CN-121785105-B - Intelligent control method for pipe culvert type gate based on data analysis

CN121785105BCN 121785105 BCN121785105 BCN 121785105BCN-121785105-B

Abstract

The invention relates to the technical field of data processing, in particular to an intelligent control method of a culvert type gate based on data analysis, which is used for acquiring flow data and water level data of a pipeline where the culvert type gate is positioned at each moment, flow velocity data of each monitoring position in the pipeline and multidimensional state data of the culvert type gate; the method comprises the steps of obtaining a primary proportion adjustment coefficient according to flow data deviation and water level data change characteristics at the current moment, presetting fluctuation characteristics of water level data and flow data in a historical period according to flow speed data of each monitoring position at the current moment, obtaining a proportion coefficient adjustment factor according to flow data deviation at the current moment and multidimensional state data in the current moment and the preset historical period, obtaining a self-adaptive proportion coefficient according to the primary proportion adjustment coefficient and the proportion coefficient adjustment factor, and performing intelligent control on a pipe culvert gate at the current moment by using a PID control method, so that control precision and stability of the pipe culvert gate are improved.

Inventors

ZHANG YUANZHEN
CHEN YONGJUAN
LI BIN
Mou Zhuangzhuang

Assignees

山东欧标信息科技有限公司

Dates

Publication Date: 20260508
Application Date: 20260303

Claims (8)

1. The intelligent control method for the pipe culvert gate based on the data analysis is characterized by comprising the following steps of: Acquiring flow data and water level data of a pipeline where the pipe culvert type gate is located at each moment when the pipe culvert type gate is cut off to the current moment, and flow speed data of each monitoring position in the pipeline, and simultaneously acquiring multidimensional state data of the pipe culvert type gate at each moment; Acquiring ideal flow data of a pipeline, and acquiring a primary proportion adjustment coefficient at the current moment according to the difference between the flow data at the current moment and the ideal flow data and the water level data change characteristic of the current moment and the last moment; Acquiring a Reynolds number corresponding to the current moment, acquiring a flow state stability adjustment factor at the current moment according to the flow velocity data of each monitoring position at the current moment, fluctuation characteristics of water level data and flow data in a preset historical period before the current moment and the Reynolds number, acquiring a gate running state adjustment factor at the current moment according to the difference between the flow data at the current moment and the ideal flow data, the current moment and multidimensional state data in the preset historical period, and acquiring a scaling factor adjustment factor at the current moment by combining the flow state stability adjustment factor and the gate running state adjustment factor; According to the preliminary proportion adjustment coefficient and the proportion coefficient adjustment factor, adjusting a preset proportion coefficient initial value in a PID control method to obtain a self-adaptive proportion coefficient at the current moment, and based on the self-adaptive proportion coefficient, using the PID control method to intelligently control the pipe culvert gate at the current moment; The flow state stability adjustment factor at the current moment is obtained according to the flow speed data of each monitoring position at the current moment, the fluctuation characteristics of the water level data and the flow data in the preset historical period before the current moment and the Reynolds number, and the flow state stability adjustment factor at the current moment comprises the following steps: Acquiring the vortex intensity of each monitoring position in a pipeline at the current moment, correspondingly acquiring a vortex intensity mean value, forming water level data in a preset history period into a water level data sequence, acquiring an addition result of the number of peak values in the water level data sequence and a constant 1 to acquire the fluctuation degree of the water level data, and carrying out normalization processing on the arithmetic square root of the product of the vortex intensity mean value and the fluctuation degree of the water level data to acquire the first flow state instability degree at the current moment; Acquiring the flow velocity data average value of all monitoring positions in the pipeline at the current moment, respectively calculating the difference absolute value of the flow velocity data of each monitoring position in the pipeline at the current moment and the flow velocity data average value, correspondingly obtaining the average value of the difference absolute value, recording the average value as an average flow velocity difference value, and carrying out normalization processing on the arithmetic square root of the product of the inverse of the average flow velocity difference value and the Reynolds number to obtain the second flow state instability degree at the current moment; The flow data in a preset history period are formed into a flow data sequence, the standard deviation of the flow data sequence is obtained, and the standard deviation is normalized to obtain a third flow state instability degree at the current moment; and normalizing the addition result of the first fluid state instability degree, the second fluid state instability degree and the third fluid state instability degree to obtain a fluid state stability adjustment factor at the current moment.
2. The intelligent control method of the culvert gate based on data analysis according to claim 1, wherein the obtaining the preliminary scaling factor at the current moment according to the difference between the flow data at the current moment and the ideal flow data and the water level data change characteristic of the current moment and the previous moment comprises: acquiring the absolute value of the difference value of the water level data at the current moment and the previous moment to obtain a water level data difference value, acquiring the interval time between the current moment and the previous moment, and calculating the ratio of the water level data difference value to the interval time to obtain the water level data change rate; Obtaining the absolute value of the difference value between the flow data at the current moment and the ideal flow data, obtaining a flow data difference value, and carrying out normalization processing on the product of the flow data difference value and the water level data change rate, so as to obtain a primary proportional adjustment coefficient at the current moment.
3. The intelligent control method of the culvert gate based on data analysis according to claim 1, wherein the obtaining the gate operation state adjustment factor at the current time according to the difference between the flow data at the current time and the ideal flow data, the current time and the multidimensional state data in the preset history period comprises: The multidimensional state data comprises gate opening and gate vibration amplitude; Acquiring a gate opening stability index at the current moment according to the gate opening at the current moment, acquiring the flow data difference degree at the current moment according to the difference between the flow data at the current moment and the ideal flow data, calculating the reciprocal of the addition result of the gate opening stability index and a preset constant, and carrying out normalization processing on the arithmetic square root of the product of the flow data difference degree and the reciprocal to obtain a first gate instability degree; Acquiring a gate instability index according to the gate vibration amplitude at each moment in a preset history period, acquiring the gate opening and closing speed of a culvert gate in the last opening and closing process before the current moment, and carrying out normalization processing on the arithmetic square root of the product of the gate opening and closing speed and the gate instability index to obtain a second gate instability degree; acquiring gate action times of a pipe culvert gate in a preset history period, and carrying out normalization processing on the gate action times to obtain a third gate instability degree; and normalizing the addition results of the first gate instability degree, the second gate instability degree and the third gate instability degree to obtain a gate running state adjustment factor at the current moment.
4. The intelligent control method for the culvert gate based on data analysis according to claim 3, wherein the step of obtaining the gate opening stability index at the current moment according to the gate opening at the current moment comprises the following steps: obtaining the maximum gate opening and the minimum gate opening of a pipe culvert gate, calculating the full square difference of the maximum gate opening and the minimum gate opening, obtaining the opposite number of the ratio of a constant 4 to the full square difference, and recording the opposite number as a stability coefficient; obtaining a difference value between the gate opening at the current moment and the minimum gate opening, marking the difference value as a first difference value, and obtaining a difference value between the gate opening at the current moment and the maximum gate opening, marking the difference value as a second difference value; and obtaining the product of the stability coefficient, the first difference value and the second difference value to obtain a gate opening stability index at the current moment.
5. The intelligent control method for the culvert gate based on data analysis according to claim 3, wherein the step of obtaining the difference degree of the flow data at the current moment according to the difference between the flow data at the current moment and the ideal flow data comprises the following steps: If the flow data at the current moment is larger than or equal to the ideal flow data, the ratio of the flow data at the current moment to the ideal flow data is obtained, and the difference value between the ratio and a constant 1 is calculated to obtain the difference degree of the flow data at the current moment; And if the flow data at the current moment is smaller than the ideal flow data, setting the difference degree of the flow data at the current moment to be 0.
6. The intelligent control method for culvert gate based on data analysis according to claim 3, wherein the obtaining gate instability index according to the gate vibration amplitude at each moment in the preset history period comprises: Forming a gate vibration amplitude data sequence from gate vibration amplitudes in a preset historical time, and respectively obtaining peak value data and valley value data of the gate vibration amplitude data sequence; if the number of peak value data or valley value data in the gate vibration amplitude data sequence is 0, setting a gate instability index as 0; If the number of the peak value data and the valley value data in the gate vibration amplitude data sequence is not 0, respectively obtaining a peak value data average value and a valley value data average value of the gate vibration amplitude data sequence, and calculating the difference value between the peak value data average value and the valley value data average value to obtain a gate instability index.
7. The intelligent control method for the culvert gate based on data analysis according to claim 1, wherein the step of obtaining the scaling factor of the current moment by combining the flow state stability adjustment factor and the gate operation state adjustment factor comprises the following steps: and normalizing the flow state stability adjustment factor, the gate running state adjustment factor and the inverse of the addition result of the preset constant to obtain the scaling factor adjustment factor at the current moment.
8. The intelligent control method of the culvert gate based on data analysis according to claim 1, wherein the adjusting the preset scaling factor initial value in the PID control method according to the preliminary scaling factor and the scaling factor to obtain the adaptive scaling factor at the current moment includes: And obtaining a preset proportional coefficient adjustment range value, a product between the primary proportional coefficient and the proportional coefficient adjustment factor to obtain a proportional coefficient adjustment value, and obtaining an addition result of a preset proportional coefficient initial value and the proportional coefficient adjustment value to obtain the self-adaptive proportional coefficient at the current moment.

Description

Intelligent control method for pipe culvert type gate based on data analysis Technical Field The invention relates to the technical field of data processing, in particular to an intelligent control method for a pipe culvert gate based on data analysis. Background In the field of hydraulic engineering, the pipe culvert type gate is used as an important water flow regulating and controlling facility and is widely applied to the fields of irrigation, flood control, water supply and the like. Traditional pipe culvert type gate control mode mainly relies on manual experience or simple timing control, is difficult to carry out accurate regulation and control according to real-time water flow condition, water level change and upstream and downstream demands. The manual operation is not only inefficient, but also is easily affected by subjective factors, so that the time and degree of opening or closing the gate are unreasonable, and the problems of water resource waste, local flooding or uneven irrigation and the like are caused. Meanwhile, the traditional control mode lacks effective utilization of historical data, and rules cannot be mined from a large amount of operation data to optimize a control strategy. Along with the development of water conservancy informatization and intellectualization, the demands on accurate, efficient and intelligent control of the culvert type gate are urgent, and the intelligent control method of the culvert type gate based on data analysis is developed, so that the water resource utilization efficiency and the water conservancy project operation management level can be improved, and the intelligent control method of the culvert type gate has important practical significance and wide application prospect. The traditional PID control structure is simple and easy to realize, the basic control framework can be quickly built by means of a mature control theory, the basic adjustment capability is provided for the general working condition, and the system can be maintained to run relatively stably. However, the core parameters of the traditional PID control generally adopt fixed parameters, such as a proportion coefficient Kp, which directly affects the response speed of the system, and the requirements of the gate on the response speed under different working conditions are different, when the pipe culvert gate faces different flow rates, water levels and flow states, the fixed proportion coefficient Kp is difficult to flexibly adapt to complex and changeable working conditions, response lag or overshoot is easy to occur, and control precision and stability are affected. Therefore, how to obtain the self-adaptive proportionality coefficient according to the water level change of water flow under different working conditions, and improving the control precision and stability of PID control on the culvert gate becomes a problem to be solved urgently. Disclosure of Invention In view of the above, the embodiment of the invention provides an intelligent control method for a pipe culvert gate based on data analysis, which aims to solve the problems of how to obtain a self-adaptive proportionality coefficient according to water level changes under different working conditions and improve the control precision and stability of the pipe culvert gate by PID control. The embodiment of the invention provides an intelligent control method for a culvert gate based on data analysis, which comprises the following steps: Acquiring flow data and water level data of a pipeline where the pipe culvert type gate is located at each moment when the pipe culvert type gate is cut off to the current moment, and flow speed data of each monitoring position in the pipeline, and simultaneously acquiring multidimensional state data of the pipe culvert type gate at each moment; Acquiring ideal flow data of a pipeline, and acquiring a primary proportion adjustment coefficient at the current moment according to the difference between the flow data at the current moment and the ideal flow data and the water level data change characteristic of the current moment and the last moment; Acquiring a Reynolds number corresponding to the current moment, acquiring a flow state stability adjustment factor at the current moment according to the flow velocity data of each monitoring position at the current moment, fluctuation characteristics of water level data and flow data in a preset historical period before the current moment and the Reynolds number, acquiring a gate running state adjustment factor at the current moment according to the difference between the flow data at the current moment and the ideal flow data, the current moment and multidimensional state data in the preset historical period, and acquiring a scaling factor adjustment factor at the current moment by combining the flow state stability adjustment factor and the gate running state adjustment factor; And adjusting the initial value of the preset proportional coef