A method, system, device and medium for predicting heavy metal pollution in flooded areas

Abstract

The present invention relates to a method, system, device and medium for predicting heavy metal pollution in flooded areas, and to the technical field of heavy metal pollution prediction, wherein the method for predicting heavy metal pollution in flooded areas includes the steps of acquiring historical information, screening information, processing data, establishing a storage database, establishing a prediction model, and outputting prediction results; the system for predicting heavy metal pollution in flooded areas includes an acquisition module, a screening module, a processing module, a storage module, a calculation module I and an output module. The present invention can accurately process historical data by screening historical data and performing array conversion on the data, thereby improving the accuracy of predicting the heavy metal pollution index in the n+1th year.

Description

Technical Field

[0001] The invention relates to the technical field of heavy metal pollution prediction, and in particular to a method and system for predicting heavy metal pollution in a flood-inundated area. Background Art

[0002] Flood disasters are one of the most frequent, widespread and most devastating natural disasters in the world. Flood disasters occur in every region almost every year. Flood disasters can cause economic losses such as damage to houses, roads and other infrastructure, flooding of crops, traffic interruptions, and business suspension, and bring about environmental damage such as soil erosion and water pollution. Generally speaking, when a region is flooded, heavy metal pollution will occur in the flooded area.

[0003] At present, the method for predicting heavy metal pollution in the flooded area is to obtain historical data, calculate the average value and variance of the historical data, reflect the fluctuation of the heavy metal pollution index through the average value and variance, and predict the range of the heavy metal pollution index for the next year based on the fluctuation value.

[0004] However, there are many conditions for heavy metal pollution in flood-inundated areas, such as the number of factories built near the flood-inundated areas, the amount of wastewater discharged by the factories, and the severity of floods in the flood-inundated areas this year, all of which affect the prediction of the heavy metal pollution index for the next year.

[0005] Therefore, there is an urgent need for a method with high prediction accuracy to predict heavy metal pollution in flooded areas. Summary of the invention

[0006] In order to improve the prediction accuracy of heavy metal pollution in flooded areas, the present invention provides a method, system, device and medium for predicting heavy metal pollution in flooded areas.

[0007] In a first aspect, the present invention provides a method for predicting heavy metal pollution in flooded areas, which adopts the following technical solution:

[0008] A method for predicting heavy metal pollution in a flood-inundated area, comprising:

[0009] Historical information acquisition: Obtain the heavy metal pollution index An in the flood-inundated area over n years, and obtain k heavy metal pollution index increases X through the heavy metal pollution index An k , and form a first incremental reference set M, the first incremental reference set M=(X 1 , X 2 , X 3 ,.....X k );

[0010] Information screening: Screen the first incremental reference set and filter out maxX kand minK k , and the remaining heavy metal pollution index increase X k Form the second incremental reference set M , , the second incremental reference set ;

[0011] Data processing: for the second incremental reference set M , Transform to form data array B j , ;in, ;

[0012] Establish a storage database: Establish a storage database and store the heavy metal pollution index An, the first incremental reference set M, and the second incremental reference set M , and data array B j Store in a database;

[0013] Establish a prediction model: predict the heavy metal pollution index C in the n+1th year. The prediction model of the heavy metal pollution index C in the n+1th year is as follows:

[0014] ;

[0015] In the formula, A 1 is the heavy metal pollution index of the first year in the flood inundation area over n years, and E is the predicted increase in the heavy metal pollution index in the n+1th year;

[0016] The calculation model for the increase in heavy metal pollution index E in year n+1 is as follows:

[0017] ;

[0018] ;

[0019] In the formula, Z and L are constants, P is the year number, e is a natural constant, and Y j+1 For data array B j The j+1th element in ;

[0020] The calculation model of constants Z and L is as follows:

[0021] ;

[0022] ;

[0023] ;

[0024] In the formula, The constants Z and L form the transposed matrix of the constant matrix, where D and G are matrix columns; D T is the transposed matrix of matrix column D, GT is the transposed matrix of the matrix column G;

[0025] Prediction result output: Output the heavy metal pollution index C in the n+1th year and the increase E in the heavy metal pollution index in the n+1th year.

[0026] When predicting heavy metal pollution in flood-inundated areas, for example, obtaining the increase in heavy metal pollution index X for 8 years k is 132, 92, 87, 118, 130, 187, 233, 207, then the first incremental reference set M = (132, 92, 87, 118, 130, 187, 233, 207), the first incremental reference set M is screened, and the second incremental reference set formed after screening = (132, 92, 118, 130, 187, 207), for the second incremental reference set Perform this column transformation to form data array B j = (132, 224, 342, 427, 659, 866), the heavy metal pollution index An, the first incremental reference set M, the second incremental reference set M , and data array B j Stored in the database, the heavy metal pollution index C of the n+1th year is predicted by the prediction model. The matrix column in the prediction model ; In the prediction model, the matrix columns ; The prediction model calculated Z=-0.205, L=56.79; The prediction model calculated the increase in the heavy metal pollution index in the 9th year to be E=256.3, and the heavy metal pollution index in the 9th year to be C=1442.3;

[0027] By adopting the above technical solution, the invention of this application has at least the following technical effects:

[0028] In establishing the prediction model, we first screen out abnormal data by screening historical data. The basis for screening abnormal data is that the flooded area in the nth year may be severely flooded, or the flood is not serious, which can affect the heavy metal pollution index of the flooded area. Secondly, the number of factories around the flooded area and the factory's wastewater discharge affect the prediction of heavy metal pollution in the flooded area. When predicting the heavy metal pollution index in the n+1th year, the historical data is screened and arrayed to achieve accurate processing of the historical data, thereby improving the accuracy of predicting the heavy metal pollution index in the n+1th year.

[0029] Optionally, the prediction of the heavy metal pollution index C in the n+1th year also includes the factory emission pollution index; the prediction model of the heavy metal pollution index C in the n+1th year is:

[0030] ;

[0031] Where R is the pollution index of factory emissions to the flood inundation area.

[0032] Optionally, the heavy metal pollution index A of the flood-inundated area during the n-year period is obtained. n When the second incremental reference set M is obtained through historical record data , Each element in , is the heavy metal pollution index A in the flood-inundated area over n years n The heavy metal pollution index A in the flood-inundated area over the past n years n-1 The difference between .

[0033] In a second aspect, the present invention provides a system for predicting heavy metal pollution in flooded areas, which adopts the following technical solution:

[0034] A heavy metal pollution prediction system for flood-inundated areas, comprising an acquisition module, a screening module, a processing module, a storage module, a calculation module I and an output module;

[0035] Acquisition module: The output end is connected to the input end of the screening module and the input end of the storage module, and is used to obtain the heavy metal pollution index An in the flood-inundated area for n years, and obtain k heavy metal pollution index increase values ​​X through the heavy metal pollution index An. k , and form a first incremental reference set M, the first incremental reference set M=(X 1 , X 2 , X 3 ,.....X k );

[0036] Screening module: The output end is connected to the input end of the processing module and the input end of the storage module for electrical signal, and is used to screen the first incremental reference set and select maxX k and minK k , and the remaining heavy metal pollution index increase X k Form the second incremental reference set M , , the second incremental reference set ;

[0037] Processing module: The output end is connected to the input end of the storage module for electrical signal processing of the second incremental reference set M , Transform to form data array B j , ;in, ;

[0038] Storage module: The output end is connected to the input end of the calculation module I by electrical signal, and is used to store the heavy metal pollution index An, the first incremental reference set M, and the second incremental reference set M, and data array B j ;

[0039] Calculation module I: The output end is connected to the input end of the output module for predicting the heavy metal pollution index C in the n+1th year. The prediction model of the heavy metal pollution index C in the n+1th year is as follows:

[0040] ;

[0041] In the formula, A 1 is the heavy metal pollution index of the first year in the flood inundation area over n years, and E is the predicted increase in the heavy metal pollution index in the n+1th year;

[0042] The calculation model for the increase in heavy metal pollution index E in year n+1 is as follows:

[0043] ;

[0044] ;

[0045] In the formula, Z and L are constants, P is the year number, e is a natural constant, and Y j+1 For data array B j The j+1th element in;

[0046] The calculation model of constants Z and L is as follows:

[0047] ;

[0048] ;

[0049] ;

[0050] In the formula, The constants Z and L form the transposed matrix of the constant matrix, where D and G are matrix columns; D T is the transposed matrix of matrix column D, G T is the transposed matrix of the matrix column G;

[0051] Output module: used to output the heavy metal pollution index C in the n+1th year and the increase E of the heavy metal pollution index in the n+1th year.

[0052] Optionally, the calculation module I also includes the factory emission pollution index when predicting the heavy metal pollution index C in the n+1th year; the prediction model of the heavy metal pollution index C in the n+1th year is:

[0053] ;

[0054] Where R is the pollution index of factory emissions to the flood inundation area.

[0055] Optionally, the heavy metal pollution index A of the flood-inundated area during the n-year period is obtained. n When the second incremental reference set M is obtained through historical record data , Each element in , is the heavy metal pollution index A in the flood-inundated area over n years n The heavy metal pollution index A in the flood-inundated area over the past n years n-1 The difference between .

[0056] In a third aspect, the present invention provides a device for predicting heavy metal pollution in flooded areas, which adopts the following technical solution:

[0057] A device comprises a processor and a storage, wherein the storage is used to store a computer program, and the processor is used to execute the computer program stored in the storage, so that the device executes the method as described in the first aspect.

[0058] In a fourth aspect, the present invention provides a computer storage medium for predicting heavy metal pollution in flooded areas, which adopts the following technical solution:

[0059] A medium having a computer program stored thereon; when the computer program is executed by a processor, the method described in the first aspect is implemented.

[0060] In summary, the present invention includes at least one of the following beneficial technical effects:

[0061] In establishing the prediction model, we first screen out abnormal data by screening historical data. The basis for screening abnormal data is that the flooded area in the nth year may be severely flooded, or the flood is not serious, which can affect the heavy metal pollution index of the flooded area. Secondly, the number of factories around the flooded area and the factory's wastewater discharge affect the prediction of heavy metal pollution in the flooded area. When predicting the heavy metal pollution index in the n+1th year, the historical data is screened and arrayed to achieve accurate processing of the historical data, thereby improving the accuracy of predicting the heavy metal pollution index in the n+1th year. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 is a flow chart of Example 1 of the present application;

[0063] Figure 2 is a schematic diagram of the system structure of Example 2 of the present application;

[0064] Figure 3 This is a device diagram of Example 3 of the present application. DETAILED DESCRIPTION

[0065] In order to make the technical problems, technical solutions and beneficial effects to be solved by 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.

[0066] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application. The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned figure descriptions are intended to cover non-exclusive inclusions.

[0067] It should be noted that when an element is referred to as being "fixed to" or "disposed on" another element, it can be directly on the other element or indirectly on the other element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or indirectly connected to the other element.

[0068] It should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0069] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0070] In this application, "and / or" is only a description of the association relationship of associated objects, indicating that there can be three relationships; for example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0071] It should be noted that, in the present application, words such as "in some embodiments", "exemplarily", "for example", etc. are used to indicate examples, illustrations or descriptions. Any embodiment or design described in the present application as "in some embodiments", "exemplarily", "for example" should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "in some embodiments", "exemplarily", "for example", etc. is intended to present related concepts in a concrete way, meaning that specific features, structures or characteristics described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the above words in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0072] The following combination Figures 1 to 3 The present invention is described in further detail.

[0073] Example 1: This example discloses a method for predicting heavy metal pollution in flooded areas. Figure 1 , a method for predicting heavy metal pollution in flooded areas comprises the following steps:

[0074] S1: Historical information acquisition: Obtain the heavy metal pollution index An in the flood-inundated area over n years, and obtain k heavy metal pollution index increase values ​​X through the heavy metal pollution index An. k , and form a first incremental reference set M, the first incremental reference set M=(X 1 , X 2 , X 3 ,.....X k ).

[0075] (For example: Get the increase in heavy metal pollution index X in 8 years k is 132, 92, 87, 118, 130, 187, 233, 207, then the first incremental reference set M = (132, 92, 87, 118, 130, 187, 233, 207)).

[0076] S2: Information screening: Screen the first incremental reference set and select maxX k and minX k , and the remaining heavy metal pollution index increase X k Form the second incremental reference set M , , the second incremental reference set The second incremental reference set M , Each element in , is the heavy metal pollution index A in the flood-inundated area over n years nThe heavy metal pollution index A in the flood-inundated area over the past n years n-1 The difference between

[0077] For example: the first incremental reference set M is screened to form a second incremental reference set =(132, 92, 118, 130, 187, 207).

[0078] S3: Data processing: for the second incremental reference set M , Transform to form data array B j , ;in, ;

[0079] For example: for the second incremental reference set Perform this column transformation to form data array B j =(132, 224, 342, 427, 659, 866);

[0080] S4: Establish a storage database: Establish a storage database and store the heavy metal pollution index An, the first incremental reference set M, and the second incremental reference set M , and data array B j Store in a database;

[0081] S5: Establish a prediction model: predict the heavy metal pollution index C in the n+1th year. The prediction model of the heavy metal pollution index C in the n+1th year is as follows:

[0082] ;

[0083] In the formula, A 1 is the heavy metal pollution index of the first year in the flood inundation area over n years, E is the predicted increase in the heavy metal pollution index in the n+1th year, and R is the pollution index of the factory emissions to the flood inundation area;

[0084] The calculation model for the increase in heavy metal pollution index E in year n+1 is as follows:

[0085] ;

[0086] ;

[0087] In the formula, Z and L are constants, P is the year number, e is a natural constant, and Y j+1 For data array B j The j+1th element in;

[0088] The calculation model of constants Z and L is as follows:

[0089] ;

[0090] ;

[0091] ;

[0092] In the formula, The constants Z and L form the transposed matrix of the constant matrix, where D and G are matrix columns; D T is the transposed matrix of matrix column D, G T is the transposed matrix of the matrix column G;

[0093] For example: In the prediction model, the matrix columns ; In the prediction model, the matrix columns ; The prediction model calculated Z=-0.205, L=56.79; The prediction model calculated the increase in the heavy metal pollution index in the 9th year E=256.3, and the heavy metal pollution index in the 9th year C=1442.3.

[0094] S6: Forecast result output: Output the heavy metal pollution index C in the n+1th year and the increase E of the heavy metal pollution index in the n+1th year.

[0095] The implementation principle of the method for predicting heavy metal pollution in a flood-inundated area in this embodiment is as follows: when predicting heavy metal pollution in a flood-inundated area, for example, obtaining the increase in the heavy metal pollution index X for 8 years k is 132, 92, 87, 118, 130, 187, 233, 207, then the first incremental reference set M = (132, 92, 87, 118, 130, 187, 233, 207), the first incremental reference set M is screened, and the second incremental reference set formed after screening = (132, 92, 118, 130, 187, 207), for the second incremental reference set Perform this column transformation to form data array B j = (132, 224, 342, 427, 659, 866), the heavy metal pollution index An, the first incremental reference set M, the second incremental reference set M , and data array B j Stored in the database, the heavy metal pollution index C of the n+1th year is predicted by the prediction model. The matrix column in the prediction model ; In the prediction model, the matrix columns ; The prediction model calculated Z=-0.205, L=56.79; The prediction model calculated the increase in the heavy metal pollution index in the 9th year to be E=256.3, and the heavy metal pollution index in the 9th year to be C=1442.3;

[0096] In establishing the prediction model, we first screen out abnormal data by screening historical data. The basis for screening abnormal data is that the flooded area in the nth year may be severely flooded, or the flood is not serious, which can affect the heavy metal pollution index of the flooded area. Secondly, the number of factories around the flooded area and the factory's wastewater discharge affect the prediction of heavy metal pollution in the flooded area. When predicting the heavy metal pollution index in the n+1th year, the historical data is screened and arrayed to achieve accurate processing of the historical data, thereby improving the accuracy of predicting the heavy metal pollution index in the n+1th year.

[0097] Example 2: This example discloses a system for predicting heavy metal pollution in flooded areas. Figure 2 ,A flood inundated area heavy metal pollution prediction system includes an acquisition module, a screening module, a processing module, a storage module, a calculation module I and an output module;

[0098] Acquisition module: The output end is connected to the input end of the screening module and the input end of the storage module, and is used to obtain the heavy metal pollution index An in the flood-inundated area for n years, and obtain k heavy metal pollution index increase values ​​X through the heavy metal pollution index An. k , and form a first incremental reference set M, the first incremental reference set M=(X 1 , X 2 , X 3 ,.....X k );

[0099] Screening module: The output end is connected to the input end of the processing module and the input end of the storage module for electrical signal, and is used to screen the first incremental reference set and select maxX k and minK k , and the remaining heavy metal pollution index increase X k Form the second incremental reference set M , , the second incremental reference set The second incremental reference set M , Each element in , is the heavy metal pollution index A in the flood-inundated area over n years n The heavy metal pollution index A in the flood-inundated area over the past n years n-1 The difference between .

[0100] Processing module: The output end is connected to the input end of the storage module for electrical signal processing of the second incremental reference set M , Transform to form data array B j , ;in, ;

[0101] Storage module: The output end is connected to the input end of the calculation module I for storing the heavy metal pollution index An, the first incremental reference set M, and the second incremental reference set M. , and data array B j ;

[0102] Calculation module I: The output end is connected to the input end of the output module for predicting the heavy metal pollution index C in the n+1th year. The prediction model of the heavy metal pollution index C in the n+1th year is as follows:

[0103] +R ;

[0104] In the formula, A 1 is the heavy metal pollution index of the first year in the flood inundation area over n years, E is the predicted increase in the heavy metal pollution index in the n+1th year, and R is the pollution index of the factory emissions to the flood inundation area;

[0105] The calculation model for the increase in heavy metal pollution index E in year n+1 is as follows:

[0106] ;

[0107] ;

[0108] In the formula, Z and L are constants, P is the year number, e is a natural constant, and Y j+1 For data array B j The j+1th element in ;

[0109] The calculation model of constants Z and L is as follows:

[0110] ;

[0111] ;

[0112] ;

[0113] In the formula, The constants Z and L form the transposed matrix of the constant matrix, where D and G are matrix columns; D T is the transposed matrix of matrix column D, G T is the transposed matrix of the matrix column G;

[0114] Output module: used to output the heavy metal pollution index C in the n+1th year and the increase E of the heavy metal pollution index in the n+1th year.

[0115] This embodiment provides a system for predicting heavy metal pollution in flooded areas: an acquisition module acquires a heavy metal pollution index An in the flooded area over n years, and obtains k heavy metal pollution index increase values ​​X through the heavy metal pollution index An. k , and form a first incremental reference set M, the first incremental reference set M=(X 1 , X 2 , X 3 ,.....X k ); The screening module screens the first incremental reference set and screens out maxX k and minK k , and the remaining heavy metal pollution index increase X k Form the second incremental reference set M , , the second incremental reference set The second incremental reference set M , Each element in , is the heavy metal pollution index A in the flood-inundated area over n years n The heavy metal pollution index A in the flood-inundated area over the past n years n-1 The processing module processes the second incremental reference set M , Transform to form data array B j , ;in, The storage module stores the heavy metal pollution index An, the first incremental reference set M, and the second incremental reference set M. , and data array B j The calculation module I calculates the heavy metal pollution index C of the n+1th year, and the output module outputs the heavy metal pollution index C of the n+1th year and the increase E of the heavy metal pollution index in the n+1th year.

[0116] Embodiment 3: This embodiment discloses a device for predicting heavy metal pollution in flooded areas, referring to Figure 3 , a device for predicting heavy metal pollution in flooded areas comprises:

[0117] Storage for storing computer programs;

[0118] The processor is used to execute the computer program stored in the memory, thereby implementing the method described in Example 1.

[0119] The storage may include a large capacity storage for storing data or instructions. By way of example and not limitation, the storage may include a hard disk, a floppy disk, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a combination of two or more of these. Where appropriate, the storage may include a removable or non-removable (or fixed) medium. Where appropriate, the storage may be inside or outside the data processing device. In a particular embodiment, the storage is a non-volatile solid-state storage. In a particular embodiment, the storage includes a read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EBROM), or a combination of two or more of these.

[0120] Embodiment 4: This embodiment discloses a computer storage medium for predicting heavy metal pollution in flood-inundated areas, wherein the computer storage medium stores a program that, when executed, can implement some or all of the steps of the method described in Embodiment 1.

[0121] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for predicting heavy metal pollution in flooded areas, characterized by: include: Historical information acquisition: Obtain the heavy metal pollution index An in the flood-inundated area over n years, and obtain k heavy metal pollution index increases X through the heavy metal pollution index An k , and form a first incremental reference set M, the first incremental reference set M=(X1, X2, X3, .....X k ); Information screening: Screen the first incremental reference set and filter out maxX k and minK k , and the remaining heavy metal pollution index increase X k Form the second incremental reference set M , , the second incremental reference set ; Data processing: for the second incremental reference set M , Transform to form data array B j , ;in, ; Establish a storage database: Establish a storage database and store the heavy metal pollution index An, the first incremental reference set M, and the second incremental reference set M , and data array B j Store in a database; Establish a prediction model: predict the heavy metal pollution index C in the n+1th year. The prediction model of the heavy metal pollution index C in the n+1th year is as follows: ; Where A1 is the heavy metal pollution index of the first year in the flood inundation area over n years, and E is the predicted increase in the heavy metal pollution index in the n+1th year; The calculation model for the increase in heavy metal pollution index E in year n+1 is as follows: ； ； In the formula, Z and L are constants, P is the year number, e is a natural constant, and Y j+1 For data array B j The jth element in ; The calculation model of constants Z and L is as follows: ; ； ； In the formula, The constants Z and L form the transposed matrix of the constant matrix, where D and G are matrix columns; D T is the transposed matrix of matrix column D, G T is the transposed matrix of the matrix column G; Prediction result output: Output the heavy metal pollution index C in the n+1th year and the increase E in the heavy metal pollution index in the n+1th year.

2. The method for predicting heavy metal pollution in flooded areas according to claim 1, characterized in that: The prediction of the heavy metal pollution index C in the n+1th year also includes the factory emission pollution index; the prediction model of the heavy metal pollution index C in the n+1th year is: ； Where R is the pollution index of factory emissions to the flood inundation area.

3. The method for predicting heavy metal pollution in flooded areas according to claim 2, characterized in that: The heavy metal pollution index A of the flood-inundated area for n years is obtained. n When the second incremental reference set M is obtained through historical record data , Each element in , is the heavy metal pollution index A in the flood-inundated area over n years n The heavy metal pollution index A in the flood-inundated area over the past n years n-1 The difference between .

4. A system for predicting heavy metal pollution in flooded areas, using the method for predicting heavy metal pollution in flooded areas according to any one of claims 1 to 3, characterized in that: It includes an acquisition module, a screening module, a processing module, a storage module, a calculation module I and an output module; Acquisition module: The output end is connected to the input end of the screening module and the input end of the storage module, and is used to obtain the heavy metal pollution index An in the flood-inundated area for n years, and obtain k heavy metal pollution index increase values ​​X through the heavy metal pollution index An. k , and form a first incremental reference set M, the first incremental reference set M=(X1, X2, X3, .....X k ); Screening module: The output end is connected to the input end of the processing module and the input end of the storage module for electrical signal, and is used to screen the first incremental reference set and select maxX k and minK k , and the remaining heavy metal pollution index increase X k Form the second incremental reference set M , , the second incremental reference set ; Processing module: The output end is connected to the input end of the storage module for electrical signal processing of the second incremental reference set M , Transform to form data array B j , ;in, ; Storage module: The output end is connected to the input end of the calculation module I for storing the heavy metal pollution index An, the first incremental reference set M, and the second incremental reference set M. , and data array B j ; Calculation module I: The output end is connected to the input end of the output module for predicting the heavy metal pollution index C in the n+1th year. The prediction model of the heavy metal pollution index C in the n+1th year is as follows: ； Where A1 is the heavy metal pollution index of the first year in the flood inundation area over n years, and E is the predicted increase in the heavy metal pollution index in the n+1th year; The calculation model for the increase in heavy metal pollution index E in year n+1 is as follows: ； ； In the formula, Z and L are constants, P is the year number, e is a natural constant, and Y j+1 For data array B j The jth element in ; The calculation model of constants Z and L is as follows: ； ； ； In the formula, The constants Z and L form the transposed matrix of the constant matrix, where D and G are matrix columns; D T is the transposed matrix of matrix column D, G T is the transposed matrix of the matrix column G; Output module: used to output the heavy metal pollution index C in the n+1th year and the increase E of the heavy metal pollution index in the n+1th year.

5. A flood inundation area heavy metal pollution prediction system according to claim 4, characterized in that: The calculation module I also includes the factory emission pollution index when predicting the heavy metal pollution index C in the n+1th year; the prediction model of the heavy metal pollution index C in the n+1th year is: ； Where R is the pollution index of factory emissions to the flood inundation area.

6. A flood inundation area heavy metal pollution prediction system according to claim 5, characterized in that: The heavy metal pollution index A of the flood-inundated area for n years is obtained. n When the second incremental reference set M is obtained through historical record data , Each element in , is the heavy metal pollution index A in the flood-inundated area over n years n The heavy metal pollution index A in the flood-inundated area over the past n years n-1 The difference between .

7. A device for predicting heavy metal pollution in flooded areas, characterized by: The device comprises a processor and a storage, wherein the storage is used to store a computer program, and the processor is used to execute the computer program stored in the storage, so that the device executes the method according to any one of claims 1 to 3.

8. A computer storage medium for predicting heavy metal pollution in flooded areas, having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 3 is implemented.

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