Full-range monitoring method for river flow based on multi-source data fusion

Abstract

The application discloses a full-range monitoring method for river flow based on multi-source data fusion, which comprises the following steps: (1) collecting real-time data obtained by various automatic flow monitoring methods according to different water level amplitudes of the river; (2) when it is found that there is data missing in the real-time data obtained by any automatic flow monitoring method within a set time period, the weight of each automatic flow monitoring value is recalculated; (3) when the real-time data obtained by any automatic flow monitoring method exceeds a historical maximum error threshold, the flow result obtained by the monitoring method is automatically filtered out; and (4) based on the real-time data obtained by the various automatic flow monitoring methods, a fusion result data of the various flow real-time data is obtained through a Kalman filtering flow fusion algorithm based on dynamic variance. The method can optimize and adjust sudden data missing conditions, and ensures the continuity, accuracy and stability of the automatic flow monitoring data.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of river flow measurement data intelligent analysis, and particularly relates to a full-range monitoring method for river flow based on multi-source data fusion. BACKGROUND

[0002] The existing river flow automatic monitoring methods include radar method, image method, acoustic Doppler method, ultrasonic time difference method, hydraulic structure method, and water level-flow relationship single value method. The research on river flow automatic monitoring is mostly the application of a single flow automatic monitoring method, but no research on full-range automatic monitoring of flow based on multi-source data fusion is conducted.

[0003] Due to the influence of climate change and ships and the limitation of hydrological monitoring technology development, it is difficult to realize flow automatic monitoring under the conditions of small flow velocity and high flood of the river. Most hydrological stations are difficult to accurately realize full-range automatic monitoring of river flow by using one method. Different flow measuring instruments have different performances in different river water environments. Since the river water environment changes over time, the flow variance measured by each instrument will fluctuate, and the corresponding flow monitoring parameters are dynamically adjusted. Single flow measuring method and instrument are not accurate. In actual field measurement, due to the small probability reasons such as change of water environment, communication interruption, and equipment failure, the flow measurement results may occasionally have short-term missing values. The conventional Kalman filter algorithm cannot process missing data, so it is necessary to optimize and adjust the conventional Kalman filter algorithm for data missing to make it cope with missing values, ensure the stability and continuity of the algorithm, and further improve the accuracy of the measurement results. SUMMARY

[0004] In order to solve the above technical problems, the present application provides a full-range monitoring method for river flow based on multi-source data fusion, which uses a multi-source data fusion algorithm to realize full-range automatic monitoring of river flow, processes the real-time data given by multiple flow automatic monitoring methods, optimizes and adjusts the occasional sudden data missing, ensures the continuity and stability of the flow automatic monitoring data, and further improves the accuracy of the measurement results.

[0005] The present application provides a full-range monitoring method for river flow based on multi-source data fusion, which includes the following steps:

[0006] (I) For different water level amplitudes of the river, real-time data obtained by multiple flow automatic monitoring methods are collected;

[0007] (II) When it is found that there is data missing in the real-time data obtained by any one of the flow automatic monitoring methods within a set time period, the weight of each flow automatic monitoring value is recalculated, and the calculation of Kalman gain, optimal estimation value, and state covariance matrix in the Kalman filter algorithm is correspondingly optimized and adjusted.

[0008] (iii) If the real-time data obtained by any one of the automatic flow monitoring methods exceeds the historical maximum error threshold value, the flow result obtained by the monitoring method exceeding the historical maximum error threshold value is filtered out through automatic screening, and does not participate in the flow fusion calculation in the next step;

[0009] (iv) Based on the real-time data obtained by the plurality of automatic flow monitoring methods, a fusion result data of the plurality of flow real-time data is obtained through a Kalman filtering flow fusion algorithm based on dynamic variance.

[0010] Further, the method step (ii) specifically comprises:

[0011] When it is found through analysis that there is data missing in the real-time data obtained by any one of the automatic flow monitoring methods within a set time period, the weight of each automatic flow monitoring value is recalculated, that is, the weight corresponding to the missing value is removed, and the weights of other automatic flow monitoring values are normalized:

[0012]

[0013] wherein D is a non-missing value set.

[0014] In the Kalman filtering algorithm, the calculation of the Kalman gain, the optimal estimate value and the state covariance matrix are adjusted accordingly, the measurement results of different flow measuring instruments on the same river water body are taken as the observation value z in the Kalman filtering calculation, the instrument measurement error is taken as the observation variance R, and the Kalman filtering formula is converted into a Kalman filtering formula for missing values,

[0015]

[0016] wherein D is a non-missing value set.

[0017] Further, the plurality of automatic flow monitoring methods in the method step (i) include at least two of the following: radar method, image method, acoustic Doppler method, ultrasonic time difference method, single value method of water level-flow relationship, ADCP automatic flow measurement method, and fall index automatic flow pushing method.

[0018] Further, step (ii) further comprises: when it is found through analysis that there is no data missing in the real-time data obtained by the plurality of automatic flow monitoring methods within a set time period, the measurement results of different flow measuring instruments on the same river water body are taken as the observation value z in the Kalman filtering calculation, the instrument measurement error is taken as the observation variance R, and the Kalman filtering formula is converted into a Kalman filtering formula for multiple observation values.

[0019] Further, in the step (three) of the method, when the real-time data obtained by any one of the automatic flow monitoring methods exceeds the historical maximum error threshold, the current measurement result of the method is compared with the past historical data to determine whether a great jump or an abnormal value occurs, which exceeds the historical maximum error threshold.

[0020] Further, the step (four) of the method specifically comprises the following sub-steps:

[0021] (1) calculating the weight w of each observation value i , or the weight of each observation value can be set as 1 / n by default, wherein n is the number of observation values;

[0022] (2) initializing parameters, state covariance P0, prediction variance Q0, and initializing the flow value x'0 at the last time as the mean value of all observation values flow change trend dx0=0;

[0023] (3) calculating the flow prediction value:

[0024] x k =x′ k-1 +dx k-1

[0025] (4) calculating the observation mean value:

[0026]

[0027] (5) calculating the prediction variance:

[0028]

[0029] (6) calculating the observation variance:

[0030]

[0031] (7) calculating the optimal fused flow x' k and state variance P' k according to the five formulas of the simplified transition Kalman filter; wherein each observation value z i and the prediction value are x;

[0032] (8) updating the flow change trend for the next operation:

[0033] dx k =x′ k -x′ k-1

[0034] (9) repeating the sub-steps (3) to (8) to obtain the fused result data of multiple flow real-time data.

[0035] Further, in the method step (four),

[0036] During the Kalman filtering algorithm process, the observation error R is a constant value, and through a variance dynamic adjustment method, the observation variance R and the prediction variance Q are dynamically adjusted according to the collected data in real-time calculation, so that the water body environment can be adapted to the continuous change;

[0037] Two gradual change coefficients are added in the adjustment of the prediction variance Q and the observation variance R, and the update formula is changed to:

[0038] Q k =(1-alpha)Q k-1 +alphaQ' k

[0039] R k,i =(1-beta)R k-1,i +betaR' k,i

[0040] The two gradual change coefficients are alpha and beta.

[0041] The technical advantages brought by the full-range monitoring method of the application will be introduced by comparing the prior art as follows:

[0042] 1) The method of the application overcomes the shortcomings of the existing single river flow monitoring method, and automatically analyzes and finds that when there are data missing in the real-time data collected in a set time period, the weight of the monitoring value of the related measurement method is timely adjusted, the real-time flow data with relatively optimal test accuracy is selected, and then a plurality of monitoring data are fused for calculation, so that the quality of the flow automatic monitoring data is better than that of the single method, and the continuity, accuracy and reliability of the flow automatic monitoring data are greatly improved.

[0043] 2) The method of the application has a wider application range, and in actual field measurement, the influence factors of the traditional measurement algorithm and the missing value situation of the short-term flow measurement result are comprehensively considered. The data analysis is more continuous and comprehensive, and a plurality of flow measurement methods can be fused to take advantage of different water level change ranges such as high water, medium water and low water, so as to realize full-range automatic monitoring of river flow, and provide real-time flow data support for flood and drought disaster prevention, water resource management, water environment protection and water ecological restoration.

[0044] 3), the monitoring method of the application also has the function of screening and filtering some flow measurement methods with serious distortion. The current measurement result of the automatic monitoring method is compared with the past historical data to determine whether there is a huge jump or abnormal value exceeding the historical maximum error threshold. If it exceeds, all flow results obtained by the monitoring method exceeding the historical maximum error threshold are automatically screened and filtered out and do not participate in the flow fusion calculation of the next step until the measurement effect is restored to normal. In this way, the interference of the flow measurement data of some flow measurement methods with serious distortion on the subsequent overall flow fusion calculation is avoided, and the accuracy of the final fusion calculation data is further improved. BRIEF DESCRIPTION OF DRAWINGS

[0045] Figure 1 The flow chart of the method of the application.

[0046] Figure 2 The schematic diagram of the variance adjustment principle of the application.

[0047] Figure 3 The flow process line diagram obtained by each flow measurement method.

[0048] Figure 4 It is a comparison situation diagram of each flow measurement method and the underway ADCP result.

[0049] Figure 5 It is the fusion flow process line diagram of the application.

[0050] Figure 6 It is a comparison result diagram of the fusion flow obtained by the method of the application and the underway ADCP. DETAILED DESCRIPTION

[0051] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application.

[0052] The full-range monitoring method provided by the first embodiment of the application comprehensively considers the condition that real-time data can be continuously collected with a high probability or the condition that data missing values obtained by a single flow measurement method may occasionally occur, and finally obtains the fusion result data of multiple flow real-time data through the Kalman filter flow fusion algorithm based on dynamic variance.

[0053] First, the Kalman filter method is introduced.

[0054] The traditional Kalman filter is an algorithm for optimally estimating the state of a system by using linear system state equations and observing the input and output data of the system.

[0055] Assuming that the state prediction value is x, the state transition matrix is F, the control input matrix is B, and the control vector is u, the state prediction formula at the kth time is:

[0056]

[0057] Assuming the state covariance matrix is P, and the prediction variance is Q, the variance update formula is:

[0058] P k =F k P k-1 F k T +Q k ②

[0059] Assuming the observation value is z, the observation variance is R, and the measurement matrix is H, the Kalman gain K is:

[0060] K=H k P k H k T (H k P k H k T +R k ) -1 =H k K′ ③

[0061] The optimal estimate of the state at the kth moment is:

[0062]

[0063] The state covariance matrix at the kth moment is updated as:

[0064] P k ′=(I-K′H k )P k ⑤

[0065] Kalman filtering iteratively updates the error term through the observation value and the above five formulas, so that the fusion result error is minimized.

[0066] Next, the application of Kalman filtering in the flow calculation algorithm of the present application is introduced.

[0067] The measurement results of different flow measuring instruments on the same water body are taken as the observation value z in the Kalman filtering calculation, and the instrument measurement error is taken as the observation variance R. Since there is more than one observation value, the Kalman filtering formula needs to be changed to the multi-observation Kalman filtering formula:

[0068]

[0069] Where n is the number of observation values, and i is the ith observation.

[0070] The research team selected the trend prediction method as the flow prediction model, so the state transition matrix F is the identity matrix, and the control term B ku k Equal to the difference dx of the last time and the last time flow value, the control item is generally to show the influence of external factors on the results, it is a variable, but in this patent scheme, we predict that the flow change is a fixed change, so a constant dx is used instead of the whole control item.

[0071] Since the observation value and the predicted value are both flow values, the measurement matrix H is also a unit matrix. Therefore, the above five formulas (i.e., formulas ①-⑤) of the traditional Kalman filter are simplified and converted into:

[0072]

[0073] P k = P k-1 + Q k ⑦

[0074]

[0075] The dynamic variance adjustment idea of the method of the application is introduced.

[0076] In the traditional Kalman filter algorithm, the observation error R is a constant value, and the application introduces a dynamic variance adjustment method idea, which dynamically adjusts the observation variance R and the prediction variance Q according to the collected data in real-time calculation, so that the algorithm can adapt to the continuous change of the water environment and calculate the optimal fused flow.

[0077] The real-time calculation obtains each observation value z i , the mean value of which is , and the variance is μ, and the predicted value is x. In theory, the observation mean value is closer to the predicted value x, the observation variance μ is smaller, and the final fused flow result is more accurate and stable.

[0078] Referring to Figure 2 , the variance dynamic adjustment principle diagram, when the observation variance is unchanged and the observation mean value and the predicted value deviation becomes larger, it is considered that the predicted value is inaccurate, and the observation value is more believed, and the predicted variance Q value is increased in the algorithm; when the observation mean value and the predicted value deviation is unchanged and the observation variance becomes larger, it is considered that the observation value stability decreases, and the predicted value is more believed, and the observation variance R value is increased in the algorithm.

[0079] In order to ensure the continuity of data change, prevent abnormal data from causing variance mutation, and prevent Q and R from mutating too much to cause the calculation result to have a large jump, the application adds two gradual change coefficients α and β in the adjustment of the predicted variance Q and the observation variance R, and the value range of the gradual change coefficient is 0-1. The update formula is changed to:

[0080] Q k =(1-α)Qk-1 + alpha Q' k

[0081] R k,i = (1-beta) R k-1,i + beta R' k,i

[0082] wherein the two gradient coefficients are alpha and beta.

[0083] As Figure 1 Embodiment two of the present application provides a full-range monitoring method for river flow based on multi-source data fusion, comprising:

[0084] Step (one) collects real-time data obtained by multiple automatic flow monitoring methods according to different water level amplitudes of the river. The multiple automatic flow monitoring methods include at least two of the following:

[0085] radar method, image method, acoustic Doppler method, ultrasonic time difference method, water level-flow relationship single value method, ADCP automatic flow measurement method, fall index automatic flow pushing method, etc.

[0086] The present application collects real-time data obtained by multiple automatic flow monitoring methods, which refers to real-time data obtained by two or more existing automatic flow monitoring methods.

[0087] In actual field measurement, due to occasional changes in water environment, communication interruption, equipment failure and other reasons, the flow measurement results may have temporary missing values. The conventional Kalman filtering algorithm cannot process missing data, so it is necessary to optimize and adjust the conventional Kalman filtering algorithm for data missing, so that it can cope with missing values.

[0088] Step (two) when it is found by analysis that there is data missing in the real-time data obtained by any one of the automatic flow monitoring methods within a set time period, such as 3 minutes or 10 minutes, the weight of each automatic flow monitoring value is recalculated, and in the Kalman filtering algorithm, the calculation of Kalman gain, optimal estimate value and state covariance matrix is correspondingly optimized and adjusted.

[0089] Specifically, in the first case, when it is found by analysis that there is data missing in the real-time data obtained by any one of the automatic flow monitoring methods within a set time period, such as 3 minutes or 10 minutes, the weight of each automatic flow monitoring value is recalculated, that is, the weight corresponding to the missing value is removed, and the other weights are normalized, and the weight corresponding to the i-th non-missing measurement result becomes:

[0090]

[0091] wherein D is a set of non-missing flow measurement methods; j is each flow measurement method in D.

[0092] In the Kalman filtering algorithm, the calculation of Kalman gain, optimal estimate value and state covariance matrix is adjusted accordingly. According to the measurement results of different flow measuring instruments on the same river water as the observation value z in the Kalman filtering calculation, and the instrument measurement error as the observation variance R, the Kalman filtering formula is converted into the Kalman filtering formula for missing value conditions.

[0093]

[0094] Where D is a non-missing value set, n is the number of observations, and i is the ith observation.

[0095] In the second case, if no data missing is found in the real-time data obtained by any automatic flow monitoring method within a set time period, such as 3 minutes or 10 minutes, according to the measurement results of different flow measuring instruments on the same river water as the observation value z in the Kalman filtering calculation, and the instrument measurement error as the observation variance R, the Kalman filtering formula is converted into the Kalman filtering formula for multiple observations.

[0096] According to the measurement results of different flow measuring instruments on the same water as the observation value z in the Kalman filtering calculation, and the instrument measurement error as the observation variance R; since there is more than one observation value, the Kalman filtering formula is converted into the Kalman filtering formula for multiple observations.

[0097]

[0098] Where n is the number of observations, and i is the ith observation.

[0099] Step (three) If the real-time data obtained by any automatic flow monitoring method exceeds the historical maximum error threshold, then filter out the flow results obtained by the monitoring method that exceeds the historical maximum error threshold through automatic screening, and do not participate in the flow fusion calculation of the next step until the measurement effect is restored to normal.

[0100] Specifically, the current measurement results of a certain automatic flow monitoring method are compared with the past historical data to determine whether there is a huge jump or abnormal value that exceeds the historical maximum error threshold. If it exceeds, filter out all the flow results obtained by the monitoring method that exceeds the historical maximum error threshold through automatic screening, and do not participate in the flow fusion calculation of the next step until the measurement effect is restored to normal. If it does not exceed, participate in the flow fusion calculation of the next step. In this way, the interference of severely distorted flow measurement data of a certain flow measuring method on the subsequent overall flow fusion calculation is avoided, and the correctness of the fusion results of multiple flow real-time data is greatly improved.

[0101] Step (four) is based on the real-time data obtained by the plurality of automatic monitoring methods, and the fusion result data of the plurality of real-time flow data is obtained by a Kalman filtering flow fusion algorithm based on dynamic variance.

[0102] Specifically includes the following sub-steps,

[0103] (1) Calculate the weight w of each observation value i , which is calculated according to the jitter degree of the observation value curve, or is calculated according to the deviation of the observation value and the measurement value on the premise of having the measurement data. If no weight calculation and distribution is made, the default weight is 1 / n, that is, the number of observation values.

[0104] For example, assuming that in the jth measurement, the measurement data value is z j , and the observation value corresponding to the time is z i,j , then the root mean square error is:

[0105]

[0106] The weight calculation is:

[0107]

[0108] (2) Initialize parameters, state covariance P0, prediction variance Q0, and initialize the flow value x'0 at the last time to be equal to the mean of all observation values The flow change trend dx0=0;

[0109] (3) Calculate the flow prediction value:

[0110] x k =x′ k-1 +dx k-1

[0111] (4) Calculate the observation mean:

[0112]

[0113] (5) Calculate the prediction variance:

[0114]

[0115] (6) Calculate the observation variance:

[0116]

[0117] (7) According to the above five formulas (i.e., formulas 11-12) of the Kalman filter after simplification and transformation, the optimal fusion flow x' k and the state variance P' k are calculated; wherein each observation value zi , the predicted value is x;

[0118] (8), update the flow change trend for the next operation:

[0119] dx k = x' k - x' k-1

[0120] (9), repeat sub-steps (3) to (8) to obtain the fusion result data of multiple flow real-time data. The method of the application filters and optimizes the data missing value through comprehensive analysis of the flow values obtained by different devices and different methods, and finally obtains more accurate, more stable and more continuous river flow results.

[0121] In the process of Kalman filtering algorithm, the observation error R is a constant value, and through a variance dynamic adjustment method, the observation variance R and the prediction variance Q are dynamically adjusted according to the collected data in real-time calculation, so that the full-range monitoring algorithm can adapt to the continuous change of the water environment.

[0122] Two gradual change coefficients are added in the adjustment of the prediction variance Q and the observation variance R, and the update formula becomes:

[0123] Q k = (1-α) Q k-1 +αQ' k

[0124] R k,i = (1-β) R k-1,i +βR' k,i

[0125] The two gradual change coefficients are α and β, and the value range can be adjusted between 0-1 according to the actual situation.

[0126] The advantages brought by the method of the application are illustrated by the flow comparison test results of a certain hydrological station.

[0127] The automatic flow monitoring data obtained by the side-scan radar automatic flow measurement method, the horizontal ADCP automatic flow measurement method and the two kinds of fall index automatic flow pushing method of a certain hydrological station are compared and analyzed with the measured flow of the walk type ADCP, and the results are shown in Figures 3-6 .

[0128] The person skilled in the art takes the walk type ADCP flow measurement result as the standard reference value, which is the most consistent with the actual flow. Figure 3 The medium red dot represents the walk type ADCP measurement result.

[0129] Figure 4In the figure, the red dotted line is y=x, indicating that the horizontal axis and the vertical axis are equal, the horizontal axis is the flow value obtained by each measurement method, and the vertical axis is the standard reference value (i.e., the measurement result of the boat ADCP), and the ideal condition is that the two are completely equal, that is, all flow measurement points are on the red dotted line, and the ideal correlation coefficient is 1. The blue points represent the measurement values of each method, and the farther away from the red dotted line, the lower the measurement accuracy and the smaller the correlation coefficient. Figure 4 It can be seen that the measurement results of the four flow measurement methods have many abnormal values and jumping points deviating from the red dotted line, resulting in low accuracy of the final flow measurement value and small correlation coefficient.

[0130] The side-scan radar automatic flow measurement method has many missing values in the flow measurement result, and has many jumps at low flow rate and low water level, and also has certain measurement error at high water level. Since the error is large when the jump occurs, the error data is easily filtered out before fusion. The relative error of the drop index method is large, especially in the low water level and low flow rate interval. The horizontal ADCP automatic flow measurement method is relatively accurate in measurement, but has dithering changes caused by noise.

[0131] By using the full-range monitoring method of the application, the data obtained by the above four measurement methods are collected respectively, and after calculation, the root mean square error of the flow value measured by the boat ADCP flow and each flow measurement method at the corresponding time is 1417.37, 558.61, 676.83 and 1297.21 respectively, and the weight of each flow measurement method is obtained after taking the reciprocal and normalizing, which is 0.15, 0.38, 0.31 and 0.16. After the full-range monitoring algorithm of the second embodiment of the application is calculated, the fusion flow result obtained is shown in Figure 5 .

[0132] The fusion flow obtained by the method of the application is compared with the boat ADCP, and the comparison result is shown in Figure 6 , the blue point represents the fusion flow value, which is close to the red dotted line, indicating high measurement accuracy and large correlation coefficient. The measurement parameter analysis result is shown in Table 1. It can be seen that the measurement parameter result of the application scheme is better than that of the single method measurement result.

[0133] The random uncertainty is an index commonly used in hydrological specifications, which is used to represent the dispersion degree of data, and the larger the value, the more dispersed the data and the worse the effect. The integrity rate refers to the percentage of the number of points with values to the total number of points. Due to accidental reasons, the measurement result of a certain method is missing, and the fusion algorithm of the application has a result as long as the other methods have a result, so the integrity rate is the highest among all methods, which reflects the continuity of the data result.

[0134] It can be seen from the test data that the full-range monitoring algorithm of river flow based on multi-source data fusion of the application is better than the above four single flow measurement methods.

[0135] Table 1 certain hydrological station flow automatic monitoring method use table

[0136]

[0137] It can be seen that the certain hydrological station realizes full range automatic monitoring of flow after fusing data, ensures the continuity of automatic monitoring data, meets various test requirements, the method of the application ensures that the final calculation result does not jump greatly, and ensures the accuracy and stability of flow measurement results. The quality of flow automatic monitoring data obtained by the method is better than that of a single method, and the continuity and reliability of flow automatic monitoring data are ensured.

Claims

1. A method for full-range monitoring of river flow based on multi-source data fusion, characterized in that, Including the following steps: (i) Collect real-time data from various automatic flow monitoring methods for different water level fluctuations in rivers; (ii) When the analysis finds that there are missing data values ​​in the real-time data obtained by any of the automatic traffic monitoring methods within the set time period, the weight of each automatic traffic monitoring value is recalculated. In the Kalman filter algorithm, the calculation of Kalman gain, optimal estimate and state covariance matrix are optimized and adjusted accordingly. Specifically, this includes: when analysis reveals missing data values ​​in the real-time data obtained by any of the automatic traffic monitoring methods within a set time period, the weights of each automatic traffic monitoring value are recalculated, i.e., the weights corresponding to the missing values ​​are removed, and the weights of other automatic traffic monitoring values ​​are normalized. Where D is the set of non-missing values, j is each flow measurement method in D, and w′ i This refers to the weight corresponding to the i-th non-missing value measurement result, w i The original weight corresponding to the i-th type of traffic automatic detection value; In the Kalman filter algorithm, the calculations of Kalman gain, optimal estimate, and state covariance matrix are all adjusted accordingly. The measurement results of the same river body by different flow measuring instruments are used as the observed value z in the Kalman filter calculation, and the instrument measurement error is used as the observation variance R. This transforms the Kalman filter formula into a Kalman filter formula for the missing value case. Where D is the set of non-missing values; the subscript i represents the i-th observation, and the subscript k represents the k-th time. (iii) If the real-time data obtained by any automatic flow monitoring method exceeds the historical maximum error threshold, the flow results obtained by the monitoring method that exceeds the historical maximum error threshold will be automatically filtered out and will not participate in the flow fusion calculation in the next step. (iv) Based on the real-time data obtained from the various automatic flow monitoring methods, a Kalman filter-based flow fusion algorithm based on dynamic variance is used to obtain the fusion result data of the various real-time flow data; including the following sub-steps: (1) Calculate the weight w of each observation. i Alternatively, the weight of each observation can be assumed to be 1 / n, where n is the number of observations. (2) Initialize parameters: state covariance P0, prediction variance Q0, and initialize the flow rate value x'0 of the previous time step to the mean of all observations. The trend of flow change dx0 = 0; (3) Calculate the predicted flow rate: x k =x′ k-1 +dx k-1 (4) Calculate the observed mean: (5) Calculate the prediction variance: (6) Calculate the observed variance: The two gradient coefficients are α and β; (7) Calculate the fusion flux x' based on the five simplified Kalman filter formulas. k and state variance P' k ; where each observation value z i The predicted value is x; (8) Update the flow change trend for the next calculation: dx k =x′ k -x′ k-1 (9) Repeat sub-steps (3) to (8) to obtain the fusion result data of multiple real-time traffic data.

2. The method for full-range monitoring of river flow based on multi-source data fusion according to claim 1, characterized in that, Step (two) also includes: When the analysis finds that there are no missing data values ​​in the real-time data obtained by various automatic flow monitoring methods within a set time period, the Kalman filter formula is transformed into a multi-observation Kalman filter formula by using the measurement results of different flow measuring instruments on the same river body as the observation value z in the Kalman filter calculation and the instrument measurement error as the observation variance R.

3. The method for full-range monitoring of river flow based on multi-source data fusion according to claim 1, characterized in that, The various automatic flow monitoring methods mentioned in step (I) include at least two of the following: radar method, image method, acoustic Doppler method, ultrasonic time difference method, water level-flow relationship single-value method, ADCP automatic flow measurement method, and drop index automatic flow propagation method.

4. The method for full-range monitoring of river flow based on multi-source data fusion according to claim 1, characterized in that, In step (iii), when it is determined that the real-time data obtained by any automatic flow monitoring method exceeds the historical maximum error threshold, the current measurement result of the method is compared with the historical data to determine whether there is a huge jump or abnormal value that exceeds the historical maximum error threshold.

5. The method for full-range monitoring of river flow based on multi-source data fusion according to claim 1, characterized in that, In step (four), During the Kalman filtering process, the observation error R is a constant. Through a variance dynamic adjustment method, the observation variance R and prediction variance Q are dynamically adjusted in real time according to the collected data, so that the full-range monitoring algorithm can adapt to the continuous changes in the water environment. Two gradient coefficients were added to the adjustment of the prediction variance Q and the observation variance R, and the update formula became: Q k =(1-α)Q k-1 +αQ′ k R k,i =(1-β)R k-1,i +βR′ k,i The two gradient coefficients are α and β.

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