CN104361111B - A kind of archives are compiled and grind method automatically - Google Patents

Abstract

The invention belongs to the classification of documents and retrieval technique field, being related to a kind of archives based on B/S framework MIS of Department Files, volume grinds method automatically；It is first according to the unified form difference typing archive information that archives list, list in volume and expert's registration card interface are provided, archive information is classified and collected automatically using automatic hierarchical classification algorithm with management module by archives typing again, and is stored in respectively in corresponding database；Then the compiling and researching condition and the information of deposit that Compiling of Files module is inputted according to user are retrieved to associated databases, inquired about and collected generation Compiling of Files result, finally Compiling of Files result is included on screen, or preserve Compiling of Files result to form paper document after export printing in the form of Word document or Excel forms, realize that the automatic volume of archives is ground；Its design principle science is reliable, and volume grinds that labor intensity is small, and operating efficiency is high, and information careless omission is few, it is ensured that compile the quality and value ground, and volume grinds efficiency high, and volume grinds environment-friendly.

Description

A Method for Automatic Compilation and Research of Archives

Technical field:

The invention belongs to the technical field of file classification and retrieval, and relates to a method for automatically compiling and researching files based on a B/S architecture file management information system, which provides automatic file compiling and research technology completed by computer software for file compilation and archive abstract compilation.

Background technique:

The archives management information system based on B/S architecture is an extension of the functions of traditional archives in the information society. It not only has the basic attributes and functions of traditional archives, but also can adapt to the development needs of the information age. , through the Internet and the establishment of a digital archive storage database to realize the collection, storage, management and utilization of various departments and various archive information, and to provide information services for the utilization of archive resources. Archives compilation and research for archives utilization is a special work in the form of compilation of archives reference materials based on archives stored in archives/rooms according to the actual needs of archives utilization. The essence of archives compilation and research work is to study and process the content of archives and documents, and compile them into volumes to make them clear at a glance, so as to improve the comprehensive management level and work efficiency of administrative departments and units, and enhance the service value of archives resources to society. At present, archives compilation and research work is mainly based on manual compilation and research, which is slow, inefficient and of poor quality.

According to the processing level of the archives, the traditional manual archives compilation and research methods are divided into two types. The first archives compilation and research processing method is to extract, shrink and edit the original archives to form summary materials; the processing archives compilation and research The forms of results include: collection of publications, collection of special topics, collection of abstracts of special archives, including collection of abstracts of experts and scholars in a certain field, collection of abstracts of academic papers, collection of abstracts of scientific and technological achievements, etc.; On the basis of analyzing, researching and summarizing the relevant content of the original archives, new materials are compiled; the forms of the processed archives compilation and research results include: yearbooks, organizational history, historical records, and comprehensive technical and economic research reports. In the second form of processing results of archives compilation and research, since people’s new understanding of things, new viewpoints, new conclusions and new suggestions are included, these are newly added information from the perspective of information volume. , so this kind of archives compilation, research and processing is generally completed by experts or scholars in related fields. However, the information contained in the compilation and research results of the first type of processing archives is the existing information in the archives. It will not increase the amount of information, and will not produce new content. The content should be comprehensive and detailed, and it is better not to miss more, and with the continuation of time, the accumulation of original archival materials that need to be edited and researched will reach a massive amount of data, and the slightest carelessness in manual editing and research of massive data will cause information omission or If mistakes are made, the quality and value of archives compilation and research results cannot be guaranteed. Its labor intensity is high, work efficiency is low, and the accuracy of compilation and research is poor, which restricts the full development and utilization of archives resources on the scale of massive data and higher technical levels. very high.

Invention content:

The purpose of the present invention is to overcome the shortcomings of the prior art, seek to design and provide an archives automatic compilation and research method based on the archives management information system, and use computer automatic classification and retrieval technology to automatically generate the form of archives compilation and research results to improve the efficiency of archives compilation and research and accuracy, reducing information omissions during manual editing.

In order to achieve the above object, the present invention realizes the automatic compilation and research of archives jointly by the archives entry and management module and the archives compilation and research module in the archives management information system based on B/S architecture, and its specific steps are:

(1) Enter the file information first, and enter the file title, file category, file number, year and various basic information of the expert according to the unified format given by the file directory, file directory and expert registration card interface displayed by the system;

(2) The file information entered in step (1) is automatically classified and summarized by the file entry and management module using the automatic hierarchical classification algorithm proposed by the present invention, and stored in the corresponding file directory, volume directory and expert basics respectively. In the information registration directory database and expert database;

(3) The archives compilation and research module searches, inquires, and summarizes the archives compilation and research results for the corresponding archives catalog, volume catalog, expert basic information registration catalog database and expert database according to the compilation and research conditions input by the user and the stored information ;

(4) Display the archive editing and research results on the screen, or export the archive editing and research results in the form of Word documents or Excel reports, print out and save them as paper documents, so as to realize automatic archive editing and research.

The automatic hierarchical classification algorithm proposed by the present invention is an improvement to the existing conventional Naive Bayesian algorithm. The Naive Bayesian algorithm refers to the classification of texts considering all the features of the text during classification, and the prediction samples are divided according to the prediction results during classification. to the category library with the highest probability for a particular document category.

The specific classification model of the Naive Bayesian algorithm involved in the present invention is as follows: Given an archive text X of unknown category, there are m categories, which are denoted as C1, C2, ..., Cm, according to the Naive Bayesian classification law, The category P(Ci|X) with the highest posterior probability under condition X is calculated as follows:

In the P(Ci|X) calculation formula, P(X) is a constant, so it is only necessary to maximize the molecule P(X|Ci)P(Ci); P(Ci) is the category distribution probability in the training set, The calculation formula is: In the formula, the numerator is the number of texts contained in the category |Ci| plus 1, and the denominator is the sum of m categories and |D| is the total number of texts contained in the training set; in order to simplify the calculation process of P(X|Ci), it is assumed that the text Multiple attributes are unrelated to each other. Therefore, calculating P(X|Ci) is to calculate the probability of feature attributes appearing on category Ci, and use two calculation models estimated by Laplace to calculate the value of P(X|Ci) :

(1) Multivariate model, whether the statistical feature attribute has appeared in the text, if it appears, it will be recorded as 1, otherwise it will be recorded as 0. The calculation formula is:

Among them, |V| represents the total number of feature values, B _xt is the mark that w _t appears in the text X, if w _t appears, B _xt is recorded as 1, otherwise it is recorded as 0, and w _t represents the tth feature, that is The tth component of the vector, therefore, the calculation formula of P(w _t |C _i ) in the formula is as follows:

(2) The multinomial model (Multinomial Model) counts the occurrence times of feature attributes in the text, and the calculation formula is:

Among them, N _xt represents the number of times feature t appears in text X; the calculation formula of P(w _t |C _i ) is as follows:

In the calculation formula of P(w _t |C _i ), N _jt is the number of times feature t has appeared in text d _j , |D| is the total number of training texts, |V| is the total number of features, and N _js is the number of features s in The number of occurrences in the text d _j ; the essence of this classification method is to count all the feature values in the text object and map to the probability of each existing category.

The present invention improves the naive Bayesian algorithm in the file entry management module, realizes the automatic hierarchical classification algorithm based on the title of the file catalog and the rough classification of keywords, directly extracts the keyword set from the file catalog and the title of the catalog in the file, and constructs Hierarchical classification model achieves classification effect with low feature dimension after appropriate dimension reduction, replaces Chinese word segmentation of traditional text classification algorithm, effectively improves classification accuracy and operation efficiency of archives; The implementation process of the automatic hierarchical classification algorithm for coarse classification is as follows:

(1) First enter the file information locally or online, and enter the file title, category, file number, year, and various basic information of the expert according to the unified format given by the file catalog displayed on the system, the catalog in the file, and the expert registration card interface. information;

(2) The system automatically extracts the text data feature parameter set of the keyword in the file title and the file text and stores it in the corresponding database;

(3) Dimensionality reduction is performed when the extracted text data feature parameter set exceeds the threshold, too many features will often lead to the disaster of dimensionality and reduce the efficiency of classification;

(4) perform rough classification of naive Bayesian classification algorithm according to extracted text data feature parameters or keywords;

(5) On the rough classification result of step (4), carry out feature extraction for each subclass respectively;

(6) Carrying out the naive Bayesian classification algorithm for the text data feature parameters of each subclass to automatically complete the subdivision;

(7) Output the classification result and save it in the corresponding database.

The data information processed by the file compilation and research module involved in the present invention is based on the basic compilation and research of the case file directory, file directory and expert registration card directory of the established file information entry and management module, and is created in the file information entry and management module. It includes 6 databases including case file directory database, in-volume directory database, file classification database, expert basic information registration database, expert paper details and expert project details; the archives compilation and research module consists of archives classification compilation and research sub-module, file size index compilation and research sub-module Module and expert information compilation and research are composed of 3 sub-modules. In the archives compilation and research module, the basic database of archives compilation and research associated with the above-mentioned 6 databases is created; the archives classification compilation and research sub-module automatically realizes the archives classification according to the compilation and research conditions input by the user Compiling and researching, file title editing and archiving time editing and researching and displaying the editing and researching results in the form of a list; the file font size index editing and researching sub-module inputs the combined editing and researching including official font size, year, file number and storage period according to the user's editing and researching needs Conditions, click to filter the data according to the conditions, so as to automatically generate and display the file size index compilation and research list; the expert information compilation and research sub-module compiles and researches the expert registration card information in the file management, according to the input expert name and research direction Carry out fuzzy query with the compilation and research conditions of the results, realize the compilation and research of expert classification, the compilation and research of expert research direction, the compilation and research of expert paper information and the compilation and research of expert project information, and summarize the compilation and research results and display them on the screen; among them, expert classification Compilation and research means that the compilation and research automatically generates a list of compilation and research results of all expert information in a certain research field, and the compilation and research results can be exported to Excel or Word documents for saving or printing.

The archives compilation and research of the archives classification compilation and research submodule involved in the present invention comprises the following steps:

(1) Create a taxonomy compilation and research view. The view takes the in-volume catalog or case file catalog as the main table, and associates the classification information table to obtain the classification information name. The in-volume catalog and case file catalog information are stored in different data tables. It is necessary to summarize two parts of information for unified query and retrieval;

(2) Data access layer code. The data access layer retrieves the file information that needs to be edited and researched from the view given in step (1). The execution function takes the query condition as a parameter to retrieve the file information that meets the conditions, and extracts the file by category. Classification, file title, filing time, number of copies, page number and file number information, and sort the editing and research results according to classification name, classification title and filing time;

(3) Realization of the application layer of classification compilation and research. Firstly, the classification compilation and research conditions are set, and classification compilation and research are performed according to the fuzzy retrieval of file classification names, fuzzy retrieval of file titles and archive time retrieval;

(4) Export the compilation and research results to EXCEL or Word documents, which is convenient for users to save, view, print and bind them into a book.

The file compilation and research of the submodule of the file size index compilation and research submodule involved in the present invention comprises the following steps:

(1) Create a document font size index view, which takes the catalog in the volume as the main table and associates the classification information table to extract the official font size, year, document serial number, file number, page number, page number and storage period information;

(2) Data access layer code, the data access layer is to extract information from the document font size index view, and sort according to the official font size, file serial number and year, the function takes query conditions as parameters, and is dynamically constructed by the application layer;

(3) The main code of the application layer, the system sets the compilation and research conditions, including the official font size, year, file number and storage period, and enters the combination according to the compilation and research needs, and filters the data according to the conditions after clicking the query;

(4) Export the compilation and research results to EXCEL or Word documents, which is convenient for users to save, view, print and bind into a book.

The file compilation and research of the expert information compilation and research submodule involved in the present invention comprises the following steps:

(1) Create an expert information compilation and research view. The view takes the expert basic information registration form as the main table, associate expert paper list to extract paper information, associate expert project list to extract project and award-winning information, expert basic information registration and expert achievement information storage In different data tables, it is necessary to summarize the information of each part for unified query when conducting information compilation and research;

(2) Data access layer code. The data access layer obtains the expert information that needs to be edited and researched from the view in step (1). The function uses the query condition as a parameter to query the qualified expert file information, and compiles and researches to extract the expert name, Expert category, research direction, paper information and project information;

(3) Implementation of expert information compilation and research application layer, setting compilation and research conditions, fuzzy retrieval of expert names, fuzzy retrieval of expert research directions, fuzzy retrieval of paper titles and paper overviews, paper publication time retrieval, fuzzy retrieval of project names, and fuzzy retrieval of project overviews and project start and end time retrieval and project award status retrieval; expert information compilation and research can realize expert classification compilation and research, expert research direction compilation and research, expert paper information compilation and expert project information compilation and research according to the input compilation and research conditions;

(4) Export the compilation and research results to an Excel or Word document, which is convenient for users to save, view, print and bind into a book.

The execution flow of the file management information system involved in the present invention is:

(1) Open a browser on the client, enter the website address of the system in the address bar to send a service request to the Web server, when the login page of the system is displayed on the desktop, after filling in the user name, password and verification code on the login page Send it to the Web server, and the Web server uses the HTTP protocol to send the home page of the file management information system to the client after verifying the user's identity, and the client browser receives the transmitted home page file and displays it on the screen;

(2) The entry of the basic information of the archives is to enter and add the category, file number, file title, year, and various basic information of the expert according to the uniform format given by the system on the homepage of the file catalog, the catalog in the file, and the expert registration card. Information; the system executes the corresponding extended application program in the business logic layer of the Web server to connect with the database server, and before storing the above-mentioned various basic information entered or added by the user in the corresponding database connected to the Web server through SQL, The system automatically classifies and archives the catalog and file titles, and then mounts the original text; the original text can be an electronic scan or an electronic original;

(3) When a certain type of archive information needs to be compiled and researched, the system will enter the corresponding file compilation and research interface according to the entry of the file compilation and research selected by the user, and input the compilation and research conditions in this interface; for example: carry out a certain When compiling and researching expert information in the field, enter the name or research direction of the research field, including the compilation and research parameters such as papers and project status, and click the query button to perform information retrieval and query on the corresponding database;

(4) According to the editing and research parameters that have been input in (3), after executing the link with the Web server, a data processing request is made to the corresponding database server through the SQL statement, that is, the basic database of archives editing and research and other associated database information are processed. Retrieval and query operations, counting, analyzing and summarizing the retrieved data items that meet the compilation and research conditions, and generating archives compilation and research results;

(5) The database server submits the generated file editing results to the Web server, and then the Web server transmits them to the client and displays them on the screen;

(6) Export the editing and research results to Word documents or Excel reports for saving or printing out.

Compared with the prior art, the present invention has scientific and reliable design principles, low editing and researching labor intensity, high work efficiency, less information omissions, guaranteed editing and researching quality and value, high editing and researching efficiency, and friendly editing and researching environment.

Description of drawings:

Fig. 1 is a schematic block diagram of the principle of hardware composition and structure of the device of the present invention.

Fig. 2 is a schematic block diagram of the logical functional structure and principles of the archives compilation and research module and the archives management module involved in the present invention.

Fig. 3 is a flow chart of the automatic compilation and research execution of the archives management information system of the present invention.

Fig. 4 is a flow chart of the implementation of the hierarchical classification algorithm of rough classification in the present invention.

detailed description:

Further description will be made below through embodiments and in conjunction with accompanying drawings.

Example 1:

In this embodiment, the classification algorithm proposed by the present invention is tested and evaluated. First, among the collected 1000 archive texts, 40 texts are randomly selected from each category to classify and train the method involved in the present invention, and the remaining 960 archive texts will be The test and evaluation of the classification results of the method involved in the present invention as the text set to be classified; wherein, there are 222 document files, 216 scientific and technological files, 162 accounting files, 95 personnel files, and 43 audio-visual files. There are 86 comprehensive photos, 35 physical archives, 40 archived documents, and 61 periodical archives. The precision, recall and F1 (harmonic average of recall and precision) are used respectively. Number) three indexes of test value evaluate classification result, and test evaluation result is as shown in table 1;

Table 1 Classification result test evaluation form

category recall Precision F1 test value Documents and archives 95.08% 91.80% 93.44% Science and Technology Archives 85.34% 87.93% 86.64% Accounting files 90.32% 93.55% 91.94% Personnel files 92.00% 94.67% 93.33% Audio and video files 93.02% 95.35% 94.19% Comprehensive photo category 97.67% 94.19% 95.93% Physical archives 91.43% 94.29% 92.86% Archive class 87.50% 85.00% 86.25% Periodical Archives 90.16% 83.61% 86.89%

The above table shows that the recall rate, precision rate and F1 test value of the classification results of this embodiment can all achieve good results, and the feature dimensions generated according to the document title and keywords in the rough classification process are all below 50 , improving the operating efficiency of the system.

The operation environment requirement of this embodiment: configuration dual-core microprocessor or higher, the networked PC and compatible machine operation of more than 2G of internal memory; Server operating system is Windows XP and above version; System requisite software is Framework 3.5, SQLServer2005; Development software It is Microsoft Visual Studio 2008; B\S three-tier architecture is adopted, and codes of presentation layer, business logic layer and data layer are respectively implemented through asp.net.

This embodiment needs to install and set the Microsoft SQL Server database server, import system database after adding user name and setting password for the server; Then publish website (i.e. the file management information system of B/S structure); After the website publishes successfully, open any one In the browser on the networked PC, enter the website address in the address bar to enter the login page, enter the account number, password and verification code, and then click Login to enter the main interface of the system management; in the tree menu bar on the left of the system administrator, click [File Compilation], the screen displays multiple items that need to be compiled, including category compilation, file size index compilation and expert information compilation, etc.; select and click [Category Compilation] to enter the category compilation interface, Input various compilation and research conditions in this interface, including file classification name, file title and filing time (greater than a certain time, less than a certain time or within a certain period of time), etc., the system will automatically generate classification and compilation according to the input compilation and research conditions. The results of the research are displayed on the screen in the form of a list, and can be exported to a Word document or Excel to save or print; select and click [File Size Index Compilation Research] to enter the file size index compilation interface, in this interface According to the needs of editing and researching, enter the content of editing and researching, such as official font size, year, file number and storage period, etc. After clicking query, the system will search and filter data according to the input editing and researching conditions to automatically generate and display the editing and researching results, or The compilation and research results are exported to Word document or Excel to save or print; select and click [Expert Information Compilation and Research] to enter the expert information compilation and research interface, enter the compilation and research conditions in this interface, and the system will perform fuzzy retrieval of expert names and research directions Fuzzy search, paper title, paper overview fuzzy search, paper publication time search, project name fuzzy search, project overview fuzzy search, project start and end time search and project award (whether award, award name) search; the system according to the input expert name, Perform fuzzy query on research directions and research results compilation conditions, realize expert classification compilation, expert research direction compilation and research, expert paper information compilation and expert project information compilation and research; and compile and research results are summarized and displayed on the screen, or Export the results to Excel or Word documents for saving or printing out.

The file classification management in this embodiment is set according to the actual needs of file compilation and research. The actual work needs to compile and research a file classification of "administrative approval". The system administrator only needs to add an "administrative approval" file classification to the file classification table. " classification data, you can maintain the corresponding classification of file information in the file directory and case file directory, and perform compilation and research operations on the classification; the system summarizes all kinds of file information through the entry of various file information , quickly retrieve the file according to the relevant information such as classification or file title, and quickly obtain the file from the corresponding physical storage location by checking the inventory location of the file, shortening the time for searching for the file and improving work efficiency.

Example 2:

The device for implementing the method for automatic file compilation and research involved in this embodiment has a main structure consisting of a client browser 1, a file information input and management module 2, a file compilation and research module 3, and 7 databases linked to it. Wherein, the file information entry and management module 2 includes three functional module units: the catalog management sub-module 4 in the volume, the file catalog management sub-module 5 and the expert registration card information sub-module 6; Module 7, file size index compilation and research sub-module 8 and expert information compilation and research sub-module 9 are composed of three functional module units connected by electronic information; file entry and management module 2 enters file information and maintains the file information. File directory and expert registration card file information are categorized and summarized; file editing and research module 3 automatically realizes file classification editing, file title editing and filing time editing and research according to user input editing conditions, and displays them in a list form Compilation and research results; file size index compilation and research sub-module 8 inputs the compilation and research conditions composed of official font size, year, file number and storage period according to the user's editing and research requirements, and after clicking query, the data can be filtered according to the compilation and research conditions, so as to automatically generate And display the file size index editing and research list; the expert information editing and research sub-module 9 compiles and researches the expert registration card information in the file management, and performs fuzzy query according to the input expert name, research direction and the compilation and research conditions of the results, so as to realize the expert Compilation and research of classification, compilation and research of expert research direction, compilation and research of expert paper information and compilation and research of expert project information, and the compilation and research results are summarized and displayed on the screen; among them, the compilation and research of expert classification is automatically generated by the compilation and research of a certain research field All expert information compilation and research results lists, and the compilation and research results can be exported to Excel or Word documents for saving or printing. The 7 databases linked with the archives information entry and management module 2 and the archives compilation and research module 3 are the file directory database, the file directory database, the file classification database, the expert basic information registration database, the expert paper detailed database, and the expert project Detailed database and basic database for file compilation and research; client browser 1 is any browser software running on any computer and terminal equipment connected to the Internet.

Claims (6)

1. a kind of archives are compiled and grind method automatically, it is characterised in that by archives in the MIS of Department Files based on B/S frameworks Typing is combined with management module and Compiling of Files module realizes that archives automatically grind by volume, and it is concretely comprised the following steps：

(1) archive information typing is first carried out, the archives list shown according to system, list in volume and expert's registration card interface are provided Unified form difference typing archives title, classification, shelves number, annual and all kinds of essential informations of expert's situation belonging to archives；

(2) step (1) is recorded using automatic hierarchical classification algorithm proposed by the present invention is automatic by archives typing and management module again The archive information entered is classified and collected automatically, and it is basic to be stored in corresponding archives catalog, list in volume and expert respectively Information is registered in catalog data base and expert database；

(3) compiling and researching condition and the information of deposit inputted again by Compiling of Files module according to user is in corresponding archives catalog, volume Catalogue registers catalog data base with expert's essential information and expert database is retrieved, inquired about and collected and generates Compiling of Files knot Really；

(4) Compiling of Files result is included on screen, or by Compiling of Files result in the form of Word document or Excel forms Printout forms paper document preservation after export, realizes that the automatic volume of archives is ground；

The implementation process of described automatic hierarchical classification algorithm is as follows：

(1) first in local or online input system archive information, the archives list shown according to system, list in volume and expert's registration card All kinds of essential informations of unified form difference typing archives title, affiliated classification, shelves number, year and expert that interface is provided；

(2) system automatically extracts the text data characteristic parameter collection of archives title and the keyword in archives text and is stored in phase In the database answered；

(3) dimensionality reduction is carried out when exceeding threshold value to the text data characteristic parameter collection of extraction, excessive feature frequently can lead to dimension Disaster, making the efficiency of classification reduces；

(4) rough sort of Naive Bayes Classification Algorithm is performed according to the text data characteristic parameter or keyword of extraction；

(5) feature extraction is carried out for each subclass respectively again in step (4) rough sort result；

(6) the text data characteristic parameter execution Naive Bayes Classification Algorithm again for each subclass is automatically performed disaggregated classification；

(7) output category result and it is saved in corresponding database；

The NB Algorithm refers to consider that all features of text classify to text during classification, will predicted during classification Sample is divided into according to predicting the outcome in particular document class probability highest class library, and its specific disaggregated model is as follows：It is given The archives text X of one unknown classification, provided with m classification, is designated as C1, C2 ... ..., Cm, according to Naive Bayes Classification law, The calculation formula of the classification P (Ci | X) with highest posterior probability is as follows under condition X：

<mrow> <mi>P</mi> <mrow> <mo>(</mo> <mi>C</mi> <mi>i</mi> <mo>|</mo> <mi>X</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <mi>P</mi> <mrow> <mo>(</mo> <mi>X</mi> <mo>|</mo> <mi>C</mi> <mi>i</mi> <mo>)</mo> </mrow> <mi>P</mi> <mrow> <mo>(</mo> <mi>C</mi> <mi>i</mi> <mo>)</mo> </mrow> </mrow> <mrow> <mi>P</mi> <mrow> <mo>(</mo> <mi>X</mi> <mo>)</mo> </mrow> </mrow> </mfrac> </mrow>

In P (Ci | X) calculation formula, P (X) is constant, therefore only needs to maximize molecule P (X | Ci) P (Ci)；P (Ci) it is category distribution probability in training set, calculation formula is：Molecule is classification in formula | Ci | include Textual data add 1, denominator is m classification and | D | be the text sum sum included in training set；In order to simplify P (X | Ci) Calculating process, it is assumed that multiple attributes of text are independent of each other, therefore, and it is exactly to calculate that characteristic attribute exists to calculate P (X | Ci) The probability occurred on classification Ci, P (X | Ci) value is calculated using 2 kinds of computation models of Laplace estimation：

(1) whether multivariate model, statistical nature attribute occurred in the text, if occurring being designated as 1, was otherwise designated as 0, calculated public Formula is：

<mrow> <mi>P</mi> <mrow> <mo>(</mo> <mi>X</mi> <mo>|</mo> <msub> <mi>C</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mo>&amp;Pi;</mo> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mo>|</mo> <mi>v</mi> <mo>|</mo> </mrow> </munderover> <mrow> <mo>(</mo> <msub> <mi>B</mi> <mrow> <mi>x</mi> <mi>t</mi> </mrow> </msub> <mi>P</mi> <mo>(</mo> <mrow> <msub> <mi>w</mi> <mi>t</mi> </msub> <mo>|</mo> <msub> <mi>C</mi> <mi>i</mi> </msub> </mrow> <mo>)</mo> <mo>+</mo> <mo>(</mo> <mrow> <mn>1</mn> <mo>-</mo> <msub> <mi>B</mi> <mrow> <mi>x</mi> <mi>t</mi> </mrow> </msub> </mrow> <mo>)</mo> <mo>(</mo> <mrow> <mn>1</mn> <mo>-</mo> <mi>P</mi> <mrow> <mo>(</mo> <mrow> <msub> <mi>w</mi> <mi>t</mi> </msub> <mo>|</mo> <msub> <mi>C</mi> <mi>i</mi> </msub> </mrow> <mo>)</mo> </mrow> </mrow> <mo>)</mo> <mo>)</mo> </mrow> </mrow>

Wherein, | V | the total quantity of representation eigenvalue, B_xtIt is w_tThe mark occurred in text X, if w_tThere is then B_xtIt is designated as 1, it is no Then it is designated as 0, w_tRepresent t-th of feature, i.e. t-th vectorial of component, therefore, the P (w in formula_t|C_i) calculation formula is as follows：

(2) multinomial model (Multinomial Model) the then occurrence number of statistical nature attribute in the text, calculation formula For：

<mrow> <mi>P</mi> <mrow> <mo>(</mo> <mi>X</mi> <mo>|</mo> <msub> <mi>C</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mo>&amp;Pi;</mo> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mo>|</mo> <mi>v</mi> <mo>|</mo> </mrow> </munderover> <mfrac> <mrow> <mi>P</mi> <msup> <mrow> <mo>(</mo> <msub> <mi>w</mi> <mi>t</mi> </msub> <mo>|</mo> <msub> <mi>C</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <msub> <mi>N</mi> <mrow> <mi>x</mi> <mi>t</mi> </mrow> </msub> </msup> </mrow> <mrow> <msub> <mi>N</mi> <mrow> <mi>x</mi> <mi>t</mi> </mrow> </msub> <mo>!</mo> </mrow> </mfrac> </mrow>

Wherein, N_xtRepresent the number of times that feature t occurs in text X；P(w_t|C_i) calculation formula it is as follows：

<mrow> <mi>P</mi> <mrow> <mo>(</mo> <msub> <mi>w</mi> <mi>t</mi> </msub> <mo>|</mo> <msub> <mi>C</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <mn>1</mn> <mo>+</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mo>|</mo> <mi>D</mi> <mo>|</mo> </mrow> </munderover> <msub> <mi>N</mi> <mrow> <mi>j</mi> <mi>t</mi> </mrow> </msub> <mi>P</mi> <mrow> <mo>(</mo> <msub> <mi>C</mi> <mi>i</mi> </msub> <mo>|</mo> <msub> <mi>d</mi> <mi>j</mi> </msub> <mo>)</mo> </mrow> </mrow> <mrow> <mo>|</mo> <mi>V</mi> <mo>|</mo> <mo>+</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>s</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mo>|</mo> <mi>V</mi> <mo>|</mo> </mrow> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mo>|</mo> <mi>D</mi> <mo>|</mo> </mrow> </munderover> <msub> <mi>N</mi> <mrow> <mi>j</mi> <mi>s</mi> </mrow> </msub> <mi>P</mi> <mrow> <mo>(</mo> <msub> <mi>C</mi> <mi>i</mi> </msub> <mo>|</mo> <msub> <mi>d</mi> <mi>j</mi> </msub> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>;</mo> </mrow>

In P (w_t|C_i) calculation formula in, N_jtT is characterized in text d_jIn the number of times that once occurred, | D | for training text sum, | V | it is characterized sum, N_jsIt is feature s in text d_jIn occurrence number；The essence of the sorting technique is to the institute in text object There is the probability that characteristic value is counted and is mapped in each already present classification.

2. archives according to claim 1 are compiled and grind method automatically, it is characterised in that the Compiling of Files resume module being related to Data message is for the archive information typing set up and archives list, list in volume and the expert's registration card mesh of management module The basic volume that record is carried out is ground, and being created in archive information typing and management module includes archives list database, list in volume number According to storehouse, classification of documents database, expert's essential information registered database, civilized thin and 6 data of expert's Item Detail of Specialists Storehouse；Compiling of Files module is compiled to grind submodule, file font size index volume and grind submodule and expert info and compile by the classification of documents grinds 3 sons Module is constituted, and the Compiling of Files basic database associated with above-mentioned 6 databases is created in Compiling of Files module；Archives point Class is compiled and grinds the compiling and researching condition that submodule is inputted according to user, realizes that classification of documents volume is ground, archives title is compiled when grinding and filing automatically Between compile grind and with tabular form show volume grind result；File font size index volume grinds submodule and grinds demand input bag according to the volume of user Official document font size, year, shelves number and the combination of retention period compiling and researching condition are included, according to condition filter data after click inquiry, so as to Automatically generate and show that file font size index volume grinds list；Expert info is compiled and grinds submodule to expert's registration card in file administration Information carries out volume and grinds statistics, and fuzzy query is carried out according to the compiling and researching condition of expert's name of input, research direction and achievement, realizes Expert classification volume is ground, expert's research direction volume is ground, expert's paper information is compiled to grind to compile with expert's project information and ground, and volume is ground into result It is shown in after being collected on screen；Wherein expert classification, which is compiled to grind to refer to compile, grinds all experts for automatically generating a certain research field Information is compiled and grinds the results list, and its volume, which grinds result, can export to preservation or printout in Excel or Word document.

3. archives according to claim 1 are compiled and grind method automatically, it is characterised in that the classification of documents being related to is compiled and grinds submodule Compiling of Files comprise the following steps：

(1) create classification volume and grind view, view is using list in volume or archives list as main table, and associative classification information table obtains classification Name of the information, list in volume is stored in from archives list information in different tables of data, is carrying out needing to collect when classification volume is ground Two parts information, carries out unified query and retrieves；

(2) data access layer identification code, retrieval needs to compile the archives letter ground during data access layer is the view provided from step (1) Breath, performs function using querying condition as parameter, retrieves qualified archive information, and classification volume grinds the extraction classification of documents, archives Title, time of filing, number, number of pages and shelves information, and result is ground according to specific name, distribution caption and time of filing to volume It is ranked up；

(3) classification volume grinds application layer realization, classification compiling and researching condition is set first, by the fuzzy search of classification of documents title, archives mark Topic fuzzy search and time of filing searching classification, which are compiled, to be ground；

(4) volume is ground into result to export in EXCEL or Word document, facilitates user's preservation to check and print and bind into book form.

4. archives according to claim 1 are compiled and grind method automatically, it is characterised in that the file font size index volume being related to grinds son The Compiling of Files of module comprises the following steps：

(1) establishment file font size indexed view, file font size indexed view is carried using list in volume as main table associative classification information table Take official document font size, year, file sequence number, shelves number, page number, number of pages and retention period information；

(2) data access layer identification code, data access layer is that information is extracted from file font size indexed view, and according to official document word Number, file sequence number and year sequence, function is using querying condition as parameter, by application layer dynamic construction；

(3) application layer main code, default compiling and researching condition, including official document font size, year, shelves number and retention period, according to Volume grinds needs and is combined input, clicks on after inquiry according to condition filter data；

(4) volume is ground into result to export in EXCEL or Word document, facilitates user's preservation to check and print and bind into book form.

5. archives according to claim 1 are compiled and grind method automatically, it is characterised in that the expert info being related to is compiled and grinds submodule Compiling of Files comprise the following steps：

(1) create expert info volume and grind view, view is using expert's essential information registration form as main table, the literary detail list of association Specialists Paper information is extracted, association expert item details extract project and winning information, the registration of expert's essential information and expert's achievement Information is stored in different tables of data, is needed to collect each several part information when row information is compiled and ground entering, is carried out unified query；

(2) data access layer identification code, data access layer is to obtain to need to compile the expert info ground from the view in step (1), Function inquires about qualified expert's archive information using querying condition as parameter, and volume is ground extraction expert name, expert's classification, ground Study carefully direction, paper information and project information；

(3) expert info, which is compiled, grinds application layer realization, sets compiling and researching condition, carries out the fuzzy search of expert's title, expert's research direction Fuzzy search, thesis topic summarizes fuzzy search with paper, and paper publishing time retrieval, project name fuzzy search, project is general State fuzzy search and the retrieval of project beginning and ending time and the prize-winning situation retrieval of project；Expert info is compiled to grind grinds bar according to the volume of input Part, realizes that expert classification volume is ground, expert's research direction volume is ground, expert's paper information is compiled to grind to compile with expert's project information and ground；

(4) volume is ground into result to export in Excel or Word document, facilitates user's preservation to check and print and bind into book form.

6. archives according to claim 1 are compiled and grind method automatically, it is characterised in that the MIS of Department Files being related to Performing flow is：

(1) a browser is opened in client, the station address of input system sends clothes to Web server in address field Business request, when showing the login page of system on desktop, is transmitted after login page fills in user name, password and identifying code To Web server, Web server passes the homepage of MIS of Department Files with http protocol after being verified to user identity Client is given, client browser receives the homepage file transmitted, and it is shown on screen；

(2) typing of archives essential information, according to system be shown in the archives list, list in volume and expert's registration card of homepage to Classification, shelves number, archives title, year and all kinds of essential informations of expert belonging to the unified form difference typing gone out and addition archives； System performs corresponding extension application in the Business Logic of Web server and is attached with database server, passes through The above-mentioned all kinds of essential informations of user's typing or addition are arrived the corresponding number being connected with Web server by SQL modes in storage Before storehouse, catalogue and archives title are carried out automatically classifying and filing by system, then original text is mounted；Original text can be electronics Scanned copy or electronic edition original paper；

(3) when needing to carry out Compiling of Files to certain class archive information, the entry for the Compiling of Files that system is selected according to user enters To corresponding Compiling of Files interface, compiling and researching condition is inputted in the interface and clicks on inquiry button, associated databases are believed Breath retrieval and inquiry；

(4) volume inputted according to step (3) grinds parameter, performed with Web server link after by SQL statement to corresponding Database server propose data processing request, i.e., to Compiling of Files basic database and be associated other database informations Retrieved and inquiry operation, and the data item that meets compiling and researching condition will be retrieved and counted, analyzed and collected, generate archives Volume is ground into fruit；

(5) result of the Compiling of Files of generation is submitted to Web server by database server, then is sent to by Web server Client is simultaneously shown on screen；

(6) volume is ground into fruit and exports to preservation or printout in Word document or Excel forms.