Multi-dimensional multi-granularity ecological sensitive area environmental impact assessment method

Abstract

The invention relates to the technical field of environment assessment, and discloses a multi-dimensional multi-granularity ecological sensitive area environment influence assessment method, which comprises the following steps: constructing an ecological sensitive area environment assessment index system, and collecting index data according to the index system and preprocessing the index data; performing data quality improvement on the preprocessed index data; performing multi-stage filtering on the enhanced index data to obtain filtered key index data; and using the ecological sensitive area environment evaluation model to receive the key index data and perform environment influence evaluation. According to the method, multi-granularity ecological sensitive area environment evaluation indexes are selected from the multi-dimensional level of pollution control, ecological environment, resource environment, environment planning, environment quality and the like of an ecological area, and abnormal data cleaning and redundant index or non-key index cleaning processing are sequentially carried out on index data; and carrying out convolution, mapping and correction processing on the cleaning result to obtain an environmental influence assessment result representing the regional ecological sensitivity.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental assessment, and in particular to a multi-dimensional and multi-granular environmental impact assessment method for ecologically sensitive areas. Background Art

[0002] Ecologically sensitive areas refer to areas in the natural environment where the ecosystem is fragile and sensitive, has low resistance to external interference, and has relatively important ecosystem functions. These areas often carry rich biodiversity and ecological functions and have extremely high ecological protection value. They cover a variety of ecosystem types, including but not limited to wetlands, forests, grasslands, deserts, coastal zones and their specific species habitats. These areas not only play an important role in maintaining regional ecological balance, regulating climate, conserving water sources, maintaining soil fertility and preventing soil erosion, but also provide a large number of ecological service functions, such as tourism, scientific research and education. However, with the intensification of human activities, these ecologically sensitive areas are facing increasingly severe environmental pressures, such as resource development, land use changes and pollution. Therefore, it is particularly important to conduct environmental impact assessments on ecologically sensitive areas. Existing environmental impact assessment methods are often too single and lack comprehensiveness and systematicity. Traditional assessment methods mainly rely on field surveys and laboratory analysis, which are difficult to fully and accurately reflect the complex environmental conditions and diverse impacts of ecologically sensitive areas. For example, some assessment methods only consider one aspect of the ecosystem, such as water quality, air quality or soil pollution, while ignoring the integrity and comprehensiveness of the ecosystem. Summary of the invention

[0003] In view of this, the present invention provides a multi-dimensional and multi-granular ecologically sensitive area environmental impact assessment method, the purpose of which is to select multi-granular ecologically sensitive area environmental assessment indicators from multi-dimensional levels such as pollution control, ecological environment, resource environment, environmental planning and environmental quality in ecological regions, clean the indicator data for abnormal data and clean redundant indicators or non-critical indicators in turn, and convolve, map and correct the cleaning results to obtain an environmental impact assessment result that characterizes the ecological sensitivity of the region.

[0004] To achieve the above object, the present invention provides a multi-dimensional and multi-granular ecologically sensitive area environmental impact assessment method, comprising the following steps: S1: Construct an environmental assessment index system for ecologically sensitive areas, collect index data according to the index system and pre-process them to obtain pre-processed index data; S2: Improve the data quality of the preprocessed indicator data to obtain enhanced indicator data; S3: Perform multi-level filtering on the enhanced indicator data to obtain filtered key indicator data; S4: Construct an environmental assessment model for ecologically sensitive areas, and use the environmental assessment model for ecologically sensitive areas to receive key indicator data and conduct environmental impact assessment.

[0005] As a further improvement method of the present invention: Optionally, the construction of an ecologically sensitive area environmental assessment index system in step S1 includes: Construct an ecologically sensitive area environmental assessment indicator system, where the set of ecologically sensitive area environmental assessment indicators in the ecologically sensitive area environmental assessment indicator system is: ; in: It represents the mth ecologically sensitive area environmental assessment index under the nth environmental index; N represents the number of environmental indicators, N=5, and the first to fifth environmental indicators are pollution control indicators, ecological environment indicators, resource and environmental indicators, environmental planning indicators, and environmental quality indicators; M represents the number of environmental assessment indicators for ecologically sensitive areas under each environmental indicator, M=4; In order Reduction reductions, air pollutant emission limits, and industrial wastewater treatment rates; They are, in order, the number of nature reserves, the proportion of environmental water resources, soil types, and ecological water consumption; in the embodiment of the present invention, soil types include construction land, water area, cultivated land, grassland, and forest land, and the ecological sensitivity of different soil types is ranked as follows: construction land < water area < cultivated land < grassland < forest land, where the higher the ecological sensitivity, the higher the possibility of ecological and environmental problems when encountering the same degree of natural and human activity interference; They are fuel resource carrying capacity, atmospheric environment carrying capacity, water resource carrying capacity and water consumption per unit of power generation; The percentage of land in the prohibited development area, the percentage of land in the ecological function area, the percentage of land in the agricultural product production area and the percentage of forest land; In order The annual average The annual average Annual average values ​​and annual average values ​​of inhalable particulate matter; The indicator data are collected according to the indicator system, and the collected indicator data are preprocessed to obtain the preprocessed indicator data.

[0006] Optionally, collecting indicator data according to the indicator system includes: The collected indicator data are: ; ; ; in: Indicates the collected indicator data; Indicates the index-related data collected for the dth area; It represents the indicator association data of the dth area under the nth environmental indicator; Represents the indicator association data of the mth ecologically sensitive area environmental assessment indicator of the dth area under the nth environmental indicator.

[0007] Optionally, preprocessing the collected indicator data to obtain preprocessed indicator data includes: The collected indicator data is preprocessed to obtain preprocessed indicator data, wherein the preprocessing process of the indicator data is as follows: S11: Calculate the maximum and minimum values ​​of the indicator-related data corresponding to the different ecologically sensitive area environmental assessment indicators in the indicator data data, where the maximum value of the mth ecologically sensitive area environmental assessment indicator under the nth environmental indicator is , the minimum value is ; S12: Normalize the indicator-related data of any ecologically sensitive area environmental assessment indicator in the indicator data data, where the indicator-related data The normalized formula is: ; in: Indicates indicator-related data The normalized result of S13: Constructing preprocessed index data : ; ; ; in: Indicates indicator-related data The preprocessing results; Indicates indicator-related data The preprocessing results; Indicates indicator-related data The preprocessing results.

[0008] Optionally, in step S2, improving the data quality of the preprocessed indicator data includes: Improve the data quality of the preprocessed indicator data, and the data quality improvement process is as follows: S21: Convert indicator data Convert to covariance matrix C: ; ; in: C represents indicator data The corresponding covariance matrix; represents the mean value of the indicator correlation data of D regions; T stands for transpose; S22: Perform eigendecomposition on the covariance matrix C to obtain G eigenvalues ​​and eigenvectors of the covariance matrix C, and select the eigenvectors corresponding to the first two eigenvalues ​​of the covariance matrix C. , and the eigenvector corresponding to the minimum eigenvalue of the covariance matrix C ; S23: Reconstruct the eigenvector into a quadratic surface function: ； in: represents the quadratic surface function, and x represents the independent variable of the quadratic surface function; S24: Calculate the distance between the indicator-related data of different regions, where the indicator-related data of the dth region Data associated with the indicator of the e-th region The distance between them is: ; in: Indicates indicator-related data Associating data with indicators The distance between represents an exponential function with a natural constant as base; represents the L2 norm; S25: Calculate the distance sequence between the indicator-related data of any region and the indicator-related data of other regions, where the indicator-related data The corresponding distance sequence is: ; in: Indicates indicator-related data The corresponding distance sequence; S26: Based on the distance sequence, the amount of information obtained that indicates that the indicator-related data corresponding to different regions has abnormal information, where the indicator-related data The amount of abnormal information is ; S27: Filter abnormal indicator-related data with information volume higher than a preset threshold to obtain indicator data with improved data quality: ; in: Indicator data indicating improvement in data quality; represents the indicator-related data of the k-th region that is retained, and K represents the total number of indicator-related data without abnormalities.

[0009] Optionally, the information amount of abnormality in the indicator association data corresponding to different regions obtained by calculating based on the distance sequence in step S26 includes: Indicator-related data The amount of abnormal information is: ; in: Indicates indicator-related data There is an abnormal amount of information; Represents distance sequence The mean of .

[0010] Optionally, in step S3, multi-level filtering is performed on the enhanced indicator data, including: The enhanced indicator data is filtered at multiple levels, and the multi-level filtering process is as follows: S31: Calculate the index data The average data corresponding to the environmental assessment indicators of different ecologically sensitive areas, among which the environmental assessment indicators of ecologically sensitive areas The corresponding average data is: ; in: Indicates environmental assessment indicators for ecologically sensitive areas The corresponding average data; Indicates indicator-related data The indicator-related data of the mth ecologically sensitive area environmental assessment indicator under the nth environmental indicator; S32: Index data The indicator-related data of any ecologically sensitive area environmental assessment indicator in the The centralization formula is: ; S33: Reconstruct the indicator-related data after the centralization process, where the reconstruction result is: ; in: Represents the reconstruction result; S34: Calculate the covariance matrix of the reconstruction result y ; S35: Covariance matrix Perform eigendecomposition to obtain the covariance matrix The eigenvalue sequence of is: ; in: Environmental assessment indicators for ecologically sensitive areas The corresponding eigenvalues; S36: Calculate the contribution weights of environmental assessment indicators for different ecologically sensitive areas, where the environmental assessment indicators for ecologically sensitive areas The contribution weight is: ; in: Indicates environmental assessment indicators for ecologically sensitive areas The contribution weight of S37: Calculate the redundancy factors of environmental assessment indicators for different ecologically sensitive areas, among which the environmental assessment indicators for ecologically sensitive areas The redundancy factor is: ; ; in: Representation sequence with sequence Pearson similarity between ; Indicates environmental assessment indicators for ecologically sensitive areas The redundancy factor of T stands for transpose; S38: The filtering level index of environmental assessment indicators of different ecologically sensitive areas is calculated by multi-level filtering method, among which the environmental assessment indicators of ecologically sensitive areas are The filter level index is: ; in: Indicates environmental assessment indicators for ecologically sensitive areas The filter level index; S39: Filter the ecologically sensitive area environmental assessment indicators whose level index is higher than the preset threshold, and the corresponding indicator-related data from the indicator data Cleaning in the middle, get the key indicator data: ; in: Indicates key indicator data; Indicates the key indicator associated data of the k-th region that is retained.

[0011] Optionally, constructing an ecologically sensitive area environmental assessment model in step S4 includes: Constructing an ecologically sensitive area environmental assessment model, wherein the ecologically sensitive area environmental assessment model takes key indicator data as input and takes environmental impact assessment of different areas as output, wherein the ecologically sensitive area environmental assessment model includes an input layer, an environmental impact assessment layer, and a correction layer; The input layer is used to receive key indicator data and divide the key indicator data into key indicator related data of different regions; The environmental impact assessment layer consists of a convolutional layer, a residual unit, and a fully connected layer in a convolutional neural network, which is used to perform residual convolution calculations on key indicator-related data to obtain key indicator-related features, and map the key indicator-related features to environmental impact assessment results; The correction layer is used to correct the environmental impact assessment results of different regions and output the corrected environmental impact assessment results; Receive key indicator data and conduct environmental impact assessment using the ecologically sensitive area environmental assessment model.

[0012] Optionally, the receiving key indicator data and performing environmental impact assessment using an ecologically sensitive area environmental assessment model includes: The ecologically sensitive area environmental assessment model is used to receive key indicator data and conduct environmental impact assessment, where the environmental impact assessment process is as follows: S41: Input layer receives key indicator data , and divide the key indicator data into key indicator related data of different regions, where the key indicator related data of the kth region is ; S42: The environmental impact assessment layer performs residual convolution calculation on the key indicator related data to obtain key indicator related features, where the key indicator related data The corresponding key indicator association characteristics are: ; in: Indicates key indicator related data Corresponding key indicator association characteristics; represents the convolution weight matrix, Represents the convolution operation; represents an activation function; in the embodiment of the present invention, the selected activation function is a ReLU function; S43: Map the key indicator correlation characteristics to the environmental impact assessment results, where the key indicator correlation characteristics The mapping formula is: ; in: represents the mapping weight matrix; Indicates key indicator related characteristics The corresponding environmental impact assessment results; S44: The correction layer corrects the environmental impact assessment results of different regions and outputs the corrected environmental impact assessment results. The correction formula is: ; in: Indicates the results of the environmental impact assessment Correction value of represents the environmental impact assessment results of the nearest neighboring area of ​​the kth area; The higher the revised environmental impact assessment result, the higher the ecological sensitivity of the area, and the higher the possibility of ecological and environmental problems occurring when encountering the same degree of interference from natural and human activities.

[0013] In order to solve the above problem, the present invention provides an electronic device, the electronic device comprising: A memory storing at least one instruction; Communication interface, enabling electronic equipment to communicate; and The processor executes the instructions stored in the memory to implement the multi-dimensional and multi-granular ecologically sensitive area environmental impact assessment method described above.

[0014] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned multi-dimensional and multi-granular ecologically sensitive area environmental impact assessment method.

[0015] Compared with the prior art, the present invention proposes a multi-dimensional and multi-granular environmental impact assessment method for ecologically sensitive areas, which has the following advantages: First, this scheme selects multi-granularity ecological sensitive area environmental assessment indicators from multiple dimensions such as pollution control, ecological environment, resource environment, environmental planning and environmental quality of ecological regions, constructs an ecological sensitive area environmental assessment indicator system, and collects indicator-related data from different regions to form indicator data. The principal component analysis is performed on the indicator data to obtain a quadratic surface that describes the direction of the indicator data. The distance between the indicator-related data is calculated based on the distance from different indicator-related data to the quadratic surface, and the distance is mapped to the amount of information about the abnormality of the indicator-related data, so as to realize the cleaning of the abnormal indicator-related data and improve the data quality of the indicator data.

[0016] At the same time, based on the variance contribution weights of environmental assessment indicators of different ecologically sensitive areas and the similarity of data between environmental assessment indicators of different ecologically sensitive areas, this scheme obtains the filtering level index of environmental assessment indicators of different ecologically sensitive areas, and cleans the corresponding indicator-related data of ecologically sensitive area environmental assessment indicators with filtering level index higher than the preset threshold from the indicator data to achieve data cleaning of redundant indicators or non-key indicators to form key indicator data. The ecologically sensitive area environmental assessment model is used to convolve, map and correct the key indicator-related data of different regions to obtain the environmental impact assessment results that characterize the regional ecological sensitivity. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic flow chart of a multi-dimensional and multi-granular ecologically sensitive area environmental impact assessment method provided by one embodiment of the present invention; Figure 2 A schematic diagram of the structure of an electronic device for implementing a multi-dimensional and multi-granular ecologically sensitive area environmental impact assessment method provided by an embodiment of the present invention.

[0018] In the figure: 1 electronic device, 10 processor, 11 memory, 12 program, 13 communication interface.

[0019] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0020] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0021] The embodiment of the present application provides a multi-dimensional and multi-granular method for environmental impact assessment of ecologically sensitive areas. The execution subject of the multi-dimensional and multi-granular method for environmental impact assessment of ecologically sensitive areas includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the multi-dimensional and multi-granular method for environmental impact assessment of ecologically sensitive areas can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0022] Embodiment 1: S1: Construct an environmental assessment index system for ecologically sensitive areas, collect index data according to the index system and preprocess them to obtain preprocessed index data.

[0023] The ecologically sensitive area environmental assessment indicator system is constructed in step S1, including: Construct an ecologically sensitive area environmental assessment indicator system, where the set of ecologically sensitive area environmental assessment indicators in the ecologically sensitive area environmental assessment indicator system is: ; in: It represents the mth ecologically sensitive area environmental assessment index under the nth environmental index; N represents the number of environmental indicators, N=5, and the first to fifth environmental indicators are pollution control indicators, ecological environment indicators, resource and environmental indicators, environmental planning indicators, and environmental quality indicators; M represents the number of environmental assessment indicators for ecologically sensitive areas under each environmental indicator, M=4; In order Reduction reductions, air pollutant emission limits, and industrial wastewater treatment rates; They are the number of nature reserves, the proportion of environmental water resources, soil types, and ecological water consumption; They are fuel resource carrying capacity, atmospheric environment carrying capacity, water resource carrying capacity and water consumption per unit of power generation; The percentage of land in the prohibited development area, the percentage of land in the ecological function area, the percentage of land in the agricultural product production area and the percentage of forest land; In order The annual average The annual average Annual average values ​​and annual average values ​​of inhalable particulate matter; The indicator data are collected according to the indicator system, and the collected indicator data are preprocessed to obtain the preprocessed indicator data.

[0024] The collecting of indicator data according to the indicator system includes: The collected indicator data are: ; ; ; in: Indicates the collected indicator data; Indicates the index-related data collected for the dth area; It represents the indicator association data of the dth area under the nth environmental indicator; Represents the indicator association data of the mth ecologically sensitive area environmental assessment indicator of the dth area under the nth environmental indicator.

[0025] The preprocessing of the collected indicator data to obtain the preprocessed indicator data includes: The collected indicator data is preprocessed to obtain preprocessed indicator data, wherein the preprocessing process of the indicator data is as follows: S11: Calculate the maximum and minimum values ​​of the indicator-related data corresponding to the different ecologically sensitive area environmental assessment indicators in the indicator data data, where the maximum value of the mth ecologically sensitive area environmental assessment indicator under the nth environmental indicator is , the minimum value is ; S12: Normalize the indicator-related data of any ecologically sensitive area environmental assessment indicator in the indicator data data, where the indicator-related data The normalized formula is: ; in: Indicates indicator-related data The normalized result of S13: Constructing preprocessed index data : ; ; ; in: Indicates indicator-related data The preprocessing results; Indicates indicator-related data The preprocessing results; Indicates indicator-related data The preprocessing results.

[0026] S2: Improve the data quality of the preprocessed indicator data to obtain enhanced indicator data.

[0027] In the step S2, the quality of the pre-processed indicator data is improved, including: Improve the data quality of the preprocessed indicator data, and the data quality improvement process is as follows: S21: Convert indicator data Convert to covariance matrix C: ; ; in: C represents indicator data The corresponding covariance matrix; represents the mean value of the indicator correlation data of D regions; T stands for transpose; S22: Perform eigendecomposition on the covariance matrix C to obtain G eigenvalues ​​and eigenvectors of the covariance matrix C, and select the eigenvectors corresponding to the first two eigenvalues ​​of the covariance matrix C. , and the eigenvector corresponding to the minimum eigenvalue of the covariance matrix C ; S23: Reconstruct the eigenvector into a quadratic surface function: ； in: represents the quadratic surface function, and x represents the independent variable of the quadratic surface function; S24: Calculate the distance between the indicator-related data of different regions, where the indicator-related data of the dth region Data associated with the indicator of the e-th region The distance between them is: ; in: Indicates indicator-related data Associating data with indicators The distance between represents an exponential function with a natural constant as base; represents the L2 norm; S25: Calculate the distance sequence between the indicator-related data of any region and the indicator-related data of other regions, where the indicator-related data The corresponding distance sequence is: ; in: Indicates indicator-related data The corresponding distance sequence; S26: Based on the distance sequence, the amount of information obtained that indicates that the indicator-related data corresponding to different regions has abnormal information, where the indicator-related data The amount of abnormal information is ; S27: Filter abnormal indicator-related data with information volume higher than a preset threshold to obtain indicator data with improved data quality: ; in: Indicator data indicating improvement in data quality; represents the indicator-related data of the k-th region that is retained, and K represents the total number of indicator-related data without abnormalities.

[0028] The information amount of abnormality in the indicator association data corresponding to different regions obtained in step S26 based on the distance sequence calculation includes: Indicator-related data The amount of abnormal information is: ; in: Indicates indicator-related data There is an abnormal amount of information; Represents distance sequence The mean of .

[0029] S3: Perform multi-level filtering on the enhanced indicator data to obtain filtered key indicator data.

[0030] In the step S3, the enhanced indicator data is subjected to multi-level filtering, including: The enhanced indicator data is filtered at multiple levels, and the multi-level filtering process is as follows: S31: Calculate the index data The average data corresponding to the environmental assessment indicators of different ecologically sensitive areas, among which the environmental assessment indicators of ecologically sensitive areas The corresponding average data is: ; in: Indicates environmental assessment indicators for ecologically sensitive areas The corresponding average data; Indicates indicator-related data The indicator-related data of the mth ecologically sensitive area environmental assessment indicator under the nth environmental indicator; S32: Index data The indicator-related data of any ecologically sensitive area environmental assessment indicator in the The centralization formula is: ; S33: Reconstruct the indicator-related data after the centralization process, where the reconstruction result is: ; in: Represents the reconstruction result; S34: Calculate the covariance matrix of the reconstruction result y ; S35: Covariance matrix Perform eigendecomposition to obtain the covariance matrix The eigenvalue sequence of is: ; in: Environmental assessment indicators for ecologically sensitive areas The corresponding eigenvalues; S36: Calculate the contribution weights of environmental assessment indicators for different ecologically sensitive areas, where the environmental assessment indicators for ecologically sensitive areas The contribution weight is: ; in: Indicates environmental assessment indicators for ecologically sensitive areas The contribution weight of S37: Calculate the redundancy factors of environmental assessment indicators for different ecologically sensitive areas, among which the environmental assessment indicators for ecologically sensitive areas The redundancy factor is: ; ; in: Representation sequence with sequence Pearson similarity between ; Indicates environmental assessment indicators for ecologically sensitive areas The redundancy factor of T stands for transpose; S38: The filtering level index of environmental assessment indicators of different ecologically sensitive areas is calculated by multi-level filtering method, among which the environmental assessment indicators of ecologically sensitive areas are The filter level index is: ; in: Indicates environmental assessment indicators for ecologically sensitive areas The filter level index; S39: Filter the ecologically sensitive area environmental assessment indicators whose level index is higher than the preset threshold, and the corresponding indicator-related data from the indicator data Cleaning in the middle, get the key indicator data: ; in: Indicates key indicator data; Indicates the key indicator associated data of the k-th region that is retained.

[0031] S4: Construct an environmental assessment model for ecologically sensitive areas, and use the environmental assessment model for ecologically sensitive areas to receive key indicator data and conduct environmental impact assessment.

[0032] The step S4 constructs an ecologically sensitive area environmental assessment model, including: Constructing an ecologically sensitive area environmental assessment model, wherein the ecologically sensitive area environmental assessment model takes key indicator data as input and takes environmental impact assessment of different areas as output, wherein the ecologically sensitive area environmental assessment model includes an input layer, an environmental impact assessment layer, and a correction layer; The input layer is used to receive key indicator data and divide the key indicator data into key indicator related data of different regions; The environmental impact assessment layer consists of a convolutional layer, a residual unit, and a fully connected layer in a convolutional neural network, which is used to perform residual convolution calculations on key indicator-related data to obtain key indicator-related features, and map the key indicator-related features to environmental impact assessment results; The correction layer is used to correct the environmental impact assessment results of different regions and output the corrected environmental impact assessment results; Receive key indicator data and conduct environmental impact assessment using the ecologically sensitive area environmental assessment model.

[0033] The method of using the ecologically sensitive area environmental assessment model to receive key indicator data and conduct environmental impact assessment includes: The ecologically sensitive area environmental assessment model is used to receive key indicator data and conduct environmental impact assessment, where the environmental impact assessment process is as follows: S41: Input layer receives key indicator data , and divide the key indicator data into key indicator related data of different regions, where the key indicator related data of the kth region is ; S42: The environmental impact assessment layer performs residual convolution calculation on the key indicator related data to obtain key indicator related features, where the key indicator related data The corresponding key indicator association characteristics are: ; in: Indicates key indicator related data Corresponding key indicator association characteristics; represents the convolution weight matrix, Represents the convolution operation; represents an activation function; in the embodiment of the present invention, the selected activation function is a ReLU function; S43: Map the key indicator correlation characteristics to the environmental impact assessment results, where the key indicator correlation characteristics The mapping formula is: ; in: represents the mapping weight matrix; Indicates key indicator related characteristics The corresponding environmental impact assessment results; S44: The correction layer corrects the environmental impact assessment results of different regions and outputs the corrected environmental impact assessment results. The correction formula is: ; in: Indicates the results of the environmental impact assessment Correction value of represents the environmental impact assessment results of the nearest neighboring area of ​​the kth area; The higher the revised environmental impact assessment result, the higher the ecological sensitivity of the area, and the higher the possibility of ecological and environmental problems occurring when encountering the same degree of interference from natural and human activities.

[0034] Embodiment 2: like Figure 2 , which is a schematic diagram of the structure of an electronic device for implementing a multi-dimensional and multi-granular ecologically sensitive area environmental impact assessment method provided by an embodiment of the present invention.

[0035] The electronic device 1 may include a processor 10 , a memory 11 , a communication interface 13 and a bus, and may also include a computer program stored in the memory 11 and executable on the processor 10 , such as a program 12 .

[0036] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 may also include both an internal storage unit of the electronic device 1 and an external storage device. The memory 11 may not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the program 12, etc., but also be used to temporarily store data that has been output or is to be output.

[0037] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules stored in the memory 11 (such as the program 12 for implementing multi-dimensional and multi-granular ecological sensitive area environmental impact assessment) and calls data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

[0038] The communication interface 13 may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices, and to achieve connection and communication between internal components of the electronic device.

[0039] The bus may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0040] Figure 2 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 2 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0041] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0042] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0043] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0044] The program 12 stored in the memory 11 in the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve: Construct an environmental assessment index system for ecologically sensitive areas, collect index data according to the index system and pre-process them to obtain pre-processed index data; Improving the data quality of the preprocessed indicator data to obtain enhanced indicator data; Perform multi-level filtering on the enhanced indicator data to obtain filtered key indicator data; Construct an environmental assessment model for ecologically sensitive areas, and use the environmental assessment model for ecologically sensitive areas to receive key indicator data and conduct environmental impact assessment.

[0045] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figure 1 to Figure 2 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0046] It should be noted that the serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments. And the terms "including", "comprising" or any other variants thereof in this article are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "including a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0047] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0048] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A multi-dimensional and multi-granular ecologically sensitive area environmental impact assessment method, characterized in that: The method comprises: S1: Construct an environmental assessment index system for ecologically sensitive areas, collect index data according to the index system and pre-process them to obtain pre-processed index data; S2: Improve the data quality of the preprocessed indicator data to obtain enhanced indicator data; S3: Perform multi-level filtering on the enhanced indicator data to obtain filtered key indicator data; S4: Construct an environmental assessment model for ecologically sensitive areas, and use the environmental assessment model for ecologically sensitive areas to receive key indicator data and conduct environmental impact assessment.

2. A multi-dimensional and multi-granular ecologically sensitive area environmental impact assessment method as claimed in claim 1, characterized in that: The ecologically sensitive area environmental assessment indicator system is constructed in step S1, including: Construct an ecologically sensitive area environmental assessment indicator system, where the set of ecologically sensitive area environmental assessment indicators in the ecologically sensitive area environmental assessment indicator system is: ； in: It represents the mth ecologically sensitive area environmental assessment index under the nth environmental index; N represents the number of environmental indicators, N=5, and the first to fifth environmental indicators are pollution control indicators, ecological environment indicators, resource and environmental indicators, environmental planning indicators, and environmental quality indicators; M represents the number of environmental assessment indicators for ecologically sensitive areas under each environmental indicator, M=4; In order Reduction reductions, air pollutant emission limits, and industrial wastewater treatment rates; They are the number of nature reserves, the proportion of environmental water resources, soil types, and ecological water consumption; They are fuel resource carrying capacity, atmospheric environment carrying capacity, water resource carrying capacity and water consumption per unit of power generation; The percentage of land in the prohibited development area, the percentage of land in the ecological function area, the percentage of land in the agricultural product production area and the percentage of forest land; In order The annual average The annual average Annual average values ​​and annual average values ​​of inhalable particulate matter; Collect indicator data according to the indicator system.

3. A multi-dimensional and multi-granular ecologically sensitive area environmental impact assessment method as claimed in claim 2, characterized in that: The collecting of indicator data according to the indicator system includes: The collected indicator data are: ； ； ； in: Indicates the collected indicator data; Indicates the index-related data collected for the dth area; It represents the indicator association data of the dth area under the nth environmental indicator; Indicator association data representing the mth ecologically sensitive area environmental assessment indicator under the nth environmental indicator for the dth area; The collected indicator data are preprocessed to obtain preprocessed indicator data.

4. A multi-dimensional and multi-granular ecologically sensitive area environmental impact assessment method as claimed in claim 3, characterized in that: The preprocessing of the collected indicator data to obtain the preprocessed indicator data includes: The collected indicator data is preprocessed to obtain preprocessed indicator data, wherein the preprocessing process of the indicator data is as follows: S11: Calculate the maximum and minimum values ​​of the indicator-related data corresponding to the different ecologically sensitive area environmental assessment indicators in the indicator data data, where the maximum value of the mth ecologically sensitive area environmental assessment indicator under the nth environmental indicator is , the minimum value is ; S12: Normalize the indicator-related data of any ecologically sensitive area environmental assessment indicator in the indicator data data, where the indicator-related data The normalized formula is: ； in: Indicates indicator-related data The normalized result of S13: Constructing preprocessed index data : ； ； ； in: Indicates indicator-related data The preprocessing results; Indicates indicator-related data The preprocessing results; Indicates indicator-related data The preprocessing results.

5. A multi-dimensional and multi-granular ecologically sensitive area environmental impact assessment method as claimed in claim 4, characterized in that: In the step S2, the quality of the pre-processed indicator data is improved, including: Improve the data quality of the preprocessed indicator data, and the data quality improvement process is as follows: S21: Convert indicator data Convert to covariance matrix C: ； ； in: C represents indicator data The corresponding covariance matrix; represents the mean value of the indicator correlation data of D regions; T stands for transpose; S22: Perform eigendecomposition on the covariance matrix C to obtain G eigenvalues ​​and eigenvectors of the covariance matrix C, and select the eigenvectors corresponding to the first two eigenvalues ​​of the covariance matrix C. , and the eigenvector corresponding to the minimum eigenvalue of the covariance matrix C ; S23: Reconstruct the eigenvector into a quadratic surface function: in: represents the quadratic surface function, and x represents the independent variable of the quadratic surface function; S24: Calculate the distance between the indicator-related data of different regions, where the indicator-related data of the dth region Data associated with the indicator of the e-th region The distance between them is: in: Indicates indicator-related data Associating data with indicators The distance between represents an exponential function with a natural constant as base; represents the L2 norm; S25: Calculate the distance sequence between the indicator-related data of any region and the indicator-related data of other regions, where the indicator-related data The corresponding distance sequence is: ； in: Indicates indicator-related data The corresponding distance sequence; S26: Based on the distance sequence, the amount of information obtained that indicates that the indicator-related data corresponding to different regions has abnormal information, where the indicator-related data The amount of abnormal information is ; S27: Filter abnormal indicator-related data with information volume higher than a preset threshold to obtain indicator data with improved data quality: ； in: Indicator data indicating improvement in data quality; represents the indicator-related data of the k-th region that is retained, and K represents the total number of indicator-related data without abnormalities.

6. A multi-dimensional and multi-granular ecologically sensitive area environmental impact assessment method as claimed in claim 5, characterized in that: The information amount of abnormality in the indicator association data corresponding to different regions obtained in step S26 based on the distance sequence calculation includes: Indicator-related data The amount of abnormal information is: ； in: Indicates indicator-related data There is an abnormal amount of information; Represents distance sequence The mean of .

7. A multi-dimensional and multi-granular ecologically sensitive area environmental impact assessment method as claimed in claim 1, characterized in that: In the step S3, the enhanced indicator data is subjected to multi-level filtering, including: The enhanced indicator data is filtered at multiple levels, and the multi-level filtering process is as follows: S31: Calculate the index data The average data corresponding to the environmental assessment indicators of different ecologically sensitive areas, among which the environmental assessment indicators of ecologically sensitive areas The corresponding average data is: ； in: Indicates environmental assessment indicators for ecologically sensitive areas The corresponding average data; Indicates indicator-related data The indicator-related data of the mth ecologically sensitive area environmental assessment indicator under the nth environmental indicator; S32: Index data The indicator-related data of any ecologically sensitive area environmental assessment indicator in the The centralization formula is: ； S33: Reconstruct the indicator-related data after the centralization process, where the reconstruction result is: ； in: Represents the reconstruction result; S34: Calculate the covariance matrix of the reconstruction result y ; S35: Covariance matrix Perform eigendecomposition to obtain the covariance matrix The eigenvalue sequence of is: ； in: Environmental assessment indicators for ecologically sensitive areas The corresponding eigenvalues; S36: Calculate the contribution weights of environmental assessment indicators for different ecologically sensitive areas, where the environmental assessment indicators for ecologically sensitive areas The contribution weight is: ； in: Indicates environmental assessment indicators for ecologically sensitive areas The contribution weight of S37: Calculate the redundancy factors of environmental assessment indicators for different ecologically sensitive areas, among which the environmental assessment indicators for ecologically sensitive areas The redundancy factor is: ； ； in: Representation sequence with sequence Pearson similarity between ; Indicates environmental assessment indicators for ecologically sensitive areas The redundancy factor of T stands for transpose; S38: The filtering level index of environmental assessment indicators of different ecologically sensitive areas is calculated by multi-level filtering method, among which the environmental assessment indicators of ecologically sensitive areas are The filter level index is: ； in: Indicates environmental assessment indicators for ecologically sensitive areas The filter level index; S39: Filter the ecologically sensitive area environmental assessment indicators whose level index is higher than the preset threshold, and the corresponding indicator-related data from the indicator data Cleaning in the middle, get the key indicator data: ； in: Indicates key indicator data; Indicates the key indicator associated data of the k-th region that is retained.

8. A multi-dimensional and multi-granular ecologically sensitive area environmental impact assessment method as claimed in claim 1, characterized in that: The step S4 constructs an ecologically sensitive area environmental assessment model, including: Constructing an ecologically sensitive area environmental assessment model, wherein the ecologically sensitive area environmental assessment model takes key indicator data as input and takes environmental impact assessment of different areas as output, wherein the ecologically sensitive area environmental assessment model includes an input layer, an environmental impact assessment layer, and a correction layer; The input layer is used to receive key indicator data and divide the key indicator data into key indicator related data of different regions; The environmental impact assessment layer consists of a convolutional layer, a residual unit, and a fully connected layer in a convolutional neural network, which is used to perform residual convolution calculations on key indicator-related data to obtain key indicator-related features, and map the key indicator-related features to environmental impact assessment results; The correction layer is used to correct the environmental impact assessment results of different regions and output the corrected environmental impact assessment results; Receive key indicator data and conduct environmental impact assessment using the ecologically sensitive area environmental assessment model.

9. A multi-dimensional and multi-granular ecologically sensitive area environmental impact assessment method as claimed in claim 8, characterized in that: The method of using the ecologically sensitive area environmental assessment model to receive key indicator data and conduct environmental impact assessment includes: The ecologically sensitive area environmental assessment model is used to receive key indicator data and conduct environmental impact assessment, where the environmental impact assessment process is as follows: S41: Input layer receives key indicator data , and divide the key indicator data into key indicator related data of different regions, where the key indicator related data of the kth region is ; S42: The environmental impact assessment layer performs residual convolution calculation on the key indicator related data to obtain key indicator related features, where the key indicator related data The corresponding key indicator association characteristics are: ； in: Indicates key indicator related data Corresponding key indicator association characteristics; represents the convolution weight matrix, Represents the convolution operation; represents the activation function; S43: Map the key indicator correlation characteristics to the environmental impact assessment results, where the key indicator correlation characteristics The mapping formula is: ； in: represents the mapping weight matrix; Indicates key indicator related characteristics The corresponding environmental impact assessment results; S44: The correction layer corrects the environmental impact assessment results of different regions and outputs the corrected environmental impact assessment results. The correction formula is: ； in: Indicates the results of the environmental impact assessment Correction value of represents the environmental impact assessment results of the nearest neighboring area of ​​the kth area; The higher the revised environmental impact assessment result, the higher the ecological sensitivity of the area, and the higher the possibility of ecological and environmental problems occurring when encountering the same degree of interference from natural and human activities.

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