An optoelectronic-based method for water environment detection

Abstract

The present invention relates to the technical field of water environment detection, and discloses a water environment detection method based on optoelectronics, including: the data analysis module can effectively identify abnormal sampling points by marking and comparing the light intensity at the sampling points, and correct them by adjusting the light intensity of the light source emission end, avoiding measurement errors caused by uneven light sources or external interference. Secondly, the verification step of the secondarily collected optical signals further ensures the accuracy of the sampling results. Finally, through the comprehensive analysis of the environmental scores of the sampling points and the overall regional environmental scores, the water environment quality can be evaluated more accurately.

Description

Technical Field

[0001] The present invention relates to the technical field of water environment detection. Specifically, it relates to a water environment detection method based on optoelectronics. Background Art

[0002] Traditional water environment detection methods usually rely on chemical analysis and physical detection means. However, these methods often have problems such as complex operation, poor real-time performance, and secondary pollution to the environment. In recent years, due to its characteristics of high sensitivity, non-contact, and real-time monitoring, optoelectronic technology has gradually become an important means in the field of water environment detection. For example, through the absorption, scattering, or reflection characteristics of optical signals, the concentration of pollutants such as dissolved substances and suspended particles in water can be detected to achieve water quality assessment.

[0003] Although optoelectronic technology shows significant advantages in water environment detection, the existing detection methods still face some technical difficulties. For example, in practical applications, due to the complexity of water bodies, the optical signals at different sampling points are affected by environmental noise and light source non-uniformity problems, resulting in inconsistent or even abnormal detection results at sampling points. In addition, most current detection systems lack the function of effectively adjusting abnormal sampling points, and the reliability and accuracy of detection results are insufficient. At the same time, traditional water quality scoring models are also relatively rough, and it is difficult to scientifically comprehensively analyze the data of sampling points for accurate assessment of regional environmental quality.

[0004] Therefore, there is an urgent need to invent a water environment detection technology to solve the problems in the existing optoelectronic water environment detection technology, such as the interference of optical signals at sampling points by environmental noise and light source non-uniformity, the lack of an effective adjustment mechanism for abnormal sampling points, and the roughness of the water quality scoring model. Summary of the Invention

[0005] In view of this, the present invention proposes a water environment detection method based on optoelectronics, aiming to solve the problems in the existing optoelectronic water environment detection technology, such as the interference of optical signals at sampling points by environmental noise and light source non-uniformity, the lack of an effective adjustment mechanism for abnormal sampling points, and the roughness of the water quality scoring model.

[0006] The present invention proposes a water environment detection method based on optoelectronics, including:

[0007] The detection module sequentially obtains the optical signals of a plurality of sampling points in the area to be detected by using an optoelectronic sensor. The data analysis module obtains the light intensity of the optical signals and obtains the average light intensity between the plurality of sampling points. The data analysis module marks the sampling points according to the light intensity of a single sampling point and the average light intensity.

[0008] The data analysis module determines whether the detection of the marked sampling points meets the standard according to the relationship between the light intensity of the marked sampling points and the average light intensity.

[0009] When the data analysis module determines that the detection of the marked sampling point does not meet the standard, it adjusts the light intensity of the light source emission end of the marked sampling point based on the relationship between the light intensity of the marked sampling point and the average light intensity;

[0010] After the adjustment of the light intensity of the light source emission end of the marked sampling point is completed, the detection module uses a photoelectric inductor to perform secondary optical signal acquisition on the marked sampling point;

[0011] The data analysis module scores the environment of the sampling point according to the optical signal of the sampling point, and determines the environmental score of the area to be detected according to the environmental scores of several of the sampling points.

[0012] Further, when the data analysis module marks the sampling point according to the light intensity of a single sampling point and the average light intensity, it includes:

[0013] When the light intensity is lower than the average light intensity, it is determined that the sampling point is marked;

[0014] When the light intensity is consistent with the average light intensity, it is determined that the sampling point is not marked;

[0015] When the light intensity is higher than the average light intensity, the light intensities of two adjacent sampling points of the sampling point are obtained, and it is determined whether to mark the sampling point.

[0016] Further, when the light intensity is higher than the average light intensity, when obtaining the light intensities of two adjacent sampling points of the sampling point and determining whether to mark the sampling point, it includes:

[0017] Obtain the average light intensity between two adjacent sampling points, and set it as the first control value;

[0018] Obtain the intensity ratio between the light intensity and the first control value, and determine whether to mark the sampling point according to the intensity ratio:

[0019] When the intensity ratio is less than or equal to 1, it is determined that the sampling point is not marked;

[0020] When the intensity ratio is greater than 1, it is determined that the sampling point is marked.

[0021] Further, when the data analysis module determines whether the detection of the marked sampling point meets the standard according to the relationship between the light intensity of the marked sampling point and the average light intensity, it includes:

[0022] The data analysis module determines a deviation threshold according to the relationship between the light intensities of a number of the sampling points:

[0023] ;

[0024] Wherein, is the deviation threshold, a is a fluctuation coefficient, N is the total data volume of a number of the sampling points, and I i is the light intensity of the i-th sampling point;

[0025] The data analysis module determines whether the detection of the marked sampling points meets the standard according to the deviation value between the light intensity and the average light intensity, and according to the relationship between the deviation value and the deviation threshold;

[0026] When the deviation value is less than or equal to the deviation threshold, it is determined that the detection of the sampling point meets the standard;

[0027] When the deviation value is greater than the deviation threshold, it is determined that the detection of the sampling point does not meet the standard.

[0028] Further, when adjusting the light intensity of the light source emission end of the marked sampling points based on the relationship between the light intensity of the marked sampling points and the average light intensity, it includes:

[0029] The data analysis module obtains the intensity difference between the light intensity of the marked sampling points and the average light intensity, determines an adjustment coefficient according to the relationship between the intensity difference and a preset first preset intensity difference and a second preset intensity difference, and adjusts the light intensity of the light source emission end of the marked sampling points according to the adjustment coefficient, wherein:

[0030] When the intensity difference is less than or equal to the first preset intensity difference, it is determined that the adjustment coefficient is L3;

[0031] When the intensity difference is greater than the first preset intensity difference and the intensity difference is less than or equal to the second preset intensity difference, it is determined that the adjustment coefficient is L2;

[0032] When the intensity difference is greater than the second preset intensity difference, it is determined that the adjustment coefficient is L1;

[0033] Wherein, the first preset intensity difference is less than zero and less than the second preset intensity difference, and L1 < L2 < L3 < 1.

[0034] Further, when the data analysis module performs an environmental score on the sampling points according to the optical signals of the sampling points, it includes:

[0035] Obtain the index data in the historical optical signals of the sampling point and the two adjacent sampling points, where the index data includes: optical signal intensity, optical signal wavelength, optical signal frequency, and optical signal phase;

[0036] Establish a feature correlation formula based on the index data of the sampling point and the two adjacent sampling points, and obtain the distance metric between each feature correlation formula;

[0037] Construct a distance matrix according to the distance metric, and perform iterative clustering on each feature correlation formula according to the distance matrix. Based on the feature correlation formula after iterative clustering, determine the feature vector of the index data between the sampling point and the two adjacent sampling points;

[0038] Obtain the index data in the optical signals of two adjacent sampling points, and substitute them into the feature vector of the index data to determine the preset index data of the sampling point;

[0039] Determine the environmental score of the sampling point according to the relationship between the index data in the optical signal of the sampling point and the preset index data.

[0040] Further, when determining the environmental score of the sampling point according to the relationship between the index data in the optical signal of the sampling point and the preset index data, it includes:

[0041] ;

[0042] Where, W is the environmental score of the sampling point, M is the total number of index data, hi is the weight coefficient of the i-th type of index data, Si is the i-th type of index data, and si is the i-th type of preset index data.

[0043] Further, when determining the feature vector of the index data between the sampling point and the two adjacent sampling points based on the feature correlation formula after iterative clustering, it includes:

[0044] Obtain the historical index data of any three adjacent groups of sampling points, substitute the historical index data on both sides into the feature vector of the index data, and obtain the middle preset index data;

[0045] Determine whether to correct the feature vector of the index data according to the relationship between the historical index data of the middle sampling point and the preset index data;

[0046] When the historical index data is consistent with the preset index data, it is determined not to correct the feature vector of the index data;

[0047] When the historical index data is inconsistent with the preset index data, the distance matrix is corrected until the historical index data is consistent with the preset index data.

[0048] Further, when determining the environmental score of the area to be detected according to the environmental scores of several sampling points, it includes:

[0049] ;

[0050] Where Y is the environmental score of the area to be detected, m is the total number of several sampling points, f k is the weight coefficient of the kth sampling point, and F k is the environmental score of the kth sampling point.

[0051] Compared with the prior art, the beneficial effects of the present invention are as follows: By sequentially collecting the optical signals of several sampling points through a photoelectric inductor, calculating the average value of the light intensity by using a data analysis module, and making marks in combination with the deviation between the light intensity of a single sampling point and the average value, the sampling points with abnormal optical signals can be accurately identified. This marking method based on data analysis overcomes the errors caused by noise or water body complexity in traditional detection, and improves the accuracy and reliability of the detection results of sampling points. Secondly, for the sampling points marked as abnormal, by adjusting the light intensity of the light source emission end of the sampling point and performing secondary collection according to the change of the optical signal, the influence of environmental interference on the optical signal can be effectively compensated. This dynamic adjustment mechanism can not only improve the detectability of the optical signal, but also optimize the data quality of abnormal sampling points, providing more accurate basic data for subsequent environmental scoring. Finally, the data analysis module calculates the environmental score according to the optical signals of the sampling points, and generates the environmental score of the area to be detected by comprehensively considering the scoring results of several sampling points. This scoring mechanism is based on the optical signals of the sampling points and combines statistical analysis methods, making the evaluation of the regional water environment quality more scientific and comprehensive, helping to intuitively reflect the water quality status of the area to be detected, and providing strong support for environmental management and decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] By reading the detailed description of the preferred embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0053] Figure 1 is a flowchart of a water environment detection method based on optoelectronics provided by an embodiment of the present invention; DETAILED DESCRIPTION OF THE EMBODIMENTS

[0054] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the drawings and in conjunction with the embodiments.

[0055] As Figure 1 shown, in some embodiments of the present application, this embodiment provides an optoelectronic-based water environment detection method, including:

[0056] Step S100: The detection module sequentially obtains the optical signals of several sampling points in the area to be detected by using an optoelectronic sensor. The data analysis module obtains the light intensity of the optical signal and obtains the average light intensity between several sampling points. The data analysis module marks the sampling points according to the light intensity of a single sampling point and the average light intensity.

[0057] Specifically, when the data analysis module marks the sampling points according to the light intensity of a single sampling point and the average light intensity, it includes: when the light intensity is lower than the average light intensity, it is determined to mark the sampling point. When the light intensity is consistent with the average light intensity, it is determined not to mark the sampling point. When the light intensity is higher than the average light intensity, the light intensities of two adjacent sampling points of the sampling point are obtained, and it is determined whether to mark the sampling point.

[0058] Specifically, when the light intensity is higher than the average light intensity, and when obtaining the light intensities of two adjacent sampling points of the sampling point and determining whether to mark the sampling point, it includes: obtaining the average light intensity between two adjacent sampling points, which is set as the first reference value. Obtaining the intensity ratio between the light intensity and the first reference value, and determining whether to mark the sampling point according to the intensity ratio: when the intensity ratio is less than or equal to 1, it is determined not to mark the sampling point. When the intensity ratio is greater than 1, it is determined to mark the sampling point.

[0059] It can be seen that the data analysis module determines whether a sampling point needs to be marked by obtaining the light intensity of the sampling point and comparing it with the average light intensity of several sampling points. When the light intensity is lower than the average light intensity, it indicates that the light signal of the sampling point may be abnormally weak, and the system directly marks the sampling point; when the light intensity is the same as the average light intensity, it means that the light signal of the sampling point is within the normal distribution range and does not need to be marked. This preliminary marking method uses the average light intensity as a benchmark to quickly screen out sampling points that may have problems, providing a basis for subsequent precise analysis. Secondly, for sampling points with light intensity higher than the average light intensity, the light intensities of the two adjacent sampling points of the sampling point are further obtained, and the average value of the light intensities of the adjacent two points (i.e., the first reference value) is calculated. By comparing the light intensity of the current sampling point with the first reference value, the abnormal degree of the light signal of the sampling point can be further analyzed. This method effectively avoids misjudgment caused by random environmental noise or local light source interference that may cause the light intensity of a single point to be too high by introducing adjacent point data, improving the scientificity and accuracy of the determination. Finally, in the in-depth determination stage, by calculating the intensity ratio between the light intensity of the sampling point and the first reference value, it is quantitatively analyzed whether the light signal of the sampling point significantly deviates from the distribution trend of the adjacent points. When the intensity ratio is less than or equal to 1, it indicates that the light intensity of the sampling point is consistent with the light signal distribution of the adjacent points and does not need to be marked; when the intensity ratio is greater than 1, it means that the light intensity of the sampling point is significantly higher and there is an obvious difference from the signal trend of the adjacent points, and it needs to be marked. The introduction of this intensity ratio makes the marking judgment more precise, can effectively identify abnormal sampling points, and ensure the reliability of data analysis.

[0060] Step S200: The data analysis module determines whether the detection of the marked sampling point meets the standard according to the relationship between the light intensity of the marked sampling point and the average light intensity.

[0061] Specifically, when the data analysis module determines whether the detection of the marked sampling point meets the standard according to the relationship between the light intensity of the marked sampling point and the average light intensity, it includes: the data analysis module determines the deviation threshold according to the relationship between the light intensities of several sampling points:

[0062] .

[0063] Among them, is the deviation threshold, a is the fluctuation coefficient, N is the total data volume of several sampling points, and I i is the light intensity of the i-th sampling point. The data analysis module determines whether the detection of the marked sampling point meets the standard according to the deviation value between the light intensity and the average light intensity and according to the relationship between the deviation value and the deviation threshold: when the deviation value is less than or equal to the deviation threshold, it is determined that the detection of the sampling point meets the standard. When the deviation value is greater than the deviation threshold, it is determined that the detection of the sampling point does not meet the standard.

[0064] It can be seen that the data analysis module first calculates the deviation threshold according to the relationship between the light intensities of a number of sampling points. The calculation formula of the deviation threshold includes the fluctuation coefficient a, the total amount of data N of the sampling points, and the light intensity Ii of each sampling point. This method obtains a global deviation threshold by comprehensively considering the light intensity fluctuations of all sampling points. The fluctuation coefficient a reflects the fluctuation characteristics of the data set, while N and Ii ensure that the calculation of the deviation threshold can reflect the change range of the overall data distribution. Through this threshold, the deviation of the sampling points can be quantitatively evaluated, providing a basis for subsequent standard determination. Secondly, the data analysis module calculates the deviation value between the light intensity of each sampling point and the average light intensity, and further compares this deviation value with the calculated deviation threshold. When the deviation value is less than or equal to the deviation threshold, it indicates that the change in the light intensity of this sampling point is within the allowable range, and the detection result meets the standard; while when the deviation value is greater than the deviation threshold, it means that the light intensity of the sampling point is abnormal and exceeds the allowable change range, and the detection result does not meet the standard. This process can judge whether the light intensity of the sampling point meets the predetermined standard by comparing the deviation value with the deviation threshold. Finally, based on the relationship between the above deviation value and the deviation threshold, the data analysis module can judge whether the detection result of each sampling point meets the standard. When the deviation value is less than or equal to the deviation threshold, it indicates that the light intensity fluctuation of the sampling point is within the normal range and will not affect the detection accuracy, so it is judged that this sampling point meets the standard; on the contrary, when the deviation value exceeds the deviation threshold, it indicates that the optical signal of the sampling point deviates from the standard range and needs to be adjusted or marked as not meeting the standard. This determination process makes the judgment of whether the optical signal of the sampling point meets the standard more accurate and scientific through quantitative deviation calculation and comparison.

[0065] Step S300: When the data analysis module determines that the detection of the marked sampling point does not meet the standard, it adjusts the light intensity of the light source emission end of the marked sampling point based on the relationship between the light intensity of the marked sampling point and the average light intensity.

[0066] Specifically, when adjusting the light intensity of the light source emission end of the marked sampling points based on the relationship between the light intensity of the marked sampling points and the average light intensity, it includes: The data analysis module obtains the intensity difference between the light intensity of the marked sampling points and the average light intensity, determines the adjustment coefficient according to the relationship between the intensity difference and the preset first preset intensity difference and the second preset intensity difference, and adjusts the light intensity of the light source emission end of the marked sampling points according to the adjustment coefficient, where: When the intensity difference is less than or equal to the first preset intensity difference, the adjustment coefficient is determined to be L3. When the intensity difference is greater than the first preset intensity difference and less than or equal to the second preset intensity difference, the adjustment coefficient is determined to be L2. When the intensity difference is greater than the second preset intensity difference, the adjustment coefficient is determined to be L1. Among them, the first preset intensity difference is less than zero and less than the second preset intensity difference, and L1 < L2 < L3 < 1.

[0067] It can be seen that the data analysis module first calculates the intensity difference between the light intensity of the marked sampling points and the average light intensity, and compares this difference with two preset intensity differences (the first preset intensity difference and the second preset intensity difference). This process provides a basis for subsequent adjustment by quantifying the deviation between the light intensity of the sampling points and the average value. By setting the upper and lower limits of the intensity difference, it is possible to accurately judge the degree of deviation according to the relationship between the light intensity of the sampling points and the average value, and adopt different adjustment strategies accordingly. Secondly, according to the relationship between the intensity difference and the preset intensity difference, the data analysis module determines an adjustment coefficient, which is used to adjust the light intensity of the light source emission end. This adjustment mechanism makes the adjustment of the light intensity more accurate and meets the requirements of different deviation degrees through hierarchical coefficient changes. Finally, by using the adjustment coefficients L1, L2, and L3 to adjust the light intensity of the light source emission end, the system can accurately correct the light intensity of the marked sampling points to make it close to the average light intensity. This step-by-step adjustment method ensures that the recovery of the light intensity is neither excessive nor insufficient, avoiding the problems of over-adjustment or under-adjustment.

[0068] Step S400: After the adjustment of the light intensity of the light source emission end of the marked sampling points is completed, the detection module uses a photoelectric sensor to collect secondary optical signals from the marked sampling points.

[0069] Step S500: The data analysis module scores the environment of the sampling points according to the optical signals of the sampling points, and determines the environmental score of the area to be detected according to the environmental scores of several sampling points.

[0070] Specifically, when the data analysis module performs an environmental score on a sampling point based on the optical signal of the sampling point, it includes: obtaining the index data in the historical optical signals of the sampling point and two adjacent sampling points, where the index data includes: optical signal intensity, optical signal wavelength, optical signal frequency, and optical signal phase. Establish a feature correlation formula based on the index data of the sampling point and the two adjacent sampling points, and obtain the distance metric between each feature correlation formula. Construct a distance matrix based on the distance metric, and perform iterative clustering on each feature correlation formula based on the distance matrix. Determine the feature vector of the index data between the sampling point and the two adjacent sampling points based on the feature correlation formula after iterative clustering. Obtain the index data in the optical signals of two adjacent sampling points, and substitute them into the feature vector of the index data to determine the preset index data of the sampling point. Determine the environmental score of the sampling point according to the relationship between the index data in the optical signal of the sampling point and the preset index data.

[0071] Specifically, when determining the feature vector of the index data between the sampling point and the two adjacent sampling points based on the feature correlation formula after iterative clustering, it includes: obtaining the historical index data of any three adjacent groups of sampling points, and substituting the historical index data on both sides into the feature vector of the index data to obtain the intermediate preset index data. Determine whether to correct the feature vector of the index data according to the relationship between the historical index data of the intermediate sampling point and the preset index data. When the historical index data is consistent with the preset index data, it is determined that the feature vector of the index data is not corrected. When the historical index data is inconsistent with the preset index data, the distance matrix is corrected until the historical index data is consistent with the preset index data.

[0072] Specifically, when determining the environmental score of the sampling point according to the relationship between the index data in the optical signal of the sampling point and the preset index data, it includes:

[0073] .

[0074] Among them, W is the environmental score of the sampling point, M is the total number of index data, hi is the weight coefficient of the i-th type of index data, Si is the i-th type of index data, and si is the i-th type of preset index data.

[0075] It can be seen that the data analysis module first obtains various index data (such as optical signal intensity, wavelength, frequency, phase, etc.) in the historical optical signals of the sampling points and their two adjacent sampling points, and uses these data to establish a feature correlation formula. The feature correlation formula is used to describe the internal relationship between these index data, and then a distance matrix is constructed through distance measurement. The role of the distance matrix is to quantify the index data differences between each sampling point and its adjacent points, ensuring that the subsequent clustering and scoring processes can fully consider the similarities and differences between the data. This construction method further refines the grouping of the data through iterative clustering, and finally extracts the feature vectors of each sampling point, providing a basis for environmental scoring. Secondly, based on the feature correlation formula after iterative clustering, the data analysis module will continue to adjust the feature vectors of the sampling points and their two adjacent sampling points. During this process, the relationship between the historical index data and the preset index data is used to correct the feature vectors. When the historical index data is consistent with the preset index data, the feature vectors do not need to be corrected; when they are inconsistent, the feature vectors are adjusted by correcting the distance matrix until the data is consistent. This correction process ensures that the final feature data is more accurate by continuously updating the feature vectors, thereby improving the accuracy of environmental scoring. Finally, after determining the relationship between the optical signal of the sampling point and the preset index data, the data analysis module will calculate the environmental score according to the differences between each index data and its preset value. Specifically, the environmental score (W) of the sampling point is obtained by weighted summing of various index data (such as light intensity, frequency, etc.), where each index data (Si) has a weight coefficient (hi), and the weight coefficient reflects the importance of the index to the environmental score. By comprehensively considering the weights of all index data and the relationship between the actual data and the preset data, a comprehensive and accurate environmental score can be obtained, thereby accurately reflecting the environmental quality of the area to be detected.

[0076] Specifically, when determining the environmental score of the area to be detected according to the environmental scores of several sampling points, it includes:

[0077] .

[0078] Among them, Y is the environmental score of the area to be detected, m is the total number of several sampling points, f k is the weight coefficient of the kth sampling point, and F k is the environmental score of the kth sampling point.

[0079] In the above embodiments, the optical sensor is used to collect the optical signals of a number of sampling points in sequence, and the data analysis module is used to calculate the mean value of the light intensity. By combining the deviation between the light intensity of a single sampling point and the mean value for marking, the sampling points with abnormal optical signals can be accurately identified. This marking method based on data analysis overcomes the errors caused by noise or water body complexity in traditional detection, improving the accuracy and reliability of the detection results for sampling points. Secondly, for the sampling points marked as abnormal, the light intensity of the light source emission end of the sampling point is adjusted, and secondary collection is carried out according to the change of the optical signal, effectively compensating for the influence of environmental interference on the optical signal. This dynamic adjustment mechanism can not only improve the detectability of the optical signal, but also optimize the data quality of abnormal sampling points, providing more accurate basic data for subsequent environmental scoring. Finally, the data analysis module calculates the environmental score based on the optical signal of the sampling point, and generates the environmental score of the area to be detected by integrating the score results of a number of sampling points. This scoring mechanism is based on the optical signal of the sampling point and combines statistical analysis methods, making the evaluation of the regional water environment quality more scientific and comprehensive, helping to intuitively reflect the water quality status of the area to be detected, and providing strong support for environmental management and decision-making.

[0080] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0081] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for realizing the functions specified in Figure 1 one or more flows or multiple flows and / or blocks Figure 1 one or more blocks or multiple blocks.

[0082] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device realizes the functions in the process Figure 1one or more processes and / or blocks Figure 1 the functions specified in one or more blocks.

[0083] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more processes and / or blocks Figure 1 one or more blocks.

[0084] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: the specific implementation manners of the present invention can still be modified or equivalently replaced, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the protection scope of the claims of the present invention.

Claims

1. A method for detecting water environment based on optoelectronics, characterized in that, Including: The detection module uses a photoelectric sensor to sequentially obtain the optical signals of several sampling points in the area to be detected. The data analysis module obtains the light intensity of the optical signals and obtains the average light intensity between several of the sampling points. The data analysis module marks the sampling points according to the light intensity of a single sampling point and the average light intensity. The data analysis module determines whether the detection of the marked sampling points meets the standard according to the relationship between the light intensity of the marked sampling points and the average light intensity. When the data analysis module determines that the detection of the marked sampling points does not meet the standard, it adjusts the light intensity of the light source emission end of the marked sampling points based on the relationship between the light intensity of the marked sampling points and the average light intensity. After the adjustment of the light intensity of the light source emission end of the marked sampling points is completed, the detection module uses a photoelectric sensor to perform secondary optical signal acquisition on the marked sampling points. The data analysis module performs an environmental score on the sampling points according to the optical signals of the sampling points, and determines the environmental score of the area to be detected according to the environmental scores of several of the sampling points. When the data analysis module performs an environmental score on the sampling points according to the optical signals of the sampling points, it includes: Obtaining the index data in the historical optical signals of the sampling point and two adjacent sampling points, where the index data includes: optical signal intensity, optical signal wavelength, optical signal frequency, and optical signal phase. Establishing a feature correlation formula according to the index data of the sampling point and two adjacent sampling points, and obtaining the distance metric between each of the feature correlation formulas. Constructing a distance matrix according to the distance metric, and performing iterative clustering on each of the feature correlation formulas according to the distance matrix. Based on the feature correlation formulas after iterative clustering, determining the feature vectors of the index data between the sampling point and two adjacent sampling points. Obtaining the index data in the optical signals of two adjacent sampling points and substituting them into the feature vectors of the index data to determine the preset index data of the sampling point. Determining the environmental score of the sampling point according to the relationship between the index data in the optical signal of the sampling point and the preset index data.

2. The optoelectronic-based water environment detection method according to claim 1, wherein When the data analysis module marks the sampling points according to the light intensity of a single sampling point and the average light intensity, it includes: When the light intensity is lower than the average light intensity, it is determined to mark the sampling point. When the light intensity is consistent with the average light intensity, it is determined not to mark the sampling point. When the light intensity is higher than the average light intensity, obtain the light intensities of two adjacent sampling points of the sampling point and determine whether to mark the sampling point.

3. The optoelectronic-based water environment detection method according to claim 2, wherein When the light intensity is higher than the average light intensity, obtain the light intensities of two adjacent sampling points of the sampling point and determine whether to mark the sampling point, it includes: Obtaining the average light intensity between two adjacent sampling points and setting it as the first reference value. Obtaining the intensity ratio between the light intensity and the first reference value, and determining whether to mark the sampling point according to the intensity ratio: When the intensity ratio is less than or equal to 1, it is determined not to mark the sampling point; When the intensity ratio is greater than 1, it is determined to mark the sampling point.

4. The optoelectronic-based water environment detection method according to claim 1, characterized in that, When the data analysis module determines whether the detection of the marked sampling point meets the standard according to the relationship between the light intensity of the marked sampling point and the average light intensity, it includes: The data analysis module determines a deviation threshold according to the relationship between the light intensities of a plurality of the sampling points: ; Among them, is the deviation threshold, a is the fluctuation coefficient, N is the total data volume of a number of the sampling points, and I i is the light intensity of the i-th sampling point; The data analysis module determines whether the detection of the marked sampling point meets the standard according to the deviation value between the light intensity and the average light intensity, and according to the relationship between the deviation value and the deviation threshold; When the deviation value is less than or equal to the deviation threshold, it is determined that the detection of the sampling point meets the standard; When the deviation value is greater than the deviation threshold, it is determined that the detection of the sampling point does not meet the standard.

5. The optoelectronic-based water environment detection method according to claim 1, wherein When adjusting the light intensity of the light source emission end of the marked sampling point based on the relationship between the light intensity of the marked sampling point and the average light intensity, it includes: The data analysis module obtains the intensity difference between the light intensity of the marked sampling point and the average light intensity, determines the adjustment coefficient according to the relationship between the intensity difference and the preset first preset intensity difference and the second preset intensity difference, and adjusts the light intensity of the light source emission end of the marked sampling point according to the adjustment coefficient, where: When the intensity difference is less than or equal to the first preset intensity difference, it is determined that the adjustment coefficient is L3; When the intensity difference is greater than the first preset intensity difference and the intensity difference is less than or equal to the second preset intensity difference, it is determined that the adjustment coefficient is L2; When the intensity difference is greater than the second preset intensity difference, it is determined that the adjustment coefficient is L1; Wherein, the first preset intensity difference is less than zero and less than the second preset intensity difference, and L1 < L2 < L3 < 1.

6. The optoelectronic-based water environment detection method according to claim 1, characterized in that, When determining the environmental score of the sampling point according to the relationship between the index data in the optical signal of the sampling point and the preset index data, it includes: ; Wherein, W is the environmental score of the sampling point, M is the total number of index data, hi is the weight coefficient of the i-th type of index data, Si is the i-th type of index data, and si is the i-th type of preset index data.

7. The optoelectronic-based water environment detection method according to claim 1, wherein When determining the feature vectors of the index data between the sampling point and the two adjacent sampling points based on the feature correlation formula after iterative clustering, it includes: Obtain the historical index data of any three adjacent groups of the sampling points, substitute the historical index data on both sides into the feature vector of the index data, and obtain the preset index data of the middle sampling point; Determine whether to correct the feature vector of the index data according to the relationship between the historical index data of the middle sampling point and the preset index data of the middle sampling point; When the historical index data of the middle sampling point is consistent with the preset index data of the middle sampling point, it is determined not to correct the feature vector of the index data; When the historical index data of the middle sampling point is inconsistent with the preset index data of the middle sampling point, the distance matrix is corrected until the historical index data is consistent with the preset index data.

8. The optoelectronic-based water environment detection method according to claim 1, characterized in that When determining the environmental score of the area to be detected based on the environmental scores of several said sampling points, it includes: ; Where Y is the environmental score of the area to be detected, m is the total number of several sampling points, f k is the weight coefficient of the k-th sampling point, F k is the environmental score of the k-th sampling point.

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