Data processing method, device, computer equipment and storage medium

Abstract

The present application relates to a data processing method, device, computer equipment and storage medium. The method comprises: obtaining a water area data set, and constructing a characteristic sequence and a reference sequence based on the water area data set; the characteristic sequence contains water area data of each water quality influencing factor index in the water area data set; the reference sequence contains reference data of each water quality influencing factor index in the water area data set; according to the grey correlation algorithm, the correlation between the water area data of each water quality influencing factor and the reference data between the characteristic sequence and the reference sequence is calculated; according to the correlation corresponding to each water quality influencing factor index, the target water quality influencing factor index is determined in each water quality influencing factor index, and based on the target water quality influencing factor index, the water quality health impact result is determined in the correlation relationship library. The use of this method improves the accuracy of the water quality health impact result.

Description

Technical Field

[0001] The present application relates to the technical field of water environment assessment, and in particular to a data processing method, apparatus, computer equipment, storage medium and computer program product. Background Art

[0002] Water resources are closely related to people's daily lives. They not only provide the necessities of people's production and life, but large-scale water pollution problems may also cause various types of diseases. Therefore, water quality always affects people's physical health.

[0003] In traditional water quality health analysis methods, a preset simulation method is usually used to simulate the distribution of pollutants in the water to determine the water quality assessment results. Then, based on the water quality assessment results, the impact of the current water quality on residents' health is artificially analyzed.

[0004] However, in traditional water quality health analysis methods, water quality assessment results can only be determined after a pollution incident occurs, and the impact of water quality assessment results on residents' health is determined by manual analysis of the water quality assessment results. Not only is the determination of water quality assessment results delayed, but manual analysis is also highly subjective, resulting in low accuracy in the impact on residents' health. Summary of the invention

[0005] Based on this, it is necessary to provide a data processing method, apparatus, computer device, computer-readable storage medium and computer program product to address the above technical issues.

[0006] In a first aspect, the present application provides a data processing method, comprising:

[0007] Acquire a water area data set, and construct a feature sequence and a reference sequence based on the water area data set; the feature sequence contains water area data of each water quality influencing factor index in the water area data set; the reference sequence contains reference data of each water quality influencing factor index in the water area data set;

[0008] According to the grey correlation algorithm, the correlation between the water area data and the reference data of each water quality influencing factor between the characteristic sequence and the reference sequence is calculated;

[0009] According to the correlation degree corresponding to each of the water quality influencing factor indicators, a target water quality influencing factor indicator is determined among the water quality influencing factor indicators, and based on the target water quality influencing factor indicator, a water quality health impact result is determined in the association relationship library.

[0010] By adopting this method, when conducting water quality assessment, the multidimensional water area data set is analyzed and processed, which enriches the dimensions of water quality assessment processing, and through the grey correlation algorithm, the target water quality influencing factor index is determined from the multidimensional water quality influencing factor index of the water area data set, and then the water quality health impact result is determined in the association relationship library through the target water quality influencing factor index, thereby improving the accuracy of the water quality health impact result.

[0011] In one embodiment, obtaining a water area dataset includes:

[0012] Get the original water area data;

[0013] The original water area data is subjected to data integration and missing value screening processing, and based on the water quality influencing factor indicators contained in the original water area data, the water area data corresponding to each water quality influencing factor indicator is standardized to obtain a water area data set.

[0014] In this embodiment, water area data of each target water area is directly collected for water quality monitoring and assessment to ensure the timeliness of the water area data, and the directly collected original water area data is processed to ensure the quality of the water area data and improve the accuracy of water quality assessment.

[0015] In one embodiment, constructing a characteristic sequence and a reference sequence based on the water area dataset includes:

[0016] Determine an object to be evaluated, and for each object to be evaluated, determine in the water area data set a water quality influencing factor index that matches the object to be evaluated and water area data and reference data corresponding to the water quality influencing factor index;

[0017] Constructing a feature sequence based on the water area data of each water quality influencing factor index corresponding to each object to be evaluated;

[0018] A reference sequence is constructed based on the reference data of each water quality influencing factor indicator corresponding to each object to be evaluated.

[0019] In this embodiment, based on the water area data set and the determined object to be evaluated, the reference sequence and the characteristic sequence are determined, and then, the degree of similarity between the determined reference sequence and the characteristic sequence can be used to determine whether the water area data and the reference data of each water quality influencing factor indicator are closely connected, thereby achieving the screening of target water quality influencing factor indicators in the water quality assessment process, and then, more accurately determining the assessment focus of the current water area environment, thereby improving the accuracy of the water quality assessment results.

[0020] In one embodiment, the calculation of the correlation between the water area data and the reference data of each water quality influencing factor between the feature sequence and the reference sequence according to the grey correlation algorithm includes:

[0021] For each characteristic sequence and reference sequence of the object to be evaluated, the correlation coefficient between the water area data and the reference data of each water quality influencing factor index is calculated according to the grey correlation algorithm;

[0022] The correlation coefficients of the same water quality influencing factor indicators of each of the objects to be evaluated are weightedly calculated to determine the correlation degree of the water quality influencing factor indicators; the correlation degree is used to characterize the degree of correlation between the water area data of the water quality influencing factor indicators and the reference data.

[0023] In this embodiment, the correlation coefficient between the water area data and the reference data of each water quality influencing factor index is calculated through the grey correlation algorithm. Thus, the correlation coefficient of the water quality influencing factor index can effectively reflect the changing trend of the correlation between the water area data and the reference data of each water quality influencing factor index among the objects to be evaluated, thereby achieving an accurate evaluation of the water quality conditions reflected by the water quality influencing factor index.

[0024] In one embodiment, determining a target water quality influencing factor index from among the water quality influencing factor indexes according to the correlation corresponding to the water quality influencing factor indexes includes:

[0025] According to the correlation degree corresponding to each of the water quality influencing factor indicators, the water quality influencing factor indicators are sorted to determine the sorting result corresponding to each of the water quality influencing factor indicators;

[0026] Based on the ranking results corresponding to each of the water quality influencing factor indicators and the preset screening conditions, a target water quality influencing factor indicator is determined from among the water quality influencing factor indicators.

[0027] In this embodiment, analyzing the correlation between reference data and characteristic data can help screen out target water quality influencing factor indicators, make water quality assessment decisions more accurately, and improve the accuracy of water quality health assessment results.

[0028] In one embodiment, before determining the water quality health impact result in the association relationship library based on the target water quality impact factor index, the method further includes:

[0029] Acquire a sample water area data set and a disease data set, wherein the sample water area data set includes indicators of various water quality influencing factors; and the disease data set includes various disease types;

[0030] For each of the disease types, construct a correlation between each water quality influencing factor index in the sample water area data set and the disease type;

[0031] Based on the correlation between each disease type and the water quality influencing factor index, a correlation database is constructed.

[0032] In this embodiment, by pre-building an association relationship library, water quality influencing factor indicators are associated with disease types, thereby achieving more accurate determination of water quality health assessment results. In addition, by determining the water quality health assessment results through the association relationship library, the efficiency of determining the water quality health assessment results can be improved.

[0033] In one embodiment, the determining of the water quality health impact result in the association relationship library based on the target water quality influencing factor index includes:

[0034] Based on the target water quality influencing factor index, determining the target disease type corresponding to the target water quality influencing factor index in the association relationship between each water quality influencing factor index and the disease type contained in the association relationship library;

[0035] Based on the target disease types, water quality health impact results are obtained.

[0036] In this embodiment, the target disease type is determined in the association relationship library based on the target water quality influencing factor index. The authenticity and rationality of the pre-built association relationship library are used to improve the accuracy of the determined target disease type, and based on the target disease type, the water quality health impact result is obtained, so that the water quality health impact result can be quickly analyzed and decided in specific application scenarios, thereby improving the efficiency of determining the water quality health impact result.

[0037] In a second aspect, the present application further provides a data processing device, comprising:

[0038] The first acquisition module is used to acquire a water area data set, and construct a feature sequence and a reference sequence based on the water area data set; the feature sequence contains water area data of each water quality influencing factor index in the water area data set; the reference sequence contains reference data of each water quality influencing factor index in the water area data set;

[0039] A calculation module, used for calculating the correlation between the water area data and the reference data of each water quality influencing factor between the characteristic sequence and the reference sequence according to a grey correlation algorithm;

[0040] The determination module is used to determine the target water quality influencing factor index among the water quality influencing factor indexes according to the correlation degree corresponding to each of the water quality influencing factor indexes, and determine the water quality health impact result in the association relationship library based on the target water quality influencing factor index.

[0041] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0042] Acquire a water area data set, and construct a feature sequence and a reference sequence based on the water area data set; the feature sequence contains water area data of each water quality influencing factor index in the water area data set; the reference sequence contains reference data of each water quality influencing factor index in the water area data set;

[0043] According to the grey correlation algorithm, the correlation between the water area data and the reference data of each water quality influencing factor between the characteristic sequence and the reference sequence is calculated;

[0044] According to the correlation degree corresponding to each of the water quality influencing factor indicators, a target water quality influencing factor indicator is determined among the water quality influencing factor indicators, and based on the target water quality influencing factor indicator, a water quality health impact result is determined in the association relationship library.

[0045] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0046] Acquire a water area data set, and construct a feature sequence and a reference sequence based on the water area data set; the feature sequence contains water area data of each water quality influencing factor index in the water area data set; the reference sequence contains reference data of each water quality influencing factor index in the water area data set;

[0047] According to the grey correlation algorithm, the correlation between the water area data and the reference data of each water quality influencing factor between the characteristic sequence and the reference sequence is calculated;

[0048] According to the correlation degree corresponding to each of the water quality influencing factor indicators, a target water quality influencing factor indicator is determined among the water quality influencing factor indicators, and based on the target water quality influencing factor indicator, a water quality health impact result is determined in the association relationship library.

[0049] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0050] Acquire a water area data set, and construct a feature sequence and a reference sequence based on the water area data set; the feature sequence contains water area data of each water quality influencing factor index in the water area data set; the reference sequence contains reference data of each water quality influencing factor index in the water area data set;

[0051] According to the grey correlation algorithm, the correlation between the water area data and the reference data of each water quality influencing factor between the characteristic sequence and the reference sequence is calculated;

[0052] According to the correlation degree corresponding to each of the water quality influencing factor indicators, a target water quality influencing factor indicator is determined among the water quality influencing factor indicators, and based on the target water quality influencing factor indicator, a water quality health impact result is determined in the association relationship library.

[0053] The above data processing method, device, computer equipment and storage medium obtain a water area data set, and construct a feature sequence and a reference sequence based on the water area data set; the feature sequence contains the water area data of each water quality influencing factor index in the water area data set; the reference sequence contains the reference data of each water quality influencing factor index in the water area data set; according to the grey correlation algorithm, the correlation between the water area data and the reference data of each water quality influencing factor between the feature sequence and the reference sequence is calculated; according to the correlation corresponding to each water quality influencing factor index, the target water quality influencing factor index is determined in each water quality influencing factor index, and the water quality health impact result is determined in the correlation relationship library based on the target water quality influencing factor index. By adopting this method, when conducting water quality assessment, the multidimensional water area data set is analyzed and processed, which enriches the dimension of water quality assessment processing, and the target water quality influencing factor index is determined in the multidimensional water quality influencing factor index of the water area data set through the grey correlation algorithm, and then the water quality health impact result is determined in the correlation relationship library through the target water quality influencing factor index, which improves the accuracy of the water quality health impact result. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0055] Figure 1 is a flow chart of a data processing method in one embodiment;

[0056] Figure 2 A schematic diagram of a flow chart of steps for obtaining a water area dataset in one embodiment;

[0057] Figure 3 A process architecture diagram for obtaining a water area data set in an embodiment;

[0058] Figure 4 A schematic diagram of a flow chart of steps for constructing a characteristic sequence and a reference sequence in one embodiment;

[0059] Figure 5 A schematic flow chart of a step of determining the correlation of water quality influencing factor indicators in one embodiment;

[0060] Figure 6 A line graph showing the correlation coefficients of various water quality influencing factor indicators of the target area A in each quarter in an embodiment;

[0061] Figure 7 A heat map of the correlation between various water quality influencing factor indicators of an object to be evaluated in one embodiment;

[0062] Figure 8 In one embodiment, a stacked bar chart is provided to reflect the differences between multiple water quality influencing factor indicators of an object to be evaluated;

[0063] Fig. 9 A line graph showing correlation coefficients of water quality influencing factor indicators of each target area in an embodiment;

[0064] Fig.10 A schematic diagram of a process for determining an index of a factor affecting target water quality in an embodiment;

[0065] Fig.11 A schematic diagram of a process for building an association relationship library in one embodiment;

[0066] Fig.12 A flowchart of the steps of determining water quality health impact results in one embodiment;

[0067] Fig.13 A flow chart of a data processing method in one embodiment;

[0068] Fig.14 is a structural block diagram of a data processing device in one embodiment;

[0069] Fig.15 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0071] In one embodiment, Figure 1 As shown, a data processing method is provided. This embodiment takes the method applied to a terminal as an example. It can be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0072] Step 102, obtaining a water area data set, and constructing a feature sequence and a reference sequence based on the water area data set.

[0073] Among them, the characteristic sequence contains the water area data of each water quality influencing factor index in the water area data set, and the reference sequence contains the reference data of each water quality influencing factor index in the water area data set.

[0074] In implementation, when conducting water quality health analysis, the terminal obtains the water area data set to be analyzed, determines the object to be evaluated, and constructs a feature sequence and a reference sequence based on the water area data set and the determined object to be evaluated. Specifically, the water area data set contains water area data and reference data of each water quality influencing factor indicator, wherein the water area data of the water quality influencing factor indicator is the actual data of the current water area (real water area data), and the reference data of the water quality influencing factor indicator is the pollution threshold of the water quality influencing factor indicator set based on industry standards, that is, if the water area data of the water quality influencing factor indicator is higher than the corresponding reference data, then the water area data of the water quality influencing factor indicator of the current water area is abnormal data indicating pollution. Therefore, for the water area data set to be analyzed, the terminal predetermines the object to be evaluated, and then processes the water area data set based on the determined object to be evaluated. For example, the object to be evaluated is the target water area of ​​each quarter. Therefore, the terminal divides the water area data set according to quarters, and divides the water area data set into 4 groups. Each group corresponds to the water area data and reference data of each water quality influencing factor indicator of the target water area of ​​one quarter. For each group of water area data, a feature sequence of the water area data corresponding to each water quality influencing factor indicator is constructed, and for each group of reference data, a reference sequence of the reference data corresponding to each water quality influencing factor indicator is constructed.

[0075] Step 104, calculating the correlation between the water area data and the reference data of each water quality influencing factor between the feature sequence and the reference sequence according to the grey correlation algorithm.

[0076] In implementation, for each object to be evaluated, the terminal calculates the correlation between the water area data of the water quality influencing factor index and the reference data between the feature sequence and the reference sequence according to the grey correlation algorithm. Specifically, for the predetermined object to be evaluated, the terminal first calculates the correlation coefficient of the water area data of each water quality influencing factor index in the feature sequence corresponding to each object to be evaluated relative to the reference data of the water quality influencing factor index according to the grey correlation algorithm, and then, the terminal calculates the correlation of each water quality influencing factor index based on the correlation coefficient calculated for each object to be evaluated.

[0077] Step 106, according to the correlation degree corresponding to each water quality influencing factor indicator, determine the target water quality influencing factor indicator among the water quality influencing factor indicators, and determine the water quality health impact result in the correlation relationship library based on the target water quality influencing factor indicator.

[0078] In implementation, the terminal determines the target water quality influencing factor index among the water quality influencing factor indexes according to the correlation degree corresponding to each water quality influencing factor index. Specifically, the correlation degree corresponding to the water quality influencing factor index represents the overall correlation degree between the water area data of the water quality influencing factor index and the reference data. If the correlation degree between the water area data of a certain water quality influencing factor index and the reference data is higher, the relationship between the water area data representing the water quality influencing factor index and the reference data is closer, and thus, the evaluation of the water quality influencing factor index is higher. Then, the evaluation results of each water quality influencing factor index are determined, and based on the evaluation results of each water quality influencing factor index, a target number of water quality influencing factor indicators with lower evaluation results are selected as the target water quality influencing factor index. Then, the terminal uses the target water quality influencing factor index as a query index, queries the disease type corresponding to the target water quality influencing factor index in the association relationship library, and determines the water quality health impact result based on the disease type.

[0079] In the above data processing method, a water area data set is obtained, and based on the water area data set, a characteristic sequence and a reference sequence are constructed; according to the grey correlation algorithm, the correlation between the water area data and the reference data of each water quality influencing factor between the characteristic sequence and the reference sequence is calculated; according to the correlation corresponding to each water quality influencing factor indicator, the target water quality influencing factor indicator is determined in each water quality influencing factor indicator, and based on the target water quality influencing factor indicator, the water quality health impact result is determined in the correlation relationship library. By adopting this method, when conducting water quality assessment, the multidimensional water area data set is analyzed and processed, which enriches the dimension of water quality assessment processing, and the target water quality influencing factor indicator is determined in the multidimensional water quality influencing factor indicator of the water area data set through the grey correlation algorithm, and then the water quality health impact result is determined in the correlation relationship library through the target water quality influencing factor indicator, which improves the accuracy of the water quality health impact result.

[0080] In an exemplary embodiment, Figure 2 As shown, the specific processing process of obtaining the original water area data in step 102 includes the following steps 202 to 204. Among them:

[0081] Step 202, obtaining original water area data.

[0082] In implementation, different monitoring dimensions and different types of water quality monitoring equipment will be set up for each monitored water area, so that each monitored water area can obtain multi-source heterogeneous original water area data. For example, the original water area data may include: spatial data related to water pollution, hydrological data, pollution data, monitoring data and vector data, etc. When conducting water quality assessment and analysis, the terminal directly obtains the original water area data collected by various on-site monitoring equipment in the target water area, and processes the original water area data to ensure the timeliness of the water area data.

[0083] Step 204 , performing data integration and missing value screening processing on the original water area data, and based on the water quality influencing factor indicators contained in the original water area data, performing standardization processing on the water area data corresponding to each water quality influencing factor indicator to obtain a water area data set.

[0084] In implementation, the terminal performs preparatory work such as aggregation, processing, and extraction on the original water area data based on business needs. After the preparatory work is completed, the terminal integrates the water area data of the water field environment and screens missing values ​​for the original water area data, and based on the water quality influencing factor indicators contained in the original water area data, standardizes the water area data corresponding to each water quality influencing factor indicator to obtain a water area data set. Specifically, the terminal obtains the original water area data of each target water area to be evaluated, and integrates the original data into a water area data set, such as Figure 3 As shown, the source of the original water area data of each target water area can be spatial data, monitoring data, hydrological data, vector data, etc. collected by space monitoring equipment (or space acquisition equipment). Through data processing methods such as standardization processing and topological calculation, the multi-source heterogeneous data can be integrated to obtain a processed water area data set. The water area data set can be used as data support for the water quality assessment process, and the data in the water area data set can be stored, explored, defined, and preprocessed.

[0085] Optionally, the terminal may also perform operations such as diffusion, dilution, and format conversion on the original water area data to improve the data quality of the original water area data. The embodiment of the present application does not limit the processing of the water area data by the terminal.

[0086] In this embodiment, water area data of each target water area is directly collected for water quality monitoring and assessment to ensure the timeliness of the water area data, and the directly collected original water area data is processed to ensure the quality of the water area data and improve the accuracy of water quality assessment.

[0087] In an exemplary embodiment, Figure 4 As shown, the specific processing process of constructing the characteristic sequence and the reference sequence based on the water area data set in step 102 includes steps 402 to 406. Among them:

[0088] Step 402, determining the object to be evaluated, and for each object to be evaluated, determining in the water area data set the water quality influencing factor index that matches the object to be evaluated and the water area data and reference data corresponding to the water quality influencing factor index.

[0089] In implementation, the terminal determines the object to be evaluated, and for each object to be evaluated, determines the water quality influencing factor index that matches the object to be evaluated in the water area data set, as well as the water area data and reference data corresponding to the water quality influencing factor index. For example, taking the target water area A of each quarter (i.e., including the first quarter, the second quarter, the third quarter, and the fourth quarter) as the object to be evaluated, the water quality influencing factor index that matches the object to be evaluated includes: other harmful chemical pollution index, bacterial pollution index, heavy metal pollution index, chlorine pollution index, and pathogenic microorganism pollution index. Furthermore, based on each water quality influencing factor index, the terminal determines the water area data and reference data corresponding to each water quality influencing factor index. Alternatively, target area A, target area B, target area C and target area D are taken as the objects to be evaluated, and the water quality influencing factor indicators matching the objects to be evaluated are determined, and the water quality influencing factor indicators may include: chloride (mg / L), arsenic (mg / L), sulfate (mg / L), chloroform (mg / L), copper (mg / L), carbon tetrachloride (mg / L), cadmium (mg / L), nitrate (mg / L), fluoride (mg / L), selenium (mg / L), chromium (hexavalent, mg / L), cyanide (mg / L), lead (mg / L), mercury (mg / L). Then, the terminal determines the water area data and reference data corresponding to each water quality influencing factor indicator.

[0090] Step 404: construct a feature sequence based on the water area data of each water quality influencing factor index corresponding to each object to be evaluated.

[0091] During implementation, the terminal constructs a feature sequence of the object to be evaluated based on the water area data of each water quality influencing factor index corresponding to each object to be evaluated.

[0092] Step 406: construct a reference sequence based on the reference data of each water quality influencing factor index corresponding to each object to be evaluated.

[0093] During implementation, the terminal constructs a reference sequence of the object to be evaluated based on the reference data of each water quality influencing factor indicator corresponding to each object to be evaluated.

[0094] In this embodiment, based on the water area data set and the determined object to be evaluated, the reference sequence and the characteristic sequence are determined, and then, the degree of similarity between the determined reference sequence and the characteristic sequence can be used to determine whether the water area data and the reference data of each water quality influencing factor indicator are closely related, thereby achieving the screening of target water quality influencing factor indicators in the process of water quality health impact assessment, and then, more accurately determining the assessment focus of the current water area environment and improving the accuracy of water quality assessment results.

[0095] In one embodiment, Figure 5 As shown, the specific processing process of step 104 includes steps 502 to 504.

[0096] Step 502: for each feature sequence and reference sequence of the object to be evaluated, the correlation coefficient between the water area data and the reference data of each water quality influencing factor index is calculated according to the grey correlation algorithm.

[0097] In the implementation, for each feature sequence and reference sequence of the object to be evaluated, the terminal calculates the correlation coefficient between the water area data and the reference data of each water quality influencing factor index according to the grey correlation algorithm. Specifically, the calculation process of the correlation coefficient between the water area data and the reference data of each water quality influencing factor index is as follows:

[0098] The terminal initializes the feature sequence composed of the water area data of each object to be evaluated and the reference sequence composed of the reference data, respectively, to obtain each water area data after the initialization processing and each corresponding reference data, and then the terminal calculates the difference sequence, and the calculation formula of the difference sequence is shown in the following formula (1):

[0099] Δ i (k) = |x′ 1i (k)-x′ i (k)|; i = 1, 2, 3, 4, 5…(1)

[0100] Among them, Δ in formula (1) i (k) represents the absolute value of the difference between the reference data and the water area data corresponding to the i-th water quality influencing factor index, x ′ 1i (k) represents the reference data of the i-th water quality influencing factor index in the reference sequence corresponding to the object to be evaluated (also called the standard value of the water quality influencing factor index), x ′ i(k) represents the water area data of the i-th water quality influencing factor index in the characteristic sequence corresponding to the object to be evaluated, and i represents the number of water quality influencing factor indexes. Then, the terminal calculates the two extremes, that is, calculates the maximum value M of the absolute value of the difference between the reference data and the water area data in each water quality influencing factor index, and calculates the minimum value m of the absolute value of the difference between the reference data and the water area data in each water quality influencing factor index. Furthermore, the calculation formula for the correlation coefficient between the water area data and the reference data of each water quality influencing factor index can be determined as shown in the following formula (2):

[0101]

[0102] Among them, γ in formula (2) 1i (k) represents the correlation coefficient of the ith water quality influencing factor index, ξ represents the resolution coefficient, which is usually taken as 0.5.

[0103] In a specific embodiment, the target water area A of each quarter is taken as the object to be evaluated. The water quality influencing factor index corresponding to the object to be evaluated is: other harmful chemical pollution index, bacterial pollution index, heavy metal pollution index, chlorine pollution index, pathogenic microorganism pollution index. Based on the calculation method of the above correlation coefficient, the correlation coefficient of each water quality influencing factor index is calculated, such as Figure 6 As shown, Figure 6 It is a line chart of the correlation coefficient of each water quality influencing factor index in target area A in each quarter. Figure 6 It reflects the changing trend of the correlation between the water area data and the reference data of each water quality influencing factor index in different quarters (i.e., reflected by the numerical value of the correlation coefficient), which facilitates a more accurate analysis of the water quality assessment of the object to be assessed (target water area A in different quarters). Furthermore, the calculation results of the correlation coefficients of the water quality influencing factor indicators are shown in Table 1 below:

[0104] Table 1

[0105]

[0106]

[0107] In an optional embodiment, in addition to the line graph of the correlation coefficient, such as Figure 7 As shown in the figure, a heat map can also be used to reflect the overall situation (i.e., the relevant situation) of each water quality influencing factor index of each object to be evaluated, or, as shown in the figure, Figure 8As shown, a stacked bar chart is used to reflect the differences between multiple water quality influencing factor indicators of each object to be evaluated. For example, the stacked bar chart can intuitively show the water area data (specific values) of each water quality influencing factor indicator of each object to be evaluated, as well as the proportion of each water quality influencing factor indicator in the whole, etc. Furthermore, based on the correlation between the various water quality influencing factor indicators reflected in the heat map and the differences between the various water quality influencing factor indicators reflected in the stacked bar chart, further analysis of the object to be evaluated can be achieved.

[0108] In another specific embodiment, each target area is taken as an object to be evaluated, and the water quality influencing factor indicators corresponding to the object to be evaluated may include: chloride (mg / L), arsenic (mg / L), sulfate (mg / L), chloroform (mg / L), copper (mg / L), carbon tetrachloride (mg / L), cadmium (mg / L), nitrate (mg / L), fluoride (mg / L), selenium (mg / L), chromium (hexavalent, mg / L), cyanide (mg / L), lead (mg / L), and mercury (mg / L). Fig. 9 As shown, Fig. 9 ( Fig. 9 Some indicators are omitted) is a line chart of the correlation coefficient of water quality influencing factors in each target area. Fig. 9 It reflects the changing trend of the correlation between the water area data and the reference data of each water quality influencing factor indicator in different target areas, which facilitates more accurate analysis of the water quality assessment of the object to be assessed (different target water areas).

[0109] Step 504: weighted calculation is performed on the correlation coefficients of the same water quality influencing factor indicators of each object to be evaluated to determine the correlation degree of the water quality influencing factor indicators.

[0110] Among them, the correlation degree is used to characterize the correlation degree between the water area data of water quality influencing factor indicators and the reference data.

[0111] In implementation, the terminal performs weighted calculation on the correlation coefficient of the same water quality influencing factor index of each object to be evaluated to determine the correlation degree of the water quality influencing factor index. Specifically, for example, for the object to be evaluated in the target area A divided by quarters, for the same water quality influencing factor index from the first quarter to the fourth quarter, for example, the bacterial pollution index, the correlation degree between the water area data of the bacterial pollution index and the reference data is calculated, that is, the correlation coefficient of the bacterial pollution index from the first quarter to the fourth quarter is weighted to determine the correlation degree of the bacterial pollution index.

[0112] In this embodiment, the correlation coefficient between the water area data and the reference data of each water quality influencing factor index is calculated through the grey correlation algorithm. Thus, the correlation coefficient of the water quality influencing factor index can effectively reflect the changing trend of the correlation between the water area data and the reference data of each water quality influencing factor index among the objects to be evaluated, thereby achieving an accurate evaluation of the water quality conditions reflected by the water quality influencing factor index.

[0113] In one embodiment, Fig.10 As shown, the specific processing process of step 106 includes steps 1002 to 1004.

[0114] Step 1002: sort the water quality influencing factor indicators according to the correlation degree corresponding to each water quality influencing factor indicator, and determine the sorting result corresponding to each water quality influencing factor indicator.

[0115] In the implementation, the terminal sorts the water quality influencing factor indicators according to the correlation degree corresponding to each water quality influencing factor indicator, and determines the sorting result corresponding to each water quality influencing factor indicator. Specifically, since the correlation degree of each water quality influencing factor indicator can reflect the degree of correlation between the water area data of the indicator and the reference data, the water quality assessment results of the above-mentioned target areas are used as examples of the objects to be assessed to further illustrate that after the terminal calculates the correlation degree of each water quality influencing factor indicator, the correlation degree corresponding to each water quality influencing factor indicator is sorted from large to small, and the sorting result corresponding to each water quality influencing factor indicator is obtained, as shown in Table 2 below:

[0116] Table 2

[0117]

[0118]

[0119] Step 1004 , based on the ranking results corresponding to the various water quality influencing factor indicators and the preset screening conditions, determine the target water quality influencing factor indicator among the various water quality influencing factor indicators.

[0120] During implementation, the terminal determines the target water quality influencing factor indicator among the water quality influencing factor indicators based on the ranking results corresponding to each water quality influencing factor indicator and the preset screening conditions. Usually, the screening condition is that the correlation of the water quality influencing indicator is lower than the target threshold. Therefore, the terminal selects the target water quality influencing factor indicator from the ranking results according to the ranking results of each water quality influencing factor indicator. Specifically, the correlation degree is obtained by weighted calculation of the correlation coefficient. The correlation coefficient reflects the changing trend of the water quality influencing factor index among the objects to be evaluated, and the correlation degree reflects an overall similar correlation result. The value of the correlation degree is between 0 and 1. The larger the value, the closer the relationship between the water area data representing the water quality influencing factor index and the reference data. The reference data is set based on industry standards and can be used to determine the indicator data threshold of water pollution. If the relationship between the water area data of the water quality influencing factor index and the reference data is closer, the water quality prevention and control work representing the water quality influencing factor index is better, and it meets the anti-pollution standards set in the industry. On the contrary, if the value of the correlation degree of the water quality influencing factor index is smaller, the water area data representing the water quality influencing factor index is farther associated with the reference data, that is, the water quality reflected by the water area data does not meet the anti-pollution standards in the industry. Therefore, the water quality influencing factor index with a smaller correlation value (i.e., lower than the target threshold) is used as the target water quality influencing factor index, and the water area data of the target water quality influencing factor index reflects the actual situation of the current water quality, so as to achieve accurate assessment of the water quality.

[0121] In a specific example, for the sorting results of the grey correlation in Table 2 above, the correlation value between the water area data (i.e., actual data) of chloride and the reference data is the largest, and the evaluation characterizing chloride is the highest, that is, the prevention and control effect of the chloride index in this water area is the best. The correlation value between the water area data of mercury and the reference data is the smallest, and the evaluation characterizing mercury is the lowest, that is, the prevention and control effect of the mercury index in this water area is the worst. After determining the two extreme data (maximum and minimum) of the sorting results, further, in order to evaluate the health impact results of water quality, a correlation threshold of 0.6 can be set, and the water quality influencing factor indicators with a correlation less than 0.6 are all determined as target water quality influencing factor indicators.

[0122] In this embodiment, analyzing the correlation between reference data and characteristic data can help screen out target water quality influencing factor indicators, make water quality assessment decisions more accurately, and improve the accuracy of water quality health assessment results.

[0123] In one embodiment, Fig.11As shown, before determining the water quality health impact result based on the target water quality influencing factor index, an association relationship library can be pre-constructed, and the association relationship library contains the association relationship between the water quality influencing factor index and the disease type, so that the water quality situation (that is, the water environment pollution situation reflected by the target water quality influencing factor index) is linked to the disease, and then, the water quality health impact result of the water quality affecting the health of residents is evaluated. Therefore, before step 106, the method also includes:

[0124] Step 1102, obtaining a sample water area data set and a disease data set.

[0125] The sample water area data set contains indicators of various water quality influencing factors. The disease data set contains indicators of various disease types.

[0126] In implementation, the terminal obtains a sample water area data set and a disease data set. The sample data volume of the sample water area data set can be as large as possible to ensure the comprehensiveness of the water quality health assessment results. The terminal sets water quality influencing factor indicators in the sample water area data set around the types of water pollutants, hydrological data, spatial data, etc. of various water pollution events. Optionally, the sample water area data set may include: multiple element indicators, such as nitrate / nitrite, copper, lead, arsenic, cadmium, phosphorus, calcium, mercury, chromium, fluorine; and other indicators, such as bacteria, viruses, rust, water hardness, etc. In addition, the terminal constructs a disease data set containing multiple disease types around disease events that are directly caused by water pollution. For example, the disease data set may include, but is not limited to: cardiovascular and cerebrovascular sclerosis, heart disease, cerebral thrombosis, arteriosclerosis, blood viscosity, immune system damage, poisoning, difficulty in detoxification, liver cancer, gastric cancer, intestinal cancer, gastritis, urinary tract infection, dysentery, neurological changes and jaundice, etc.

[0127] Optionally, the sample water area data set and the water area data set obtained when evaluating the water quality health impact results are two independent data sets. The data in the sample water area data set and the data in the water area data set may be the same or different, and the embodiments of the present application do not limit this.

[0128] Step 1104, for each disease type, construct a correlation between each water quality influencing factor index and the disease type in the sample water area data set.

[0129] In implementation, the terminal constructs the correlation between the target water quality influencing factor index and the disease type in the sample water area data set for each disease type. Specifically, the terminal extracts features from the sample water area data set and the disease data set, and constructs the correlation between each water quality influencing factor index and the disease type in the sample water area data set. Optionally, the terminal can directly construct a direct correlation between each water quality influencing factor index and the disease type, such as Fig.12 As shown, Fig.12 It is a correlation diagram between each water quality influencing factor index and disease type. Each disease type may be induced by multiple water quality influencing factor indicators. Therefore, the correlation between disease type and water quality influencing factor indicators can be a one-to-many direct mapping relationship. Alternatively, the terminal can first classify the water quality influencing factor indicators, and then, based on the classification results of the water quality influencing factor indicators, construct a fuzzy mapping relationship between disease type and water quality influencing factor indicators, as shown in Table 3 below:

[0130] Table 3

[0131]

[0132]

[0133] Step 1106, constructing an association relationship library based on the association relationship between each disease type and the water quality influencing factor index.

[0134] In the implementation, the terminal constructs an association relationship library based on the association relationship between each disease type and the sample water quality influencing factor index. The association relationship library can also use the water quality influencing factor index as an index item, and query the corresponding disease type through the determined target water quality influencing factor index, so as to realize the prediction and evaluation of the diseases that may be induced in the current water area.

[0135] In this embodiment, by pre-building an association relationship library, water quality influencing factor indicators are associated with disease types, thereby achieving more accurate determination of water quality health assessment results. In addition, by determining the water quality health assessment results through the association relationship library, the efficiency of determining the water quality health assessment results can be improved.

[0136] In one embodiment, Fig.12 As shown, the specific processing process of determining the water quality health impact result in the association relationship library based on the target water quality influencing factor index in step 106 includes steps 1202 to 1204.

[0137] Step 1202, based on the target water quality influencing factor index, determine the target disease type corresponding to the target water quality influencing factor index from the association relationship between each water quality influencing factor index and the disease type contained in the association relationship library.

[0138] During implementation, the terminal determines the target disease type corresponding to the target water quality influencing factor indicator based on the target water quality influencing factor indicator and the association relationship between each water quality influencing factor indicator and the disease type contained in the association relationship library, thereby realizing the prediction and evaluation of diseases that may be caused by water pollution incidents.

[0139] Step 1204, based on the target disease type, obtain water quality health impact results.

[0140] In implementation, the terminal obtains the water quality health impact result based on the target disease type. Specifically, the terminal analyzes and outputs the water quality health impact result based on the determined target disease type and the target water quality influencing factor index corresponding to the target disease type. For example, the target water quality influencing factor index includes: mercury, arsenic, etc. The target disease types determined in the association library include: neurotoxicosis, mental disorder, neuritis, then the water quality health impact result can be: the water quality of the current object to be evaluated (for example, the target water area A in different quarters) has excessive mercury and arsenic. If the water quality of the target water area A continues to have this problem in the future, it will cause neurotoxicosis, mental disorder, neuritis and other disease problems in nearby residents, affecting the residents' physical health.

[0141] Optionally, the association library can also use the disease type as an index item, and reversely query the water quality influencing factor index corresponding to the disease type through the disease type, so as to determine the cause of the current disease. Specifically, for example, taking the disease type of cancer as an index item, after determining the target water quality influencing factor index through the gray correlation algorithm, the water quality influencing factor index that induces the disease can be reversely determined in the association library based on the disease type of cancer. Then, the terminal verifies the water quality influencing factor index based on the queried water quality influencing factor index and the target water quality influencing factor index to determine whether the target water quality influencing factor index of the current object to be evaluated contains the queried water quality influencing factor index. If it is included, it indicates that the water quality of the current object to be evaluated has a higher carcinogenic risk. If it is not included (including completely not included and incompletely included), it indicates that the water quality of the current object to be evaluated has a lower carcinogenic risk. Therefore, the embodiment of the present application does not limit the index items of the association library and how to determine the water quality health impact results.

[0142] In this embodiment, the target disease type is determined in the association relationship library according to the target water quality influencing factor index. The pre-constructed association relationship library has authenticity and rationality, which can improve the accuracy of the determined target disease type, and based on the target disease type, the water quality health impact result is obtained, so as to realize the rapid analysis and decision-making of the water quality health impact result in the specific application scenario, and improve the efficiency of determining the water quality health impact result.

[0143] In an example of a specific data processing method, such as Fig.13 As shown, Fig.13It is a flow chart of the data processing method. In this data processing method, each monitoring device at the water source end and the medical end can collect raw data based on different time granularities. Then, the terminal can efficiently obtain the water data set and the disease data set. The terminal can build and update the association relationship library based on the water data set and the disease data set. The association relationship library is divided into two different types of relationship sub-libraries. The first relationship sub-library can include the classification results of the water quality influencing factor index. The classification result can build a fuzzy mapping relationship between the disease type and the water quality influencing factor index. The second relationship sub-library may include a direct mapping relationship between the water quality influencing factor index and the disease type, which is not limited in the embodiment of the present application. Then, when evaluating the health impact of water quality, the terminal can determine the correlation of each water quality influencing factor index based on the gray correlation algorithm, and then determine the target water quality influencing factor index. Thus, with the help of the target water quality influencing factor index and the association relationship library, the water data and disease data can be processed and analyzed, and the health impact of water quality can be determined.

[0144] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0145] Based on the same inventive concept, the embodiment of the present application also provides a data processing device for implementing the data processing method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the one or more data processing device embodiments provided below can refer to the limitations on the data processing method above, and will not be repeated here.

[0146] In an exemplary embodiment, Fig.14 As shown, a data processing device 1400 is provided, including: an acquisition module 1401, a calculation module 1402 and a determination module 1403, wherein:

[0147] The first acquisition module 1401 is used to acquire a water area data set, and construct a feature sequence and a reference sequence based on the water area data set; the feature sequence includes water area data of various water quality influencing factor indicators in the water area data set; the reference sequence includes reference data of various water quality influencing factor indicators in the water area data set;

[0148] The calculation module 1402 is used to calculate the correlation between the water area data of each water quality influencing factor and the reference data between the characteristic sequence and the reference sequence according to the grey correlation algorithm;

[0149] The determination module 1403 is used to determine the target water quality influencing factor index among the water quality influencing factor indexes according to the correlation degree corresponding to each water quality influencing factor index, and determine the water quality health impact result in the correlation relationship library based on the target water quality influencing factor index.

[0150] In one embodiment, the first acquisition module 1401 is specifically used to acquire original water area data;

[0151] The original water area data are integrated and missing value screening is performed, and based on the water quality influencing factor indicators contained in the original water area data, the water area data corresponding to each water quality influencing factor indicator are standardized to obtain the water area data set.

[0152] In one embodiment, the first acquisition module 1401 is specifically used to determine the object to be evaluated, and for each object to be evaluated, determine the water quality influencing factor index that matches the object to be evaluated and the water area data and reference data corresponding to the water quality influencing factor index in the water area data set;

[0153] Based on the water area data of each water quality influencing factor index corresponding to each object to be evaluated, a characteristic sequence is constructed;

[0154] A reference sequence is constructed based on the reference data of each water quality influencing factor indicator corresponding to each object to be evaluated.

[0155] In one embodiment, the calculation module 1402 is specifically used to calculate the correlation coefficient between the water area data and the reference data of each water quality influencing factor index according to the grey correlation algorithm for each feature sequence and reference sequence of the object to be evaluated;

[0156] The correlation coefficient of the same water quality influencing factor indicator of each object to be evaluated is weightedly calculated to determine the correlation degree of the water quality influencing factor indicator; the correlation degree is used to characterize the degree of correlation between the water area data of the water quality influencing factor indicator and the reference data.

[0157] In one embodiment, the determination module 1403 is specifically used to sort the water quality influencing factor indicators according to the correlation degree corresponding to each water quality influencing factor indicator, and determine the sorting result corresponding to each water quality influencing factor indicator;

[0158] Based on the ranking results corresponding to each water quality influencing factor indicator and the preset screening conditions, the target water quality influencing factor indicator is determined among the water quality influencing factor indicators.

[0159] In one embodiment, the apparatus 1400 further includes:

[0160] The second acquisition module is used to acquire a sample water area data set and a disease data set. The sample water area data set contains indicators of various water quality influencing factors; the disease data set contains various disease types;

[0161] The first construction module is used to construct the correlation between each water quality influencing factor index and the disease type in the sample water area data set for each disease type;

[0162] The second construction module is used to construct an association relationship library based on the association relationship between each disease type and the water quality influencing factor indicators.

[0163] In one embodiment, the determination module 1403 is specifically used to determine the target disease type corresponding to the target water quality influencing factor indicator in the association relationship between each water quality influencing factor indicator and the disease type contained in the association relationship library based on the target water quality influencing factor indicator;

[0164] Based on the target disease types, water quality health impact results are obtained.

[0165] Each module in the above data processing device can be implemented in whole or in part by software, hardware or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0166] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Fig.15As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a data processing method is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0167] Those skilled in the art will understand that Fig.15 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0168] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0169] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0170] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0171] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0172] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0173] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A data processing method, characterized in that: The method comprises: Acquire a water area data set, and construct a feature sequence and a reference sequence based on the water area data set; the feature sequence contains water area data of each water quality influencing factor index in the water area data set; the reference sequence contains reference data of each water quality influencing factor index in the water area data set; According to the grey correlation algorithm, the correlation between the water area data and the reference data of each water quality influencing factor between the characteristic sequence and the reference sequence is calculated; According to the correlation degree corresponding to each of the water quality influencing factor indicators, a target water quality influencing factor indicator is determined among the water quality influencing factor indicators, and based on the target water quality influencing factor indicator, a water quality health impact result is determined in the correlation relationship library; Determining the target water quality influencing factor index from among the water quality influencing factor indexes includes: Determine the evaluation results of each water quality influencing factor indicator, and based on the evaluation results of each water quality influencing factor indicator, select a target number of water quality influencing factor indicators whose correlation degree of the water quality influencing indicator in the evaluation results is lower than the target threshold as the target water quality influencing factor indicators; The calculation formula for determining the correlation coefficient between the water area data and the reference data for each water quality influencing factor index is as follows: Among them, γ 1i (k) represents the correlation coefficient of the i-th water quality influencing factor index, ξ represents the resolution coefficient, and the resolution coefficient is 0.5; Δ i (k) represents the absolute value of the difference between the reference data and the water area data corresponding to the i-th water quality influencing factor index, M is the maximum value among the absolute values ​​of the difference between the reference data and the water area data in each water quality influencing factor index; m is the minimum value among the absolute values ​​of the difference between the reference data and the water area data in each water quality influencing factor index; the correlation coefficient is used to determine the correlation degree of the water quality influencing factor index.

2. The method according to claim 1, characterized in that The obtaining of the water area dataset comprises: Get the original water area data; The original water area data is subjected to data integration and missing value screening processing, and based on the water quality influencing factor indicators contained in the original water area data, the water area data corresponding to each water quality influencing factor indicator is standardized to obtain a water area data set.

3. The method according to claim 1, characterized in that The step of constructing a characteristic sequence and a reference sequence based on the water area data set includes: Determine an object to be evaluated, and for each object to be evaluated, determine in the water area data set a water quality influencing factor index that matches the object to be evaluated and water area data and reference data corresponding to the water quality influencing factor index; Constructing a feature sequence based on the water area data of each water quality influencing factor index corresponding to each object to be evaluated; A reference sequence is constructed based on the reference data of each water quality influencing factor indicator corresponding to each object to be evaluated.

4. The method according to claim 1, characterized in that The step of calculating the correlation between the water area data and the reference data of each water quality influencing factor between the characteristic sequence and the reference sequence according to the grey correlation algorithm includes: For each characteristic sequence and reference sequence of the object to be evaluated, the correlation coefficient between the water area data and the reference data of each water quality influencing factor index is calculated according to the grey correlation algorithm; The correlation coefficients of the same water quality influencing factor indicators of each of the objects to be evaluated are weightedly calculated to determine the correlation degree of the water quality influencing factor indicators; the correlation degree is used to characterize the degree of correlation between the water area data of the water quality influencing factor indicators and the reference data.

5. The method according to claim 1, characterized in that Before determining the water quality health impact result in the association relationship library based on the target water quality influencing factor index, the method further includes: Acquire a sample water area data set and a disease data set, wherein the sample water area data set includes indicators of various water quality influencing factors; and the disease data set includes various disease types; For each of the disease types, construct a correlation between each water quality influencing factor index in the sample water area data set and the disease type; Based on the correlation between each disease type and the water quality influencing factor index, a correlation database is constructed.

6. The method according to claim 1, characterized in that Determining the water quality health impact result in the association relationship library based on the target water quality influencing factor index includes: Based on the target water quality influencing factor index, determining the target disease type corresponding to the target water quality influencing factor index in the association relationship between each water quality influencing factor index and the disease type contained in the association relationship library; Based on the target disease types, water quality health impact results are obtained.

7. A data processing device, characterized in that: The device comprises: The first acquisition module is used to acquire a water area data set, and construct a feature sequence and a reference sequence based on the water area data set; the feature sequence contains water area data of each water quality influencing factor index in the water area data set; the reference sequence contains reference data of each water quality influencing factor index in the water area data set; A calculation module, used for calculating the correlation between the water area data and the reference data of each water quality influencing factor between the characteristic sequence and the reference sequence according to a grey correlation algorithm; A determination module, for determining a target water quality influencing factor index among the water quality influencing factor indexes according to the correlation degree corresponding to each of the water quality influencing factor indexes, and determining a water quality health impact result in an association relationship library based on the target water quality influencing factor index; The determination module is specifically used to determine the evaluation results of each water quality influencing factor index, and based on the evaluation results of each water quality influencing factor index, select a target number of water quality influencing factor indicators whose correlation degree of the water quality influencing index in the evaluation results is lower than the target threshold as the target water quality influencing factor indicators; Determine the evaluation results of each water quality influencing factor indicator, and based on the evaluation results of each water quality influencing factor indicator, select a target number of water quality influencing factor indicators whose correlation degree of the water quality influencing indicator in the evaluation results is lower than the target threshold as the target water quality influencing factor indicators; The calculation formula for determining the correlation coefficient between the water area data and the reference data for each water quality influencing factor index is as follows: Among them, γ 1i (k) represents the correlation coefficient of the i-th water quality influencing factor index, ξ represents the resolution coefficient, and the resolution coefficient is 0.5; Δ i (k) represents the absolute value of the difference between the reference data and the water area data corresponding to the i-th water quality influencing factor index, M is the maximum value among the absolute values ​​of the difference between the reference data and the water area data in each water quality influencing factor index; m is the minimum value among the absolute values ​​of the difference between the reference data and the water area data in each water quality influencing factor index; the correlation coefficient is used to determine the correlation degree of the water quality influencing factor index.

8. The device according to claim 7, characterized in that The first acquisition module is specifically used to acquire original water area data; The original water area data is subjected to data integration and missing value screening processing, and based on the water quality influencing factor indicators contained in the original water area data, the water area data corresponding to each water quality influencing factor indicator is standardized to obtain a water area data set.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images