicator, is the important coefficient, is the current gate frequency, is the current water flow frequency.

[0061] In this embodiment, it should be noted that the denominator Reflects the current water flow frequency With the current gate frequency When the two are closer, the denominator approaches zero, and the value of the entire fraction increases sharply, reflecting the nonlinear growth characteristics of the resonance risk. As a reference amplification factor, to ensure the normalization of frequency amplitude, when and When the numerator is large (such as in high-frequency scenarios), the denominator is too small to cause numerical instability. Furthermore, the square operation further amplifies the small frequency deviation in the critical state. For example, when gradually approaching , the linear decrease of denominator will cause quadratic growth of fraction value, so that the risk warning can be triggered in advance. Further, the important coefficient is dynamically adjusted according to the gate structure health, historical failure rate and real-time load; for example, for the high-wear gate, is increased to improve the risk sensitivity; in high-risk working conditions such as heavy rain, is temporarily increased to match the intensity of environmental disturbance; for example, when an abnormal temperature rise of the gate bearing is monitored, is increased by 20%, so that the risk index is increased by 20% under the same frequency difference, and if the historical failure record shows that 3 jamming events have occurred this month, is additionally increased by 15%.

[0062] In summary, the gradual resonance risk of early warning is realized, for example, scenario 1 (slowly approaching): when approaches at a rate of 0.5 Hz / min , even if the current difference is still 5 Hz (not reaching the minimum deviation threshold, such as 4 Hz), the risk index has grown through the fraction to a value that meets the warning; for example, scenario 2 (dynamic threshold adaptation): if is adjusted to 1.5 due to equipment aging, the same frequency difference increases by 50%, avoiding false negatives. Further, the non-linear coupling of sudden disturbance is handled, and the square operation makes the risk index more sensitive to sudden frequency fluctuations; for example, sudden disturbance, heavy rain causes to jump from 45 Hz to 49 Hz in 10 seconds ( = 50 Hz), at this time from (95 / 5)^2=361 , suddenly increases to (99 / 1)^2=9801 , the risk assessment is high risk, which must trigger a large degree of control adjustment. Further, the robustness of false action is also avoided, when randomly fluctuates around the threshold value (such as fluctuates at 19 Hz ± 0.1 Hz), the square operation smooths the transient noise and prevents frequent false triggering; at the same time, whether is higher or lower than , the square operation eliminates the positive and negative signs, and the risk is evaluated with the same logic, avoiding direction discrimination.

[0063] For example, the current gate frequency = 12 Hz, the current water flow frequency = 21 Hz, and the dynamic important coefficient =1.8 (Base 1, ① Red alert for heavy rain + 0.4; ② Two jams have occurred this month + 0.2; ③ Bearing temperature exceeds the limit + 0.2). Substitute this into the expression to calculate the risk index If the current water flow frequency changes, =15Hz, substitute into the expression to calculate the risk index , which means that the risk indicator has increased significantly.

[0064] In summary, the expression amplifies the critical sensitivity through the fractional structure, strengthens the nonlinear response through square operation, and adapts the dynamic coefficient to multiple working conditions. It can identify risks before the frequency difference reaches the minimum deviation threshold, thus achieving early warning. Adapts to equipment health and environmental disturbances; squares and smoothes random fluctuations, is highly resistant to noise, and avoids false operations.

[0065] like Figure 3 As shown, in one embodiment, obtaining the target opening according to the risk index, the gradient correction value, and the trend correction value in S4 includes:

[0066] S41. Obtaining an adjustment indicator based on the risk indicator, the gradient correction value, and the trend correction value;

[0067] S42, obtaining a plurality of opening adjustment values ​​corresponding to different numerical ranges, and obtaining the corresponding opening adjustment value according to the numerical range in which the adjustment index falls;

[0068] S43. Obtain a target opening according to the current opening and the opening adjustment amount.

[0069] In this embodiment, it should be noted that in S41, a directionally sensitive adjustment index is constructed by integrating the dynamic characteristics of trend direction and risk energy. First, the gradient correction value (reflecting the increase or decrease in the historical deviation and the directional trend) is linearly superimposed with the real-time risk index (quantifying the current frequency proximity) to form a base adjustment value. Subsequently, the base adjustment value is weighted by the trend correction value (indicating the rate of risk accumulation, with a minimum value of 1). When the historical trend indicates a continued accumulation of risk (e.g., the flow frequency slowly approaches the gate frequency), a positive gradient correction value amplifies the adjustment index. Conversely, if the deviation shows a easing trend, a negative gradient correction value suppresses the adjustment value to avoid excessive response. For example, if the gate's natural frequency drifts due to mechanical wear, even if the current flow frequency does not reach the original threshold, if the historical gradient indicates a continued approaching trend, this mechanism will still generate a high-amplitude positive adjustment index, triggering pre-adjustment of the opening.

[0070] In S42, the continuous adjustment index is discretized into a stepped opening action using pre-set multi-level threshold intervals. Specifically, the adjustment index is divided into several non-uniform intervals (e.g., low-risk zone, warning zone, and emergency response zone), each corresponding to a different opening adjustment amplitude (e.g., fine adjustment 1%, medium adjustment 3%, and emergency adjustment 5%). The interval boundaries are designed nonlinearly, with a narrower span in the high-risk interval, thereby achieving a jump in sensitivity under critical conditions. For example, if the adjustment index enters the emergency response zone due to a sudden increase in water flow frequency, even if the index value only slightly exceeds the threshold, the maximum adjustment amplitude will be triggered to ensure rapid braking before the critical resonance point. Furthermore, hysteresis bands can be set for each interval to avoid frequent switching near the threshold and enhance control stability.

[0071] In S43, the final target opening command is generated based on the sum of the current actual opening and the discrete adjustment value. To balance response speed and equipment safety, a smoothing constraint can be introduced: when the adjustment value exceeds a preset safety threshold, the single adjustment is automatically decomposed into multiple incremental steps, and the opening error is calibrated in real time using pressure feedback from the hydraulic actuator. For example, if the current opening is 60% and the discrete adjustment value requires an abrupt -8%, the system will not jump directly to 52%. Instead, it will make a two-stage step-by-step adjustment (first -5% and then -3%).

[0072] In one embodiment, the adjustment indicator is obtained according to the risk indicator, the gradient correction value, and the trend correction value in S41 as follows:

[0073] ;in,

[0074] To adjust the index, is the trend correction value corresponding to the current time point, is the gradient correction value corresponding to the current time point, As a risk indicator.

[0075] In this embodiment, it should be noted that the linear superposition basic adjustment amount The physical meaning of is to combine the historical trend (gradient correction value) with the current risk (risk indicator) to form the basic adjustment amount. :Capture the time-varying cumulative effect of risk energy by weighting the rate of change of historical deviation degree. Positive values ​​indicate accelerated growth of deviation degree, while negative values ​​indicate risk mitigation. Risk Indicator : Quantify the closeness between the current water flow frequency and the gate frequency. The larger the value, the closer it is to the resonance threshold. The trend correction value is used as a nonlinear weight and a comprehensive factor for amplitude improvement to nonlinearly amplify the basic adjustment amount.

[0076] For example, the radial gate of the reservoir has a rapid increase in water flow frequency due to heavy rain; the gradient correction value =3.2 (historical deviation from accelerated growth), risk indicator =24.8 (current water flow frequency is close to gate frequency), trend correction value =2 (the trend direction is risk growth, and it is lower than the threshold twice in a row). Substitute it into the expression to calculate and adjust the indicator .

[0077] Furthermore, the index mapping is adjusted (S42 preset interval), and the adjustment interval is defined as: <20, low risk area → fine-tuning 1%; 20 <60, warning zone → medium adjustment 3%; 60 , Emergency Zone → Emergency Adjustment 5%. Action Trigger: =56 falls into the warning zone, triggering an emergency adjustment of 3% of the opening. Since the current water flow frequency is higher than the gate frequency, the gate is reduced to 97% of the current opening. The reduction in opening leads to a decrease in water flow, thereby increasing the water flow frequency, making it further deviate from the gate frequency, and reducing the risk of frequency coupling.

[0078] A gate opening adaptive control system based on dynamic evaluation of resonance risk is also provided, the system comprising:

[0079] A data acquisition module is used to obtain the current time point and the processing time period, to use time points separated from the current time point by different processing time periods as different time points to be processed, and to obtain the historical water flow frequency and historical gate frequency corresponding to each time point to be processed;

[0080] A first data processing module is configured to obtain a degree of deviation corresponding to each time point to be processed based on a historical flow frequency and a historical gate frequency corresponding to each time point to be processed, obtain a gradient correction value corresponding to the current time point based on a prediction model and the degrees of deviation corresponding to the multiple time points to be processed, and obtain a trend correction value corresponding to the current time point based on a trend model and the degrees of deviation corresponding to the multiple time points to be processed;

[0081] The second data processing module is used to obtain the current water flow frequency and the current gate frequency at the current time point, and obtain the risk index based on the current water flow frequency and the current gate frequency;

[0082] The adaptive control module is used to obtain the target opening according to the risk index, gradient correction value and trend correction value, and control the gate opening according to the target opening.

[0083] In one embodiment, the data acquisition module is also used to: obtain a first adjustment parameter based on current weather conditions, and obtain a second adjustment parameter based on the historical failure frequency of the gate; obtain basic monitoring density, and obtain a processing time period based on the basic monitoring density, the first adjustment parameter and the second adjustment parameter.

[0084] In one embodiment, the adaptive control module is further used to: obtain an adjustment index based on the risk index, the gradient correction value and the trend correction value; obtain multiple opening adjustment amounts corresponding to different numerical ranges, and obtain the corresponding opening adjustment amount based on the numerical range in which the adjustment index falls; and obtain the target opening based on the current opening and the opening adjustment amount.

[0085] In this embodiment, it should be noted that, regarding the above-mentioned gate opening adaptive control system based on dynamic evaluation of resonance risk, the specific method of performing the operation has been described in detail in the implementation method of the gate opening adaptive control method based on dynamic evaluation of resonance risk, and will not be elaborated here.

[0086] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings. However, the present disclosure is not limited to the specific details of the above embodiments. Within the technical concept of the present disclosure, various simple modifications can be made to the technical solutions of the present disclosure, and these simple modifications all fall within the scope of protection of the present disclosure.

[0087] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0088] In addition, the various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

[0089] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and description of the present invention.

Claims

1. A gate opening adaptive control method based on dynamic evaluation of resonance risk, characterized in that: include: Obtain the current time point and the processing time period, use time points separated from the current time point by different processing time periods as different time points to be processed, and obtain the historical water flow frequency and historical gate frequency corresponding to each time point to be processed; Obtain the degree of deviation corresponding to each time point to be processed based on the historical water flow frequency and the historical gate frequency corresponding to each time point to be processed, and obtain the gradient correction value corresponding to the current time point based on the prediction model and the degree of deviation corresponding to multiple time points to be processed, and obtain the trend correction value corresponding to the current time point based on the trend model and the degree of deviation corresponding to multiple time points to be processed; Get the current water flow frequency and the current gate frequency at the current time point, and obtain the risk index based on the current water flow frequency and the current gate frequency; the risk index obtained based on the current water flow frequency and the current gate frequency is expressed as: ;in, is a risk indicator, is the important coefficient, is the current gate frequency, is the current water flow frequency; Obtain the target opening according to the risk index, gradient correction value and trend correction value, and control the gate opening according to the target opening; The step of obtaining the target opening according to the risk indicator, the gradient correction value, and the trend correction value includes: obtaining an adjustment indicator according to the risk indicator, the gradient correction value, and the trend correction value; obtaining a plurality of opening adjustment amounts corresponding to different numerical ranges, and obtaining the corresponding opening adjustment amount according to the numerical range within which the adjustment indicator falls; obtaining the target opening according to the current opening and the opening adjustment amount; obtaining the adjustment indicator according to the risk indicator, the gradient correction value, and the trend correction value is expressed as follows: ;in, To adjust the index, is the trend correction value corresponding to the current time point, is the gradient correction value corresponding to the current time point, As a risk indicator.

2. The gate opening adaptive control method based on dynamic evaluation of resonance risk according to claim 1 is characterized in that: The acquisition processing time period includes: Obtaining a first adjustment parameter based on current weather conditions and obtaining a second adjustment parameter based on the gate's historical failure frequency; A basic monitoring density is obtained, and a processing time period is obtained according to the basic monitoring density, the first adjustment parameter, and the second adjustment parameter.

3. The gate opening adaptive control method based on dynamic evaluation of resonance risk according to claim 1 is characterized in that: The prediction model in which the gradient correction value corresponding to the current time point is obtained based on the prediction model and the deviation degrees corresponding to multiple time points to be processed is expressed as: ;in, is the gradient correction value corresponding to the current time point, is the number of deviations, For the The degree of deviation corresponding to the time point to be processed, For the The degree of deviation corresponding to the time point to be processed.

4. The gate opening adaptive control method based on dynamic evaluation of resonance risk according to claim 1 is characterized in that: The trend model in which the trend correction value corresponding to the current time point is obtained based on the trend model and the deviation degrees corresponding to multiple time points to be processed is expressed as: ;in, is the trend correction value corresponding to the current time point, is the number of time points to be processed in the sampling window, is the minimum deviation threshold, The first The degree of deviation corresponding to the time point to be processed, The first The degree of deviation corresponding to the time point to be processed.

5. A gate opening adaptive control system based on dynamic evaluation of resonance risk, characterized in that: The system comprises: A data acquisition module is used to obtain the current time point and the processing time period, to use time points separated from the current time point by different processing time periods as different time points to be processed, and to obtain the historical water flow frequency and historical gate frequency corresponding to each time point to be processed; A first data processing module is configured to obtain a degree of deviation corresponding to each time point to be processed based on a historical flow frequency and a historical gate frequency corresponding to each time point to be processed, obtain a gradient correction value corresponding to the current time point based on a prediction model and the degrees of deviation corresponding to the multiple time points to be processed, and obtain a trend correction value corresponding to the current time point based on a trend model and the degrees of deviation corresponding to the multiple time points to be processed; The second data processing module is used to obtain the current water flow frequency and the current gate frequency at the current time point, and obtain the risk index based on the current water flow frequency and the current gate frequency; the risk index obtained based on the current water flow frequency and the current gate frequency is expressed as: ;in, is a risk indicator, is the important coefficient, is the current gate frequency, is the current water flow frequency; Adaptive control module, used to obtain the target opening according to the risk index, gradient correction value and trend correction value, and control the gate opening according to the target opening; The adaptive control module is further configured to: obtain an adjustment index based on the risk index, the gradient correction value, and the trend correction value; obtain a plurality of opening adjustment amounts corresponding to different numerical ranges, and obtain the corresponding opening adjustment amount based on the numerical range within which the adjustment index falls; obtain a target opening based on the current opening and the opening adjustment amount; and obtain the adjustment index based on the risk index, the gradient correction value, and the trend correction value, which is expressed as: ;in, To adjust the index, is the trend correction value corresponding to the current time point, is the gradient correction value corresponding to the current time point, As a risk indicator.

6. The gate opening adaptive control system based on dynamic evaluation of resonance risk according to claim 5, characterized in that: The data acquisition module is also used for: Obtaining a first adjustment parameter based on current weather conditions and obtaining a second adjustment parameter based on the gate's historical failure frequency; A basic monitoring density is obtained, and a processing time period is obtained according to the basic monitoring density, the first adjustment parameter, and the second adjustment parameter.

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