WO2019196226A1 - 制度信息查询方法、装置、计算机设备和存储介质 - Google Patents

制度信息查询方法、装置、计算机设备和存储介质 Download PDF

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Publication number
WO2019196226A1
WO2019196226A1 PCT/CN2018/095449 CN2018095449W WO2019196226A1 WO 2019196226 A1 WO2019196226 A1 WO 2019196226A1 CN 2018095449 W CN2018095449 W CN 2018095449W WO 2019196226 A1 WO2019196226 A1 WO 2019196226A1
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information
file
split
node
tree
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PCT/CN2018/095449
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English (en)
French (fr)
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韩梅
张安元
邓华威
王科
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平安科技（深圳）有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/279—Recognition of textual entities
    • G06F40/284—Lexical analysis, e.g. tokenisation or collocates
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/279—Recognition of textual entities
    • G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis

Definitions

  • the present application relates to a system information query method, device, computer device and storage medium.
  • Institutional norms are the rules and guidelines that employees must abide by in their production and operation activities, including laws and policies, organizational structure, management systems, job responsibilities, technical systems, and work processes.
  • institutions institutions
  • the inventor realized that in order to meet the job requirements of all positions, the company may record all the institutional information applicable to different positions to the same system document, so that users can only conduct system inquiry based on the entire information content of the system documents, thereby making the system Information query efficiency is reduced.
  • a system information query method, apparatus, computer device, and storage medium are provided.
  • a system information query method includes: monitoring system information issued by a first terminal; the system information includes system description information and an associated system file; the system file includes a plurality of system terms and corresponding applicable object identifiers; The system information is classified, and the system information is added to a preset one or more target information trees according to the classification result; and multiple associated information trees corresponding to the target information tree are acquired; each of the associated information trees has a corresponding Applicable object identifier; split the system file, use the system clause corresponding to each applicable object identifier to generate a system sub-file corresponding to the applicable object identifier; generate an information node according to the system description information, and correspondingly the system a subfile is associated with the information node, and the information node is added to an associated information tree corresponding to the same applicable object identifier; and when the system query request sent by the second terminal is received, the system is responsive to the system according to the associated information tree Query request.
  • the system information query device comprises: an information classification module, configured to monitor system information issued by the first terminal; the system information includes system description information and an associated system file; the system file includes multiple system terms and corresponding corresponding Applying the object identifier; classifying the system information, and adding the system information to the preset one or more target information trees according to the classification result; the information splitting module is configured to acquire multiple corresponding to the target information tree Correlation information tree; each of the associated information trees has a corresponding applicable object identifier; the system file is split, and the system sub-file corresponding to the applicable object identifier is generated by using the system clause corresponding to each applicable object identifier; a module, configured to generate an information node according to the system description information, associate the corresponding system sub-file to the information node, add the information node to an associated information tree corresponding to the same applicable object identifier, and an information query module, For receiving an institutional query request sent by the second terminal, based on the The associated information tree is responsive to the system query request.
  • an information classification module configured
  • a computer device comprising a memory and one or more processors having stored therein computer readable instructions that, when executed by the processor, implement the steps of the system information query method provided in any one of the embodiments of the present application.
  • One or more non-volatile storage media storing computer readable instructions, when executed by one or more processors, causing one or more processors to implement a system as provided in any one embodiment of the present application The steps of the information query method.
  • FIG. 1 is an application scenario diagram of a system information query method according to one or more embodiments.
  • FIG. 2 is a flow diagram of a method for querying institutional information in accordance with one or more embodiments.
  • FIG. 3 is a schematic diagram of a target information tree in a system information query method in accordance with one or more embodiments.
  • FIG. 4 is a schematic diagram of an associated information tree in a system information query method in accordance with one or more embodiments.
  • FIG. 5 is a flow diagram showing the steps of classifying and archiving system information according to one or more embodiments.
  • FIG. 6 is a structural block diagram of a system information inquiry apparatus according to one or more embodiments.
  • FIG. 7 is a block diagram of a computer device in accordance with one or more embodiments.
  • the system information query method provided by the present application can be applied to the application environment as shown in FIG. 1.
  • the first terminal 102 communicates with the server 104 over a network.
  • the second terminal 106 communicates with the server 104 over a network.
  • the first terminal 102 and the second terminal 106 can be, but are not limited to, various personal computers, notebook computers, smart phones, tablets, and portable wearable devices.
  • the first terminal 102 is a system management terminal, and the user can perform system drafting, opinion collection, approval, and release at the first terminal 102.
  • the second terminal 106 is a service terminal, and the user can perform operations such as system learning at the second terminal 106.
  • the first terminal 102 and the second terminal 106 may be the same terminal or different terminals.
  • the server 104 can be implemented by a stand-alone server or a server cluster composed of a plurality of servers.
  • a plurality of target information trees are stored in the server 104. Each target information tree has a corresponding category label.
  • the server 104 monitors whether the first terminal 102 issues new system information. Institutional information includes institutional description information and associated institutional documents. When it is detected that the first terminal 102 issues new institutional information, the server 104 classifies the system information, and incorporates the system information into the corresponding one or more target information trees according to the classification result. Each target information tree has a corresponding plurality of associated information trees. Each associated information tree has a corresponding applicable object identifier.
  • the server 104 incorporates the system information into the corresponding associated information tree. Specifically, after the system information is added to the corresponding target information tree, the server 104 acquires multiple associated information trees corresponding to the target information tree.
  • the institutional document includes a number of institutional provisions and corresponding applicable object identifiers.
  • the server 104 splits the system file according to the applicable object identifier corresponding to each system clause in the system file, and uses the system clause corresponding to each applicable object identifier to generate a system sub-file corresponding to the applicable object identifier.
  • the server 104 generates an information node according to the system description information, and associates the split plurality of system sub-files to the information node.
  • the server 104 adds a plurality of information nodes associated with different system sub-files to the associated information trees corresponding to the same applicable object identifier.
  • the server 104 Upon receiving the institutional query request sent by the second terminal 106, the server 104 responds to the system query request based on the associated information tree.
  • a system information query method is provided, which is applied to the server in FIG. 1 as an example, and includes the following steps:
  • Step 202 Monitor system information issued by the first terminal; the system information includes system description information and associated system files; and the system file includes multiple system terms and corresponding applicable object identifiers.
  • the server monitors whether the first terminal issues new system information.
  • Institutional information includes institutional description information and associated institutional documents.
  • the system description information includes the system code, the system name, the system level, the issuing unit, the release date, the applicable object identifier or the information summary.
  • the system information may be text information, voice information, image information, video information, and the like. If it is voice information, image information or video information, voice information, image information and video information can be converted into text information by voice recognition or image processing.
  • the institutional document includes a number of institutional provisions and the applicable object identifier for each system clause. Applicable object identification refers to the identification information of the object that needs to perform or understand the system, and may be a post identification or an organization identification.
  • step 204 the system information is classified, and the system information is added to the preset one or more target information trees according to the classification result.
  • the server classifies the system information. Specifically, the server performs word segmentation on the system information to obtain a corresponding set of original words.
  • the original set of words includes a plurality of original words.
  • the server performs synonymous expansion on each original word to generate a set of extended words corresponding to each original word.
  • the server forms a set of extended system information corresponding to the system information according to each set of extended words, and inputs the set of extended system information into a preset system management model to obtain a target category corresponding to the system information.
  • each target information tree includes a plurality of information nodes and an institutional file associated with each information node.
  • Institutional files can be many types of files, such as pdf documents, jpg images, xls tables, mp3 audio or avi videos.
  • Different information nodes can be arranged in the target information tree according to the release time. It is easy to understand that an institutional information may also have no associated institutional documents, and may also have multiple associated institutional documents, which is not limited.
  • Each target information tree has a corresponding category label.
  • the category label is used to identify the category of information nodes that the corresponding target information tree can contain, such as administrative management, sales management, or risk management.
  • the server obtains the category label corresponding to the target category, and filters one or more target information trees including the obtained category label.
  • the server generates an information node based on the system description information. For example, the system number and/or the system name can be used as information nodes.
  • the server associates the system file to the information node, and adds the information node associated with the system file to the target information tree obtained by the screening.
  • Step 206 Acquire multiple association information trees corresponding to the target information tree; each associated information tree has a corresponding applicable object identifier.
  • Each target information tree has a corresponding plurality of associated information trees.
  • Each information node in the target information tree has a corresponding one or more applicable object identifiers.
  • the different applicable object identifiers in the target information tree respectively have a corresponding associated information tree.
  • the number of applicable object identifiers in the target information tree is equal to the number of corresponding association information trees, so that each applicable object identifier corresponding post has a corresponding associated information tree.
  • the target information tree is used to record institutional information that applies to all positions in the enterprise.
  • the associated information tree only needs to record the institutional information applicable to a position.
  • Each associated information tree has a corresponding applicable object identifier.
  • the associated information tree corresponding to the object identifier "post 1" is applied, and the information node 4 and the information are not present in the target information tree of FIG. Node 9. It is easy to understand that the directory hierarchy of multiple information nodes in the associated information tree is not necessarily consistent with the target information tree, and can be adaptively adjusted. The content of the system file record associated with other information nodes still existing in the associated information tree may be different from the content of the system file record associated with the corresponding information node in the target information tree.
  • step 208 the system file is split, and the system sub-file corresponding to the applicable object identifier is generated by using the system clause corresponding to each applicable object identifier.
  • the server splits the multiple system terms in the system file according to the applicable object identifier corresponding to each system clause in the system file, and generates a system sub-file corresponding to each applicable object identifier.
  • the institutional document A includes four system clauses X1 to X4.
  • X1 corresponds to the applicable object identifier including A and B
  • X2 corresponds to the applicable object identifier including A
  • X3 corresponds to the applicable object identifier including A, B, C, D and E
  • X4 corresponds to the applicable object identifier including A and D.
  • Institutional Document A consists of five applicable object identifiers: A, B, C, D and E. The corresponding splits are obtained in five system sub-documents A1 to A5.
  • the system sub-file A1 corresponding to the applicable object identifier A includes four system clauses X1 to X4;
  • the system sub-file A2 corresponding to the applicable object identifier B includes two system clauses of X1 and X3; and so on.
  • Step 210 Generate an information node according to the system description information, associate the corresponding system sub-file to the information node, and add the information node to the associated information tree corresponding to the same applicable object identifier.
  • the server After the server adds the system information to the corresponding target information tree, the server obtains the corresponding associated information tree corresponding to the target information tree according to the applicable object identifier recorded by the system file. It is easy to understand that the server only needs to obtain the associated information tree corresponding to the applicable object identifier of the system file record.
  • the institutional information classification is added to three target information trees, including the target information tree M.
  • the target information tree M corresponds to the applicable object identifiers including A, B, C, D, E, and E.
  • the system file only includes information content applicable to A, B, C, D, and E according to the above example, the server only needs to obtain the target information.
  • the associated information tree corresponding to A, B, C, D, and E corresponding to tree M.
  • the server generates an information node according to the system description information, and associates the split multiple system sub-files with the information node.
  • the server adds multiple information nodes associated with different system sub-files to the associated information tree corresponding to the same applicable object identifier. For example, in the above example, the association has added system subfile information of the nodes A1 to object information tree M applicable object ID A corresponding association information tree M A; and is associated with adding system subfile information of the node A2 to the target information
  • the tree M corresponds to the associated information tree M B corresponding to the object identifier B , and so on.
  • Step 212 When receiving the system query request sent by the second terminal, query the request based on the associated information tree response system.
  • the server When receiving the system query request sent by the second terminal, the server acquires the associated information tree corresponding to the applicable object identifier.
  • the system query request carries the applicable object identifier and query conditions.
  • the server searches for an information node that satisfies the query condition in the association information tree, acquires a system sub-file associated with the information node that satisfies the query condition, and sends the system sub-file to the second terminal.
  • the traditional target information tree needs to include all the system information in the corresponding target information tree, which leads to the lack of specificity of the target information tree.
  • a special association information tree is separately constructed for different positions, which is convenient for the user to perform query based on the information content applicable to the user, thereby improving the efficiency of the system query.
  • the system information when the newly released system information is monitored, the system information is classified, and the system information may be added to the preset one or more target information trees according to the classification result; the corresponding application of each system clause in the system file Object identification, splitting the multiple system terms in the system file, and obtaining the system sub-file corresponding to each applicable object identifier; according to the applicable object identifier in the target information tree, the corresponding related information tree can be obtained, and then the system can be obtained according to the system
  • the description information generation information node associated with the different system sub-files is added to the association information tree corresponding to the same applicable object identifier; when receiving the system query request sent by the second terminal, the request may be queried based on the association information tree response system.
  • the system documents that are applicable to the system information of different positions will be recorded, and the system clauses that need to be implemented or understood in each position will be selected to meet the individualized needs of different positions, and only separate positions for different positions will be constructed.
  • the associated information tree containing the content of the corresponding post requirements, and the process of generating all associated information trees is fully automated, saving time and effort; subsequent users only need to perform system query based on the associated information tree applicable to them, and can also improve the efficiency of system query.
  • the step of classifying the system information that is, classifying the system information, and adding the system information to the preset one or more target information trees includes:
  • Step 502 Perform word segmentation on the system information to obtain a corresponding original word set; the original word set includes a plurality of original words.
  • the server segments the system information through a word segmentation algorithm to obtain a collection of original words.
  • the original set of words includes a plurality of original words.
  • words that have a small effect on the classification such as stop words, modal particles, and punctuation marks, are removed, thereby improving the efficiency of subsequent feature extraction.
  • a stop word refers to a word in the system information that appears more than a preset threshold but has little practical meaning, such as me, he, etc.
  • the terminal may also pre-specify the category information of the system information, so that the server can incorporate the system information into the corresponding target information tree according to the category information. If the system description information already contains category information of the system information, the system information can be added to the corresponding target information tree according to the category information. If the system description information does not include the category information of the system information, the system information may be classified and managed according to the system information processing method provided by the present application.
  • Step 504 Synonymously expand each original word to generate an extended word set corresponding to each original word.
  • the server separately obtains the synonym corresponding to each original word in the original word set, and forms the extended word set by the original word and the corresponding synonym.
  • Synonyms refer to words that have the same or similar meaning as the original words.
  • the original words are “not allowed”, and the synonyms can be “no”, “forbidden”, “avoided”, “cancelled”, etc., and the original words and corresponding synonyms are formed. Expand the collection of words, such as the original words "not allowed” corresponding to the set of extended words as ⁇ no, no, prohibit, avoid, eliminate ⁇ .
  • each original word in the original word set has a corresponding extended word set, such as a corresponding extended word set of a is ⁇ a, a1, a2 ⁇ , b corresponding
  • the set of extended words is ⁇ b, b1, b2, b3 ⁇
  • the set of extended words corresponding to c is ⁇ c, c1, c2 ⁇ .
  • Step 506 Form an extended system information set corresponding to the system information according to each extended word set.
  • the server arbitrarily selects one word from the set of extended words corresponding to each original word according to the order in which the original words appear in the system information, and forms an extended system information in order.
  • different extended system information is formed, and different extended system information constitutes an expanded system information set.
  • the server obtains a Cartesian product for the set of extended words corresponding to each original word, and forms a set of extended system information composed of different extended system information.
  • the Cartesian product of the two sets X and Y also known as the direct product, is expressed as X ⁇ Y.
  • the first object is a member of X and the second object is one of all possible ordered pairs of Y.
  • step 508 the extended system information set is input into a preset system management model, and the target category corresponding to the system information is obtained.
  • the institutional management model is for determining a target category corresponding to the input from among a plurality of candidate types based on the input.
  • the system management model can be a model obtained by training such as logistic regression algorithm and support vector machine algorithm.
  • the system management model can be formed by multiple sub-management model connections. Since the input of the system management model is an expanded set of extended system information, the expanded information of each extended system expresses the same or similar meaning as the institutional information, and improves the effective coverage of the institutional information, thereby inputting the trained After the institutional management model, the accuracy of the target category can be improved.
  • Step 510 Acquire a category label corresponding to each of the plurality of target information trees, filter a target information tree including the category label corresponding to the target category, and add the system information to the filtered target information tree.
  • the server obtains the category label corresponding to the target category, and filters one or more target information trees including the obtained category label.
  • the server generates an information node according to the system description information, and detects whether the same information node already exists in the target information tree obtained by the screening. If not, the server associates the system file to the information node, and adds the information node associated with the system file to the filtered target information tree.
  • the server determines, according to the system description information, whether the generated information node belongs to a parallel node or a parent child node with the same information node that already exists. When the generated information node and the existing information node belong to the parallel node, the server distinguishes the generated information node from the existing information node, and adds the marked information node to the corresponding target information tree.
  • the system file is associated with the information node after the difference mark.
  • the server When the generated information node and the existing same information node belong to the parallel node, the server describes and defines the generated information node according to the system description information, that is, extracts the keyword in the system description information, and generates the generated keyword pair.
  • the information node performs semantic expansion.
  • the information node generated according to the name of the system is the “company welfare management system”
  • the keyword “research and development department” is extracted from the system description information
  • the information node after the semantic expansion may be the “company development department welfare management system”.
  • the server adds the semantically expanded information node as a child node of the existing same information node to the corresponding target information tree, and associates the system file to the child node.
  • the extended word set corresponding to each original word is formed first, and then the expanded system information set is formed by expanding the word set, thereby greatly expanding the expansion degree of the extended system information, and the extended extended system information is expressed and institutional information.
  • the same or similar meanings improve the effective coverage of institutional information, so that after the input of the trained system management model, the accuracy of the target category can be improved, and the system information can be accurately incorporated into the corresponding target information tree, and the system can be improved. Information classification efficiency and accuracy.
  • splitting the system file includes: identifying a file type of the system file; when the file type of the system file is the first type, calling a preset split interface to split the system file; When the file type of the file is the second type, obtain the split expression corresponding to the preset multiple applicable object identifiers, traverse the system file, and match each split expression with the system file, according to the matching result.
  • the system documents are split.
  • Split rules for different file types can contain the same split dimension but contain different split methods.
  • the corresponding splitting manner may be splitting by using a preset splitting interface.
  • the default split interface may be OLEDB (an application program interface).
  • the corresponding split mode may be split by using multiple preset split expressions.
  • the server pre-stores split expressions corresponding to multiple applicable object identifiers, and each split expression includes one or more split fields.
  • the server traverses the system files line by line, and matches the split expression corresponding to each applicable object identifier with multiple system clauses in the system file, and separates the system clauses in the system file that match each split expression successfully. It is divided into a system sub-file corresponding to the corresponding applicable object identifier, thereby obtaining the system sub-file of the system file in multiple split dimensions.
  • splitting rules including different splitting modes are configured for different types of files, so that splitting of multiple types of files can be supported.
  • splitting the system file includes: calculating a data amount of the system file, detecting whether the data amount exceeds a threshold; when the data amount exceeds the threshold, acquiring a preset target data amount, and determining a system according to the target data amount The split position of the file; detects whether the split position is between adjacent separators; when the split position is at a separator, splits the system file into multiple intermediate files at the split position; when the split position is located When the adjacent separators are separated, the system file is split into multiple intermediate files at any one of the adjacent separators; multiple intermediate files are split according to the preset splitting rules.
  • the server calculates the amount of data in the system file and checks whether the amount of data exceeds the threshold.
  • the threshold may be preset or may be temporarily generated based on the load monitoring result of the server.
  • the server may pre-separate the system file into multiple intermediate files with a small amount of data, and then split the intermediate files into multiple system sub-files.
  • the server acquires a preset target data amount, and determines a split location of the system file according to the target data amount.
  • the target data amount may be preset or may be temporarily generated based on load monitoring results of other servers in the plurality of clusters. For example, the data volume of the system file A is 720M. If the target data volume is 80M, the 80M size position of the system file is marked as the first split position, and the 160M size position is marked as the second split position. And so on.
  • the server identifies if each split location is between adjacent separators. When the split location is located at a location where the separator is located, the server splits the system file at the split location to obtain a plurality of intermediate files corresponding to the system file. When the split position is between adjacent separators, the server splits the corresponding system file at any one of the adjacent separators, that is, the previous separator or the next separator in the adjacent separator The symbol is split to obtain multiple intermediate files corresponding to the system file.
  • the server calls multi-threading to split the intermediate file into multiple system sub-files as described above, or send the intermediate files to other servers in the cluster for splitting to improve file splitting efficiency.
  • the system files with large data volume are split into intermediate files with small data volume and then transmitted to other servers in the cluster for splitting, which can also improve data transmission efficiency.
  • two-level splitting is performed on the system file with a large amount of data: wherein the split of the first level is split according to the amount of data, and the split of the second level is performed according to the preset split dimension.
  • Splitting splitting; splitting the system file with a large amount of data into intermediate files with a small amount of data, and splitting the intermediate file into multiple system sub-files in parallel, thereby improving the efficiency of file splitting.
  • the information node is generated according to the system description information, and the corresponding system sub-file is associated with the information node, and the information node is added to the associated information tree corresponding to the same applicable object identifier, including: multiple systems obtained by splitting The total amount of data of the file is verified; the total number of system clauses corresponding to the split is verified by the total number of system clauses; the preset multiple key fields are obtained, and multiple system sub-files obtained by splitting are obtained.
  • the server obtains the data amount of the system file before the split, and records it as the first data amount; calculates the total data amount of the plurality of system sub-files obtained by the split, and records it as the second data amount.
  • the server checks whether the difference between the first data amount and the second data amount exceeds a threshold.
  • the server calculates the total number of system clauses in the system file before the split according to the system description information, and records it as the first quantity.
  • the number of system sub-files obtained by server statistical splitting corresponds to the total number of system clauses, which is recorded as the second quantity.
  • the server verifies whether the first quantity and the second quantity are equal.
  • the system description information includes a summary of the information.
  • the server extracts multiple key fields in the message digest.
  • the server extracts a key segment from the plurality of system sub-files obtained by the splitting, and matches the extracted keyword segment with the preset keyword segment.
  • the difference between the first data amount and the second data amount does not exceed the threshold, the first quantity is equal to the second quantity, and the extracted key field matches the preset key field successfully, indicating data consistency check Through, the server adds multiple system sub-files to the corresponding association information tree in the above manner.
  • the data consistency check is performed on the plurality of system sub-files obtained by the splitting and the system files before the splitting, so as to ensure the accuracy of the file splitting and avoid the files.
  • the lack of data caused by the split has an impact on the accuracy of the information in the associated information tree, which in turn can improve the accuracy of the system query.
  • the association information tree includes a plurality of information nodes; each information node is associated with a corresponding information digest; the system query request carries the employee identification and the query condition; and the query request based on the associated information tree response system includes: obtaining the employee Identifying the applicable object identifier corresponding to the identifier; searching for the information node that satisfies the query condition in the corresponding association information tree according to the applicable object identifier; if yes, acquiring the information summary associated with the information node that satisfies the query condition, and returning the information summary to the first The second terminal; when receiving the system read request sent by the second terminal, the system read request carries the information node identifier; obtains the system file corresponding to the information node identifier, and returns the system file to the second terminal.
  • the server pushes the associated information tree to the second terminal corresponding to the corresponding post, and the corresponding post user refers to the query learning.
  • the second terminal triggers the system query request or the system read request according to the query operation of the user to the associated information tree, and sends the system query request or the system read request to the server. For example, when the second terminal detects that the mouse stays at an information node for more than a threshold, the second terminal sends a system query request to the server. When the second terminal detects a mouse click operation of the mouse on an information node, the system sends a system read request to the server.
  • the system query request carries the employee identification and query conditions.
  • the server obtains the applicable object identifier corresponding to the employee identifier according to the system query request, obtains the corresponding association information tree according to the applicable object identifier, and searches for the information node that satisfies the query condition in the obtained association information tree.
  • Each information node in the associated information tree is associated with a corresponding information digest.
  • the information summary records the purpose of the corresponding system information, the main content introduction or the scope of application. When the information node or its associated information digest contains multiple keywords in the query condition, it indicates that the information node satisfies the query condition.
  • the server When there is an information node that satisfies the query condition, the server acquires the information digest associated with the information node, and returns the information digest to the second terminal.
  • the message summary can be generated based on the system description information.
  • the second terminal popup window displays a summary of information corresponding to the directory node, so that the user determines whether the information node is the system information that needs to be searched for. If yes, the corresponding clause details are further obtained from the server through the system read request to reduce unnecessary data transmission between the second terminal and the server.
  • the item details may be the system file corresponding to the clicked information node.
  • the server when the second terminal performs the system query, the server feeds back the information digest to the second terminal, and then feeds back the corresponding system file, so as to reduce unnecessary data transmission between the second terminal and the server, thereby saving the server. Resources.
  • the method further includes: generating a crawler tag corresponding to the system information according to the preset rule; and crawling the current political information related to the system information from the preset website according to the crawler tag;
  • the current affairs political information and the multiple institutional clauses are semantically analyzed to judge whether the semantics of the institutional clauses and the semantics of the current political information are contradictory; if there are institutional clauses that are contrary to the semantics of current political information, the review prompts corresponding to the system clauses will be reviewed. The prompt is sent to the first terminal.
  • the server associates the corresponding crawler tag with the information node before adding the information node to the target information tree.
  • the crawler tag can be one or more institutional keywords related to laws and regulations or current affairs politics in the system information.
  • the server crawls relevant current political information on the preset website according to the crawler tag during the idle time of the database. Therefore, before the server performs the crawling operation, it is necessary to analyze the idle time of the database in advance.
  • the server runs the monitoring script, and monitors the execution status and resource consumption status of the batch task in the database in the preset time period by using the monitoring script, and obtains the execution time and resource consumption time of the batch task in the preset time period.
  • Batch tasks include classifying system information and querying requests based on the corresponding information tree.
  • the preset time period may be set to a time period of the entire non-working time or a period of part of the non-working time. For example, according to the crawler tag, the time period from 9:00 am to 5:30 am in the month before the crawling of the relevant current affairs political information on the preset website.
  • the server will perform statistics on the resource consumption time in the preset time period to obtain a resource consumption statistics table.
  • the resource consumption time in the server extraction resource consumption statistics table is compared with the execution time of the plurality of batch processing tasks, and the resource consumption time capable of avoiding the execution time of the plurality of batch processing tasks is filtered out. Since the batch task consumes more database resources when it is executed, the resource consumption time of avoiding the execution time of multiple batch tasks can be used as the idle time of the database. Since the idle time of the database is within a preset time period, and the preset time period may be a time period of non-working time, the idle time of the database obtained by the above manner may be regarded as an idle time with optimal database performance.
  • the server crawls relevant current political information on the default website according to the crawler tag during the idle time of the database, and semantically analyzes the current political information of the crawled current political information and the corresponding system documents in the corresponding system file to determine whether there is a semantically related system. Terms. If yes, the server generates a review prompt corresponding to the system clause, and sends a review prompt to the first terminal. The server detects whether there are corresponding sub-clauses in the system clauses that are contrary to the semantics of current affairs political information. If yes, the server generates a linkage review prompt corresponding to the sub-clause, and sends a linkage review prompt to the first terminal.
  • a corresponding validity period and a review reminding time limit may be respectively set for each information node in the target information tree.
  • the server adds the system to the corresponding target information tree, it starts timing, generates a review prompt when the review period of the validity period is reached, and sends the review prompt to the first terminal.
  • the first terminal may initiate a monitoring request to the server for the published system at any time to actively review the system information corresponding to the information node. The server extracts the corresponding information node in the stored multiple target information trees according to the monitoring request and returns to the first terminal.
  • the first terminal is convenient to have a global understanding of a large amount of system information that has been released, thereby facilitating the tracking, revision, or abolition of the issued system information.
  • FIGS. 2 and 5 are sequentially displayed as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Except as explicitly stated herein, the execution of these steps is not strictly limited, and the steps may be performed in other orders. Moreover, at least some of the steps in FIGS. 2 and 5 may include a plurality of sub-steps or stages, which are not necessarily performed at the same time, but may be performed at different times, or The order of execution of the stages is also not necessarily sequential, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
  • an institutional information querying apparatus including: an information classification information splitting module 604, an information archiving module 606, and an information querying module 608, wherein:
  • the information classification module 602 is configured to monitor system information issued by the first terminal; the system information includes system description information and associated system files; the system file includes multiple system terms and corresponding applicable object identifiers; and the system information is classified according to The classification result adds the system information to the preset one or more target information trees.
  • the information splitting module 604 is configured to acquire a plurality of associated information trees corresponding to the target information tree; each associated information tree has a corresponding applicable object identifier; the system file is split, and the system clause corresponding to each applicable object identifier is generated. The corresponding system sub-file corresponding to the applicable object identifier.
  • the information archiving module 606 is configured to generate an information node according to the system description information, associate the corresponding system sub-file to the information node, and add the information node to the associated information tree corresponding to the same applicable object identifier.
  • the information querying module 608 is configured to query the request based on the associated information tree response system when receiving the system query request sent by the second terminal.
  • the information classification module 602 is further configured to perform word segmentation on the system information to obtain a corresponding original word set; the original word set includes a plurality of original words; synonymously expand each original word to generate each original word corresponding a set of extended words; forming a set of extended system information corresponding to the system information according to each set of extended words; inputting the expanded system information set into a preset institutional management model, obtaining a target category corresponding to the institutional information; acquiring a plurality of target information trees respectively corresponding The category labeling screen filters the target information tree including the category label corresponding to the target category, and adds the system information to the filtered target information tree.
  • the information splitting module 604 is further configured to identify a file type of the system file; when the file type of the system file is the first type, the preset split interface is invoked to split the system file; When the file type of the file is the second type, obtain the split expression corresponding to the preset multiple applicable object identifiers, traverse the system file, and match each split expression with the system file, according to the matching result.
  • the system documents are split.
  • the information splitting module 604 is further configured to calculate a data amount of the system file, and detect whether the data amount exceeds a threshold; when the data amount exceeds the threshold, obtain a preset target data amount, and determine a system according to the target data amount.
  • the split position of the file detects whether the split position is between adjacent separators; when the split position is at a separator, splits the system file into multiple intermediate files at the split position; when the split position is located When the adjacent separators are separated, the system file is split into multiple intermediate files at any one of the adjacent separators; multiple intermediate files are split according to the preset splitting rules.
  • the information archiving module 606 is further configured to verify the total data amount of the plurality of system sub-files obtained by the splitting; and perform the total number of system clauses corresponding to the split multiple system sub-files respectively. Verification; obtain a preset plurality of key fields, extract key fields in the plurality of system sub-files obtained by the splitting, and match the extracted keyword segments with the preset keyword segments; The total data amount of the file and the total number of corresponding system terms are respectively verified, and when the extracted keyword segment and the preset keyword segment are successfully matched, the information node is generated according to the system description information, and the corresponding system sub-file is associated with The information node adds the information node to the associated information tree corresponding to the same applicable object identifier.
  • the association information tree includes a plurality of information nodes; each information node is associated with a corresponding information digest; the system query request carries the employee identification and the query condition; and the information query module 608 is further configured to obtain the employee identifier corresponding to Applicable object identifier; according to the applicable object identifier, in the corresponding association information tree, it is found whether there is an information node that satisfies the query condition; if yes, the information digest associated with the information node that satisfies the query condition is obtained, and the information digest is returned to the second terminal; When receiving the system read request sent by the second terminal, the system read request carries the information node identifier; the system file corresponding to the information node identifier is obtained, and the system file is returned to the second terminal.
  • the device further includes a review prompting module 610, configured to generate a crawler tag corresponding to the system information according to the preset rule; and according to the crawler tag, crawling the current information related to the system information from the preset website during the idle time of the database Political information; semantic analysis of current political information and multiple institutional clauses, to determine whether there is semantic difference between the semantics of institutional clauses and current political information; if there are institutional clauses that are contrary to the semantics of current political information, generate institutional clauses
  • the corresponding review prompt sends the review prompt to the first terminal.
  • Each module in the above-mentioned system information inquiry device may be implemented in whole or in part by software, hardware, and a combination thereof.
  • Each of the above modules may be embedded in or independent of the processor in the computer device, or may be stored in a memory in the computer device in a software form, so that the processor invokes the operations corresponding to the above modules.
  • a computer device which may be a server, and its internal structure diagram may be as shown in FIG.
  • the computer device includes a processor, memory, network interface, and database connected by a system bus.
  • the processor of the computer device is used to provide computing and control capabilities.
  • the memory of the computer device includes a non-volatile storage medium, an internal memory.
  • the non-volatile storage medium stores an operating system, computer readable instructions, and a database.
  • the internal memory provides an environment for operation of an operating system and computer readable instructions in a non-volatile storage medium.
  • the database of the computer device is used to store institutional information.
  • the network interface of the computer device is used to communicate with an external terminal via a network connection. The step of implementing the system information query method provided in any one of the embodiments of the present application when the computer readable instructions are executed by the processor.
  • FIG. 7 is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation of the computer device to which the solution of the present application is applied.
  • the specific computer device may It includes more or fewer components than those shown in the figures, or some components are combined, or have different component arrangements.
  • One or more non-volatile storage media storing computer readable instructions, when executed by one or more processors, causing one or more processors to implement a system as provided in any one embodiment of the present application The steps of the information query method.
  • Non-volatile memory can include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory.
  • Volatile memory can include random access memory (RAM) or external cache memory.
  • RAM is available in a variety of formats, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronization chain.
  • SRAM static RAM
  • DRAM dynamic RAM
  • SDRAM synchronous DRAM
  • DDRSDRAM double data rate SDRAM
  • ESDRAM enhanced SDRAM
  • Synchlink DRAM SLDRAM
  • Memory Bus Radbus
  • RDRAM Direct RAM
  • DRAM Direct Memory Bus Dynamic RAM
  • RDRAM Memory Bus Dynamic RAM

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Abstract

一种制度信息查询方法，包括：监测第一终端发布的制度信息；制度信息包括制度描述信息及关联的制度文件；制度文件包括多个制度条款及分别对应的适用对象标识；对制度信息进行分类，将制度信息添加至预设的一个或多个目标信息树；获取目标信息树对应的多个关联信息树及分别对应的适用对象标识；对制度文件进行拆分，利用每个适用对象标识对应的制度条款生成相应的制度子文件；根据制度描述信息生成信息节点，将相应制度子文件关联至信息节点，将信息节点添加至相同适用对象标识对应的关联信息树；基于关联信息树响应制度查询请求。

Description

制度信息查询方法、装置、计算机设备和存储介质
本申请要求于2018年4月9日提交中国专利局，申请号为2018103127498，申请名称为“制度信息查询方法、装置、计算机设备和存储介质”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及一种制度信息查询方法、装置、计算机设备和存储介质。
背景技术
企业制度化是对企业生产经营与管理等活动中的重复性事物和概念，通过制订、发布和实施制度规范达到统一，以提高企业管理水平。制度规范(以下简称“制度”)是员工在生产经营活动中须共同遵守的规定和准则，包括法律与政策、企业组织结构、管理制度、岗位职责、技术制度、工作流程等制度文件。但发明人意识到，为了满足所有岗位的工作需求，企业可能将适用于不同岗位的制度信息全部记录至同一个制度文件中，使得用户只能基于制度文件全部信息内容进行制度查询，进而使制度信息查询效率降低。
发明内容
根据本申请公开的各种实施例，提供一种制度信息查询方法、装置、计算机设备和存储介质。
一种制度信息查询方法包括：监测第一终端发布的制度信息；所述制度信息包括制度描述信息及关联的制度文件；所述制度文件包括多个制度条款以及分别对应的适用对象标识；对所述制度信息进行分类，根据分类结果将所述制度信息添加至预设的一个或多个目标信息树；获取所述目标信息树对应的多个关联信息树；每个所述关联信息树具有对应的适用对象标识；对所述制度文件进行拆分，利用每个适用对象标识对应的制度条款生成相应适用对象标识对应的制度子文件；根据所述制度描述信息生成信息节点，将相应所述制度子文件关联至所述信息节点，将所述信息节点添加至相同适用对象标识对应的关联信息树；及当接收到第二终端发送的制度查询请求时，基于所述关联信息树响应所述制度查询请求。
一种制度信息查询装置包括：信息分类模块，用于监测第一终端发布的制度信息；所述制度信息包括制度描述信息及关联的制度文件；所述制度文件包括多个制度条款以及分别对应的适用对象标识；对所述制度信息进行分类，根据分类结果将所述制度信息添加至预设的一个或多个目标信息树；信息拆分模块，用于获取所述目标信息树对应的多个关联信息树；每个所述关联信息树具有对应的适用对象标识；对所述制度文件进行拆分，利用每个适用对象标识对应的制度条款生成相应适用对象标识对应的制度子文件；信息归档 模块，用于根据所述制度描述信息生成信息节点，将相应所述制度子文件关联至所述信息节点，将所述信息节点添加至相同适用对象标识对应的关联信息树；及信息查询模块，用于当接收到第二终端发送的制度查询请求时，基于所述关联信息树响应所述制度查询请求。
一种计算机设备，包括存储器和一个或多个处理器，存储器中存储有计算机可读指令，计算机可读指令被处理器执行时实现本申请任意一个实施例中提供的制度信息查询方法的步骤。
一个或多个存储有计算机可读指令的非易失性存储介质，计算机可读指令被一个或多个处理器执行时，使得一个或多个处理器实现本申请任意一个实施例中提供的制度信息查询方法的步骤。
本申请的一个或多个实施例的细节在下面的附图和描述中提出。本申请的其它特征和优点将从说明书、附图以及权利要求书变得明显。
附图说明
为了更清楚地说明本申请实施例中的技术方案，下面将对实施例中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其它的附图。
图1为根据一个或多个实施例中制度信息查询方法的应用场景图。
图2为根据一个或多个实施例中制度信息查询方法的流程示意图。
图3为根据一个或多个实施例中制度信息查询方法中目标信息树的示意图。
图4为根据一个或多个实施例中制度信息查询方法中关联信息树的示意图。
图5为根据一个或多个实施例中对制度信息分类归档步骤的流程示意图。
图6为根据一个或多个实施例中制度信息查询装置的结构框图。
图7为根据一个或多个实施例中计算机设备的框图。
具体实施方式
为了使本申请的技术方案及优点更加清楚明白，以下结合附图及实施例，对本申请进行进一步详细说明。应当理解，此处描述的具体实施例仅仅用以解释本申请，并不用于限定本申请。
本申请提供的制度信息查询方法，可以应用于如图1所示的应用环境中。第一终端102与服务器104通过网络进行通信。第二终端106与服务器104通过网络进行通信。第一终端102和第二终端106可以但不限于是各种个人计算机、笔记本电脑、智能手机、平板电脑和便携式可穿戴设备。第一终端102为制度管理终端，用户可以在第一终端102进行制度起草、意见征集、审批和发布等。第二终端106为业务终端，用户可以在第二终端106进行制度学习等操作。第一终端102与第二终端106可以同一终端，也可以是不同的 终端。服务器104可以用独立的服务器或者是多个服务器组成的服务器集群来实现。
服务器104中存储了多种目标信息树。每种目标信息树具有对应的类别标注。服务器104对第一终端102是否发布新的制度信息进行监测。制度信息包括制度描述信息及关联的制度文件。当监测到第一终端102发布了新的制度信息时，服务器104对制度信息进行分类，根据分类结果将制度信息纳入相应的一种或多种目标信息树。每种目标信息树具有对应的多个关联信息树。每种关联信息树具有对应的适用对象标识。服务器104将制度信息纳入相应的关联信息树。具体的，将制度信息添加至相应的目标信息树后，服务器104获取目标信息树对应的多个关联信息树。制度文件包括多个制度条款以及分别对应的适用对象标识。服务器104根据制度文件中每个制度条款对应的适用对象标识，对制度文件进行拆分，利用每个适用对象标识对应的制度条款生成相应适用对象标识对应的制度子文件。服务器104根据制度描述信息生成信息节点，将拆分得到的多个制度子文件分别关联至信息节点。服务器104将多个关联有不同制度子文件的信息节点分别添加至相同适用对象标识对应的关联信息树。当接收到第二终端106发送的制度查询请求时，服务器104基于关联信息树响应该制度查询请求。在制度信息发布时，将记录来了适用于不同岗位的制度信息的制度文件拆分，为不同岗位分别构建专门的关联信息树，后续用户只需基于适用于自己的内容进行制度查询，提高制度查询效率。
在其中一个实施例中，如图2所示，提供了一种制度信息查询方法，以该方法应用于图1中的服务器为例进行说明，包括以下步骤：
步骤202，监测第一终端发布的制度信息；制度信息包括制度描述信息及关联的制度文件；制度文件包括多个制度条款以及分别对应的适用对象标识。
服务器对第一终端是否发布新的制度信息进行监测。制度信息包括制度描述信息及关联的制度文件。制度描述信息包括制度编码、制度名称、制度级别、发布单位、发布日期、适用对象标识或信息摘要等。制度信息可以是文本信息，也可以是语音信息、图像信息、视频信息等。如果是语音信息、图像信息或视频信息，则可先通过语音识别或图像处理，将语音信息、图像信息和视频信息转化为文本信息。制度文件包括多项制度条款以及每项制度条款对应的适用对象标识。适用对象标识是指需要执行或了解该制度的对象的标识信息，可以是岗位标识或机构标识等。
步骤204，对制度信息进行分类，根据分类结果将制度信息添加至预设的一个或多个目标信息树。
当监测到第一终端发布了新的制度信息时，服务器对制度信息进行分类。具体的，服务器对制度信息进行分词得到对应的原始词语集合。原始词语集合包括多个原始词语。服务器对各个原始词语进行同义扩展，生成每个原始词语对应的扩展词语集合。服务器根据各个扩展词语集合形成制度信息对应的扩展制度信息集合，将扩展制度信息集合输入预设的制度管理模型，得到制度信息对应的目标类别。
服务器中存储了多种目标信息树。不同目标信息树可以理解为不同的制度体系，用 于存储不同类别和用途的制度信息。如图3所示，每种目标信息树包括多个信息节点及每个信息节点关联的制度文件。制度文件可以是多种类型的文件，如pdf文档、jpg图像、xls表格、mp3音频或avi视频等。不同的信息节点在目标信息树中可以按照发布时间先后排列。容易理解，一项制度信息也可以不具有关联的制度文件，也还可以具有多个关联的制度文件，对此不作限制。
每种目标信息树具有对应的类别标注。类别标注用于标识相应目标信息树能够包含的信息节点的类别，如行政管理类、销售管理类或风险管理类等。服务器获取与目标类别对应的类别标注，筛选包含获取到的类别标注的一种或多种目标信息树。服务器根据制度描述信息生成信息节点。例如，可以将制度编号和/或制度名称作为信息节点。服务器将制度文件关联至该信息节点，将关联有制度文件的信息节点添加至筛选得到的目标信息树。
步骤206，获取目标信息树对应的多个关联信息树；每个关联信息树具有对应的适用对象标识。
每种目标信息树具有对应的多个关联信息树。目标信息树中每个信息节点具有对应的一个或多个适用对象标识。目标信息树中不同适用对象标识分别具有对应的一个关联信息树。换言之，目标信息树中包含适用对象标识的数量与对应的关联信息树的数量相等，从而每个适用对象标识对应岗位具有对应的关联信息树。
目标信息树用于记录适用于企业全部岗位的制度信息。而关联信息树则只需记录适用于一个岗位的制度信息。每种关联信息树具有对应的适用对象标识。如图4所示，岗位1无需执行或了解信息节点4和信息节点9对应的制度，则适用对象标识“岗位1”对应的关联信息树，相对图3目标信息树不存在信息节点4和信息节点9。容易理解，关联信息树中多个信息节点的目录层级，并非一定与目标信息树一致，可以自适应调整。关联信息树仍存在的其他信息节点关联的制度文件记录的内容，与目标信息树中相应信息节点关联的制度文件记录的内容可以不同。
步骤208，对制度文件进行拆分，利用每个适用对象标识对应的制度条款生成相应适用对象标识对应的制度子文件。
服务器根据制度文件中每个制度条款对应的适用对象标识，对制度文件中多个制度条款进行拆分，生成每个适用对象标识分别对应的制度子文件。例如，制度文件A包括X1～X4四项制度条款。其中，X1对应适用对象标识包括甲和乙，X2对应适用对象标识包括甲，X3对应适用对象标识包括甲、乙、丙、丁和戊，X4对应适用对象标识包括甲和丁。制度文件A共包括甲、乙、丙、丁和戊五个适用对象标识，对应的拆分得到五个制度子文件A1～A5。其中，适用对象标识甲对应的制度子文件A1包括X1～X4四项制度条款；适用对象标识乙对应的制度子文件A2包括X1和X3两项制度条款；如此类推。
步骤210，根据制度描述信息生成信息节点，将相应制度子文件关联至信息节点，将信息节点添加至相同适用对象标识对应的关联信息树。
服务器将制度信息添加至相应的目标信息树后，服务器根据制度文件记录的适用对象标识，获取目标信息树对应的相应关联信息树。容易理解，服务器只需获取制度文件记录的适用对象标识对应的关联信息树。例如，制度信息分类添加至三种目标信息树，其中包括目标信息树M。目标信息树M对应适用对象标识包括甲、乙、丙、丁、戊和己，假设依上述举例制度文件只包括适用于甲、乙、丙、丁和戊的信息内容，则服务器只需获取目标信息树M对应的甲、乙、丙、丁和戊分别对应的关联信息树。
服务器根据制度描述信息生成信息节点，将拆分得到的多个制度子文件分别关联至信息节点。服务器将多个关联有不同制度子文件的信息节点分别添加至相同适用对象标识对应的关联信息树。例如，在上述举例中，将关联有制度子文件A1的信息节点添加至目标信息树M中适用对象标识甲对应的关联信息树M 甲；将关联有制度子文件A2的信息节点添加至目标信息树M中适用对象标识乙对应的关联信息树M 乙，如此类推。
步骤212，当接收到第二终端发送的制度查询请求时，基于关联信息树响应制度查询请求。
当接收到第二终端发送的制度查询请求时，服务器获取适用对象标识对应的关联信息树。制度查询请求携带了适用对象标识和查询条件。服务器在关联信息树中查找满足查询条件的信息节点，获取与满足查询条件的信息节点关联的制度子文件，将制度子文件发送至第二终端。传统的目标信息树为了满足所有用户的需求，需要将所有制度信息都包含到相应目标信息树中，这样导致目标信息树缺乏针对性。本实施例为不同岗位分别构建专门的关联信息树，方便用户基于适用于自己的信息内容进行查询，提高制度查询效率。
本实施例中，在监测到新发布的制度信息时，对制度信息分类，可以根据分类结果将制度信息添加至预设的一个或多个目标信息树；制度文件中每个制度条款对应的适用对象标识，对制度文件中多个制度条款进行拆分，可以得到每个适用对象标识对应的制度子文件；根据目标信息树中适用对象标识，可以获取对应的关联信息树，进而可以将根据制度描述信息生成关联有不同制度子文件的信息节点添加至相同适用对象标识对应的关联信息树；在接收到第二终端发送的制度查询请求时，可以基于关联信息树响应制度查询请求。在制度信息发布时，将记录来了适用于不同岗位的制度信息的制度文件拆分，将每个岗位需要执行或了解的制度条款挑选出来，满足不同岗位个性化需求，为不同岗位分别构建只包含相应岗位需求内容的关联信息树，且所有关联信息树的生成过程全自动进行，省时省力；后续用户只需基于适用于自己的关联信息树进行制度查询，也可以提高制度查询效率。
在其中一个实施例中，如图5所示，对制度信息分类归档的步骤，即对制度信息进行分类，将制度信息添加至预设的一个或多个目标信息树包括：
步骤502，对制度信息进行分词得到对应的原始词语集合；原始词语集合包括多个原始词语。
当监测到第一终端发布的制度信息，服务器通过分词算法对制度信息进行分词，得 到原始词语集合。原始词语集合包括多个原始词语。在其中一个实施例中，得到各个原始词语后，去除停用词、语气词、标点符号等对分类影响作用小的词语，从而提高后续特征提取的效率。停用词指的是制度信息中出现频率超过预设阈值但实际意义不大的词，如我，的，他等。
终端在发布制度信息时，也可以预先标明制度信息的类别信息，以便服务器可以根据该类别信息，将制度信息纳入相应的目标信息树。若制度描述信息已包含制度信息的类别信息，可以根据类别信息将制度信息添加至相应的目标信息树。若制度描述信息并未包含制度信息的类别信息，则可以按照本申请提供的制度信息处理方法对制度信息进行分类管理。
步骤504，对各个原始词语进行同义扩展，生成每个原始词语对应的扩展词语集合。
服务器分别获取原始词语集合中各个原始词语对应的同义词，将原始词语与对应的同义词形成扩展词语集合。每个原始词语都存在对应的扩展词语集合。同义词是指与原始词语含义相同或相近的词语，如原始词语为“不得”，同义词可为“切勿”、“禁止”、“避免”、“杜绝”等，将原始词语与对应的同义词形成扩展词语集合，如原始词语“不得”对应的扩展词语集合为{不得，切勿，禁止，避免，杜绝}。如原始词语集合为{a,b,c}，则原始词语集合中的每个原始词语都存在对应的扩展词语集合，如a对应的扩展词语集合为{a,a1,a2}，b对应的扩展词语集合为{b,b1,b2,b3}，c对应的扩展词语集合为{c,c1,c2}。
步骤506，根据各个扩展词语集合形成制度信息对应的扩展制度信息集合。
服务器按照与制度信息中各个原始词语出现的顺序，从各个原始词语对应的扩展词语集合中任意选择一个词语，按顺序形成一个扩展制度信息。当从扩展词语集合中选择不同的词语时，则形成不同的扩展制度信息，不同的扩展制度信息组成扩展制度信息集合。在其中一个实施例中，服务器对各个原始词语对应的扩展词语集合求笛卡尔积，形成由不同的扩展制度信息组成的扩展制度信息集合。两个集合X和Y的笛卡尔积，又称直积，表示为X×Y。第一个对象是X的成员而第二个对象是Y的所有可能有序对的其中一个成员。
步骤508，将扩展制度信息集合输入预设的制度管理模型，得到制度信息对应的目标类别。
制度管理模型用于根据输入从多个候选类型中确定与输入对应的目标类别。制度管理模型可以是通过逻辑回归算法、支持向量机算法等训练得到的模型。制度管理模型内部可以由多个子管理模型连接形成。由于制度管理模型的输入是经过扩展了的扩展制度信息集合，扩展后的各个扩展制度信息表达了与制度信息相同或相近的含义，提高了制度信息的有效覆盖范围，从而在后续输入已训练的制度管理模型后，可提高目标类别的精准性。
步骤510，获取多个目标信息树分别对应的类别标注，筛选包含与目标类别对应类别标注的目标信息树，将制度信息添加至筛选得到的目标信息树。
服务器获取与目标类别对应的类别标注，筛选包含获取到的类别标注的一种或多种目标信息树。服务器根据制度描述信息生成信息节点，检测筛选得到的目标信息树中是否 已存在相同的信息节点。若不存在，服务器将制度文件关联至该信息节点，将关联有制度文件的信息节点添加至筛选得到的目标信息树。
若筛选得到的目标信息树中已经存在相应的信息节点，则服务器只需将制度文件关联至已存在相应的信息节点。在另一个实施例中，服务器根据制度描述信息判断生成的信息节点与已存在的相同信息节点属于并列节点还是父子节点。当生成的信息节点与已存在的相同信息节点属于并列节点时，服务器对生成的信息节点与已存在的相同信息节点进行区别标记，将区别标记后的信息节点添加至相应的目标信息树，将制度文件关联至区别标记后的信息节点。
当生成的信息节点与已存在的相同信息节点属于并列节点时，服务器根据制度描述信息对生成的信息节点进行描述限定，即在制度描述信息中提取关键词，利用提取到的关键词对生成的信息节点进行语义扩充。例如，根据制度名称生成的信息节点为“公司福利管理制度”，在制度描述信息中提取关键词“研发部”，则语义扩充后的信息节点可以是“公司研发部福利管理制度”。服务器将语义扩充后的信息节点作为已存在的相同信息节点的子节点添加至相应的目标信息树，将制度文件关联至该子节点。
本实施例中，先形成每个原始词语对应的扩展词语集合，再通过扩展词语集合形成扩展制度信息集合，大大提高了扩展制度信息的扩展度，扩展后的各个扩展制度信息表达了与制度信息相同或相近的含义，提高了制度信息的有效覆盖范围，从而在后续输入已训练的制度管理模型后，可提高目标类别的精准性，进而可以准确将制度信息纳入相应的目标信息树，提高制度信息分类效率和准确率。
在其中一个实施例中，对制度文件进行拆分包括：识别制度文件的文件类型；当制度文件的文件类型为第一类型时，调用预设的拆分接口对制度文件进行拆分；当制度文件的文件类型为第二类型时，获取预设的多个适用对象标识分别对应的拆分表达式，对制度文件进行遍历，将每个拆分表达式与制度文件进行匹配，根据匹配结果对制度文件进行拆分。
不同文件类型对应的拆分规则可以包含相同的拆分维度，但包含不同的拆分方式。具体的，当制度文件的文件类型为第一类型时，对应的拆分方式可以是利用预设的拆分接口进行拆分。例如，当第一类型的制度文件为dbf.数据库表文件时，预设的拆分接口可以是OLEDB(一种应用程序接口)。当制度文件的文件类型为第二类型时，对应的拆分方式可以是利用预设的多个拆分表达式进行拆分。服务器预存储了多个适用对象标识分别对应的拆分表达式，每个拆分表达式包括一个或多个拆分字段。服务器对制度文件进行逐行遍历，将每个适用对象标识对应的拆分表达式与制度文件中多项制度条款分别进行匹配，将制度文件中与每个拆分表达式匹配成功的制度条款拆分为一个相应适用对象标识对应的制度子文件，从而得到制度文件在多个拆分维度的制度子文件。
本实施例中，针对不同类型的文件配置包含不同拆分方式的拆分规则，从而可以支持多种类型文件的拆分。
在其中一个实施例中，对制度文件进行拆分包括：计算制度文件的数据量，检测数 据量是否超过阈值；当数据量超过阈值时，获取预设的目标数据量，根据目标数据量确定制度文件的拆分位置；检测拆分位置是否位于相邻分隔符之间；当拆分位置位于一个分隔符处时，在拆分位置将制度文件拆分为多个中间文件；当拆分位置位于相邻分隔符之间时，在相邻分隔符中任意一个分隔符处将制度文件拆分为多个中间文件；按照预设的拆分规则，对多个中间文件进行拆分。
服务器计算制度文件的数据量，检测数据量是否超过阈值。该阈值可以是预先设定的，也可以是根据服务器的负载监测结果临时生成的。当数据量超过阈值时，服务器可以将制度文件预先拆分为多个数据量小的中间文件，再将中间文件分别拆分为多个制度子文件。具体的，服务器获取预设的目标数据量，根据目标数据量确定制度文件的拆分位置。目标数据量可以是预先设定的，也可以是根据对多个集群内其他服务器的负载监测结果临时生成的。例如，制度文件A的数据量为720M，假设目标数据量为80M，则将制度文件的第80M大小的位置标记为第一个拆分位置，第160M大小的位置标记为第二个拆分位置，以此类推。
服务器识别每个拆分位置是否位于相邻分隔符之间。当拆分位置位于一个分隔符所在的位置时，服务器在该拆分位置对制度文件进行拆分，得到该制度文件对应的多个中间文件。当拆分位置位于相邻分隔符之间时，服务器在相邻分隔符中任意一个分隔符处对相应制度文件进行拆分，即对该相邻分隔符中的前一个分隔符或后一个分隔符处进行拆分，得到制度文件对应的多个中间文件。服务器调用多线程按照上述方式将中间文件拆分为多个制度子文件，或者将中间文件发送至集群内其他服务器进行拆分，以提高文件拆分效率。将数据量较大的制度文件拆分为数据量较小的中间文件后传输至集群内其他服务器进行拆分，还可以提高数据传输效率。
本实施例中，对于数据量较大的制度文件进行两级拆分：其中，第一层级的拆分是根据数据量进行拆分，第二层级的拆分是根据预设的拆分维度进行拆分；将数据量较大的制度文件拆分为数据量较小的中间文件，可以并行将中间文件拆分为多个制度子文件，进而可以提高文件拆分效率。
在其中一个实施例中，根据制度描述信息生成信息节点，将相应制度子文件关联至信息节点，将信息节点添加至相同适用对象标识对应的关联信息树包括：对拆分得到的多个制度子文件的总数据量进行校验；对拆分得到的多个制度子文件分别对应制度条款的总数量进行校验；获取预设的多个关键字段，在拆分得到的多个制度子文件提取关键字段，对提取到的关键字段与预设的关键字段进行匹配；当多个制度子文件的总数据量以及对应制度条款的总数量分别校验通过，且提取到的关键字段与预设的关键字段匹配成功时，根据制度描述信息生成信息节点，将相应制度子文件关联至信息节点，将信息节点添加至相同适用对象标识对应的关联信息树。
为了提高文件拆分的准确性，在将拆分得到的多个制度子文件添加至相应关联信息树之前，对拆分后的多个制度子文件与拆分前的制度文件的数据一致性进行校验。具体的， 服务器获取拆分前制度文件的数据量，记作第一数据量；计算拆分得到的多个制度子文件的总数据量，记作第二数据量。服务器对第一数据量与第二数据量的差值是否超过阈值进行校验。
服务器根据制度描述信息测算拆分前制度文件包含制度条款的总数量，记作第一数量。服务器统计拆分得到的多个制度子文件分别对应制度条款的总数量，记作第二数量。服务器对第一数量与第二数量是否相等进行校验。
制度描述信息包括信息摘要。服务器在信息摘要中提取多个关键字段。服务器在拆分得到的多个制度子文件提取关键字段，对提取到的关键字段与预设的关键字段进行匹配。当第一数据量与第二数据量的差值未超过阈值，第一数量与第二数量相等，且提取到的关键字段与预设的关键字段匹配成功时，表示数据一致性校验通过，服务器按照上述方式将多个制度子文件分别添加至至相应关联信息树。
本实施例中，每个制度文件在拆分完成后，对拆分得到的多个制度子文件与拆分之前的制度文件进行数据一致性校验，可以保证文件拆分的准确性，避免文件拆分造成数据缺失对关联信息树的信息准确性造成影响，进而可以提高制度查询准确率。
在其中一个实施例中，关联信息树包括多个信息节点；每个信息节点关联有对应的信息摘要；制度查询请求携带了员工标识和查询条件；基于关联信息树响应制度查询请求包括：获取员工标识对应的适用对象标识；根据适用对象标识，在对应的关联信息树中查找是否存在满足查询条件的信息节点；若存在，获取满足查询条件的信息节点关联的信息摘要，将信息摘要返回至第二终端；当接收到第二终端发送的制度阅读请求时，制度阅读请求携带了信息节点标识；获取信息节点标识对应的制度文件，将制度文件返回至第二终端。
服务器在生成每个适用对象标识对应的关联信息树后，将关联信息树推送至相应岗位对应的第二终端，供相应岗位用户参考查询学习。第二终端根据用户对关联信息树的查询操作，触发制度查询请求或制度阅读请求，将制度查询请求或制度阅读请求发送至服务器。例如，第二终端检测到鼠标停留在某个信息节点的时间超过阈值，则向服务器发送制度查询请求。第二终端检测到鼠标在某个信息节点的鼠标点击操作，则向服务器发送制度阅读请求。
制度查询请求携带了员工标识和查询条件。服务器根据制度查询请求，获取员工标识对应的适用对象标识，根据适用对象标识获取对应的关联信息树，在获取到的关联信息树中查找是否存在满足查询条件的信息节点。关联信息树中每个信息节点关联有对应的信息摘要。信息摘要记录了相应制度信息的用途、主要内容简介或适用范围等。当信息节点或其关联的信息摘要中包含查询条件中多个关键词则表示该信息节点满足查询条件。
当存在满足查询条件的信息节点时，服务器获取该信息节点关联的信息摘要，将信息摘要返回至第二终端。信息摘要可以是根据制度描述信息生成的。第二终端弹窗展示该目录节点对应的信息摘要，以便用户判断该信息节点是否为自己需要查找的制度信息。如果是，再通过制度阅读请求向服务器进一步获取对应的条款详细信息，以减少第二终端与服 务器之间不必要的数据传输。条款详细信息可以是被点击信息节点对应的制度文件。
本实施例中，在第二终端进行制度查询时，服务器向第二终端反馈信息摘要后，再反馈对应的制度文件，以减少第二终端与服务器之间不必要的数据传输，进而可以节约服务器资源。
在其中一个实施例中，方法还包括：按照预设规则生成制度信息对应的爬虫标签；根据爬虫标签，在数据库空闲时间从预设网站爬取与制度信息相关的时事政治信息；对爬取到的时事政治信息与多个制度条款进行语义解析，判断是否存在制度条款的语义与时事政治信息的语义相悖；若存在与时事政治信息语义相悖的制度条款，生成制度条款对应的复审提示，将复审提示发送至第一终端。检测与时事政治信息语义相悖的制度条款是否存在对应的子条款；若存在，生成子条款对应的联动复审提示，将联动复审提示发送至第一终端。
服务器在将信息节点添加至目标信息树之前，针对信息节点关联对应的爬虫标签。爬虫标签可以是制度信息中一个或多个与法律法规或时事政治相关的制度关键词。为了缓解服务器的资源消耗，服务器在数据库的空闲时间根据爬虫标签，在预设网站爬取相关的时事政治信息。因而，服务器进行爬虫操作之前，需要提前分析出数据库的空闲时间。
具体的，服务器运行监控脚本，通过监控脚本对预设时间段内数据库中的批处理任务执行状况和资源消耗状况进行监控，得到在预设时间段内批处理任务的执行时间和资源消耗时间。批处理任务包括对制度信息进行分类以及基于关联信息树相应制度查询请求等。为了能够充分缓解数据库资源消耗的压力，可以将预设时间段设置为整个非工作时间的时间段或者部分非工作时间的时间段。例如，根据爬虫标签，在预设网站爬取相关的时事政治信息之前的一个月内1号～5号的晚上9:00～凌晨5:30的时间段等。服务器将在预设时间段内资源消耗时间进行统计，得到资源消耗统计表。服务器提取资源消耗统计表中的资源消耗时间与多个批处理任务的执行时间进行比对，筛选出能够避开多个批处理任务执行时间的资源消耗时间。由于批处理任务执行时会消耗较多的数据库资源，因此避开多个批处理任务执行时间的资源消耗时间，可以作为数据库的空闲时间。由于数据库的空闲时间是在预设时间段内的，预设时间段可以是非工作时间的时间段，因此通过上述方式得到的数据库的空闲时间可以视为数据库性能最优的空闲时间。
服务器在数据库的空闲时间根据爬虫标签，在预设网站爬取相关的时事政治信息，对爬取到的时事政治信息和相应制度文件中多个制度条款进行语义解析，判断是否存在语义相悖的制度条款。若存在，服务器生成该制度条款对应的复审提示，将复审提示发送至第一终端。服务器检测与时事政治信息语义相悖的制度条款是否存在对应的子条款。若存在，服务器生成该子条款对应的联动复审提示，将联动复审提示发送至第一终端。
在另一个实施例中，可以对目标信息树中每个信息节点分别设定对应的有效期和复审提醒时限。服务器在将制度添加至相应的目标信息树后，开始计时，在达到有效期的复审提醒时限生成复审提示，将复审提示发送至第一终端。在又一个实施例中，第一终端随时 可以向服务器发起对已发布制度的监控请求，以主动对信息节点对应的制度信息进行复审。服务器根据监控请求，在存储的多个目标信息树提取对应的信息节点返回至第一终端。
传统的，在制度信息发布之后缺乏对制度信息及时进行复审提示的方法，主要是以人工翻阅文件及人工记录的方式进行检查，耗费大量人力和时间成本。对于大型企业，其工作量更大。本实施例，便于第一终端对已经发布的大量制度信息有全局的了解，进而方便对已发布的制度信息进行跟踪修订或废止等管理。
应该理解的是，虽然图2和图5的流程图中的各个步骤按照箭头的指示依次显示，但是这些步骤并不是必然按照箭头指示的顺序依次执行。除非本文中有明确的说明，这些步骤的执行并没有严格的顺序限制，这些步骤可以以其它的顺序执行。而且，图2和图5中的至少一部分步骤可以包括多个子步骤或者多个阶段，这些子步骤或者阶段并不必然是在同一时刻执行完成，而是可以在不同的时刻执行，这些子步骤或者阶段的执行顺序也不必然是依次进行，而是可以与其它步骤或者其它步骤的子步骤或者阶段的至少一部分轮流或者交替地执行。
在其中一个实施例中，如图6所示，提供了一种制度信息查询装置，包括：信息分类信息拆分模块604、信息归档模块606和信息查询模块608，其中：
信息分类模块602，用于监测第一终端发布的制度信息；制度信息包括制度描述信息及关联的制度文件；制度文件包括多个制度条款以及分别对应的适用对象标识；对制度信息进行分类，根据分类结果将制度信息添加至预设的一个或多个目标信息树。
信息拆分模块604，用于获取目标信息树对应的多个关联信息树；每个关联信息树具有对应的适用对象标识；对制度文件进行拆分，利用每个适用对象标识对应的制度条款生成相应适用对象标识对应的制度子文件。
信息归档模块606，用于根据制度描述信息生成信息节点，将相应制度子文件关联至信息节点，将信息节点添加至相同适用对象标识对应的关联信息树。
信息查询模块608，用于当接收到第二终端发送的制度查询请求时，基于关联信息树响应制度查询请求。
在其中一个实施例中，信息分类模块602还用于对制度信息进行分词得到对应的原始词语集合；原始词语集合包括多个原始词语；对各个原始词语进行同义扩展，生成每个原始词语对应的扩展词语集合；根据各个扩展词语集合形成制度信息对应的扩展制度信息集合；将扩展制度信息集合输入预设的制度管理模型，得到制度信息对应的目标类别；获取多个目标信息树分别对应的类别标注，筛选包含与目标类别对应类别标注的目标信息树，将制度信息添加至筛选得到的目标信息树。
在其中一个实施例中，信息拆分模块604还用于识别制度文件的文件类型；当制度文件的文件类型为第一类型时，调用预设的拆分接口对制度文件进行拆分；当制度文件的文件类型为第二类型时，获取预设的多个适用对象标识分别对应的拆分表达式，对制度文件 进行遍历，将每个拆分表达式与制度文件进行匹配，根据匹配结果对制度文件进行拆分。
在其中一个实施例中，信息拆分模块604还用于计算制度文件的数据量，检测数据量是否超过阈值；当数据量超过阈值时，获取预设的目标数据量，根据目标数据量确定制度文件的拆分位置；检测拆分位置是否位于相邻分隔符之间；当拆分位置位于一个分隔符处时，在拆分位置将制度文件拆分为多个中间文件；当拆分位置位于相邻分隔符之间时，在相邻分隔符中任意一个分隔符处将制度文件拆分为多个中间文件；按照预设的拆分规则，对多个中间文件进行拆分。
在其中一个实施例中，信息归档模块606还用于对拆分得到的多个制度子文件的总数据量进行校验；对拆分得到的多个制度子文件分别对应制度条款的总数量进行校验；获取预设的多个关键字段，在拆分得到的多个制度子文件提取关键字段，对提取到的关键字段与预设的关键字段进行匹配；当多个制度子文件的总数据量以及对应制度条款的总数量分别校验通过，且提取到的关键字段与预设的关键字段匹配成功时，根据制度描述信息生成信息节点，将相应制度子文件关联至信息节点，将信息节点添加至相同适用对象标识对应的关联信息树。
在其中一个实施例中，关联信息树包括多个信息节点；每个信息节点关联有对应的信息摘要；制度查询请求携带了员工标识和查询条件；信息查询模块608还用于获取员工标识对应的适用对象标识；根据适用对象标识，在对应的关联信息树中查找是否存在满足查询条件的信息节点；若存在，获取满足查询条件的信息节点关联的信息摘要，将信息摘要返回至第二终端；当接收到第二终端发送的制度阅读请求时，制度阅读请求携带了信息节点标识；获取信息节点标识对应的制度文件，将制度文件返回至第二终端。
在其中一个实施例中，该装置还包括复审提示模块610，用于按照预设规则生成制度信息对应的爬虫标签；根据爬虫标签，在数据库空闲时间从预设网站爬取与制度信息相关的时事政治信息；对爬取到的时事政治信息与多个制度条款进行语义解析，判断是否存在制度条款的语义与时事政治信息的语义相悖；若存在与时事政治信息语义相悖的制度条款，生成制度条款对应的复审提示，将复审提示发送至第一终端。检测与时事政治信息语义相悖的制度条款是否存在对应的子条款；若存在，生成子条款对应的联动复审提示，将联动复审提示发送至第一终端。
关于制度信息查询装置的具体限定可以参见上文中对于制度信息查询方法的限定，在此不再赘述。上述制度信息查询装置中的各个模块可全部或部分通过软件、硬件及其组合来实现。上述各模块可以硬件形式内嵌于或独立于计算机设备中的处理器中，也可以以软件形式存储于计算机设备中的存储器中，以便于处理器调用执行以上各个模块对应的操作。
在其中一个实施例中，提供了一种计算机设备，该计算机设备可以是服务器，其内部结构图可以如图7所示。该计算机设备包括通过系统总线连接的处理器、存储器、网络接 口和数据库。其中，该计算机设备的处理器用于提供计算和控制能力。该计算机设备的存储器包括非易失性存储介质、内存储器。该非易失性存储介质存储有操作系统、计算机可读指令和数据库。该内存储器为非易失性存储介质中的操作系统和计算机可读指令的运行提供环境。该计算机设备的数据库用于存储制度信息。该计算机设备的网络接口用于与外部的终端通过网络连接通信。该计算机可读指令被处理器执行时实现本申请任意一个实施例中提供的制度信息查询方法的步骤。
本领域技术人员可以理解，图7中示出的结构，仅仅是与本申请方案相关的部分结构的框图，并不构成对本申请方案所应用于其上的计算机设备的限定，具体的计算机设备可以包括比图中所示更多或更少的部件，或者组合某些部件，或者具有不同的部件布置。
一个或多个存储有计算机可读指令的非易失性存储介质，计算机可读指令被一个或多个处理器执行时，使得一个或多个处理器实现本申请任意一个实施例中提供的制度信息查询方法的步骤。
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程，是可以通过计算机可读指令来指令相关的硬件来完成，计算机可读指令可存储于一非易失性计算机可读取存储介质中，该计算机可读指令在执行时，可包括如上述各方法的实施例的流程。其中，本申请所提供的各实施例中所使用的对存储器、存储、数据库或其它介质的任何引用，均可包括非易失性和/或易失性存储器。非易失性存储器可包括只读存储器(ROM)、可编程ROM(PROM)、电可编程ROM(EPROM)、电可擦除可编程ROM(EEPROM)或闪存。易失性存储器可包括随机存取存储器(RAM)或者外部高速缓冲存储器。作为说明而非局限，RAM以多种形式可得，诸如静态RAM(SRAM)、动态RAM(DRAM)、同步DRAM(SDRAM)、双数据率SDRAM(DDRSDRAM)、增强型SDRAM(ESDRAM)、同步链路(Synchlink)DRAM(SLDRAM)、存储器总线(Rambus)直接RAM(RDRAM)、直接存储器总线动态RAM(DRDRAM)、以及存储器总线动态RAM(RDRAM)等。
以上实施例的各技术特征可以进行任意的组合，为使描述简洁，未对上述实施例中的各个技术特征所有可能的组合都进行描述，然而，只要这些技术特征的组合不存在矛盾，都应当认为是本说明书记载的范围。
以上实施例仅表达了本申请的几种实施方式，其描述较为具体和详细，但并不能因此而理解为对发明专利范围的限制。应当指出的是，对于本领域的普通技术人员来说，在不脱离本申请构思的前提下，还可以做出若干变形和改进，这些都属于本申请的保护范围。因此，本申请专利的保护范围应以所附权利要求为准。

Claims (20)

  1. 一种制度信息查询方法，包括：
    监测第一终端发布的制度信息；所述制度信息包括制度描述信息及关联的制度文件；所述制度文件包括多个制度条款以及分别对应的适用对象标识；
    对所述制度信息进行分类，根据分类结果将所述制度信息添加至预设的一个或多个目标信息树；
    获取所述目标信息树对应的多个关联信息树；每个所述关联信息树具有对应的适用对象标识；
    对所述制度文件进行拆分，利用每个所述适用对象标识对应的制度条款生成相应适用对象标识对应的制度子文件；
    根据所述制度描述信息生成信息节点，将相应所述制度子文件关联至所述信息节点，将所述信息节点添加至相同适用对象标识对应的关联信息树；及
    当接收到第二终端发送的制度查询请求时，基于所述关联信息树响应所述制度查询请求。
  2. 根据权利要求1所述的方法，其特征在于，所述对所述制度信息进行分类，将所述制度信息添加至预设的一个或多个目标信息树，包括：
    对所述制度信息进行分词得到对应的原始词语集合；所述原始词语集合包括多个原始词语；
    对各个原始词语进行同义扩展，生成每个原始词语对应的扩展词语集合；
    根据各个扩展词语集合形成所述制度信息对应的扩展制度信息集合；
    将所述扩展制度信息集合输入预设的制度管理模型，得到所述制度信息对应的目标类别；及
    获取多个所述目标信息树分别对应的类别标注，筛选包含与所述目标类别对应类别标注的目标信息树，将所述制度信息添加至筛选得到的目标信息树。
  3. 根据权利要求1所述的方法，其特征在于，所述对所述制度文件进行拆分，包括：
    识别所述制度文件的文件类型；
    当所述制度文件的文件类型为第一类型时，调用预设的拆分接口对所述制度文件进行拆分；及
    当所述制度文件的文件类型为第二类型时，获取预设的多个适用对象标识分别对应的拆分表达式，对所述制度文件进行遍历，将每个拆分表达式与所述制度文件进行匹配，根据匹配结果对所述制度文件进行拆分。
  4. 根据权利要求1所述的方法，其特征在于，所述对所述制度文件进行拆分，包括：
    计算所述制度文件的数据量，检测所述数据量是否超过阈值；
    当所述数据量超过阈值时，获取预设的目标数据量，根据所述目标数据量确定所述制度文件的拆分位置；
    检测所述拆分位置是否位于相邻分隔符之间；
    当所述拆分位置位于一个分隔符处时，在所述拆分位置将所述制度文件拆分为多个中间文件；
    当所述拆分位置位于相邻分隔符之间时，在所述相邻分隔符中任意一个分隔符处将所述制度文件拆分为多个中间文件；及
    按照预设的拆分规则，对多个所述中间文件进行拆分。
  5. 根据权利要求1所述的方法，其特征在于，根据所述制度描述信息生成信息节点，将相应所述制度子文件关联至所述信息节点，将所述信息节点添加至相同适用对象标识对应的关联信息树，包括：
    对拆分得到的多个制度子文件的总数据量进行校验；
    对拆分得到的多个制度子文件分别对应制度条款的总数量进行校验；
    获取预设的多个关键字段，在拆分得到的多个制度子文件提取关键字段，对提取到的关键字段与预设的关键字段进行匹配；及
    当多个制度子文件的总数据量以及对应制度条款的总数量分别校验通过，且提取到的关键字段与预设的关键字段匹配成功时，根据所述制度描述信息生成信息节点，将相应所述制度子文件关联至所述信息节点，将所述信息节点添加至相同适用对象标识对应的关联信息树。
  6. 根据权利要求1所述的方法，其特征在于，所述关联信息树包括多个信息节点；每个信息节点关联有对应的信息摘要；所述制度查询请求携带了员工标识和查询条件；基于所述关联信息树响应所述制度查询请求，包括：
    获取所述员工标识对应的适用对象标识；
    根据所述适用对象标识，在对应的关联信息树中查找是否存在满足所述查询条件的信息节点；
    若存在，获取满足所述查询条件的信息节点关联的信息摘要，将所述信息摘要返回至所述第二终端；及
    当接收到所述第二终端发送的制度阅读请求时，所述制度阅读请求携带了信息节点标识；获取所述信息节点标识对应的制度文件，将所述制度文件返回至所述第二终端。
  7. 根据权利要求1所述的方法，其特征在于，还包括：
    按照预设规则生成所述制度信息对应的爬虫标签；
    根据所述爬虫标签，在数据库空闲时间从预设网站爬取与所述制度信息相关的时事政治信息；
    对爬取到的所述时事政治信息与多个所述制度条款进行语义解析，判断是否存在所述制度条款的语义与所述时事政治信息的语义相悖；
    若存在与所述时事政治信息语义相悖的制度条款，生成所述制度条款对应的复审提示，将所述复审提示发送至所述第一终端；
    检测与所述时事政治信息语义相悖的制度条款是否存在对应的子条款；及
    若存在，生成所述子条款对应的联动复审提示，将所述联动复审提示发送至所述第一终端。
  8. 一种制度信息查询装置，包括：
    信息分类模块，用于监测第一终端发布的制度信息；所述制度信息包括制度描述信息及关联的制度文件；所述制度文件包括多个制度条款以及分别对应的适用对象标识；对所述制度信息进行分类，根据分类结果将所述制度信息添加至预设的一个或多个目标信息树；
    信息拆分模块，用于获取所述目标信息树对应的多个关联信息树；每个所述关联信息树具有对应的适用对象标识；对所述制度文件进行拆分，利用每个适用对象标识对应的制度条款生成相应适用对象标识对应的制度子文件；
    信息归档模块，用于根据所述制度描述信息生成信息节点，将相应所述制度子文件关联至所述信息节点，将所述信息节点添加至相同适用对象标识对应的关联信息树；及
    信息查询模块，用于当接收到第二终端发送的制度查询请求时，基于所述关联信息树响应所述制度查询请求。
  9. 一种计算机设备，包括存储器及一个或多个处理器，所述存储器中储存有计算机可读指令，所述计算机可读指令被所述一个或多个处理器执行时，使得所述一个或多个处理器执行以下步骤：
    监测第一终端发布的制度信息；所述制度信息包括制度描述信息及关联的制度文件；所述制度文件包括多个制度条款以及分别对应的适用对象标识；
    对所述制度信息进行分类，根据分类结果将所述制度信息添加至预设的一个或多个目标信息树；
    获取所述目标信息树对应的多个关联信息树；每个所述关联信息树具有对应的适用对象标识；
    对所述制度文件进行拆分，利用每个所述适用对象标识对应的制度条款生成相应适用对象标识对应的制度子文件；
    根据所述制度描述信息生成信息节点，将相应所述制度子文件关联至所述信息节点，将所述信息节点添加至相同适用对象标识对应的关联信息树；及
    当接收到第二终端发送的制度查询请求时，基于所述关联信息树响应所述制度查询请求。
  10. 根据权利要求9所述的计算机设备，其特征在于，所述对所述制度信息进行分类，所述处理器执行所述计算机可读指令时还执行以下步骤：
    对所述制度信息进行分词得到对应的原始词语集合；所述原始词语集合包括多个原始词语；
    对各个原始词语进行同义扩展，生成每个原始词语对应的扩展词语集合；
    根据各个扩展词语集合形成所述制度信息对应的扩展制度信息集合；
    将所述扩展制度信息集合输入预设的制度管理模型，得到所述制度信息对应的目标类别；及
    获取多个所述目标信息树分别对应的类别标注，筛选包含与所述目标类别对应类别标注的目标信息树，将所述制度信息添加至筛选得到的目标信息树。
  11. 根据权利要求9所述的计算机设备，其特征在于，所述处理器执行所述计算机可读指令时还执行以下步骤：识别所述制度文件的文件类型；
    当所述制度文件的文件类型为第一类型时，调用预设的拆分接口对所述制度文件进行拆分；及
    当所述制度文件的文件类型为第二类型时，获取预设的多个适用对象标识分别对应的拆分表达式，对所述制度文件进行遍历，将每个拆分表达式与所述制度文件进行匹配，根据匹配结果对所述制度文件进行拆分。
  12. 根据权利要求9所述的计算机设备，其特征在于，所述处理器执行所述计算机可读指令时还执行以下步骤：
    计算所述制度文件的数据量，检测所述数据量是否超过阈值；
    当所述数据量超过阈值时，获取预设的目标数据量，根据所述目标数据量确定所述制度文件的拆分位置；
    检测所述拆分位置是否位于相邻分隔符之间；
    当所述拆分位置位于一个分隔符处时，在所述拆分位置将所述制度文件拆分为多个中间文件；
    当所述拆分位置位于相邻分隔符之间时，在所述相邻分隔符中任意一个分隔符处将所述制度文件拆分为多个中间文件；及
    按照预设的拆分规则，对多个所述中间文件进行拆分。
  13. 根据权利要求9所述的计算机设备，其特征在于，所述处理器执行所述计算机可读指令时还执行以下步骤：
    对拆分得到的多个制度子文件的总数据量进行校验；
    对拆分得到的多个制度子文件分别对应制度条款的总数量进行校验；
    获取预设的多个关键字段，在拆分得到的多个制度子文件提取关键字段，对提取到的关键字段与预设的关键字段进行匹配；及
    当多个制度子文件的总数据量以及对应制度条款的总数量分别校验通过，且提取到的关键字段与预设的关键字段匹配成功时，根据所述制度描述信息生成信息节点，将相应所述制度子文件关联至所述信息节点，将所述信息节点添加至相同适用对象标识对应的关联信息树。
  14. 根据权利要求9所述的计算机设备，其特征在于，所述关联信息树包括多个信息节点；每个信息节点关联有对应的信息摘要；所述制度查询请求携带了员工标识和查询条 件；所述处理器执行所述计算机可读指令时还执行以下步骤：
    获取所述员工标识对应的适用对象标识；
    根据所述适用对象标识，在对应的关联信息树中查找是否存在满足所述查询条件的信息节点；
    若存在，获取满足所述查询条件的信息节点关联的信息摘要，将所述信息摘要返回至所述第二终端；及
    当接收到所述第二终端发送的制度阅读请求时，所述制度阅读请求携带了信息节点标识；获取所述信息节点标识对应的制度文件，将所述制度文件返回至所述第二终端。
  15. 一个或多个存储有计算机可读指令的非易失性计算机可读存储介质，所述计算机可读指令被一个或多个处理器执行时，使得所述一个或多个处理器执行以下步骤：
    监测第一终端发布的制度信息；所述制度信息包括制度描述信息及关联的制度文件；所述制度文件包括多个制度条款以及分别对应的适用对象标识；
    对所述制度信息进行分类，根据分类结果将所述制度信息添加至预设的一个或多个目标信息树；
    获取所述目标信息树对应的多个关联信息树；每个所述关联信息树具有对应的适用对象标识；
    对所述制度文件进行拆分，利用每个所述适用对象标识对应的制度条款生成相应适用对象标识对应的制度子文件；
    根据所述制度描述信息生成信息节点，将相应所述制度子文件关联至所述信息节点，将所述信息节点添加至相同适用对象标识对应的关联信息树；及
    当接收到第二终端发送的制度查询请求时，基于所述关联信息树响应所述制度查询请求。
  16. 根据权利要求15所述的存储介质，其特征在于，所述对所述制度信息进行分类，所述计算机可读指令被所述处理器执行时还执行以下步骤：
    对所述制度信息进行分词得到对应的原始词语集合；所述原始词语集合包括多个原始词语；
    对各个原始词语进行同义扩展，生成每个原始词语对应的扩展词语集合；
    根据各个扩展词语集合形成所述制度信息对应的扩展制度信息集合；
    将所述扩展制度信息集合输入预设的制度管理模型，得到所述制度信息对应的目标类别；及
    获取多个所述目标信息树分别对应的类别标注，筛选包含与所述目标类别对应类别标注的目标信息树，将所述制度信息添加至筛选得到的目标信息树。
  17. 根据权利要求15所述的存储介质，其特征在于，所述计算机可读指令被所述处理器执行时还执行以下步骤：
    识别所述制度文件的文件类型；
    当所述制度文件的文件类型为第一类型时，调用预设的拆分接口对所述制度文件进行拆分；及
    当所述制度文件的文件类型为第二类型时，获取预设的多个适用对象标识分别对应的拆分表达式，对所述制度文件进行遍历，将每个拆分表达式与所述制度文件进行匹配，根据匹配结果对所述制度文件进行拆分。
  18. 根据权利要求15所述的存储介质，其特征在于，所述计算机可读指令被所述处理器执行时还执行以下步骤：
    计算所述制度文件的数据量，检测所述数据量是否超过阈值；
    当所述数据量超过阈值时，获取预设的目标数据量，根据所述目标数据量确定所述制度文件的拆分位置；
    检测所述拆分位置是否位于相邻分隔符之间；
    当所述拆分位置位于一个分隔符处时，在所述拆分位置将所述制度文件拆分为多个中间文件；
    当所述拆分位置位于相邻分隔符之间时，在所述相邻分隔符中任意一个分隔符处将所述制度文件拆分为多个中间文件；及
    按照预设的拆分规则，对多个所述中间文件进行拆分。
  19. 根据权利要求15所述的存储介质，其特征在于，所述计算机可读指令被所述处理器执行时还执行以下步骤：对拆分得到的多个制度子文件的总数据量进行校验；
    对拆分得到的多个制度子文件分别对应制度条款的总数量进行校验；
    获取预设的多个关键字段，在拆分得到的多个制度子文件提取关键字段，对提取到的关键字段与预设的关键字段进行匹配；及
    当多个制度子文件的总数据量以及对应制度条款的总数量分别校验通过，且提取到的关键字段与预设的关键字段匹配成功时，根据所述制度描述信息生成信息节点，将相应所述制度子文件关联至所述信息节点，将所述信息节点添加至相同适用对象标识对应的关联信息树。
  20. 根据权利要求15所述的存储介质，其特征在于，所述关联信息树包括多个信息节点；每个信息节点关联有对应的信息摘要；所述制度查询请求携带了员工标识和查询条件；所述计算机可读指令被所述处理器执行时还执行以下步骤：
    获取所述员工标识对应的适用对象标识；
    根据所述适用对象标识，在对应的关联信息树中查找是否存在满足所述查询条件的信息节点；
    若存在，获取满足所述查询条件的信息节点关联的信息摘要，将所述信息摘要返回至所述第二终端；及
    当接收到所述第二终端发送的制度阅读请求时，所述制度阅读请求携带了信息节点标识；获取所述信息节点标识对应的制度文件，将所述制度文件返回至所述第二终端。
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