CN111695348A - Method and device for recommending case handling units according to case handling experience - Google Patents
Method and device for recommending case handling units according to case handling experience Download PDFInfo
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Abstract
The invention provides a method and a device for recommending case handling units according to the experience of case handling. The method, the device, the electronic equipment and the computer storage medium solve the problem that the manual judgment of the docking unit is easy to cause errors due to meticulous labor division and large quantity of functional departments. The proposal can be accurately submitted to a case handling unit, so that the case handling time is shortened, and meanwhile, the historical case handling experience is used for reference, and the historical case handling experience is effectively utilized.
Description
Technical Field
The invention belongs to the technical field of proposal processing methods, and particularly relates to a method and a device for recommending a proposal handling unit according to the experience of proposal handling.
Background
A proposal refers to a suggestion to submit a meeting discussion decision. In the operation management of large groups and companies, proposals are often generated, and the proposals decided in discussion need to be handed over to different departments. For large groups and enterprises, departments have detailed labor division and a large number, and manual judgment of docking units is easy to cause errors, so that a proposal cannot be accurately submitted to a case handling unit. The last proposal handling experience is not effectively used for reference and fully utilized. The proposed proposal has longer handling time.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art. Therefore, the invention provides a method and a device for recommending case handling units according to the experience of case handling.
According to an embodiment of the first aspect of the invention, a method for recommending a case handling unit according to a proposal handling experience comprises the following steps:
obtaining proposal database data information, wherein the proposal database data information comprises Internet public proposal sample information;
obtaining first keyword phrase information, wherein the first keyword phrase information consists of proposal keywords in a proposal text;
obtaining proposal text weight vector matrix information, wherein the weight vector matrix information is obtained by assigning values to the first keyword phrase information according to the weight of the proposal keywords;
and obtaining the matrix distance between the proposed text weight vector matrix information and the Internet public proposed sample information to obtain a case handling unit.
The method for recommending the case handling unit according to the proposal handling experience provided by the embodiment of the invention at least has the following technical effects:
the problem that the manual judgment of the docking units is easy to cause errors due to meticulous labor division and numerous quantities of functional departments is solved. The proposal can be accurately submitted to a case handling unit, the case handling time is shortened, and the historical case handling experience is effectively utilized.
In the method, the data information of the proposal database comprises internet public proposal sample information, and the internet public proposal sample information covers the proposal handling data of all regions across the country and can be obtained by capturing all public proposals on the internet by the prior art. The flow is shown in fig. 2.
According to some embodiments of the invention, the internet public proposal sample information is a sample vector matrix.
According to some embodiments of the present invention, the calculation method of the weight of the proposed keyword is a word frequency-inverse document frequency calculation method.
The word frequency-inverse document frequency calculation method is used for evaluating the importance degree of a word on one document in a document set or a corpus, and the importance degree of the word is increased in proportion to the occurrence frequency of the word in the document, but is reduced in inverse proportion to the occurrence frequency of the word in a word bank.
Wherein, the word frequency refers to the frequency of a certain word appearing in the file, and the calculation method comprises the following steps:
word frequency (TF) is the number of occurrences of a word in an article/the total number of words in the article.
The Inverse Document Frequency (IDF) refers to the total number of documents divided by the number of documents containing the term, and is calculated by:
the Inverse Document Frequency (IDF) is log (total number of documents in corpus/(number of documents containing the word +1)), and the denominator is increased by 1 in order to prevent the word from being absent from the corpus and resulting in a dividend of zero.
The calculation method of the word frequency-inverse file frequency comprises the following steps: the word frequency-inverse file frequency is the word frequency (TF) × inverse file frequency (IDF).
The calculated word frequency-inverse file frequency value is the weight value of the corresponding keyword.
According to some embodiments of the invention, the method of calculating the matrix distance comprises a spatial distance algorithm.
According to some embodiments of the invention, the spatial distance algorithm comprises a cosine algorithm.
An apparatus for recommending a transaction unit according to a proposed content according to an embodiment of a second aspect of the present invention includes a unit for performing the above method.
An electronic device according to an embodiment of the third aspect of the present invention includes a memory for storing a computer program including program instructions and a processor configured to call the program instructions to execute the above method.
A computer storage medium according to an embodiment of the fourth aspect of the invention stores a computer program comprising program instructions which, when executed by a processor, cause the processor to perform the method described above.
Drawings
FIG. 1 is a flow chart of a method for recommending a proposed case handling organization based on a proposed case handling experience.
FIG. 2 is a flow chart of a method for obtaining proposal library data information using all published proposals on the Internet.
FIG. 3 is a schematic diagram of the structure of the apparatus for recommending a proposed office based on the experience of proposal handling.
FIG. 4 is a schematic diagram of an electronic device for recommending a proposed case office based on a proposed case handling experience.
Detailed Description
The following are specific examples of the present invention, and the technical solutions of the present invention will be further described with reference to the examples, but the present invention is not limited to the examples.
Example 1
The embodiment provides a method for recommending a case handling unit according to the experience of case handling, the flow is shown in figure 1, and the method comprises the following steps:
obtaining proposal database data information, wherein the proposal database data information comprises Internet public proposal sample information;
obtaining first keyword phrase information, wherein the first keyword phrase information consists of proposal keywords in a proposal text;
obtaining proposal text weight vector matrix information, wherein the weight vector matrix information is obtained by assigning values to the first keyword phrase information according to the weight of the proposal keywords;
and obtaining the matrix distance between the proposed text weight vector matrix information and the Internet public proposed sample information to obtain a case handling unit.
The data information of the proposal library comprises internet public proposal sample information, the internet public proposal sample information covers the proposal handling data of all regions across the country, and all public proposals on the internet can be captured and obtained through the prior art. The flow is shown in fig. 2. And the Internet publicly proposes sample information as a sample vector matrix. The calculation method of the weight of the proposal keyword is a word frequency-inverse file frequency calculation method.
The word frequency-inverse document frequency calculation method is used for evaluating the importance degree of a word on one document in a document set or a corpus, and the importance degree of the word is increased in proportion to the occurrence frequency of the word in the document, but is reduced in inverse proportion to the occurrence frequency of the word in a word bank.
Wherein, the word frequency refers to the frequency of a certain word appearing in the file, and the calculation method comprises the following steps:
word frequency (TF) is the number of occurrences of a word in an article/the total number of words in the article.
The Inverse Document Frequency (IDF) refers to the total number of documents divided by the number of documents containing the term, and is calculated by:
the Inverse Document Frequency (IDF) is log (total number of documents in corpus/(number of documents containing the word +1)), and the denominator is increased by 1 in order to prevent the word from being absent from the corpus and resulting in a dividend of zero.
The calculation method of the word frequency-inverse file frequency comprises the following steps: the word frequency-inverse file frequency is the word frequency (TF) × inverse file frequency (IDF).
The calculated word frequency-inverse file frequency value is the weight value of the corresponding keyword.
The calculation method of the matrix distance comprises a space distance algorithm. The spatial distance algorithm includes a cosine algorithm.
Taking a certain proposal disclosed by the internet as an example, the text excerpts are as follows:
with the increasing demand for high-quality educational resources, school work is under increasing pressure. One of the biggest puzzles faced at present is: under the condition that the teacher resource is not sufficient originally, the problem of temporary teachers and resources shortage occurs in schools when the teacher needs to go out for learning for a plurality of days and asks for a plurality of days for illness or when the woman teacher needs to be pregnant for bearing the students for one year and a half (especially after the two-child policy is opened, the woman teacher seems to be more frequent in vacation), and the influence on school teaching is great. Because schools are 'one radish and one pit', teachers need to perform normal teaching work and also need to take the safety management, the health and epidemic prevention monitoring, the lunch break management, the second class training, the organization of school activities, the self study of entering repair and the like. If the problem of the shortage of teachers and resources depends on the internal digestion of schools, the schools which are busy in work can not be operated at all, and the normal education and teaching work can be disturbed certainly. Under the condition that 'people are wasted' everywhere, it is not easy to find out the teacher in the course of lessons in time. In addition, even if the system is found, the teacher in the lecture can not guarantee the payroll treatment, is lack of the belonging sense, is busy with the recruitment and other problems, and the like, and the instability of the work is brought. And a suggestion of 'building a resource library of a forthcoming teacher' is provided aiming at the problems.
After Chinese word segmentation is carried out on contents and word weight scoring is carried out according to tf-idf, the extracted main keywords and the weights are as follows (part of words with particularly low weights do not influence the classification of articles, so that the words are omitted to avoid influencing the calculation efficiency):
3.0 of course substitution, 4.1 of teacher, 1.2 of teaching, 2.3 of work, 2.4 of teacher, 5.0 of second classroom, 3.0 of school, 4.2 of teacher's resource, 0.4 of leave asking, 4.3 of epidemic prevention, 0.1 of work, 1 of resource bank, 0.5 of work and resources, 0.03 of shortage, 0.8 of noon break, 1 of school worker, 2.3 of repair and study, 1 of busy, 1 of pregnancy, 1.5 of illness, 1.2 of going out, 1.5 of going to dinner, 0.2 of lunch, 1.3 of vacation, and 1.8 of recruitment.
Then, the similarity comparison is carried out with a proposal submitted to an education department in a sample library, and the content of the proposal is extracted as follows:
according to statistics at the end of 2012, the city of the student has 1968 places of various levels of civil schools, 91 ten thousands of students account for 41.4 percent of the total number of students, and 38731 exclusively-designated teachers of the civil schools (excluding education training institutions) account for 33.5 percent of the total number of exclusively-designated teachers of the whole city. Wherein the professional teachers of the civil school in the compulsory education stage account for 26.44 percent of the total number of the professional teachers in the compulsory education stage. Therefore, the number of the teachers in the civil school is one third of that of the teachers in the city, and the construction of the team of the teachers in the civil school directly influences the education quality shared by nearly 4 adult students in the city. However, at present, the following problems still exist in the construction of a teacher team for the citizen to handle schools.
It obtains the following keywords:
3.2 teachers, 2.3 folk offices, 1.6 schools, 0.8 team construction, 1.8 obligate education, 1.2 my cities, 1.2 education quality, 3.2 students at school, 0.1 total, 1.2 students, 0.1 direct influence, 0.5 citizens, 0.12 city, 0.03 ten thousands of people, 0.1 year end, 0.1 statistics, 1.2 training and 0.3 organization.
And merging the collected entries with the entry set of the first proposal to obtain a summarized entry set:
class-substitute, teacher, teaching, work, teacher, second classroom, school, teacher's materials, leave, health epidemic prevention, work, resource pool, wage, treatment, shortage, noon break, school in, school workers, study, busy, pregnant, sick, go out, go, lunch, vacation, employment, civil affairs, team construction, compulsory education, I city, education quality, at school student, total number, direct influence, citizen, city, ten thousand, year end, statistics, training, organization.
Based on the entry set, respective weight arrays are obtained.
Proposal one (the current proposal to be analyzed):
3.0 of course substitution, 4.1 of teacher, 1.2 of teaching, 2.3 of work, 2.4 of teacher, 5.0 of second classroom, 3.0 of school, 4.2 of teacher's resource, 0.4 of leave asking, 4.3 of epidemic prevention, 0.1 of work, 1 of resource bank, 0.5 of work and resources, 0.03 of shortage, 0.8 of noon break, 1 of school worker, 2.3 of repair and study, 1 of busy, 1 of pregnancy, 1.5 of illness, 1.2 of going out, 1.5 of going to dinner, 0.2 of lunch, 1.3 of vacation, and 1.8 of recruitment.
0.0 part of a civil office, 0.0 part of team construction, 0.0 part of compulsory education, 0.0 part of the city of I, 0.0 part of education quality, 0.0 part of a school student and 0.0 part of a total number, 0.0 part of direct influence, 0.0 part of citizen, 0.0 part of the whole city, 0.0 part of ten thousand people, 0.0 part of the year end, 0.0 part of statistics, 0.0 part of training and 0.0 part of organization.
Proposal two (sample):
class 0.0, teacher 0.0, teaching 0.0, work 0.0, teacher 3.2, second classroom 0.0, school 1.6, teacher 0.0, leave 0.0, health epidemic prevention 0.0, work 0.0, resource library 0.0, wage 0.0, treatment 0.0, shortage 0.0, noon break 0.0, school worker 0.0, school 0.0, busy 0.0, pregnancy 0.0, illness 0.0, go out 0.0, employment 0.0, lunch 0.0, vacation 0.0, recruitment, 0.0, teacher, 3.2, civil, 2.3, school, 1.6, team construction, 0.8, compulsory education, 1.8, city, 1.2, quality, 1.2, student 3.2, training, 0.0, 1.0.0, 0.5, 0.0.0, 1.0.0, 1.5, 0.0, 0, 1.0.0.0, 1.0, 1.0.0, 1.8, 1.2, 3, 1.2, 3, 1.0.2, 1..
The angle between the two vectors is calculated using a cosine algorithm to obtain a value of 0.135.
Similarly, the cosine distance value between the sample library and other proposals is calculated, and after the sample library is arranged from big to small, if the sample proposal processing unit of 40 in the first 100 is the education department, 25 are the health hall and 13 are the development and modification committee, the current proposal is judged to be most suitable for being processed by the education department.
Example 2
The present example provides an apparatus 20 for recommending a proposed case handling organization based on a proposed case handling experience, as shown in FIG. 3, the apparatus including means for performing the method of example 1, the apparatus may include:
the receiving unit 201 is configured to receive a proposal text, where the proposal keywords in the proposal text constitute first keyword phrase information.
A processing unit 202, configured to obtain proposal database data information, where the proposal database data information includes internet public proposal sample information;
the processing unit 202 is further configured to obtain first keyword phrase information, where the first keyword phrase information is composed of proposal keywords in a proposal text;
the processing unit 202 is further configured to obtain proposed text weight vector matrix information, where the weight vector matrix information is obtained by assigning a value to the first keyword phrase information according to the weight of the proposed keyword;
the processing unit 202 is further configured to obtain a matrix distance between the proposed text weight vector matrix information and the internet public proposed sample information, so as to obtain a case handling unit.
Example 3
This example provides an electronic device 30 which, as shown in fig. 4, may comprise a receiver 301, a memory 302 and a processor 303, the receiver 301, the memory 302 and the processor 303 being connected by one or more communication buses.
The receiver 301 may be used to receive data, for example, the receiver 301 may be used to receive proposal text.
The processor 303 may be a central processing unit, and the processor 303 may also be other general purpose processors, digital signal processors, application specific integrated circuits, off-the-shelf programmable gate arrays or other editable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor, and optionally, the processor 303 may be any conventional processor or the like. The memory 302 is used for storing program instructions. A processor 303 for calling program instructions stored in the memory 302 for performing the method of embodiment 1, namely:
obtaining proposal database data information, wherein the proposal database data information comprises Internet public proposal sample information;
obtaining first keyword phrase information, wherein the first keyword phrase information consists of proposal keywords in a proposal text;
obtaining proposal text weight vector matrix information, wherein the weight vector matrix information is obtained by assigning values to the first keyword phrase information according to the weight of the proposal keywords;
and obtaining the matrix distance between the proposed text weight vector matrix information and the Internet public proposed sample information to obtain a case handling unit.
Example 4
The present example provides a computer storage medium storing a computer program comprising program instructions that, when executed by a processor, cause the processor to perform the method of embodiment 1.
Claims (8)
1. A method for recommending a case handling unit according to a proposal handling experience is characterized by comprising the following steps:
obtaining proposal database data information, wherein the proposal database data information comprises Internet public proposal sample information;
obtaining first keyword phrase information, wherein the first keyword phrase information consists of proposal keywords in a proposal text;
obtaining proposal text weight vector matrix information, wherein the weight vector matrix information is obtained by assigning values to the first keyword phrase information according to the weight of the proposal keywords;
and obtaining the matrix distance between the proposed text weight vector matrix information and the Internet public proposed sample information to obtain a case handling unit.
2. The method of claim 1, wherein the internet public proposal sample information is a sample vector matrix.
3. The method of claim 1, wherein the calculation method of the weight of the proposed keyword is a word frequency-inverse document frequency calculation method.
4. The method of claim 1, wherein the matrix distance calculation method comprises a spatial distance algorithm.
5. The method of claim 1, wherein the office units comprise a host unit and a split unit, and wherein the vector group distance of the host unit is greater than the vector group distance of the split unit.
6. An apparatus for recommending a proposed office based on a proposed work experience, the apparatus comprising means for performing the method of any of claims 1-5.
7. An electronic device comprising a memory for storing a computer program comprising program instructions and a processor configured to invoke the program instructions to perform the method of any of claims 1 to 5.
8. A computer storage medium, characterized in that the computer storage medium stores a computer program comprising program instructions that, when executed by a processor, cause the processor to perform the method according to any of claims 1-5.
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| CN112395416A (en) * | 2020-11-11 | 2021-02-23 | 湖南正宇软件技术开发有限公司 | Proposal processing method, proposal processing device, computer equipment and storage medium |
| CN116644175A (en) * | 2023-07-26 | 2023-08-25 | 山东唐和智能科技有限公司 | Recommendation system and method for proposal handling units |
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Application publication date: 20200922 |