CN113947290A - Distribution network evaluation and review auxiliary method and system based on artificial intelligence - Google Patents
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Abstract
The invention provides a distribution network evaluation and review auxiliary method based on artificial intelligence, which comprises the following steps: s101, importing a historical evaluation data record packet into a distribution network evaluation auxiliary system; s102, the distribution network revivable evaluation auxiliary system obtains historical review data by analyzing the historical review data record packet, generates a data map according to the historical review data, associates all reviews and establishes a map database; s103, under a full-text search mode, establishing an intelligent review recommendation algorithm model based on distance similarity by taking fuzzy matching as a prototype and combining attribute factors and associated information in a graph database; and S104, calling an intelligent review recommendation algorithm model according to review data input by the user, and selecting similar review from the historical review data to recommend to the user. The method can improve the efficiency and the quality of the distribution network which can be researched and reviewed.
Description
Technical Field
The invention relates to the technical field of evaluation auxiliary systems, in particular to a distribution network evaluation auxiliary method based on artificial intelligence.
Background
The distribution network is an important infrastructure directly facing terminal electricity users, the distribution network project evaluation and review efficiency and quality directly influence the distribution network investment progress, the investment rationality and the investment economy, and as the power consumption of the whole society increases and slows down, under the condition that most of the power supply and the skeleton of the power transmission line are gradually improved, the investment gravity center of the power grid in China is shifted from a backbone network to a distribution network side. The key points of power grid investment are shifted, and the exploitable projects of the investment and construction planning of the distribution network are increased rapidly, so that the exploitable and auditable workload of the distribution network in the coming years is increased greatly. However, compared with power transmission and transformation projects, the limit of the power grid company on the research requirements of the distribution network project is relatively small, the management control is relatively loose, the problems of low quality of the research and evaluation, improper management and the like exist, and the research and evaluation personnel easily neglect errors in the process of the verification due to carelessness and carelessness, so that the overall progress and quality of the research and evaluation are influenced.
Disclosure of Invention
In view of the above, the present invention provides a distribution network evaluable review assistance method and system based on artificial intelligence, so as to overcome or at least partially solve the above problems in the prior art.
In order to achieve the above object, a first aspect of the present invention provides an artificial intelligence-based distribution network evaluation and review assistance method, which is applied to a distribution network evaluation and review assistance system, and the method specifically includes the following steps:
s101, importing a historical evaluation data record packet into a distribution network evaluation auxiliary system;
s102, the distribution network revivable evaluation auxiliary system obtains historical review data by analyzing the historical review data record packet, generates a data map according to the historical review data, associates all reviews and establishes a map database;
s103, under a full-text search mode, establishing an intelligent review recommendation algorithm model based on distance similarity by taking fuzzy matching as a prototype and combining attribute factors and associated information in a graph database;
and S104, calling an intelligent review recommendation algorithm model according to review data input by the user, and selecting similar review from the historical review data to recommend to the user.
Further, the data map is generated according to the historical review data, and specifically, the data map is generated according to the key points, the specifications and the technical principles of each historical review data for acquiring the key points, the specifications and the technical principles of each historical review data.
Further, the method for obtaining the key points, the specifications and the technical principles of the historical review data and generating the data map according to the key points, the specifications and the technical principles of the historical review data specifically comprises the following steps:
s201, detecting each chapter and paragraph in each historical review material to form a chapter list of the historical review material as a main point;
s202, detecting information related to the specification information and the technical principle file, which is mentioned by each piece of historical review data, taking the information related to the specification information and the technical principle file as query conditions, querying the related technical specification file, establishing an incidence relation between the historical review data and the related technical specification file, and forming a data map of the historical review data.
Further, the distance similarity-based review intelligent recommendation algorithm model specifically comprises the following steps:
s301, setting an attribute factor review weight value of the review data file;
s302, traversing a graph database, inquiring data with at least one dimension same as the attribute factor review weight of the review data file, and taking the maximum review weight when the attribute factor review weights of all dimensions are the same to form a set of graph structures;
s303, assigning a weight distance value to each dimension of the attribute factors respectively to form a distance value between the files;
s304, calculating the shortest distance between the review data and each file in the graph database;
s305, sorting according to the shortest distance between the review data and each file in the graph database, and selecting the file with the shortest distance for recommendation.
Further, the step S304 specifically includes the following steps:
s401, taking the review data as a source point, taking each file in the graph database as other vertexes, setting a mark number group book [ ], establishing a vertex set P with a known shortest path and a vertex set Q with an unknown shortest path, wherein the set P only comprises one vertex of the source point in an initial state, and a book [ i ] is 1 and is expressed in the set P;
s402, setting a shortest path array dst [ ] and continuously updating, wherein the updating specifically comprises the following steps: in the initial state, let dst [ i ] ═ edge [ s ] [ i ], where s denotes the source point and edge is the adjacency matrix, at this time, dst [ s ] ═ 0 and book [ s ] ═ 1, select a vertex u in the set Q nearest to the source point s to add to the set P, and perform a relaxation operation on each edge according to the new central point of u, let book [ u ] ═ 1;
s403, selecting a vertex v closest to the source point S from the set Q again, adding the vertex v into the set P, and performing relaxation operation on each edge according to the fact that v is a new central point, wherein book [ v ] is made to be 1;
and S404, repeating the step S403 until the set Q is empty.
Further, the relaxation operation specifically includes: let dst [ j ] ═ min { dst [ j ], dst [ x ] + edge [ x ] [ j ] }, where x denotes the center point and j denotes the end point.
Further, the attribute factors include item types, query heat and file attributes, and the file attributes include points, specifications and technical principles.
Further, the method further comprises the steps of:
the method comprises the steps of obtaining voice data input by a user, calling a single-machine intelligent voice recognition conversion module, converting the voice data into text contents meeting the specification, and inquiring a database according to the text contents to obtain corresponding historical review data.
The second aspect of the present invention provides a distribution network evaluable review auxiliary system, including:
the import module is used for importing a historical review data record packet into the system;
the analysis module is used for analyzing the historical review data record packet to obtain historical review data, generating a data map according to the historical review data, associating all reviews and establishing a map database;
the modeling module is used for establishing an intelligent review recommendation algorithm model based on distance similarity by taking fuzzy matching as a prototype and combining the attribute factors and the associated information in the graph database in a full-text search mode;
and the recommending module calls the intelligent recommending algorithm for review according to the review data input by the user and selects similar review from the historical review data to recommend to the user.
Compared with the prior art, the invention has the beneficial effects that:
according to the distribution network review auxiliary method and the distribution network review auxiliary system, the historical review data record packet is imported into the system for analysis to form the data map related to related data, so that a user can conveniently and quickly retrieve historical review data during review, review key points, specifications and technical principles which are easy to ignore and mistake are distinguished from the historical review record according to the review intelligent recommendation algorithm model and are recommended to the user, the user can conveniently refer to the distribution network during review, and therefore the efficiency and the quality of the distribution network which can be reviewed are improved.
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 apparent that the drawings in the following description are only preferred embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without inventive efforts.
Fig. 1 is a schematic overall flow chart of an artificial intelligence-based distribution network evaluation and review assisting method provided by an embodiment of the invention.
Fig. 2 is a schematic diagram of a data map structure provided by an embodiment of the present invention.
FIG. 3 is a schematic flow chart illustrating a working principle of a review intelligent recommendation algorithm model according to an embodiment of the present invention.
FIG. 4 is a flowchart illustrating a method for calculating a shortest distance between each document in the review data and the database according to an embodiment of the present invention.
Fig. 5 is a schematic overall structure diagram of a distribution network evaluable review auxiliary system based on artificial intelligence according to another embodiment of the present invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, the illustrated embodiments are provided to illustrate the invention and not to limit the scope of the invention.
Referring to fig. 1, the present embodiment provides an artificial intelligence-based distribution network evaluable review auxiliary method, which is applied to a distribution network evaluable review auxiliary system, and the method specifically includes the following steps:
s101, importing the historical evaluation data record package into a distribution network evaluation auxiliary system. The historical review data record packet is internally provided with a plurality of pieces of historical review data.
S102, the distribution network revivable evaluation auxiliary system obtains historical review data by analyzing the historical review data record packet, generates a data map according to the historical review data, associates all reviews and establishes a map database.
S103, under a full-text search mode, fuzzy matching is used as a prototype to combine attribute factors and associated information in a graph database, and an intelligent review recommendation algorithm model based on distance similarity is established.
And S104, calling an intelligent review recommendation algorithm model according to review data input by the user, and selecting similar review from the historical review data to recommend to the user.
Specifically, in step S102, the data map is generated according to the historical review data, specifically, the data map is generated according to the key points, specifications, and technical principles of each historical review data to obtain the key points, specifications, and technical principles of each historical review data.
As an optional implementation manner, the obtaining of the key points, specifications, and technical principles of each historical review material and the generating of the data map according to the key points, specifications, and technical principles of each historical review material specifically include the following steps:
s201, detecting chapter sections in each piece of historical review data to form a chapter list of the historical review data as a main point;
s202, detecting information related to the specification information and the technical principle file in each piece of historical review data, taking the information related to the specification information and the technical principle file as query conditions, querying the related technical specification file, establishing an incidence relation between the historical review data and the related technical specification file, and forming a data map of the review data. Illustratively, as shown in fig. 2, the representation form of the data map may be a structure in the form of a map cloud.
As an alternative implementation manner, referring to fig. 3, in step S103, the intelligent review recommendation algorithm model based on distance similarity specifically includes the following steps:
s301, setting an attribute factor evaluation weight value of the evaluation data file. The attribute factors at least comprise three dimensions of item types, query heat degrees and file attributes, and the file attributes at least comprise the main points, specifications and technical principles of the files. The larger the review weight value, the smaller the distance.
S302, traversing the graph database, inquiring data with at least one dimension same as the attribute factor evaluation weight of the evaluation data file in the graph database, and taking the maximum evaluation weight when the attribute factor evaluation weights of all dimensions of the evaluation data file and the certain data in the graph database are the same to form a set of graph-like structures.
S303, assigning a weight distance value to each dimension of the attribute factors respectively to form a distance value between the files.
S304, calculating the shortest distance between the review data and each file in the graph database.
S305, sorting according to the shortest distance between the review data and each file in the graph database, and selecting the file with the shortest distance to the review data for recommendation.
As a further optional implementation manner, referring to fig. 4, the step S304 specifically includes the following steps;
s401, taking the review data as a source point, taking each file in the graph database as other vertexes, setting a mark number group book [ ], establishing a vertex set P with a known shortest path and a vertex set Q with an unknown shortest path, wherein only one vertex is included in the set P in an initial state, and when the book [ i ] is 1, the point is shown in the set P.
S402, setting a shortest path array dst [ ] and continuously updating the shortest path array dst [ ], wherein the updating specifically comprises the following steps: in the initial state, let dst [ i ] ═ edge [ S ] [ i ], where S denotes the source point and edge is the adjacency matrix, at this time, dst [ S ] ═ 0 and book [ S ] ═ 1, select a vertex u in the set Q closest to the source point S to add to the set P, and take u as a new center point, perform a relaxation operation on each edge, and let book [ u ] ═ 1. The relaxation operation means that a vertex u can be passed by the vertex s- > j on the way, and dst [ j ] ═ min { dst [ j ], dst [ u ] + edge [ u ] [ j ] }, wherein u represents a central point and j represents an end point.
S403, selecting a vertex v nearest to the source point S from the set Q again, adding the vertex v into the set P, and taking v as a new central point, performing a relaxation operation on each edge (i.e., dst [ j ] ═ min { dst [ j ], dst [ v ] + edge [ v ] [ j ] }, where v denotes a central point and j denotes an end point), and making book [ v ] ═ 1.
And S404, repeatedly executing the step S403 until the set Q is empty.
As an optional implementation, the method further comprises the steps of:
the method comprises the steps of obtaining voice data input by a user, calling a single-machine intelligent voice recognition conversion module, converting the voice data into text contents meeting the specification, and inquiring a database according to the text contents to obtain corresponding historical review data.
Illustratively, in the embodiment, the single-computer voice recognition SDK is adopted to intelligently recognize the voice input by the user, and the voice input by the user is converted into the text content meeting the specification by combining with the associated data in the graph database, so as to realize the single-computer intelligent voice recognition conversion module, thereby facilitating the user to quickly input and query the specification and the specification during the review.
According to the distribution network evaluation and review auxiliary method based on artificial intelligence, a user can import a historical evaluation data record packet into a distribution network evaluation and review auxiliary system, and the system forms a related data map according to historical evaluation data, so that the user can conveniently and quickly read the historical evaluation data during evaluation. Meanwhile, single-machine voice recognition SDK is adopted, the voice input by the user is intelligently recognized and converted into the actual text content, so that the convenience is brought to the user, and the review efficiency is improved. Meanwhile, the method extracts evaluation key points, specifications and technical principles which are related to evaluation data and easy to ignore and error from the historical evaluation process record through the evaluation intelligent recommendation algorithm model, and recommends the evaluation key points, specifications and technical principles to the user, so that the user can conveniently refer to the evaluation.
On the basis of the foregoing method embodiment, based on the same inventive concept, another embodiment of the present invention further provides an artificial intelligence-based distribution network evaluation and review assistance system, including:
the import module is used for importing a historical review data record packet into the system;
the analysis module is used for analyzing the historical review data record packet to obtain historical review data, generating a data map according to the historical review data, associating all reviews and establishing a map database;
the modeling module is used for establishing an intelligent review recommendation algorithm model based on distance similarity by taking fuzzy matching as a prototype and combining the attribute factors and the associated information in the graph database in a full-text search mode;
and the recommending module calls the intelligent recommending algorithm for review according to the review data input by the user and selects similar review from the historical review data to recommend to the user.
The system embodiment is used for executing the method described in the method embodiment, and the working principle and the beneficial effects of the system embodiment can refer to the method embodiment, which is not described herein again.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (9)
1. The distribution network evaluation and review auxiliary method based on artificial intelligence is applied to a distribution network evaluation and review auxiliary system, and specifically comprises the following steps:
s101, importing a historical evaluation data record packet into a distribution network evaluation auxiliary system;
s102, the distribution network revivable evaluation auxiliary system obtains historical review data by analyzing the historical review data record packet, generates a data map according to the historical review data, associates all reviews and establishes a map database;
s103, under a full-text search mode, establishing an intelligent review recommendation algorithm model based on distance similarity by taking fuzzy matching as a prototype and combining attribute factors and associated information in a graph database;
and S104, calling an intelligent review recommendation algorithm model according to review data input by the user, and selecting similar review from the historical review data to recommend to the user.
2. The distribution network evaluation auxiliary method based on artificial intelligence as claimed in claim 1, wherein the data map is generated according to historical evaluation data, and specifically, the data map is generated according to the main points, specifications and technical principles of each historical evaluation data for obtaining the main points, specifications and technical principles of each historical evaluation data.
3. The distribution network evaluation and review auxiliary method based on artificial intelligence as claimed in claim 2, wherein the method for acquiring the main points, specifications and technical principles of each historical evaluation data and generating the data map according to the main points, specifications and technical principles of each historical evaluation data specifically comprises the following steps:
s201, detecting each chapter and paragraph in each historical review material to form a chapter list of the historical review material as a main point;
s202, detecting information related to the specification information and the technical principle file, which is mentioned by each piece of historical review data, taking the information related to the specification information and the technical principle file as query conditions, querying the related technical specification file, establishing an incidence relation between the historical review data and the related technical specification file, and forming a data map of the historical review data.
4. The distribution network evaluable review auxiliary method based on artificial intelligence as recited in claim 1, wherein the intelligent review recommendation algorithm model based on distance similarity specifically comprises the following steps:
s301, setting an attribute factor review weight value of the review data file;
s302, traversing a graph database, inquiring data with at least one dimension same as the attribute factor review weight of the review data file, and taking the maximum review weight when the attribute factor review weights of all dimensions are the same to form a set of graph structures;
s303, assigning a weight distance value to each dimension of the attribute factors respectively to form a distance value between the files;
s304, calculating the shortest distance between the review data and each file in the graph database;
s305, sorting according to the shortest distance between the review data and each file in the graph database, and selecting the file with the shortest distance for recommendation.
5. The distribution network evaluable review auxiliary method based on artificial intelligence as claimed in claim 4, wherein the step S304 specifically comprises the steps of:
s401, taking the review data as a source point, taking each file in the graph database as other vertexes, setting a mark number group book [ ], establishing a vertex set P with a known shortest path and a vertex set Q with an unknown shortest path, wherein the set P only comprises one vertex of the source point in an initial state, and a book [ i ] is 1 and is expressed in the set P;
s402, setting a shortest path array dst [ ] and continuously updating, wherein the updating specifically comprises the following steps: in the initial state, let dst [ i ] ═ edge [ s ] [ i ], where s denotes the source point and edge is the adjacency matrix, at this time, dst [ s ] ═ 0 and book [ s ] ═ 1, select a vertex u in the set Q nearest to the source point s to add to the set P, and perform a relaxation operation on each edge according to the new central point of u, let book [ u ] ═ 1;
s403, selecting a vertex v closest to the source point S from the set Q again, adding the vertex v into the set P, and performing relaxation operation on each edge according to the fact that v is a new central point, wherein book [ v ] is made to be 1;
and S404, repeating the step S403 until the set Q is empty.
6. The distribution network evaluable review auxiliary method based on artificial intelligence as recited in claim 5, wherein the relaxation operation specifically comprises: let dst [ j ] ═ min { dst [ j ], dst [ x ] + edge [ x ] [ j ] }, where x denotes the center point and j denotes the end point.
7. The distribution network evaluable review auxiliary method based on artificial intelligence as claimed in any one of claims 4-6, wherein the attribute factors include project type, query heat and file attributes, and the file attributes include points, specifications, and technical principles.
8. The distribution network evaluable review auxiliary method based on artificial intelligence as claimed in claim 1, wherein the method further comprises the steps of:
the method comprises the steps of obtaining voice data input by a user, calling a single-machine intelligent voice recognition conversion module, converting the voice data into text contents meeting the specification, and inquiring a database according to the text contents to obtain corresponding historical review data.
9. An artificial intelligence-based distribution network evaluation and review auxiliary system is characterized by comprising:
the import module is used for importing a historical review data record packet into the system;
the analysis module is used for analyzing the historical review data record packet to obtain historical review data, generating a data map according to the historical review data, associating all reviews and establishing a map database;
the modeling module is used for establishing an intelligent review recommendation algorithm model based on distance similarity by taking fuzzy matching as a prototype and combining the attribute factors and the associated information in the graph database in a full-text search mode;
and the recommending module calls the intelligent recommending algorithm for review according to the review data input by the user and selects similar review from the historical review data to recommend to the user.
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Application publication date: 20220118 |