CN111651479A - Article evaluation method, device and related equipment - Google Patents
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Abstract
The application discloses an article evaluation method, which is applied to an elastic search server and comprises the steps of obtaining article information of an article to be evaluated according to a received evaluation request; inquiring and acquiring target evaluation information corresponding to the article to be evaluated from a Mongo database according to the article information; calculating to obtain a target evaluation value according to the target evaluation information, and feeding back the target evaluation value to a request terminal; the item evaluation method can meet the requirement of system stability and effectively improve the data query efficiency. The application also discloses an article evaluation system, an article evaluation device, a server and a computer-readable storage medium, which have the beneficial effects.
Description
Technical Field
The present application relates to the field of server technologies, and in particular, to an item assessment method, and further, to an item assessment system, apparatus, server, and computer-readable storage medium.
Background
In the specific development process of a project, the backend service is the most basic, and the size degree of one project directly determines the number and the complexity degree of interfaces of the backend service. In a scenario with a high real-time requirement, a fast data return speed is required, and in a batch query with a large data volume, a large amount of data needs to be stably transmitted and evaluated. However, since the evaluation is based on fuzzy query of massive sample data accumulated in the sample library, and further processing is sometimes required to be performed on the query result before the query result is finally fed back to the user, which puts a high requirement on the overall performance of the system, the conventional relational database has difficulty in supporting the current application scenario due to the defects of slow data query and low efficiency.
Therefore, how to effectively improve the data query efficiency while meeting the requirement of system stability is an urgent problem to be solved by those skilled in the art.
Disclosure of Invention
The object of the application is to provide an article evaluation method, which can effectively improve the data query efficiency while meeting the requirement of system stability; it is another object of the present application to provide an item assessment system, apparatus, server and computer readable storage medium, also having the above-mentioned advantageous effects.
In order to solve the above technical problem, in a first aspect, the present application provides an article evaluation method applied to an ElasticSearch server, including:
acquiring article information of an article to be evaluated according to the received evaluation request;
inquiring and acquiring target evaluation information corresponding to the article to be evaluated from a Mongo database according to the article information;
and calculating to obtain a target evaluation value according to the target evaluation information, and feeding back the target evaluation value to the request terminal.
Preferably, the obtaining of the target evaluation information corresponding to the article to be evaluated by querying from the Mongo database according to the article information includes:
performing word segmentation processing on the article information to obtain each article keyword;
and inquiring and obtaining target evaluation information corresponding to each article keyword from the Mongo database.
Preferably, the obtaining of the target evaluation information corresponding to each item keyword by querying from the Mongo database includes:
and obtaining target evaluation information corresponding to each article keyword through a preset API (application program interface) of the Mongo database.
Preferably, before the obtaining of the target evaluation value by calculation according to the target evaluation information, the method further includes:
and performing de-duplication processing on each target evaluation information to obtain processed target evaluation information.
Preferably, the calculating to obtain the target evaluation value according to the target evaluation information includes:
obtaining the matching degree of each target evaluation information;
screening and retaining the target evaluation information with the matching degree exceeding the preset matching degree;
and carrying out mean value calculation on each target evaluation information to obtain the target evaluation value.
Preferably, the feeding back the target evaluation value to the requesting terminal includes:
and feeding back the target evaluation value to the request terminal through a webSocket protocol.
In a second aspect, the application further provides an article evaluation system, which comprises an elastic search server, a Mongo database and a client, wherein evaluation information of various articles is stored in the Mongo database;
the client is used for sending an evaluation request to the ElasticSearch server;
the ElasticSearch server is used for inquiring and obtaining target evaluation information corresponding to the article to be evaluated from the Mongo database according to the evaluation request, calculating and obtaining a target evaluation value according to the target evaluation information, and feeding the target evaluation value back to the client.
In a third aspect, the present application further provides an article evaluation apparatus applied to an ElasticSearch server, including:
the information determining module is used for acquiring the article information of the article to be evaluated according to the received evaluation request;
the information query module is used for querying and obtaining target evaluation information corresponding to the article to be evaluated from the Mongo database according to the article information;
and the information feedback module is used for calculating and obtaining a target evaluation value according to the target evaluation information and feeding back the target evaluation value to the request terminal.
In a fourth aspect, the present application further discloses a server, including:
a memory for storing a computer program;
a processor for executing the computer program to implement the steps of any of the item assessment methods described above.
In a fifth aspect, the present application also discloses a computer-readable storage medium having stored thereon a computer program for implementing the steps of any one of the item assessment methods described above when executed by a processor.
The article evaluation method is applied to an elastic search server and comprises the steps of obtaining article information of an article to be evaluated according to a received evaluation request; inquiring and acquiring target evaluation information corresponding to the article to be evaluated from a Mongo database according to the article information; and calculating to obtain a target evaluation value according to the target evaluation information, and feeding back the target evaluation value to the request terminal.
Therefore, the article evaluation method provided by the application carries out target data query based on the ElasticSearch server and the Mongo database so as to realize article evaluation, the ElasticSearch server is provided with a distributed full-text search engine, data can be quickly returned according to conditions, and real-time search is achieved, so that the data query efficiency is effectively improved, and the article evaluation efficiency is further improved; meanwhile, the Mongo database is a distributed database, a cluster deployment mode is adopted, the stability and the reliability are high, and compared with a traditional relational database mode, the implementation mode can meet the requirement of system stability, effectively improve the data query speed and guarantee the item evaluation efficiency.
The article evaluation system, the apparatus, the server and the computer-readable storage medium provided by the present application all have the above beneficial effects, and are not described herein again.
Drawings
In order to more clearly illustrate the technical solutions in the prior art and the embodiments of the present application, the drawings that are needed to be used in the description of the prior art and the embodiments of the present application will be briefly described below. Of course, the following description of the drawings related to the embodiments of the present application is only a part of the embodiments of the present application, and it will be obvious to those skilled in the art that other drawings can be obtained from the provided drawings without any creative effort, and the obtained other drawings also belong to the protection scope of the present application.
FIG. 1 is a schematic flow chart of an item assessment method provided herein;
FIG. 2 is a block diagram of an item evaluation system provided herein;
FIG. 3 is a schematic diagram of an article evaluation system according to the present application;
FIG. 4 is a schematic structural diagram of an article evaluation device provided in the present application;
fig. 5 is a schematic structural diagram of a server provided in the present application.
Detailed Description
The core of the application is to provide an article evaluation method, which can effectively improve the data query efficiency while meeting the requirement of system stability; another core of the present application is to provide an item assessment system, apparatus, server, and computer-readable storage medium, which also have the above-mentioned advantages.
In order to more clearly and completely describe the technical solutions in the embodiments of the present application, the technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application. It is to be understood that the embodiments described are only a few embodiments of the present application and not all 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 application.
In the specific development process of a project, the backend service is the most basic, and the size degree of one project directly determines the number and the complexity degree of interfaces of the backend service. In a scenario with a high real-time requirement, a fast data return speed is required, and in a batch query with a large data volume, a large amount of data needs to be stably transmitted and evaluated. However, since the evaluation is based on fuzzy query of massive sample data accumulated in the sample library, and further processing is sometimes required to be performed on the query result before the query result is finally fed back to the user, which puts a high requirement on the overall performance of the system, the conventional relational database has difficulty in supporting the current application scenario due to the defects of slow data query and low efficiency. Therefore, in order to solve the above technical problem, the present application provides an article evaluation method, which can effectively improve data query efficiency while satisfying the requirement of system stability.
Referring to fig. 1, fig. 1 is a schematic flow chart of an item evaluation method provided in the present application, where the item evaluation method is applied to an ElasticSearch server, and the method includes:
s101: acquiring article information of an article to be evaluated according to the received evaluation request;
firstly, the article evaluation method provided by the application is realized through an ElasticSearch server, the ElasticSearch server is a search server based on Lucene, a full-text search engine with distributed multi-user capability is provided, real-time search can be achieved, and the method has the advantages of stability, reliability, quickness, convenience in installation and use and the like. Further, in this step, it is intended to achieve acquisition of item information, where the item information is related to an item to be evaluated and is obtained by performing data analysis on a received evaluation request, where the evaluation request is a data request for the item to be evaluated, which is initiated by a request terminal, that is, a client, and is used to request implementation of item evaluation.
S102: inquiring and acquiring target evaluation information corresponding to the article to be evaluated from the Mongo database according to the article information;
the method comprises the steps of obtaining target evaluation information, specifically, constructing a database in advance, wherein the database is a Mongo database, the Mongo database is a database based on distributed file storage and can provide extensible high-performance data for WEB application, and the stability and reliability of the database can be effectively guaranteed by adopting a cluster deployment mode. Therefore, after the article information is obtained based on the evaluation request, the corresponding evaluation information, namely the target evaluation information, can be inquired and obtained from the Mongo database according to the article information. It can be understood that the number of the target evaluation information is not unique, that is, the number of the target items corresponding to the items to be evaluated, which are determined by querying the Mongo database, may be multiple, but does not affect the implementation of the technical solution.
As a preferred embodiment, the above querying and obtaining target evaluation information corresponding to an item to be evaluated from a Mongo database according to item information may include: performing word segmentation processing on the article information to obtain each article keyword; and inquiring and obtaining target evaluation information corresponding to each article keyword from the Mongo database.
The preferred embodiment provides a more specific method for acquiring target evaluation information, and specifically, the article information may be subjected to word segmentation to extract and acquire corresponding article keywords, and similarly, since the number of the article keywords is not unique, the number of the target evaluation information acquired by matching from the Mongo database is also not unique. Therefore, the data query is carried out by extracting the keywords, the comprehensiveness of the data query can be effectively ensured, and the accuracy of the article evaluation result is further improved.
As a preferred embodiment, the above querying and obtaining the target evaluation information corresponding to each item keyword from the Mongo database may include: and obtaining target evaluation information corresponding to each article keyword through a preset API (application program interface) of the Mongo database.
Specifically, for data transmission between the ElasticSearch server and the Mongo database, the preferred embodiment provides a more specific implementation manner, that is, the implementation is realized through an API interface set in the Mongo database, the Mongo database provides rich API interfaces, and has higher expandability, and the data query efficiency can be effectively ensured.
S103: and calculating to obtain a target evaluation value according to the target evaluation information, and feeding back the target evaluation value to the request terminal.
This step is intended to realize the evaluation of the article to obtain a target evaluation value of the article to be evaluated. Specifically, calculation can be performed according to the target evaluation information, and a preset calculation strategy is used for realizing calculation, such as mean value calculation, weight calculation and the like, and further, after the target evaluation value is obtained, the target evaluation value is fed back to the request terminal, so that the article evaluation is completed.
As a preferred embodiment, before the calculating the target evaluation value according to the target evaluation information, the method may further include: and carrying out duplicate removal processing on each target evaluation information to obtain the processed target evaluation information.
Specifically, in order to further improve the article evaluation efficiency, data deduplication processing can be performed before the calculation of the target evaluation value, that is, duplicate data is eliminated, so that data redundancy is avoided, unnecessary data calculation is effectively reduced, and the article evaluation efficiency is further improved.
As a preferred embodiment, the above calculating and obtaining the target evaluation value according to the target evaluation information may include: obtaining the matching degree of each target evaluation information; screening and retaining target evaluation information with the matching degree exceeding a preset matching degree; and carrying out mean value calculation on each target evaluation information to obtain a target evaluation value.
The preferred embodiment provides a more specific target evaluation value calculation method, which includes firstly, obtaining a matching degree corresponding to each target evaluation information, and specifically obtaining the matching degree in a data query process, at this time, in order to ensure the item evaluation efficiency, only a part of target evaluation information with a higher matching degree can be reserved, and the problem of low efficiency caused by the need of calculating all target evaluation information is avoided, and further, performing mean value calculation on each piece of target evaluation information reserved after screening, so as to obtain a corresponding target evaluation value, namely the evaluation value of the item to be evaluated. The specific value of the preset matching degree does not affect the implementation of the technical scheme, and can be set by technical personnel according to actual requirements, so that the method is not limited by the application.
As a preferred embodiment, the feeding back the target evaluation value to the requesting terminal may include: and feeding back the target evaluation value to the request terminal through a webSocket protocol.
The preferred embodiment provides a more specific feedback method of a target evaluation value, namely, the feedback method is realized based on a webSocket protocol, which is a new network protocol based on TCP, and can realize full-duplex communication between a browser and a server.
Therefore, the article evaluation method provided by the application carries out target data query based on the ElasticSearch server and the Mongo database so as to realize article evaluation, the ElasticSearch server is provided with a distributed full-text search engine, data can be quickly returned according to conditions, and real-time search is achieved, so that the data query efficiency is effectively improved, and the article evaluation efficiency is further improved; meanwhile, the Mongo database is a distributed database, a cluster deployment mode is adopted, the stability and the reliability are high, and compared with a traditional relational database mode, the implementation mode can meet the requirement of system stability, effectively improve the data query speed and guarantee the item evaluation efficiency.
On the basis of the above embodiments, taking the escort evaluation as an example, the embodiment of the present application provides a more specific article evaluation method, please refer to fig. 2, and fig. 2 is a block diagram of an article evaluation system provided by the present application.
Specifically, the item evaluation system is based on a Spring boot architecture, and specifically adopts a Spring boot + Transportclient + MongoTemplate + json format output mode, wherein:
1. the Springboot is a Spring project which can run independently, is provided with an embedded Servlet container and a starter, and simplifies the Maven configuration and the XML configuration into an annotation configuration, thereby being more convenient to use; in addition, the Springboot is integrated with a Shiro framework for realizing authority control;
2. the Transportclient can perform operations such as increasing, deleting, modifying, checking and the like on the ElasticSearch, and establishes a connection relation with the server through the connection with the Java API of the ElasticSearch;
3. MongoTemplate provides rich API interfaces for Mongodb, the interfaces are standardized and easy to expand, and the CURD (basic atomic operations for processing data, including creating, updating, reading, deleting and the like) of Mongodb can be easily realized.
Therefore, when the value of the to-be-evaluated escort is evaluated, for the input related conditions, the server side can firstly carry out interference item filtering and word segmentation processing aiming at possible input errors so as to ensure the success of matching to the maximum extent; further, by utilizing the strong search capability of the ElasticSearch, comprehensively inquiring and analyzing data of a plurality of sources in the database, quickly matching a plurality of results with the highest correlation degree, and carrying out average calculation according to the prices of the results to obtain the evaluation value of the escort to be evaluated so as to effectively prevent the interference of dirty data of individual sources and ensure the accuracy of the evaluation result to the maximum extent; and finally, actively pushing the message to the front end by using a webSocket protocol.
In addition, besides value evaluation, according to different requirements on data instantaneity and stability, the server side also provides different functional modules to meet different business requirements, and as shown in fig. 2, the functions of batch evaluation, market classification, historical curves, address identification, quantity statistics, timing reevaluation, risk early warning, Chinese word segmentation and the like can be realized. Therefore, each module in the framework has clear responsibility and has the characteristics of traceability and flexible adjustment.
Therefore, the article evaluation method provided by the embodiment of the application queries target data based on the ElasticSearch server and the Mongo database so as to realize article evaluation, the ElasticSearch server is provided with a distributed full-text search engine, and data can be quickly returned according to conditions so as to achieve real-time search, so that the data query efficiency is effectively improved, and the article evaluation efficiency is further improved; meanwhile, the Mongo database is a distributed database, a cluster deployment mode is adopted, the stability and the reliability are high, and compared with a traditional relational database mode, the implementation mode can meet the requirement of system stability, effectively improve the data query speed and guarantee the item evaluation efficiency.
To solve the above technical problem, the present application further provides an article evaluation system, please refer to fig. 3, fig. 3 is a schematic structural diagram of the article evaluation system provided by the present application, including an ElasticSearch server 100, a Mongo database 200 and a client 300, wherein the Mongo database 200 stores evaluation information of various articles;
a client 300, configured to send an evaluation request to the ElasticSearch server 100;
the ElasticSearch server 100 is configured to query and obtain target evaluation information corresponding to the article to be evaluated from the Mongo database 200 according to the evaluation request, calculate and obtain a target evaluation value according to the target evaluation information, and feed back the target evaluation value to the client 300.
Therefore, the article evaluation system provided by the embodiment of the application queries target data based on the ElasticSearch server and the Mongo database so as to realize article evaluation, the ElasticSearch server is provided with a distributed full-text search engine, and data can be quickly returned according to conditions so as to achieve real-time search, so that the data query efficiency is effectively improved, and the article evaluation efficiency is further improved; meanwhile, the Mongo database is a distributed database, a cluster deployment mode is adopted, the stability and the reliability are high, and compared with a traditional relational database mode, the implementation mode can meet the requirement of system stability, effectively improve the data query speed and guarantee the item evaluation efficiency.
For the introduction of the system provided by the present application, please refer to the above method embodiment, which is not described herein again.
To solve the above technical problem, the present application further provides an article evaluation apparatus, which is applied to an elastic search server, please refer to fig. 4, where fig. 4 is a schematic structural diagram of the article evaluation apparatus provided by the present application, and the article evaluation apparatus includes:
the information determining module 1 is used for acquiring article information of an article to be evaluated according to the received evaluation request;
the information query module 2 is used for querying and obtaining target evaluation information corresponding to the article to be evaluated from the Mongo database according to the article information;
and the information feedback module 3 is used for calculating and obtaining a target evaluation value according to the target evaluation information and feeding back the target evaluation value to the request terminal.
As a preferred embodiment, the information query module 2 may include:
the word segmentation unit is used for carrying out word segmentation processing on the article information to obtain each article keyword;
and the query unit is used for querying and obtaining the target evaluation information corresponding to each article keyword from the Mongo database.
As a preferred embodiment, the query unit may be specifically configured to obtain target evaluation information corresponding to each item keyword through a preset API interface of the Mongo database.
As a preferred embodiment, the article evaluation device may further include a deduplication module, configured to perform deduplication processing on each target evaluation information before the target evaluation value is obtained by the calculation according to the target evaluation information, so as to obtain processed target evaluation information.
As a preferred embodiment, the information feedback module 3 may include:
the acquisition unit is used for acquiring the matching degree of each target evaluation information;
the screening unit is used for screening and retaining target evaluation information with the matching degree exceeding the preset matching degree;
the calculating unit is used for carrying out mean value calculation on each target evaluation information to obtain a target evaluation value;
and a feedback unit for feeding back the target evaluation value to the requesting terminal.
As a preferred embodiment, the feedback unit may be specifically configured to feed back the target evaluation value to the requesting terminal through a webSocket protocol.
For the introduction of the apparatus provided in the present application, please refer to the above method embodiments, which are not described herein again.
To solve the above technical problem, the present application further provides a server, please refer to fig. 5, where fig. 5 is a schematic structural diagram of the server provided in the present application, and the server may include:
a memory 10 for storing a computer program;
the processor 20, when executing the computer program, may implement the steps of any of the above-described methods of item assessment.
For the introduction of the server provided in the present application, please refer to the above method embodiment, which is not described herein again.
To solve the above problem, the present application further provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, can implement the steps of any one of the above-mentioned article evaluation methods.
The computer-readable storage medium may include: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
For the introduction of the computer-readable storage medium provided in the present application, please refer to the above method embodiments, which are not described herein again.
The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. 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 application.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The technical solutions provided by the present application are described in detail above. The principles and embodiments of the present application are explained herein using specific examples, which are provided only to help understand the method and the core idea of the present application. It should be noted that, for those skilled in the art, without departing from the principle of the present application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall into the protection scope of the present application.
Claims (10)
1. An item evaluation method applied to an ElasticSearch server, comprising:
acquiring article information of an article to be evaluated according to the received evaluation request;
inquiring and acquiring target evaluation information corresponding to the article to be evaluated from a Mongo database according to the article information;
and calculating to obtain a target evaluation value according to the target evaluation information, and feeding back the target evaluation value to the request terminal.
2. The item evaluation method according to claim 1, wherein the obtaining of the target evaluation information corresponding to the item to be evaluated by querying from a Mongo database according to the item information comprises:
performing word segmentation processing on the article information to obtain each article keyword;
and inquiring and obtaining target evaluation information corresponding to each article keyword from the Mongo database.
3. The item evaluation method according to claim 2, wherein the obtaining of the target evaluation information corresponding to each item keyword from the monto database comprises:
and obtaining target evaluation information corresponding to each article keyword through a preset API (application program interface) of the Mongo database.
4. The item evaluation method according to claim 1, wherein before calculating a target evaluation value from the target evaluation information, further comprising:
and performing de-duplication processing on each target evaluation information to obtain processed target evaluation information.
5. The item evaluation method according to claim 4, wherein said calculating a target evaluation value from the target evaluation information includes:
obtaining the matching degree of each target evaluation information;
screening and retaining the target evaluation information with the matching degree exceeding the preset matching degree;
and carrying out mean value calculation on each target evaluation information to obtain the target evaluation value.
6. The item evaluation method according to claim 1, wherein the feeding back the target evaluation value to the requesting terminal includes:
and feeding back the target evaluation value to the request terminal through a webSocket protocol.
7. An article evaluation system is characterized by comprising an ElasticSearch server, a Mongo database and a client, wherein the Mongo database stores evaluation information of various articles;
the client is used for sending an evaluation request to the ElasticSearch server;
the ElasticSearch server is used for inquiring and obtaining target evaluation information corresponding to the article to be evaluated from the Mongo database according to the evaluation request, calculating and obtaining a target evaluation value according to the target evaluation information, and feeding the target evaluation value back to the client.
8. An article evaluation apparatus applied to an ElasticSearch server, comprising:
the information determining module is used for acquiring the article information of the article to be evaluated according to the received evaluation request;
the information query module is used for querying and obtaining target evaluation information corresponding to the article to be evaluated from the Mongo database according to the article information;
and the information feedback module is used for calculating and obtaining a target evaluation value according to the target evaluation information and feeding back the target evaluation value to the request terminal.
9. A server, comprising:
a memory for storing a computer program;
a processor for executing the computer program to carry out the steps of the item assessment method according to any one of claims 1 to 6.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, is adapted to carry out the steps of the item assessment method according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010295889.6A CN111651479A (en) | 2020-04-15 | 2020-04-15 | Article evaluation method, device and related equipment |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202010295889.6A CN111651479A (en) | 2020-04-15 | 2020-04-15 | Article evaluation method, device and related equipment |
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| CN111651479A true CN111651479A (en) | 2020-09-11 |
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