Intelligent manuscript publishing system and method

Abstract

The present invention provides an intelligent manuscript publishing system and method, which belongs to the field of intelligent push and solves the problem of low manuscript push efficiency. The specific steps are as follows: a manuscript acquisition module acquires manuscripts that are about to be pushed; a manuscript processing module performs a primary classification on the manuscripts that are about to be pushed and extracts priority manuscripts; a secondary classification is performed on the manuscripts that are about to be pushed to obtain manuscripts of interest; pictures are added to the manuscripts of interest based on the content in the manuscripts of interest to obtain manuscripts to be pushed; a manuscript promotion module combines the manuscripts to be pushed and calculates the additional supplementary amount of the manuscripts to be pushed; the number of views of different types of manuscripts for each user is obtained, the acceptance value of each user for different types of manuscripts is calculated, and the manuscripts to be pushed are pushed; a manuscript update module updates original manuscript data and pushes manuscripts; the present invention enables users to read high-quality manuscripts by acquiring, screening, classifying and pushing manuscripts.

Description

Technical Field

[0001] The present invention provides an intelligent manuscript publishing system and method, relating to the field of intelligent push. Background Art

[0002] The existing intelligent publishing systems and methods have the following deficiencies:

[0003] Technology dependence and talent shortage: The existing news publishing system is highly dependent on advanced technology and algorithms, which requires media companies to continuously invest resources in technology research and development and optimization. Currently, there is a large demand for professional talents in the field of artificial intelligence in China, but the supply is insufficient, which may lead to companies facing a shortage of technical talents in the short term.

[0004] Limited scope of application: The existing publishing system is mainly suitable for pushing manuscripts with weak logic and fixed formats such as finance, sports, and emergencies. It is unable to accurately push manuscripts with diverse formats and contents, especially those focusing on current affairs, science and technology, popular science, culture, education, health, and economy, resulting in a poor user experience.

[0005] Unstable content quality: Although existing publishing systems can push relevant content based on user interests and behavioral data, the quality of the pushed content varies greatly. This is mainly due to the limited algorithm and data processing capabilities of the system, which can only match based on keywords and tags, but cannot deeply understand the content of the article. As a result, the pushed content deviates from the actual needs of users, or the pushed content is too superficial and lacks depth. Summary of the Invention

[0006] In view of the shortcomings of the existing technology, the purpose of the present invention is to provide an intelligent manuscript publishing system and method, aiming to solve the problem of low manuscript push efficiency.

[0007] In order to achieve the above-mentioned purpose, the present invention is implemented through the following technical solutions: an intelligent manuscript publishing system includes:

[0008] Manuscript acquisition module: used to obtain original manuscripts and their submission information, eliminate manuscripts and submission information with bad content, and obtain manuscripts and submission information that are ready for push;

[0009] Manuscript processing module: including manuscript classification submodule and manuscript illustration submodule;

[0010] Manuscript classification submodule: This module is used to build a news recognition model and use it to classify the manuscripts to be pushed, extracting the news broadcast manuscripts as priority manuscripts.

[0011] Change the training object of the news recognition model and build an interest recognition model; perform secondary classification on the articles to be pushed based on the interest recognition model, add interest tags to non-news broadcast articles, and obtain articles of interest;

[0012] Manuscript picture submodule: uses natural language processing technology to identify the content of interesting manuscripts, and adds pictures to the interesting manuscripts based on the content, to obtain the manuscripts to be pushed;

[0013] Manuscript promotion module: including manuscript statistics submodule and manuscript push submodule;

[0014] Manuscript Statistics Submodule: used to count the historical views of different types of manuscripts to be pushed, and calculate the additional supplementary amount of manuscripts to be pushed based on the manuscripts to be pushed;

[0015] Collect the information of prospective contributors with continuous submission records as manuscript source information; obtain the number of views of news articles and articles of interest corresponding to the manuscript source information in the past three months to determine the high-quality manuscript source information;

[0016] Manuscript push submodule: used to obtain the number of all users and push priority manuscripts to all users; obtain the number of views of different types of manuscripts for each user, calculate the acceptance value of each user for different types of manuscripts, and recommend manuscripts to users based on the user's acceptance value;

[0017] Manuscript update module: used to obtain manuscripts with high-quality source information and manuscripts with non-high-quality source information in real time, update original manuscript data, and push manuscripts.

[0018] Furthermore, the workflow of the manuscript classification submodule is as follows:

[0019] Process B1: Count the number of quasi-submission information, recorded as si; count the number of quasi-pushed manuscripts corresponding to each quasi-submission information, and get do1, do2~do si ;

[0020] Among them, do1 represents the number of manuscripts that are to be pushed corresponding to the first quasi-submission information; do2 represents the number of manuscripts that are to be pushed corresponding to the second quasi-submission information; and so on. si Indicates the number of manuscripts that are about to be pushed corresponding to the si-th submission information;

[0021] Process B2: Obtain news articles as training manuscripts a1 and non-news articles as training manuscripts a2, and use the logistic regression model to build a news recognition model;

[0022] Process B3: Using the joblib library, load the quasi-push manuscripts corresponding to the 1st to sith quasi-submission information into the news recognition model, classify the quasi-push manuscripts, and extract the news broadcast manuscript corresponding to each quasi-submission information as the priority manuscript;

[0023] Count the number of priority manuscripts corresponding to each prospective submission information and get: ne1, ne2~ne si ;

[0024] Among them, ne1 represents the number of priority manuscripts corresponding to the first quasi-submission information; ne2 represents the number of priority manuscripts corresponding to the second quasi-submission information; and so on, ne si Indicates the number of priority manuscripts corresponding to the si-th prospective submission information;

[0025] Process B4: Treat the non-priority manuscripts that are about to be pushed as manuscripts to be classified, and define interest tags for the manuscripts to be classified. Interest tags include: technology, life, shopping, entertainment, and others;

[0026] Change the training object of the news recognition model and build an interest recognition model; perform secondary classification of the manuscripts to be classified according to the interest recognition model, and count the number of scientific manuscripts in the manuscripts to be classified corresponding to the 1st to sith quasi-submission information, and obtain: sc1, sc2~sc si ;

[0027] The number of life manuscripts is: li1, li2~li si ;

[0028] The number of shopping manuscripts is: sh1, sh2~sh si ;

[0029] The number of entertainment articles: re1, re2~re si ;

[0030] The number of other manuscripts is: ot1, ot2~ot si ;

[0031] Among them, sc1, li1, sh1, re1 and ot1 respectively represent the number of scientific manuscripts, life manuscripts, shopping manuscripts, entertainment manuscripts and other manuscripts corresponding to the first quasi-submission information;

[0032] sc2, li2, sh2, re2, and ot2 respectively represent the number of scientific manuscripts, life manuscripts, shopping manuscripts, entertainment manuscripts, and other manuscripts corresponding to the second quasi-submission information;

[0033] And so on, sc si 、li si ,sh si 、re siand ot si , respectively, represent the number of scientific manuscripts, life manuscripts, shopping manuscripts, entertainment manuscripts and other manuscripts corresponding to the si-th quasi-submission information;

[0034] Process B5: Send the science and technology manuscripts, life manuscripts, shopping manuscripts, entertainment manuscripts and other manuscripts corresponding to the 1st to sith quasi-submission information as interest manuscripts to the manuscript illustration submodule for illustration, obtain the manuscripts to be pushed, and enter the manuscript statistics submodule.

[0035] Furthermore, the specific process of process B2 is as follows:

[0036] Step B21: Import the pandas and scikit-learn libraries;

[0037] Create a list yes_texts to store training manuscript a1; create a list no_texts to store training manuscript a2;

[0038] Combine news and non-news article texts as feature X, that is:

[0039] X=news_texts+non_news_texts;

[0040] Define label y, mark news articles as 1 and non-news articles as 0, that is:

[0041] y=[1]*len(yes_texts)+[0]*len(no_texts);

[0042] Process B22: Use the load_data function to load the features X and labels y as the combined dataset; divide the combined dataset into a training set and a test set;

[0043] The text of the training set is denoted as X_train and the label is denoted as y_train;

[0044] The text of the test set is recorded as X_test and the label is recorded as y_test;

[0045] The number of training sets and test sets satisfies: training set > test set;

[0046] Process B23: Use the manuscripts in the training set as training text and the manuscripts in the test set as test text; initialize the TF-IDF vectorizer and perform TF-IDF feature extraction on the training set X_train;

[0047] Use the fit_transform method to learn the vocabulary in the training text, calculate the TF-IDF value of each word in each training text, and obtain the sparse matrix X_train_tfidf;

[0048] In the sparse matrix X_train_tfidf, each row represents a training text, each column represents a word, and the values ​​in the matrix correspond to the TF-IDF values ​​of the word in different training texts;

[0049] Use the transform method to extract TF-IDF features from the test set text to obtain the training feature X_test_tfidf;

[0050] Process B24: Initialize the logistic regression model model and train the model with the training set data, that is:

[0051] model.fit(X_train_tfidf,y_train);

[0052] Use the trained model to predict the test set, that is:

[0053] y_pred=model.predict(X_test_tfidf);

[0054] Use the accuracy_score() method to calculate the accuracy of the model on the test set; and use the classification_report() method to calculate the precision until the accuracy and precision between any article in the training set with label y_train = 1 and any article in the test set with label y_test = 1 are close to 100%; the accuracy and precision between the articles with label y_train = 1 and the articles with label y_test = 0, the articles with label y_train = 0 and the articles with label y_test = 1, and the articles with label y_train = 0 and the articles with label y_test = 0 are all 0%, completing the training of the news recognition model;

[0055] Load the joblib library to save the news recognition model and load manuscripts for the news recognition model.

[0056] Furthermore, the workflow of the manuscript statistics submodule is as follows:

[0057] Process C1: Count the views of scientific articles in the past three months and get: hsc3, hsc2, and hsc1;

[0058] The number of views of life articles is: hli3, hli2 and hli1;

[0059] The number of views of shopping articles is: hsh3, hsh2 and hsh1;

[0060] The number of views of entertainment articles is: hre3, hre2 and hre1;

[0061] The views of other manuscripts are: hot3, hot2 and hot1;

[0062] Among them, hsc3, hli3, hsh3, hre3 and hot3 represent the page views of science and technology articles, life articles, shopping articles, entertainment articles and other articles in the third month;

[0063] hsc2, hli2, hsh2, hre2, and hot2 represent the page views of science and technology articles, lifestyle articles, shopping articles, entertainment articles, and other articles in the second month;

[0064] hsc1, hli1, hsh1, hre1, and hot1 represent the number of page views of science and technology articles, lifestyle articles, shopping articles, entertainment articles, and other articles in the first month;

[0065] Process C2: Calculate the growth rate of scientific manuscripts from the third month to the second month, recorded as usc (3-2) ;usc (3-2) The calculation formula is: usc (3-2) =(hsc2-hsc3) / hsc3;

[0066] The growth rate of life articles from the third month to the second month, denoted as uli (3-2) ;uli (3-2) The calculation formula is: (3-2) =(hli2-hli3) / hli3;

[0067] The growth rate of shopping articles from the third month to the second month, recorded as ush (3-2) ;ush (3-2) The calculation formula is: (3-2) =(hsh2-hsh3) / hsh3;

[0068] The growth rate of entertainment articles from the third month to the second month, recorded as ure (3-2) ure (3-2) The calculation formula is:ure (3-2) =(hre2-hre3) / hre3;

[0069] The growth rate of other manuscripts from the third month to the second month is denoted as uot (3-2) ;uot (3-2) The calculation formula is: uot (3-2) =(hot2-hot3) / hot3.

[0070] Furthermore, the subsequent process of process C2 is as follows:

[0071] Process C3: Calculate the growth rate of scientific manuscripts from the second month to the first month, recorded as usc (2-1) ;usc (2-1) The calculation formula is: usc (2-1) =(hsc1-hsc2) / hsc2;

[0072] The growth rate of life articles from the second month to the first month, denoted as uli (2-1) ;uli (2-1) The calculation formula is: (2-1) =(hli1-hli2) / hli2;

[0073] The growth rate of shopping articles from the second month to the first month, recorded as ush (2-1) ;ush (2-1) The calculation formula is: (2-1) =(hsh1-hsh2) / hsh2;

[0074] The growth rate of entertainment articles from the second month to the first month, recorded as ure (2-1) ure (2-1) The calculation formula is:ure (2-1) =(hre1-hre2) / hre2;

[0075] The growth rate of other manuscripts from the second month to the first month, denoted as uot (2-1) ;uot (2-1) The calculation formula is: uot (2-1) =(hot1-hot2) / hot2;

[0076] Process C4: Calculation of sc1~sc si The sum of li1, li2~li is recorded as asc; si The sum of is written as ali; sh1, sh2 ~ sh si The sum of re1, re2 and re si The sum of ot1, ot2 and ot si The sum of is written as aot;

[0077] {usc (3-2) , usc (2-1) , hsc2, hsc1} is group a1, with {uli (3-2) ,uli (2-1) , hli2, hli1} is group a2, with {ush (3-2) ,ush (2-1) , hsh2, hsh1} is group a3, with {ure (3-2) ,ure (2-1), hre2, hre1} is group a4, with {uot (3-2) ,uot (2-1) , hot2, hot1} is group a5;

[0078] Using the state transition matrix, the expected number of scientific manuscripts submitted based on group a1 is calculated, recorded as Esc; the expected number of scientific manuscripts submitted based on group a2 is calculated, recorded as Eli; the expected number of scientific manuscripts submitted based on group a3 is calculated, recorded as Esh; the expected number of scientific manuscripts submitted based on group a4 is calculated, recorded as Ere; the expected number of scientific manuscripts submitted based on group a5 is calculated, recorded as Eot;

[0079] Process C5: Compare asc and Esc. If asc ≤ Esc, then Δsc = (Esc - asc); if asc > Esc, then Δsc = 0. Δsc represents the number of additional supplementary manuscripts for the scientific manuscript.

[0080] Compare ali and Eli. If ali≤Eli, then Δli=(Eli-ali); if ali>Eli, then Δli=0; where Δli represents the number of additional supplementary articles for daily life.

[0081] Compare ash and Esh. If ash ≤ Esh, then Δsh = (Esh - ash); if ash > Esh, then Δsh = 0. Here, Δsh represents the number of additional manuscripts for the shopping manuscript.

[0082] Compare are and Ere. If are ≤ Ere, then Δre = (Ere - are); if are > Ere, then Δre = 0. Δre represents the number of additional drafts for entertainment articles.

[0083] Compare aot and Eot. If aot≤Eot, then Δot=(Eot-aot); if aot>Eot, then Δot=0; where Δot represents the number of additional manuscripts.

[0084] According to Δsc, Δli, Δsh, Δre and Δot, the corresponding types of manuscripts are supplemented;

[0085] Process C6: Collect the information of prospective manuscripts with continuous submission records in the past three months as manuscript source information;

[0086] The number of manuscript source information is counted, denoted as ni; the relationship between ni and si satisfies: ni≤si; si represents the number of quasi-submission information;

[0087] Obtain the number of views of news articles and articles of interest corresponding to the source information in the past three months, determine the high-quality source information, and enter the article push sub-module.

[0088] Furthermore, the specific process of process C6 is as follows:

[0089] Process C61: Obtain the number of news article views corresponding to the source information in the past three months to determine the high-quality source information of the news articles;

[0090] Get the news article page views corresponding to the source information in the past three months, and get ne(1,3), ne(1,2), ne(1,1) to ne(ni,1);

[0091] Among them, ne(1,3) represents the number of news article views in the third month corresponding to the first source information; ne(1,2) represents the number of news article views in the second month corresponding to the first source information; ne(1,1) represents the number of news article views in the first month corresponding to the first source information;

[0092] Similarly, ne(ni,1) represents the number of news article views in the first month corresponding to the ni-th source information;

[0093] Process C62: Calculate the sum of the number of page views of the news articles corresponding to the first to the nith source information in the third month, denoted as ane(-, 3); (i.e., the sum of ne(1, 3), ne(2, 3) to ne(ni, 3))

[0094] The sum of the number of news article views in the second month is recorded as ane(-, 2); (i.e., the sum of ne(1, 2), ne(2, 2) to ne(ni, 2))

[0095] The sum of the number of news article views in the first month is recorded as ane(-, 1); (i.e., the sum of ne(1, 1), ne(2, 1) to ne(ni, 1))

[0096] Process C63: Calculate the continuous variation coefficient of the first source information news article page views, recorded as up (1) ; up (1) The calculation formula is:

[0097]

[0098] Calculate the continuous variation coefficient of the second source news article page views, recorded as up (2) ; up (2) The calculation formula is:

[0099]

[0100] Similarly, calculate the continuous change coefficient of the number of views of the ni-th source information news article, recorded as up (ni) ; up (ni) The calculation formula is:

[0101]

[0102] Process C64: Get the priority number of manuscripts corresponding to the manuscript source information from 1 to ni, and get: nne1, nne2~nne ni ;nne1, nne2~nne ni With ne1, ne2~ne si The relationship satisfies:

[0103] Among them, nne1 represents the number of priority manuscripts corresponding to the first manuscript source information; nne2 represents the number of priority manuscripts corresponding to the second manuscript source information; and so on, nne ni Indicates the number of priority manuscripts corresponding to the ni-th manuscript source information;

[0104] Process C65: Calculate the news priority coefficient of the first source information, denoted as fne (1) ;fne (1) The calculation formula is: fne (1) =up (1) ×nne1;

[0105] Calculate the news priority coefficient of the second source information, denoted as fne (2) ;fne (2) The calculation formula is: fne (2) =up (2) ×nne2;

[0106] Similarly, calculate the news priority coefficient of the ni-th source information, denoted as fne (ni) ;fne (ni) The calculation formula is: fne (ni) =up (ni) ×nne ni ;

[0107] Process C66: Extraction of fne (1) ~fne (ni) The largest news priority coefficient is recorded as nem; the source information corresponding to nem is used as the high-quality source information of the news article;

[0108] Process C67: Repeat the same process of determining the high-quality source information of news articles to determine the high-quality source information corresponding to each article of interest.

[0109] Furthermore, the workflow of the manuscript push submodule is as follows:

[0110] Process D1: Get the number of all users, denoted as us; push priority articles to all users;

[0111] Get the number of views of each user's scientific articles in the past three months, and get: usc(1,3), usc(1,2), usc(1,1)~usc(us,1);

[0112] Among them, usc(1,1) represents the number of views of the scientific articles of the first user in the third month; usc(1,2) represents the number of views of the scientific articles of the first user in the second month; usc(1,1) represents the number of views of the scientific articles of the first user in the first month;

[0113] Similarly, usc(us,1) represents the number of views of scientific articles by the us-th user in the first month;

[0114] Get the number of views of each user's life articles in the past three months, and get: uli(1,3), uli(1,2), uli(1,1)~uli(us,1);

[0115] Among them, uli(1,1) represents the number of views of the first user's life article in the third month; uli(1,2) represents the number of views of the first user's life article in the second month; uli(1,1) represents the number of views of the first user's life article in the first month;

[0116] Similarly, uli(us,1) represents the number of views of the life articles of the us-th user in the first month;

[0117] Get the number of views of each user's shopping articles in the past three months, and get: ush(1,3), ush(1,2), ush(1,1) ~ ush(us,1);

[0118] Among them, ush(1,3) represents the number of views of the first user's shopping articles in the third month; ush(1,2) represents the number of views of the first user's shopping articles in the second month; ush(1,1) represents the number of views of the first user's shopping articles in the first month;

[0119] Similarly, ush(us,1) represents the number of views of the shopping article of the us-th user in the first month;

[0120] Get the number of views of each user's entertainment articles in the past three months, and get: ure(1,3), ure(1,2), ure(1,1) ~ ure(us,1);

[0121] Among them, ure(1,3) represents the number of page views of the entertainment articles of the first user in the third month; ure(1,2) represents the number of page views of the entertainment articles of the first user in the second month; ure(1,1) represents the number of page views of the entertainment articles of the first user in the first month;

[0122] Similarly, ure(us,1) represents the number of views of entertainment articles by the us-th user in the first month;

[0123] Get the number of views of other articles of each user in the past three months, and get: uot(1,3), uot(1,2), uot(1,1) ~ uot(us,1);

[0124] Among them, uot(1,3) represents the number of views of other articles of the first user in the third month; uot(1,2) represents the number of views of other articles of the first user in the second month; uot(1,1) represents the number of views of other articles of the first user in the first month;

[0125] Similarly, uot(us,1) represents the number of views of other articles by the us-th user in the first month;

[0126] It should be noted that the “user” in the manuscript push submodule refers to: a person or unit that accepts the manuscript push service of the present invention;

[0127] Process D2: Calculate the average number of scientific article views of all users in the past three months, recorded as msc;

[0128] The average number of views of life articles in the past three months is recorded as mli;

[0129] The average number of shopping article views in the past three months, denoted as msh;

[0130] The average number of entertainment article views in the past three months, recorded as mre;

[0131] The average number of views of other articles in the past three months is recorded as mot;

[0132] Process D3: Using the first user's page views of science and technology articles, lifestyle articles, shopping articles, entertainment articles, and other articles in the past three months as sample data, construct a sample matrix, denoted as matrix o. The mathematical expression of matrix o is:

[0133]

[0134] Calculate the acceptance value of the first user for different types of manuscripts based on the matrix o, and push the manuscripts to be pushed to the first user;

[0135] Process D4: Repeat the same process of pushing the manuscript to be pushed for the first user, and push the manuscript to be pushed for the second to us users.

[0136] Furthermore, the specific process of process D3 is as follows:

[0137] Process D31: Define calculation formula d1-1: X (1,j)=1-[usc(1,j) / (msc / 3)];

[0138] Calculation formula d1-2: Y (1,j) =1-[uli(1,j) / (mli / 3)];

[0139] Calculation formula d1-3: Z (1,j) =1-[ush(1,j) / (msh / 3)];

[0140] Calculation formula d1-4: P (1,j) =1-[ure(1,j) / (mre / 3)];

[0141] Calculation formula d1-5: Q (1,j) =1-[uot(1,j) / (mot / 3)];

[0142] Among them, X (1,j) It represents the deviation of the reading volume of scientific articles of the first user in the jth month relative to the average reading volume of scientific articles of all users;

[0143] Y (1,j) It represents the deviation of the reading volume of the first user's life article in the jth month relative to the average reading volume of all users' life articles;

[0144] Z (1,j) It represents the deviation of the number of shopping articles read by the first user in the jth month relative to the average number of shopping articles read by all users;

[0145] P (1,j) It represents the deviation of the entertainment article reading volume of the first user in the jth month relative to the average entertainment article reading volume of all users;

[0146] Q (1,j) It represents the deviation of the number of other articles read by the first user in the jth month relative to the average number of other articles read by all users;

[0147] The value range of j is: 1 to 3;

[0148] Process D32: Calculate the offsets of the parameters in matrix o according to equations d1-1 to d1-5 in process D31 to obtain matrix o1. The mathematical expression of matrix o1 is:

[0149]

[0150] Calculate X (1,1) 、X (1,2) and X (1,3) The average value of is denoted as aX;Y (1,1) 、Y (1,2) and Y (1,3) The average value of is denoted as aY; Z(1,1) , Z (1,2) and Z (1,3) The average value is denoted as aZ; P (1,1) 、P (1,2) and P (1,3) The average value is recorded as aP;Q (1,1) , Q (1,2) and Q (1,3) The average value is denoted as aQ;

[0151] Process D33: Calculate X (1,1) 、X (1,2) and X (1,3) The standard deviation of is denoted as SX; the calculation formula of SX is:

[0152]

[0153] Calculate Y (1,1) 、Y (1,2) and Y (1,3) The standard deviation of is denoted as SY; the calculation formula of SY is:

[0154]

[0155] Calculate Z (1,1) , Z (1,2) and Z (1,3) The standard deviation of is denoted as SZ; the calculation formula of SZ is:

[0156]

[0157] Calculate P (1,1) 、P (1,2) and P (1,3) The standard deviation of is recorded as SP; the calculation formula of SP is:

[0158]

[0159] Calculate Q (1,1) , Q (1,2) and Q (1,3) The standard deviation of is denoted as SQ; the calculation formula of SQ is:

[0160]

[0161] Process D34: Define calculation formula d2-1: dX (1,j) =(X (1,j) -aX) / SX;

[0162] Calculation formula d2-2: dY (1,j) =(Y (1,j) -aY) / SY;

[0163] Calculation formula d2-3: dZ(1,j) =(Z (1,j) -aZ) / SZ;

[0164] Calculation formula d2-4: dP (1,j) =(P (1,j) -aP) / SP;

[0165] Calculation formula d2-5: dQ (1,j) =(Q (1,j) -aQ) / SQ;

[0166] Among them, dX (1，j) Indicates X (1，j) The value of the second normalization, dY (1，j) Indicates Y (1，j) The value of the second normalization, dZ (1，j) Indicates Z (1，j) The value of secondary normalization, dP (1，j) Indicates P (1，j) The value of the second normalization, dQ (1，j) Express Q (1，j) The value for secondary normalization; the value range of j is: 1 to 3.

[0167] Furthermore, the subsequent process of process D34 is as follows:

[0168] Process D35: According to calculation formulas d2-1 to d2-5 in process D34, the quadratic normalized values ​​of the parameters in matrix o1 are calculated to obtain matrix o2. The mathematical expression of matrix o2 is:

[0169]

[0170] Calculate dX (1,1) 、dX (1,2) and dX (1,3) The average value of (1,-) ; Calculate dY (1,1) 、dY (1,2) and dY (1,3) The average value of (1,-) ; Calculate dZ (1,1) 、dZ (1,2) and dZ (1,3) The average value of (1,-) Calculate dP (1,1) 、dP (1,2) and dP (1,3) The average value, denoted as dP (1,-) ; Calculate dQ (1,1) 、dQ (1,2) and dQ (1,3) The average value of (1,-) ;

[0171] Calculate the average value of the parameters in the first row of matrix o2, recorded as dD (-，1) ;

[0172] Calculate the average value of the parameters in the second row of matrix o2, recorded as dD (-，2) ;

[0173] Calculate the average value of the parameters in the third row of matrix o2, recorded as dD (-，3) ;

[0174] Process D36: Define calculation formula d3-1:

[0175] Among them, rX (1，j) represents dX (1，j) The covariance of , dD (-，j) Represents the average value of the jth row in matrix o2; the value range of j is: 1 to 3;

[0176] Calculation formula d3-2:

[0177] Among them, rY (1，j) represents dY (1，j) covariance of

[0178] Calculation formula d3-3:

[0179] Among them, rZ (1，j) represents dZ (1，j) covariance of

[0180] Calculation formula d3-4:

[0181] Among them, rP (1，j) Indicates dP (1，j) covariance of

[0182] Calculation formula d3-5:

[0183] Among them, rQ (1，j) Indicates dQ (1，j) covariance of

[0184] Process D37: Calculate the covariance of each parameter in the matrix o2 according to equations d3-1 to d3-5 in process D36 to obtain the matrix o3. The mathematical expression of the matrix o3 is:

[0185]

[0186] Split the matrix o3 to obtain matrices o4-1 to o4-5;

[0187] The mathematical expression of matrix o4-1 is:

[0188]

[0189] The mathematical expression of matrix o4-2 is:

[0190]

[0191] The mathematical expression of matrix o4-3 is:

[0192]

[0193] The mathematical expression of the matrix o4-4 is:

[0194]

[0195] The mathematical expression of matrix o4-5 is:

[0196]

[0197] Process D38: Use Intel MKL library functions to define and calculate weight values ​​λ1, λ2, λ3, λ4, and λ54, as well as offsets μ1, μ2, μ3, μ4, and μ3, so that matrices o4-1 to o4-5 satisfy the following mathematical relationship:

[0198]

[0199] Process D39: Calculate the acceptance value of the first user's scientific manuscript, denoted as bsc, bsc = λ1 / μ1;

[0200] Calculate the acceptance value of the first user's life manuscript and record it as bli, bli = λ2 / μ2;

[0201] Calculate the acceptance value of the first user's shopping manuscript and record it as bsh, bsh = λ3 / μ3;

[0202] Calculate the acceptance value of the first user's entertainment manuscript and record it as bre, bre = λ4 / μ4;

[0203] Calculate the acceptance value of other manuscripts of the first user and record it as bot, bot = λ5 / μ5;

[0204] Arrange bsc, bli, bsh, bre and bot in descending order to obtain the interest sequence of the first user, and push the articles to be pushed to the first user in the order of the interest sequence.

[0205] An intelligent publishing method includes:

[0206] Step S1: obtaining original manuscripts and their submission information, eliminating manuscripts and submission information containing inappropriate content, and obtaining manuscripts to be pushed and submission information to be submitted;

[0207] Step S2: Build a news recognition model and use it to classify the articles to be pushed, extracting the news broadcast articles as the priority articles;

[0208] Change the training object of the news recognition model and build an interest recognition model; perform secondary classification on the articles to be pushed based on the interest recognition model, add interest tags to non-news broadcast articles, and obtain articles of interest;

[0209] Using natural language processing technology, the content of the manuscripts of interest is identified, and pictures are added to the manuscripts of interest based on the content of the manuscripts of interest, thereby obtaining the manuscripts to be pushed;

[0210] Step S3: Count the historical views of different types of manuscripts to be pushed, and calculate the additional supplementary amount of manuscripts to be pushed based on the manuscripts to be pushed;

[0211] Collect the information of prospective contributors with continuous submission records as manuscript source information; obtain the number of views of news articles and articles of interest corresponding to the manuscript source information in the past three months to determine the high-quality manuscript source information;

[0212] Obtain the total number of users and push priority articles to all users; obtain the number of views of each user's articles of different types, calculate each user's acceptance value for different types of articles, and recommend articles to users based on their acceptance values;

[0213] Step S4: Obtain manuscripts with high-quality manuscript source information and manuscripts with non-high-quality manuscript source information in real time, update original manuscript data, and push manuscripts.

[0214] Compared with the prior art, the present invention has the following beneficial effects:

[0215] Precise delivery: The present invention has powerful data analysis capabilities and is designed for multiple fields including but not limited to news, technology, life, shopping, etc.; in the process of screening manuscripts for users, it also analyzes user needs and the acceptance of different manuscripts based on the reading habits of different users, and selects different media resources for users to achieve precise delivery and wide coverage.

[0216] Improve production efficiency: The present invention can process massive amounts of data in a short period of time, and achieve the collection, screening, organization, illustration and push of manuscripts, simplifying the tedious manuscript processing links and greatly improving the dissemination speed of traditional media; at the same time, compared with traditional manual creation, the present invention can save labor costs and reduce the operating costs of enterprises.

[0217] Data monitoring and feedback: The present invention has data monitoring and feedback functions, which allow media workers to understand the publishing effect of manuscripts and users' reading tendencies within a certain period of time in real time, and screen high-quality manuscript sources for media workers. This processing method helps media workers adjust and optimize subsequent manuscript content. BRIEF DESCRIPTION OF THE DRAWINGS

[0218] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments with reference to the following drawings:

[0219] Figure 1 Schematic diagram of the system of the present invention;

[0220] Figure 2 Schematic diagram of the method of the present invention;

[0221] Figure 3 This is a schematic diagram of the logic design of the present invention. DETAILED DESCRIPTION

[0222] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0223] Example 1

[0224] See also Figure 1 and Figure 3 An intelligent manuscript publishing system includes: a manuscript acquisition module, a manuscript processing module, a manuscript promotion module, a manuscript update module, a database, and a server; wherein the manuscript acquisition module, the manuscript processing module, the manuscript promotion module, and the manuscript update module are connected to the database and the server respectively;

[0225] Among them, the database and server are used for data transmission and data calculation between the manuscript acquisition module, manuscript processing module, manuscript promotion module and manuscript update module;

[0226] Manuscript acquisition module: used to obtain original manuscripts and their submission information, eliminate manuscripts and submission information with bad content, and obtain manuscripts and submission information that are ready for push;

[0227] Submission information means: the identity information of the original manuscript contributor or contributing organization;

[0228] It should be noted that the "original manuscript" in the present invention refers to the manuscript pushed using the present invention (an intelligent manuscript publishing system and method); "submitting unit" refers to the media organization;

[0229] Process A: The workflow of the manuscript acquisition module is as follows:

[0230] Process A1: Obtain the original manuscript and its submission information;

[0231] Use the Worktile tool to remove manuscripts with bad content from the original manuscripts and obtain manuscripts that are ready for push;

[0232] Mark submissions containing inappropriate content as violators and report them to the police;

[0233] It should be noted that "bad content" in this invention refers to: violent, reactionary, pornographic and other content that endangers public security and social atmosphere;

[0234] Process A2: Summarize the submission information of all manuscripts to be pushed as the submission information;

[0235] Count the number of quasi-submission information, summarize the manuscripts in the format of quasi-submission information - quasi-pushed manuscripts, obtain the manuscripts to be assigned, and enter the manuscript processing module.

[0236] Manuscript processing module: including manuscript classification submodule and manuscript illustration submodule;

[0237] Manuscript classification submodule: This module is used to build a news recognition model and use it to classify the manuscripts to be pushed, extracting the news broadcast manuscripts as priority manuscripts.

[0238] Change the training object of the news recognition model and build an interest recognition model; perform secondary classification on the articles to be pushed based on the interest recognition model, add interest tags to non-news broadcast articles, and obtain articles of interest;

[0239] Process B: The workflow of the manuscript classification submodule is as follows:

[0240] Process B1: Count the number of quasi-submission information, recorded as si; count the number of quasi-pushed manuscripts corresponding to each quasi-submission information, and get do1, do2~do si ;

[0241] Among them, do1 represents the number of manuscripts that are to be pushed corresponding to the first quasi-submission information; do2 represents the number of manuscripts that are to be pushed corresponding to the second quasi-submission information; and so on. si Indicates the number of manuscripts that are about to be pushed corresponding to the si-th submission information;

[0242] Process B2: Obtain news articles (existing online) as training articles a1 and non-news articles (existing online) as training articles a2, and use the logistic regression model to build a news recognition model;

[0243] Step B21: Import the pandas and scikit-learn libraries;

[0244] Create a list yes_texts to store training manuscript a1; create a list no_texts to store training manuscript a2;

[0245] Combine news and non-news article texts as feature X, that is:

[0246] X=news_texts+non_news_texts;

[0247] Define label y, mark news articles as 1 and non-news articles as 0, that is:

[0248] y=[1]*len(yes_texts)+[0]*len(no_texts);

[0249] Process B22: Use the load_data function to load the features X and labels y as the combined dataset; divide the combined dataset into a training set and a test set;

[0250] The text of the training set is denoted as X_train and the label is denoted as y_train;

[0251] The text of the test set is recorded as X_test and the label is recorded as y_test;

[0252] The number of training sets and test sets satisfies: training set > test set;

[0253] Process B23: Use the manuscripts in the training set as training text and the manuscripts in the test set as test text; initialize the TF-IDF vectorizer and perform TF-IDF feature extraction on the training set X_train;

[0254] Use the fit_transform method to learn the vocabulary in the training text (that is, all non-repeated words), calculate the TF-IDF value of each word in each training text, and obtain the sparse matrix X_train_tfidf;

[0255] In the sparse matrix X_train_tfidf, each row represents a training text, each column represents a word, and the values ​​in the matrix correspond to the TF-IDF values ​​of the word in different training texts;

[0256] Use the transform method to extract TF-IDF features from the test set text to obtain the training features X_test_tfidf. (It should be noted that the transform method is used here instead of fit_transform to ensure that the vocabulary used in the test set is consistent with the vocabulary used in the training set. This is done to avoid introducing new words that do not appear in the training set into the test set, ensuring that the model evaluation on the test set is fair.)

[0257] Process B24: Initialize the logistic regression model model and train the model with the training set data, that is:

[0258] model.fit(X_train_tfidf,y_train);

[0259] Use the trained model to predict the test set, that is:

[0260] y_pred=model.predict(X_test_tfidf);

[0261] Use the accuracy_score() method to calculate the accuracy of the model on the test set; and use the classification_report() method to calculate the precision until the accuracy and precision between any article in the training set with label y_train = 1 and any article in the test set with label y_test = 1 are close to 100%; the accuracy and precision between the articles with label y_train = 1 and the articles with label y_test = 0, the articles with label y_train = 0 and the articles with label y_test = 1, and the articles with label y_train = 0 and the articles with label y_test = 0 are all 0%, completing the training of the news recognition model;

[0262] Load the joblib library to save the (trained) news recognition model and load manuscripts for the news recognition model;

[0263] Process B3: Using the joblib library, load the quasi-push manuscripts corresponding to the 1st to sith quasi-submission information into the news recognition model, classify the quasi-push manuscripts, and extract the news broadcast manuscript corresponding to each quasi-submission information as the priority manuscript;

[0264] Count the number of priority manuscripts (i.e. news broadcast manuscripts) corresponding to each quasi-submission information, and get: ne1, ne2~ne si ;

[0265] Among them, ne1 represents the number of priority manuscripts corresponding to the first quasi-submission information; ne2 represents the number of priority manuscripts corresponding to the second quasi-submission information; and so on, ne si Indicates the number of priority manuscripts corresponding to the si-th prospective submission information;

[0266] Process B4: Treat the non-priority manuscripts that are about to be pushed as manuscripts to be classified, and define interest tags for the manuscripts to be classified. Interest tags include: technology, life, shopping, entertainment, and others;

[0267] The training object of the news recognition model is changed to build an interest recognition model. According to the interest recognition model, the articles to be classified are reclassified and interest tags are added to the articles to be classified to obtain articles of interest. Articles of interest include: science and technology articles, life articles, shopping articles, entertainment articles, and other articles.

[0268] It should be noted that "other" refers to articles "not related to science and technology, life, shopping, or entertainment";

[0269] Process B41: Obtain scientific and non-scientific articles (existing online), replace training article a1 in process B2 with scientific articles (existing online), and replace training article a2 with non-scientific articles (existing online). Repeat the same process of building a news recognition model (i.e., process B21 and process B24), build a science and technology-interest recognition model, use the science and technology-interest recognition model to extract scientific articles from the articles to be classified, and add scientific labels;

[0270] Process B42: Obtain (existing online) lifestyle articles and non-lifestyle articles, shopping articles and non-shopping articles, and entertainment articles and non-entertainment articles, and repeat the same process of constructing the technology-interest recognition model (as in process B41) to construct the lifestyle-interest recognition model, the shopping-interest recognition model, and the entertainment-interest recognition model;

[0271] Use the life-interest recognition model, shopping-interest recognition model, and entertainment-interest recognition model to extract life manuscripts, shopping manuscripts, and entertainment manuscripts from the manuscripts to be classified, and add life, shopping, and entertainment tags in sequence;

[0272] Process B43: Count the number of scientific manuscripts in the manuscripts to be classified corresponding to the 1st to sith quasi-submission information, and get: sc1, sc2~sc si ;

[0273] The number of life manuscripts is: li1, li2~li si ;

[0274] The number of shopping manuscripts is: sh1, sh2~sh si ;

[0275] The number of entertainment articles: re1, re2~re si ;

[0276] Among them, sc1, li1, sh1 and re1 respectively represent the number of scientific manuscripts, life manuscripts, shopping manuscripts and entertainment manuscripts corresponding to the first quasi-submission information;

[0277] sc2, li2, sh2, and re2 respectively represent the number of science and technology manuscripts, life manuscripts, shopping manuscripts, and entertainment manuscripts corresponding to the second quasi-submission information;

[0278] And so on, sc si 、li si ,sh si and re si , respectively, represent the number of scientific manuscripts, life manuscripts, shopping manuscripts, and entertainment manuscripts corresponding to the si-th quasi-submission information;

[0279] Process B44: Calculate the number of other manuscripts corresponding to the first quasi-submission information, ot1, where ot1 is calculated as follows: ot1 = do1 - (ne1 + sc1 + li1 + sh1 + re1);

[0280] Calculate the number of other manuscripts corresponding to the second quasi-submission information, ot2, using the following formula: ot2 = do2 - (ne2 + sc2 + li2 + sh2 + re2);

[0281] Similarly, calculate the number of other manuscripts corresponding to the si-th quasi-submission information ot si ,ot si The calculation formula is: si =do si -(ne si +sc si +li si +sh si +re si );

[0282] The number of other manuscripts corresponding to the 1st to sith quasi-submission information is: ot1, ot2~ot si ;

[0283] Manuscript picture submodule: used to use natural language processing technology to identify the content in the manuscripts of interest, and add pictures to the manuscripts of interest based on the content in the manuscripts of interest, so as to obtain the manuscripts to be pushed;

[0284] Summarize the manuscripts to be pushed and enter the manuscript promotion module; the tags and quantity of the manuscripts to be pushed are the same as the tags and quantity of the interested manuscripts

[0285] Manuscript promotion module: including manuscript statistics submodule and manuscript push submodule;

[0286] Manuscript Statistics Submodule: used to count the historical page views of different types of non-news articles (in the past three months), and calculate the additional supplementary amount of articles to be pushed in combination with the articles to be pushed;

[0287] Collect statistics on prospective manuscript submissions with continuous submission records (in the past three months) as manuscript source information; obtain the number of views of news articles and articles of interest corresponding to the manuscript source information in the past three months to determine high-quality manuscript source information;

[0288] Process C: The workflow of the manuscript statistics submodule is as follows:

[0289] Process C1: Count the views of scientific articles in the past three months and get: hsc3, hsc2, and hsc1;

[0290] The number of views of life articles is: hli3, hli2 and hli1;

[0291] The number of views of shopping articles is: hsh3, hsh2 and hsh1;

[0292] The number of views of entertainment articles is: hre3, hre2 and hre1;

[0293] The views of other manuscripts are: hot3, hot2 and hot1;

[0294] Among them, hsc3, hli3, hsh3, hre3 and hot3 represent the page views of science and technology articles, life articles, shopping articles, entertainment articles and other articles in the third month;

[0295] hsc2, hli2, hsh2, hre2, and hot2 represent the page views of science and technology articles, lifestyle articles, shopping articles, entertainment articles, and other articles in the second month;

[0296] hsc1, hli1, hsh1, hre1, and hot1 represent the number of page views of science and technology articles, lifestyle articles, shopping articles, entertainment articles, and other articles in the first month;

[0297] Process C2: Calculate the growth rate of scientific manuscripts from the third month to the second month, recorded as usc (3-2) ;usc (3-2) The calculation formula is: usc (3-2) =(hsc2-hsc3) / hsc3;

[0298] The growth rate of life articles from the third month to the second month, denoted as uli (3-2) ;uli (3-2) The calculation formula is: (3-2) =(hli2-hli3) / hli3;

[0299] The growth rate of shopping articles from the third month to the second month, recorded as ush (3-2) ;ush (3-2) The calculation formula is: (3-2)=(hsh2-hsh3) / hsh3;

[0300] The growth rate of entertainment articles from the third month to the second month, recorded as ure (3-2) ure (3-2) The calculation formula is:ure (3-2) =(hre2-hre3) / hre3;

[0301] The growth rate of other manuscripts from the third month to the second month is denoted as uot (3-2) ;uot (3-2) The calculation formula is: uot (3-2) =(hot2-hot3) / hot3;

[0302] Process C3: Calculate the growth rate of scientific manuscripts from the second month to the first month, recorded as usc (2-1) ;usc (2-1) The calculation formula is: usc (2-1) =(hsc1-hsc2) / hsc2;

[0303] The growth rate of life articles from the second month to the first month, denoted as uli (2-1) ;uli (2-1) The calculation formula is: (2-1) =(hli1-hli2) / hli2;

[0304] The growth rate of shopping articles from the second month to the first month, recorded as ush (2-1) ;ush (2-1) The calculation formula is: (2-1) =(hsh1-hsh2) / hsh2;

[0305] The growth rate of entertainment articles from the second month to the first month, recorded as ure (2-1) ure (2-1) The calculation formula is:ure (2-1) =(hre1-hre2) / hre2;

[0306] The growth rate of other manuscripts from the second month to the first month, denoted as uot (2-1) ;uot (2-1) The calculation formula is: uot (2-1) =(hot1-hot2) / hot2;

[0307] Process C4: Calculation of sc1~sc si The sum of li1, li2~li is recorded as asc; si The sum of is written as ali; sh1, sh2 ~ sh si The sum of re1, re2 and resi The sum of ot1, ot2 and ot si The sum of is written as aot;

[0308] {usc (3-2) , usc (2-1) , hsc2, hsc1} is group a1, with {uli (3-2) ,uli (2-1) , hli2, hli1} is group a2, with {ush (3-2) ,ush (2-1) , hsh2, hsh1} is group a3, with {ure (3-2) ,ure (2-1) , hre2, hre1} is group a4, with {uot (3-2) ,uot (2-1) , hot2, hot1} is group a5;

[0309] Using the state transition matrix, the expected number of scientific manuscripts submitted based on group a1 is calculated, recorded as Esc; the expected number of scientific manuscripts submitted based on group a2 is calculated, recorded as Eli; the expected number of scientific manuscripts submitted based on group a3 is calculated, recorded as Esh; the expected number of scientific manuscripts submitted based on group a4 is calculated, recorded as Ere; the expected number of scientific manuscripts submitted based on group a5 is calculated, recorded as Eot;

[0310] Process C5: Compare asc and Esc. If asc ≤ Esc, then Δsc = (Esc - asc); if asc > Esc, then Δsc = 0. Δsc represents the number of additional supplementary manuscripts for the scientific manuscript.

[0311] Compare ali and Eli. If ali≤Eli, then Δli=(Eli-ali); if ali>Eli, then Δli=0; where Δli represents the number of additional supplementary articles for life articles.

[0312] Compare ash and Esh. If ash ≤ Esh, then Δsh = (Esh - ash); if ash > Esh, then Δsh = 0. Here, Δsh represents the number of additional manuscripts for the shopping manuscript.

[0313] Compare are and Ere. If are ≤ Ere, then Δre = (Ere - are); if are > Ere, then Δre = 0. Δre represents the number of additional drafts for entertainment articles.

[0314] Compare aot and Eot. If aot≤Eot, then Δot=(Eot-aot); if aot>Eot, then Δot=0; where Δot represents the number of additional manuscripts.

[0315] According to Δsc, Δli, Δsh, Δre and Δot, corresponding types of manuscripts (i.e., science and technology manuscripts, life manuscripts, shopping manuscripts, entertainment manuscripts and other manuscripts) are supplemented;

[0316] Process C6: Collect the information of prospective manuscripts with continuous submission records in the past three months as manuscript source information;

[0317] The number of manuscript source information is counted, denoted as ni; the relationship between ni and si satisfies: ni≤si; si represents the number of quasi-submission information;

[0318] Obtain the number of views of news articles and articles of interest corresponding to the source information in the past three months to determine high-quality source information;

[0319] Process C61: Obtain the number of news article views corresponding to the source information in the past three months to determine the high-quality source information of the news articles;

[0320] Get the news article page views corresponding to the source information in the past three months, and get ne(1,3), ne(1,2), ne(1,1) to ne(ni,1);

[0321] Among them, ne(1,3) represents the number of news article views in the third month corresponding to the first source information; ne(1,2) represents the number of news article views in the second month corresponding to the first source information; ne(1,1) represents the number of news article views in the first month corresponding to the first source information;

[0322] Similarly, ne(ni,1) represents the number of news article views in the first month corresponding to the ni-th source information;

[0323] Process C62: Calculate the sum of the number of page views of the news articles corresponding to the first to the nith source information in the third month, denoted as ane(-, 3); (i.e., the sum of ne(1, 3), ne(2, 3) to ne(ni, 3))

[0324] The sum of the number of news article views in the second month is recorded as ane(-, 2); (i.e., the sum of ne(1, 2), ne(2, 2) to ne(ni, 2))

[0325] The sum of the number of news article views in the first month is recorded as ane(-, 1); (i.e., the sum of ne(1, 1), ne(2, 1) to ne(ni, 1))

[0326] Process C63: Calculate the continuous variation coefficient of the first source information news article page views, recorded as up (1) ; up (1) The calculation formula is:

[0327]

[0328] Calculate the continuous variation coefficient of the second source news article page views, recorded as up (2) ; up (2) The calculation formula is:

[0329]

[0330] Similarly, calculate the continuous change coefficient of the number of views of the ni-th source information news article, recorded as up (ni) ; up (ni) The calculation formula is:

[0331]

[0332] Process C64: Get the priority number of manuscripts corresponding to the manuscript source information from 1 to ni, and get: nne1, nne2~nne ni ;nne1, nne2~nne ni With ne1, ne2~ne si The relationship satisfies:

[0333] Among them, nne1 represents the number of priority manuscripts corresponding to the first manuscript source information; nne2 represents the number of priority manuscripts corresponding to the second manuscript source information; and so on, nne ni Indicates the number of priority manuscripts corresponding to the ni-th manuscript source information;

[0334] Process C65: Calculate the news priority coefficient of the first source information, denoted as fne (1) ;fne (1) The calculation formula is: fne (1) =up (1) ×nne1;

[0335] Calculate the news priority coefficient of the second source information, denoted as fne (2) ;fne (2) The calculation formula is: fne (2) =up (2) ×nne2;

[0336] Similarly, calculate the news priority coefficient of the ni-th source information, denoted as fne (ni) ;fne (ni) The calculation formula is: fne (ni) =up (ni) ×nne ni ;

[0337] Process C66: Extraction of fne (1) ~fne (ni)The largest news priority coefficient is recorded as nem; the source information corresponding to nem is used as the high-quality source information of the news article;

[0338] Process C67: Repeat the same process of determining high-quality source information of news articles (i.e., processes C61 to C66) to determine the high-quality source information corresponding to each article of interest;

[0339] Article push submodule: used to obtain the number of all users and push priority articles to all users; obtain the number of views of different types of articles (i.e., non-news articles) for each user (in the past three months), calculate each user's acceptance value for different types of articles (i.e., non-news articles), and recommend articles to be pushed to users based on the user's acceptance value;

[0340] Process D: The workflow of the manuscript push submodule is as follows:

[0341] Process D1: Get the number of all users, denoted as us; push priority articles to all users;

[0342] Get the number of views of each user's scientific articles in the past three months, and get: usc(1,3), usc(1,2), usc(1,1)~usc(us,1);

[0343] Among them, usc(1,1) represents the number of views of the scientific articles of the first user in the third month; usc(1,2) represents the number of views of the scientific articles of the first user in the second month; usc(1,1) represents the number of views of the scientific articles of the first user in the first month;

[0344] Similarly, usc(us,1) represents the number of views of scientific articles by the us-th user in the first month;

[0345] Get the number of views of each user's life articles in the past three months, and get: uli(1,3), uli(1,2), uli(1,1)~uli(us,1);

[0346] Among them, uli(1,1) represents the number of views of the first user's life article in the third month; uli(1,2) represents the number of views of the first user's life article in the second month; uli(1,1) represents the number of views of the first user's life article in the first month;

[0347] Similarly, uli(us,1) represents the number of views of the life articles of the us-th user in the first month;

[0348] Get the number of views of each user's shopping articles in the past three months, and get: ush(1,3), ush(1,2), ush(1,1) ~ ush(us,1);

[0349] Among them, ush(1,3) represents the number of views of the first user's shopping articles in the third month; ush(1,2) represents the number of views of the first user's shopping articles in the second month; ush(1,1) represents the number of views of the first user's shopping articles in the first month;

[0350] Similarly, ush(us,1) represents the number of views of the shopping article of the us-th user in the first month;

[0351] Get the number of views of each user's entertainment articles in the past three months, and get: ure(1,3), ure(1,2), ure(1,1) ~ ure(us,1);

[0352] Among them, ure(1,3) represents the number of page views of the entertainment articles of the first user in the third month; ure(1,2) represents the number of page views of the entertainment articles of the first user in the second month; ure(1,1) represents the number of page views of the entertainment articles of the first user in the first month;

[0353] Similarly, ure(us,1) represents the number of views of entertainment articles by the us-th user in the first month;

[0354] Get the number of views of other articles of each user in the past three months, and get: uot(1,3), uot(1,2), uot(1,1) ~ uot(us,1);

[0355] Among them, uot(1,3) represents the number of views of other articles of the first user in the third month; uot(1,2) represents the number of views of other articles of the first user in the second month; uot(1,1) represents the number of views of other articles of the first user in the first month;

[0356] Similarly, uot(us,1) represents the number of views of other articles by the us-th user in the first month;

[0357] It should be noted that the “user” in the manuscript push submodule refers to: a person or unit that accepts the manuscript push service of the present invention (an intelligent manuscript publishing system and method);

[0358] Process D2: Calculate the average number of scientific article views of all users in the past three months (i.e., the average of usc(1,3), usc(1,2), usc(1,1) to usc(us,1)), recorded as msc;

[0359] The average number of views of life articles in the past three months (i.e., the average of uli(1,3), uli(1,2), and uli(1,1) to uli(us,1)), denoted as mli;

[0360] The average number of shopping article views in the past three months (i.e., the average of ush(1,3), ush(1,2), ush(1,1) to ush(us,1)), denoted as msh;

[0361] The average number of entertainment article views in the past three months (i.e., the average of ure(1,3), ure(1,2), ure(1,1) to ure(us,1)), denoted as mre;

[0362] The average number of views of other manuscripts in the past three months (i.e., the average of uot(1,3), uot(1,2), uot(1,1) to uot(us,1)), is recorded as mot;

[0363] Process D3: Using the first user's page views of science and technology articles, lifestyle articles, shopping articles, entertainment articles, and other articles in the past three months as sample data, construct a sample matrix, denoted as matrix o. The mathematical expression of matrix o is:

[0364]

[0365] Calculate the acceptance value of the first user for different types of articles (i.e., non-news articles) based on the matrix o, and push the articles to be pushed to the first user;

[0366] Process D31: Define calculation formula d1-1: X (1,j) =1-[usc(1,j) / (msc / 3)];

[0367] Calculation formula d1-2: Y (1,j) =1-[uli(1,j) / (mli / 3)];

[0368] Calculation formula d1-3: Z (1,j) =1-[ush(1,j) / (msh / 3)];

[0369] Calculation formula d1-4: P (1,j) =1-[ure(1,j) / (mre / 3)];

[0370] Calculation formula d1-5: Q (1,j) =1-[uot(1,j) / (mot / 3)];

[0371] Among them, X (1,j) It represents the deviation of the reading volume of scientific articles of the first user in the jth month relative to the average reading volume of scientific articles of all users;

[0372] Y (1,j) It represents the deviation of the reading volume of the first user's life article in the jth month relative to the average reading volume of all users' life articles;

[0373] Z (1,j) It represents the deviation of the number of shopping articles read by the first user in the jth month relative to the average number of shopping articles read by all users;

[0374] P (1,j) It represents the deviation of the entertainment article reading volume of the first user in the jth month relative to the average entertainment article reading volume of all users;

[0375] Q (1,j) It represents the deviation of the number of other articles read by the first user in the jth month relative to the average number of other articles read by all users;

[0376] The value range of j is: 1 to 3;

[0377] Process D32: Calculate the offsets of the parameters in matrix o according to equations d1-1 to d1-5 in process D31 to obtain matrix o1. The mathematical expression of matrix o1 is:

[0378]

[0379] Calculate X (1,1) 、X (1,2) and X (1,3) The average value of is denoted as aX;Y (1,1) 、Y (1,2) and Y (1,3) The average value of is denoted as aY; Z (1,1) , Z (1,2) and Z (1,3) The average value is denoted as aZ; P (1,1) 、P (1,2) and P (1,3) The average value is recorded as aP;Q (1,1) , Q (1,2) and Q (1,3) The average value is denoted as aQ;

[0380] Process D33: Calculate X (1,1) 、X (1,2) and X (1,3) The standard deviation of is denoted as SX; the calculation formula of SX is:

[0381]

[0382] Calculate Y (1,1) 、Y (1,2) and Y (1,3) The standard deviation of is denoted as SY; the calculation formula of SY is:

[0383]

[0384] Calculate Z (1,1) , Z (1,2) and Z(1,3) The standard deviation of is denoted as SZ; the calculation formula of SZ is:

[0385]

[0386] Calculate P (1,1) 、P (1,2) and P (1,3) The standard deviation of is recorded as SP; the calculation formula of SP is:

[0387]

[0388] Calculate Q (1,1) , Q (1,2) and Q (1,3) The standard deviation of is denoted as SQ; the calculation formula of SQ is:

[0389]

[0390] Process D34: Define calculation formula d2-1: dX (1,j) =(X (1,j) -aX) / SX;

[0391] Calculation formula d2-2: dY (1,j) =(Y (1,j) -aY) / SY;

[0392] Calculation formula d2-3: dZ (1,j) =(Z (1,j) -aZ) / SZ;

[0393] Calculation formula d2-4: dP (1,j) =(P (1,j) -aP) / SP;

[0394] Calculation formula d2-5: dQ (1,j) =(Q (1,j) -aQ) / SQ;

[0395] Among them, dX (1，j) Indicates X (1，j) The value of the second normalization, dY (1，j) Indicates Y (1，j) The value of the second normalization, dZ (1，j) Indicates Z (1，j) The value of secondary normalization, dP (1，j) Indicates P (1，j) The value of the second normalization, dQ (1，j) Express Q (1，j) The value of secondary normalization; the value range of j is: 1 to 3;

[0396] Process D35: According to calculation formulas d2-1 to d2-5 in process D34, the quadratic normalized values ​​of the parameters in matrix o1 are calculated to obtain matrix o2. The mathematical expression of matrix o2 is:

[0397]

[0398] Calculate dX (1,1) 、dX (1,2) and dX (1,3) The average value of (1,-) Calculate dY (1,1) 、dY (1,2) and dY (1,3) The average value of (1,-) ; Calculate dZ (1,1) 、dZ (1,2) and dZ (1,3) The average value of (1,-) ; Calculate dP (1,1) 、dP (1,2) and dP (1,3) The average value, denoted as dP (1,-) ; Calculate dQ (1,1) 、dQ (1,2) and dQ (1,3) The average value of (1,-) ;

[0399] Calculate the average value of each parameter in the first row of matrix o2 (ie dX (1,1) 、dY (1,1) 、dZ (1,1) 、dP (1,1) and dQ (1,1) The average value of (-，1) ;

[0400] Calculate the average value of each parameter in the second row of matrix o2 (ie dX (1,2) 、dY (1,2) 、dZ (1,2) 、dP (1,2) and dQ (1,2) The average value of (-，2) ;

[0401] Calculate the average value of each parameter in the third row of matrix o2 (ie dX (1,3) 、dY (1,3) 、dZ (1,3) 、dP (1,3) and dQ (1,3) The average value of (-，3) ;

[0402] Process D36: Define calculation formula d3-1:

[0403] Among them, rX (1，j) represents dX (1，j) The covariance of , dD (-，j) Represents the average value of the jth row in matrix o2; the value range of j is: 1 to 3;

[0404] Calculation formula d3-2:

[0405] Among them, rY (1，j) represents dY (1，j) covariance of

[0406] Calculation formula d3-3:

[0407] Among them, rZ (1，j) represents dZ (1，j) covariance of

[0408] Calculation formula d3-4:

[0409] Among them, rP (1，j) Indicates dP (1，j) covariance of

[0410] Calculation formula d3-5:

[0411] Among them, rQ (1，j) Indicates dQ (1，j) covariance of

[0412] Process D37: Calculate the covariance of each parameter in the matrix o2 according to equations d3-1 to d3-5 in process D36 to obtain the matrix o3. The mathematical expression of the matrix o3 is:

[0413]

[0414] Split the matrix o3 to obtain matrices o4-1 to o4-5;

[0415] The mathematical expression of matrix o4-1 is:

[0416]

[0417] The mathematical expression of matrix o4-2 is:

[0418]

[0419] The mathematical expression of matrix o4-3 is:

[0420]

[0421] The mathematical expression of the matrix o4-4 is:

[0422]

[0423] The mathematical expression of matrix o4-5 is:

[0424]

[0425] Process D38: Use Intel MKL library functions to define and calculate weight values ​​λ1, λ2, λ3, λ4, and λ54, as well as offsets μ1, μ2, μ3, μ4, and μ3, so that matrices o4-1 to o4-5 satisfy the following mathematical relationship:

[0426]

[0427] Process D39: Calculate the acceptance value of the first user's scientific manuscript, denoted as bsc, bsc = λ1 / μ1;

[0428] Calculate the acceptance value of the first user's life manuscript and record it as bli, bli = λ2 / μ2;

[0429] Calculate the acceptance value of the first user's shopping manuscript and record it as bsh, bsh = λ3 / μ3;

[0430] Calculate the acceptance value of the first user's entertainment manuscript and record it as bre, bre = λ4 / μ4;

[0431] Calculate the acceptance value of other manuscripts of the first user and record it as bot, bot = λ5 / μ5;

[0432] Arrange bsc, bli, bsh, bre, and bot in descending order to obtain the interest sequence of the first user, and push the articles to be pushed to the first user in the order of the interest sequence;

[0433] Process D4: Repeat the same process of pushing the manuscript to be pushed for the first user (ie, process D3), and push the manuscript to be pushed for the second to us-th users.

[0434] Manuscript update module: used to obtain manuscripts with high-quality source information and manuscripts with non-high-quality source information in real time, update original manuscript data, and push manuscripts.

[0435] It should be noted that the present invention only lists four specific types of labels: "technology, life, shopping, and entertainment". Media workers or relevant technical personnel can define or design more labels on their own when using the present invention to increase the benefits of the present invention to different user groups.

[0436] Example 2

[0437] See also Figure 2 , an intelligent publishing method includes:

[0438] Step S1: obtaining original manuscripts and their submission information, eliminating manuscripts and submission information containing inappropriate content, and obtaining manuscripts to be pushed and submission information to be submitted;

[0439] Step S2: Build a news recognition model and use it to classify the articles to be pushed, extracting the news broadcast articles as the priority articles;

[0440] Change the training object of the news recognition model and build an interest recognition model; perform secondary classification on the articles to be pushed based on the interest recognition model, add interest tags to non-news broadcast articles, and obtain articles of interest;

[0441] Using natural language processing technology, the content of the manuscripts of interest is identified, and pictures are added to the manuscripts of interest based on the content of the manuscripts of interest, thereby obtaining the manuscripts to be pushed;

[0442] Step S3: Count the historical page views of different types of non-news articles (in the past three months), and calculate the additional supplementary amount of articles to be pushed in combination with the articles to be pushed;

[0443] Collect statistics on prospective manuscript submissions with continuous submission records (in the past three months) as manuscript source information; obtain the number of views of news articles and articles of interest corresponding to the manuscript source information in the past three months to determine high-quality manuscript source information;

[0444] Obtain the total number of users and push priority articles to all users; obtain the number of views of different types of articles (i.e., non-news articles) for each user (in the past three months), calculate each user's acceptance value for different types of articles (i.e., non-news articles), and recommend articles to be pushed to users based on their acceptance value;

[0445] Step S4: Obtain manuscripts with high-quality manuscript source information and manuscripts with non-high-quality manuscript source information in real time, update original manuscript data, and push manuscripts.

[0446] The above formulas are all dimensionless and calculated by taking their numerical values. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions. For example, if there are weight coefficients and proportional coefficients, the size of the settings is to quantify each parameter to obtain a specific value, which is convenient for subsequent comparison. Regarding the size of the weight coefficient and the proportional coefficient, as long as it does not affect the proportional relationship between the parameter and the quantized value, it is fine.

[0447] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. An intelligent manuscript publishing system, characterized in that: The manuscript publishing system includes: Manuscript acquisition module: used to obtain original manuscripts and their submission information, eliminate manuscripts and submission information with bad content, and obtain manuscripts and submission information that are ready for push; Manuscript processing module: including manuscript classification submodule and manuscript illustration submodule; Manuscript classification submodule: This module is used to build a news recognition model and use it to classify the manuscripts to be pushed, extracting the news broadcast manuscripts as priority manuscripts. Change the training object of the news recognition model and build an interest recognition model; perform secondary classification on the articles to be pushed based on the interest recognition model, add interest tags to non-news broadcast articles, and obtain articles of interest; Manuscript picture submodule: uses natural language processing technology to identify the content of interesting manuscripts, and adds pictures to the interesting manuscripts based on the content, to obtain the manuscripts to be pushed; Manuscript promotion module: including manuscript statistics submodule and manuscript push submodule; Manuscript Statistics Submodule: used to count the historical views of different types of manuscripts to be pushed, and calculate the additional supplementary amount of manuscripts to be pushed based on the manuscripts to be pushed; Collect the information of prospective contributors with continuous submission records as manuscript source information; obtain the number of views of news articles and articles of interest corresponding to the manuscript source information in the past three months to determine the high-quality manuscript source information; Manuscript push submodule: used to obtain the number of all users and push priority manuscripts to all users; obtain the number of views of different types of manuscripts for each user, calculate the acceptance value of each user for different types of manuscripts, and recommend manuscripts to users based on the user's acceptance value; Manuscript update module: used to obtain manuscripts with high-quality source information and manuscripts with non-high-quality source information in real time, update original manuscript data, and push manuscripts.

2. The intelligent manuscript publishing system according to claim 1, characterized in that: The workflow of the manuscript classification submodule is as follows: Process B1: Count the number of quasi-submission information, recorded as si; count the number of quasi-pushed manuscripts corresponding to each quasi-submission information, and get do1, do2~do si ; Among them, do1 represents the number of manuscripts that are to be pushed corresponding to the first quasi-submission information; do2 represents the number of manuscripts that are to be pushed corresponding to the second quasi-submission information; and so on. si Indicates the number of manuscripts that are about to be pushed corresponding to the si-th submission information; Process B2: Obtain news articles as training manuscripts a1 and non-news articles as training manuscripts a2, and use the logistic regression model to build a news recognition model; Process B3: Using the joblib library, load the quasi-push manuscripts corresponding to the 1st to sith quasi-submission information into the news recognition model, classify the quasi-push manuscripts, and extract the news broadcast manuscript corresponding to each quasi-submission information as the priority manuscript; Count the number of priority manuscripts corresponding to each prospective submission information and get: ne1, ne2~ne si ; Among them, ne1 represents the number of priority manuscripts corresponding to the first quasi-submission information; ne2 represents the number of priority manuscripts corresponding to the second quasi-submission information; and so on, ne si Indicates the number of priority manuscripts corresponding to the si-th prospective submission information; Process B4: Treat the non-priority manuscripts that are about to be pushed as manuscripts to be classified, and define interest tags for the manuscripts to be classified. Interest tags include: technology, life, shopping, entertainment, and others; Change the training object of the news recognition model and build an interest recognition model; perform secondary classification of the manuscripts to be classified according to the interest recognition model, and count the number of scientific manuscripts in the manuscripts to be classified corresponding to the 1st to sith quasi-submission information, and obtain: sc1, sc2~sc si ; The number of life manuscripts is: li1, li2~li si ; The number of shopping manuscripts is: sh1, sh2~sh si ; The number of entertainment articles: re1, re2~re si ; The number of other manuscripts is: ot1, ot2~ot si ; Among them, sc1, li1, sh1, re1 and ot1 respectively represent the number of scientific manuscripts, life manuscripts, shopping manuscripts, entertainment manuscripts and other manuscripts corresponding to the first quasi-submission information; sc2, li2, sh2, re2, and ot2 respectively represent the number of scientific manuscripts, life manuscripts, shopping manuscripts, entertainment manuscripts, and other manuscripts corresponding to the second quasi-submission information; And so on, sc si 、li si ,sh si 、re si and ot si , respectively, represent the number of scientific manuscripts, life manuscripts, shopping manuscripts, entertainment manuscripts and other manuscripts corresponding to the si-th quasi-submission information; Process B5: Send the science and technology manuscripts, life manuscripts, shopping manuscripts, entertainment manuscripts and other manuscripts corresponding to the 1st to sith quasi-submission information as interest manuscripts to the manuscript illustration submodule for illustration, obtain the manuscripts to be pushed, and enter the manuscript statistics submodule.

3. The intelligent manuscript publishing system according to claim 2, characterized in that: The specific process of process B2 is as follows: Step B21: Import the pandas and scikit-learn libraries; Create a list yes_texts to store training manuscript a1; create a list no_texts to store training manuscript a2; Combine news and non-news article texts as feature X, that is: X = news_texts + non_news_texts; Define label y, mark news articles as 1 and non-news articles as 0, that is: Process B22: Use the load_data function to load the features X and labels y as the combined dataset; divide the combined dataset into a training set and a test set; The text of the training set is denoted as X_train and the label is denoted as y_train; The text of the test set is recorded as X_test and the label is recorded as y_test; The number of training sets and test sets satisfies: training set > test set; Process B23: Use the manuscripts in the training set as training text and the manuscripts in the test set as test text; initialize the TF-IDF vectorizer and perform TF-IDF feature extraction on the training set X_train; Use the fit_transform method to learn the vocabulary in the training text, calculate the TF-IDF value of each word in each training text, and obtain the sparse matrix X_train_tfidf; In the sparse matrix X_train_tfidf, each row represents a training text, each column represents a word, and the values ​​in the matrix correspond to the TF-IDF values ​​of the word in different training texts; Use the transform method to extract TF-IDF features from the test set text to obtain the training feature X_test_tfidf; Process B24: Initialize the logistic regression model model and train the model with the training set data, that is: model.fit(X_train_tfidf, y_train); Use the trained model to predict the test set, that is: y_pred = model.predict(X_test_tfidf); Use the accuracy_score() method to calculate the accuracy of the model on the test set; and use the classification_report() method to calculate the precision until the accuracy and precision between any article in the training set with label y_train = 1 and any article in the test set with label y_test = 1 are close to 100%; the accuracy and precision between the articles with label y_train = 1 and the articles with label y_test = 0, the articles with label y_train = 0 and the articles with label y_test = 1, and the articles with label y_train = 0 and the articles with label y_test = 0 are all 0%, completing the training of the news recognition model; Load the joblib library to save the news recognition model and load manuscripts for the news recognition model.

4. The intelligent manuscript publishing system according to claim 2, characterized in that: The workflow of the manuscript statistics submodule is as follows: Process C1: Count the number of views of scientific articles in the past three months and get: hsc3, hsc2 and hsc1; The number of views of life articles is: hli3, hli2 and hli1; The number of views of shopping articles is: hsh3, hsh2 and hsh1; The number of views of entertainment articles is: hre3, hre2 and hre1; The views of other manuscripts are: hot3, hot2 and hot1; Among them, hsc3, hli3, hsh3, hre3 and hot3 represent the page views of science and technology articles, life articles, shopping articles, entertainment articles and other articles in the third month; hsc2, hli2, hsh2, hre2, and hot2 represent the page views of science and technology articles, lifestyle articles, shopping articles, entertainment articles, and other articles in the second month; hsc1, hli1, hsh1, hre1, and hot1 represent the number of page views of science and technology articles, lifestyle articles, shopping articles, entertainment articles, and other articles in the first month; Process C2: Calculate the growth rate of scientific manuscripts from the third month to the second month, recorded as usc （3-2） ;usc （3-2） The calculation formula is: usc （3-2） = (hsc2-hsc3) / hsc3; The growth rate of life articles from the third month to the second month, denoted as uli （3-2） ;uli （3-2） The calculation formula is: （3-2） = (hli2-hli3) / hli3; The growth rate of shopping manuscripts from the third month to the second month, recorded as ush （3-2） ;ush （3-2） The calculation formula is: （3-2） = (hsh2-hsh3) / hsh3; The growth rate of entertainment articles from the third month to the second month, recorded as ure （3-2） ure （3-2） The calculation formula is:ure （3-2） = (hre2-hre3) / hre3; The growth rate of other manuscripts from the third month to the second month is denoted as uot （3-2） ;uot （3-2） The calculation formula is: uot （3-2） = (hot2-hot3) / hot3.

5. The intelligent manuscript publishing system according to claim 4, characterized in that: The subsequent process of process C2 is as follows: Process C3: Calculate the growth rate of scientific manuscripts from the second month to the first month, recorded as usc （2-1） ;usc （2-1） The calculation formula is: usc （2-1） = (hsc1-hsc2) / hsc2; The growth rate of life articles from the second month to the first month, denoted as uli （2-1） ;uli （2-1） The calculation formula is: （2-1） = (hli1-hli2) / hli2; The growth rate of shopping articles from the second month to the first month, recorded as ush （2-1） ;ush （2-1） The calculation formula is: （2-1） = (hsh1-hsh2) / hsh2; The growth rate of entertainment articles from the second month to the first month, recorded as ure （2-1） ure （2-1） The calculation formula is:ure （2-1） = (hre1-hre2) / hre2; The growth rate of other manuscripts from the second month to the first month, denoted as uot （2-1） ;uot （2-1） The calculation formula is: uot （2-1） = (hot1-hot2) / hot2; Process C4: Calculation of sc1~sc si The sum of li1~li is recorded as asc; si The sum of sh1 and sh2 is written as ali; si The sum of re1 and re2 is written as ash; si The sum of ot1 and ot2 is written as are; si The sum of is written as aot; {usc （3-2） , usc （2-1） , hsc2, hsc1} is group a1, with {uli （3-2） ,uli （2-1） , hli2, hli1} is group a2, with {ush （3-2） ,ush （2-1） , hsh2, hsh1} is group a3, with {ure （3-2） ,ure （2-1） , hre2, hre1} is group a4, with {uot （3-2） ,uot （2-1） , hot2, hot1} is group a5; Using the state transition matrix, the expected number of scientific manuscript submissions is calculated based on group a1, denoted as Esc; The expected number of scientific manuscripts submitted based on group a2 is calculated as Eli; the expected number of scientific manuscripts submitted based on group a3 is calculated as Esh; the expected number of scientific manuscripts submitted based on group a4 is calculated as Ere; the expected number of scientific manuscripts submitted based on group a5 is calculated as Eot; Process C5: Compare asc and Esc. If asc ≤ Esc, then Δsc = (Esc - asc); if asc > Esc, then Δsc = 0. Δsc represents the number of additional supplementary manuscripts for the scientific manuscript. Compare ali and Eli. If ali≤Eli, then Δli=(Eli-ali); if ali>Eli, then Δli=0; where Δli represents the number of additional supplementary articles for daily life. Compare ash and Esh. If ash ≤ Esh, then Δsh = (Esh - ash); if ash > Esh, then Δsh = 0. Δsh represents the number of additional manuscripts for the shopping manuscript. Compare the size of are and Ere. If are ≤ Ere, then Δre = (Ere - are); if are > Ere, then Δre = 0; where Δre represents the number of additional drafts for entertainment articles. Compare aot and Eot. If aot ≤ Eot, then Δot = (Eot - aot); if aot > Eot, then Δot = 0. Δot represents the number of additional manuscripts. According to Δsc, Δli, Δsh, Δre and Δot, the corresponding types of manuscripts are supplemented; Process C6: Collect the information of prospective manuscripts with continuous submission records in the past three months as manuscript source information; The number of manuscript source information is counted, denoted as ni; the relationship between ni and si satisfies: ni≤si; si represents the number of quasi-submission information; Obtain the number of views of news articles and articles of interest corresponding to the source information in the past three months, determine the high-quality source information, and enter the article push sub-module.

6. The intelligent manuscript publishing system according to claim 5, characterized in that: The specific process of process C6 is as follows: Process C61: Obtain the number of news article views corresponding to the source information in the past three months to determine the high-quality source information of the news articles; Get the number of news article views corresponding to the source information in the past three months, and get ne(1,3), ne(1,2), ne(1,1) to ne(ni,1); Among them, ne(1,3) represents the number of news article views in the third month corresponding to the first source information; ne(1,2) represents the number of news article views in the second month corresponding to the first source information; ne(1,1) represents the number of news article views in the first month corresponding to the first source information; Similarly, ne(ni,1) represents the number of news article views in the first month corresponding to the ni-th source information; Process C62: Calculate the sum of the number of news article page views in the third month corresponding to the first to ni-th source information, record it as ane(-, 3); the sum of the number of news article page views in the second month as ane(-, 2); the sum of the number of news article page views in the first month as ane(-, 1); Process C63: Calculate the continuous variation coefficient of the first source information news article page views, recorded as up （1） ; up （1） The calculation formula is: ； Calculate the continuous variation coefficient of the second source news article page views, recorded as up （2） ; up （2） The calculation formula is: ； Similarly, calculate the continuous change coefficient of the number of views of the ni-th source information news article, recorded as up （ni） ; up （ni） The calculation formula is: ； Process C64: Get the priority number of manuscripts corresponding to the manuscript source information from 1 to ni, and get: nne1, nne2~nne ni ;nne1, nne2~nne ni With ne1, ne2~ne si The relationship satisfies: {nne1, nne2~nne ni } {ne1, ne2~ne si }; Among them, nne1 represents the number of priority manuscripts corresponding to the first manuscript source information; nne2 represents the number of priority manuscripts corresponding to the second manuscript source information; and so on, nne ni Indicates the number of priority manuscripts corresponding to the ni-th manuscript source information; Process C65: Calculate the news priority coefficient of the first source information, denoted as fne （1） ;fne （1） The calculation formula is: fne （1） =up （1） ×nne1; Calculate the news priority coefficient of the second source information, denoted as fne （2） ;fne （2） The calculation formula is: fne （2） =up （2） ×nne2; Similarly, calculate the news priority coefficient of the ni-th source information, denoted as fne （ni） ;fne （ni） The calculation formula is: fne （ni） =up （ni） ×nne ni ; Process C66: Extraction of fne （1） ~fne （ni） The largest news priority coefficient is recorded as nem; the source information corresponding to nem is used as the high-quality source information of the news article; Process C67: Repeat the same process of determining the high-quality source information of news articles to determine the high-quality source information corresponding to each article of interest.

7. The intelligent manuscript publishing system according to claim 5, characterized in that: The workflow of the manuscript push submodule is as follows: Process D1: Get the number of all users, denoted as us; push priority articles to all users; Get the number of views of each user's scientific articles in the past three months, and get: usc(1,3), usc(1,2), usc(1,1)~usc(us,1); Among them, usc(1,1) represents the number of views of the scientific articles of the first user in the third month; usc(1,2) represents the number of views of the scientific articles of the first user in the second month; usc(1,1) represents the number of views of the scientific articles of the first user in the first month; Similarly, usc(us,1) represents the number of views of scientific articles by the us-th user in the first month; Get the number of views of each user's life articles in the past three months, and get: uli (1,3), uli (1,2), uli (1,1) ~ uli (us,1); Among them, uli (1,1) represents the number of views of the first user's life article in the third month; uli (1,2) represents the number of views of the first user's life article in the second month; uli (1,1) represents the number of views of the first user's life article in the first month; Similarly, uli(us,1) represents the number of views of the life articles of the us-th user in the first month; Get the number of views of each user's shopping articles in the past three months, and get: ush(1,3), ush(1,2), ush(1,1) ~ ush(us,1); Among them, ush(1,3) represents the number of views of the first user's shopping articles in the third month; ush(1,2) represents the number of views of the first user's shopping articles in the second month; ush(1,1) represents the number of views of the first user's shopping articles in the first month; Similarly, ush(us,1) represents the number of views of the shopping article of the us-th user in the first month; Get the number of views of each user's entertainment articles in the past three months, and get: ure(1,3), ure(1,2), ure(1,1) ~ ure(us,1); Among them, ure(1,3) represents the number of page views of the entertainment articles of the first user in the third month; ure(1,2) represents the number of page views of the entertainment articles of the first user in the second month; ure(1,1) represents the number of page views of the entertainment articles of the first user in the first month; Similarly, ure(us,1) represents the number of views of entertainment articles by the us-th user in the first month; Get the number of views of other articles of each user in the past three months, and get: uot(1,3), uot(1,2), uot(1,1)~uot(us,1); Among them, uot(1,3) represents the number of views of other articles of the first user in the third month; uot(1,2) represents the number of views of other articles of the first user in the second month; uot(1,1) represents the number of views of other articles of the first user in the first month; Similarly, uot(us,1) represents the number of views of other articles by the us-th user in the first month; Process D2: Calculate the average number of scientific article views of all users in the past three months, recorded as msc; The average number of views of life articles in the past three months is recorded as mli; The average number of shopping article views in the past three months, denoted as msh; The average number of entertainment article views in the past three months, recorded as mre; The average number of views of other articles in the past three months is recorded as mot; Process D3: Using the first user's page views of science and technology articles, lifestyle articles, shopping articles, entertainment articles, and other articles in the past three months as sample data, construct a sample matrix, denoted as matrix o. The mathematical expression of matrix o is: ； Calculate the acceptance value of the first user for different types of manuscripts based on the matrix o, and push the manuscripts to be pushed to the first user; Process D4: Repeat the same process of pushing the manuscript to be pushed for the first user, and push the manuscript to be pushed for the second to us users.

8. The intelligent manuscript publishing system according to claim 7, characterized in that: The specific process of process D3 is as follows: Process D31: Define calculation formula d1-1: X （1,j） =1-[usc(1,j) / (msc / 3)]; Calculation formula d1-2: Y （1,j） =1-[uli(1,j) / (mli / 3)]; Calculation formula d1-3: Z （1,j） =1-[ush(1,j) / (msh / 3)]; Calculation formula d1-4: P （1,j） =1-[ure(1,j) / (mre / 3)]; Calculation formula d1-5: Q （1,j） =1-[uot(1,j) / (mot / 3)]; Among them, X （1,j） It represents the deviation of the reading volume of scientific articles of the first user in the jth month relative to the average reading volume of scientific articles of all users; Y （1,j） It represents the deviation of the reading volume of the first user's life article in the jth month relative to the average reading volume of all users' life articles; Z （1,j） It represents the deviation of the number of shopping articles read by the first user in the jth month relative to the average number of shopping articles read by all users; P （1,j） It represents the deviation of the entertainment article reading volume of the first user in the jth month relative to the average entertainment article reading volume of all users; Q （1,j） It represents the deviation of the number of other articles read by the first user in the jth month relative to the average number of other articles read by all users; The value range of j is: 1 to 3; Process D32: Calculate the offsets of the parameters in matrix o according to equations d1-1 to d1-5 in process D31 to obtain matrix o1. The mathematical expression of matrix o1 is: ； Calculate X （1,1） 、X （1,2） and X （1,3） The average value of is denoted as aX;Y （1,1） 、Y （1,2） and Y （1,3） The average value of is denoted as aY; Z （1,1） , Z （1,2） and Z （1,3） The average value is denoted as aZ; P （1,1） 、P （1,2） and P （1,3） The average value is recorded as aP;Q （1,1） , Q （1,2） and Q （1,3） The average value is denoted as aQ; Process D33: Calculate X （1,1） 、X （1,2） and X （1,3） The standard deviation of is denoted as SX; the calculation formula of SX is: ； Calculate Y （1,1） 、Y （1,2） and Y （1,3） The standard deviation of is denoted as SY; the calculation formula of SY is: ； Calculate Z （1,1） , Z （1,2） and Z （1,3） The standard deviation of is denoted as SZ; the calculation formula of SZ is: ； Calculate P （1,1） 、P （1,2） and P （1,3） The standard deviation of is recorded as SP; the calculation formula of SP is: ； Calculate Q （1,1） , Q （1,2） and Q （1,3） The standard deviation of is denoted as SQ; the calculation formula of SQ is: ； Process D34: Define calculation formula d2-1: dX （1,j） =(X （1,j） -aX) / SX; Calculation formula d2-2: dY （1,j） =(Y （1,j） -aY) / SY; Calculation formula d2-3: dZ （1,j） =(Z （1,j） -aZ) / SZ; Calculation formula d2-4: dP （1,j） =(P （1,j） -aP) / SP; Calculation formula d2-5: dQ （1,j） =(Q （1,j） -aQ) / SQ; Among them, dX （1，j） Indicates X （1，j） The value of the second normalization, dY （1，j） Indicates Y （1，j） The value of the second normalization, dZ （1，j） Indicates Z （1，j） The value of secondary normalization, dP （1，j） Indicates P （1，j） The value of the second normalization, dQ （1，j） Express Q （1，j） The value for secondary normalization; the value range of j is: 1 to 3.

9. The intelligent manuscript publishing system according to claim 8, characterized in that: The subsequent process of process D34 is as follows: Process D35: According to calculation formulas d2-1 to d2-5 in process D34, the quadratic normalized values ​​of the parameters in matrix o1 are calculated to obtain matrix o2. The mathematical expression of matrix o2 is: ； Calculate dX （1,1） 、dX （1,2） and dX （1,3） The average value, denoted as dX （1,-） ; Calculate dY （1,1） 、dY （1,2） and dY （1,3） The average value of （1,-） ; Calculate dZ （1,1） 、dZ （1,2） and dZ （1,3） The average value of （1,-） Calculate dP （1,1） 、dP （1,2） and dP （1,3） The average value, denoted as dP （1,-） ; Calculate dQ （1,1） 、dQ （1,2） and dQ （1,3） The average value of （1,-） ; Calculate the average value of the parameters in the first row of matrix o2, recorded as dD （-，1） ; Calculate the average value of the parameters in the second row of matrix o2, recorded as dD （-，2） ; Calculate the average value of the parameters in the third row of matrix o2, recorded as dD （-，3） ; Process D36: Define calculation formula d3-1: ; Among them, rX （1，j） represents dX （1，j） The covariance of , dD （-，j） Represents the average value of the jth row in matrix o2; the value range of j is: 1 to 3; Calculation formula d3-2: ; Among them, rY （1，j） represents dY （1，j） covariance of Calculation formula d3-3: ; Among them, rZ （1，j） represents dZ （1，j） covariance of Calculation formula d3-4: ; Among them, rP （1，j） Indicates dP （1，j） covariance of Calculation formula d3-5: Among them, rQ （1，j） Indicates dQ （1，j） covariance of Process D37: Calculate the covariance of each parameter in the matrix o2 according to equations d3-1 to d3-5 in process D36 to obtain the matrix o3. The mathematical expression of the matrix o3 is: ； Split the matrix o3 to obtain matrices o4-1 to o4-5; The mathematical expression of matrix o4-1 is: ； The mathematical expression of matrix o4-2 is: ； The mathematical expression of matrix o4-3 is: ； The mathematical expression of the matrix o4-4 is: ； The mathematical expression of matrix o4-5 is: ； Process D38: Use Intel MKL library functions to define and calculate weight values ​​λ1, λ2, λ3, λ4, and λ5, as well as offsets μ1, μ2, μ3, μ4, and μ5, so that matrices o4-1 through o4-5 satisfy the following mathematical relationship: ； Process D39: Calculate the acceptance value of the first user's scientific manuscript, denoted as bsc, bsc = λ1 / μ1; Calculate the acceptance value of the first user's life manuscript and record it as bli, bli = λ2 / μ2; Calculate the acceptance value of the first user's shopping manuscript and record it as bsh, bsh = λ3 / μ3; Calculate the acceptance value of the first user's entertainment manuscript and record it as bre, bre = λ4 / μ4; Calculate the acceptance value of other manuscripts of the first user and record it as bot, bot = λ5 / μ5; Arrange bsc, bli, bsh, bre and bot in descending order to obtain the interest sequence of the first user, and push the articles to be pushed to the first user in the order of the interest sequence.

10. An intelligent manuscript publishing method, applicable to an intelligent manuscript publishing system according to any one of claims 1 to 9, characterized in that: The publication method includes: Step S1: obtaining original manuscripts and their submission information, eliminating manuscripts and submission information containing inappropriate content, and obtaining manuscripts to be pushed and submission information to be submitted; Step S2: Build a news recognition model and use it to classify the articles to be pushed, extracting the news broadcast articles as the priority articles; Change the training object of the news recognition model and build an interest recognition model; perform secondary classification on the articles to be pushed based on the interest recognition model, add interest tags to non-news broadcast articles, and obtain articles of interest; Using natural language processing technology, the content of the manuscripts of interest is identified, and pictures are added to the manuscripts of interest based on the content of the manuscripts of interest, thereby obtaining the manuscripts to be pushed; Step S3: Count the historical views of different types of manuscripts to be pushed, and calculate the additional supplementary amount of manuscripts to be pushed based on the manuscripts to be pushed; Collect the information of prospective contributors with continuous submission records as manuscript source information; obtain the number of views of news articles and articles of interest corresponding to the manuscript source information in the past three months to determine the high-quality manuscript source information; Obtain the total number of users and push priority articles to all users; obtain the number of views of each user's articles of different types, calculate each user's acceptance value for different types of articles, and recommend articles to users based on their acceptance values; Step S4: Obtain manuscripts with high-quality manuscript source information and manuscripts with non-high-quality manuscript source information in real time, update original manuscript data, and push manuscripts.

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