A method and device for identifying and attributing the non-uniform change of sediment transport volume in a river basin

Abstract

The present invention discloses a method and device for identifying and attributing the non-uniform change of sediment transport volume in a basin. By performing mean and variance mutation point analysis and testing on the entropy value sequences corresponding to different time scales, the mean and variance mutation points are obtained. According to the mean and variance mutation points, the annual sediment transport volume sequence of the basin is decomposed into a near-natural sediment transport volume sequence and a human disturbance sequence. The near-natural sediment transport volume sequence is input into a pre-trained near-natural sediment transport volume sequence prediction model to obtain a predicted near-natural sediment transport volume sequence. When the human disturbance sequence is inconsistent with the predicted near-natural sediment transport volume sequence, the correlation index between the human activity impact factor and the annual sediment transport volume of the basin is calculated, and hierarchical analysis is performed on the correlation index. According to the correlation index and the results of hierarchical analysis, the comprehensive weight of the human activity impact factor is calculated by using the game weight hierarchical analysis method. The present invention can make the identification and analysis more accurate and reliable.

Description

Technical Field

[0001] The present invention relates to the field of hydrological water resources analysis, and in particular to a method and device for identifying and attributing the non-uniform change of sediment transport volume in a basin. Background Technique

[0002] The non-uniformity of sediment transport volume in a basin means that within a certain period in a certain basin, due to the continuous long-term influence of the development of human activities and climate change, the overall hydrological characteristic sequence in this basin will inevitably undergo some major changes, breaking the original long-term stability in the sediment transport volume sequence of the basin, gradually entering another stable new state from the long-term stable hydrological state in the initial stage, that is, a mutation occurs in the overall sediment transport volume sequence of the basin. The accurate diagnosis technology of the change trend of the sediment transport volume sequence in a basin is the research foundation for studying the key technology of optimal design in the changing spatio-temporal environment, and is also a key to completing the feasibility research and analysis of current engineering problems. Whether the judgment result is reasonably, accurately and feasibly applied is directly related to a series of strategic issues such as the scale of the design team, the operation organization strategy, the risk of hydraulic structures and the prevention and control technology.

[0003] For the non-uniformity diagnosis of the sediment transport volume sequence in a basin, the research objects mainly focus on three aspects: trend variation, jump variation, and periodic variation. The statistical parameters mainly include: mean or median, trend, variance, short-range autocorrelation, long-range autocorrelation, period (frequency), probability distribution type, etc. Among them, the mean, trend and variance variation tests are the most widely studied hydrological statistical parameters at present. Common diagnosis methods mainly include statistical parameters such as mean or median, trend, variance, short-range autocorrelation, long-range autocorrelation, period (frequency), probability distribution type, etc. Only several general methods such as mean, variance and trend are used for the analysis of the sediment transport volume sequence in a basin, so it is difficult to distinguish the differences between different series. At present, in the attribution analysis of the non-uniformity of the sediment transport volume sequence in a basin, the main methods for determining the weight of human activity impact factors include the fuzzy AHP method, the entropy weight method, the principal component analysis method, etc. The analysis of the change of human activity impact factors is relatively single. In previous attribution analyses, usually a single weight determination model is used to analyze the impact factors, and the subjectivity and objectivity of different methods for the same factor are different, and the weight coefficients vary greatly. Summary of the Invention

[0004] Aiming at the problems existing in the prior art, the present invention provides a method and device for identifying and attributing the non-uniform change of sediment transport volume in a basin, which can make the identification and analysis more accurate and reliable.

[0005] In order to solve the above technical problems, the present invention is realized through the following technical solutions:

[0006] A method for identifying and attributing the non-uniform change of sediment transport volume in a basin, comprising:

[0007] Obtain the annual sediment transport volume sequence of the basin;

[0008] Conduct a periodic analysis on the annual sediment transport volume sequence of the basin to obtain the time scales corresponding to different periods;

[0009] Input the annual sediment transport volume sequence of the basin under the time scales corresponding to different periods into the multi-scale entropy model to obtain the entropy value sequences corresponding to different time scales;

[0010] Conduct a mean and variance mutation point analysis test on the entropy value sequences corresponding to different time scales to obtain the mean and variance mutation points;

[0011] According to the mean and variance mutation points, decompose the annual sediment transport volume sequence of the basin into a near-natural sediment transport volume sequence and a human disturbance sequence, and input the near-natural sediment transport volume sequence into a pre-trained near-natural sediment transport volume sequence prediction model to obtain the predicted near-natural sediment transport volume sequence;

[0012] When the human disturbance sequence is inconsistent with the predicted near-natural sediment transport volume sequence, calculate the correlation index between the human activity impact factor and the annual sediment transport volume of the basin, conduct a hierarchical analysis on the correlation index, and calculate the comprehensive weight of the human activity impact factor by using the game weight hierarchical analysis method according to the correlation index and the hierarchical analysis result.

[0013] Further, the step of inputting the annual sediment transport volume sequence of the basin under the time scales corresponding to different periods into the multi-scale entropy model to obtain the entropy value sequences corresponding to different time scales includes:

[0014] 1) For the annual sediment transport volume sequence X(i) of length N, conduct different time scale divisions. If the time scale is s, divide the annual sediment transport volume sequence into a sequence composed of N / s average values, that is, the annual sediment transport volume sequence Z(i) under the time scales corresponding to different periods:

[0015]

[0016] 2) Calculate the distance d between the annual sediment transport volume sequence under the time scales corresponding to different periods and X(k), and respectively find the maximum distance among the N / s sequences, where k = N - s + 1;

[0017] d = max|Z(i) - Z(j)|

[0018] 3) Count the number Y of the maximum distances less than the similarity tolerance among the N / s sequences i s , and divide it by the total number of vectors to calculate the corresponding average value Y s :

[0019] Y s ＝ ΣY i s / (N - s + 1)

[0020] 4) Change the time scale to s + 1, repeat steps 2) - 3), and calculate to obtain Y s+1 ;

[0021] 5) Calculate the entropy value sequence E corresponding to different time scales:

[0022] E = lnY s - lnY s+1 .

[0023] Furthermore, use the Pettitt mutation test method to conduct mean and variance mutation point analysis tests on the entropy value sequences corresponding to the different time scales, and obtain the mean and variance mutation points, including:

[0024] 1) Construct a sequence r based on the entropy value sequence E corresponding to different time scales i , and sum the first k terms to obtain y k :

[0025]

[0026]

[0027] 2) If the maximum value of the absolute value of y appears at time t0 k , then t0 is the mutation point of the mean and variance;

[0028]

[0029] 3) Calculate the significance level P:

[0030]

[0031] If P ≤ 0.5, then the mutation point at this t0 is significant.

[0032] Furthermore, according to the correlation index and the hierarchical analysis result, use the game weight hierarchical analysis method to calculate the comprehensive weight of the human activity influence factor, including:

[0033] 1) Conduct weight analysis on m human activity influence factors according to the correlation index and the hierarchical analysis result, and denote the corresponding weight group as u p = {u p1 , u p2 ,..., u pm}(p = 1, 2), and construct the corresponding weight vector set u;

[0034]

[0035]

[0036] Wherein, is the transposed matrix of the weight group u p , and α p is the weight coefficient;

[0037] 2) To minimize the deviation between weights, optimize the coefficients;

[0038]

[0039] Wherein, ui represents the linear combination coefficient of the correlation index and the analytic hierarchy process result;

[0040] 3) Obtain the optimal solution and perform normalization calculation:

[0041]

[0042]

[0043] 4) Thus, obtain the comprehensive weight u* of each human activity impact factor:

[0044]

[0045] Furthermore, the training method of the near-natural sediment transport volume sequence prediction model includes:

[0046] Use the near-natural sediment transport volume sequence as the training set to train the ARMA model to obtain the near-natural sediment transport volume sequence prediction model.

[0047] Furthermore, use the Morlet wavelet method and / or the HP filtering method to perform periodic analysis on the annual sediment transport volume sequence of the basin to obtain the time scales corresponding to different periods, specifically as follows:

[0048] S11. Split the annual sediment transport volume sequence X(i) (i = 1, 2,..., N) of the basin into two parts, namely the trend component M(i) and the periodic component C(i);

[0049] X(i) = M(i) + C(i)

[0050] S12. Solve the trend component M(i), which is usually obtained from the solution of the minimization problem. The minimization model is:

[0051]

[0052] B(L) = (L -1 - 1) - (1 - L)

[0053] Wherein, λ is the conversion factor, and L is the delay operator of M(i);

[0054] S13. Substitute the delay operator B(L) into the above formula to solve for the periodic component C(i).

[0055] An apparatus for identifying and attributing the non-uniform change of sediment transport volume in a basin, comprising:

[0056] An acquisition module, configured to acquire the annual sediment transport volume sequence of the basin;

[0057] An analysis module, configured to perform periodic analysis on the annual sediment transport volume sequence of the basin to obtain the time scales corresponding to different periods;

[0058] An entropy value sequence module, configured to input the annual sediment transport volume sequence of the basin under the time scales corresponding to different periods into a multi-scale entropy model to obtain the entropy value sequences corresponding to different time scales;

[0059] A test module, configured to perform mean and variance mutation point analysis and test on the entropy value sequences corresponding to different time scales to obtain mean and variance mutation points;

[0060] A prediction module, configured to decompose the annual sediment transport volume sequence of the basin into a near-natural sediment transport volume sequence and a human disturbance sequence according to the mean and variance mutation points, and input the near-natural sediment transport volume sequence into a pre-trained near-natural sediment transport volume sequence prediction model to obtain a predicted near-natural sediment transport volume sequence;

[0061] A calculation module, configured to calculate the correlation index between the human activity impact factor and the annual sediment transport volume of the basin when the human disturbance sequence is inconsistent with the predicted near-natural sediment transport volume sequence, perform hierarchical analysis on the correlation index, and calculate the comprehensive weight of the human activity impact factor by using the game weight hierarchical analysis method according to the correlation index and the hierarchical analysis result.

[0062] A device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method for identifying and attributing the non-uniform change of sediment transport volume in a basin are implemented.

[0063] A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the steps of the method for identifying and attributing the non-uniform change of sediment transport volume in a basin are implemented.

[0064] Compared with the prior art, the present invention has at least the following beneficial effects:

[0065] In the method for identifying and attributing the inconsistent change of sediment transport volume in a river basin proposed by the present invention, the entropy value is an important linear hydrological index parameter, which can clearly analyze the details in the sequence. If only a few common methods such as mean, variance, rank, etc. are used to numerically analyze these hydrological sequence indicators, it is difficult to distinguish the differences in different sequences. However, the entropy value analysis method can effectively distinguish the differences that cannot be intuitively represented by these linear statistical method parameters. Therefore, the introduction of the entropy value is of great significance for the study of hydrological non - consistency. Compared with sample entropy, multi - scale entropy can calculate the sample entropy of the observed time series from any infinite or even multiple time - scale systems. It can more accurately reflect the autocorrelation degree of entropy between at least several different spatial - scale points of a time - series sample and evaluate the complexity of the time series. In previous attribution analyses, a single right - confirmation model was usually used to analyze influencing factors, and the game - weight analytic hierarchy process was rarely used to analyze each influencing factor. Based on combining the game - weight analytic hierarchy process with multiple right - confirmation models, the comprehensive weight result obtained not only has objectivity but also guarantees the accuracy of the result. The game - weight analytic hierarchy process proposed by the present invention can use a variety of methods to deeply study the relationship between attributes, fully consider the differences between the subjective and objective weights of various methods, reach an agreement or compromise on the subjective and objective weights, so as to minimize the gap between the subjective and objective weights.

[0066] To make the above - mentioned objects, features, and advantages of the present invention more obvious and understandable, the following specifically enumerates preferred embodiments and, in conjunction with the accompanying drawings, makes a detailed description as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the technical solutions in the specific embodiments of the present invention, the following will briefly introduce the drawings required for use in the description of the specific embodiments. Obviously, the following - described drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0068] Figure 1 It is a flowchart of a method for identifying and attributing the inconsistent change of sediment transport volume in a river basin according to the present invention.

[0069] Figure 2 It is a flowchart of the multi - scale entropy method.

[0070] Figure 3 It is the Morlet wavelet analysis of Huaxian Station in the embodiment of the present invention.

[0071] Figure 4 It is the HP filtering method of Huaxian Station in the embodiment of the present invention.

[0072] Figure 5This is the mutation diagnosis result with a time scale of 3 in the embodiments of the present invention.

[0073] Figure 6 This is the mutation diagnosis result with a time scale of 7 in the embodiments of the present invention.

[0074] Figure 7 This is the weight result calculated by the correlation index, analytic hierarchy process, and game weight method in the embodiments of the present invention.

[0075] Figure 8 This is the prediction result of the natural annual sediment transport volume sequence at Huaxian Station in the embodiments of the present invention.

[0076] Figure 9 This is the attribution analysis of each influencing factor in the embodiments of the present invention. Detailed implementation manners

[0077] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0078] As a specific implementation manner of the present invention, as Figure 1 shown, the present invention provides a method for identifying and attributing the non-uniform change of sediment transport volume in a basin, specifically including the following steps:

[0079] S1. Obtain the annual sediment transport volume sequence of the basin;

[0080] S2. Conduct a periodic analysis on the annual sediment transport volume sequence of the basin to obtain the time scales corresponding to different periods;

[0081] S3. Input the annual sediment transport volume sequence of the basin at the time scales corresponding to different periods into a multi-scale entropy model to obtain an entropy value sequence corresponding to different time scales;

[0082] S4. Conduct a mean and variance mutation point analysis test on the entropy value sequence corresponding to different time scales to obtain mean and variance mutation points;

[0083] S5. According to the mean and variance mutation points, convert the annual sediment transport volume sequence of the basin into a near-natural sediment transport volume sequence and a human disturbance sequence, and input the near-natural sediment transport volume sequence into a pre-trained near-natural sediment transport volume sequence prediction model to obtain a predicted near-natural sediment transport volume sequence;

[0084] S6. When the human disturbance sequence is inconsistent with the predicted near-natural sediment transport volume sequence, calculate the correlation index between the human activity impact factor and the annual sediment transport volume of the basin, conduct a hierarchical analysis of the correlation index, and calculate the comprehensive weight of the human activity impact factor using the game weight hierarchical analysis method according to the correlation index and the hierarchical analysis result.

[0085] Among them, the human activity impact factors include precipitation, relative humidity, wind speed, evaporation, water conservancy projects, social water consumption, land use change, soil and water conservation, etc.

[0086] On the basis of the above implementation manner, as a more preferred implementation manner, in step S2, the Morlet wavelet method and / or the HP filtering method are used to perform periodic analysis on the annual sediment transport volume sequence of the basin to obtain the time scales corresponding to different periods.

[0087] Specifically, the Morlet wavelet method performs periodic analysis on the annual sediment transport volume sequence of the basin as follows:

[0088] a. The principle of the wavelet function is:

[0089]

[0090] b. The calculation formula is:

[0091]

[0092] In the formula, a is the scale factor and b is the translation factor.

[0093] Specifically, the specific calculation steps of the HP filtering method are as follows:

[0094] a. Split the annual sediment transport volume sequence X(i) (i = 1, 2,..., N) of the basin into two parts, namely the trend component M(i) and the periodic component C(i);

[0095] X(i) = M(i) + C(i)

[0096] b. Solving the trend component M(i) is usually obtained from the solution of the minimization problem, and the minimization model is:

[0097]

[0098] B(L) = (L -1 - 1) - (1 - L)

[0099] In the formula, λ is the conversion factor and L is the delay operator of M(i);

[0100] c. Substitute the delay operator B(L) into the above formula to solve for the periodic component C(i).

[0101] Based on the above embodiments, as a more preferred embodiment, in step S3, the annual sediment yield sequence of the basin under different time scales of different periods is input into the multi-scale entropy model to obtain the entropy value sequence corresponding to different time scales. The specific steps are as follows:

[0102] S31. For the annual sediment yield sequence X(i) of the basin with a length of N, perform different time scale divisions. If the time scale is s, then the annual sediment yield sequence of the basin is divided into a sequence composed of N / s average values, that is, the annual sediment yield sequence Z(i) of the basin under different time scales corresponding to different periods:

[0103]

[0104] S32. Calculate the distance d between the annual sediment yield sequence of the basin under different time scales corresponding to different periods and X(k), and find the maximum distance among the N / s sequences respectively, where k = N - s + 1;

[0105] d = max|Z(i) - Z(j)|

[0106] d = max|Z(i) - Z(j)|

[0107] S33. Count the number Y of the maximum distances in the N / s sequences that are less than the similarity tolerance i s , and divide it by the total number of vectors to calculate the corresponding average value Y s :

[0108] Ys = ∑Y i s / (N - s + 1)

[0109] S34. Change the time scale to s + 1, repeat S32 - S33, and calculate to obtain Y s+1 ;

[0110] S35. Calculate the entropy value sequence E corresponding to different time scales:

[0111] E = lnY s -lnY s+1 .

[0112] Based on the above embodiments, as a more preferred embodiment, in step S4, the Pettitt mutation test method is used to perform mean and variance mutation point analysis and test on the entropy value sequence corresponding to different time scales to obtain the mean and variance mutation points. The specific steps are as follows:

[0113] S41. Construct a sequence r according to the entropy value sequence E corresponding to different time scales i , and sum the first k terms to obtain y k :

[0114]

[0115]

[0116] S42. If y appears at time t0 k the maximum value of the absolute value, then the mutation point at t0 is the mutation point of the mean value and variance;

[0117]

[0118] S43. Calculate the significance level P:

[0119]

[0120] If P ≤ 0.5, then the mutation point at this t0 is significant.

[0121] On the basis of the above embodiments, as a more preferred embodiment, in step S5, the training method of the near-natural sediment transport volume sequence prediction model includes:

[0122] Using the near-natural sediment transport volume sequence as the training set, train the ARMA model to obtain the near-natural sediment transport volume sequence prediction model.

[0123] Before inputting the near-natural sediment transport volume sequence into the pre-trained near-natural sediment transport volume sequence prediction model in step S5, it further includes:

[0124] a. Smooth the near-natural sediment transport volume sequence by using difference and moving average, and check whether the near-natural sediment transport volume sequence is smooth and non-white noise;

[0125] b. If the near-natural sediment transport volume sequence meets the requirements of stability and randomness, select the ACF method to order the sequence, determine the p and q orders in the model, and perform residual analysis.

[0126] On the basis of the above embodiments, as a more preferred embodiment, in step S6, according to the correlation index and the hierarchical analysis result, the comprehensive weight of the human activity influence factor is calculated by using the game weight hierarchical analysis method, and the specific steps are as follows:

[0127] S61. Perform weight analysis on m human activity influence factors according to the correlation index and the hierarchical analysis result, and record the corresponding weight group as u p (p = 1, 2), and construct the corresponding weight vector set u;

[0128]

[0129]

[0130] In the formula, is the transposed matrix of the weight group u p , and α p is the weight coefficient;

[0131] S62. To minimize the deviation between weights, optimize the coefficients;

[0132]

[0133] In the formula, u i represents the linear combination coefficient of the correlation index and the analytic hierarchy process result;

[0134] S63. Obtain the optimal solution and perform normalization calculation:

[0135]

[0136]

[0137] S64. Thus, obtain the comprehensive weight u* of each human activity impact factor:

[0138]

[0139] Example

[0140] Step 1. Select the Weihe River Basin as the experimental area. The sediment transport volume in this basin shows an increasing trend from the middle reaches to the lower reaches. In the present invention, the annual sediment transport volume series from 1919 to 2010 for a total of 92 years in this basin is used as the hydrological data for the calibration period. It can be seen from the basin station data that the annual sediment transport volume decreases with time and shows an obvious decrease in the 1970s. It is obtained by Morlet wavelet analysis that there are small cycles of 3 years, medium cycles of 7 years, and large cycles of 13 years. The small cycle of 3 years disappeared in 1974, so 1974 is regarded as the periodic variation point of the basin. The calculation and analysis results are as Figure 3 shown.

[0141] Step 2. Based on the annual sediment transport volume data of the basin, select the HP filtering method to perform calculation and analysis on the data sequence. It can be seen from the trend curve that there is an obvious downward trend in 1974, and the cycle also weakens in 1974. The calculation and analysis results are as Figure 4 shown.

[0142] Step 3. Using the multi-scale entropy model, analyze the entropy value variation of the basin, and conduct the Pettitt test to perform mutation analysis on the mean and variance of the entropy values. The results show that the mean mutation of the sequence with a scale of 3 occurred in 1970, while the variance mutation occurred in 1974. The mean mutation of the sequence with a scale of 7 occurred in 1972, while the variance variation occurred in 1991. The high entropy value indicates a high degree of irregularity in the sequence, and there is a general correlation between the mutation points of the variance and the jumps in the mean. The calculation results are as Figure 5 and 6 shown.

[0143] Step 4. To further study the impact of human activities on the periodic changes, decompose the measured annual sediment transport volume into two parts: the natural annual sediment transport volume sequence and the human activity impact sequence. The human activity impact sequence is the difference sequence between the measured annual sediment transport volume and the natural annual sediment transport volume sequence. Using the annual sediment transport volume data from 1919 to 1970 as the training set, and using the ARMA model for prediction, the absolute value of the relative error between the prediction result and the measured data is 0.1103. Therefore, it is considered that the prediction result is good, and the prediction result is as Figure 7 shown.

[0144] Step 5. According to the influencing factors of the sediment transport volume in the basin, select precipitation, relative humidity, wind speed, evaporation, water conservancy projects, social water consumption, land use change, and soil and water conservation as the influencing factors, calculate using the correlation index, conduct hierarchical analysis based on the calculation results, and use the game method to calculate the two sets of weights obtained above to obtain the comprehensive weight. It can be seen from the calculation results that the comprehensive weight of water conservancy projects accounts for the largest proportion, and its comprehensive weight is 0.3063. Water conservancy projects, as the main factor affecting the sediment transport volume by human activities, are the main factors affecting the change of sediment transport volume. There are many water conservancy projects in the Weihe River Basin. Taking reservoirs as the research object of water conservancy projects, it is found that the completion time of the main reservoirs is around 1970, which is consistent with the results of the mutation test. It is calculated that the mean jump variation of social water consumption also occurred in 1970, which is consistent with the mutation test results. The Weihe River Basin is one of the regions where the soil and water conservation policy was carried out earliest. Therefore, the impact of the implementation of the soil and water conservation policy on the annual sediment transport volume cannot be ignored. The impact of the land use change rate is relatively small. The relative humidity and wind speed both show a significant downward trend on the time scale, the precipitation shows an insignificant downward trend, and the evaporation shows an insignificant upward trend. The calculation results are shown in Table 1, Figure 8 and 9 , Figure 9 where the dark part is the correlation index result, the darker part is the AHP weight result, and the light part is the comprehensive weight result.

[0145] Table 1 Weight values calculated by different weight calculation methods

[0146]

[0147] Combined with the above calculation results, it can be seen that the results of the multi-scale entropy non-uniformity analysis method based on the game weight analytic hierarchy process proposed by the present invention are as follows: (1) The results of the Pettitt test and the periodic mutation results are consistent, both occurring in the 1970s. (2) The entropy values of the time series with time scales of 3 and 7 are generally high, indicating that the sequence has strong disorder and high irregularity. (3) By decomposing the measured runoff into two parts: the natural runoff sequence and the human activity influence sequence, and predicting the sequence, the relative error of the prediction is small, and it is considered that the prediction has good results. (4) The comprehensive weight of the water conservancy project is 0.3063, and most of the water conservancy projects were built in the 1970s. The analysis results are consistent with the periodic mutation results, so it is considered that the results of using this analysis method are good.

[0148] The present invention provides an important theoretical judgment basis for the sustainable research and exploration of the hydrological system and the calculation and analysis of various evolution systems of the hydrological system. For sequences with high entropy values, it is difficult to reflect their own regularity, the periodicity is not strong, and the complexity of the hydrological system is relatively high. This indicates that for hydrological sequences with low autocorrelation, the less information they carry, and the fewer predictable reference data they have. The accuracy of the comprehensive weight calculated by using the game weight analytic hierarchy process is also relatively high, and the objectivity is stronger.

[0149] The present invention provides a device for identifying and attributing the non-uniform change of the sediment transport volume in a basin, including:

[0150] An acquisition module, configured to acquire the annual sediment transport volume sequence of the basin.

[0151] An analysis module, configured to perform periodic analysis on the annual sediment transport volume sequence of the basin to obtain time scales corresponding to different periods.

[0152] An entropy value sequence module, configured to input the annual sediment transport volume sequence of the basin under the time scales corresponding to different periods into a multi-scale entropy model to obtain entropy value sequences corresponding to different time scales.

[0153] A test module, configured to perform mean and variance mutation point analysis and test on the entropy value sequences corresponding to different time scales to obtain mean and variance mutation points.

[0154] A prediction module, configured to decompose the annual sediment transport volume sequence of the basin into a near-natural sediment transport volume sequence and a human disturbance sequence according to the mean and variance mutation points, and input the near-natural sediment transport volume sequence into a pre-trained near-natural sediment transport volume sequence prediction model to obtain a predicted near-natural sediment transport volume sequence.

[0155] A calculation module, configured to calculate a correlation index between a human activity impact factor and the annual sediment yield of a basin when the human disturbance sequence is inconsistent with the predicted near-natural sediment yield sequence, perform hierarchical analysis on the correlation index, and calculate a comprehensive weight of the human activity impact factor by using the game weight hierarchical analysis method according to the correlation index and the hierarchical analysis result.

[0156] The device for identifying and attributing the inconsistent change of the sediment yield of a basin provided by the present invention is used to implement the foregoing method for identifying and attributing the inconsistent change of the sediment yield of a basin. Therefore, the specific implementation manners in the device for identifying and attributing the inconsistent change of the sediment yield of a basin can be seen in the embodiment part of the method for identifying and attributing the inconsistent change of the sediment yield of a basin in the foregoing text.

[0157] In one embodiment of the present invention, a computer device is provided. The computer device includes a processor and a memory. The memory is used to store a computer program, and the computer program includes program instructions. The processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or the corresponding function. The processor described in the embodiment of the present invention can be used to implement the operations of the method for identifying and attributing the inconsistent change of the sediment yield of a basin.

[0158] In one embodiment of the present invention, when a method for identifying and attributing the non-uniform change of sediment transport volume in a basin is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present invention, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable storage medium includes permanent and non-permanent, removable and non-removable media, and information storage can be achieved by any method or technology. The information can be computer-readable instructions, data structures, program modules or other data.

[0159] The computer storage medium can be any available medium that can be accessed by a computer or a data storage device, including but not limited to magnetic memory (such as floppy disks, hard disks, magnetic tapes, magneto-optical discs (MO), etc.), optical memory (such as CDs, DVDs, BDs, HVDs, etc.), and semiconductor memory (such as ROM, EPROM, EEPROM, non-volatile memory (NANDFLASH), solid state drives (SSD)), etc.

[0160] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program code.

[0161] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products of the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0162] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction means that implements the function specified in one or more processes and / or blocks Figure 1 in one or more processes and / or blocks Figure 1 specified in the block or blocks.

[0163] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable apparatus provide steps for implementing the function specified in one or more processes and / or blocks Figure 1 in one or more processes and / or blocks Figure 1 specified in the block or blocks.

[0164] Finally, it should be noted that the above-described embodiments are only specific embodiments of the present invention, used to illustrate the technical solutions of the present invention, rather than limiting it. The protection scope of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: any person skilled in the art within the technical scope disclosed by the present invention can still modify the technical solutions recorded in the foregoing embodiments, or can easily conceive of changes, or make equivalent replacements for some of the technical features; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A method for identifying and attributing the non-uniform change of sediment transport volume in a basin, characterized in that, Including: Obtain the annual sediment transport volume sequence of the basin; Conduct a periodic analysis on the annual sediment transport volume sequence of the basin to obtain the time scales corresponding to different periods; Input the annual sediment transport volume sequences of the basin under the time scales corresponding to different periods into the multi-scale entropy model to obtain the entropy value sequences corresponding to different time scales, including: 1) For the annual sediment transport volume sequence X(i) of length N, perform different time scale divisions. If the time scale is s, divide the annual sediment transport volume sequence of the basin into a sequence composed of N / s average values, that is, the annual sediment transport volume sequence Z(i) of the basin under the time scales corresponding to different periods: 2) Calculate the distance d between the annual sediment transport volume sequences of the basin under the time scales corresponding to different periods and X(k), and respectively find the maximum distance among the N / s sequences, where k = N - s + 1; d = max|Z(i) - Z(j)| 3) Count the number Y of sequences among N / s sequences whose maximum distance is less than the similarity tolerance, and divide it by the total number of vectors to calculate the corresponding average value Y i s s :​ Y s = ∑Y i s / (N - s + 1) 4) Change the time scale to s + 1, repeat steps 2) - 3), and calculate to obtain Y s+1 ; 5) Calculate the entropy value sequence E corresponding to different time scales: E = lnY s -lnY s+1 Conduct mean and variance mutation point analysis and testing on the entropy value sequences corresponding to different time scales to obtain mean and variance mutation points; According to the mean and variance mutation points, decompose the annual sediment transport volume sequence of the basin into a near-natural sediment transport volume sequence and a human disturbance sequence, input the near-natural sediment transport volume sequence into a pre-trained near-natural sediment transport volume sequence prediction model to obtain a predicted near-natural sediment transport volume sequence; When the human disturbance sequence is inconsistent with the predicted near-natural sediment transport volume sequence, calculate the correlation index between the human activity impact factor and the annual sediment transport volume of the basin, conduct hierarchical analysis on the correlation index, and according to the correlation index and the hierarchical analysis result, calculate the comprehensive weight of the human activity impact factor by using the game weight hierarchical analysis method.

2. The method for identifying and attributing the inconsistent change of sediment transport volume in a basin according to claim 1, wherein Use the Pettitt mutation test method to conduct mean and variance mutation point analysis and testing on the entropy value sequences corresponding to different time scales to obtain mean and variance mutation points, including: 1) Construct sequence r based on the entropy value sequence E corresponding to different time scales i , and sum the first k terms to obtain y k : 2) If y appears at time t0 k which is the maximum value of the absolute value, then t0 is the mutation point of the mean and variance; 3) Calculate the significance level P: If P ≤ 0.5, the mutation point at t0 is significant.

3. The method for identifying and attributing the inconsistent change of sediment transport volume in a river basin according to claim 1, wherein, The calculating the comprehensive weight of the human activity impact factor by using the game weight hierarchical analysis method according to the correlation index and the hierarchical analysis result includes: 1) Perform a weight analysis on m human activity impact factors based on the correlation index and the hierarchical analysis results, and denote the corresponding weight group as u p ={u p1 , u p2 ,..., u pm}(p = 1, 2), and construct the corresponding weight vector set u; In the formula, is the transposed matrix of the weight group u p , and α p is the weight coefficient; 2) Optimize the coefficients to minimize the deviation between the weights; where, u i represents the linear combination coefficient of the correlation index and the analytic hierarchy process result; 3) Obtain the optimal solution and conduct normalization calculation: 4) Thus, obtain the comprehensive weight u* of each human activity impact factor:

4. The method for identifying and attributing the non-uniform change of sediment transport volume in a basin according to claim 1, characterized in that The training method of the near-natural sediment transport volume sequence prediction model includes: Use the near-natural sediment transport volume sequence as the training set to train the ARMA model to obtain the near-natural sediment transport volume sequence prediction model.

5. A method for identifying and attributing the non-uniform change of sediment transport volume in a river basin according to claim 1, characterized in that Use the Morlet wavelet method and / or the HP filtering method to conduct a periodic analysis on the annual sediment transport volume sequence of the basin to obtain the time scales corresponding to different periods, specifically as follows: S11. Split the annual sediment transport volume sequence X(i) (i = 1, 2,..., N) of the basin into two parts, namely the trend component M(i) and the periodic component C(i); X(i) = M(i) + C(i) S12. The solution of the trend component M(i) is usually obtained from the solution of the minimization problem, and the minimization model is: B(L) = (L - 1 -1) - (1 - L) In the formula, λ is the conversion factor, and L is the delay operator of M(i); S13. Substitute the delay operator B(L) into the above formula to solve for the periodic component C(i).

6. An apparatus for identifying and attributing the non-uniform change of sediment transport volume in a basin, characterized in that, Including: An acquisition module for acquiring the annual sediment transport volume sequence of the basin; An analysis module for performing periodic analysis on the annual sediment transport volume sequence of the basin to obtain the time scales corresponding to different periods; An entropy value sequence module for inputting the annual sediment transport volume sequence of the basin under the time scales corresponding to different periods into a multi-scale entropy model to obtain the entropy value sequences corresponding to different time scales, including: 1) For the annual sediment transport volume sequence X(i) of length N, perform different time scale divisions. If the time scale is s, divide the annual sediment transport volume sequence of the basin into a sequence composed of N / s average values, that is, the annual sediment transport volume sequence Z(i) of the basin under the time scales corresponding to different periods: 2) Calculate the distance d between the annual sediment transport volume sequence of the basin under the time scales corresponding to different periods and X(k), and respectively find the maximum distance among the N / s sequences, where k = N - s + 1; d = max|Z(i) - Z(j)| 3) Count the number Y of sequences among N / s sequences whose maximum distance is less than the similarity tolerance, and divide it by the total number of vectors to calculate the corresponding average value Y i s and divide it by the total number of vectors to calculate the corresponding average value Y s : Y s = ∑Y i s / (N - s + 1) 4) Change the time scale to s + 1, repeat steps 2) to 3), and calculate to obtain Y s+1 ; 5) Calculate the entropy value sequence E corresponding to different time scales: E = lnY s -lnY s+1 A test module for performing mean and variance mutation point analysis tests on the entropy value sequences corresponding to different time scales to obtain mean and variance mutation points; A prediction module for decomposing the annual sediment transport volume sequence of the basin into a near-natural sediment transport volume sequence and a human disturbance sequence according to the mean and variance mutation points, and inputting the near-natural sediment transport volume sequence into a pre-trained near-natural sediment transport volume sequence prediction model to obtain a predicted near-natural sediment transport volume sequence; A calculation module for calculating the correlation index between the human activity influence factor and the annual sediment transport volume of the basin when the human disturbance sequence is inconsistent with the predicted near-natural sediment transport volume sequence, performing hierarchical analysis on the correlation index, and calculating the comprehensive weight of the human activity influence factor by using the game weight hierarchical analysis method according to the correlation index and the hierarchical analysis result.

7. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of a method for identifying and attributing the non-uniform change of sediment transport volume in a basin as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of a method for identifying and attributing the non-uniform change of sediment transport volume in a basin as described in any one of claims 1 to 5.

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