CN115455051A

CN115455051A - Professional entity generation method, device, equipment and medium for natural resources

Info

Publication number: CN115455051A
Application number: CN202211108829.4A
Authority: CN
Inventors: 甘兵; 廖瑞毅
Original assignee: Digital Guangdong Network Construction Co Ltd
Current assignee: Digital Guangdong Network Construction Co Ltd
Priority date: 2022-09-13
Filing date: 2022-09-13
Publication date: 2022-12-09

Abstract

The invention discloses a professional entity generation method, a professional entity generation device, a professional entity generation equipment and a professional entity generation medium for natural resources. The method comprises the following steps: acquiring a natural resource professional entity generation task, and performing entity identification to obtain a standard entity name contained in the natural resource professional entity generation task; generating a professional entity intention classification result according to the task generated by the natural resource professional entity; obtaining a target path relation matched with a natural resource professional entity generation task according to a preset basic entity gallery; and constructing a target query statement according to the professional entity intention classification result, the standard entity name and the target path relation, and querying in a preset basic entity gallery according to the target query statement to obtain a target professional entity corresponding to the natural resource professional entity generation task. By the technical scheme, the professional entity of the natural resource can be generated quickly and accurately, and the generation efficiency and accuracy of the professional entity of the natural resource are improved.

Description

Professional entity generation method, device, equipment and medium for natural resources

Technical Field

The invention relates to the technical field of information search, in particular to a professional entity generation method, a professional entity generation device, a professional entity generation equipment and a professional entity generation medium for natural resources.

Background

In the existing natural resource investigation scene, the existing situation of the natural resource is determined by the existing natural resource standard map layer, such as: the situation of the pattern spot of the facility agricultural land, the situation of the land class of the 'three-tone' of the filed facility agricultural land, the distribution change situation of the facility agricultural land in the ten years, whether the land belongs to the permanent basic farmland and the like becomes important to be understood and supervised.

At present, when a data analysis demand appears, a method of manual off-line analysis is generally needed to be adopted to send a professional to analyze the demand, and then the demand is compiled into a database storing a natural resource standard map layer to generate a corresponding professional entity. However, the manual offline analysis method is adopted, so that the time consumption of data processing is long, the automation degree is low, the accuracy cannot be guaranteed in time, and when the service requirement is newly increased again, the calculation needs to be performed again. The efficiency and accuracy of generating professional entities of natural resources are reduced. Therefore, how to improve the generation efficiency and accuracy of professional entities of natural resources is a problem to be solved urgently.

Disclosure of Invention

The invention provides a method, a device, equipment and a medium for generating professional entities of natural resources, which can improve the efficiency and the accuracy of generating the professional entities of the natural resources.

According to an aspect of the present invention, there is provided a method for generating a professional entity of a natural resource, including:

acquiring a natural resource professional entity generation task, and performing entity identification on the natural resource professional entity generation task to obtain a standard entity name contained in the natural resource professional entity generation task;

generating a professional entity intention classification result according to the task generated by the natural resource professional entity;

obtaining a target path relation matched with a natural resource professional entity generation task according to a preset basic entity gallery;

and constructing a target query statement according to the professional entity intention classification result, the standard entity name and the target path relation, and querying in a preset basic entity gallery according to the target query statement to obtain a target professional entity corresponding to the natural resource professional entity generation task.

According to another aspect of the present invention, there is provided a professional entity generating apparatus of natural resources, including:

the standard entity name acquisition module is used for acquiring a natural resource professional entity generation task and performing entity identification on the natural resource professional entity generation task to obtain a standard entity name contained in the natural resource professional entity generation task;

the classification result generation module is used for generating a professional entity intention classification result according to the natural resource professional entity generation task;

the path relation acquisition module is used for acquiring a target path relation matched with the task generated by the professional natural resource entity according to a preset basic entity gallery;

and the professional entity acquisition module is used for constructing a target query statement according to the professional entity intention classification result, the standard entity name and the target path relation, and inquiring in a preset basic entity gallery according to the target query statement to obtain a target professional entity corresponding to the natural resource professional entity generation task.

According to another aspect of the present invention, there is provided an electronic apparatus including:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform a method for professional entity generation of natural resources according to any of the embodiments of the present invention.

According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement a method for generating a professional entity of a natural resource according to any one of the embodiments of the present invention when the computer instructions are executed.

According to the technical scheme of the embodiment of the invention, entity recognition is carried out on the acquired professional entity generation task of the natural resource to obtain a standard entity name contained in the professional entity generation task of the natural resource; generating a professional entity intention classification result according to the task generated by the natural resource professional entity; obtaining a target path relation matched with a natural resource professional entity generation task according to a preset basic entity image library; and constructing a target query statement according to the professional entity intention classification result, the standard entity name and the target path relation, and querying in a preset basic entity gallery according to the target query statement to obtain a target professional entity corresponding to the natural resource professional entity generation task, so that the problem of low generation efficiency and accuracy of the professional entity of the natural resource is solved, and the generation efficiency and accuracy of the professional entity of the natural resource can be improved.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a flowchart of a professional entity generating method for natural resources according to an embodiment of the present invention;

fig. 2a is a flowchart of a professional entity generation method for natural resources according to a second embodiment of the present invention;

FIG. 2b is a flowchart of a generation process of a basic entity according to the second embodiment of the present invention;

FIG. 2c is a schematic diagram of a process for generating a basic entity gallery according to the second embodiment of the present invention;

fig. 3 is a schematic structural diagram of a professional entity generating apparatus for natural resources according to a third embodiment of the present invention;

fig. 4 is a schematic structural diagram of an electronic device implementing the professional entity generation method of natural resources according to the embodiment of the present invention.

Detailed Description

In order to make those skilled in the art better understand the technical solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It should be noted that the terms "candidate," "alternative," "object," and the like in the description and claims of the invention and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Example one

Fig. 1 is a flowchart of a method for generating a professional entity of a natural resource according to an embodiment of the present invention, where the method is applicable to a case of automatically generating a professional entity of a natural resource, and the method can be executed by a professional entity generating apparatus of a natural resource, where the professional entity generating apparatus of a natural resource can be implemented in a form of hardware and/or software, and the professional entity generating apparatus of a natural resource can be configured in an electronic device. As shown in fig. 1, the method includes:

s110, acquiring a natural resource professional entity generating task, and performing entity identification on the natural resource professional entity generating task to obtain a standard entity name contained in the natural resource professional entity generating task.

Among them, natural resources may refer to substances and energy that are directly obtained from the nature and used for production and life. Generally, the method can be divided into land resources, water resources, climate resources, biological resources, mineral resources and the like. In an embodiment of the invention, the natural resource may be selected as a land resource.

The professional entity may be an analysis result matched with the natural resource obtained after analyzing the basic information of the natural resource.

The natural resource professional generating task may refer to a statement initiated by a user to learn about the existing condition of a natural resource. For example, if the natural resource is an agricultural land-type land resource, the corresponding natural resource professional entity generation task may be: what is the distribution of the agricultural land.

The entity identification may refer to identifying an entity name corresponding to a natural resource name included in the natural resource professional entity generation task. The standard entity name may refer to an entity name in a standard format corresponding to an entity name in the natural resource professional entity generation task.

Therefore, a set of uniform data architecture can be established by converting the entity name in the natural resource professional entity generation task into the standard entity name, so that the execution of the subsequent task is facilitated, and the generation efficiency of the professional entity is improved.

And S120, generating a professional entity intention classification result according to the task generated by the natural resource professional entity.

The professional entity intention classification result may refer to a result of analyzing the number of standard entities and the number of relationships between the standard entities in the natural resource professional entity generation task. Illustratively, the professional entity intention classification result may be classified into a one-hop structural intention type or a multi-hop structural intention type, wherein the one-hop structural intention type may indicate that each standard entity in the natural resource professional entity generation task is adjacent; the multi-hop structure intent type may indicate that standard entities in the natural resource professional entity generation task are non-adjacent.

Therefore, by generating the professional entity intention classification result corresponding to the natural resource professional entity generating task, the number of the standard entities and the relation number among the standard entities in the natural resource professional entity generating task can be timely obtained, an effective basis is provided for the follow-up establishment of query sentences, and the accuracy of the query sentences is ensured.

And S130, obtaining a target path relation matched with the task generated by the professional natural resource entity according to a preset basic entity gallery.

The basic entity gallery may refer to a pre-established database including a plurality of basic entities and association relations between the basic entities. The basic entity may refer to an entity that summarizes the universe data of the natural resource, and may exemplarily include basic information such as a life cycle, an approval cycle, an area, and a usage corresponding to the natural resource.

The target path relationship may refer to a relationship stored in the basic entity gallery corresponding to a relationship between standard entities in the natural resource professional entity generation task. The relationship result between the standard entities in the task generated by the professional entity of the natural resource can be obtained through the target path relationship.

S140, constructing a target query statement according to the professional entity intention classification result, the standard entity name and the target path relation, and querying in a preset basic entity gallery according to the target query statement to obtain a target professional entity corresponding to the natural resource professional entity generation task.

The target query statement may refer to a machine language statement for querying a target path relationship between standard entities. The query result corresponding to the target path relation can be automatically obtained by querying in a preset basic entity gallery through the target query statement.

The target professional entity may refer to an analysis result obtained after analyzing a standard entity and a target path relationship included in the natural resource professional entity generation task.

It is worth noting that after the target professional entity corresponding to the natural resource professional entity generation task is obtained, confirmation information can be fed back to the user, wherein the confirmation information can include the target professional entity, and after a confirmation instruction fed back by the user is received, the target professional entity is displayed. Therefore, the accuracy of professional entities can be further ensured.

Example two

Fig. 2a is a flowchart of a method for generating a professional entity of natural resources according to a second embodiment of the present invention, where the embodiment is refined based on the above embodiment, and in the embodiment, the operation of performing entity identification on a task generated by a professional entity of natural resources to obtain a standard entity name included in the task generated by the professional entity of natural resources is refined, and the method specifically includes: converting the task generated by the professional natural resource entity into an abstract syntax tree, and extracting an operand of the abstract syntax tree; performing entity identification on the operand to obtain a candidate entity name contained in a natural resource professional entity generation task; and carrying out standardization processing on the candidate entity names to obtain standard entity names aligned with a preset basic entity gallery. As shown in fig. 2a, the method comprises:

s210, generating a file pasting source library according to file data in a pre-stored natural resource standard layer.

The natural resource standard map layer may refer to a map layer which is drawn in advance according to the survey result and is related to the natural resource. In the prior art, the natural resource standard layer is usually stored in a relational database management System PgSQL (postgrid Structured Query Language) or a relational database management System ORACLE, and is assisted by a Post-Geographic Information System (Post-GIS) plug-in.

The file data may refer to stored data in the natural resource standard layer. The file pasting source library can refer to a database formed by file data stored in a pasting layer, and the file pasting source library can be stored in a distributed massive column-type non-relational database system HBase generally.

Specifically, file data in a pre-stored natural resource standard map layer, such as file data of a facility agricultural land, a permanent basic farmland, a filed facility agricultural land or an administrative division, is collected to generate a file pasting source library.

And S220, performing data processing on the file pasting source library according to the cleaning treatment rule and the error calibration rule to obtain a standard file pasting source library.

Wherein, the cleaning treatment rule may refer to a rule for screening out non-compliant data in the file pasting source library. A miscalibration rule may refer to a rule that miscalibrates non-compliant data. The standard file pasting source library can refer to a file pasting source library processed by the cleaning treatment rule and the error calibration rule.

Therefore, the file pasting source library is subjected to cleaning treatment rules and error calibration rule treatment, so that the accuracy of file data in the standard file pasting source library can be ensured, and an effective basis is provided for the establishment of the basic entity gallery.

And S230, calculating attribute information and data relation of each source layer in the standard file source library according to the space function.

The space function may refer to a function for extracting attribute information and data relationship of each source layer in the file source library. For example, the occupation function can be used to calculate the occupation relation, the spatial superposition relation, the intersection relation, and the like between various types of map layers in the source layer.

The attribute information may refer to basic information of each source layer, such as a spot number, a place name, a data year, and the like. The data relationship may refer to an association relationship between attribute information.

It is worth noting that specific processing of each source pasting layer can be achieved according to calculation rules such as a point aggregation analysis operator, a vector diagram cutting or point track reconstruction operator and the like.

And S240, splicing the attribute information of each source pasting layer into a basic entity according to the target dimension.

The target dimension may refer to a measurement scale used as a basis for attribute information classification when attribute information is spliced. The target dimension includes a time dimension, a space dimension, or a business dimension.

The time dimension may refer to a metric that takes time as description attribute information. Illustratively, time attributes may correspond, such as, for example, the year of the data, etc. Usually, the names of land types change with the change of time of a natural resource, for example, the last year of a land is a field, and the last year is a building, so that the entity can be more accurately extracted and assembled by adding a time dimension, and a professional entity can be generated according to different dimension information and application scenes.

The spatial dimension may refer to a metric that takes space as description attribute information. Illustratively, spatial attributes such as a spot number, a land name, and a basic farmland _ grade level may be corresponded. Generally, the two plots have an intersecting, occupying or separating relationship, so that the entities can be more accurately extracted and assembled by adding spatial dimensions, and the professional entities can be generated according to different dimensional information and subsequent application scenes.

The business dimension may refer to a metric that takes a management process as description attribute information. For example, the service attribute may be corresponding to a record _ item unique code, a record _ reporting time, a record _ use name, and the like. Generally, the filing condition of a basic entity needs to be timely reflected in the investigation process, so that the entity can be more accurately extracted and assembled by adding service dimensions, and a professional entity can be generated according to different dimension information and subsequent application scenes.

As shown in fig. 2b, a flow chart of the generation process of the underlying entity. Specifically, firstly, a file pasting source library is obtained from a pre-stored natural resource standard map layer, such as a survey facility agricultural land, a filing facility agricultural land, a permanent basic field and an administrative division, and is subjected to data processing to obtain a standard file pasting source library; further, layer data of each source layer in a standard file source pasting library is obtained, attribute information and data relation of each source layer are calculated by using a space function or a related calculation rule, and finally all space attribute information, time attribute information and service attribute information aiming at the same basic entity are spliced to generate the basic entity. It is noted that each base entity corresponds to a unique code of the generated result.

For example, if a facility agricultural foundation entity needs to be generated, after the attribute information and the data relationship of each source pasting layer are obtained, the layer data of the investigation facility agricultural land and the record facility agricultural land can be associated and connected in series, and time dimension information is given; then, respectively carrying out association and concatenation with the layer data of the permanent basic field, and giving management dimension information; and finally, associating and connecting with an administrative division in series, and giving spatial dimension information to form the facility agricultural land foundation entity.

And S250, associating the basic entities according to the data relation to form a basic entity gallery.

FIG. 2c is a schematic diagram of the generation process of the basic entity gallery. Specifically, a data relationship of each pasting layer in a standard file pasting source library is obtained, and illustratively, a time inclusion relationship exists between a basic entity 1 and a basic entity 2; the basic entity 1 and the basic entity 4 have a permanent agricultural land inclusion relationship; if the foundation entity 4 and the foundation entity 5 have a recorded farmland area containing relationship, the foundation entity 1 and the foundation entity 2 can be connected, the foundation entity 1 and the foundation entity 4 are connected, and the foundation entity 4 and the foundation entity 5 form a foundation entity gallery. Further, after the basic entity 6 is added again through dynamic expansion, a region relationship exists between the basic entity 1 and the basic entity 6; the basic entity 5 and the basic entity 6 have a farmland area adjacent relation; the basic entity 3 and the basic entity 6 have a region adjacent relation, the basic entity 1 is connected with the basic entity 6, the basic entity 5 is connected with the basic entity 6, and the basic entity 3 is connected with the basic entity 6 to form an updated basic entity gallery.

And S260, acquiring a natural resource professional entity generating task, converting the natural resource professional entity generating task into an abstract syntax tree, and extracting an operand of the abstract syntax tree.

Wherein, the abstract syntax tree can refer to a tree representation formed by the natural resource professional generating task in an abstract syntax structure. Each node on the abstract syntax tree may represent a structure in a natural resource professional generation task, which may include, for example, operands. An operand may refer to data in the abstract syntax tree that contains a physical object.

And S270, carrying out entity identification on the operand to obtain a candidate entity name contained in the natural resource professional entity generation task.

The candidate entity name may refer to an entity name displayed in the natural resource professional entity generating task. Illustratively, the natural resource professional entity generation task is as follows: the agricultural land of the filed research facility occupies the number and the area of the permanent basic farmland, the task generated by the professional entity of the natural resource is converted into an abstract syntax tree, the operand of the abstract syntax tree is extracted, and then the entity recognition is carried out on the operand, so that the candidate entity names named as the agricultural land of the filed research facility and the permanent basic farmland can be obtained.

S280, carrying out standardization processing on the candidate entity names to obtain standard entity names aligned with a preset basic entity gallery.

In an optional embodiment, normalizing the candidate entity name to obtain a standard entity name aligned with a preset basic entity gallery includes: inquiring the alternative entity name matched with the candidate entity name according to a preset dictionary; and carrying out entity disambiguation on the alternative entity names, and mapping the alternative entity names into standard entity names aligned with a preset basic entity gallery.

The preset dictionary may refer to a preset dictionary containing a plurality of entity names, and may be established in the generation process of the basic entity. In the embodiment of the present invention, in addition to the query using the preset dictionary, an elastic search Engine (ES) may be used.

Wherein entity disambiguation may refer to a process of memorializing alternative entity names such that the alternative entity names map to standard entity names.

Specifically, taking the candidate entity names of 'recorded research facility agricultural land' and 'permanent basic farmland' as examples, the 'recorded research facility agricultural land and permanent basic farmland' are input into a preset dictionary to be inquired to obtain alternative entity names, and then entity disambiguation is carried out on the alternative entity names to finally obtain a standard entity name named 'recorded facility agricultural land and permanent basic farmland'. Therefore, the standard entity name aligned with the preset basic entity gallery can be obtained, an effective basis is provided for the subsequent generation of the professional entity, and the accuracy of the professional entity can be ensured.

And S290, generating a professional entity intention classification result according to the natural resource professional entity generation task.

Specifically, the task generated by the professional entity of natural resources is as follows: the number and the area of permanent basic farmlands occupied by agricultural land pressure of the recorded research facilities are taken as an example, and the fact that the classification result of the intention of the professional entity comprises two basic entity semantic slot positions and a relation slot position can be known.

S2100, according to a preset basic entity gallery, inquiring to obtain a full-scale path relation matched with the standard entity name.

Wherein, the full-scale path relationship may refer to all path relationships matching the standard entity name.

Specifically, the standard entity name "filed facility agricultural land, permanent basic farmland" is searched in a preset basic entity gallery to obtain all the path relations related to the "filed facility agricultural land, permanent basic farmland".

And S2110, screening the full-quantity path relationship to obtain a target path relationship matched with the task generated by the natural resource professional entity.

In an optional embodiment, the screening of the full-scale path relationship to obtain the target path relationship matched with the natural resource professional entity generation task includes: performing semantic matching and path sequencing on the task generated by the professional natural resource entity and the full path relation to obtain a candidate path relation; and screening out candidate path relations meeting the target sorting standard as target path relations.

The candidate path relationship may be a path relationship that satisfies semantic matching and is obtained after sorting. The target ranking criterion may refer to a criterion for screening candidate path relationships according to a ranking result, and may be, for example, a candidate path relationship ranked first as a target path relationship.

Specifically, semantic matching and path sorting are carried out on the natural resource professional entity generation task and the full path relation, and a candidate path relation is obtained; and screening out the first ranked candidate path relation 'override' as a target path relation.

S2120, obtaining the query statement template, and determining the number of the slot positions of the target path relation and the number of the slot positions of the standard entity name in the query statement template according to the professional entity intention classification result.

The query statement template may refer to a preset statement template for generating a professional entity. Illustratively, it may be "SELECTX WHERE { <? x. } ", wherein < > can represent the slot position filled with the target path relation and the standard entity name, and the number of < > is determined by the classification result of the professional entity intention; x. may represent a query result. The slot number may refer to the number of target path relationships and the number of standard entity names included in the task of generating the professional entity of the natural resource, and may be determined by the intent classification result of the professional entity.

And S2130, filling the standard entity name and the target path relation into the corresponding slot position of the query statement template to generate a target query statement.

Specifically, the number of slots of a target path relation in the query statement template is determined to be 1 and the number of slots of a standard entity name is determined to be 2 according to the professional entity intention classification result, so that the query statement template can be constructed into' SELECTX WHERE { < > < > <? x. } "; further, the standard entity name and the target path relation are filled into the corresponding slot of the query statement template, and the generation is shaped as "select WHERE { < filed facility agricultural land > < permanent basic farmland > < occupied pressure >? x. } "target query statement.

S2140, inquiring in a preset basic entity gallery according to the target inquiry statement to obtain a target professional entity corresponding to the natural resource professional entity generating task.

Specifically, according to the target query statement "select x WHERE { < filed facility agricultural land > < permanent basic farmland > < pressure occupation >? And x. inquiring in a preset basic entity graph library to obtain the attribute area and the number of the occupation relation, thereby realizing the generation of professional entities.

According to the technical scheme of the embodiment of the invention, a file pasting source library is generated according to the file data in the pre-stored natural resource standard layer; then according to the cleaning treatment rule and the error calibration rule, performing data processing on the file pasting source library to obtain a standard file pasting source library; calculating attribute information and data relation of each source layer in the standard file source library according to the space function; splicing the attribute information of each source pasting layer into a basic entity according to the target dimension; associating each basic entity according to the data relationship to form a basic entity gallery; further, converting the task generated by the professional natural resource entity into an abstract syntax tree, and extracting an operand of the abstract syntax tree; performing entity identification on the operand to obtain a candidate entity name contained in a natural resource professional entity generation task; then, carrying out standardization processing on the candidate entity names to obtain standard entity names aligned with a preset basic entity gallery; generating a professional entity intention classification result according to the task generated by the natural resource professional entity; further, according to a preset basic entity gallery, inquiring to obtain a full path relation matched with the standard entity name; screening to obtain a target path relation matched with the task generated by the professional entity of the natural resource; acquiring a query statement template, and determining the number of slot positions of a target path relation and the number of slot positions of a standard entity name in the query statement template according to a professional entity intention classification result; filling the standard entity name and the target path relation into a corresponding slot position of the query statement template to generate a target query statement; and finally, inquiring in a preset basic entity gallery according to the target inquiry statement to obtain a target professional entity corresponding to the generation task of the professional entity of the natural resource, solving the problem of low generation efficiency and accuracy of the professional entity of the natural resource, and improving the generation efficiency and accuracy of the professional entity of the natural resource.

On the basis of the above embodiment, the embodiment of the present invention is also applicable to application scenarios of different dimension information. For example, if the scene a needs to know and research the distribution and the record rate of the facility agricultural land, the distribution and the record rate of the facility agricultural land can be obtained by extracting the facility agricultural land area and the number of the recorded facility agricultural land areas of each county according to the spatial dimension. And the scene B needs to pay attention to the stable situation and the variable flow direction situation of the facility agricultural land during the period from the second investigation to the third investigation for ten years, the same-caliber achievement data of the second investigation facility agricultural land and the third investigation can be extracted according to the time dimension to be assembled into a professional entity facing the scene B, and the stable situation and the variable flow direction situation of the land of the second investigation facility agricultural land are obtained by extracting the land class and the area of two years. The scenario C needs to pay attention to the situation that the filed third investigation facility agricultural land occupies the permanent basic farmland, so that the attribute association situations of the third investigation facility agricultural land, the filed facility agricultural land and the permanent basic farmland can be extracted from the service dimension, and the rationality of occupying the permanent basic farmland can be traced in the service management. Therefore, by establishing a set of unified data architecture, the corresponding professional entities can be generated according to different dimension information, and the generation efficiency of the professional entities is improved.

EXAMPLE III

Fig. 3 is a schematic structural diagram of a professional entity generating device for natural resources according to a third embodiment of the present invention. As shown in fig. 3, the apparatus includes: a standard entity name acquisition module 310, a classification result generation module 320, a path relation acquisition module 330 and a professional entity acquisition module 340;

the standard entity name obtaining module 310 is configured to obtain a natural resource professional entity generating task, perform entity identification on the natural resource professional entity generating task, and obtain a standard entity name included in the natural resource professional entity generating task;

the classification result generation module 320 is used for generating a professional entity intention classification result according to the natural resource professional entity generation task;

the path relation obtaining module 330 is configured to obtain a target path relation matched with the natural resource professional entity generating task according to a preset basic entity gallery;

and the professional entity obtaining module 340 is configured to construct a target query statement according to the professional entity intention classification result, the standard entity name and the target path relationship, and query in a preset basic entity gallery according to the target query statement to obtain a target professional entity corresponding to the natural resource professional entity generating task.

According to the technical scheme of the embodiment of the invention, entity recognition is carried out on the acquired professional entity generation task of the natural resource to obtain a standard entity name contained in the professional entity generation task of the natural resource; generating a professional entity intention classification result according to the task generated by the natural resource professional entity; obtaining a target path relation matched with a natural resource professional entity generation task according to a preset basic entity gallery; and constructing a target query statement according to the professional entity intention classification result, the standard entity name and the target path relation, and querying in a preset basic entity gallery according to the target query statement to obtain a target professional entity corresponding to the natural resource professional entity generation task, so that the problem of low generation efficiency and accuracy of the professional entity of the natural resource is solved, and the generation efficiency and accuracy of the professional entity of the natural resource can be improved.

Optionally, the professional entity generating device for natural resources may further include: the basic entity image library generating module is used for generating a file pasting source library according to file data in a pre-stored natural resource standard image layer before acquiring a natural resource professional entity generating task and performing entity identification on the natural resource professional entity generating task to obtain a standard entity name contained in the natural resource professional entity generating task; according to the cleaning treatment rule and the error calibration rule, performing data processing on the file pasting source library to obtain a standard file pasting source library; calculating attribute information and data relation of each source layer in a standard file source database according to a space function; splicing the attribute information of each source pasting layer into a basic entity according to the target dimension; wherein the target dimension comprises a time dimension, a space dimension or a business dimension; and associating each basic entity according to the data relationship to form a basic entity gallery.

Optionally, the standard entity name obtaining module 310 may specifically include: an operand extraction unit, a candidate entity name acquisition unit and a standard entity name acquisition unit;

the operand extraction unit is used for converting the task generated by the professional natural resource entity into an abstract syntax tree and extracting operands of the abstract syntax tree;

the candidate entity name acquisition unit is used for carrying out entity identification on the operand to obtain a candidate entity name contained in the natural resource professional entity generation task;

and the standard entity name acquisition unit is used for standardizing the candidate entity names to obtain standard entity names aligned with the preset basic entity gallery.

Optionally, the standard entity name obtaining unit may be specifically configured to: inquiring the alternative entity name matched with the candidate entity name according to a preset dictionary; and carrying out entity disambiguation on the alternative entity name, and mapping the alternative entity name into a standard entity name aligned with a preset basic entity gallery.

Optionally, the path relation obtaining module 330 may specifically include: a full quantity path relation obtaining unit and a target path relation obtaining unit;

the system comprises a full-quantity path relation obtaining unit, a full-quantity path relation obtaining unit and a full-quantity path relation obtaining unit, wherein the full-quantity path relation obtaining unit is used for inquiring and obtaining a full-quantity path relation matched with a standard entity name according to a preset basic entity gallery;

and the target path relation obtaining unit is used for screening the full-scale path relation to obtain a target path relation matched with the natural resource professional entity generation task.

Optionally, the target path relation obtaining unit may be specifically configured to: performing semantic matching and path sequencing on the task generated by the professional natural resource entity and the full path relation to obtain a candidate path relation; and screening out candidate path relations meeting the target sorting standard as target path relations.

Optionally, the professional entity obtaining module 340 may be specifically configured to: acquiring a query statement template, and determining the number of slot positions of a target path relation and the number of slot positions of a standard entity name in the query statement template according to a professional entity intention classification result; and filling the standard entity name and the target path relation into the corresponding slot position of the query statement template to generate the target query statement.

The professional entity generation device for natural resources provided by the embodiment of the invention can execute the professional entity generation method for natural resources provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.

Example four

FIG. 4 illustrates a block diagram of an electronic device 410 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 4, the electronic device 410 includes at least one processor 420, and a memory communicatively connected to the at least one processor 420, such as a Read Only Memory (ROM) 430, a Random Access Memory (RAM) 440, and the like, wherein the memory stores computer programs executable by the at least one processor, and the processor 420 may perform various suitable actions and processes according to the computer programs stored in the Read Only Memory (ROM) 430 or the computer programs loaded from the storage unit 490 into the Random Access Memory (RAM) 440. In the RAM440, various programs and data required for the operation of the electronic device 410 may also be stored. The processor 420, the ROM 430, and the RAM440 are connected to each other by a bus 450. An input/output (I/O) interface 460 is also connected to bus 450.

Various components in the electronic device 410 are connected to the I/O interface 460, including: an input unit 470 such as a keyboard, a mouse, etc.; an output unit 480 such as various types of displays, speakers, and the like; a storage unit 490, such as a magnetic disk, optical disk, or the like; and a communication unit 4100 such as a network card, a modem, a wireless communication transceiver, and the like. The communication unit 4100 allows the electronic device 410 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.

Processor 420 may be a variety of general and/or special purpose processing components with processing and computing capabilities. Some examples of processor 420 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. Processor 420 performs the various methods and processes described above, such as professional entity generation of natural resources.

The method can comprise the following steps:

In some embodiments, the method for professional entity generation of natural resources may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 490. In some embodiments, part or all of a computer program may be loaded onto and/or installed onto electronic device 410 via ROM 430 and/or communications unit 4100. When loaded into RAM440 and executed by processor 420, the computer programs may perform one or more steps of the natural resource professional generation method described above. Alternatively, in other embodiments, processor 420 may be configured by any other suitable means (e.g., by way of firmware) to perform a professional entity generation method of natural resources.

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.

The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired result of the technical solution of the present invention can be achieved.

The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A professional entity generation method of natural resources is characterized by comprising the following steps:

obtaining a target path relation matched with a natural resource professional entity generation task according to a preset basic entity image library;

2. The method of claim 1, wherein before obtaining the natural resource professional entity generating task and performing entity recognition on the natural resource professional entity generating task to obtain a standard entity name included in the natural resource professional entity generating task, the method further comprises:

generating a file pasting source library according to file data in a pre-stored natural resource standard layer;

according to the cleaning treatment rule and the error calibration rule, performing data processing on the file pasting source library to obtain a standard file pasting source library;

calculating attribute information and data relation of each source layer in a standard file source database according to a space function;

splicing the attribute information of each source pasting layer into a basic entity according to the target dimension; wherein the target dimension comprises a time dimension, a space dimension or a business dimension;

and associating the basic entities according to the data relationship to form a basic entity gallery.

3. The method according to claim 1, wherein the entity identifying the natural resource professional entity generation task to obtain a standard entity name included in the natural resource professional entity generation task includes:

converting the task generated by the professional natural resource entity into an abstract syntax tree, and extracting an operand of the abstract syntax tree;

performing entity identification on the operand to obtain a candidate entity name contained in a natural resource professional entity generation task;

and carrying out standardization processing on the candidate entity names to obtain standard entity names aligned with a preset basic entity gallery.

4. The method of claim 3, wherein the normalizing the candidate entity names to obtain standard entity names aligned with a predetermined underlying entity gallery comprises:

inquiring the alternative entity name matched with the candidate entity name according to a preset dictionary;

and carrying out entity disambiguation on the alternative entity name, and mapping the alternative entity name into a standard entity name aligned with a preset basic entity gallery.

5. The method according to claim 1, wherein obtaining the target path relationship matched with the task generated by the professional entity of natural resources according to a preset basic entity gallery comprises:

according to a preset basic entity gallery, inquiring to obtain a full path relation matched with the standard entity name;

and screening the full path relation to obtain a target path relation matched with the task generated by the professional entity of the natural resource.

6. The method of claim 5, wherein the screening of the full-scale path relationships to obtain target path relationships matching the natural resource professional generation task comprises:

performing semantic matching and path sequencing on the task generated by the professional natural resource entity and the full path relation to obtain a candidate path relation;

and screening out candidate path relations meeting the target sorting standard as target path relations.

7. The method of claim 1, wherein constructing the target query statement according to the professional entity intention classification result, the standard entity name and the target path relationship comprises:

acquiring a query statement template, and determining the number of slot positions of a target path relation and the number of slot positions of a standard entity name in the query statement template according to a professional entity intention classification result;

and filling the standard entity name and the target path relation into the corresponding slot position of the query statement template to generate the target query statement.

8. An apparatus for generating professional entities of natural resources, comprising:

the path relation acquisition module is used for acquiring a target path relation matched with the natural resource professional entity generation task according to a preset basic entity gallery;

9. An electronic device, characterized in that the electronic device comprises:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein, the first and the second end of the pipe are connected with each other,

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of professional entity generation of natural resources of any of claims 1-7.

10. A computer-readable storage medium storing computer instructions for causing a processor to perform the method for professional entity generation of natural resources of any one of claims 1-7 when executed.