Hydropower station gate opening degree cooperative control method based on beidou technology

Abstract

This invention relates to the field of data prediction technology, specifically to a collaborative control method for the opening degree of hydropower station gates based on BeiDou technology. This method constructs state parameters and analyzes the transition of these parameters over historical periods to obtain the transition probability for each state parameter transition. By combining the correlation between meteorological data and opening data, as well as the correlation between historical meteorological data and predicted meteorological data, the transition probabilities are continuously weighted to obtain a second weighted transition probability. The quantitative characteristics of the state transition of the flood discharge channel gate under its predicted state transition are statistically analyzed for the non-flood discharge channel gate, thereby obtaining the upper-level influence weight, obtaining the second state transition matrix of the flood discharge channel gate, and performing state prediction. This embodiment of the invention, by combining the influence of historical meteorological data and further combining the control influence between gates, obtains accurate gate state prediction results.

Description

Technical Field

[0001] This invention relates to the field of data prediction technology, specifically to a collaborative control method for the gate opening of a hydropower station based on BeiDou technology. Background Technology

[0002] Gate control in hydropower stations is an important means of ensuring multiple tasks such as reservoir water level and flow regulation and flood control. In large-scale hydropower stations, real-time data transmission of multiple hydropower station gates is involved, and this data is crucial for equipment monitoring and control.

[0003] The emergence of the BeiDou Navigation Satellite System (BDS) enables passive communication and control systems in areas where ordinary mobile communication signals cannot cover or in emergency situations where communication fiber optic lines are damaged. It provides high-precision positioning and time synchronization functions. By using the BeiDou satellite system to establish a comprehensive monitoring model, and combining historical and real-time data, the operating status of the gate can be effectively evaluated and the opening error can be precisely adjusted.

[0004] In existing technologies, Markov chains can be used to predict the state transition of gates. However, in actual prediction, the future state predicted by the Markov chain is only strongly correlated with the current state and does not take into account historical states. As a fixed large-scale water conservancy project, the data of a hydropower station is obviously periodic and seasonal. Therefore, the state prediction results of the Markov chain in the existing technology may be inaccurate. Summary of the Invention

[0005] To address the technical problem of inaccurate prediction results when using Markov chains to predict the opening status of hydropower station gates due to the lack of consideration for historical data in existing technologies, this invention aims to provide a collaborative control method for hydropower station gate opening based on BeiDou technology. The specific technical solution adopted is as follows:

[0006] This invention proposes a collaborative control method for the gate opening of a hydropower station based on BeiDou technology, the method comprising:

[0007] For each gate, the state parameters at each historical moment are determined based on the historical water level data and historical opening data; the number of transitions between state parameters at adjacent historical moments is counted to obtain the transition probability under each state parameter transition condition;

[0008] Based on the correlation between historical meteorological data and the historical opening data of each gate, the meteorological impact weight of each gate is obtained, and the transfer probability is weighted to obtain a first weighted transfer probability; based on the correlation between predicted meteorological data and historical meteorological data, the meteorological change weight is obtained, and the first weighted transfer probability and the transfer probability are weighted and adjusted to obtain a second weighted transfer probability.

[0009] The second weighted transition probabilities under each state parameter transition condition of the non-flood discharge gate form the first state transition matrix of each non-flood discharge gate. The predicted state transition condition of each non-flood discharge gate is determined by using a Markov chain based on the first state transition matrix.

[0010] In historical data, the number of transitions for each state parameter of the flood discharge channel gate under the predicted state transition is counted to obtain the upper-level influence weight. Based on the upper-level influence weight, the second weighted transition probability of the flood discharge channel gate and the transition probability are weighted and adjusted to obtain the third weighted transition probability. The second state transition matrix of the flood discharge channel gate is constructed, and the predicted state transition is determined.

[0011] Furthermore, the method for obtaining the state parameters includes:

[0012] For each dimension in the historical water level data and historical opening data, the data range in the dimension is divided into ten level intervals; the two level intervals corresponding to each gate at a historical moment constitute a binary tuple, and the binary tuple serves as a state parameter.

[0013] Furthermore, the historical meteorological data includes historical temperature data and historical precipitation data.

[0014] Furthermore, the method for obtaining the meteorological impact weights includes:

[0015] Either the historical temperature data or the historical precipitation data can be used as the meteorological dimension to be analyzed.

[0016] For the meteorological dimension to be analyzed, the historical period is divided into segments with a year as the time length to obtain the data sequence of the dimension to be analyzed and the gate opening data sequence for each year.

[0017] Obtain the difference distance and periodic difference between the data sequence of the dimension to be analyzed and the gate opening data sequence in the same time period; use the product of the periodic difference and the difference distance as the initial influence factor in each time period; perform negative correlation mapping and normalization on the average initial influence factors of all time periods to obtain the meteorological influence factor in the meteorological dimension to be analyzed.

[0018] The meteorological influence factor under the historical temperature data dimension is used as the denominator, and the meteorological influence factor under the historical precipitation data dimension is used as the numerator. The obtained ratio is normalized to obtain the meteorological influence weight.

[0019] Furthermore, the method for obtaining the meteorological change weights includes:

[0020] The predicted meteorological data includes predicted temperature data and predicted precipitation data; a first Pearson correlation coefficient is obtained between the predicted meteorological data and the historical temperature data; a second Pearson correlation coefficient is obtained between the predicted precipitation data and the historical precipitation data; and the average of the first Pearson correlation coefficient and the second Pearson correlation coefficient is used as the meteorological change weight.

[0021] Furthermore, the method for obtaining the first weighted transition probability includes:

[0022] The first weighted transition probability is obtained by multiplying the meteorological impact weight by the transition probability.

[0023] Furthermore, the method for obtaining the second weighted transition probability includes:

[0024] The meteorological change weight is used as the weight of the first weighted transition probability, and the result of the negative correlation mapping of the meteorological change weight is used as the weight of the transition probability. The first weighted transition probability and the transition probability are weighted and summed to obtain the second weighted transition probability.

[0025] Furthermore, the method for obtaining the superior influence weight includes:

[0026] For any state parameter transition in the flood discharge channel gate, the state parameter transition is taken as the target transition; the number of times the flood discharge channel gate is the target transition under the predicted state transition of the previous gate in the historical data is counted to obtain a first quantity; the total number of target transitions of the flood discharge channel gate in the historical data is counted to obtain a second quantity; the ratio of the first quantity to the second quantity is taken as the initial superior influence weight, and the ratio of the initial superior influence weight to the second weighted transition probability of the previous gate under the predicted state transition is taken as the superior influence weight.

[0027] Furthermore, the method for obtaining the third weighted transition probability includes:

[0028] For each state parameter transition of the flood discharge channel gate, the corresponding superior influence weight is used as the weight of the second weighted transition probability, and the negative correlation mapping result of the superior influence weight is used as the weight of the transition probability. The second weighted transition probability and the transition probability are weighted and summed to obtain the third weighted transition probability.

[0029] Furthermore, the method for obtaining the transition probability includes:

[0030] Take any state parameter as the target state parameter, and take the total number of all state parameter transitions of the target state parameter in the historical period as the third quantity; for each state parameter transition of the target state parameter, take the ratio of the number of each state parameter transition to the third quantity as the transition probability.

[0031] The present invention has the following beneficial effects:

[0032] This invention first analyzes the historical data of each gate. For each gate, the opening degree and water level at each moment characterize the main state of the gate. Therefore, state parameters are constructed and the transition of state parameters within historical periods is analyzed to obtain the transition probability under each state parameter transition condition. The transition probability is the basic data characterizing the gate state transition characteristics of historical data. Furthermore, the correlation between meteorological data and opening degree data, as well as the correlation between historical meteorological data and predicted meteorological data, is used to continuously weight the transition probability. This results in a second weighted transition probability that incorporates the changing factors of historical meteorological information and the correlation factors with opening degree data, thereby ensuring the accuracy of the state transition matrix. Furthermore, considering that the flood discharge channel gate is a lower-level gate, its opening degree is affected by the gate at the next higher level. Therefore, the quantitative characteristics of the state transition of the flood discharge channel gate under the predicted state transition conditions of the gate at the next higher level (non-flood discharge channel gate) are statistically analyzed to obtain the influence weight of the higher level, thus obtaining the second state transition matrix of the flood discharge channel gate. In other words, the flood discharge channel gate, based on the historical data factors considered above, also incorporates the influence of the gate at the next higher level, making the predicted state results more accurate. Attached Figure Description

[0033] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 A flowchart of a collaborative control method for the gate opening of a hydropower station based on BeiDou technology, provided in one embodiment of the present invention;

[0035] Figure 2 This is a schematic diagram of a hydroelectric power station structure provided in one embodiment of the present invention. Detailed Implementation

[0036] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a hydropower station gate opening collaborative control method based on BeiDou technology proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0037] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0038] The following description, in conjunction with the accompanying drawings, details a specific scheme for a collaborative control method for the opening degree of a hydropower station gate based on BeiDou technology, provided by the present invention.

[0039] Please see Figure 1 The diagram illustrates a flowchart of a collaborative control method for the opening degree of a hydropower station gate based on BeiDou technology, according to an embodiment of the present invention. The method includes:

[0040] Step S1: For each gate, determine the state parameters at each historical moment based on historical water level data and historical opening data; count the number of transitions between state parameters at adjacent historical moments in the historical period, and obtain the transition probability under each state parameter transition condition.

[0041] This invention relates to a hydropower station that incorporates a BeiDou communication system. BeiDou communication terminal units are deployed at the locations of the hydropower station's generating units, and several sensors are installed at each gate. The BeiDou satellite positioning system is used to periodically and quantitatively acquire relevant monitoring status information data at the gates, including gate opening data, water level data, and meteorological data. In this embodiment, data is acquired daily and statistically integrated quarterly. The BeiDou system transmits the collected information data to a server terminal, where the data is recorded, stored, and analyzed.

[0042] In large hydropower stations, the opening control of multiple gates directly affects the normal operation of the station. Gate opening typically depends on various factors, primarily water level, temperature, and precipitation. Under different hydrological conditions, such as when the reservoir reaches warning levels during the flood season or when a heavy rain is imminent, multiple gates need to be opened simultaneously to rapidly discharge excess water and prevent dam failure or flooding. The opening degree between different gates needs to be precisely adjusted based on the current reservoir water level, weather changes, and the discharge capacity of each gate. This invention aims to optimize the gate state prediction process using Markov chains in existing technologies. Therefore, it first needs to determine the gate state transitions in historical data and establish the basic transition probabilities.

[0043] This invention uses a gate as an example. For a gate, its opening degree and water level data are crucial states. Therefore, the state parameters at each historical moment are first determined based on historical water level and opening degree data. Each state parameter represents a state, and the change from one state to another can be observed through temporal progression. Therefore, this invention statistically analyzes the number of transitions between state parameters at adjacent historical moments within a historical period to obtain the transition probability for each state parameter transition. For example, if the transition from state 1 to state 2 occurs frequently within a historical period, then this state parameter transition has a strong transition probability and can serve as an important basis for Markov chain prediction of gate states.

[0044] It should be noted that, in order to facilitate the prediction of gate states using Markov chains, this embodiment of the invention uses the various levels of gates in a hydropower station as nodes in a graph structure to construct a gate graph structure. In the gate graph structure, the edge weights between nodes represent the magnitude of the gate cooperation relationship, and the edges between nodes include the water flow control relationship between the gates. The hierarchical relationship between gates can be intuitively obtained through the graph structure. For example... Figure 2 It shows a schematic diagram of a hydropower station structure provided by an embodiment of the present invention. Figure 2 In the diagram, node A is the main control gate of the hydropower station, which directly controls gates B, C, and D. Gate B is the dam gate, and gate C is the spillway gate. The opening of gate C is influenced by both gates A and B. Gate D is the intake gate for the power generation equipment. The diagram clearly illustrates the control and influence between the gates.

[0045] It should be noted that the method of constructing graph structures is a well-known technique in the art and will not be elaborated here.

[0046] Preferably, in one embodiment of the present invention, the method for obtaining the state parameters includes:

[0047] For each dimension in the historical water level data and historical gate opening data, the data range within the dimension is divided into ten level intervals. The two level intervals corresponding to each gate at a given historical moment form a tuple, which serves as the state parameter. For example, if the tuple at a certain historical moment is (5,6), it means that the water level data corresponding to the gate at that moment is in level 5, and the opening is in level 6. If the tuple at the next moment is (6,5), it indicates that a state parameter transition has occurred, and the transition from (5,6) to (6,5) is one type of state parameter transition.

[0048] Preferably, in one embodiment of the present invention, the method for obtaining the transition probability includes:

[0049] Let any state parameter be taken as the target state parameter, and the total number of all state parameter transitions of the target state parameter within the historical time period be taken as the third quantity. For each state parameter transition of the target state parameter, the ratio of the number of each state parameter transition to the third quantity is taken as the transition probability. The transition probability is expressed by the formula:

[0050] ;in Let be the transition probability when state parameter i transitions to state parameter j. This represents the number of transitions that occur in the historical data for this state parameter transition. Let be the number of transitions when state parameter i transitions to state parameter k, and n be the number of different types of state parameters other than state parameter i. The total number of transitions from state parameter i to each other state parameter, i.e. It is the third quantity.

[0051] Step S2: Based on the correlation between historical meteorological data and the historical opening data of each gate, obtain the meteorological impact weight of each gate and weight the transfer probability to obtain the first weighted transfer probability; based on the correlation between predicted meteorological data and historical meteorological data, obtain the meteorological change weight, and adjust the first weighted transfer probability and the transfer probability to obtain the second weighted transfer probability.

[0052] The transition probabilities obtained in step S1 for each state parameter transition scenario are only statistically derived baseline data. A higher transition probability indicates a greater probability of that state parameter transition occurring, but it does not consider the impact of meteorological data on gate opening. Regarding the impact of meteorological data on gate opening, increased precipitation increases water storage at the hydropower station, thus requiring a larger gate opening for flood discharge; conversely, rising temperatures lead to water evaporation within the hydropower station environment, necessitating a smaller gate opening to ensure normal power generation. Therefore, meteorological data has a significant impact on gate opening, requiring further correction of the transition probabilities by combining historical meteorological and gate opening data.

[0053] Firstly, the meteorological impact weight of each gate can be obtained based on the correlation between historical meteorological data and historical gate opening data. For example... Figure 2Gate D in the system primarily functions for power generation. To ensure its operation, its water level and opening are not easily affected by weather conditions. Gates C and B, on the other hand, primarily function for water storage or flood discharge, and are significantly influenced by weather data. Therefore, for each gate, the degree of influence can be determined based on the correlation between historical weather data and historical opening data. This allows for the weighting of the transition probabilities, resulting in the first weighted transition probability.

[0054] Preferably, in this embodiment of the invention, historical meteorological data includes historical temperature data and historical precipitation data. Because it includes two dimensions, the method for obtaining the meteorological influence weights includes:

[0055] Either historical temperature data or historical precipitation data can be used as the meteorological dimension to be analyzed.

[0056] For the meteorological dimension to be analyzed, the historical time period is segmented with a year as the time length to obtain the data sequence of the dimension to be analyzed and the gate opening data sequence for each year. That is, a historical time period can contain multiple sequences, and each sequence represents a time period.

[0057] The study obtains the distance between the data series of the dimension to be analyzed and the data series of gate openings within the same time period, as well as the periodicity difference. Since meteorological data exhibits significant periodicity, the greater the difference between the period of Zaman's gate opening and the period of precipitation, the less the gate opening is affected by meteorological conditions. Similarly, a larger distance indicates that the data changes between the two series are less similar, and the gate opening is less affected by meteorological conditions.

[0058] The product of the periodic differences and the distance between them is used as the initial influence factor for each time period. That is, the larger the initial influence factor, the less the opening data of the gate is affected by meteorology. Therefore, the average initial influence factors of all time periods are negatively correlated and normalized to obtain the meteorological influence factor under the meteorological dimension to be analyzed.

[0059] Considering that temperature and gate opening are negatively correlated, while precipitation and gate opening are positively correlated, the meteorological impact factor under historical temperature data is used as the denominator and the meteorological impact factor under historical precipitation data is used as the numerator. The resulting ratio is then normalized to obtain the meteorological impact weight.

[0060] In one embodiment of the present invention, the meteorological influence factor is expressed by the formula:

[0061] ;in M represents the total number of meteorological influencing factors over a given period. To determine the periodicity of the data sequence of the dimension to be analyzed in the k-th time period, To determine the periodicity of the gate opening data sequence in the k-th time period, For the k-th time period, the data sequence of the dimension to be analyzed is... Let be the gate opening data sequence for the k-th time period, DTW() is the dynamic time warping distance calculation function, and exp() is the exponential function with the natural constant as the base.

[0062] In the above formula, the dynamic time warp distance is used as the difference distance, and the negative correlation mapping and normalization of the data are achieved through the exponential function.

[0063] In this embodiment of the invention, the periodicity of the sequence can be obtained through STL decomposition, which is a technique well known to those skilled in the art and will not be described in detail here.

[0064] In this embodiment of the invention, the normalization method may be linear normalization, or other techniques well known to those skilled in the art, such as function mapping, and is not limited thereto.

[0065] Preferably, in one embodiment of the present invention, the meteorological influence weight is multiplied by the transition probability to obtain a first weighted transition probability. That is, by multiplying, the transition probability of each state parameter transition of the gate is combined with the influence of meteorological data on the gate to obtain the first weighted transition probability.

[0066] Because this embodiment of the invention aims to predict the state transition of the gate at future times, and the meteorological impact data obtained above are all obtained through statistical historical data, their reference value for future times needs to be reassessed. For future times, the stronger the correlation between the predicted meteorological data and historical meteorological data, the stronger the reference value of the obtained data results. Therefore, this embodiment of the invention further obtains meteorological change weights based on the correlation between the predicted meteorological data and historical meteorological data. Based on these meteorological change weights, the first weighted transition probability and the transition probability can be weighted and adjusted to obtain the second weighted transition probability. That is, the larger the meteorological change weight, the more important the first weighted transition probability information, and the weaker the reference value of the original transition probability data.

[0067] It should be noted that the forecast meteorological data can be integrated and processed based on local meteorological forecast results, which will not be elaborated further.

[0068] Preferably, in one embodiment of the present invention, the method for obtaining the meteorological change weight includes:

[0069] The forecast meteorological data includes forecast temperature data and forecast precipitation data; the first Pearson correlation coefficient between the forecast meteorological data and historical temperature data is obtained; the second Pearson correlation coefficient between the forecast precipitation data and historical precipitation data is obtained; and the average of the first and second Pearson correlation coefficients is used as the weight of meteorological changes.

[0070] Preferably, in one embodiment of the present invention, the method for obtaining the second weighted transition probability includes:

[0071] The meteorological change weight is used as the weight of the first weighted transition probability, and the result of the negative correlation mapping of the meteorological change weight is used as the weight of the transition probability. The first weighted transition probability and the second weighted transition probability are then summed using weights to obtain the second weighted transition probability. It should be noted that the meteorological change weight in this embodiment of the invention is a result with a value between 0 and 1. Therefore, the selected negative correlation mapping method can directly obtain the weight of the transition probability by subtracting the meteorological change weight from the positive integer 1. That is, the relationship between the weight of the transition probability and the weight of the first weighted transition probability is inverse; therefore, a weighted summation can obtain the accurate second weighted transition probability after incorporating the influence of meteorological data.

[0072] Step S3: The second weighted transition probabilities of each state parameter transition case of the non-flood discharge channel gate form the first state transition matrix of each non-flood discharge channel gate. Based on the first state transition matrix, the predicted state transition situation of each non-flood discharge channel gate is determined using a Markov chain.

[0073] For non-flood discharge channel gates, their opening degree has strong autonomy, while flood discharge channel gates, because they bear the important responsibility of flood discharge and water storage, are significantly affected by the gates at the next higher level. Therefore, this embodiment of the invention analyzes non-flood discharge channel gates and flood discharge channel gates separately. For non-flood discharge channel gates, the first state transition matrix of each non-flood discharge channel gate can be directly constructed from the second weighted transition probabilities obtained from the transition of each state parameter. The predicted state transition of each non-flood discharge channel gate can be determined by combining the existing Markov chain with the real-time state.

[0074] Step S4: In historical data, count the number of transitions for each state parameter of the flood discharge channel gate under the predicted state transition conditions, obtain the upper-level influence weight, adjust the second weighted transition probability and the transition probability of the flood discharge channel gate according to the upper-level influence weight, obtain the third weighted transition probability, construct the second state transition matrix of the flood discharge channel gate, and determine the predicted state transition conditions.

[0075] The function of flood discharge channel gates is to promptly discharge excess water to designated areas when water levels are too high or floods occur, preventing dam breaches due to excessive water accumulation. Therefore, the opening degree of these gates is significantly affected by the upstream gates during water level regulation. It is necessary to determine the impact of the upstream gates on the flood discharge channel gates based on historical statistical data of the predicted conditions of the upstream gates.

[0076] In this embodiment of the invention, historical data is used to statistically analyze the number of transitions for each state parameter of the flood discharge channel gate under predicted state transition conditions, thus obtaining the upper-level influence weight. Similar to the meteorological change weight mentioned above, the second weighted transition probability and the aforementioned transition probability of the flood discharge channel gate are weighted and adjusted according to the upper-level influence weight to obtain a third weighted transition probability. The second state transition matrix of the flood discharge channel gate obtained based on the third weighted transition probability can then determine the predicted state transition of the flood discharge channel gate.

[0077] Preferably, in one embodiment of the present invention, the method for obtaining the influence weight of the superior includes:

[0078] For any state parameter transition in the flood discharge channel gate, this state parameter transition is taken as the target transition. The number of times the flood discharge channel gate was the target transition under the predicted state transition conditions in historical data is counted, obtaining the first quantity. The total number of target transitions for the flood discharge channel gates in historical data is counted, obtaining the second quantity. The ratio of the first quantity to the second quantity is used as the initial superior influence weight. That is, the higher the first quantity, the more state transitions were made under the influence of the superior among all the second quantities, and the greater the influence of the superior gate.

[0079] To further normalize the initial superior influence weight, the ratio of the initial superior influence weight to the second weighted transition probability of the previous gate under the predicted state transition is used as the superior influence weight.

[0080] Preferably, in one embodiment of the present invention, similar to the method for obtaining the second weighted transition probability, the method for obtaining the third weighted transition probability includes:

[0081] For each state parameter transition of the flood discharge channel gate, the corresponding superior influence weight is used as the weight of the second weighted transition probability, and the negative correlation mapping result of the superior influence weight is used as the weight of the transition probability. The second weighted transition probability and the transition probability are weighted and summed to obtain the third weighted transition probability. It should be noted that the value range of the superior influence weight in this embodiment is also between 0 and 1, and its corresponding negative correlation mapping result is also implemented by subtracting the superior influence weight from the positive integer 1.

[0082] It should be noted that the state transition matrix in this embodiment of the invention is in the form of: ;in, This represents the second or third weighted transition probability of state parameter i transitioning to state parameter j.

[0083] In this embodiment of the invention, after obtaining the predicted state transition status of all gates, the BeiDou satellite system sends control commands to the gate opening controller through the terminal. The opening controller can compare the current state with the predicted state transition status and perform real-time adjustment and feedback.

[0084] In summary, this invention constructs state parameters and analyzes their transitions over historical periods to obtain the transition probability for each state parameter transition. By combining the correlation between meteorological data and gate opening data, as well as the correlation between historical and predicted meteorological data, the transition probabilities are continuously weighted to obtain a second weighted transition probability. The quantitative characteristics of the state transitions of the flood discharge channel gates under the predicted state transition conditions of the non-flood discharge channel gates at the previous level are statistically analyzed to obtain the influence weight of the previous level, resulting in a second state transition matrix for the flood discharge channel gates, which is then used for state prediction. This invention, by combining the influence of historical meteorological data and further considering the control influence between gates, obtains accurate gate state prediction results.

[0085] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0086] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for coordinated control of gate opening in a hydropower station based on BeiDou technology, characterized in that, The method is executed on a BeiDou system terminal, which records meteorological data and opening data of the gate and feeds back control commands to the gate; the method includes: For each gate, the state parameters at each historical moment are determined based on historical water level data and historical opening data; the number of transitions between state parameters at adjacent historical moments is counted to obtain the transition probability under each state parameter transition condition; Based on the correlation between historical meteorological data and the historical opening data of each gate, the meteorological impact weight of each gate is obtained, and the transfer probability is weighted to obtain a first weighted transfer probability; based on the correlation between predicted meteorological data and historical meteorological data, the meteorological change weight is obtained, and the first weighted transfer probability and the transfer probability are weighted and adjusted to obtain a second weighted transfer probability. The second weighted transition probabilities under each state parameter transition condition of the non-flood discharge gate form the first state transition matrix of each non-flood discharge gate. The predicted state transition condition of each non-flood discharge gate is determined by using a Markov chain based on the first state transition matrix. In historical data, the number of transitions for each state parameter of the flood discharge channel gate under the predicted state transition is counted to obtain the upper-level influence weight. Based on the upper-level influence weight, the second weighted transition probability of the flood discharge channel gate and the transition probability are weighted and adjusted to obtain the third weighted transition probability. The second state transition matrix of the flood discharge channel gate is constructed, and the predicted state transition is determined.

2. The method for coordinated control of gate opening in a hydropower station based on BeiDou technology according to claim 1, characterized in that, The method for obtaining the state parameters includes: For each dimension in the historical water level data and historical opening data, the data range in the dimension is divided into ten level intervals; the two level intervals corresponding to each gate at a historical moment constitute a binary tuple, and the binary tuple serves as a state parameter.

3. The method for coordinated control of gate opening in a hydropower station based on BeiDou technology according to claim 1, characterized in that, The historical meteorological data includes historical temperature data and historical precipitation data.

4. The method for coordinated control of gate opening in a hydropower station based on BeiDou technology according to claim 3, characterized in that, The methods for obtaining the meteorological impact weights include: Either the historical temperature data or the historical precipitation data can be used as the meteorological dimension to be analyzed. For the meteorological dimension to be analyzed, the historical period is divided into segments with a year as the time length to obtain the data sequence of the dimension to be analyzed and the gate opening data sequence for each year. Obtain the difference distance and periodic difference between the data sequence of the dimension to be analyzed and the gate opening data sequence in the same time period; use the product of the periodic difference and the difference distance as the initial influence factor in each time period; perform negative correlation mapping and normalization on the average initial influence factors of all time periods to obtain the meteorological influence factor in the meteorological dimension to be analyzed. The meteorological influence factor under the historical temperature data dimension is used as the denominator, and the meteorological influence factor under the historical precipitation data dimension is used as the numerator. The obtained ratio is normalized to obtain the meteorological influence weight.

5. The method for coordinated control of gate opening in a hydropower station based on BeiDou technology according to claim 4, characterized in that, The methods for obtaining the meteorological change weights include: The predicted meteorological data includes predicted temperature data and predicted precipitation data; a first Pearson correlation coefficient is obtained between the predicted meteorological data and the historical temperature data; a second Pearson correlation coefficient is obtained between the predicted precipitation data and the historical precipitation data; and the average of the first Pearson correlation coefficient and the second Pearson correlation coefficient is used as the meteorological change weight.

6. The method for coordinated control of gate opening in a hydropower station based on BeiDou technology according to claim 1, characterized in that, The method for obtaining the first weighted transition probability includes: The first weighted transition probability is obtained by multiplying the meteorological impact weight by the transition probability.

7. The method for coordinated control of gate opening in a hydropower station based on BeiDou technology according to claim 1, characterized in that, The methods for obtaining the second weighted transition probability include: The meteorological change weight is used as the weight of the first weighted transition probability, and the result of the negative correlation mapping of the meteorological change weight is used as the weight of the transition probability. The first weighted transition probability and the transition probability are weighted and summed to obtain the second weighted transition probability.

8. The method for coordinated control of gate opening in a hydropower station based on BeiDou technology according to claim 1, characterized in that, The methods for obtaining the superior influence weight include: For any state parameter transition in the flood discharge channel gate, the state parameter transition is taken as the target transition; the number of times the flood discharge channel gate is the target transition under the predicted state transition of the previous gate in the historical data is counted to obtain a first quantity; the total number of target transitions of the flood discharge channel gate in the historical data is counted to obtain a second quantity; the ratio of the first quantity to the second quantity is taken as the initial superior influence weight, and the ratio of the initial superior influence weight to the second weighted transition probability of the previous gate under the predicted state transition is taken as the superior influence weight.

9. The method for coordinated control of gate opening in a hydropower station based on BeiDou technology according to claim 1, characterized in that, The method for obtaining the third weighted transition probability includes: For each state parameter transition of the flood discharge channel gate, the corresponding superior influence weight is used as the weight of the second weighted transition probability, and the negative correlation mapping result of the superior influence weight is used as the weight of the transition probability. The second weighted transition probability and the transition probability are weighted and summed to obtain the third weighted transition probability.

10. A method for coordinated control of gate opening in a hydropower station based on BeiDou technology according to claim 1, characterized in that, The method for obtaining the transition probability includes: Take any state parameter as the target state parameter, and take the total number of all state parameter transitions of the target state parameter in the historical period as the third quantity; for each state parameter transition of the target state parameter, take the ratio of the number of each state parameter transition to the third quantity as the transition probability.

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