CN117634894B - Ecological environment risk assessment method and device, electronic equipment and storage medium - Google Patents
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Abstract
The invention is applicable to the technical field of environmental risk assessment, and provides an ecological environmental risk assessment method, an ecological environmental risk assessment device, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring keyword information of an object to be evaluated; determining an evaluation index under at least one evaluation dimension of the object to be evaluated from a knowledge graph of the ecological environment risk field, which is established in advance, according to the keyword information; determining at least one evaluation dimension and a weight coefficient of an evaluation index to obtain an evaluation index system corresponding to the object to be evaluated; and carrying out risk assessment on the object to be assessed based on the assessment index system. The invention can improve the accuracy of risk assessment on the ecological environment.
Description
Technical Field
The invention belongs to the technical field of environmental risk assessment, and particularly relates to an ecological environmental risk assessment method, an ecological environmental risk assessment device, electronic equipment and a storage medium.
Background
In the ecological environment risk assessment process, the establishment of a scientific and reasonable evaluation index system is still a key for determining the accuracy of ecological risk assessment. However, when an evaluation index system is constructed, the problems of strong subjectivity, incomplete evaluation indexes and the like exist, so that an evaluation result is inaccurate.
Disclosure of Invention
In view of the above, the embodiments of the present invention provide an ecological environment risk assessment method, an apparatus, an electronic device, and a storage medium, so as to improve the accuracy of risk assessment for an ecological environment.
A first aspect of an embodiment of the present invention provides an ecological environment risk assessment method, including:
acquiring keyword information of an object to be evaluated;
Determining an evaluation index under at least one evaluation dimension of the object to be evaluated from a knowledge graph of the ecological environment risk field, which is established in advance, according to the keyword information;
Determining at least one evaluation dimension and a weight coefficient of an evaluation index to obtain an evaluation index system corresponding to the object to be evaluated;
and carrying out risk assessment on the object to be assessed based on the assessment index system.
With reference to the first aspect, in a possible implementation manner of the first aspect, the keyword information is an environment type;
according to the keyword information, determining an evaluation index under at least one evaluation dimension of the object to be evaluated from a knowledge graph of the ecological environment risk field, which is established in advance, comprises the following steps:
Screening all the evaluation indexes related to the environment types from the knowledge graph;
and determining the evaluation dimension of each evaluation index related to the environment type according to the knowledge graph to obtain the evaluation index under at least one evaluation dimension of the object to be evaluated.
With reference to the first aspect, in a possible implementation manner of the first aspect, the keyword information is an environment type and at least one evaluation dimension;
according to the keyword information, determining an evaluation index under at least one evaluation dimension of the object to be evaluated from a knowledge graph of the ecological environment risk field, which is established in advance, comprises the following steps:
screening all evaluation indexes related to the environment types and the evaluation indexes under each evaluation dimension from the knowledge graph;
And determining an evaluation index of each evaluation dimension coincident with the environment type to obtain an evaluation index of the object to be evaluated under at least one evaluation dimension.
With reference to the first aspect, in a possible implementation manner of the first aspect, after obtaining the evaluation index in at least one evaluation dimension of the object to be evaluated, the method further includes:
Clustering each evaluation index according to the similarity of each evaluation index under each evaluation dimension to obtain at least one cluster;
and merging all the evaluation indexes of the same cluster into one evaluation index.
With reference to the first aspect, in a possible implementation manner of the first aspect, after obtaining the evaluation index in at least one evaluation dimension of the object to be evaluated, the method further includes:
Acquiring the occurrence frequency of each evaluation index from the knowledge graph;
and deleting the evaluation index with the occurrence frequency lower than the preset threshold value.
With reference to the first aspect, in a possible implementation manner of the first aspect, determining a weight coefficient of the at least one evaluation dimension and the evaluation index includes:
Determining the weight coefficient of each evaluation dimension according to the occurrence frequency of each evaluation dimension; wherein the weight coefficient of each evaluation dimension is positively correlated with the occurrence frequency;
Under each evaluation dimension, determining a weight coefficient of each evaluation index according to the occurrence frequency of each evaluation index; wherein, the weight coefficient of each evaluation index is positively correlated with the occurrence frequency.
With reference to the first aspect, in one possible implementation manner of the first aspect, the knowledge graph of the ecological environment risk area is established by:
acquiring ecological environment risk assessment data;
Extracting an entity and an entity relationship from ecological environment risk assessment data through a preset deep learning model, and establishing a mode layer for obtaining a knowledge graph;
The deep learning model is formed by sequentially connecting a BERT module, a Graph module and a GlobalPointer module; the mode layer is divided into an object layer, a space-time attribute layer, a risk assessment method layer, a risk problem and influence factor layer, a risk management activity layer and a relation layer from top to bottom.
A second aspect of an embodiment of the present invention provides an ecological environment risk assessment apparatus, including:
the acquisition module is used for acquiring keyword information of the object to be evaluated;
The establishing module is used for determining an evaluation index under at least one evaluation dimension of the object to be evaluated from a knowledge graph of the ecological environment risk field, which is established in advance, according to the keyword information; determining at least one evaluation dimension and a weight coefficient of an evaluation index to obtain an evaluation index system corresponding to the object to be evaluated;
And the evaluation module is used for performing risk evaluation on the object to be evaluated based on the evaluation index system.
A third aspect of the embodiments of the present invention provides an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method as described above in the first aspect or any implementation of the first aspect when the computer program is executed.
A fourth aspect of embodiments of the present invention provides a computer readable storage medium storing a computer program which when executed by a processor performs the steps of a method as in the first aspect or any implementation of the first aspect.
Compared with the prior art, the embodiment of the invention has the beneficial effects that:
The embodiment of the invention constructs the knowledge graph in the ecological environment risk field. The user can input keyword information according to the needs for different research purposes and research objects. By combining keyword information with a knowledge graph, proper evaluation dimension and evaluation index can be selected in a targeted manner, so that a powerful support is provided for scientifically and reasonably constructing an evaluation index system, the problem that evaluation is inaccurate and comprehensive due to subjective factors is solved to a certain extent, and the accuracy of ecological environment risk evaluation is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of an ecological environment risk domain knowledge graph provided by an embodiment of the invention;
FIG. 2 is a schematic flow chart of an ecological environment risk assessment method according to an embodiment of the present invention;
FIG. 3 is a second flow chart of an ecological environment risk assessment method according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of an ecological environment risk assessment device according to an embodiment of the present invention;
fig. 5 is a schematic diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to illustrate the technical scheme of the invention, the following description is made by specific examples.
The ecological environment is used as a material foundation and a space carrier for human survival and development, and supports the continuous development of socioeconomic performance. The ecological environment risk condition of the evaluation area can reflect urban planning, construction and management effects, and the urban ecological civilization construction work is promoted. Under the age background of information technology development, the ecological environment risk knowledge is promoted to be accurate, the internal requirements of the current ecological civilization construction are promoted, and the objective requirements of sustainable utilization of regional resources are promoted.
In the ecological environment risk assessment process, the construction of an evaluation index system is still a key for determining the accuracy and scientificity of ecological risk assessment, but the prior art has the problems of strong subjectivity and the like in the construction of the evaluation index system. Along with the development of information technology, the internet becomes a main source for acquiring knowledge, however, the knowledge in the current ecological environment risk field is mainly stored in a traditional text form, and the problems of poor aggregation capability, low utilization rate, difficult knowledge sharing and the like exist. In view of the good performance of the knowledge graph on knowledge management, if the knowledge graph related to the ecological environment risk field can be established, an evaluation index system of ecological environment risk evaluation can be constructed in a visualized manner, related indexes can be selected in a targeted manner according to the research purpose and the research object, the problems of inaccurate and comprehensive evaluation and the like caused by subjective factors can be reduced to a certain extent, the ecological civilization status is reflected objectively, and information support is provided for the related departments of ecological environment space management.
According to the embodiment of the invention, the theoretical basis and the deep learning of the knowledge graph are utilized, the entity (the entity can be understood as the space element in the ecological environment risk) in the objective world and the relation among the entities are expressed in the form of a network-shaped structure and a graph, the ecological environment risk assessment knowledge graph is established, and effective data and decision support are provided for the establishment of an evaluation index system of the ecological environment risk. The basic composition units of the knowledge graph are entity-relation-entity triples and entity and related attribute-value pairs thereof, and the entities are mutually connected through relations to form a net-shaped knowledge structure.
Here, the knowledge graph of the ecological environment risk domain may be established by:
acquiring ecological environment risk assessment data;
And extracting the entity and entity relation from the ecological environment risk assessment data through a preset deep learning model, and establishing a mode layer for obtaining the knowledge graph.
In this embodiment, the ecological environment risk assessment data may be obtained through documents and website data in the ecological environment field.
Further, a deep learning model (BERT-Graph-GlobalPointer model) formed by sequentially connecting a BERT module, a Graph module and a GlobalPointer module is constructed to extract the entity and the entity relationship. Firstly, acquiring a dynamic word vector rich in context information by using a BERT module; then, local and non-local information of the word vector is obtained by utilizing a Graph module; and finally, converting the triplet extraction into the quintuple extraction by using a GlobalPointer module decoder, so that the problems of difficult overlapping extraction of the triples in the relation extraction and the like are solved to a certain extent. The deep learning model can be trained by semantic annotation of the ecological environment risk assessment data. Semantic annotation should be from the model layer of the knowledge graph, including the annotation of entities and entity relationships. The relationship is expressed as a triplet (E1, R, E2), R characterizing the relationship of the entities E1 and E2.
And extracting the entity and entity relation by using the trained deep learning model, and constructing a model layer of the knowledge graph. The final purpose of the knowledge graph in the ecological environment wind field is to serve an evaluation index system. Along this line, referring to fig. 1, from the perspective of a hierarchical framework, the model layer construction of the knowledge graph of the ecological environment risk domain can be divided into 6 layers from top to bottom according to an evaluation theory framework: object layer, attribute layer (spatiotemporal attribute layer), method model layer (risk assessment method layer), state layer (risk problem and impact factor layer), activity layer (risk management activity layer) and relationship layer.
The object layer is an environment type of the evaluation object, and comprises the following steps: land utilization systems, field systems, desert oasis, city systems, tilling systems, etc.
The attribute layer is attribute information of the evaluation object, and comprises: regional, temporal, spatial, distributed, etc.
The method model layer comprises the following steps: evaluation scale, evaluation dimension, method model, risk level, etc., and a plurality of evaluation indexes are further subdivided under each evaluation dimension.
The state layer comprises: risk sources, impact factors, ecological problems, etc.
The active layer includes: optimizing land utilization structure, ecological risk early warning and the like.
The relationship layer comprises relationships among various entities and between the entities and the ecological environment risks.
According to the established knowledge graph of the ecological environment wind field, the embodiment of the invention provides an ecological environment risk assessment method, which is shown in fig. 2 and comprises the following steps:
Step S201, obtaining keyword information of an object to be evaluated.
Here, the user can input keyword information according to his own study purpose and study object. And searching related evaluation indexes from the knowledge graph according to the keyword information by the algorithm.
Step S202, determining an evaluation index under at least one evaluation dimension of the object to be evaluated from a knowledge graph of the ecological environment risk field, which is established in advance, according to the keyword information.
In this embodiment, as shown in fig. 1, in the ecological environment risk knowledge graph, the evaluation dimension has a high occurrence frequency, such as "potential-connectivity-restoring force", "risk source-risk receptor", "production-life-ecology", "landscape pattern-ecological process", and "stress-state-response". The evaluation indexes of different evaluation dimension partitions are different. In addition, the emphasis of the general evaluation dimension is also different for different evaluation objects, so that the established evaluation index system should have pertinence.
Thus, in one possible implementation, the keyword information may be an environment type.
Determining an evaluation index under at least one evaluation dimension of the object to be evaluated from a knowledge graph of the pre-established ecological environment risk domain according to the keyword information, wherein the evaluation index comprises:
Screening all the evaluation indexes related to the environment types from the knowledge graph;
and determining the evaluation dimension of each evaluation index related to the environment type according to the knowledge graph to obtain the evaluation index under at least one evaluation dimension of the object to be evaluated.
In this embodiment, when the keyword input by the user is an environment type, for example, a wetland system, an urban system, or the like, the algorithm may utilize a knowledge graph to screen out evaluation indexes related to the environment type, to obtain a second-level index, where the evaluation dimension to which each evaluation index belongs is used as a first-level index, and an evaluation index system may be constructed by using the first-level index and the second-level index.
Or in one possible implementation the keyword information may be an environment type and at least one evaluation dimension.
Determining an evaluation index under at least one evaluation dimension of the object to be evaluated from a knowledge graph of the pre-established ecological environment risk domain according to the keyword information, wherein the evaluation index comprises:
screening all evaluation indexes related to the environment types and the evaluation indexes under each evaluation dimension from the knowledge graph;
And determining an evaluation index of each evaluation dimension coincident with the environment type to obtain an evaluation index of the object to be evaluated under at least one evaluation dimension.
In this embodiment, when the user inputs the environment type, the user may also input the desired evaluation dimension according to his own requirement, and then the algorithm may use the knowledge graph to screen out the evaluation index related to the evaluation dimension of the environment type, and construct an evaluation index system.
The above is merely two preferable examples, and the keyword information may be content such as a space-time attribute, an influence factor, and the like, which is not limited in this embodiment.
Through the method, a user can establish a proper evaluation index system according to the purpose and the needs of the user, and the ecological environment risk is evaluated scientifically and reasonably.
Step S203, determining at least one evaluation dimension and a weight coefficient of an evaluation index to obtain an evaluation index system corresponding to the object to be evaluated.
The evaluation index system also needs to determine the weight coefficients of the first and second indexes. Here, the weight coefficient may be preset or calculated by an algorithm, which is not limited in this embodiment.
Step S204, performing risk assessment on the object to be assessed based on the assessment index system.
By acquiring the data of the evaluation index of the object to be evaluated and combining the evaluation index system, the object to be evaluated can be evaluated, and the method is not the key point of the scheme and is not excessively introduced.
The embodiment of the invention constructs the knowledge graph in the ecological environment risk field. The user can input keyword information according to the needs for different research purposes and research objects. By combining keyword information with a knowledge graph, proper evaluation dimension and evaluation index can be selected in a targeted manner, so that a powerful support is provided for scientifically and reasonably constructing an evaluation index system, the problem that evaluation is inaccurate and comprehensive due to subjective factors is solved to a certain extent, and the accuracy of ecological environment risk evaluation is improved.
FIG. 3 is a flowchart of another method for evaluating a risk of a state environment according to an embodiment of the present invention, referring to FIG. 3, the method includes:
step S301, obtaining keyword information of an object to be evaluated.
Step S302, according to the keyword information, determining an evaluation index under at least one evaluation dimension of the object to be evaluated from a knowledge graph of the ecological environment risk field, which is established in advance.
Step S303, clustering each evaluation index according to the similarity of each evaluation index under each evaluation dimension to obtain at least one cluster; and merging all the evaluation indexes of the same cluster into one evaluation index.
In the embodiment, the evaluation indexes are analyzed by using clustering statistics, the similar evaluation indexes are combined, and complex network relations among a plurality of evaluation indexes are combined into a plurality of relatively less categories, so that the comprehensiveness and systemicity of an evaluation index system can be ensured.
Step S304, obtaining the occurrence frequency of each evaluation index from the knowledge graph; and deleting the evaluation index with the occurrence frequency lower than the preset threshold value.
In this embodiment, the evaluation index with the occurrence frequency lower than the preset threshold is deleted, and the evaluation index with the higher occurrence frequency is used as the main index, so that automatic screening adjustment of the evaluation index according to the change of time and situation can be realized, and the hot spot problem is studied. In this way, real-time tracking can be performed when the research direction and evaluation index of risk assessment change with the social development.
Here, the screening operation is performed after the cluster analysis, so that deletion of low-frequency words similar to the meaning of high-frequency words can be avoided, and the accuracy of the algorithm is further improved.
Step S305, determining at least one evaluation dimension and a weight coefficient of an evaluation index to obtain an evaluation index system corresponding to the object to be evaluated; specifically, determining a weight coefficient of each evaluation dimension according to the occurrence frequency of each evaluation dimension; wherein the weight coefficient of each evaluation dimension is positively correlated with the occurrence frequency; under each evaluation dimension, determining a weight coefficient of each evaluation index according to the occurrence frequency of each evaluation index; wherein, the weight coefficient of each evaluation index is positively correlated with the occurrence frequency.
The weight coefficient reflects the evaluation dimension and the importance degree of the evaluation index, and the reasonable determination of the weight coefficient is the key of accurate evaluation. In the embodiment, the weight is determined by the occurrence frequency of the evaluation dimension and the occurrence frequency of the evaluation index, and the higher the occurrence frequency is, the larger the weight coefficient is, so that the key problem and the hot spot problem of risk evaluation are reflected, and the evaluation result of the evaluation index system is more accurate.
Step S305, performing risk assessment on the object to be assessed based on the assessment index system.
The implementation manners of step S301, step S302, and step S305 may be referred to the description in the embodiment of fig. 2, and the description of this embodiment is omitted.
For example, when the urban system is subjected to ecological environment risk assessment, the urban system is considered to be a typical man-ground system, is a product of mutual balance of multiple elements of the social-ecological system, has complex characteristics of multiple risk sources and risk acceptors, determination of administrative boundaries, spatial heterogeneity of internal functional partitions and the like, and therefore, an evaluation index system is combed and constructed from the dimension of the social-ecological system, and the results are shown in table 1.
TABLE 1 evaluation index System for urban systems
The embodiment of the invention establishes the knowledge graph in the ecological environment risk field, and can provide guidance for the establishment of an ecological environment risk evaluation index system, measures for avoiding ecological environment risks and the like. Based on the knowledge graph of the ecological environment risk field, an ecological environment risk analysis and evaluation index system can be constructed in a visualized mode, relevant indexes can be selected in a targeted mode according to research purposes and research objects, and the problems that evaluation is not accurate and comprehensive enough due to subjective factors can be reduced to a certain extent.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
Fig. 4 is a schematic structural diagram of an ecological environment risk assessment apparatus 40 according to an embodiment of the present invention, referring to fig. 4, the apparatus includes:
An obtaining module 41, configured to obtain keyword information of the object to be evaluated.
The establishing module 42 is configured to determine, according to the keyword information, an evaluation index under at least one evaluation dimension of the object to be evaluated from a knowledge graph in a pre-established ecological environment risk domain; and determining at least one evaluation dimension and a weight coefficient of the evaluation index to obtain an evaluation index system corresponding to the object to be evaluated.
And the evaluation module 43 is used for performing risk evaluation on the object to be evaluated based on the evaluation index system.
As one possible implementation, the keyword information is an environment type.
The setup module 42 is configured to:
Screening all the evaluation indexes related to the environment types from the knowledge graph;
and determining the evaluation dimension of each evaluation index related to the environment type according to the knowledge graph to obtain the evaluation index under at least one evaluation dimension of the object to be evaluated.
As one possible implementation, the keyword information is an environment type and at least one evaluation dimension.
The setup module 42 is configured to:
screening all evaluation indexes related to the environment types and the evaluation indexes under each evaluation dimension from the knowledge graph;
And determining an evaluation index of each evaluation dimension coincident with the environment type to obtain an evaluation index of the object to be evaluated under at least one evaluation dimension.
As a possible implementation, after obtaining the evaluation index in at least one evaluation dimension of the object to be evaluated, the establishing module 42 is further configured to:
Clustering each evaluation index according to the similarity of each evaluation index under each evaluation dimension to obtain at least one cluster;
and merging all the evaluation indexes of the same cluster into one evaluation index.
As a possible implementation, after obtaining the evaluation index in at least one evaluation dimension of the object to be evaluated, the establishing module 42 is further configured to:
Acquiring the occurrence frequency of each evaluation index from the knowledge graph;
and deleting the evaluation index with the occurrence frequency lower than the preset threshold value.
As a possible implementation, the establishing module 42 is configured to:
Determining the weight coefficient of each evaluation dimension according to the occurrence frequency of each evaluation dimension; wherein the weight coefficient of each evaluation dimension is positively correlated with the occurrence frequency;
Under each evaluation dimension, determining a weight coefficient of each evaluation index according to the occurrence frequency of each evaluation index; wherein, the weight coefficient of each evaluation index is positively correlated with the occurrence frequency.
As one possible implementation, the knowledge graph of the ecological environment risk domain may be established by:
acquiring ecological environment risk assessment data;
Extracting an entity and an entity relationship from ecological environment risk assessment data through a preset deep learning model, and establishing a mode layer for obtaining a knowledge graph;
The deep learning model is formed by sequentially connecting a BERT module, a Graph module and a GlobalPointer module; the mode layer is divided into an object layer, a space-time attribute layer, a risk assessment method layer, a risk problem and influence factor layer, a risk management activity layer and a relation layer from top to bottom.
Fig. 5 is a schematic diagram of an electronic device 50 according to an embodiment of the invention. As shown in fig. 5, the electronic device 50 of this embodiment includes: a processor 51, a memory 52, and a computer program 53, such as an ecological risk assessment program, stored in the memory 52 and executable on the processor 51. The steps in the above-described respective embodiments of the method for risk assessment of ecological environment are implemented when the processor 51 executes the computer program 53, for example, steps S201 to S204 shown in fig. 2. Or the processor 51 when executing the computer program 53 performs the functions of the modules of the above-described embodiments of the apparatus, such as the functions of the modules 41 to 44 shown in fig. 4.
By way of example, the computer program 53 may be divided into one or more modules/units, which are stored in the memory 52 and executed by the processor 51 to complete the present invention. One or more of the modules/units may be a series of computer program instruction segments capable of performing particular functions for describing the execution of the computer program 53 in the electronic device 50.
The electronic device 50 may be a desktop computer, a notebook computer, a palm computer, a cloud server, or the like. The electronic device 50 may include, but is not limited to, a processor 51, a memory 52. It will be appreciated by those skilled in the art that fig. 5 is merely an example of electronic device 50 and is not intended to limit electronic device 50, and may include more or fewer components than shown, or may combine certain components, or different components, e.g., electronic device 50 may also include input-output devices, network access devices, buses, etc.
The Processor 51 may be a central processing unit (Central Processing Unit, CPU), other general purpose Processor, digital signal Processor (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), off-the-shelf Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 52 may be an internal storage unit of the electronic device 50, such as a hard disk or a memory of the electronic device 50. The memory 52 may also be an external storage device of the electronic device 50, such as a plug-in hard disk provided on the electronic device 50, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD), or the like. Further, the memory 52 may also include both internal and external storage units of the electronic device 50. The memory 52 is used to store computer programs and other programs and data required by the electronic device 50. The memory 52 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/electronic device and method may be implemented in other manners. For example, the apparatus/electronic device embodiments described above are merely illustrative, e.g., the division of modules or units is merely a logical functional division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the steps of each method embodiment described above may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, executable files or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.
Claims (5)
1. An ecological environment risk assessment method, comprising:
acquiring keyword information of an object to be evaluated; wherein the keyword information is an environment type and at least one evaluation dimension; the evaluation dimensions include potential-connectivity-resilience, risk source-risk receptor, production-life-ecology, landscape pattern-ecology process and stress-status-response;
screening all evaluation indexes related to the environment types and evaluation indexes under each evaluation dimension from a pre-established knowledge graph of the ecological environment risk field;
Determining an evaluation index of each evaluation dimension coincident with the environment type to obtain an evaluation index of the object to be evaluated under at least one evaluation dimension;
Clustering each evaluation index according to the similarity of each evaluation index under each evaluation dimension to obtain at least one cluster;
Combining all the evaluation indexes of the same cluster into one evaluation index;
Acquiring the occurrence frequency of each evaluation index from the knowledge graph, and deleting the evaluation index with the occurrence frequency lower than a preset threshold value;
Determining the weight coefficient of the at least one evaluation dimension and the evaluation index to obtain an evaluation index system corresponding to the object to be evaluated; the determining the weight coefficients of the at least one evaluation dimension and the evaluation index comprises: determining the weight coefficient of each evaluation dimension according to the occurrence frequency of each evaluation dimension; wherein the weight coefficient of each evaluation dimension is positively correlated with the occurrence frequency; under each evaluation dimension, determining a weight coefficient of each evaluation index according to the occurrence frequency of each evaluation index; wherein, the weight coefficient of each evaluation index and the occurrence frequency form positive correlation;
Performing risk assessment on the object to be assessed based on the assessment index system;
the mode layer frame of the knowledge graph is divided into:
The evaluation object layer comprises a land utilization system, a field system, a desert oasis, a city system and a cultivated land system;
a space-time attribute layer comprising area, time, space and distribution;
the risk assessment method model layer comprises an assessment scale, an assessment dimension, a method model and a risk grade;
a risk status layer comprising a risk source, an influence factor, and an ecological problem;
The risk management active layer comprises an optimized land utilization structure and ecological risk early warning;
and the relation layer comprises relations among various entities and relations between the entities and ecological environment risks.
2. The ecological environment risk assessment method according to claim 1, wherein the knowledge graph of the ecological environment risk field is established by:
acquiring ecological environment risk assessment data;
extracting an entity and an entity relationship from the ecological environment risk assessment data through a preset deep learning model, and establishing a mode layer for obtaining the knowledge graph; the deep learning model is formed by sequentially connecting a BERT module, a Graph module and a GlobalPointer module.
3. An ecological environment risk assessment device, comprising:
The acquisition module is used for acquiring keyword information of the object to be evaluated; wherein the keyword information is an environment type and at least one evaluation dimension; the evaluation dimensions include potential-connectivity-resilience, risk source-risk receptor, production-life-ecology, landscape pattern-ecology process and stress-status-response;
The building module is used for screening all evaluation indexes related to the environment types and evaluation indexes under each evaluation dimension from a knowledge graph of the pre-built ecological environment risk field; determining an evaluation index of each evaluation dimension coincident with the environment type to obtain an evaluation index of the object to be evaluated under at least one evaluation dimension; clustering each evaluation index according to the similarity of each evaluation index under each evaluation dimension to obtain at least one cluster; combining all the evaluation indexes of the same cluster into one evaluation index; acquiring the occurrence frequency of each evaluation index from the knowledge graph, and deleting the evaluation index with the occurrence frequency lower than a preset threshold value; determining the weight coefficient of the at least one evaluation dimension and the evaluation index to obtain an evaluation index system corresponding to the object to be evaluated; the determining the weight coefficients of the at least one evaluation dimension and the evaluation index comprises: determining the weight coefficient of each evaluation dimension according to the occurrence frequency of each evaluation dimension; wherein the weight coefficient of each evaluation dimension is positively correlated with the occurrence frequency; under each evaluation dimension, determining a weight coefficient of each evaluation index according to the occurrence frequency of each evaluation index; wherein, the weight coefficient of each evaluation index and the occurrence frequency form positive correlation;
the evaluation module is used for performing risk evaluation on the object to be evaluated based on the evaluation index system;
the mode layer of the knowledge graph is divided into:
The evaluation object layer comprises a land utilization system, a field system, a desert oasis, a city system and a cultivated land system;
a space-time attribute layer comprising area, time, space and distribution;
the risk assessment method model layer comprises an assessment scale, an assessment dimension, a method model and a risk grade;
a risk status layer comprising a risk source, an influence factor, and an ecological problem;
The risk management active layer comprises an optimized land utilization structure and ecological risk early warning;
and the relation layer comprises relations among various entities and relations between the entities and ecological environment risks.
4. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to claim 1 or 2 when executing the computer program.
5. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor realizes the steps of the method according to claim 1 or 2.
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