Enterprise-level total station search construction method and device

Abstract

The invention relates to the technical field of data search, and discloses an enterprise-level full-station search construction method and device, and the method comprises the steps: obtaining the intranet information of a target enterprise, and constructing a nested index field based on the intranet information of the target enterprise; defining a word segmentation device, and matching the nested index field with the word segmentation device; obtaining enterprise demand information, and constructing a search sorting algorithm based on the enterprise demand information; and performing distributed deployment on the nested index field matched with the word segmentation device and a search sorting algorithm to obtain an enterprise-level total-station search architecture. According to the method, the data query efficiency is improved, and the collaborative office efficiency of enterprises is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data search, and in particular to a method and device for constructing an enterprise-level full-site search. Background Art

[0002] In an enterprise intranet, there are several types of data: department information, employee information, project information, wiki document information, and some file information. For enterprise intranet users, it is very important to be able to search this data conveniently and get the target results accurately and quickly.

[0003] However, related data search services face performance bottlenecks and low query efficiency, resulting in low office efficiency. Summary of the invention

[0004] In view of this, the present invention provides a method and device for constructing an enterprise-level full-site search to solve the problem of low office efficiency of related data search services.

[0005] In a first aspect, the present invention provides a method for constructing an enterprise-level full-site search, the method comprising:

[0006] Obtain the target enterprise intranet information, and build a nested index field based on the target enterprise intranet information;

[0007] Define a word breaker and match the nested index field with the word breaker;

[0008] Obtain enterprise demand information and build a search and ranking algorithm based on the enterprise demand information;

[0009] The nested index fields after matching the word segmenter and the search ranking algorithm are deployed in a distributed manner to obtain an enterprise-level full-site search architecture.

[0010] The present embodiment provides a method for constructing an enterprise-level full-site search, which constructs a nested index field, matches the nested index field with a word segmenter, and constructs a search sorting algorithm, and then distributes the nested index field and the search sorting algorithm after matching the word segmenter to obtain an enterprise-level full-site search architecture, thereby integrating and indexing various data sources within the enterprise, and can quickly and accurately obtain various types of information within the enterprise, thereby improving data query efficiency and greatly enhancing the collaborative office efficiency of the enterprise.

[0011] In an optional implementation, building a nested index field based on the target enterprise intranet information includes:

[0012] Determine employee type information based on the target enterprise intranet information, and determine employee object fields based on the employee type information;

[0013] The nested object field is obtained, and other information is nested into the employee object field using the nested object field to obtain a nested index field; the other information is the remaining information in the target enterprise intranet information except the employee type information.

[0014] This embodiment provides a method for constructing an enterprise-level full-site search, which utilizes nested object fields to nest other information into employee object fields to obtain nested index fields, thereby realizing associative search. The method is not limited to searching for target data, but can also search for related data associated with the target data, thereby improving data search efficiency.

[0015] In an optional implementation, building a nested index field based on the target enterprise intranet information further includes:

[0016] Perform field optimization on the nested index field to obtain the optimized nested index field.

[0017] This embodiment provides a method for constructing an enterprise-level full-site search, which reduces the storage pressure and search response speed of ElasticSearch by optimizing the nested index fields.

[0018] In an optional implementation, a word breaker is defined, and the nested index field is matched with the word breaker, including:

[0019] According to the preset combination rules, the word segmenters are combined to obtain a combined word segmenter;

[0020] The index field type and subfield type are determined based on the nested index field, and the index field type and subfield type are matched with the combined word breaker and the word breaker respectively.

[0021] This embodiment provides a method for constructing an enterprise-level full-site search. According to the characteristics of the word segmenters, multiple word segmenters are combined, and different combined word segmenter types are used for different fields of different indexes, thereby realizing the search of multiple texts and ensuring the accuracy of search results.

[0022] In an optional implementation, building a search ranking algorithm based on enterprise demand information includes:

[0023] Obtaining an open application programming interface, and configuring a data tag for the open application programming interface;

[0024] Determine the level and weight of data labels based on the enterprise's sorting requirements;

[0025] Construct a search and sorting algorithm based on the level of data labels and data weights.

[0026] This embodiment provides a method for constructing an enterprise-level full-site search, which constructs a search ranking algorithm through the level of data tags and data weights. Enterprises can highly customize the ranking algorithm according to their needs to make search results more accurate.

[0027] In an optional implementation, it also includes:

[0028] Based on the target enterprise intranet information, the enterprise intranet organizational structure service information and wiki service information are determined, and full data synchronization is performed on the enterprise intranet organizational structure service information and wiki service information.

[0029] This embodiment provides a method for constructing an enterprise-level full-site search, which avoids data loss due to network jitter, Kafka exceptions or other exceptions by setting full data synchronization of the enterprise intranet organizational structure service information and wiki service information, thereby ensuring the stability of search results.

[0030] In a second aspect, the present invention provides a device for constructing an enterprise-level full-site search, the device comprising:

[0031] The first construction module is used to obtain the target enterprise intranet information and construct a nested index field based on the target enterprise intranet information;

[0032] The matching module is used to define a tokenizer and match the nested index field with the tokenizer;

[0033] The second building module is used to obtain enterprise demand information and build a search ranking algorithm based on the enterprise demand information;

[0034] The deployment module is used to distribute the nested index fields and search ranking algorithms after matching the word segmenter to obtain an enterprise-level full-site search architecture.

[0035] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the method for constructing an enterprise-level full-site search according to the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0036] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method for constructing an enterprise-level full-site search according to the first aspect or any corresponding embodiment thereof.

[0037] In a fifth aspect, the present invention provides a computer program product, including computer instructions, which are used to enable a computer to execute the method for constructing an enterprise-level full-site search according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 is a flow chart of a method for constructing an enterprise-level full-site search according to an embodiment of the present invention;

[0040] Figure 2 is a schematic diagram of a data acquisition architecture according to an embodiment of the present invention;

[0041] Figure 3 is a flow chart of another method for constructing an enterprise-level full-site search according to an embodiment of the present invention;

[0042] Figure 4 is a flow chart of another method for constructing an enterprise-level full-site search according to an embodiment of the present invention;

[0043] Figure 5 is a structural block diagram of a device for constructing an enterprise-level full-site search according to an embodiment of the present invention;

[0044] Figure 6 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0046] SaaS (Software as a Service) platforms provide similar search services, but they have the following defects: insufficient data sources, inability to search for data associations, inability to perform one-stop search (all relevant data can be searched through an input box without switching search target items), inability to display customized results, etc.

[0047] To solve the above technical problems, an embodiment of the present invention provides an enterprise-level full-site search construction method, which avoids the performance bottleneck and low query efficiency that a relational database or file system may face when processing massive text data through technologies such as ElasticSearch+Kafka+Restful API, as well as nested index construction and optimization, multi-word segmenter combination and selection, onestop-es1 sorting algorithm, and real-time incremental data synchronization and stability assurance solutions. By using high-performance, highly scalable, distributed ElasticSearch (search server), Kafka (an open source stream processing platform), Lucene (full-text search engine) and other technologies to realize content search in the enterprise intranet, the collaborative office efficiency of the enterprise is greatly improved, and efficient, accurate and customizable search result return and display can be achieved.

[0048] The embodiment of the present invention provides a method for constructing an enterprise-level full-site search. It should be noted that the execution subject of the method for constructing an enterprise-level full-site search provided by the embodiment of the present invention can be a construction device for an enterprise-level full-site search. The construction device for an enterprise-level full-site search can be implemented as part or all of an electronic device through software, hardware, or a combination of software and hardware. The electronic device can be a server or a terminal. The server in the embodiment of the present application can be a single server or a server cluster composed of multiple servers. The terminal in the embodiment of the present application can be a smart phone, a personal computer, a tablet computer, a wearable device, an intelligent robot, and other intelligent hardware devices. In the following method embodiments, the execution subject is an electronic device as an example for explanation.

[0049] According to an embodiment of the present invention, an embodiment of a method for constructing an enterprise-level full-site search is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0050] In this embodiment, a method for constructing an enterprise-level full-site search is provided, which can be used in the above-mentioned electronic device. Figure 1 is a flow chart of a method for constructing an enterprise-level full-site search according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0051] Step S101, obtaining the target enterprise intranet information, and constructing a nested index field based on the target enterprise intranet information.

[0052] Specifically, the data sources of the target enterprise intranet information are the enterprise intranet organizational structure service information and wiki (a hypertext system that is open on the Internet and can be collaboratively created by multiple people) service information. The MySQL (a relational database management system) of the above service stores all department, employee, project, wiki, file and other data.

[0053] Furthermore, for ElasticSearch, a separate index is created for one type of document, and the search results only contain data of this type (for example, only employee information or department information whose names contain "xxx" can be searched), and there is a lack of search for related data. Therefore, nested index fields are designed, such as employee_index, department_index, product_index, wiki_index, file_index, etc.

[0054] Step S102, define a word segmenter, and match the nested index field with the word segmenter.

[0055] Specifically, in ElasticSearch, in order to achieve more accurate search results, multiple word segmenters are defined to handle word segmentation tasks of different languages ​​and text types: Standard Analyzer (ES default standard word segmenter, suitable for general text search), English Analyzer (English word segmenter, more suitable for search of English documents), ChineseAnalyzer (Chinese word segmenter, suitable for processing text in Chinese voice) and Ngram Analyzer (segmented according to n-gram, also called sliding window word segmenter). However, the problem with the above word segmenters is that they can only be used in a specific single semantic scenario, that is, either only English can be searched, or only Chinese can be searched, or there is no sliding window matching search under the semantic state. For inputting any text: Zhang, Zhang San, Zhang Sanfeng, zhangsanfeng, zhangsf, Zhang san, zsf, zsf@zsf@163.cn, the target data "Zhang Sanfeng" can be searched, or the search demand of inputting: polaris movie to search for wiki documents containing this English paragraph cannot be met.

[0056] Furthermore, a nested combination scheme of multiple word breakers is used: in the document object mappings of ElasticSearch, for each field, multiple sub-fields are set, and different word breakers are used respectively. Then a custom word breaker is created, and multiple word breakers are combined and designed so that the custom word breaker has the word breaker function of different word breakers.

[0057] Step S103, obtaining enterprise demand information, and constructing a search ranking algorithm based on the enterprise demand information.

[0058] Step S104, distribute the nested index fields and search ranking algorithm after matching the word segmenter to obtain an enterprise-level full-site search architecture.

[0059] Specifically, Figure 2 As shown in the figure, the search service provides an interface to the organizational structure service. When maintaining data, the data can be written to Kafka by calling the interface and returned (without blocking the organizational structure service). Then the search service consumes Kafka messages to synchronize incremental data to ElasticSearch in real time. Among them, both ElasticSearch and the search service are deployed in a distributed manner to ensure service reliability, and the automatic sharding mechanism of ElasticSearch is also used to cope with the storage of large-scale data volumes of enterprises.

[0060] Furthermore, based on the target enterprise intranet information, the enterprise intranet organizational structure service information and the wiki service information are determined, and full data synchronization is performed on the enterprise intranet organizational structure service information and the wiki service information.

[0061] Furthermore, in order to avoid data loss due to network jitter, Kafka exceptions or other abnormal data, a full synchronization solution based on the organizational structure service and wiki service MySQL is set up in the enterprise-level full-site search architecture. That is, the relevant data and association relationships in MySQL are queried and parsed into corresponding ElasticSearch documents, and the data in ElasticSearch is fully updated to achieve data completeness.

[0062] The present embodiment provides a method for constructing an enterprise-level full-site search, which constructs a nested index field, matches the nested index field with a word segmenter, and constructs a search sorting algorithm, and then distributes the nested index field and the search sorting algorithm after matching the word segmenter to obtain an enterprise-level full-site search architecture, thereby integrating and indexing various data sources within the enterprise, and can quickly and accurately obtain various types of information within the enterprise, thereby improving data query efficiency and greatly enhancing the collaborative office efficiency of the enterprise.

[0063] In this embodiment, a method for constructing an enterprise-level full-site search is provided, which can be used in the above-mentioned electronic device. Figure 3 is a flow chart of a method for constructing an enterprise-level full-site search according to an embodiment of the present invention. Figure 3 As shown, the process includes the following steps:

[0064] Step S301, obtaining the target enterprise intranet information, and constructing a nested index field based on the target enterprise intranet information.

[0065] Specifically, the above step S301 includes:

[0066] Step S3011, determining employee type information based on the target enterprise intranet information, and determining employee object fields based on the employee type information.

[0067] Specifically, the fields id, name, username, describe, and is_leave belong to employee type information (i.e., employee type information), and the fields they have themselves represent the basic information of the employee himself.

[0068] Step S3012, obtaining the nested object field, and using the nested object field to nest other information into the employee object field to obtain a nested index field; the other information is the remaining information in the target enterprise intranet information except the employee type information.

[0069] Specifically, department information, project information, and wiki information may be nested in the employee index object. Taking the nested index field employee_index as an example, employee_index is shown in Table 1 below.

[0070] Table 1:

[0071]

[0072] Furthermore, the nested index field is optimized to obtain an optimized nested index field.

[0073] Furthermore, the fields in the nested index fields that are associated with the employee type information are filtered, and irrelevant fields are deleted. The filtering rule is: select the fields that have the employee type information.

[0074] Furthermore, when storing / updating employee data, the search results after nested indexing include not only the basic information of the employee, but also the department, project, and related work item information of the employee. The query of the search object and information related to the search object is realized; for example, after entering "xxx" in the search box, the following can be searched: 1) Basic information of the search object itself: employee information with "xxx" in the name / description, department information with "xxx" in the name / description, project information with "xxx" in the name / description, and wiki document information with "xxx" in the title / content; 2) Information associated with the search object: department information with "xxx" in the name / description of an employee (or project) in a department, and project information with "xxx" in the name / description of an employee (or department) in a project; 3) Information associated with the search object: employee information with "xxx" in the name / description of the team (or project or wiki document) to which an employee belongs.

[0075] Step S302, define a word segmenter, and match the nested index field with the word segmenter.

[0076] Specifically, the above step S302 includes:

[0077] Step S3021, combining the word segmenters according to the preset combination rule to obtain a combined word segmenter.

[0078] Specifically, the combined word segmenters include: custom_pinyin_jieba_analyzer: using the pinyin word segmenter + jieba Chinese word segmenter for combination to achieve Chinese + pinyin mixed search; custom_pinyin_ngram_analyzer: using the pinyin word segmenter + ngram word segmenter for combination to achieve pinyin + ngram sliding window mixed search; custom_english_jieba_analyzer: using the english word segmenter + jieba word segmenter for combination to achieve English + Chinese mixed search.

[0079] Step S3022: determine the index field type and sub-field type based on the nested index field, and match the index field type and sub-field type with the combined word segmenter and the word segmenter respectively.

[0080] Specifically, for more precise search, word segmentation matching is performed on nested index fields containing subfields. For example, the name field is divided into the following subfields: pinyin: using the custom_pinyin_jieba_analyzer word segmenter; chinese: using the chinese word segmenter; keyword: using the keyword type for exact matching; username: using the custom_pinyin_ngram_analyzer word segmenter; describe: using the custom_english_jieba_analyzer word segmenter.

[0081] Furthermore, for the word segmentation of fields and word segmenters, stop words are used as filters (filters used to specify query conditions), and letter case conversion and other means are used to optimize the search structure.

[0082] Step S303: Obtain enterprise demand information and construct a search and ranking algorithm based on the enterprise demand information. Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0083] Step S304: Distribute the nested index fields and search ranking algorithms after matching the word segmenter to obtain an enterprise-level full-site search architecture. Figure 1 Step S104 of the illustrated embodiment will not be described in detail here.

[0084] The present embodiment provides a method for constructing an enterprise-level full-site search, which utilizes nested object fields to nest other information into employee object fields to obtain nested index fields, thereby realizing associative search. The method is not limited to searching for target data, but can also search for related data associated with the target data, thereby improving data search efficiency. Furthermore, according to the characteristics of word segmenters, a plurality of word segmenters are combined, and different combined word segmenter types are used for different fields of different indexes, thereby realizing the search of multiple texts and ensuring the accuracy of search results.

[0085] In this embodiment, a method for constructing an enterprise-level full-site search is provided, which can be used in the above-mentioned electronic device. Figure 4 is a flow chart of a method for constructing an enterprise-level full-site search according to an embodiment of the present invention. Figure 4 As shown, the process includes the following steps:

[0086] Step S401, obtain the target enterprise intranet information, and construct a nested index field based on the target enterprise intranet information. Figure 3 Step S301 of the illustrated embodiment will not be described in detail here.

[0087] Step S402: define a word segmenter and match the nested index field with the word segmenter. Figure 3 Step S302 of the illustrated embodiment will not be described in detail here.

[0088] Step S403, obtaining enterprise demand information, and constructing a search ranking algorithm based on the enterprise demand information.

[0089] Specifically, the above step S403 includes:

[0090] Step S4031, obtaining an open application programming interface, and configuring a data tag for the open application programming interface.

[0091] Specifically, define openapi (open application programming interface), flexibly configure access data, and define data labels when calling openapi to write data (such as leadership level: company leaders, department leaders, ordinary employees, etc.; department level: first-level department, second-level department, third-level group, etc.).

[0092] Step S4032, determine the level and data weight of the data label based on the enterprise sorting requirement information.

[0093] Step S4033, construct a search and sorting algorithm based on the level of the data label and the data weight.

[0094] Specifically, the level of data labels (for example, according to leadership level, department level, etc.) and data weights (for example, employee weight is the highest, department weight is the second, project weight is the third, and document weight is the fourth) are used as sorting reference values; and sorting rules are set, that is, sort by data weight first, and if the data weights are the same (that is, all are employee data), then sort according to the level of data labels (that is, leaders are in front, and ordinary employees are in the back); and then construct the onestop-es1 sorting algorithm according to the above sorting reference values ​​and sorting rules.

[0095] Step S404: Distribute the nested index fields and search ranking algorithms after matching the word segmenter to obtain an enterprise-level full-site search architecture. Figure 3 Step S304 of the illustrated embodiment will not be described in detail here.

[0096] This embodiment provides a method for constructing an enterprise-level full-site search, which constructs a search ranking algorithm through the level of data tags and data weights. Enterprises can highly customize the ranking algorithm according to their needs to make search results more accurate.

[0097] The following is a specific example to illustrate the specific steps of a method for constructing an enterprise-level full-site search.

[0098] Embodiment 1:

[0099] The specific steps of the enterprise-level full-site search construction method include:

[0100] Taking employee_index as an example, the computer language of specific mappings in nested indexes is as follows:

[0101]

[0102]

[0103] Based on the above nested index and mappings settings, the data format is designed when creating a document (doc):

[0104]

[0105]

[0106] According to the above nested index, mappings design, document data format design, etc., the constructed ES document (doc) contains the fields of the employee itself (such as: id, name, username, describe, is_leave, etc.), as well as the department (departments) field, the project (products) field, and the associated document (wikis) field of the employee.

[0107] Therefore, when searching, multiple field attributes of the document and sub-field attributes of the nested fields are searched together; for example, if the search condition is "Zhang San", the query statement is:

[0108]

[0109] }

[0110] Query all documents in ES (including employee_index, department_index, and product_index). The query filtering conditions are: documents containing "Zhang San" in the name field, username field, and describe field in the current document, as well as documents containing "Zhang San" in the name field subfield under the employees field, the name field subfield under the departments field, and the name field subfield under the product field in the nested fields. All of them will be searched out.

[0111] Through the above steps, the nested index is matched and selected with the input data to obtain the search results.

[0112] When the input data is the employee's name, the custom_pinyin_jieba_analyzer word segmenter is used, and the employee's email username uses the custom_pinyin_ngram_analyzer word segmenter; different word segmenters are used based on different fields, and the word segmentation effects are obtained; when searching for the name "Zhang Sanfeng" or the email address "zhangsanfeng@testcom.cn", you can enter any content such as "Zhang San, Zhang Sanfeng, zhang, zhangsan, zhangsanfeng, zhangsanfeng@c", etc., and you can search for the corresponding ES documents.

[0113] Embodiment 2:

[0114] Assume that the constructed data includes: employee data: Zhang San, Zhang Sanfeng, Zhang Meng, Zhang Mengmeng, Lin Xiaoqi (linxq@163.com), Li Ning, Yang Yi, Zhao Ding, Kang Le; department data: data monitoring department, technical middle office department, network security department; the employees in each department are: 1) Data monitoring department: Zhang San, Zhang Meng, Lin Xiaoqi; 2) Technical middle office department: Zhang Sanfeng, Li Ning, Yang Yi; 3) Network security department: Zhang Mengmeng, Zhao Ding, Kang Le; project data: virtual machine monitoring project, QPS (Queries-per-second) monitoring project Control project, code base project, devops (a general term for a set of processes, methods and systems) project, network operation and maintenance project, topology security project; the employees and departments under each project are as follows: 1) Virtual machine monitoring project: Zhang San, Lin Xiaoqi; belong to the data monitoring department; 2) QPS monitoring project: Zhang Meng; belong to the data monitoring department; 3) Code base project: Li Ning; belong to the technical middle office department; 4) Devops project: Zhang Sanfeng; belong to the technical middle office department; 5) Network operation and maintenance project: Kang Le; belong to the network security department; Topology security project: no employees; belong to the network security department.

[0115] The steps for searching using the enterprise-level full-site search architecture obtained by the construction method of enterprise-level full-site search are:

[0116] There are two types of searches: recommended search (target data) and list search (target data + related data). For recommended search, you can enter partial content and the search service will automatically complete all content for recommendation. The following types of searches are supported:

[0117] 1) Zhang: You can fuzzily search for Zhang Meng, Zhang San, Zhang Mengmeng, and Zhang Sanfeng;

[0118] 2) zhang: You can fuzzy search for Zhang Meng, Zhang San, Zhang Mengmeng, and Zhang Sanfeng;

[0119] 3) Zhang San: You can fuzzily search for Zhang San and Zhang Sanfeng;

[0120] 4) zhangsan: can fuzzily search for Zhangsan and Zhangsanfeng;

[0121] 5)zhangsanf: can fuzzily search for Zhang Sanfeng;

[0122] 6) lxq: Search for Lin Xiaoqi by the first letter of her pinyin;

[0123] 7) Lin Xiaoqi: The exact match search results in Lin Xiaoqi;

[0124] 8)linxiaoqi: exact match search results in Lin Xiaoqi;

[0125] 9) Linxiaoqi: exact match search results in Lin Xiaoqi;

[0126] 10) Data: fuzzy matching to data monitoring department;

[0127] 11) Department: fuzzy matching to data monitoring department, technical middle office department, and network security department;

[0128] 12) Monitoring: fuzzy matching to data monitoring department, QPS monitoring project, virtual machine monitoring project;

[0129] 13)QPS: Fuzzy match to QPS monitoring items.

[0130] For list search, the search service can associate and search for data related to the search content in all types of data based on the search content, and prioritize and paginate the recalled data, as follows:

[0131] 1) Zhang San: Search for employee data of Zhang San and Zhang Sanfeng, department data of the data monitoring department where Zhang San works and the technical middle office department where Zhang Sanfeng works, and project data of the virtual machine monitoring project where Zhang San works and the devops project where Zhang Sanfeng works;

[0132] 2) Technical middle platform: Search for department type data of the technical middle platform department, search for two project type data of code base project and devops project belonging to the technical middle platform department, and search for three employee type data of Zhang Sanfeng, Li Ning, and Yang Yi belonging to the technical middle platform department;

[0133] 3) Virtual machine monitoring: Search out the project type data of the virtual machine monitoring project, search out the type data of the two employees Zhang San and Lin Xiaoqi belonging to the virtual machine monitoring project, and search out the data of the data monitoring department to which the virtual machine monitoring project belongs.

[0134] In the above embodiments, the following technical effects are achieved:

[0135] 1) Innovation in accessing multiple data sources in the enterprise intranet (functional level):

[0136] Similar enterprise-level systems have certain defects: the search function of the Beisen platform can only search employee information. Even if there is department information, it does not support search, let alone project information; the search function of WeChat for Enterprise can only search employee information + group chat information + document information, and there is no search for departments and projects; to address the above defects, through one-stop search, various data sources within the enterprise are integrated and indexed, including but not limited to employee information, department information, project information, wiki document information, file information, etc.; users can quickly and accurately obtain various types of information within the enterprise by entering any content in the search box.

[0137] 2) Innovation in data relevance search (functional level):

[0138] Since the search results of Beisen platform and enterprise WeChat platform are independent of the relationship between employees, departments, projects, etc., it is impossible to search for related data; therefore, through association search, it is not limited to the search of target data, but also to search for related data associated with the target data. For example, when searching for employee information, not only the employee is searched out, but also the department and project to which the employee belongs.

[0139] 3) Innovation in the combination and selection of multiple word segmenters (technical implementation level):

[0140] ElasticSearch provides some tokenizers, but a single tokenizer can only support a single type of search. Therefore, we deeply studied the tokenizer characteristics of ElasticSearch and transformed the ElasticSearch tokenizer. On the basis of the original tokenizer, we combined multiple tokenizers and used different combined tokenizer types for different fields of different indexes to realize the search of multiple texts. For example, the following inputs can search for "Zhang Sanfeng": input zhangsanfeng, which is implemented by using the pinyin tokenizer; input Zhang san, which is implemented by using the Chinese tokenizer + pinyin tokenizer; input zsf, which is implemented by using the pinyin tokenizer + English tokenizer; input zsf@163.cn, which is implemented by using the English tokenizer + sliding window tokenizer.

[0141] 4) Innovation of customizable sorting requirements (functionality + technical implementation level):

[0142] The onestop-es1 sorting algorithm can support placing specially designated data at the front or the back, providing customized sorting requirements for enterprises. For example: sorting and displaying search results in order of priority such as company leaders, department leaders, and ordinary employees; or sorting and displaying search results in order of priority such as first-level departments, second-level departments, and XX-level groups; or sorting resigned employees at the back, and resigned employees must enter their full names to be searched, etc. The above sorting designs allow enterprises to search for the most satisfactory data according to their own needs.

[0143] In this embodiment, a device for constructing an enterprise-level full-site search is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and will not be repeated hereafter. As used below, the term "module" may be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0144] This embodiment provides a device for constructing an enterprise-level full-site search, such as Figure 5 As shown, including:

[0145] The first construction module 501 is used to obtain the target enterprise intranet information and construct a nested index field based on the target enterprise intranet information;

[0146] A matching module 502 is used to define a word segmenter and match the nested index field with the word segmenter;

[0147] The second building module 503 is used to obtain enterprise demand information and build a search ranking algorithm based on the enterprise demand information;

[0148] The deployment module 504 is used to distribute the nested index fields and search ranking algorithm after matching the word segmenter to obtain an enterprise-level full-site search architecture.

[0149] In some optional implementations, the first building block 501 includes:

[0150] A first determining unit, configured to determine employee type information based on target enterprise intranet information, and determine employee object fields based on the employee type information;

[0151] The nesting unit is used to obtain the nested object field, and use the nested object field to nest other information into the employee object field to obtain the nested index field; the other information is the remaining information in the target enterprise intranet information except the employee type information.

[0152] In some optional implementations, the first building module 501 further includes:

[0153] The optimization unit is used to optimize the nested index field to obtain the optimized nested index field.

[0154] In some optional implementations, the matching module 502 includes:

[0155] A combining unit, used for combining the word segmenters according to a preset combination rule to obtain a combined word segmenter;

[0156] The matching unit is used to determine the index field type and the subfield type based on the nested index field, and match the index field type and the subfield type with the combined word breaker and the word breaker respectively.

[0157] In some optional implementations, the second building module 503 includes:

[0158] A configuration unit, used for obtaining an open application programming interface and configuring a data tag for the open application programming interface;

[0159] A second determination unit, used to determine the level and data weight of the data label based on the enterprise sorting requirement information;

[0160] A building block for constructing a search and ranking algorithm based on the level of data labels and data weights.

[0161] In some optional embodiments, it also includes:

[0162] The setting module is used to determine the enterprise intranet organizational structure service information and wiki service information based on the target enterprise intranet information, and perform full data synchronization setting of the enterprise intranet organizational structure service information and wiki service information.

[0163] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0164] In this embodiment, a device for constructing an enterprise-level full-site search is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above-mentioned functions.

[0165] The embodiment of the present invention also provides a computer device having the above Figure 5 A device for constructing an enterprise-level full-site search is shown.

[0166] See also Figure 6 , Figure 6 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 6As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 6 A processor 10 is taken as an example.

[0167] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0168] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0169] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0170] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0171] The computer device also includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 6 The example of connecting through bus is taken in the following.

[0172] The input device 30 can receive input digital or character information, and generate key signal input related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0173] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0174] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of computer program instructions in computer-readable media includes, but is not limited to, source files, executable files, installation package files, etc., and accordingly, the way in which computer program instructions are executed by a computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0175] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for constructing an enterprise-level full-site search, characterized in that: The method comprises: Acquire the target enterprise intranet information, and construct a nested index field based on the target enterprise intranet information; Define a word segmenter, and match the nested index field with the word segmenter; Acquire enterprise demand information, and construct a search and ranking algorithm based on the enterprise demand information; The nested index fields after matching the word segmenter and the search ranking algorithm are distributed and deployed to obtain an enterprise-level full-site search architecture.

2. The method according to claim 1, characterized in that The step of constructing a nested index field based on the target enterprise intranet information includes: Determine employee type information based on the target enterprise intranet information, and determine employee object fields based on the employee type information; The nested object field is obtained, and other information is nested into the employee object field using the nested object field to obtain the nested index field; the other information is the remaining information in the target enterprise intranet information except the employee type information.

3. The method according to claim 2, characterized in that The step of constructing a nested index field based on the target enterprise intranet information further includes: Field optimization is performed on the nested index field to obtain an optimized nested index field.

4. The method according to claim 1, characterized in that The defining a word segmenter and matching the nested index field with the word segmenter includes: Combining the word segmenters according to a preset combination rule to obtain a combined word segmenter; An index field type and a subfield type are determined based on the nested index field, and the index field type and the subfield type are matched with the combined word segmenter and the word segmenter respectively.

5. The method according to claim 1, characterized in that The step of constructing a search ranking algorithm based on the enterprise demand information includes: Obtaining an open application programming interface, and configuring a data tag for the open application programming interface; Determine the level and data weight of the data label based on the enterprise sorting requirement information; The search ranking algorithm is constructed based on the level of the data label and the data weight.

6. The method according to claim 1, characterized in that Also includes: Based on the target enterprise intranet information, the enterprise intranet organizational structure service information and the wiki service information are determined, and full data synchronization is performed on the enterprise intranet organizational structure service information and the wiki service information.

7. A device for constructing an enterprise-level full-site search, characterized in that: The device comprises: A first construction module is used to obtain the target enterprise intranet information and construct a nested index field based on the target enterprise intranet information; A matching module, used for defining a word segmenter and matching the nested index field with the word segmenter; The second building module is used to obtain enterprise demand information and build a search ranking algorithm based on the enterprise demand information; A deployment module is used to distribute the nested index fields after matching the word segmenter and the search ranking algorithm to obtain an enterprise-level full-site search architecture.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for constructing an enterprise-level full-site search as described in any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method for constructing an enterprise-level full-site search according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the method for constructing an enterprise-level full-site search according to any one of claims 1 to 6.

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