CN116611700A - Knowledge graph-based regional soil erosion dynamic monitoring method and device - Google Patents
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Abstract
The method establishes a water and soil loss evaluation index system of a water and soil loss monitoring area; calculating rainfall index values, soil type index values and slope length index values for the static indexes; extracting geographic knowledge facts by using knowledge extraction means to form a geographic knowledge map; acquiring a high-resolution remote sensing image of a water and soil loss monitoring area, and realizing automatic classification and intelligent extraction of land utilization types and engineering measures by using a constructed geographic knowledge graph; calculating a gradient index value, a vegetation coverage and biological measure index value and an engineering measure index value for the dynamic index; and multiplying the static index and the dynamic index grid result to obtain a soil erosion quantized value, judging the soil erosion intensity according to the soil erosion intensity judging model, and correcting the artificial disturbance map erosion intensity. The application greatly reduces the manual interpretation and field workload and improves the dynamic monitoring efficiency of the regional soil erosion.
Description
Technical Field
The application relates to a dynamic monitoring method and device for regional soil erosion based on a knowledge graph, and belongs to the technical field of soil erosion monitoring.
Background
Soil erosion is one of serious natural disasters in China, and not only causes the destruction of land resources, but also causes the deterioration of agricultural production environment, the imbalance of ecological balance and frequent flood and drought, and affects the development of various industries. Relevant law prescribes that relevant departments should perfect a national water and soil conservation monitoring network to dynamically monitor the national water and soil loss. The dynamic monitoring of water and soil loss refers to the long-term investigation, observation and analysis of water and soil loss occurrence, development, harm and water and soil conservation benefits.
The current land utilization and engineering measure interpretation mainstream is a method combining manual visual interpretation of remote sensing images and field verification, and the shortcoming is that the manual interpretation and field verification have huge workload. Other methods also adopt algorithm thinking, such as a machine learning algorithm, and utilize sample learning to acquire remote sensing information to realize an automatic interpretation method, but the premise is that the visual characteristics of a sample picture are locally relevant or local feature features are not changed along with the position, so that high accuracy can be ensured, the earth surface system is heterogeneous, and in addition, the result of a deep learning model sometimes conflicts with priori knowledge and expert knowledge of people, so that the quality of water and soil loss monitoring results is affected.
Disclosure of Invention
The application aims to provide a dynamic monitoring method and device for water and soil loss of a region based on a knowledge graph, which are used for rapidly calculating related indexes to obtain an erosion modulus by extracting land utilization types and engineering measures of high-resolution remote sensing images so as to realize dynamic monitoring for water and soil loss of the region.
The technical scheme for solving the technical problems is as follows: a dynamic monitoring method for regional soil erosion based on a knowledge graph comprises the following steps:
analyzing water and soil loss influence factors of a water and soil loss monitoring area, and establishing a water and soil loss evaluation index system of the water and soil loss monitoring area, wherein the water and soil loss evaluation index system comprises a static index and a dynamic index;
calculating the static index: acquiring data of a rainfall observation site, obtaining rainfall index values of the water and soil loss monitoring area through interpolation analysis, performing superposition analysis assignment calculation according to a national soil map according to physicochemical analysis data of soil general investigation, obtaining each soil type index value of the water and soil loss monitoring area, obtaining DEM data of the water and soil loss monitoring area, and calculating slope length index values;
acquiring specified structured, unstructured and semi-structured data of the water and soil loss monitoring area, and extracting geographic knowledge facts by using knowledge extraction means to form a geographic knowledge map;
acquiring a high-resolution remote sensing image of the water and soil loss monitoring area, and realizing automatic classification and intelligent extraction of land utilization types and engineering measures by utilizing the constructed geographical knowledge graph;
calculating the dynamic index: calculating a gradient index value by using the DEM data and the interpreted land use type data; calculating a normalized vegetation index NDVI and vegetation coverage FVC of the water and soil loss monitoring area by using the TM image and the MODIS image to obtain vegetation coverage and biological measure index values; assigning values to water and soil conservation engineering measure indexes for the water and soil loss monitoring areas with engineering measures;
and multiplying the static index by the dynamic index grid result to obtain a soil erosion quantized value, judging the soil erosion intensity according to a soil erosion intensity judging model, and correcting the artificially disturbed plaque erosion intensity.
As a preferable scheme of the regional soil erosion dynamic monitoring method based on the knowledge graph, the static indexes comprise rainfall indexes, soil type indexes and slope length indexes, the dynamic indexes are indexes related to land utilization types and engineering measure interpretation results, and the dynamic indexes comprise slope indexes, vegetation coverage and biological measure indexes and engineering measure indexes.
As a preferred scheme of the regional soil erosion dynamic monitoring method based on the knowledge graph, the static index is calculated in the following steps:
interpolation is carried out by utilizing geographic information system software to generate a contour map and a grid map layer, so as to obtain a rainfall index value A of the water and soil loss monitoring area 1 ；
According to the physicochemical analysis data of the second national soil census, carrying out superposition analysis assignment calculation according to the national soil map to obtain each soil type index value A of the soil erosion monitoring area 2 ；
Calculating a slope length index value A according to the acquired DEM data of the water and soil loss monitoring area by using geographic information system software 3 。
As a preferred scheme of the dynamic monitoring method for the water and soil loss of the area based on the knowledge graph, the construction process of the geographical knowledge graph adopts data comprising high-resolution remote sensing image data, a land three-adjustment land utilization change database and a key management engineering database;
land use types include cultivated land, garden land, woodland, grassland, artificial land for harassment, construction land, transportation land, water area, and water conservancy facility land;
engineering measures include terraces, stems, horizontal steps, and horizontal furrows.
As a preferred scheme of the regional soil erosion dynamic monitoring method based on the knowledge graph, the geographical knowledge fact process is extracted by using a knowledge extraction means:
storing the extracted geographic knowledge facts into a data layer and a mode layer of a geographic knowledge base, and extracting resource description framework triples to form a geographic knowledge map;
and fusing and correlating the geographic environment, the geographic entity and the human factor resources to obtain the related knowledge of the geographic space.
As a preferred scheme of the regional soil erosion dynamic monitoring method based on the knowledge graph, the dynamic index is calculated in the following way:
wherein B is 1 Indicating a gradient index, θ indicating a gradient;
when the land is woodland and grassland and the gradient is larger than 30 degrees, theta is substituted into the formula to calculate the gradient index.
As a preferred scheme of the dynamic monitoring method for regional soil erosion based on the knowledge graph, the dynamic index is calculated, and vegetation coverage and biological measure index value B is calculated 2 The calculation mode of (a) is as follows:
if the land use type is cultivated land or artificial disturbance land, the vegetation coverage and biological measure index value is 1;
if the index value is the construction land or the transportation land, the index value is 0.01;
if the index value is 0 for the water area and the water conservancy facility land, the index value is 0;
if the land use type is garden, forest or grassland, calculating vegetation coverage and biological measure index values by using rainfall weight of 24 periods, wherein the calculation formula is as follows:
in WR i The ratio of the i-th half-month rainfall erosion force calculated for the previous calculation to the annual erosion force; SLR (SLR) i Soil loss ratio for ith half-moon garden, woodland and grassland.
As a preferred scheme of the regional soil erosion dynamic monitoring method based on the knowledge graph, the dynamic index is calculated, and the engineering measure index value B is calculated 3 The assignment mode is as follows:
index value of terrace is 0.084; the index value of the ground stalk is 0.347; the index of the horizontal order takes on a value of 0.151; the index value of the horizontal ditch is 0.335;
for farmland with gradient less than or equal to 2 degrees, if no terrace soil and water conservation engineering measures are adopted, the index is assigned to be 0.431.
As a preferred scheme of the regional soil erosion dynamic monitoring method based on the knowledge graph, the calculation formula of the soil erosion quantized value QS is as follows:
QS＝A 1 ×A 2 ×A 3 ×B 1 ×B 2 ×B 3
wherein A is 1 For rainfall index value, A 2 For soil type index value, A 3 For the index value of slope length, B 1 B is the gradient index 2 For vegetation cover and biological measure index value, B 3 Is an engineering measure index value;
the soil erosion strength judgment model is as follows:
a grid region having a QS value <5 is determined to be a micro-scale; the QS value interval [5,25 ] is determined to be mild, the QS value interval [25,50 ] is determined to be moderate, the QS value interval [50, 80) is determined to be strong, the QS value interval [80,150) is determined to be extremely strong, and the QS value >150 is determined to be severe.
The application also provides a dynamic monitoring device for regional soil erosion based on the knowledge graph, which comprises:
the evaluation index system construction module is used for analyzing water and soil loss influence factors of a water and soil loss monitoring area and establishing a water and soil loss evaluation index system of the water and soil loss monitoring area, wherein the water and soil loss evaluation index system comprises a static index and a dynamic index;
the static index calculation module is used for calculating the static index: acquiring data of a rainfall observation site, obtaining rainfall index values of the water and soil loss monitoring area through interpolation analysis, performing superposition analysis assignment calculation according to a national soil map according to physicochemical analysis data of soil general investigation, obtaining each soil type index value of the water and soil loss monitoring area, obtaining DEM data of the water and soil loss monitoring area, and calculating slope length index values;
the geographical knowledge map construction module is used for acquiring specified structured, unstructured and semi-structured data of the water and soil loss monitoring area, extracting geographical knowledge facts by using a knowledge extraction means and forming a geographical knowledge map;
the image information classification and extraction module is used for acquiring a high-resolution remote sensing image of the water and soil loss monitoring area and realizing automatic classification and intelligent extraction of land utilization types and engineering measures by utilizing the constructed geographic knowledge graph;
the dynamic index calculation module is used for calculating the dynamic index: calculating a gradient index value by using the DEM data and the interpreted land use type data; calculating a normalized vegetation index NDVI and vegetation coverage FVC of the water and soil loss monitoring area by using the TM image and the MODIS image to obtain vegetation coverage and biological measure index values; assigning values to water and soil conservation engineering measure indexes for the water and soil loss monitoring areas with engineering measures;
and the soil erosion analysis processing module is used for multiplying the static index and the dynamic index grid result to obtain a soil erosion quantized value, judging the soil erosion intensity according to the soil erosion intensity judging model, and correcting the artificial disturbance map spot erosion intensity.
The method has the beneficial effects that by analyzing the water and soil loss influence factors of the water and soil loss monitoring area, a water and soil loss evaluation index system of the water and soil loss monitoring area is established, wherein the water and soil loss evaluation index system comprises a static index and a dynamic index; calculating a static index: acquiring data of a rainfall observation site, obtaining rainfall index values of a water and soil loss monitoring area through interpolation analysis, performing superposition analysis and assignment calculation according to a national soil map according to physicochemical analysis data of soil general investigation, obtaining each soil type index value of the water and soil loss monitoring area, obtaining DEM data of the water and soil loss monitoring area, and calculating a slope length index value; acquiring specified structured, unstructured and semi-structured data of a water and soil loss monitoring area, and extracting geographic knowledge facts by using knowledge extraction means to form a geographic knowledge map; acquiring a high-resolution remote sensing image of a water and soil loss monitoring area, and realizing automatic classification and intelligent extraction of land utilization types and engineering measures by using a constructed geographic knowledge graph; calculating dynamic indexes: calculating a gradient index value by using the DEM data and the interpreted land use type data; calculating a normalized vegetation index NDVI and vegetation coverage FVC of the water and soil loss monitoring area by using the TM image and the MODIS image to obtain vegetation coverage and biological measure index values; assigning values to water and soil conservation engineering measure indexes in a water and soil loss monitoring area with engineering measures; and multiplying the static index and the dynamic index grid result to obtain a soil erosion quantized value, judging the soil erosion intensity according to the soil erosion intensity judging model, and correcting the artificial disturbance map erosion intensity. According to the application, the geographic knowledge map is formed by constructing the geographic knowledge map, fully utilizing the existing knowledge and related data, reflecting the geographic knowledge on a spatial map model structure, and forming the geographic knowledge map through data acquisition, information extraction, knowledge fusion and knowledge processing, so that a brand-new automatic intelligent means is provided for the remote sensing interpretation of the dynamic monitoring of the water loss and soil erosion, the manual interpretation and the field workload are greatly reduced, and the dynamic monitoring efficiency of the water loss and soil erosion in a region is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It will be apparent to those skilled in the art from this disclosure that the drawings described below are merely exemplary and that other embodiments may be derived from the drawings provided without undue effort.
The structures, proportions, sizes, etc. shown in the present specification are shown only for the purposes of illustration and description, and are not intended to limit the scope of the application, which is defined by the claims, so that any structural modifications, changes in proportions, or adjustments of sizes, which do not affect the efficacy or the achievement of the present application, should fall within the scope of the application.
Fig. 1 is a schematic diagram of a dynamic monitoring method for regional soil erosion based on a knowledge graph according to an embodiment of the present application;
fig. 2 is a schematic diagram of an area water and soil loss dynamic monitoring device based on a knowledge graph according to an embodiment of the present application.
Detailed Description
In order that the above objects, features and advantages of the application will be readily understood, a more particular description of the application will be rendered by reference to the appended drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. The present application may be embodied in many other forms than described herein and similarly modified by those skilled in the art without departing from the spirit of the application, whereby the application is not limited to the specific embodiments disclosed below.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.
Example 1
Referring to fig. 1, an embodiment of the application provides a method for dynamically monitoring regional soil erosion based on a knowledge graph, which comprises the following steps:
s1, analyzing water and soil loss influence factors of a water and soil loss monitoring area, and establishing a water and soil loss evaluation index system of the water and soil loss monitoring area, wherein the water and soil loss evaluation index system comprises static indexes and dynamic indexes;
s2, calculating the static index: acquiring data of a rainfall observation site, obtaining rainfall index values of the water and soil loss monitoring area through interpolation analysis, performing superposition analysis assignment calculation according to a national soil map according to physicochemical analysis data of soil general investigation, obtaining each soil type index value of the water and soil loss monitoring area, obtaining DEM data of the water and soil loss monitoring area, and calculating slope length index values;
s3, acquiring specified structured, unstructured and semi-structured data of the water and soil loss monitoring area, and extracting geographic knowledge facts by using knowledge extraction means to form a geographic knowledge map;
s4, acquiring a high-resolution remote sensing image of the water and soil loss monitoring area, and realizing automatic classification and intelligent extraction of land utilization types and engineering measures by utilizing the constructed geographical knowledge graph;
s5, calculating the dynamic index: calculating a gradient index value by using the DEM data and the interpreted land use type data; calculating a normalized vegetation index NDVI and vegetation coverage FVC of the water and soil loss monitoring area by using the TM image and the MODIS image to obtain vegetation coverage and biological measure index values; assigning values to water and soil conservation engineering measure indexes for the water and soil loss monitoring areas with engineering measures;
and S6, multiplying the static index by the dynamic index grid result to obtain a soil erosion quantized value, judging the soil erosion intensity according to a soil erosion intensity judging model, and correcting the artificial disturbance map erosion intensity.
In this embodiment, the static indexes include a rainfall index, a soil type index and a slope length index, the dynamic indexes are indexes related to land utilization types and interpretation results of engineering measures, and the dynamic indexes include a slope index, a vegetation coverage and biological measure index and an engineering measure index.
Wherein, in the process of calculating the static index:
acquiring data of a rainfall observation site, performing interpolation by using ArcGIS software of a geographic information system to generate a contour map and a grid map layer, and obtaining a rainfall index value A of the water and soil loss monitoring area 1 The method comprises the steps of carrying out a first treatment on the surface of the According to the physicochemical analysis data of the second national soil census, carrying out superposition analysis assignment calculation by utilizing ArcGIS software according to the national soil map to obtain each soil type index value A of the water and soil loss monitoring area 2 The method comprises the steps of carrying out a first treatment on the surface of the Calculating a slope length index value A according to the acquired DEM data of the water and soil loss monitoring area by using geographic information system software 3 。
The rainfall index, the soil type index and the slope length index can also adopt data issued by water conservancy departments.
In the embodiment, the geographical knowledge graph construction process adopts data comprising high-resolution remote sensing image data, a land three-adjustment land utilization change database and a key management engineering database; land utilization types include cultivated land, garden land, woodland, grassland, construction land, transportation land, water area, water conservancy facility land and other lands, and artificial disturbance land types are added according to soil erosion characteristics, and 9 land types are added. Engineering measures include terraces, stems, horizontal steps and horizontal furrows in 4 categories.
In this embodiment, the process of extracting the geographical knowledge facts by knowledge extraction means: extracting geographical knowledge facts from basic data and other semi-structured and unstructured data, storing the extracted geographical knowledge facts into a data layer and a mode layer of a geographical knowledge base, and extracting resource description framework triples (RDF triples, h, r and t) to form a geographical knowledge map; and fusing and correlating the geographic environment, the geographic entity and the human factor resources to obtain the related knowledge of the geographic space.
Specifically, the process of extracting the geographical knowledge facts by using knowledge extraction means comprises the following steps: collecting related geographic information and data, carding to summarize geographic knowledge, adopting a hierarchical and gradual refining mode, abstracting and refining from three knowledge levels of data knowledge, conceptual knowledge and regular knowledge from a plurality of dimensions such as topological relation, azimuth, spectrum, texture, tone and shape, constructing a characteristic body of a typical geographic entity, and realizing geographic knowledge body expression; extracting geographical knowledge facts from an existing basic database and other structured, semi-structured and unstructured data, extracting required attributes, relationships and entities, correcting the data, eliminating various contradictions, completing knowledge fusion, carrying out knowledge processing such as ontology extraction, knowledge reasoning and verification, storing the extracted geographical knowledge facts into a data layer and a mode layer of a geographical knowledge base, and extracting resource description framework triples (RDF triples, h, r and t) to form a geographical knowledge map; the geographic environment, the geographic entity and the human factor resources are fused and associated, so that the related knowledge of the geographic space is obtained more comprehensively, the more perfect geographic space understanding capability is achieved, and intelligent interpretation is realized.
In this embodiment, in the calculating process of the dynamic index, the slope index value is calculated by using DEM data and the interpreted land use type data, and the calculating manner of the slope index value is as follows:
wherein B is 1 Indicating a gradient index, θ indicating a gradient; when the land is woodland and grassland and the gradient is larger than 30 degrees, theta is substituted into the formula to calculate the gradient index.
In this embodiment, the dynamic index is calculated by calculating the NDVI and vegetation coverage FVC, vegetation coverage and biological measure index B using TM image and 24 half-month MODIS image 2 The calculation mode of (a) is as follows: if the land use type is cultivated land or artificial disturbance land, the vegetation coverage and biological measure index value is 1 (which is equivalent to no vegetation coverage); if the index value is 0.01 (corresponding to 80% vegetation coverage) for construction land or transportation land; if the index value is 0 (erosion amount is 0) for the water area and the water conservancy facilities land; if the land use type is garden, forest or grassland, calculating vegetation coverage and biological measure index values by using rainfall weight of 24 periods, wherein the calculation formula is as follows:
in WR i Is the front partThe calculated i-th half-month rainfall erosion force accounts for the annual erosion force proportion, and the value range is 0-1; SLR (SLR) i The soil loss ratio of the ith half-moon garden, woodland and grassland is 0-1.
In the present embodiment, the engineering measure index value B 3 The effect of water and soil conservation engineering measures is reflected, the different engineering measures have different action procedures and assignment on water and soil loss. Calculating the dynamic index, namely taking engineering measures in the area where the engineering measures are taken, and obtaining an engineering measure index value B 3 The assignment mode is as follows: index value of terrace is 0.084; the index value of the ground stalk is 0.347; the index of the horizontal order takes on a value of 0.151; the index value of the horizontal ditch is 0.335; for farmland with gradient less than or equal to 2 degrees, if no terrace soil and water conservation engineering measures are adopted, the index value is assigned to be 0.431, and the index value is taken to be 1 except for the conditions.
In this embodiment, the calculated static index and dynamic index value are utilized, in ArcGIS software, each grid layer is resampled, the resolution is the same, the grid positions are aligned, and then the grid product operation of the layers is performed, and the calculation formula of the soil erosion quantized value QS is:
QS＝A 1 ×A 2 ×A 3 ×B 1 ×B 2 ×B 3
wherein A is 1 For rainfall index value, A 2 For soil type index value, A 3 For the index value of slope length, B 1 B is the gradient index 2 For vegetation cover and biological measure index value, B 3 Is an engineering measure index value; if the soil is cultivated land, multiplying the soil erosion quantized value by a coefficient of 0.338 to obtain the soil erosion quantized value of each grid.
Wherein, soil erosion strength judgement model is:
a grid region having a QS value <5 is determined to be a micro-scale; the QS value interval [5,25 ] is determined to be mild, the QS value interval [25,50 ] is determined to be moderate, the QS value interval [50, 80) is determined to be strong, the QS value interval [80,150) is determined to be extremely strong, and the QS value >150 is determined to be severe.
In this embodiment, for the map spots for artificial disturbance extracted based on the knowledge graph, erosion intensity correction is required, and the soil erosion intensity is determined according to the gradient: the hardening area is 50% or more, slightly less than 5 DEG, moderately 5-15 DEG, strongly 15-30 DEG and extremely strongly 30 DEG or more.
In summary, according to the method, the water and soil loss evaluation index system of the water and soil loss monitoring area is established by analyzing the water and soil loss influence factors of the water and soil loss monitoring area, wherein the water and soil loss evaluation index system comprises static indexes and dynamic indexes; calculating a static index: acquiring data of a rainfall observation site, obtaining rainfall index values of a water and soil loss monitoring area through interpolation analysis, performing superposition analysis and assignment calculation according to a national soil map according to physicochemical analysis data of soil general investigation, obtaining each soil type index value of the water and soil loss monitoring area, obtaining DEM data of the water and soil loss monitoring area, and calculating a slope length index value; acquiring specified structured, unstructured and semi-structured data of a water and soil loss monitoring area, and extracting geographic knowledge facts by using knowledge extraction means to form a geographic knowledge map; acquiring a high-resolution remote sensing image of a water and soil loss monitoring area, and realizing automatic classification and intelligent extraction of land utilization types and engineering measures by using a constructed geographic knowledge graph; calculating dynamic indexes: calculating a gradient index value by using the DEM data and the interpreted land use type data; calculating a normalized vegetation index NDVI and vegetation coverage FVC of the water and soil loss monitoring area by using the TM image and the MODIS image to obtain vegetation coverage and biological measure index values; assigning values to water and soil conservation engineering measure indexes in a water and soil loss monitoring area with engineering measures; and multiplying the static index and the dynamic index grid result to obtain a soil erosion quantized value, judging the soil erosion intensity according to the soil erosion intensity judging model, and correcting the artificial disturbance map erosion intensity. According to the application, the geographic knowledge map is formed by constructing the geographic knowledge map, fully utilizing the existing knowledge and related data, reflecting the geographic knowledge on a spatial map model structure, and forming the geographic knowledge map through data acquisition, information extraction, knowledge fusion and knowledge processing, so that a brand-new automatic intelligent means is provided for the remote sensing interpretation of the dynamic monitoring of the water loss and soil erosion, the manual interpretation and the field workload are greatly reduced, and the dynamic monitoring efficiency of the water loss and soil erosion in a region is improved.
It should be noted that the method of the embodiments of the present disclosure may be performed by a single device, such as a computer or a server. The method of the embodiment can also be applied to a distributed scene, and is completed by mutually matching a plurality of devices. In the case of such a distributed scenario, one of the devices may perform only one or more steps of the methods of embodiments of the present disclosure, the devices interacting with each other to accomplish the methods.
It should be noted that the foregoing describes some embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments described above and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
Example 2
Referring to fig. 2, embodiment 2 of the present application provides a dynamic monitoring device for regional soil erosion based on a knowledge graph, including:
the evaluation index system construction module 1 is used for analyzing water and soil loss influence factors of a water and soil loss monitoring area and establishing a water and soil loss evaluation index system of the water and soil loss monitoring area, wherein the water and soil loss evaluation index system comprises a static index and a dynamic index;
the static index calculation module 2 is configured to calculate the static index: acquiring data of a rainfall observation site, obtaining rainfall index values of the water and soil loss monitoring area through interpolation analysis, performing superposition analysis assignment calculation according to a national soil map according to physicochemical analysis data of soil general investigation, obtaining each soil type index value of the water and soil loss monitoring area, obtaining DEM data of the water and soil loss monitoring area, and calculating slope length index values;
the geographical knowledge graph construction module 3 is used for acquiring specified structured, unstructured and semi-structured data of the water and soil loss monitoring area, extracting geographical knowledge facts by using knowledge extraction means and forming a geographical knowledge graph;
the image information classification and extraction module 4 is used for acquiring a high-resolution remote sensing image of the water and soil loss monitoring area and realizing automatic classification and intelligent extraction of land utilization types and engineering measures by utilizing the constructed geographic knowledge graph;
the dynamic index calculation module 5 is configured to calculate the dynamic index: calculating a gradient index value by using the DEM data and the interpreted land use type data; calculating a normalized vegetation index NDVI and vegetation coverage FVC of the water and soil loss monitoring area by using the TM image and the MODIS image to obtain vegetation coverage and biological measure index values; assigning values to water and soil conservation engineering measure indexes for the water and soil loss monitoring areas with engineering measures;
and the soil erosion analysis processing module 6 is used for multiplying the static index and the dynamic index grid result to obtain a soil erosion quantized value, judging the soil erosion intensity according to a soil erosion intensity judging model, and correcting the artificial disturbance map plaque erosion intensity.
It should be noted that, because the content of information interaction and execution process between the modules of the above-mentioned device is based on the same concept as the method embodiment in the embodiment 1 of the present application, the technical effects brought by the content are the same as the method embodiment of the present application, and the specific content can be referred to the description in the foregoing illustrated method embodiment of the present application, which is not repeated herein.
Example 3
Embodiment 3 of the present application provides a non-transitory computer readable storage medium, in which program code for a knowledge-graph-based dynamic monitoring method for regional soil erosion is stored, the program code including instructions for executing the knowledge-graph-based dynamic monitoring method for regional soil erosion of embodiment 1 or any possible implementation thereof.
Computer readable storage media can be any available media that can be accessed by a computer or data storage devices, such as servers, data centers, etc., that contain an integration of one or more available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid State Disk, SSD), etc.
Example 4
Embodiment 4 of the present application provides an electronic device, including: a memory and a processor;
the processor and the memory complete communication with each other through a bus; the memory stores program instructions executable by the processor, and the processor invokes the program instructions to perform the knowledge-graph-based dynamic monitoring method for regional soil erosion of the water according to embodiment 1 or any possible implementation thereof.
Specifically, the processor may be implemented by hardware or software, and when implemented by hardware, the processor may be a logic circuit, an integrated circuit, or the like; when implemented in software, the processor may be a general-purpose processor, implemented by reading software code stored in a memory, which may be integrated in the processor, or may reside outside the processor, and which may reside separately.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present application, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable devices. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by wired (e.g., coaxial cable, optical fiber, digital Subscriber Line (DSL)), or wireless (e.g., infrared, wireless, microwave, etc.).
It will be appreciated by those skilled in the art that the modules or steps of the application described above may be implemented in a general purpose computing device, they may be concentrated on a single computing device, or distributed across a network of computing devices, they may alternatively be implemented in program code executable by computing devices, so that they may be stored in a memory device for execution by computing devices, and in some cases, the steps shown or described may be performed in a different order than that shown or described, or they may be separately fabricated into individual integrated circuit modules, or multiple modules or steps within them may be fabricated into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.
While the application has been described in detail in the foregoing general description and specific examples, it will be apparent to those skilled in the art that modifications and improvements can be made thereto. Accordingly, such modifications or improvements may be made without departing from the spirit of the application and are intended to be within the scope of the application as claimed.
Claims (10)
1. The regional soil erosion dynamic monitoring method based on the knowledge graph is characterized by comprising the following steps of:
analyzing water and soil loss influence factors of a water and soil loss monitoring area, and establishing a water and soil loss evaluation index system of the water and soil loss monitoring area, wherein the water and soil loss evaluation index system comprises a static index and a dynamic index;
calculating the static index: acquiring data of a rainfall observation site, obtaining rainfall index values of the water and soil loss monitoring area through interpolation analysis, performing superposition analysis assignment calculation according to a national soil map according to physicochemical analysis data of soil general investigation, obtaining each soil type index value of the water and soil loss monitoring area, obtaining DEM data of the water and soil loss monitoring area, and calculating slope length index values;
acquiring specified structured, unstructured and semi-structured data of the water and soil loss monitoring area, and extracting geographic knowledge facts by using knowledge extraction means to form a geographic knowledge map;
acquiring a high-resolution remote sensing image of the water and soil loss monitoring area, and realizing automatic classification and intelligent extraction of land utilization types and engineering measures by utilizing the constructed geographical knowledge graph;
calculating the dynamic index: calculating a gradient index value by using the DEM data and the interpreted land use type data; calculating a normalized vegetation index NDVI and vegetation coverage FVC of the water and soil loss monitoring area by using the TM image and the MODIS image to obtain vegetation coverage and biological measure index values; assigning values to water and soil conservation engineering measure indexes for the water and soil loss monitoring areas with engineering measures;
and multiplying the static index by the dynamic index grid result to obtain a soil erosion quantized value, judging the soil erosion intensity according to a soil erosion intensity judging model, and correcting the artificially disturbed plaque erosion intensity.
2. The knowledge-graph-based regional soil erosion dynamic monitoring method according to claim 1, wherein the static indexes comprise rainfall indexes, soil type indexes and slope length indexes, the dynamic indexes are indexes related to land utilization types and engineering measure interpretation results, and the dynamic indexes comprise slope indexes, vegetation coverage and biological measure indexes and engineering measure indexes.
3. The knowledge-graph-based regional soil erosion dynamic monitoring method according to claim 2, wherein the static index is calculated by:
interpolation is carried out by utilizing geographic information system software to generate a contour map and a grid map layer, so as to obtain a rainfall index value A of the water and soil loss monitoring area 1 ；
According to the physicochemical analysis data of the second national soil screening, according toPerforming superposition analysis assignment calculation according to the national soil map to obtain index values A of each soil type of the water and soil loss monitoring area 2 ；
Calculating a slope length index value A according to the acquired DEM data of the water and soil loss monitoring area by using geographic information system software 3 。
4. The knowledge-based regional soil erosion dynamic monitoring method according to claim 3, wherein the geographical knowledge construction process comprises high-resolution remote sensing image data, a land three-tone land utilization change database and a key management engineering database;
land use types include cultivated land, garden land, woodland, grassland, artificial land for harassment, construction land, transportation land, water area, and water conservancy facility land;
engineering measures include terraces, stems, horizontal steps, and horizontal furrows.
5. The knowledge graph-based regional soil erosion dynamic monitoring method according to claim 4, wherein the geographical knowledge fact process is extracted by knowledge extraction means:
storing the extracted geographic knowledge facts into a data layer and a mode layer of a geographic knowledge base, and extracting resource description framework triples to form a geographic knowledge map;
and fusing and correlating the geographic environment, the geographic entity and the human factor resources to obtain the related knowledge of the geographic space.
6. The knowledge-graph-based regional soil erosion dynamic monitoring method according to claim 5, wherein the calculation process is performed on the dynamic index, and the calculation mode of the gradient index value is as follows:
wherein B is 1 Represents a gradient index, θ representsGradient;
when the land is woodland and grassland and the gradient is larger than 30 degrees, theta is substituted into the formula to calculate the gradient index.
7. The knowledge-graph-based regional soil erosion dynamic monitoring method according to claim 6, wherein the dynamic index is calculated, and vegetation coverage and biological measure index value B is calculated 2 The calculation mode of (a) is as follows:
if the land use type is cultivated land or artificial disturbance land, the vegetation coverage and biological measure index value is 1;
if the index value is the construction land or the transportation land, the index value is 0.01;
if the index value is 0 for the water area and the water conservancy facility land, the index value is 0;
if the land use type is garden, forest or grassland, calculating vegetation coverage and biological measure index values by using rainfall weight of 24 periods, wherein the calculation formula is as follows:
in WR i The ratio of the i-th half-month rainfall erosion force calculated for the previous calculation to the annual erosion force; SLR (SLR) i Soil loss ratio for ith half-moon garden, woodland and grassland.
8. The knowledge-graph-based regional soil erosion dynamic monitoring method according to claim 7, wherein the dynamic index is calculated, and the engineering measure index value B is calculated 3 The assignment mode is as follows:
index value of terrace is 0.084; the index value of the ground stalk is 0.347; the index of the horizontal order takes on a value of 0.151; the index value of the horizontal ditch is 0.335;
for farmland with gradient less than or equal to 2 degrees, if no terrace soil and water conservation engineering measures are adopted, the index is assigned to be 0.431.
9. The knowledge-graph-based regional soil erosion dynamic monitoring method of claim 8, wherein the calculation formula of the soil erosion quantized value QS is:
QS＝A 1 ×A 2 ×A 3 ×B 1 ×B 2 ×B 3
wherein A is 1 For rainfall index value, A 2 For soil type index value, A 3 For the index value of slope length, B 1 B is the gradient index 2 For vegetation cover and biological measure index value, B 3 Is an engineering measure index value;
the soil erosion strength judgment model is as follows:
a grid region having a QS value <5 is determined to be a micro-scale; the QS value interval [5,25 ] is determined to be mild, the QS value interval [25,50 ] is determined to be moderate, the QS value interval [50, 80) is determined to be strong, the QS value interval [80,150) is determined to be extremely strong, and the QS value >150 is determined to be severe.
10. Regional soil erosion and water loss dynamic monitoring device based on knowledge graph, its characterized in that includes:
the evaluation index system construction module is used for analyzing water and soil loss influence factors of a water and soil loss monitoring area and establishing a water and soil loss evaluation index system of the water and soil loss monitoring area, wherein the water and soil loss evaluation index system comprises a static index and a dynamic index;
the static index calculation module is used for calculating the static index: acquiring data of a rainfall observation site, obtaining rainfall index values of the water and soil loss monitoring area through interpolation analysis, performing superposition analysis assignment calculation according to a national soil map according to physicochemical analysis data of soil general investigation, obtaining each soil type index value of the water and soil loss monitoring area, obtaining DEM data of the water and soil loss monitoring area, and calculating slope length index values;
the geographical knowledge map construction module is used for acquiring specified structured, unstructured and semi-structured data of the water and soil loss monitoring area, extracting geographical knowledge facts by using a knowledge extraction means and forming a geographical knowledge map;
the image information classification and extraction module is used for acquiring a high-resolution remote sensing image of the water and soil loss monitoring area and realizing automatic classification and intelligent extraction of land utilization types and engineering measures by utilizing the constructed geographic knowledge graph;
the dynamic index calculation module is used for calculating the dynamic index: calculating a gradient index value by using the DEM data and the interpreted land use type data; calculating a normalized vegetation index NDVI and vegetation coverage FVC of the water and soil loss monitoring area by using the TM image and the MODIS image to obtain vegetation coverage and biological measure index values; assigning values to water and soil conservation engineering measure indexes for the water and soil loss monitoring areas with engineering measures;
and the soil erosion analysis processing module is used for multiplying the static index and the dynamic index grid result to obtain a soil erosion quantized value, judging the soil erosion intensity according to the soil erosion intensity judging model, and correcting the artificial disturbance map spot erosion intensity.
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