Water body reflected light wavelength prediction method and device and terminal equipment

Abstract

The invention provides a water body reflection light wavelength prediction method and device and terminal equipment, and is suitable for the technical field of data processing, and the method comprises the steps: obtaining water body reflection spectrum information; performing time-frequency domain conversion on the water body reflection spectrum information to generate initial water body reflection light wavelength information; according to a preset calculation time threshold, preset population quantity information and preset population movement information, performing screening calculation on the initial water body reflected light wavelength information, and determining first reflected light wavelength information; performing classified calculation on the first reflected light wavelength information according to a plurality of randomly generated population center points, and determining second reflected light wavelength information; and according to a preset nonlinear function, a preset weight matrix and a preset offset variable, performing prediction calculation on the second reflected light wavelength information to obtain the reflected light wavelength information of the target water body. The method is used for ensuring the determination efficiency and accuracy of the chemical oxygen demand of the water body through the ultraviolet-visible spectrophotometry.

Description

Technical Field

[0001] The present application belongs to the field of data processing technology, and in particular relates to a method, device and terminal equipment for predicting the wavelength of light reflected from water. Background Art

[0002] Chemical oxygen demand (COD) is an important and quantitatively measurable organic pollution parameter, and is an important indicator of the degree of water pollution. The determination of chemical oxygen demand is usually used to quickly detect important pollutants in the food, drug and environmental fields, thereby helping the Food, Drug and Environmental Crime Investigation Bureau to conduct crime dynamic analysis and investigation in the fields of food, drugs, ecological environment, etc.

[0003] In the prior art, there are methods for determining chemical oxygen demand, including potassium dichromate oxidation method (GB 11914-89), permanganate index method (GB 11892-89) and ultraviolet-visible spectrophotometry (UV method). Among them, potassium dichromate oxidation method refers to the oxidation of reducing substances in water samples by a certain amount of potassium dichromate in a strong acid solution, the excess potassium dichromate uses ferrochlore as an indicator, and is back-titrated with ammonium ferrous sulfate solution. By measuring the amount of oxygen consumed, the content of reducing substances in the water sample can be calculated; the permanganate index method refers to the use of potassium permanganate as an oxidant to treat water samples in an acidic or alkaline medium, and the degree of pollution of organic and inorganic oxidizable substances in the water is evaluated by measuring the amount of potassium permanganate consumed. Ultraviolet-visible spectrophotometry is a method that uses the continuous spectrum of electromagnetic waves in the ultraviolet-visible light region as a light source to irradiate the sample and study the relative intensity of light absorption by the molecules of the substance.

[0004] The COD detection of heavily polluted industrial wastewater and domestic sewage adopts the potassium dichromate oxidation method in accordance with the current national standard of my country. However, the potassium dichromate method has the following problems: huge pollution of the detection reagent; long detection time; large interference in the COD detection of high-chlorine wastewater; high material cost, personnel cost and energy consumption. In contrast, the UV-visible spectrophotometry method has obvious advantages in detecting COD and can completely solve the above problems. However, the UV-visible spectrophotometry detection method lacks an accurate mathematical prediction model. The large amount of measured wavelength data cannot be effectively screened and classified, which increases the complexity of analysis and calculation. In addition, the COD obtained by manually analyzing the large amount of collected wavelength data one by one is prone to inaccurate detection results, making the UV method unable to be promoted and applied. Summary of the invention

[0005] In view of this, the embodiments of the present application provide a method, device and terminal device for predicting the wavelength of reflected light from water bodies, which can solve the problem that a large amount of collected wavelength data cannot be effectively screened and classified, resulting in huge data processing volume and high computational complexity, resulting in a large workload, low efficiency and low accuracy in manual experimental analysis.

[0006] A first aspect of an embodiment of the present application provides a method for predicting the wavelength of reflected light from a water body, comprising: Obtain water body reflectance spectrum information; Performing time-frequency conversion on the water body reflection spectrum information to generate initial water body reflection light wavelength information; According to a preset calculation time threshold, preset population quantity information, and preset population movement information, the initial water body reflected light wavelength information is screened and calculated to determine the first reflected light wavelength information; Classify and calculate the first reflected light wavelength information according to a plurality of randomly generated population center points to determine the second reflected light wavelength information; According to a preset nonlinear function, a preset weight matrix and a preset offset variable, the second reflected light wavelength information is predicted and calculated to obtain the target water body reflected light wavelength information.

[0007] A second aspect of an embodiment of the present application provides a device for predicting the wavelength of reflected light from a water body, comprising: A water body reflection spectrum information acquisition module is used to obtain water body reflection spectrum information; An initial water body reflected light wavelength information generation module is used to perform time-frequency domain conversion on the water body reflected light spectrum information to generate initial water body reflected light wavelength information; A first reflected light wavelength information determination module, used to screen and calculate the initial water body reflected light wavelength information according to a preset calculation time threshold, preset population quantity information, and preset population movement information, to determine the first reflected light wavelength information; A second reflected light wavelength information determination module, configured to classify and calculate the first reflected light wavelength information according to a plurality of randomly generated population center points to determine the second reflected light wavelength information; The target water body reflected light wavelength information calculation module is used to predict and calculate the second reflected light wavelength information according to a preset nonlinear function and a preset weight matrix to obtain the target water body reflected light wavelength information.

[0008] A third aspect of an embodiment of the present application provides a terminal device, which includes a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the steps of the method for predicting the wavelength of light reflected from a water body as described in the first aspect above are implemented.

[0009] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium, comprising: a computer program stored therein, which, when executed by a processor, implements the steps of the method for predicting the wavelength of light reflected from a water body as described in the first aspect above.

[0010] Compared with the prior art, the embodiments of the present application have the following beneficial effects: a large amount of collected water body reflection spectrum information is converted into time-frequency domain to obtain a large amount of water body reflection light wavelength information, and then the wavelength information with reference in the water body reflection light wavelength is screened, and the wavelength information with large measurement error in the water body reflection light wavelength information is filtered out, and the screening efficiency is ensured by setting the calculation time to reduce the calculation complexity of the subsequent chemical oxygen demand measurement, and the wavelength information obtained by the screening is classified, and the classified wavelength information can be used to determine the effective band that can be used for chemical oxygen demand measurement, and then the classified wavelength information is mapped to the nonlinear space through a preset nonlinear function, and the important features in the classified wavelength information are extracted through a preset weight matrix. Then, the possible offset of wavelength information in nonlinear space is taken into consideration, and the wavelength information in nonlinear space is calculated according to a preset weight matrix and a preset offset variable. The calculation result is the predicted wavelength information, which is used to measure the chemical oxygen demand of the water body. The present application ensures the validity and reliability of the available wavelengths by screening and classifying the effective wavelengths, and then predicts the screened and classified wavelengths. The predicted wavelength data can be used to make up for the shortage of wavelength measurement data in quantity, and avoids the problem in the prior art that timeliness cannot be ensured by manually measuring multiple spectral data, and the data accuracy cannot be guaranteed due to improper operation or measurement errors during the measurement process, thereby improving the detection efficiency and accuracy of the degree of pollution of water bodies. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0012] Figure 1 It is a schematic diagram of the implementation flow of the method for predicting the wavelength of reflected light from a water body provided in an embodiment of the present application; Figure 2 It is a schematic diagram of the implementation flow of the method for predicting the wavelength of reflected light from a water body provided in an embodiment of the present application; Figure 3 It is a schematic diagram of the implementation flow of the method for predicting the wavelength of reflected light from a water body provided in an embodiment of the present application; Figure 4 It is a schematic diagram of the implementation flow of the method for predicting the wavelength of reflected light from a water body provided in an embodiment of the present application; Figure 5 It is a schematic diagram of the implementation flow of the method for predicting the wavelength of reflected light from a water body provided in an embodiment of the present application; Figure 6 It is a schematic diagram of the implementation flow of the method for predicting the wavelength of reflected light from a water body provided in an embodiment of the present application; Figure 7 It is a schematic diagram of the implementation flow of the method for predicting the wavelength of reflected light from a water body provided in an embodiment of the present application; Figure 8 It is a schematic diagram of the structure of a device for predicting the wavelength of reflected light from a water body provided in an embodiment of the present application; Fig. 9 It is a schematic diagram of a terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0013] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0014] In order to illustrate the technical solution described in this application, a specific embodiment is provided below for illustration.

[0015] Figure 1 The following is a flowchart of the method for predicting the wavelength of reflected light from a water body provided in Example 1 of the present application, which is described in detail as follows: Step S101, obtaining water body reflectance spectrum information.

[0016] In this embodiment, the water body reflection spectrum refers to the spectrum of the water body reflection light obtained by irradiating the water body to be detected with the continuous spectrum of electromagnetic waves in the ultraviolet-visible light region as the light source, which can be obtained by measuring the reflected light with an ultraviolet-visible spectrometer. It is understandable that the directly extracted spectral data needs to be preprocessed to remove redundant data before it can be used as water body reflection spectrum information for subsequent analysis and calculation.

[0017] Step S102, performing time-frequency domain conversion on the water body reflection spectrum information to generate initial water body reflection light wavelength information.

[0018] In this embodiment, the time-frequency domain conversion can be carried out by Fourier transform, short-time Fourier transform, or wavelet transform. The data after the water body reflection spectrum is converted from the frequency domain to the time domain is used as the initial water body reflection light wavelength information for subsequent screening and classification calculation.

[0019] In this embodiment, preferably, the wavelet transform algorithm is used to preprocess the ultraviolet spectrum data, that is, the water body reflection spectrum information is converted into the reflected light wavelength information in the time domain through the wavelet transform algorithm. Wavelet transform inherits and develops the localized idea of ​​short-time Fourier transform, while overcoming the shortcomings such as the window size not changing with frequency, and can provide a "time-frequency" window that changes with frequency, which is an ideal tool for signal time-frequency analysis and data preprocessing. This embodiment can fully highlight the characteristics of ultraviolet absorption through transformation, and analyze the localization of time and frequency; through the telescopic translation operation, the ultraviolet absorption signal is gradually refined at multiple scales, and finally the time subdivision at high frequency and the frequency subdivision at low frequency are achieved, which can automatically adapt to the requirements of time-frequency signal analysis and effectively focus on any details of the signal.

[0020] Step S103, screening and calculating the initial water body reflected light wavelength information according to a preset calculation time threshold, preset population quantity information and preset population movement information, to determine the first reflected light wavelength information.

[0021] In this embodiment, the preset calculation time threshold refers to the maximum calculation time that can be allowed in the screening calculation, which can be set manually to limit the available time for the screening calculation, avoid the calculation iteration process from falling into a high-complexity calculation, resulting in a long calculation time or unpredictable calculation time, thereby ensuring the efficiency of screening the initial water body reflected light wavelength. The preset population quantity information refers to the number of populations formed when all wavelengths in the initial water body reflected light wavelength information are used as population individuals to form multiple populations, which can be set manually. It can be understood that the screening in this embodiment can be to use a large amount of wavelength information as population individuals, and then screen out effective and reliable wavelength information from each population when determining the chemical oxygen demand, and the population quantity information is used to indirectly determine the data volume of the wavelength information finally screened. The preset population movement information can refer to the movement distance value that different population individuals will generate when calculating in the population to which they belong, which can be set manually. It can be understood that during the calculation process, the individuals in the population move within the population. When multiple iterative calculations occur, the number of movements and the moving distances of the individuals in the population change. When the iterative calculation is completed, the moving distances of each individual in a population can be calculated, and the individuals in all populations with the shortest moving distances can be screened out as the first reflected light wavelength to be substituted into subsequent prediction calculations.

[0022] Step S104: classify and calculate the first reflected light wavelength information according to a plurality of randomly generated population center points to determine the second reflected light wavelength information.

[0023] In this embodiment, the population center point can be manually set to determine the classified population during the classification process. The number of population center points indicates how many categories all the light wavelength information in the first reflected light wavelength information can be divided into. It can be understood that all the wavelength information in the first reflected light wavelength information is taken as population individuals, and then the logical distances between all population individuals and all population center points can be calculated respectively, so that multiple population individuals are allocated to sub-populations to which each population center point belongs according to the logical distance, and one sub-population represents one class, and the wavelength information in all classes is composed of the second reflected wavelength information. It can be understood that the logical distance can be the Euclidean distance or the Manhattan distance. Classifying the wavelength information is used to determine the band information that multiple wavelength information can be composed of, and the band information is subsequently predicted in the form of numerical points of multiple wavelengths. It is understandable that bands are long and short, but not any two wavelengths can form a band. Therefore, it is necessary to classify the wavelengths and screen the wavelengths that can form effective bands by classification. The wavelengths in the same category can be combined with each other to form bands. The formed bands can be used to measure the chemical oxygen demand of water bodies.

[0024] Step S105 , predicting and calculating the second reflected light wavelength information according to a preset nonlinear function, a preset weight matrix, and a preset offset variable to obtain the target water body reflected light wavelength information.

[0025] In this embodiment, the preset nonlinear function may be a sigmoid function, a Tanh function, a ReLU function, or a Leaky ReLU function, which is used to map the second reflected light wavelength information into a nonlinear space for calculation, so that multiple wavelength data features in the second reflected light wavelength information can be fully considered. The preset weight matrix may be set manually, and by calculating with the second reflected wavelength information, all data features in the second reflected wavelength information can be distinguished by setting different weights, so that the important features in the second reflected wavelength information can be highlighted, so that these important features are considered in the process of prediction calculation to improve the accuracy of the prediction calculation. The preset offset variable may be set manually, which refers to the relative position offset that may occur in the nonlinear space during the prediction calculation of each wavelength information in the second reflected light wavelength information, and is used to more effectively reverse the absolute position of the predicted data in the nonlinear space, thereby improving the robustness of the prediction calculation process and ensuring the reliability of the prediction calculation results.

[0026] The method for predicting the wavelength of water body reflected light provided in the embodiment of the present application performs time-frequency domain conversion on a large amount of collected water body reflected light spectrum information to obtain a large amount of water body reflected light wavelength information, and then filters out the wavelength information with reference in the water body reflected light wavelength, and filters out the wavelength information with large measurement error in the water body reflected light wavelength information, and ensures the screening efficiency by setting the calculation time to reduce the calculation complexity of the subsequent chemical oxygen demand measurement, and classifies the wavelength information obtained by the screening, and the classified wavelength information can be used to determine the effective wavelength band that can be used for chemical oxygen demand measurement, and then by mapping the classified wavelength information to the nonlinear space through a preset nonlinear function, the important features of the classified wavelength information are extracted through a preset weight matrix, Then, the possible offset of wavelength information in nonlinear space is taken into consideration, and the wavelength information in nonlinear space is calculated according to a preset weight matrix and a preset offset variable. The calculation result is the predicted wavelength information, which is used to measure the chemical oxygen demand of the water body. The present application ensures the validity and reliability of the available wavelengths by screening and classifying the effective wavelengths, and then predicts the screened and classified wavelengths. The predicted wavelength data can be used to make up for the shortage of wavelength measurement data in quantity, and avoids the problem in the prior art that timeliness cannot be ensured by manually measuring multiple spectral data, and the data accuracy cannot be guaranteed due to improper operation or measurement errors during the measurement process, thereby improving the detection efficiency and accuracy of the degree of pollution of water bodies.

[0027] Figure 2 The flowchart of the method for predicting the wavelength of reflected light from water provided in the second embodiment of the present application is shown. The difference between the method and the first embodiment is as follows: The preset population movement information includes movement path information; the movement path information includes movement node information and movement node weight information; wherein the movement node information corresponds to the movement node weight information; The step S103 specifically includes: Step S201, generating a plurality of water body reflected light wavelength sets according to preset population quantity information and the water body reflected light wavelength information; the water body reflected light wavelength set includes a plurality of water body reflected light wavelength information.

[0028] In this embodiment, all water body reflected light wavelength information in the water body reflected light wavelength information are randomly formed into multiple water body reflected light wavelength sets, and the number of water body reflected light wavelengths is the same as the preset population quantity information. The number of water body reflected light wavelength information in each water body reflected light wavelength set can be the same.

[0029] Step S202: randomly matching the plurality of water body reflected light wavelength information in each of the water body reflected light wavelength sets with the plurality of mobile node information to obtain initial mobile node information and initial mobile node weight information.

[0030] In this embodiment, the water body reflected light wavelength information is taken as an individual in a set, that is, an individual in the water body reflected light wavelength set, and the number of mobile nodes in the preset moving path information may be greater than the number of individuals in the set. The moving path information may be taken as a mobile node set, and the mobile nodes in the mobile node set are randomly matched with the water body reflected light wavelength information in the water body reflected light wavelength set, ensuring that each water body reflected light wavelength information can be matched with a mobile node, so as to subsequently implement iterative calculation of the individuals in the water body reflected light wavelength set through the change of the mobile node corresponding to the individual in the single water body reflected light wavelength set and the update of the mobile node weight information.

[0031] Step S203: randomly adjusting the matching relationship between the plurality of water body reflected light wavelength information and the plurality of mobile node information in each water body reflected light wavelength set according to the moving path information, and generating intermediate mobile node information.

[0032] In this embodiment, by randomly adjusting the matching relationship between multiple water body reflected light wavelength information and multiple mobile node information in each water body reflected light wavelength set, the change of the mobile node corresponding to the individual in a single water body reflected light wavelength set and the random update of the mobile node weight information are realized, so as to get rid of the influence of the initial mobile node, thereby realizing iterative calculation of the individuals in the water body reflected light wavelength set, and avoiding the iterative result from falling into the local optimal solution. The result of an iterative operation is used as the intermediate mobile node information, which can be used to further solve the global optimal solution.

[0033] Step S204: Calculate intermediate mobile node weight information according to the mobile path information and the initial mobile node weight information.

[0034] In this embodiment, the initial mobile node weight information and the weight information of the intermediate mobile node when not matched by the wavelength information of the light reflected by the current water body can be determined based on the moving path information. When the intermediate mobile node matches the wavelength information of the light reflected by the current water body, the initial mobile node weight information and the weight information of the intermediate mobile node when not matched by the wavelength information of the light reflected by the current water body are summed, and the result of the calculation is used as the weight information of the intermediate mobile node.

[0035] Step S205 , screening and calculating a plurality of water body reflected light wavelength information in the water body reflected light wavelength set according to the intermediate mobile node weight information and a preset calculation time threshold, to determine first reflected light wavelength information.

[0036] In this embodiment, the matching relationship between the multiple water body reflected light wavelength information and the multiple mobile node information in each water body reflected light wavelength set can be continuously adjusted, and then the intermediate mobile calculation and the intermediate mobile node weight information can be updated until the calculation time of the screening process reaches a preset calculation time threshold. The screening calculation is then stopped, and the sum of all the intermediate mobile node weight information in all current mobile paths is summed, and the sum is sorted in descending order, and the water body reflected light wavelength information that has been matched with all the mobile nodes in the top-ranked mobile paths is determined as the first reflected light wavelength information.

[0037] The method for predicting the wavelength of light reflected by a water body provided in an embodiment of the present application first generates multiple sets of information on the wavelength of light reflected by a large amount of water body, so as to perform batch calculations on the multiple sets to reduce the calculation complexity, and then realizes parallel update iterative calculation of the movement path by matching the mobile node information in the population movement information with the water body reflected light wavelength information and updating the matching relationship, so that the iterative result is free from the dependence on the initial solution, and the time for updating the iterative calculation is limited by a preset calculation time threshold, and the weight value of the mobile node in the movement path after the update iteration is determined to determine the water body reflected light wavelength information that has been matched with the mobile node in the movement path as the global optimal solution, that is, the first reflected light wavelength information, thereby effectively ensuring the accuracy and efficiency of continuing to screen the reflected light wavelength information.

[0038] Figure 3 The flowchart of the method for predicting the wavelength of reflected light from water provided in the third embodiment of the present application is shown. The difference between the method and the second embodiment is that the step S205 specifically includes: Step S301, obtaining actual calculation time.

[0039] In this embodiment, the actual calculation time refers to the time from the start of generating the wavelength set of water body reflected light to the time of calculating the intermediate mobile node weight information, which is used to measure the time currently consumed by the screening calculation and can be obtained through the computer's built-in timer.

[0040] Step S302, determine whether the actual calculation time is less than the preset calculation time threshold, if so, proceed to step S303; if not, proceed to step S304.

[0041] In this embodiment, when the actual calculation time is less than the preset calculation time threshold, it means that the current screening calculation time is not too long, and the screening calculation can continue, that is, continue to adjust the matching relationship between the multiple water body reflected light wavelength information and the multiple mobile node information in each water body reflected light wavelength set, so as to obtain the global optimal solution as the first reflected light wavelength information. When the actual calculation time is greater than or equal to the preset calculation time threshold, it means that the current time used for screening calculation is too long, and it is not suitable to continue the screening calculation, then it is necessary to stop adjusting the matching relationship between the multiple water body reflected light wavelength information and the multiple mobile node information in each water body reflected light wavelength set, and use the currently obtained result as the global optimal solution to calculate the first reflected light wavelength information.

[0042] Step S303: Use the intermediate mobile node weight information as initial mobile node weight information, and return to step S203.

[0043] In this embodiment, when the actual calculation time is less than the preset calculation time threshold, it means that the current screening calculation time is not long, and the screening calculation can continue. By using the intermediate mobile node weight information as the initial mobile node weight information, the matching relationship between the multiple water body reflected light wavelength information and the multiple mobile node information in each water body reflected light wavelength set is re-adjusted, and then the intermediate mobile node weight information is updated, so that the calculation result is freed from the influence of the initial solution and gradually approaches the global optimal solution.

[0044] Step S304: summing up the intermediate mobile node weight information in each of the mobile path information to obtain a plurality of mobile node total weight information corresponding to the water body reflected light wavelength set.

[0045] In this embodiment, when the actual calculation time is greater than or equal to the preset calculation time threshold, it indicates that the current time used for screening calculation is too long and it is not suitable to continue the screening calculation. Therefore, the adjustment of the matching relationship between multiple water body reflected light wavelength information and multiple mobile node information in each water body reflected light wavelength set is stopped, and the intermediate mobile node weight information of the intermediate mobile nodes matched with the water body reflected light wavelength information in each path information is summed. The sum value is the total weight of the mobile node, which is used to represent the weight value of each mobile path information, so as to determine the first reflected light wavelength information later.

[0046] Step S305 , determining the destination intermediate node information corresponding to each of the water body reflected light wavelength sets according to the maximum value in the total weight information of the mobile node corresponding to each of the water body reflected light wavelength sets.

[0047] In this embodiment, because all water body reflected light wavelength information is calculated by substituting the water body reflected light wavelength set in batches, it is necessary to determine the maximum value in the total weight information of the mobile node corresponding to each water body reflected light wavelength set, and determine the moving path corresponding to this maximum value, and all nodes in the moving path corresponding to this maximum value that have matched the water body reflected light wavelength information are determined as the destination intermediate node information.

[0048] Step S306: determining the water body reflected light wavelength information that matches the plurality of destination intermediate nodes in each of the water body reflected light wavelength sets as the first reflected light wavelength information.

[0049] In this embodiment, the maximum value in the total weight information of the mobile node corresponding to each water body reflected light wavelength set is calculated, and the moving path corresponding to the maximum value is determined, and then the water body reflected light wavelength information matched with all nodes in the moving path is determined, so that the water body reflected light wavelength information is used as the global optimal solution and determined as the first reflected light wavelength information.

[0050] The method for predicting the wavelength of reflected light of a water body provided in the embodiment of the present application limits the calculation time of the screening process by a preset calculation time threshold, and continuously readjusts the matching relationship between the multiple water body reflected light wavelength information and the multiple mobile node information in each water body reflected light wavelength set, so that the calculation result is freed from the influence of the initial solution and continuously approaches the global optimal solution. When the actual calculation time reaches the preset calculation time threshold, the adjustment of the matching relationship between the multiple water body reflected light wavelength information and the multiple mobile node information in each water body reflected light wavelength set is stopped, and the current calculation result is used as the global optimal solution. By calculating the total weight of the mobile node, the moving path corresponding to the total weight of the mobile node is determined, and according to the nodes in the moving path, the water body reflected light wavelength information that has been matched with all the nodes in the moving path is determined as the first reflected light wavelength information, which is used to determine the number of valid reflected light wavelength information, thereby effectively ensuring the screening efficiency of the water body reflected light wavelength and improving the screening accuracy.

[0051] Figure 4 The flowchart of the method for predicting the wavelength of reflected light from water provided in the fourth embodiment of the present application is shown. The difference between the method and the first embodiment is that the step S104 specifically includes: Step S401: using a plurality of randomly generated population center points as a plurality of initial population center points.

[0052] In this embodiment, the population center point is used to classify the first reflected light wavelength information. The number of population center points determines the number of categories into which the water reflected light wavelength in the first reflected light wavelength will be divided. The population center point is initially randomly generated by a computer and is used to perform the initial classification of the first reflected light wavelength. The population center point used for the initial classification is called the initial population center point.

[0053] Step S402: generating a reflected light wavelength population according to the first reflected light wavelength information; the reflected light wavelength population includes a plurality of reflected light wavelength information.

[0054] In this embodiment, each water body reflected light wavelength information in the first reflected light wavelength information is used as a population individual to generate a reflected light wavelength population. Therefore, the reflected light wavelength population includes a plurality of reflected light wavelength information, and the reflected light wavelength information here refers to the water body reflected light wavelength information in the first reflected light wavelength information, and is not the initial water body reflected light wavelength information generated after the water body reflected light spectrum information is transformed in the time-frequency domain.

[0055] Step S403, calculating the distance values ​​between a plurality of reflected light wavelength information in the reflected light wavelength population and a plurality of the initial population center points.

[0056] In this embodiment, the distance value may be a Euclidean distance value or a Manhattan distance value.

[0057] Step S404: generating a plurality of reflected light wavelength sub-populations according to the distance value and the initial population center point.

[0058] In this embodiment, a distance threshold may be set, and the reflected light wavelength information whose distance from the initial population center point is less than or equal to the distance threshold is used as the reflected light wavelength sub-population where the initial population center point is located. Each initial population center point belongs to a reflected light wavelength sub-population. When the distances between a reflected light wavelength information and multiple initial population center points are all less than or equal to the distance threshold, the reflected light wavelength information will belong to the reflected light wavelength sub-population where the initial population center point closest to it is located.

[0059] Step S405 , performing classification calculation according to the multiple pieces of reflected light wavelength information in the reflected light wavelength sub-population and a preset population individual offset function to determine the second reflected light wavelength information.

[0060] In this embodiment, the preset population individual offset function may be a Griewank function, a Rastrigin function, a Schaffer function, an Ackley function, or a Rosenbrock function.

[0061] In this embodiment, optionally, the reflected light wavelength information may be used as the independent variable of the population individual offset function, and the function value of the population individual offset function may be calculated as the offset of the reflected light wavelength information in the reflected light wavelength sub-population, and the population center point may be re-determined according to the offset, and it may be determined whether the re-determined population center point is consistent with the initial population center point. If not, the distance values ​​between multiple reflected light wavelength information in the reflected light wavelength population and multiple population center points may be recalculated, and then based on the re-determination of the reflected light wavelength sub-population, the offset of each reflected light wavelength sub-population in the current reflected light wavelength sub-population may be calculated according to the preset population individual offset function, and then the population center point may be re-determined according to the offset, until the re-determined population center point is consistent with the population center point calculated in the previous iteration cycle, and the current reflected light wavelength sub-population may be determined as the second reflected light wavelength information.

[0062] The method for predicting the wavelength of reflected light of a water body provided in the embodiment of the present application classifies each reflected light wavelength information in the first reaction light wavelength information through a randomly generated population center point, and determines the reflected light wavelength sub-population to which multiple reflected light wavelength information belongs by calculating the logical distance between the population center and each reflected light wavelength information, thereby realizing dividing each reflected light wavelength information into different categories according to the multiple randomly generated population centers.

[0063] Figure 5 The flowchart of the method for predicting the wavelength of reflected light from water provided in the fifth embodiment of the present application is shown. The difference between the method and the fourth embodiment is that the step S405 specifically includes: Step S501, calculating the individual shift function value of each reflected light wavelength information in the reflected light wavelength subpopulation according to the plurality of reflected light wavelength information in the reflected light wavelength subpopulation and a preset population individual shift function.

[0064] In this embodiment, the reflected light wavelength information can be used as the independent variable of the population individual offset function, and the function value of the population individual offset function is calculated as the offset of the reflected light wavelength information in the reflected light wavelength sub-population, that is, the individual offset function value of each reflected light wavelength information in the reflected light wavelength sub-population.

[0065] Step S502: taking the reflected light wavelength information corresponding to the extreme value of the individual shift function value as the center point of the population after the shift.

[0066] In this embodiment, the reflected light wavelength information corresponding to the maximum value of the individual shift function value may be used as the center point of the shifted population to reclassify the reflected light wavelength information so that the classification result approaches the global optimal solution.

[0067] Step S503, determining whether the shifted population center point is the same as the initial population center point, if so, proceeding to step S504; if not, proceeding to step S505.

[0068] In this embodiment, when the population center point after the shift is the same as the initial population center point, it means that the current calculation of the population center point has converged, and there is no need to further calculate the population center point. The second reflected light wavelength information calculated based on the population center point analysis is the global optimal solution. When the population center point after the shift is different from the initial population center point, it means that the current calculation of the population center point has not converged, and further calculation of the population center point is required until the population center point after the shift is the same as the initial population center point.

[0069] Step S504: determine the plurality of reflected light wavelength sub-populations as second reflected light wavelength information.

[0070] In this embodiment, a plurality of reflected light wavelength subpopulations are determined as the second reflected light wavelength information, and a band can be determined according to the reflected light wave information in each reflected light wavelength subpopulation, thereby analyzing the structural characteristics of water body organic matter according to the band.

[0071] Step S505, taking the shifted population center point as the initial population center point, and returning to step S402.

[0072] In this embodiment, when the offset population center point is different from the initial population center point, it means that the current calculation of the population center point has not converged, and further calculation is required based on the current offset population center point until the offset population center point is the same as the initial population center point.

[0073] The method for predicting the wavelength of reflected light in water provided in the embodiment of the present application calculates the individual offsets of the individuals in the population, readjusts the center point of the population according to the extreme value of the offset, and ensures that the adjusted center point of the population gradually approaches the global optimal solution. When the center point of the population after the offset is consistent with the center point of the population before the offset, it indicates that the current calculation of the center point of the population has converged, and there is no need to further readjust the reflected light wavelength sub-population. The current reflected light wavelength sub-population is determined as the second reflected light wavelength information. The effective band can be determined through the reflected light wavelength information in each reflected light wavelength sub-population, which is convenient for the subsequent analysis of the material structure of organic matter in the water body, thereby ensuring the efficiency and accuracy of the calculation of chemical oxygen demand.

[0074] Figure 6 The flowchart of the method for predicting the wavelength of reflected light from water provided in the sixth embodiment of the present application is shown. The difference between the method and the first embodiment is that the step S105 specifically includes: Step S601: Perform a weighted sum calculation according to a preset weight matrix and the second reflected light wavelength information to obtain a first reflected light wavelength prediction variable.

[0075] In this embodiment, the preset weight matrix may be manually set. The numerical values ​​in the preset weight matrix may be used as coefficients, the coefficients are multiplied by the water body reflected light wavelength information in the second reflected light wavelength information, and the multiplication results are summed to obtain the first reflected light wavelength prediction variable, which is used to subsequently calculate the prediction result for the second reflected light wavelength information.

[0076] Step S602: Obtain a second reflected light wavelength prediction variable according to a preset offset variable and the first reflected light wavelength prediction variable.

[0077] In this embodiment, the preset offset variable may be manually set, and the preset offset variable may be added to the first reflected light wavelength to obtain the second reflected light wavelength prediction variable, which is used for subsequent calculation of the prediction result of the second reflected light wavelength information.

[0078] Step S603: performing nonlinear calculation on the second reflected light wavelength prediction variable according to a preset nonlinear function to obtain the reflected light wavelength information of the target water body.

[0079] In this embodiment, the second reflected light wavelength prediction variable may be used as the independent variable of the nonlinear function, and the calculated function value is the reflected light wavelength information of the target water body, that is, the result of the prediction calculation.

[0080] The method for predicting the wavelength of reflected light from a water body provided in an embodiment of the present application maps the classified wavelength information to a nonlinear space through a preset nonlinear function, extracts important features from the classified wavelength information through a preset weight matrix, and then considers the possible offset of the wavelength information in the nonlinear space, thereby calculating the wavelength information in the nonlinear space according to the preset weight matrix and the preset offset variable, and the calculation result is the predicted wavelength information, which is used to measure the chemical oxygen demand of the water body.

[0081] Figure 7 The flowchart of the method for predicting the wavelength of reflected light from water provided in the seventh embodiment of the present application is shown. The method differs from the first embodiment in that after step S105, the method further includes: Step S701, obtaining water body organic matter quality information.

[0082] In this embodiment, the water body organic matter quality information can be obtained by measuring the detected water body using spectroscopy.

[0083] Step S702, based on a preset water body organic matter structure analysis model, obtain water body organic matter density information according to the wavelength information of the reflected light of the target water body.

[0084] In this embodiment, the preset water body organic matter structure analysis model can be a Gaussian03 software package, using density functional theory (DFT), B3LYP / 6-31+G* basis set level, selecting ethanol as the solvent, and using the CPCM solvent model to optimize the configuration of all AGEs monomers and protonated structures, and based on this, vibration analysis is performed to determine that the stable configuration is located at the lowest point of the molecular potential energy surface without generating imaginary frequencies, that is, the minimum energy configuration. On this basis, the 6-31+G* basis set of TD-DFT can be used to simulate the ultraviolet-visible absorption spectrum of each molecule, and 50 electron transfer states are considered for calculation and analysis, so that the wavelength information of the target water body reflected light is analyzed and calculated through the preset water body organic matter structure analysis model to obtain the water body organic matter density information.

[0085] Step S703, obtaining chemical oxygen demand information of the water body according to the water body organic matter quality information, water body organic matter density information and a preset standard organic matter volume.

[0086] In this embodiment, based on the water body organic matter quality information and water body organic matter density information, the volume information of the detected water body organic matter can be calculated, and then the volume information of the detected water body is analyzed and calculated with the preset standard organic matter volume to obtain the chemical oxygen demand of the water body, which is used to evaluate the degree of water pollution.

[0087] The method for predicting the wavelength of reflected light from a water body provided in the embodiment of the present application analyzes the characteristic structures of various organic matter in the water body according to the reflected light wavelength information of the target water body through a water body organic matter structure analysis model, and uses quantum chemical calculation methods to perform structural optimization and vibration analysis on such structures, calculate their electronic spectra, and analyze the key structures with a quantitative relationship with COD in combination with their redox behavior. The method can fully consider the complexity of the water body and the diversity of the sources of organic matter in the water body, and then perform organic matter density analysis through the key structure combined with density functional theory, and then calculate the volume of organic matter according to the density and mass of organic matter, and then determine the chemical oxygen demand information of the water body through the volume of organic matter, which is used to evaluate the degree of pollution of the detected water body and help the Food, Drug and Environmental Crime Investigation Bureau to conduct dynamic crime analysis and investigation work in the fields of food, drugs, ecological environment, etc.

[0088] Corresponding to the method of the above embodiment, Figure 8 A structural block diagram of a device for predicting the wavelength of light reflected from a water body provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown. Figure 8The exemplary water body reflected light wavelength prediction device may be the execution body of the water body reflected light wavelength prediction method provided in the aforementioned first embodiment.

[0089] Reference Figure 8 , the water body reflected light wavelength prediction device comprises: A water body reflection spectrum information acquisition module 810 is used to acquire water body reflection spectrum information; An initial water body reflected light wavelength information generating module 820 is used to perform time-frequency domain conversion on the water body reflected light spectrum information to generate initial water body reflected light wavelength information; The first reflected light wavelength information determination module 830 is used to screen and calculate the initial water body reflected light wavelength information according to a preset calculation time threshold, preset population quantity information, and preset population movement information to determine the first reflected light wavelength information; A second reflected light wavelength information determination module 840 is used to classify and calculate the first reflected light wavelength information according to a plurality of randomly generated population center points to determine the second reflected light wavelength information; The target water body reflected light wavelength information calculation module 850 is used to predict and calculate the second reflected light wavelength information according to a preset nonlinear function, a preset weight matrix and a preset offset variable to obtain the target water body reflected light wavelength information.

[0090] The process of each module in the water body reflected light wavelength prediction device provided in the embodiment of the present application realizing its own function can be specifically referred to the aforementioned Figure 1 The description of the first embodiment is not repeated here.

[0091] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0092] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0093] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0094] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0095] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish descriptions, and cannot be understood as indicating or suggesting relative importance. It should also be understood that although the terms "first", "second", etc. are used to describe various elements in some embodiments of the present application in the text, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, the first table can be named as the second table, and similarly, the second table can be named as the first table without departing from the scope of the various described embodiments. The first table and the second table are both tables, but they are not the same table.

[0096] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0097] The method for predicting the wavelength of reflected light from a water body provided in the embodiment of the present application can be applied to terminal devices such as mobile phones, tablet computers, wearable devices, vehicle-mounted devices, augmented reality (AR) / virtual reality (VR) devices, laptop computers, ultra-mobile personal computers (UMPC), netbooks, personal digital assistants (PDA), etc. The embodiment of the present application does not impose any restrictions on the specific type of the terminal device.

[0098] For example, the terminal device can be a station (STAION, ST) in a WLAN, a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA) device, a handheld device with wireless communication function, a computing device or other processing device connected to a wireless modem, a vehicle-mounted device, a vehicle networking terminal, a computer, a laptop computer, a handheld communication device, a handheld computing device, a satellite wireless device, a wireless modem card, a TV set top box (STB), a customer premises equipment (CPE) and / or other devices for communicating on a wireless system and a next-generation communication system, such as a mobile terminal in a 5G network or a mobile terminal in a future evolved Public Land Mobile Network (PLMN) network, etc.

[0099] As an example but not limitation, when the terminal device is a wearable device, the wearable device can also be a general term for wearable devices that are intelligently designed and developed using wearable technology for daily wear, such as glasses, gloves, watches, clothing and shoes. A wearable device is a portable device that is worn directly on the body or integrated into the user's clothes or accessories. Wearable devices are not just hardware devices, but also achieve powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include those that are fully functional, large in size, and can achieve complete or partial functions without relying on smartphones, such as smart watches or smart glasses, as well as those that only focus on a certain type of application function and need to be used in conjunction with other devices such as smartphones, such as various smart bracelets and smart jewelry for vital sign monitoring.

[0100] Fig. 9 Schematic diagram of the structure of a terminal device provided by an embodiment of the present application. Fig. 9 As shown, the terminal device 9 of this embodiment includes: at least one processor 90 ( Fig. 9 Only one is shown in the figure), a memory 91, wherein the memory 91 stores a computer program 92 that can be run on the processor 90. When the processor 90 executes the computer program 92, the steps in the above-mentioned various water body reflected light wavelength prediction method embodiments are implemented, such as Figure 1 Alternatively, when the processor 90 executes the computer program 92, the functions of each module / unit in the above-mentioned device embodiments are realized, for example, Figure 8Functions of modules 810 to 850 are shown.

[0101] The terminal device 9 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal device may include, but is not limited to, a processor 90 and a memory 91. Those skilled in the art will appreciate that Fig. 9 It is only an example of the terminal device 9 and does not constitute a limitation on the terminal device 9. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal device may also include an input sending device, a network access device, a bus, etc.

[0102] The processor 90 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0103] In some embodiments, the memory 91 may be an internal storage unit of the terminal device 9, such as a hard disk or memory of the terminal device 9. The memory 91 may also be an external storage device of the terminal device 9, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal device 9. Further, the memory 91 may also include both an internal storage unit of the terminal device 9 and an external storage device. The memory 91 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory 91 may also be used to temporarily store data that has been sent or is to be sent.

[0104] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0105] An embodiment of the present application also provides a terminal device, which includes at least one memory, at least one processor, and a computer program stored in the at least one memory and executable on the at least one processor, wherein when the processor executes the computer program, the terminal device implements the steps in any of the above-mentioned method embodiments.

[0106] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0107] An embodiment of the present application provides a computer program product. When the computer program product runs on a terminal device, the terminal device can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0108] If the integrated module / 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 present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0109] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0110] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0111] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0112] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for predicting the wavelength of reflected light from a water body, characterized in that: include: Obtain water body reflectance spectrum information; Performing time-frequency conversion on the water body reflection spectrum information to generate initial water body reflection light wavelength information; According to a preset calculation time threshold, preset population quantity information, and preset population movement information, the initial water body reflected light wavelength information is screened and calculated to determine the first reflected light wavelength information; Classify and calculate the first reflected light wavelength information according to a plurality of randomly generated population center points to determine the second reflected light wavelength information; According to a preset nonlinear function, a preset weight matrix and a preset offset variable, the second reflected light wavelength information is predicted and calculated to obtain the target water body reflected light wavelength information.

2. The method for predicting the wavelength of reflected light from a water body according to claim 1, wherein: The preset population movement information includes movement path information; the movement path information includes movement node information and movement node weight information; wherein the movement node information corresponds to the movement node weight information; The step of screening and calculating the initial water body reflected light wavelength information according to the preset calculation time threshold, the preset population quantity information and the preset population movement information to determine the first reflected light wavelength information specifically includes: Generate multiple water body reflected light wavelength sets according to preset population quantity information and the water body reflected light wavelength information; the water body reflected light wavelength set includes multiple water body reflected light wavelength information; Randomly matching the multiple water body reflected light wavelength information in each of the water body reflected light wavelength sets with the multiple mobile node information to obtain initial mobile node information and initial mobile node weight information; According to the moving path information, randomly adjust the matching relationship between the plurality of water body reflected light wavelength information and the plurality of mobile node information in each of the water body reflected light wavelength sets to generate intermediate mobile node information; Calculating intermediate mobile node weight information according to the mobile path information and initial mobile node weight information; According to the intermediate mobile node weight information and a preset calculation time threshold, a plurality of water body reflected light wavelength information in the water body reflected light wavelength set are screened and calculated to determine the first reflected light wavelength information.

3. The method for predicting the wavelength of reflected light from a water body according to claim 2, wherein: The step of screening and calculating a plurality of water body reflected light wavelength information in the water body reflected light wavelength set according to the intermediate mobile node weight information and a preset calculation time threshold to determine the first reflected light wavelength information specifically includes: Get the actual calculation time; Determine whether the actual calculation time is less than a preset calculation time threshold; If yes, the intermediate mobile node weight information is used as the initial mobile node weight information, and the process returns to the step of randomly adjusting the matching relationship between the plurality of water body reflected light wavelength information and the plurality of mobile node information in each water body reflected light wavelength set according to the moving path information to generate the intermediate mobile node information; If not, summing up the intermediate mobile node weight information in each of the mobile path information to obtain a plurality of mobile node total weight information corresponding to the water body reflected light wavelength set; Determine the destination intermediate node information corresponding to each of the water body reflected light wavelength sets according to the maximum value of the total weight information of the mobile nodes corresponding to each of the water body reflected light wavelength sets; The water body reflected light wavelength information that matches the plurality of destination intermediate nodes in each of the water body reflected light wavelength sets is determined as the first reflected light wavelength information.

4. The method for predicting the wavelength of reflected light from a water body according to claim 1, wherein: The step of classifying and calculating the first reflected light wavelength information according to a plurality of randomly generated population center points to determine the second reflected light wavelength information specifically includes: Using multiple randomly generated population center points as multiple initial population center points; Generate a reflected light wavelength population according to the first reflected light wavelength information; the reflected light wavelength population includes a plurality of reflected light wavelength information; Calculating the distance values ​​between a plurality of reflected light wavelength information in the reflected light wavelength population and a plurality of the initial population center points; Generating a plurality of reflected light wavelength sub-populations according to the distance value and the center point of the initial population; According to the information of multiple reflected light wavelengths in the reflected light wavelength sub-population and a preset population individual offset function, classification calculation is performed to determine the second reflected light wavelength information.

5. The method for predicting the wavelength of reflected light from a water body according to claim 4, wherein: The step of performing classification calculation according to the multiple pieces of reflected light wavelength information in the reflected light wavelength subpopulation and a preset population individual offset function to determine the second reflected light wavelength information specifically includes: Calculating the individual shift function value of each reflected light wavelength information in the reflected light wavelength subpopulation according to the plurality of reflected light wavelength information in the reflected light wavelength subpopulation and the preset population individual shift function; The reflected light wavelength information corresponding to the extreme value of the individual shift function value is used as the center point of the population after the shift; Determining whether the center point of the population after the shift is the same as the center point of the initial population; If yes, determining the plurality of reflected light wavelength subpopulations as second reflected light wavelength information; If not, the shifted population center point is used as the initial population center point, and the process returns to the step of generating a reflected light wavelength population according to the first reflected light wavelength information.

6. The method for predicting the wavelength of reflected light from a water body according to claim 1, wherein: The step of predicting and calculating the second reflected light wavelength information according to a preset nonlinear function, a preset weight matrix, and a preset offset variable to obtain the target water body reflected light wavelength information specifically includes: Performing a weighted sum calculation according to a preset weight matrix and the second reflected light wavelength information to obtain a first reflected light wavelength prediction variable; Obtaining a second reflected light wavelength prediction variable according to a preset offset variable and the first reflected light wavelength prediction variable; According to a preset nonlinear function, a nonlinear calculation is performed on the second reflected light wavelength prediction variable to obtain the reflected light wavelength information of the target water body.

7. The method for predicting the wavelength of reflected light from a water body according to claim 1, wherein: After the step of predicting and calculating the second reflected light wavelength information according to the preset nonlinear function, the preset weight matrix and the preset offset variable to obtain the reflected light wavelength information of the target water body, the method further includes: Obtain information on the quality of organic matter in water bodies; Based on a preset water body organic matter structure analysis model, the water body organic matter density information is obtained according to the reflected light wavelength information of the target water body; The chemical oxygen demand information of the water body is obtained according to the water body organic matter quality information, the water body organic matter density information and the preset standard organic matter volume.

8. A device for predicting the wavelength of reflected light from a water body, characterized in that: include: A water body reflection spectrum information acquisition module is used to obtain water body reflection spectrum information; An initial water body reflected light wavelength information generating module is used to convert the water body reflected light spectrum information into time-frequency domain to generate initial water body reflected light wavelength information; A first reflected light wavelength information determination module, configured to screen and calculate the initial water body reflected light wavelength information according to a preset calculation time threshold, preset population quantity information, and preset population movement information, and determine the first reflected light wavelength information; A second reflected light wavelength information determination module, configured to classify and calculate the first reflected light wavelength information according to a plurality of randomly generated population center points to determine the second reflected light wavelength information; The target water body reflected light wavelength information calculation module is used to predict and calculate the second reflected light wavelength information according to a preset nonlinear function and a preset weight matrix to obtain the target water body reflected light wavelength information.

9. A terminal device, characterized in that: The terminal device includes a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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