River and lake health evaluation method, standard determination method, system, equipment and medium

Abstract

The embodiment of the invention provides a river and lake health evaluation method, a standard determination method, a system, equipment and a medium, and belongs to the technical field of data processing. The method comprises the steps of obtaining an incoming water quantity time sequence of a target river and lake, sequencing the incoming water quantities in the incoming water quantity time sequence to obtain an incoming water quantity sequencing sequence, and then dividing the incoming water quantity sequencing sequence to determine a plurality of incoming water quantity subsequences, for each incoming water quantity sub-sequence, obtaining an index value corresponding to unit time to which each incoming water quantity belongs, forming an index sub-sequence according to each index value, and then constructing a corresponding threshold sequence according to the index sub-sequence, and checking each element in the threshold sequence to determine a target evaluation standard of the corresponding water inflow sub-sequence. The accuracy of the river and lake health evaluation standard can be improved, and the reliability of river and lake health evaluation is improved.

Description

Technical Field

[0001] The present application relates to the technical field of data processing, and particularly relates to a method for evaluating the health of rivers and lakes, a method for determining standards, a system, a device, and a medium. Background Art

[0002] Carrying out the evaluation of the health of rivers and lakes is a necessary measure to master the health status of rivers and lakes, scientifically analyze river and lake problems, improve the level of river and lake management and protection, and promote the construction of water ecological civilization. The evaluation of the health of rivers and lakes can be carried out from aspects such as water quality and biomass. Defining the evaluation criteria for evaluation indicators can provide an objective and quantifiable discrimination scale for the health status of rivers and lakes, help ensure the consistency and comparability of evaluation results, and enhance the public's awareness of the health of rivers and lakes. At present, the evaluation criteria for river and lake health indicators are generally determined based on artificial experience, and the determination of evaluation criteria is greatly affected by subjective human factors. In addition, in the process of determining the evaluation criteria, the chain reaction of river water environment and water ecology caused by the difference in incoming water is not considered, and the health evaluation criteria of rivers and lakes cannot adapt to different water flow variation situations, resulting in inaccurate evaluation results. Summary of the Invention

[0003] The main purpose of the embodiments of the present application is to propose a method for evaluating the health of rivers and lakes, a method for determining standards, a system, a device, and a medium, aiming to improve the accuracy of the evaluation criteria for the health of rivers and lakes and the reliability of the evaluation of the health of rivers and lakes.

[0004] To achieve the above object, on the one hand, an embodiment of the present application proposes a method for determining the evaluation criteria for the health of rivers and lakes, including the following steps:

[0005] Obtain the time series of the incoming water volume of the target river or lake, where the time series of the incoming water volume includes the incoming water volume within multiple unit times;

[0006] Sort the incoming water volumes in the time series of the incoming water volume to obtain a sorted sequence of the incoming water volume;

[0007] Divide the sorted sequence of the incoming water volume to determine multiple subsequences of the incoming water volume;

[0008] Obtain the index value corresponding to the unit time to which each incoming water volume in the subsequence of the incoming water volume belongs, and form an index subsequence according to each of the index values;

[0009] Construct a corresponding threshold sequence according to the index subsequence, and check each element in the threshold sequence to determine the target evaluation criteria for the corresponding subsequence of the incoming water volume, where each element of the threshold sequence represents a candidate evaluation criterion.

[0010] In some embodiments, the dividing the sorted sequence of the incoming water volume to determine multiple subsequences of the incoming water volume includes the following steps:

[0011] Calculate the design frequency of the water inflow according to the number corresponding to each water inflow in the water inflow sorting sequence.

[0012] Divide the water inflows in the water inflow sorting sequence according to the preset division range of the design frequency to obtain multiple water inflow subsequences.

[0013] In some embodiments, dividing the design frequencies of the water inflow sorting sequence according to the division range of the design frequency to obtain multiple water inflow subsequences includes the following steps:

[0014] Perform curve fitting based on each water inflow and the corresponding design frequency in the water inflow sorting sequence to obtain a theoretical frequency curve.

[0015] Divide the water inflows of the theoretical frequency curve according to the preset division range of the design frequency to obtain multiple water inflow subsequences.

[0016] In some embodiments, constructing a corresponding threshold sequence according to the index subsequence and testing each element in the threshold sequence to determine the target evaluation criteria for the corresponding water inflow subsequence includes the following steps:

[0017] Construct a threshold sequence with zero as the lower limit and the maximum index value in the index subsequence as the upper limit according to a preset step size.

[0018] Select an element in the threshold sequence as the criterion to be tested.

[0019] Filter out the index values in the index subsequence that are greater than the criterion to be tested to obtain a sequence to be tested.

[0020] Test the data distribution of the sequence to be tested. When the sequence to be tested passes the test, determine the criterion to be tested as the target evaluation criteria for the corresponding water inflow subsequence; when the sequence to be tested fails the test, select the next element in the threshold sequence as the criterion to be tested, and repeat the steps of filtering out the index values in the index subsequence that are greater than the criterion to be tested to obtain a sequence to be tested until testing the data distribution of the sequence to be tested.

[0021] In some embodiments, testing the data distribution of the sequence to be tested includes the following steps:

[0022] Use the K-S test to calculate the goodness of fit between the sequence to be tested and the optimal distribution function to obtain a sequence goodness of fit.

[0023] When the sequence goodness of fit is greater than the expected goodness of fit, determine that the sequence to be tested passes the test.

[0024] When the sequence fitness is less than or equal to the expected fitness, it is determined that the sequence to be tested fails the test.

[0025] In some embodiments, the optimal distribution function of the index subsequence is determined through the following steps:

[0026] Obtain a plurality of candidate distribution functions;

[0027] Calculate the information entropy and partial entropy between the index subsequence and the candidate distribution function, and calculate the correlation coefficient between the index subsequence and the candidate distribution function according to the information entropy and the partial entropy;

[0028] Select the candidate distribution function with the largest correlation coefficient with the index subsequence as the optimal distribution function of the index subsequence.

[0029] To achieve the above object, on the other hand, an embodiment of the present application proposes a method for evaluating the health of rivers and lakes, including the following steps:

[0030] Obtain the target evaluation data of the target river or lake, where the target evaluation data includes the target incoming water volume and the target index value;

[0031] Obtain the incoming water volume range of each incoming water volume subsequence, determine the corresponding incoming water volume subsequence according to the incoming water volume range to which the target incoming water volume belongs, and obtain the corresponding target evaluation criteria according to the incoming water volume subsequence; where the target evaluation criteria are determined by the method for determining the health evaluation criteria of rivers and lakes in the above embodiments;

[0032] Analyze the target index value according to the target evaluation criteria to obtain the evaluation result of the health of the river or lake.

[0033] To achieve the above object, on the other hand, an embodiment of the present application proposes a system for determining the health evaluation criteria of rivers and lakes, including:

[0034] A first module for obtaining the incoming water volume time series of the target river or lake, where the incoming water volume time series includes the incoming water volume within a plurality of unit times;

[0035] A second module for sorting the incoming water volumes in the incoming water volume time series to obtain an incoming water volume sorting sequence;

[0036] A third module for dividing the incoming water volume sorting sequence to determine a plurality of incoming water volume subsequences;

[0037] A fourth module for obtaining the index value corresponding to the unit time to which each incoming water volume in the incoming water volume subsequence belongs, and forming an index subsequence according to each of the index values;

[0038] A fifth module is configured to construct a corresponding threshold sequence according to the index subsequence, and inspect each element in the threshold sequence to determine the target evaluation criteria for the corresponding incoming water subsequence, wherein each element of the threshold sequence represents a candidate evaluation criterion.

[0039] To achieve the above object, on the other hand, an embodiment of the present application provides an electronic device, which includes a memory, a processor, a program stored on the memory and executable on the processor, and a data bus for realizing connection communication between the processor and the memory. When the program is executed by the processor, the method described in the above embodiment is realized.

[0040] To achieve the above object, on the other hand, an embodiment of the present application provides a storage medium, which is a computer-readable storage medium for computer-readable storage. The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to realize the method described in the above embodiment.

[0041] The method for evaluating the health of rivers and lakes, the method for determining the standard, the system, the device and the medium provided by the present application obtain the incoming water time series of the target river or lake. The incoming water time series includes the incoming water volume in multiple unit times. The incoming water volumes in the incoming water time series are sorted in size to obtain an incoming water sorting sequence, and then the incoming water sorting sequence is divided to determine multiple incoming water subsequences. For each incoming water subsequence, the index value corresponding to the unit time to which each incoming water belongs is obtained, and an index subsequence is formed according to each index value. Then, a corresponding threshold sequence is constructed according to the index subsequence, and each element in the threshold sequence is inspected to determine the target evaluation criteria for the corresponding incoming water subsequence. The present application considers the variation of the incoming water volume in historical data, divides the historical data based on the incoming water volume, and then determines the appropriate target evaluation criteria within their respective incoming water ranges through the threshold inspection method, improving the accuracy of the evaluation criteria for the health of rivers and lakes within each incoming water range, thereby improving the reliability of the evaluation of the health of rivers and lakes. Description of the Drawings

[0042] Figure 1 is a flowchart of the method for determining the evaluation criteria for the health of rivers and lakes provided by the embodiment of the present application;

[0043] Figure 2 is a flowchart of the method for evaluating the health of rivers and lakes provided by the embodiment of the present application;

[0044] Figure 3 is a schematic diagram of the theoretical frequency curve provided by the embodiment of the present application;

[0045] Figure 4 is a schematic diagram of the hardware structure of the electronic device provided by the embodiment of the present application. Detailed implementation manners

[0046] In order to make the objectives, technical solutions and advantages of the present application more clear and understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0047] It should be noted that although functional modules are divided in the system and the logical sequence is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order from the module division in the system or the sequence in the flowchart. Terms such as "first" and "second" in the specification, claims and the above-mentioned drawings are used to distinguish similar objects and do not necessarily need to describe a specific order or sequence.

[0048] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0049] Carrying out the evaluation of river and lake health is a necessary measure to master the health status of rivers and lakes, scientifically analyze river and lake problems, improve the level of river and lake governance and protection, and promote the construction of water ecological civilization. Defining the evaluation criteria for evaluation indicators can provide an objective and quantifiable discrimination scale for the health status of rivers and lakes, help ensure the consistency and comparability of evaluation results, and enhance the public's awareness of river and lake health. In related technologies, the determination of evaluation criteria generally has the following methods:

[0050] The empirical method determines the evaluation criteria based on the experience accumulated by experts and practitioners in the long-term work or research process. This method is applicable to scenarios with a mature practical basis and relatively unified opinions of experts in the field. However, the evaluation criteria are prone to large differences due to the limitations of personal experience and subjective biases.

[0051] The statistical analysis method determines the evaluation criteria by calculating the statistical parameters of evaluation indicators. Representative statistical parameters include mean-standard deviation, quantiles, etc. This method is applicable to scenarios with sufficient data volume, good data foundation and certain regularity.

[0052] The machine learning method uses the powerful data feature learning capabilities of algorithms such as clustering analysis (K-Means clustering, hierarchical clustering, etc.) and deep learning (convolutional neural network, recurrent neural network, etc.) to analyze the potential structure of evaluation indicators to determine the evaluation criteria. This method is applicable to scenarios of complex, high-dimensional and non-linear data analysis, but it also requires artificial setting or discrimination of the rationality of the evaluation standard level division.

[0053] The fuzzy comprehensive evaluation method converts fuzzy evaluation into quantitative evaluation by establishing a membership function corresponding to the fuzzy evaluation index, so as to determine the evaluation criteria. This method is applicable to fields such as social sciences and humanistic management. The evaluation criteria are easily interfered by subjective influencing factors of people, and it is difficult to accurately grasp the change law of the membership function of the evaluation index.

[0054] The above-mentioned methods all take the overall sample of the evaluation index as the object and do not consider the impact of incoming water differences on river health. Water resources are an important foundation to support the sustainable development of river health, and the incoming water differences have a huge impact on river health. The above-mentioned methods still adopt the same standard under the condition of changing incoming water volume, and do not consider the chain reaction of river water environment and water ecology caused by incoming water differences to determine the evaluation criteria, resulting in low reliability of the evaluation results of river and lake health.

[0055] In addition, the above-mentioned methods are greatly affected by subjective factors of people, and a method for adaptively determining evaluation criteria based on the natural endowment of rivers has not been formed. The empirical method, machine learning method and fuzzy comprehensive evaluation method require artificial definition of evaluation criteria and evaluation grades; the selection of statistical parameters in the statistical analysis method is highly subjective and will directly affect the evaluation criteria, which also leads to low reliability of the evaluation results of river and lake health.

[0056] Based on this, the embodiments of the present application provide a method for evaluating river and lake health, a method for determining standards, a system, a device and a medium, aiming to improve the accuracy of the evaluation criteria for river and lake health and the reliability of the evaluation of river and lake health.

[0057] The method for evaluating river and lake health, the method for determining standards, the system, the device and the medium provided by the embodiments of the present application are specifically described through the following embodiments. First, the method for determining the evaluation criteria for river and lake health and the method for evaluating river and lake health in the embodiments of the present application are described.

[0058] The method for determining the evaluation criteria for river and lake health and the method for evaluating river and lake health provided by the embodiments of the present application relate to the technical field of data processing. The method for determining the evaluation criteria for river and lake health and the method for evaluating river and lake health provided by the embodiments of the present application can be applied to terminals, can also be applied to server sides, and can also be software running on terminals or server sides. In some embodiments, the terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, etc.; the server side can be configured as an independent physical server, can also be configured as a server cluster or distributed system composed of multiple physical servers, and can also be configured as a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application for implementing the method for determining the evaluation criteria for river and lake health or the method for evaluating river and lake health, etc., but is not limited to the above forms.

[0059] This application can be used in numerous general-purpose or special-purpose computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and so on. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This application can also be practiced in a distributed computing environment where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0060] Figure 1 is an optional flowchart of the method for determining the river and lake health assessment criteria provided by an embodiment of this application. Figure 1 The method in may include but is not limited to steps S101 to S106.

[0061] Step S101, obtain the incoming water volume time series of the target river or lake, where the incoming water volume time series includes the incoming water volumes within multiple unit times.

[0062] Step S102, sort the incoming water volumes in the incoming water volume time series in terms of magnitude to obtain the incoming water volume sorting sequence.

[0063] Step S103, divide the incoming water volume sorting sequence to determine multiple incoming water volume subsequences.

[0064] Step S104, obtain the index values corresponding to the unit times to which each incoming water volume in the incoming water volume subsequence belongs, and form an index subsequence based on each index value.

[0065] Step S105, construct a corresponding threshold sequence based on the index subsequence, and check each element in the threshold sequence to determine the target assessment criteria for the corresponding incoming water volume subsequence, where each element of the threshold sequence represents a candidate assessment criteria.

[0066] In step S101 of some embodiments, the target river or lake refers to the lake for which health analysis is required. The incoming water volume time series includes the incoming water volumes of the river or lake within multiple consecutive unit times in terms of time, and the unit time is set according to requirements, generally with "year" as the unit time. Exemplarily, obtain the annual average incoming water volume of a certain river or lake from 1961 to 2022, so as to form the incoming water volume time series.

[0067] In step S102 of some embodiments, the water inflow data in the water inflow time series is sorted in chronological order. In this embodiment, it is necessary to sort the water inflow data in the series according to the magnitude of the water inflow. Specifically, the annual water inflow in the water inflow time series can be sorted in descending order, or the annual water inflow in the water inflow time series can be sorted in ascending order to obtain the water inflow sorting sequence.

[0068] In step S103 of some embodiments, according to a certain data volume ratio, the water inflow sorting sequence is divided to obtain multiple water inflow subsequences, and each water inflow subsequence corresponds to a corresponding water inflow range. Exemplarily, the annual water inflow in the water inflow time series is sorted in descending order to obtain the water inflow sorting sequence, and then according to the data volume ratios of 50%, 25%, and 25%, the water inflow sorting sequence with a data volume of 100 is divided into three water inflow subsequences, and the data volumes of the three water inflow subsequences are 50, 25, and 25 respectively.

[0069] In some embodiments, in step S103, the steps of dividing the water inflow sorting sequence to determine multiple water inflow subsequences may include but are not limited to the following steps:

[0070] Step S201, calculating the design frequency of the water inflow according to the number corresponding to each water inflow in the water inflow sorting sequence;

[0071] Step S202, dividing the water inflow in the water inflow sorting sequence according to the preset division range of the design frequency to obtain multiple water inflow subsequences.

[0072] In this embodiment, the water inflow time series Q(t) is arranged in descending order, where the water inflow data is numbered from large to small as n = 1, 2,..., N in sequence to obtain the water inflow sorting sequence. Then, the design frequency corresponding to each water inflow in the water inflow sorting sequence is calculated through the following formula:

[0073]

[0074] Among them, n represents the number of the water inflow in the water inflow sorting sequence, and N represents the total number of data in the water inflow sorting sequence.

[0075] The preset division range of the design frequency is defined as P <= 25%, 25% < P <= 50%, 50% < P <= 75%, 75% < P <= 90%, and 90% < P. It can be understood that this preset division range is only exemplary, and the preset division range can be set according to actual needs.

[0076] According to the division range of the design frequency, the water inflow in the water inflow sorting sequence can be divided. That is, the water inflow with a design frequency P <= 25% in the water inflow sorting sequence is divided into one group, the water inflow with a design frequency belonging to 25% < P <= 50% is divided into one group, the water inflow with a design frequency belonging to 50% < P <= 75% is divided into one group, the water inflow with a design frequency belonging to 75% < P <= 90% is divided into one group, and the water inflow with a design frequency belonging to 90% < P is divided into one group. Thus, the water inflow sorting sequence is divided into five water inflow subsequences. Further, since the years corresponding to the water inflow subsequence with P > 90% belong to extremely dry years, the health status of rivers and lakes may need to be analyzed separately during actual evaluation and is not included in the scope of analysis of the evaluation criteria for the time being. That is, the water inflow subsequence with a design frequency P > 90% of Q(t) can be temporarily not analyzed later.

[0077] This embodiment divides the water inflow based on the design frequency. Compared with directly dividing by manual experience, it can reduce the uncertainty brought by subjective factors and improve the rationality of the division of water inflow differences.

[0078] In some embodiments, in step S202, the step of dividing the design frequency of the water inflow sorting sequence according to the division range of the design frequency to obtain multiple water inflow subsequences may include, but is not limited to, the following steps:

[0079] Step S301, perform curve fitting according to each water inflow and the corresponding design frequency in the water inflow sorting sequence to obtain a theoretical frequency curve;

[0080] Step S302, divide the water inflow of the theoretical frequency curve according to the preset division range of the design frequency to obtain multiple water inflow subsequences.

[0081] In this embodiment, after calculating the design frequency of each water inflow in the water inflow sorting sequence, curve fitting can be performed on each water inflow in the water volume sorting sequence to calculate its theoretical frequency curve. The theoretical frequency curve helps to evaluate the occurrence probability of water inflows of different magnitudes. By fitting the water inflow and the design frequency into a theoretical frequency curve and then dividing the water inflow, the accuracy of the water inflow range corresponding to each water inflow subsequence can be improved, thereby improving the accuracy of subsequent evaluations. Exemplarily, a P-III type hydrological frequency analysis software with goodness-of-fit calculation can be used to achieve curve fitting, that is, input the water inflows and the corresponding design frequencies in the above-mentioned water inflow sorting sequence into the software to obtain a theoretical frequency curve. Then divide the water inflow of the theoretical frequency curve according to the preset division range to obtain multiple water inflow subsequences.

[0082] In step S104 of some embodiments, after different incoming water subsequences are obtained by partitioning, for each incoming water subsequence, each incoming water data in the incoming water subsequence corresponds to a unit time. According to actual evaluation requirements (such as evaluation requirements for indicators such as oxygen content and organic matter content in rivers and lakes), the indicator value of the target river or lake at this unit time can be obtained, so as to obtain the indicator subsequence corresponding to the incoming water subsequence.

[0083] In step S105 of some embodiments, after the indicator subsequence corresponding to the incoming water subsequence is obtained, a corresponding threshold sequence is constructed according to the indicator subsequence. Each element of the threshold sequence represents a candidate evaluation criterion for the evaluation requirement, and each element in the threshold sequence is tested to determine the target evaluation criterion for the corresponding incoming water subsequence. This embodiment considers the variation of the incoming water in the historical data, divides the historical data based on the incoming water, and then determines the appropriate target evaluation criterion within each incoming water range through the threshold test method, improving the accuracy of the river and lake health evaluation criteria within each incoming water range, thereby improving the reliability of the river and lake health evaluation.

[0084] In some embodiments, in step S105, the steps of constructing a corresponding threshold sequence according to the indicator subsequence and testing each element in the threshold sequence to determine the target evaluation criterion for the corresponding incoming water subsequence may include but are not limited to the following steps:

[0085] Step S401, construct a threshold sequence with zero as the lower limit and the maximum indicator value in the indicator subsequence as the upper limit according to a preset step size;

[0086] Step S402, select an element in the threshold sequence as the criterion to be tested;

[0087] Step S403, screen out the indicator values in the indicator subsequence that are greater than the criterion to be tested to obtain the sequence to be tested;

[0088] Step S404, test the data distribution of the sequence to be tested. When the sequence to be tested passes the test, the criterion to be tested is determined as the target evaluation criterion for the corresponding incoming water subsequence; when the sequence to be tested fails the test, select the next element in the threshold sequence as the criterion to be tested, and repeat the steps of screening out the indicator values in the indicator subsequence that are greater than the criterion to be tested to obtain the sequence to be tested until the data distribution of the sequence to be tested is tested.

[0089] In this embodiment, taking the maximum indicator value of the indicator subsequence X(t) as the upper limit, denoted as X(t) Max , with 0 as the lower limit and a preset step size of 0.1, from 0 to X(t) Max generate the threshold sequence TH[X(t)] jIt can be understood that the preset step size can be set according to the accuracy requirements of the actual evaluation criteria, and the embodiments of the present application do not make specific limitations.

[0090] Starting from the first element in the threshold sequence (i.e., j = 1), use it as the criterion to be tested, and screen out the index values in the index subsequence that are greater than the criterion to be tested to obtain the sequence to be tested KS[X(t)]. j , test the data distribution of the sequence to be tested. If the sequence to be tested passes the test, determine the current criterion to be tested as the target evaluation criterion for the corresponding incoming water subsequence, and then end the loop; if the sequence to be tested fails the test, select the next element in the threshold sequence as the criterion to be tested, and repeat steps S403 to S404.

[0091] In some embodiments, in step S404, testing the data distribution of the sequence to be tested may include, but is not limited to, the following steps:

[0092] Step S501, use the K-S test to calculate the goodness of fit between the sequence to be tested and the optimal distribution function to obtain the sequence goodness of fit.

[0093] Step S502, when the sequence goodness of fit is greater than the expected goodness of fit, determine that the sequence to be tested passes the test.

[0094] Step S503, when the sequence goodness of fit is less than or equal to the expected goodness of fit, determine that the sequence to be tested fails the test.

[0095] In this embodiment, the Chinese name of the K-S test is the Kolmogorov-Smirnov test, which is a statistical method for testing the goodness of fit between sample data and a reference distribution, and is widely used in fields such as hydrology, finance, and biology. Use the K-S test to calculate the goodness of fit between the sequence to be tested and the optimal distribution function to obtain the sequence goodness of fit p[X(t)]. j , specifically as follows:

[0096] p[X(t)] j is a statistical parameter calculated by the test, called the asymptotic significance (two-tailed). The K-S test method judges the goodness of fit between the sequence to be tested and the optimal distribution function based on the maximum vertical distance between the two empirical distribution functions. In this example, denote the sequence to be tested as X(t), and the empirical distribution function of the sequence to be tested X(t) is:

[0097]

[0098] In the formula, F n [X(t)] is the empirical distribution function of the sequence to be tested X(t); is the indicator function of the sequence X(t); T is the sequence length, t = 1, 2,..., T.

[0099] The null hypothesis H0 of the K-S test: F1[X(t)] = F2[X(t)], that is, the empirical distribution of the sequence to be tested is consistent with the empirical distribution of the optimal distribution function; the alternative hypothesis H1: F1[X(t)] ≠ F2[X(t)], that is, the empirical distributions of the two sequences are inconsistent. To quantify the difference between the empirical distributions of the two sequences, the maximum difference D is defined, and the calculation formula is as follows:

[0100]

[0101] where sup represents the least upper bound of the set, and D T,α represents the rejection region of the sequence X(t) with a capacity of T at the confidence level α. When D ≥ D T,α , reject H0; otherwise, accept H0. To further quantify the significance of the difference between the two sequences, p[X(t)] j is introduced to specify the confidence level α. The value of the confidence level α is usually 95% or 99%, and the corresponding p[X(t)] j values are 0.05 and 0.01 respectively. If p[X(t)] j < 0.01, it indicates that the determination result is strong, and H0 should be rejected, that is, the two sequences follow different distributions and do not have consistency; if 0.01 < p[X(t)] j < 0.05, it indicates that the determination result is weak. In this case, the p[X(t)] j p value is considered to have a certain marginality, and usually H0 is also rejected; if p[X(t)] j > 0.05, then H0 can be accepted.

[0102] If the sequence goodness-of-fit p[X(t)] j > 0.05, it is considered that the empirical distribution of the sequence to be tested is consistent with the empirical distribution of the optimal distribution function, and the sequence to be tested KS[X(t)] j passes the test, and the j-th candidate evaluation criterion in the threshold sequence is used as the target evaluation criterion for the evaluation requirement.

[0103] In some embodiments, the optimal distribution function of the index subsequence in step S501 can be determined through the following steps:

[0104] Step S601, obtain multiple candidate distribution functions;

[0105] Step S602, calculate the information entropy and partial entropy between the index subsequence and the candidate distribution functions, and calculate the correlation coefficient between the index subsequence and the candidate distribution functions according to the information entropy and partial entropy;

[0106] Step S603, select the candidate distribution function with the largest correlation coefficient with the index subsequence as the optimal distribution function of the index subsequence.

[0107] In this embodiment, the correlation coefficient α[X(t)] is used i to measure the goodness of fit between each index subsequence X(t) and the distribution curve F[·] i and determine the optimal distribution function F[X(t)] Best . Among them, i = 1, 2, …, 7 are the distribution curve numbers, which are normal distribution, lognormal distribution, exponential distribution, Weibull distribution, gamma distribution, binomial distribution, and Poisson distribution in sequence. The correlation coefficient α[X(t)] i is a statistical parameter in the information entropy theory, and the calculation steps are as follows:

[0108] First, calculate the information entropy of X(t) and F[X(t)] i .

[0109]

[0110] Second, X(t) is the t-th index value of the index subsequence; H X(t) is the information entropy of X(t); F[X(t)] i is the function value of the i-th distribution function at the t-th index value of the index subsequence; is the information entropy of F[X(t)] i .

[0111] Second, calculate the partial entropy of X(t) and F[X(t)] i .

[0112]

[0113] Among them, H X(t) {F[X(t)] i} is the partial entropy of X(t) with respect to the i-th distribution function; is the partial entropy of the i-th distribution function with respect to the t-th index value of the index subsequence.

[0114] Then, calculate the correlation coefficient of X(t) and F[X(t)] i .

[0115]

[0116] Finally, determine the optimal distribution function of X(t). With α[X(t)] i being the maximum as the goal, select the corresponding F[X(t)] i as the optimal distribution function F[X(t)] Best .

[0117] Please refer to Figure 2, Another aspect of the embodiments of the present application proposes a method for evaluating the health of rivers and lakes, which may include but is not limited to the following steps:

[0118] Step S701, obtain the target evaluation data of the target river or lake, where the target evaluation data includes the target incoming water volume and the target index value;

[0119] Step S702, obtain the incoming water volume range of each incoming water volume subsequence, determine the corresponding incoming water volume subsequence according to the incoming water volume range to which the target incoming water volume belongs, and obtain the corresponding target evaluation criteria according to the incoming water volume subsequence; wherein, the target evaluation criteria are determined by the method for determining the river and lake health evaluation criteria in the above embodiments;

[0120] Step S703, analyze the target index value according to the target evaluation criteria to obtain the river and lake health evaluation result.

[0121] In this embodiment, after obtaining the target evaluation criteria corresponding to each incoming water volume subsequence (incoming water volume range) according to the method for determining the river and lake health evaluation criteria in the above embodiments, it can be applied to the actual evaluation of the health of rivers and lakes. Exemplarily, the incoming water volume value of the target river or lake in 2024 is obtained as 80 and the oxygen content index value is 0.5 (i.e., the target evaluation data). Assume that the oxygen content evaluation criteria for the incoming water volume range of 90-100 are determined to be 0.4 through the above method for determining the river and lake health evaluation criteria, and the incoming water volume value of 80 belongs to the incoming water volume range of 90-100. Select the oxygen content evaluation criteria of 0.4, and compare that the oxygen content index value of 0.5 is greater than the oxygen content evaluation criteria of 0.7. Therefore, an evaluation result that the oxygen content of the river and lake is appropriate can be obtained.

[0122] Exemplarily, taking River A as an example, the basin area is 5813 km², the river length is 163 km, and the average channel slope is 1.29‰. Taking the annual average incoming water volume time series Q(t) (t = 1, 2,..., 62) of River A from 1961 to 2022 as an example, the specific implementation process of the method in the embodiments of the present application is described.

[0123] S1, design frequency calculation:

[0124] Use the P-III type hydrological frequency analysis software with goodness-of-fit calculation to determine the design frequency of the annual incoming water volume of River A, and fit its theoretical frequency curve. The theoretical frequency curve is as Figure 3 shown.

[0125] S2, screening of evaluation index samples:

[0126] From Figure 3It can be seen that according to the division of the annual water inflow Q(t), the index value samples QB(t) of the evaluation index of the variation degree of the flow process are screened into 4 groups, and the respective index subsequences QB(t)-1, QB(t)-2, QB(t)-3 and QB(t)-4 are obtained. The water inflow ranges corresponding to the water inflow subsequences obtained after the division of Q(t) are shown in Table 1. Table 1 Water inflow range

[0127] Serial number 1 2 3 4 Annual water inflow (100 million m³) 67 ≤ Q(t) 54 ≤ Q(t) < 67 43.5 ≤ Q(t) < 54 36.2 ≤ Q(t) < 43.5

[0128] S3, Distribution function optimization:

[0129] Calculate the correlation coefficient α[X(t)] between each group of QB(t) and the distribution curve F[·] i as shown in Table 2. Since QB(t) is a continuous sample, the correlation coefficients with the binomial distribution and Poisson distribution of the discrete distribution function are not calculated. i

[0130] Table 2 Calculation results of correlation coefficients

[0131] Correlation coefficient QB(t) - 1 QB(t) - 2 QB(t) - 3 QB(t) - 4 Normal distribution 0.968 0.987 0.961 0.953 Log - normal distribution 0.986 0.974 0.984 0.966 Exponential distribution 0.952 0.977 0.982 0.991 Weibull distribution 0.946 0.965 0.976 0.984 Gamma distribution 0.955 0.943 0.968 0.978

[0132] As can be seen from Table 2, the optimal distribution function of QB(t)-1 is the lognormal distribution, the optimal distribution function of QB(t)-2 is the normal distribution, the optimal distribution function of QB(t)-3 is the lognormal distribution, and the optimal distribution function of QB(t)-4 is the exponential distribution.

[0133] S4, Evaluation criteria determination:

[0134] Use the K-S test to traverse the threshold sequences TH[X(t)] of QB(t)-1, QB(t)-2, QB(t)-3 and QB(t)-4 j to determine the evaluation criteria. The corresponding threshold sequences for each group are as follows:

[0135] TH[X(t)]1 = {0, 0.1, 0.2, …, 75.6};

[0136] TH[X(t)]2 = {0, 0.1, 0.2, …, 73.5};

[0137] TH[X(t)]3 = {0, 0.1, 0.2, …, 47.6};

[0138] TH[X(t)]3 = {0, 0.1, 0.2, …, 38.1}.

[0139] After screening, the evaluation criteria for QB(t)-1, QB(t)-2, QB(t)-3 and QB(t)-4 are 54.3, 42.8, 35.4 and 10.6 respectively.

[0140] ​In 2022, the water inflow of River A, Q(t) = 6.947 billion m³, and the index value QB(t) = 62.4. According to the screening of Q(t), it belongs to the water inflow range corresponding to QB(t)-1. Since QB(t) = 62.4 > 54.3, the degree of variation of the flow process conforms to the natural law of the river and should get full marks.

[0141] In some embodiments, on the other hand, an evaluation standard determination system for river and lake health proposed in an embodiment of the present application includes:

[0142] A first module for obtaining the time series of water inflow of a target river or lake, where the time series of water inflow includes the water inflow within multiple unit times;

[0143] A second module for sorting the water inflows in the time series of water inflow to obtain a sorted water inflow sequence;

[0144] A third module for dividing the sorted water inflow sequence to determine multiple subsequences of water inflow;

[0145] A fourth module for obtaining the index value corresponding to the unit time to which each water inflow in the subsequence of water inflow belongs, and forming an index subsequence according to each index value;

[0146] A fifth module for constructing a corresponding threshold sequence according to the index subsequence, and testing each element in the threshold sequence to determine the target evaluation standard of the corresponding subsequence of water inflow, where each element of the threshold sequence represents a candidate evaluation standard.

[0147] It can be understood that the content in the above embodiments of the method for determining the evaluation standard for river and lake health is applicable to the embodiments of this system. The functions specifically implemented by the embodiments of this system are the same as those of the above embodiments of the method for determining the evaluation standard for river and lake health, and the beneficial effects achieved are also the same as those of the above embodiments of the method for determining the evaluation standard for river and lake health.

[0148] An embodiment of the present application also provides an electronic device, which includes: a memory, a processor, a program stored on the memory and executable on the processor, and a data bus for realizing the connection and communication between the processor and the memory. When the program is executed by the processor, it implements the above method for determining the evaluation standard for river and lake health or the method for evaluating river and lake health. This electronic device can be any intelligent terminal including a tablet computer, an in-vehicle computer, etc.

[0149] Please refer to Figure 4 , Figure 4 which shows the hardware structure of an electronic device in another embodiment. The electronic device includes:

[0150] The processor 401 can be implemented in the form of a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0151] The memory 402 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM), etc. The memory 402 can store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 402 and are called by the processor 401 to execute the method for determining the river and lake health evaluation criteria or the river and lake health evaluation method in the embodiments of the present application;

[0152] The input / output interface 403 is used to implement information input and output;

[0153] The communication interface 404 is used to implement communication interaction between this device and other devices, and can implement communication through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WI-FI, Bluetooth, etc.);

[0154] The bus 405 transmits information between the various components of the device (such as the processor 401, the memory 402, the input / output interface 403, and the communication interface 404);

[0155] Among them, the processor 401, the memory 402, the input / output interface 403, and the communication interface 404 are communicatively connected to each other inside the device through the bus 405.

[0156] The embodiments of the present application also provide a storage medium, which is a computer-readable storage medium for computer-readable storage. The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the above-mentioned method for determining the river and lake health evaluation criteria or the river and lake health evaluation method.

[0157] As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0158] The embodiments described in the embodiments of the present application are for more clearly illustrating the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art will know that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.

[0159] Those skilled in the art can understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than those shown, or combine certain steps, or different steps.

[0160] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0161] Those of ordinary skill in the art can understand that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices can be implemented as software, firmware, hardware, and appropriate combinations thereof.

[0162] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present application described here can be implemented in an order different from those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0163] It should be understood that in this application, "at least one (item)" means one or more, and "a plurality" means two or more. "And / or" is used to describe the association relationship of associated objects and indicates that there can be three relationships. For example, "A and / or B" can mean: only A exists, only B exists, and both A and B exist at the same time. Among them, A and B can be singular or plural. The character " / " generally indicates that the associated objects before and after are in an "or" relationship. "At least one (item) of the following" or its similar expression refers to any combination of these items, including any combination of single item (item) or plural items (items). For example, at least one (item) of a, b, or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0164] In several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the above units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of systems or units can be in electrical, mechanical or other forms.

[0165] The units described above as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0166] In addition, in each embodiment of this application, each functional unit can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0167] When an integrated unit 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 this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of this application. The aforementioned storage medium includes: various media that can store programs such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs.

[0168] The preferred embodiments of the embodiments of this application have been described above with reference to the accompanying drawings, and thus do not limit the scope of the rights of the embodiments of this application. Any modifications, equivalent replacements, and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of this application shall be within the scope of the rights of the embodiments of this application.

Claims

1. A method for determining river and lake health assessment standards, characterized in that: The following steps are involved: Obtaining a time series of water inflow of a target river or lake, wherein the time series of water inflow includes water inflow in multiple unit times; Sorting the water inflow in the water inflow time series by size to obtain a water inflow sorting sequence; Dividing the water inflow quantity sorting sequence to determine a plurality of water inflow quantum sequences; Obtaining the index value corresponding to the unit time to which each water volume in the water volume subsequence belongs, and forming an index subsequence according to each of the index values; A corresponding threshold sequence is constructed according to the indicator subsequence, and each element in the threshold sequence is tested to determine the target evaluation standard of the corresponding incoming water quantum sequence, wherein each element of the threshold sequence represents a candidate evaluation standard.

2. The method for determining river and lake health assessment standards according to claim 1, characterized in that: The step of dividing the water inflow quantity sorting sequence to determine a plurality of water inflow quantity subsequences comprises the following steps: Calculate the design frequency of the water volume according to the serial number corresponding to each water volume in the water volume sorting sequence; The water inflow of the water inflow sorting sequence is divided according to a preset division range of the design frequency to obtain a plurality of water inflow quantum sequences.

3. The method for determining river and lake health assessment standards according to claim 2 is characterized in that: The step of dividing the design frequency of the water inflow quantity sorting sequence according to the division range of the design frequency to obtain a plurality of water inflow quantity subsequences comprises the following steps: Performing curve fitting according to each water inflow in the water inflow sorting sequence and the corresponding design frequency to obtain a theoretical frequency curve; The water inflow of the theoretical frequency curve is divided according to a preset division range of the design frequency to obtain a plurality of water inflow quantum sequences.

4. The method for determining river and lake health assessment standards according to claim 1, characterized in that: The step of constructing a corresponding threshold sequence according to the indicator subsequence and testing each element in the threshold sequence to determine the target evaluation standard of the corresponding incoming water quantum sequence comprises the following steps: Taking zero as the lower limit and the maximum indicator value in the indicator subsequence as the upper limit, constructing a threshold sequence according to a preset step size; Selecting an element in the threshold sequence as a standard to be tested; Filter out the index values ​​in the index subsequence that are greater than the to-be-tested standard to obtain a to-be-tested sequence; The data distribution of the sequence to be tested is tested. When the sequence to be tested passes the test, the standard to be tested is determined as the target evaluation standard of the corresponding incoming water quantum sequence; when the sequence to be tested fails the test, the next element in the threshold sequence is selected as the standard to be tested, and the steps of screening out the index value in the index subsequence that is greater than the standard to be tested to obtain the sequence to be tested and testing the data distribution of the sequence to be tested are repeated.

5. The method for determining river and lake health assessment standards according to claim 4 is characterized in that: The step of testing the data distribution of the sequence to be tested comprises the following steps: The KS test is used to calculate the degree of fit between the sequence to be tested and the optimal distribution function to obtain the sequence fit; When the sequence fit is greater than the expected fit, it is determined that the sequence to be tested passes the test; When the sequence fit is less than or equal to the expected fit, it is determined that the sequence to be tested fails the test.

6. The method for determining river and lake health assessment standards according to claim 5, characterized in that: The optimal distribution function of the indicator subsequence is determined by the following steps: Obtain multiple candidate distribution functions; Calculating the information entropy and partial entropy between the indicator subsequence and the candidate distribution function, and calculating the correlation coefficient between the indicator subsequence and the candidate distribution function according to the information entropy and the partial entropy; A candidate distribution function having the largest correlation coefficient with the indicator subsequence is selected as the optimal distribution function of the indicator subsequence.

7. A method for evaluating the health of rivers and lakes, characterized in that: The following steps are involved: Obtaining target evaluation data of target rivers and lakes, wherein the target evaluation data includes target water inflow and target index value; Obtaining the water volume range of each water volume quantum sequence, and determining the corresponding water volume quantum sequence according to the water volume range to which the target water volume belongs, and obtaining the corresponding target evaluation standard according to the water volume quantum sequence; wherein the target evaluation standard is determined by the river and lake health evaluation standard determination method as described in claim 1; The target indicator values ​​are analyzed according to the target evaluation standards to obtain river and lake health evaluation results.

8. A system for determining river and lake health assessment standards, characterized in that: include: The first module is used to obtain the time series of water inflow of the target river or lake, wherein the time series of water inflow includes the water inflow in multiple unit times; The second module is used to sort the water inflow in the water inflow time series to obtain a water inflow sorting sequence; The third module is used to divide the water inflow sorting sequence to determine multiple water inflow quantum sequences; The fourth module is used to obtain the index value corresponding to the unit time of each water volume in the water volume quantum sequence, and form an index subsequence according to each of the index values; The fifth module is used to construct a corresponding threshold sequence according to the indicator subsequence, and to test each element in the threshold sequence to determine the target evaluation standard of the corresponding incoming water quantum sequence, wherein each element of the threshold sequence represents a candidate evaluation standard.

9. An electronic device, characterized in that: The electronic device includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory. When the program is executed by the processor, the steps of the method described in any one of claims 1 to 7 are realized.

10. A storage medium, the storage medium being a computer-readable storage medium, used for computer-readable storage, characterized in that: The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of any one of claims 1 to 7.

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