Rapid detection and early warning method and system for excessive water bodies based on multi-dimensional sensors

Abstract

The present invention relates to the technical field of chemical property testing and analysis, and specifically to a method and system for rapid detection and early warning of excessive water bodies based on a multidimensional sensor, including: obtaining pH value data of dry, lightless water bodies, pH value data of illuminated water bodies, and light intensity data; comparing the overall change amplitude trend of pH value data of lightless water bodies and pH value data of illuminated water bodies, combining light intensity data, and obtaining the normal degree of light pH; analyzing the difference in the smooth change of constructed data segments and overall data between pH value data of different light water bodies and the normal degree of light pH, and obtaining light saturated pH value data segments; obtaining adaptive voting weights according to light saturated pH value data segments; performing data classification according to the adaptive voting weights, obtaining actual abnormal water pH value data, and issuing an early warning. The present invention improves the accuracy of classification results and improves the accuracy of detection results.

Description

Technical Field

[0001] The present invention relates to the technical field of chemical property testing and analysis, and in particular to a method and system for rapid detection and early warning of excessive water bodies based on a multi-dimensional sensor. Background Art

[0002] Exceeding standard water refers to water bodies with certain indicators or exceeding standard limits. Such water bodies will cause harm to human health, so rapid detection and early warning of water bodies are needed. There are usually a large number of microorganisms in water bodies. These microorganisms will constantly change various indicators in the water body due to their own ongoing metabolic processes. At the same time, the pH value in the water body will not only greatly affect the activity of microorganisms, thereby greatly affecting other indicators, but also directly affect human health; therefore, the pH value in the water body can be detected first, the collected water body pH value data can be classified and detected, and the abnormal water body pH value data can be separated, thereby completing the rapid detection of the water body.

[0003] Existing technologies usually use the KNN (K-Nearest Neighbor) K nearest neighbor algorithm to classify and detect water pH value data; however, some microorganisms in the water body adjust their own metabolic processes to different degrees under the influence of light, and have different effects on the pH value in the water body, resulting in certain errors in the real pH value data in the water body; the traditional KNN algorithm only determines the voting score by comparing the number of data, and does not take into account the impact of the process of some microorganisms in the water body adjusting their metabolism under the influence of light on the real data of the water body pH value, resulting in the classification result cannot classify the real abnormal pH data well, thereby affecting the accuracy of the detection results of excessive water bodies. Summary of the invention

[0004] The present invention provides a method and system for rapid detection and early warning of excessive water bodies based on a multidimensional sensor to solve the existing problem that the traditional KNN algorithm only determines the voting score by comparing the number of data, and does not take into account the impact of the process of some microorganisms in the water body adjusting their metabolism under the influence of light on the real data of the pH value of the water body, resulting in the classification result being unable to classify the real abnormal pH data well.

[0005] The method and system for rapid detection and early warning of excessive water bodies based on multi-dimensional sensors of the present invention adopt the following technical solutions:

[0006] The present invention proposes a method for rapid detection and early warning of excessive water bodies based on a multi-dimensional sensor, the method comprising the following steps:

[0007] Acquire a number of pH value data of non-illuminated water bodies, pH value data of illuminated water bodies, and light intensity data according to a preset frequency;

[0008] Compare the overall change trend of pH value data of non-illuminated water body and pH value data of illuminated water body, and combine with the ideal change trend of light intensity data to obtain the normal degree of light pH value data of each illuminated water body;

[0009] Combining any two pH value data of illuminated water bodies in pairs, several initial photosynthetic water body pH data combinations are obtained; based on the local differences of the data on both sides of the initial photosynthetic water body pH data combination time series, the difference in the smooth change of the constructed data segments and the overall data between the pH value data of different illuminated water bodies is analyzed, and combined with the normal degree of illumination pH, the light saturated pH value data segment is divided from all the illuminated water body pH value data; according to the distance distribution between the illuminated water body pH value data and the light saturated pH value data segment and the normal degree of illumination pH, the weight is adaptively adjusted to obtain the adaptive voting weight of each illuminated water body pH value data;

[0010] Data is classified according to the adaptive voting weights to obtain some actual abnormal water pH value data and issue early warning.

[0011] Preferably, the method for obtaining the normal pH level under illumination is:

[0012] Obtain the normality of the pH value data of each light-free water body;

[0013] According to the normality of the indicators, a number of normal pH value data of light-free water bodies are selected from all pH value data of light-free water bodies, and the ideal pH curve of light-free water bodies is obtained by fitting; all light intensity data are fitted to obtain the ideal light curve of water bodies;

[0014] Get the The Euclidean distance between the light intensity data and the corresponding value on the ideal water body light curve;

[0015] Get the The pH difference between the pH value data of the light-free water body and the corresponding value on the ideal pH curve of the light-free water body;

[0016] According to the Euclidean distance and pH difference, we get The illumination pH normality of the illumination water body pH value data is negatively correlated with the Euclidean distance and negatively correlated with the pH difference.

[0017] Preferably, the method for obtaining the normality of the indicator is:

[0018] For any pH value data of a lightless water body, obtain the local comparison and overall difference of the pH value data of the lightless water body;

[0019] According to the local comparison and overall difference, the normality of the indicator of the pH value data of the light-free water body is obtained; the normality of the indicator is negatively correlated with the local comparison and overall difference.

[0020] Preferably, the method for obtaining the local-to-global difference is:

[0021] Obtain the local difference of adjacent data within the local range around the pH value data of the light-free water body;

[0022] Obtain the overall average difference of adjacent data in the overall range of pH value data of light-free water bodies;

[0023] The difference between the local difference and the overall average difference is taken as the local comparison overall difference of the pH value data of the light-free water body.

[0024] Preferably, the method for acquiring the light saturated pH value data segment is:

[0025] For any initial photosynthetic water pH data combination, in the initial photosynthetic water pH data combination, the first illuminated water pH value data is recorded as the initial photosynthetic starting pH value data; the second illuminated water pH value data is recorded as the initial photosynthetic ending pH value data;

[0026] Obtain the neighborhood data stability factor for each initial photosynthetic water pH data combination;

[0027] According to the neighborhood data stability factor, the photosynthetic water pH saturation of each initial photosynthetic water pH data combination is obtained;

[0028] In the initial photosynthetic water pH data combination with the maximum photosynthetic water pH saturation, the data segment consisting of all illuminated water pH data between the corresponding moments of the initial photosynthetic starting pH data and the photosynthetic ending pH data is recorded as the illumination saturated pH data segment.

[0029] Preferably, the method for obtaining the neighborhood data stability factor is:

[0030] For any initial photosynthetic water pH data combination, obtain the left and right local contrast difference of the initial photosynthetic water pH data combination;

[0031] According to the left and right local contrast difference, the neighborhood data stabilization factor of the initial photosynthetic starting pH value data is obtained; the neighborhood data stabilization factor is negatively correlated with the left and right local contrast difference.

[0032] Preferably, the method for obtaining the pH saturation of the photosynthetic water body is:

[0033] For any initial photosynthetic water pH data combination, obtain the local and overall photosynthetic contrast difference mean of the initial photosynthetic water pH data combination; obtain the light pH normality mean of the initial photosynthetic water pH data combination;

[0034] According to the neighborhood data stability factor, the local overall photosynthetic contrast difference mean and the light pH normality mean, the photosynthetic water pH saturation of the initial photosynthetic water pH data combination is obtained; the photosynthetic water pH saturation is negatively correlated with the local overall photosynthetic contrast difference mean, positively correlated with the neighborhood data stability factor, and positively correlated with the light pH normality mean.

[0035] Preferably, the method for obtaining the adaptive voting weight is:

[0036] For any pH value data of illuminated water body, the minimum time interval between the pH value data of illuminated water body and the light saturation pH value data segment is taken as the light saturation time distance of the pH value data of illuminated water body;

[0037] Get the ideal fluctuation value of pH value data of illuminated water body;

[0038] According to the comparison of ideal fluctuation value, illumination saturation time distance and illumination pH normality, the adaptive voting weight of the illumination water pH value data is obtained; the comparison of ideal fluctuation value is positively correlated with the adaptive voting weight, negatively correlated with the illumination saturation time distance, and negatively correlated with the illumination pH normality.

[0039] Preferably, the method for obtaining the actual abnormal water pH value data is:

[0040] The adaptive voting weight of each illuminated water pH value data is taken as the number of data; according to the number of data, the KNN algorithm is used to classify all illuminated water pH value data to obtain several categories of illuminated water pH value data; the cumulative sum of the adaptive voting weights of all illuminated water pH value data in each category is taken as the true abnormality degree of each category; among all categories, the category with the largest true abnormality degree is recorded as the water body true abnormal category, and each illuminated water pH value data under the water body true abnormal category is recorded as the actual abnormal water pH value data.

[0041] The present invention also proposes a rapid detection and early warning system for excessive water bodies based on a multidimensional sensor, comprising a memory and a processor, wherein the processor executes a computer program stored in the memory to implement the steps of the above-mentioned rapid detection and early warning method for excessive water bodies based on a multidimensional sensor.

[0042] The beneficial effects of the technical solution of the present invention are as follows: by comparing the overall change amplitude trends of pH value data of water bodies without illumination and pH value data of water bodies with illumination, the normal degree of illumination pH is obtained; wherein the normal degree of illumination pH is used to describe the degree to which the pH value data of water bodies with illumination approach the water body index corresponding to the actual normal change of pH value, thereby improving the accuracy of identifying data anomalies; analyzing the difference in the smooth change of the constructed data segment and the overall data between the pH value data of water bodies with different illumination, the illumination saturated pH value data segment is obtained; wherein the illumination saturated pH value data segment is used to describe the corresponding pH data segment when the photosynthesis of water microorganisms is in a saturated state, which is more conducive to analyzing the smooth change of pH data before and after the photosynthetic state is saturated ; The distance distribution between the illuminated water pH value data and the illuminated saturated pH value data segment is combined with the normal degree of illuminated pH, and the weight is adaptively adjusted to obtain an adaptive voting weight; wherein the adaptive voting weight is used to describe the degree to which the illuminated water pH value data belongs to real abnormal data, thereby reducing the interference of light on the pH index in the water body; the present invention obtains an adaptive voting weight combined with the influence of light by analyzing the influence of different lighting conditions on the real water pH value data, and adds adaptive weights on the basis of the original traditional KNN algorithm to compare the number of data, thereby improving the accuracy of the classification results, reducing the possibility of real abnormal pH data being misclassified as other categories of data, and improving the accuracy of the detection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, 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 invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 The present invention is a flowchart of the steps of the method for rapid detection and early warning of excessive water bodies based on a multi-dimensional sensor. DETAILED DESCRIPTION

[0045] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the method and system for rapid detection and early warning of excessive water bodies based on multidimensional sensors proposed by the present invention, its specific implementation method, structure, characteristics and effects, in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0046] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0047] The specific scheme of the method and system for rapid detection and early warning of excessive water bodies based on a multi-dimensional sensor provided by the present invention is described in detail below with reference to the accompanying drawings.

[0048] See also Figure 1 , which shows a flowchart of a method for rapid detection and early warning of excessive water bodies based on a multi-dimensional sensor provided by an embodiment of the present invention, the method comprising the following steps:

[0049] Step S001: acquiring a number of pH value data of non-illuminated water bodies, pH value data of illuminated water bodies, and light intensity data according to a preset frequency.

[0050] Existing technologies usually use the KNN (K-Nearest Neighbor) K nearest neighbor algorithm to classify and detect water pH value data; however, some microorganisms in the water body adjust their own metabolic processes to different degrees under the influence of light, and have different effects on the pH value in the water body, resulting in certain errors in the real pH value data in the water body; the traditional KNN algorithm only determines the voting score by comparing the number of data, and does not take into account the impact of the process of some microorganisms in the water body adjusting their metabolism under the influence of light on the real data of the water body pH value, resulting in the classification result cannot classify the real abnormal pH data well, thereby affecting the accuracy of the detection results of excessive water bodies.

[0051] Specifically, we first need to obtain the pH value data of water bodies without light, the pH value data of water bodies with light, and the light intensity data. The specific process is: under no light conditions, use a multidimensional sensor to detect the pH value in the water once every 5 seconds, and obtain the pH value data of water bodies without light, for a total of one day; under light conditions, use a multidimensional sensor to detect the pH value and light intensity in the water once every 5 seconds, and obtain the pH value data and light intensity data of water bodies with light, for a total of one day.

[0052] It should be noted that the detection time and the total detection duration in this embodiment can be determined according to the specific implementation situation.

[0053] So far, some pH value data of non-illuminated water bodies, pH value data of illuminated water bodies and light intensity data have been obtained through the above method.

[0054] Step S002: Compare the overall change trend of pH value data of non-illuminated water body with that of illuminated water body, and combine with the ideal change trend of light intensity data to obtain the normal degree of illumination pH of each illuminated water body pH value data.

[0055] It should be noted that in a lightless environment, some microorganisms in the water body cannot photosynthesize to produce related metabolites to affect the pH index in the water body because they do not receive light, and the pH index in the water body in a lightless environment can better reflect the change trend of the real pH index than the pH index in the water body in a light environment. Under normal circumstances, the pH index in the water body is similar to the change characteristics of the light intensity, both of which are in a state of continuous change, and will not produce a large degree of abnormal mutation at a certain detection moment; at the same time, because there is a mutual influence between the light intensity and the water body pH index in the light environment, the change difference of the water body pH index between the lightless environment and the light environment can be compared, combined with the light intensity, to obtain the normal degree of light pH of the light water body pH value data in the light environment, so that more accurate and real data can be determined in the light environment through the KNN algorithm in the future.

[0056] Specifically, preset a data quantity , wherein this embodiment is based on This example is described as an example, and this embodiment is not specifically limited. It can be determined according to the specific implementation situation; taking any pH value data of a lightless water body as an example, the pH value data of the lightless water body is pH value data of water without light and the following The data segment composed of the pH value data of the light-free water body is recorded as the light-free reference pH value data segment of the pH value data of the light-free water body.

[0057] It is especially noted that if the actual number of data before and after the pH value of the water body without light does not meet the preset , then the actual number of data before and after the pH value data of the light-free water body is used as the basis to obtain the light-free reference pH value data segment.

[0058] Furthermore, by comparing the change difference between the local adjacent data and the overall adjacent data in the lightless reference water body pH value data segment of the lightless water body pH value data, the normality of the indicator of the lightless water body pH value data is obtained; wherein the specific process of obtaining the normality of the indicator is: obtaining the local comparison and overall difference of the lightless water body pH value data; according to the local comparison and overall difference, obtaining the normality of the indicator of the lightless water body pH value data. The normality of the indicator is negatively correlated with the local comparison and overall difference. As an example, the normality of the indicator of the lightless water body pH value data can be calculated by the following formula:

[0059]

[0060] In the formula, Indicates the normality of the pH value data of the light-free water body; Indicates the number of all lightless water body pH value data in the lightless reference pH value data segment of the lightless water body pH value data; In the pH value data segment of the light-free water body, the first The pH value data of the light-free water body are compared with the The absolute value of the difference between the pH value data of the light-free water body; Indicates taking the absolute value; represents an exponential function with a natural constant as the base, and the embodiment adopts Model to present inverse proportional relationship and normalization processing, As the input of the model, the implementer can choose the inverse proportional function and the normalization function according to the actual situation.

[0061] It should be noted that It represents the local difference of adjacent data in the local range around the pH value data of the light-free water body; It represents the overall average difference between adjacent data in the overall range of pH value data of non-light water body; It represents the overall difference of the local comparison of the pH value data of the lightless water body; if the normality of the indicator of the pH value data of the lightless water body is greater, it means that the data change trends on both sides of the pH value data of the lightless water body are closer, reflecting that the pH value data of the lightless water body is more consistent with the data under normal conditions.

[0062] Further, the normality of the indicator of each pH value data of the light-free water body is obtained; a normality threshold of the indicator is preset , wherein this embodiment is based on This example is described as an example, and this embodiment is not specifically limited. It can be determined according to the specific implementation situation; the normal level of the indicator is greater than The pH value data of the light-free water body is recorded as the normal pH value data of the light-free water body; the least squares method is used to fit all the normal pH value data of the light-free water body to obtain a fitting curve, which is recorded as the ideal pH curve of the light-free water body. The least squares method is a well-known technology and will not be described in detail in this embodiment. Each pH value data of the light-free water body corresponds to a value in the ideal pH curve of the light-free water body.

[0063] Furthermore, referring to the method for obtaining the ideal pH curve of non-illuminated water body based on the normal pH value data of non-illuminated water body, the pH value data of non-illuminated water body is replaced with the light intensity data to obtain the ideal pH curve of non-illuminated water body based on the light intensity data, and recorded as the ideal water body illumination curve.

[0064] Furthermore, by combining The trend changes between the pH value data of the illuminated water body and the ideal pH curve of the water body without light and the ideal light curve of the water body are similar, and the first The normality of the pH value of the water body under illumination; the specific process of obtaining the normality of the pH value of the water body under illumination is as follows: The Euclidean distance between the light intensity data and the corresponding value on the ideal water body light curve; Get the The pH difference between the pH value data of the light-free water body and the corresponding value on the ideal pH curve of the light-free water body; according to the Euclidean distance and the pH difference, the The normality of the illumination pH of the illumination water body pH value data. The normality of the illumination pH is negatively correlated with the Euclidean distance and negatively correlated with the pH difference; as an example, the following formula can be used to calculate the normality of the illumination pH of the illumination water body pH value data. Normality of light pH for each light water pH data:

[0065]

[0066] In the formula, Indicates Normality of light pH value of each light water body pH value data; Indicates The pH value data of the light-free water body in the ideal pH curve of the light-free water body are different from the pH value data of the light-free water body in the ideal pH curve of the light-free water body. The absolute value of the difference between the pH value data of the illuminated water bodies; Indicates The value of the light intensity data in the ideal water body light curve is different from the The Euclidean distance between the pH value data of the illuminated water bodies is obtained. The normality of the illumination pH of all the pH value data of the illuminated water bodies is obtained. The acquisition of the Euclidean distance is a well-known technology and will not be repeated in this embodiment.

[0067] It should be noted that if The greater the normality of the pH value data of the water body under illumination, the The pH value data of the light water body and the The trend distribution of pH value data of no light water, and the The pH value data of the light water body and the The trend distribution between the pH value data of the water body without light is closer, which reflects the influence of light on the pH value of the water body. At the time of the detection of the pH value data of the illuminated water body, the smaller the influence of the illumination conditions on the metabolic process of microorganisms, the smaller the interference to the pH value index of the water body, which further reflects the The pH value data of a water body with a certain amount of illumination is closer to the water body index corresponding to the actual normal pH value.

[0068] So far, the normal degree of illumination pH of all illuminated water pH value data is obtained through the above method.

[0069] Step S003: any two illuminated water body pH value data are combined in pairs to obtain a number of initial photosynthetic water body pH data combinations; based on the local differences in the data on both sides of the initial photosynthetic water body pH data combination time series, the difference in the smooth change of the constructed data segments and the overall data between the different illuminated water body pH value data is analyzed, and combined with the normal degree of illumination pH, the illumination saturated pH value data segment is divided from all the illuminated water body pH value data; according to the distance distribution between the illuminated water body pH value data and the illumination saturated pH value data segment and the normal degree of illumination pH, the weight is adaptively adjusted to obtain the adaptive voting weight of each illuminated water body pH value data.

[0070] It should be noted that in the actual lighting environment, although some microorganisms in the water body can continuously perform photosynthesis, the corresponding metabolic process itself also has a conversion limit; with the increase of light intensity, the photosynthetic conversion process corresponding to the microorganisms that can perform photosynthesis in the water body continues to approach the limit, so that the photosynthesis of the corresponding microorganisms approaches the saturation state, and the pH index in the water body is in a dynamic equilibrium state as a whole. In this case, the light intensity at this time continues to increase and cannot affect the pH index in the water body to a large extent, indicating that the light intensity corresponding to this dynamic equilibrium state has relatively weak interference with the pH index. The process of photosynthesis of microorganisms in the water body with the light intensity is a state of continuous and asymptotic change, so the impact on the pH index in the water body is also a continuous and asymptotic change; the closer the photosynthesis is to the light saturation state, the lower the correlation between the numerical changes of light intensity and pH index, the weaker the impact on the pH index in the water body, and the higher the authenticity of the pH index in the corresponding stage. Therefore, based on the normal degree of light pH, the stability of changes in local data and overall data contained in the pH value data of water bodies with different light exposures can be analyzed to obtain the pH saturation of photosynthetic water bodies, thereby determining the light-saturated pH value data segment that represents the saturation of photosynthesis; then, based on the normal degree of light pH, the time distance distribution between the pH value data of water bodies with different light exposures and the light-saturated pH value data segment can be analyzed to obtain the adaptive voting weight of each light water body pH value data.

[0071] Specifically, preset a number of pH data of illuminated water bodies , wherein this embodiment is based on This example is described as an example, and this embodiment is not specifically limited. It can be determined according to the specific implementation situation; taking any pH value data of the illuminated water body as an example, the pH value data of the illuminated water body is The data segment consisting of the pH value data of the illuminated water body is recorded as the preceding pH data segment of the illuminated water body pH value data; The data segment composed of the illuminated water body pH value data is recorded as the post-illuminated water body pH data segment of the illuminated water body pH value data.

[0072] It is particularly noted that if the number of pH value data of the illuminated water body before and after the pH value data of the illuminated water body does not meet the preset , then based on the number of illuminated water body pH value data actually existing before and after the illuminated water body pH value data, the preceding illuminated water body pH data segment and the following illuminated water body pH data segment of the illuminated water body pH value data are obtained; wherein if the illuminated water body pH value data is the first illuminated water body pH value data, then this embodiment defaults that the preceding illuminated water body pH data segment of the illuminated water body pH value data is itself; wherein if the illuminated water body pH value data is the last illuminated water body pH value data, then this embodiment defaults that the following illuminated water body pH data segment of the illuminated water body pH value data is itself.

[0073] Further, the data combination formed by any two illuminated water body pH value data is recorded as the initial photosynthetic water body pH data combination; taking any initial photosynthetic water body pH data combination as an example, in the initial photosynthetic water body pH data combination, the first illuminated water body pH value data is recorded as the initial photosynthetic starting pH value data; and the second illuminated water body pH value data is recorded as the initial photosynthetic ending pH value data. In the initial photosynthetic water body pH data combination, by analyzing the difference in data distribution trends outside the data segment constructed between the initial photosynthetic starting pH value data and the initial photosynthetic ending pH value data, the neighborhood data stability factor of the initial photosynthetic water body pH data combination is obtained; wherein the specific process of obtaining the domain data stability factor is: obtaining the left and right local contrast difference of the initial photosynthetic water body pH data combination; according to the left and right local contrast difference, obtaining the neighborhood data stability factor of the initial photosynthetic starting pH value data. The neighborhood data stability factor is negatively correlated with the left and right local contrast difference; as an example, the neighborhood data stability factor of the initial photosynthetic water body pH data combination can be calculated by the following formula:

[0074]

[0075] In the formula, represents the neighborhood data stability factor of the initial photosynthetic water pH data combination; Indicates the number of all illuminated water body pH value data in the preceding illuminated water body pH data segment of the initial photosynthetic starting pH value data; Indicates the pH value of the initial photosynthetic starting pH value in the preceding illumination water body pH data segment. The absolute value of the difference between the pH value data of the water body at the illumination and the initial photosynthetic starting pH value data; Indicates the number of all illuminated water body pH value data in the post-illuminated water body pH data segment of the initial photosynthetic termination pH value data; Indicates the first post-illumination water pH data segment of the initial photosynthetic termination pH data. The absolute value of the difference between the pH value data of the water body at illumination and the pH value data at the end of photosynthesis; Indicates taking the absolute value.

[0076] It should be noted that It represents the local contrast difference on the left side of the initial photosynthetic starting pH value data; It represents the right local contrast difference of the initial photosynthetic termination pH value data; It indicates the difference between the left and right local contrasts of the initial photosynthetic water pH data combination. If the neighborhood data stability factor of the initial photosynthetic water pH data combination is larger, it means that the data fluctuations on both sides of the data segment between the two illuminated water pH data in the initial photosynthetic water pH data combination are more similar, reflecting that the two illuminated water pH data in the initial photosynthetic water pH data combination are more likely to belong to the pH index of the same photosynthetic stage.

[0077] Furthermore, within the initial photosynthetic water pH data combination, based on the normality of illumination pH, the stability of the overall variation range of illumination water pH data between the initial photosynthetic starting pH data and the initial photosynthetic ending pH data is analyzed, and combined with the neighborhood data stability factor of the initial photosynthetic water pH data combination, the photosynthetic water pH saturation of the initial photosynthetic water pH data combination is obtained; wherein the specific process of obtaining the photosynthetic water pH saturation is: obtaining the local overall photosynthetic contrast difference mean of the initial photosynthetic water pH data combination; obtaining the illumination pH normality mean of the initial photosynthetic water pH data combination; and obtaining the photosynthetic water pH saturation of the initial photosynthetic water pH data combination according to the neighborhood data stability factor, the local overall photosynthetic contrast difference mean and the illumination pH normality mean. The photosynthetic water pH saturation is negatively correlated with the local overall photosynthetic contrast difference mean, positively correlated with the neighborhood data stability factor, and positively correlated with the light pH normality mean; as an example, the photosynthetic water pH saturation of the initial photosynthetic water pH data combination can be calculated by the following formula:

[0078]

[0079] In the formula, Indicates the photosynthetic water pH saturation of the initial photosynthetic water pH data combination; Indicates the number of all illuminated water pH value data between the corresponding detection moments of the initial photosynthetic starting pH value data and the initial photosynthetic ending pH value data; Indicates the pH value between the initial photosynthetic start pH value data and the initial photosynthetic end pH value data. pH value data of light water bodies; It represents the average value of all the pH value data of the illuminated water body between the corresponding detection moments of the initial photosynthetic starting pH value data and the initial photosynthetic ending pH value data; Indicates the average of the normal light pH values ​​of the initial photosynthetic starting pH value data and the initial photosynthetic ending pH value data; represents the neighborhood data stability factor of the initial photosynthetic water pH data combination; Indicates the preset hyperparameters. In this embodiment, , used to prevent the denominator from being 0; Indicates taking the absolute value.

[0080] It should be noted that It represents the mean value of the local-to-overall photosynthetic contrast difference of the initial photosynthetic water pH data combination. If the photosynthetic water pH saturation of the initial photosynthetic water pH data combination is greater, it means that in the data segment formed between the two illuminated water pH value data in the initial photosynthetic water pH data combination, the influence of the continuous change of light intensity on the corresponding pH value in the data segment is lower, which is reflected in that the data segment formed between the two illuminated water pH value data in the initial photosynthetic water pH data combination can better represent that the photosynthesis of microorganisms is closer to the saturation state, and the interference of water pH index under the influence of light is lower.

[0081] Furthermore, the photosynthetic water pH saturation of all initial photosynthetic water pH data combinations is obtained; in the initial photosynthetic water pH data combination with the largest photosynthetic water pH saturation, the initial photosynthetic starting pH value data is recorded as photosynthetic starting pH value data, and the initial photosynthetic ending pH value data is recorded as photosynthetic ending pH value data; a data segment composed of all illuminated water pH value data between the corresponding detection moments of the photosynthetic starting pH value data and the photosynthetic ending pH value data is recorded as an illuminated saturated pH value data segment. Among all the illuminated water body pH value data, take any illuminated water body pH value data as an example, if the illuminated water body pH value data is outside the illuminated saturated pH value data segment, the minimum time interval from the illuminated water body pH value data to the illuminated saturated pH value data segment will be used as the illuminated saturated time distance of the illuminated water body pH value data; if the illuminated water body pH value data is within the illuminated saturated pH value data segment, this embodiment defaults the illuminated saturated time distance of the illuminated water body pH value data to 1; obtain the illuminated saturated time distance of all illuminated water body pH value data. Perform least squares fitting on all illuminated water body pH value data to obtain a fitting curve, and record it as the illuminated water body pH ideal curve. Each illuminated water body pH value data corresponds to a value in the illuminated water body pH ideal curve.

[0082] Furthermore, combined with The illumination saturation time distance of the pH value data of the illuminated water body, as well as the difference between the values ​​in the ideal pH curve of the illuminated water body, are obtained. The specific process of obtaining the adaptive voting weight is: obtaining the comparative ideal fluctuation value of the pH value data of the illuminated water body; according to the comparative ideal fluctuation value, the illumination saturation time distance and the normal degree of illumination pH, the adaptive voting weight of the pH value data of the illuminated water body is obtained. The comparative ideal fluctuation value is positively correlated with the adaptive voting weight, negatively correlated with the illumination saturation time distance, and negatively correlated with the normal degree of illumination pH. As an example, the following formula can be used to calculate the first Adaptive voting weights for pH data of illuminated water bodies:

[0083]

[0084] In the formula, Indicates Adaptive voting weights for pH value data of light-irradiated water bodies; Indicates The value of the pH value of the first illumination water body in the ideal pH curve of the illumination water body is The absolute value of the difference between the pH value data of the illuminated water bodies; Indicates Normality of light pH value of each light water body pH value data; Indicates The illumination saturation time distance of the pH value data of the illuminated water body is obtained. The adaptive voting weight of the pH value data of all illuminated water bodies is obtained.

[0085] It should be noted that Indicates The ideal fluctuation value of the pH value data of the light water body; if the The larger the adaptive voting weight of the pH value data of the light water body, the The closer the pH value data of the first illumination water body is to the light saturation time range, the more abnormal the pH index change in the water body is, and the weaker the interference by light is, which reflects the first illumination water body pH value data of ... The more light the water pH data is exposed to, the more likely it is to be a true abnormal pH indicator.

[0086] So far, the adaptive voting weights of pH value data of all illuminated water bodies are obtained through the above method.

[0087] Step S004: Classify the data according to the adaptive voting weights to obtain some actual abnormal water pH value data and issue an early warning.

[0088] Specifically, the adaptive voting weight of each illuminated water body pH value data is used as the number of data; according to the number of data, the KNN algorithm is used to classify all illuminated water body pH value data to obtain several categories of illuminated water body pH value data. The sum of the adaptive voting weights of all illuminated water body pH value data under each category is used as the true abnormality degree of each category; among all categories, the category with the largest true abnormality degree is recorded as the water body true abnormality category, and each illuminated water body pH value data under the water body true abnormality category is recorded as the actual abnormal water body pH value data; and each actual abnormal water body pH value data is displayed on a digital display screen for early warning. The process of classifying all data according to the number of data is a well-known content of the KNN (K-Nearest Neighbor) K nearest neighbor algorithm, which will not be repeated in this embodiment.

[0089] Through the above steps, rapid detection and early warning of water bodies exceeding the standard based on multi-dimensional sensors can be completed.

[0090] Another embodiment of the present invention provides a rapid detection and early warning system for excessive water bodies based on a multi-dimensional sensor. The system includes a memory and a processor. When the processor executes the computer program stored in the memory, the above method steps S001 to S004 are executed.

[0091] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A rapid detection and early warning method for excessive water bodies based on multi-dimensional sensors, characterized in that: The method comprises the following steps: Acquire a number of pH value data of non-illuminated water bodies, pH value data of illuminated water bodies, and light intensity data according to a preset frequency; Compare the overall change trend of pH value data of non-illuminated water body and pH value data of illuminated water body, and combine with the ideal change trend of light intensity data to obtain the normal degree of light pH value data of each illuminated water body; Combining any two pH value data of illuminated water bodies in pairs, several initial photosynthetic water body pH data combinations are obtained; based on the local differences of the data on both sides of the initial photosynthetic water body pH data combination time series, the difference in the smooth change of the constructed data segments and the overall data between the pH value data of different illuminated water bodies is analyzed, and combined with the normal degree of illumination pH, the light saturated pH value data segment is divided from all the illuminated water body pH value data; according to the distance distribution between the illuminated water body pH value data and the light saturated pH value data segment and the normal degree of illumination pH, the weight is adaptively adjusted to obtain the adaptive voting weight of each illuminated water body pH value data; Data is classified according to the adaptive voting weights to obtain some actual abnormal water pH value data and issue early warnings; The method for obtaining the normal pH level under illumination is as follows: Obtain the normality of the pH value data of each light-free water body; According to the normality of the indicators, a number of normal pH value data of light-free water bodies are selected from all pH value data of light-free water bodies, and the ideal pH curve of light-free water bodies is obtained by fitting; all light intensity data are fitted to obtain the ideal light curve of water bodies; Get the The Euclidean distance between the light intensity data and the corresponding value on the ideal water body light curve; Get the The pH difference between the pH value data of the light-free water body and the corresponding value on the ideal pH curve of the light-free water body; According to the Euclidean distance and pH difference, we get The illumination pH normality of the illumination water body pH value data; the illumination pH normality is negatively correlated with the Euclidean distance and negatively correlated with the pH difference; The method for obtaining the normality of the indicator is: For any pH value data of a lightless water body, obtain the local comparison and overall difference of the pH value data of the lightless water body; According to the local comparison and overall difference, the normality of the indicator of the pH value data of the light-free water body is obtained; the normality of the indicator is negatively correlated with the local comparison and overall difference; The method for obtaining the light saturated pH value data segment is: Obtain the local difference of adjacent data within the local range around the pH value data of the light-free water body; Obtain the overall average difference of adjacent data in the overall range of pH value data of light-free water bodies; The difference between the local difference and the overall average difference is used as the local-to-overall difference of the pH value data of the light-free water body; The method for obtaining the adaptive voting weight is: For any pH value data of illuminated water body, the minimum time interval between the pH value data of illuminated water body and the light saturation pH value data segment is taken as the light saturation time distance of the pH value data of illuminated water body; Get the ideal fluctuation value of pH value data of illuminated water body; According to the comparison of ideal fluctuation value, illumination saturation time distance and illumination pH normality, an adaptive voting weight of the illumination water pH value data is obtained; the comparison of ideal fluctuation value is positively correlated with the adaptive voting weight, negatively correlated with the illumination saturation time distance, and negatively correlated with the illumination pH normality; The calculation formula for adaptive voting weight is: In the formula, Indicates Adaptive voting weights for pH value data of light-irradiated water bodies; Indicates The value of the pH value of the first illumination water body in the ideal pH curve of the illumination water body is The absolute value of the difference between the pH value data of the illuminated water bodies; Indicates Normality of light pH value of each light water body pH value data; Indicates The illumination saturation time distance of each illumination water pH value data; Indicates Comparison of ideal fluctuation values ​​of pH value data of illuminated water bodies.

2. According to claim 1, the method for rapid detection and early warning of excessive water bodies based on multidimensional sensors is characterized in that: The method for obtaining the light saturated pH value data segment is: For any initial photosynthetic water pH data combination, in the initial photosynthetic water pH data combination, the first illuminated water pH value data is recorded as the initial photosynthetic starting pH value data; The pH value data of the second illuminated water body is recorded as the initial photosynthetic termination pH value data; Obtain the neighborhood data stability factor for each initial photosynthetic water pH data combination; According to the neighborhood data stability factor, the photosynthetic water pH saturation of each initial photosynthetic water pH data combination is obtained; In the initial photosynthetic water body pH data combination with the maximum photosynthetic water body pH saturation, the data segment consisting of all the illuminated water body pH value data between the corresponding moments of the initial photosynthetic starting pH value data and the photosynthetic ending pH value data is recorded as the illumination saturated pH value data segment; The method for obtaining the neighborhood data stability factor is: For any initial photosynthetic water pH data combination, obtain the left and right local contrast difference of the initial photosynthetic water pH data combination; According to the left and right local contrast difference, a neighborhood data stabilization factor of the initial photosynthetic starting pH value data is obtained; the neighborhood data stabilization factor is negatively correlated with the left and right local contrast difference; The calculation formula of the neighborhood data stability factor of the initial photosynthetic water pH data combination is: In the formula, represents the neighborhood data stability factor of the initial photosynthetic water pH data combination; Represents the number of all illuminated water pH value data in the preceding illuminated water pH data segment of the initial photosynthetic starting pH value data; The first pH value of the initial photosynthetic starting pH value in the pre-illumination water pH data segment The absolute value of the difference between the pH value data of the water body at the illumination and the initial photosynthetic starting pH value data; Represents the number of all illuminated water pH value data in the post-illuminated water pH data segment of the initial photosynthetic termination pH value data; The first post-illumination water pH data segment represents the initial photosynthetic termination pH data. The absolute value of the difference between the pH value data of the water body at illumination and the pH value data at the end of photosynthesis; Indicates taking the absolute value; It represents the local contrast difference on the left side of the initial photosynthetic starting pH value data; It represents the right local contrast difference of the initial photosynthetic termination pH data; Represents the left and right local contrast difference of the initial photosynthetic water pH data combination.

3. The method for rapid detection and early warning of excessive water bodies based on multidimensional sensors according to claim 2 is characterized in that: The method for obtaining the pH saturation of the photosynthetic water body is: For any initial photosynthetic water pH data combination, obtain the local and overall photosynthetic contrast difference mean of the initial photosynthetic water pH data combination; obtain the light pH normality mean of the initial photosynthetic water pH data combination; According to the neighborhood data stability factor, the local overall photosynthetic contrast difference mean and the light pH normality mean, the photosynthetic water pH saturation of the initial photosynthetic water pH data combination is obtained; the photosynthetic water pH saturation is negatively correlated with the local overall photosynthetic contrast difference mean, positively correlated with the neighborhood data stability factor, and positively correlated with the light pH normality mean.

4. The method for rapid detection and early warning of excessive water bodies based on multidimensional sensors according to claim 1 is characterized in that: The method for obtaining the actual abnormal water pH value data is: The adaptive voting weight of each illuminated water pH value data is taken as the number of data; according to the number of data, the KNN algorithm is used to classify all illuminated water pH value data to obtain several categories of illuminated water pH value data; the cumulative sum of the adaptive voting weights of all illuminated water pH value data in each category is taken as the true abnormality degree of each category; Among all categories, the category with the largest degree of true anomaly is recorded as the true abnormal water body category, and each illumination water body pH value data under the true abnormal water body category is recorded as the actual abnormal water body pH value data.

5. A rapid detection and early warning system for excessive water bodies based on multi-dimensional sensors, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the computer program is executed by the processor, the steps of the method for rapid detection and early warning of excessive water bodies based on a multidimensional sensor as described in any one of claims 1 to 4 are implemented.

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