CN112487257A - Office personnel relationship analysis method - Google Patents
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Abstract
The invention provides an office staff relation analysis method, which comprises the steps of monitoring contact activities of office staff, constructing a staff contact relation chart database, analyzing a daily report of work, extracting staff item relations, and constructing a staff item chart database; based on a person contact relationship chart database, calculating the degree of person contact and the degree of contact relationship between every two persons, and detecting a person contact group; calculating the item correlation degree between every two personnel based on a personnel item map database; based on the calculation result, the degree of deviation between the degree of contact between persons and the degree of correlation of the matter is calculated. The office staff relation analysis method provided by the invention analyzes the relevance of the staff contact relation, the actual activities of the staff and the matters responsible for or participating in the staff, provides data level support for monitoring the abnormal contact relation and improving the working efficiency, and realizes comprehensive and objective evaluation on the staff value.
Description
Technical Field
The invention relates to the technical field of personnel relationship analysis, in particular to an office personnel relationship analysis method.
Background
In an office, the mutual contact between the personnel occurs every day, wherein some contact is effective contact and some contact is ineffective contact, from the contact relationship of the office personnel and the personnel item relationship in a work daily report, the degree of the contact relationship between the personnel and the others, the degree of the contact relationship between every two personnel, the item correlation degree between every two personnel and the personnel contact group detection can provide some data level support for the work evaluation, the abnormal contact relationship detection and the work effectiveness improvement of the office personnel, the technical scheme for analyzing and evaluating the office personnel at present mainly analyzes and evaluates from the working duration, the working content and the leadership evaluation, lacks the attention to the personnel contact relationship, cannot analyze the correlation degree between the actual activities and the responsible or participating items of the personnel, and influences the comprehensive and objective evaluation on the value of the office personnel, therefore, it is necessary to design a method for analyzing office staff relationship to analyze office staff.
Disclosure of Invention
The invention aims to provide an office staff relation analysis method, which analyzes the relevance of staff contact relation, actual staff activities and matters responsible for or participated by the staff, provides data level support for monitoring abnormal contact relation and improving working efficiency, and realizes comprehensive and objective evaluation on staff value.
In order to achieve the purpose, the invention provides the following scheme:
an office staff relationship analysis method, comprising the steps of:
step 1, analyzing the contact relationship of people, and establishing a people contact relationship chart database:
monitoring the contact relationship and the contact duration between the personnel and recording the contact relationship and the contact duration into a personnel contact relationship chart database; calculating the contact degree of each person based on a person contact relation chart database, ranking the persons according to the contact degree from high to low, calculating the contact degree of each person, and ranking the persons according to the contact degree from high to low; calculating the contact relationship degree between every two persons, ranking the contact relationship between each person and other persons according to the contact relationship degree from high to low, and detecting the person contact group according to the contact relationship degree between every two persons;
step 2, analyzing personnel item relationship, and establishing a personnel item map database:
analyzing the daily work report, extracting the personnel item relationship and the item keyword relationship, and recording the personnel item relationship and the item keyword relationship into a personnel item relationship map database; calculating the importance degree of each item and the item participation degree of each person according to the personnel item relation chart database, ranking the persons according to the item participation degree, calculating the item correlation degree between every two persons, and ranking the item correlation relation between the person and other persons according to the item correlation degree from high to low;
and 3, analyzing the deviation degree of the personnel contact relation and the personnel item relation:
collecting the contact degree and the ranking, and the item participation degree and the ranking of each person, calculating the deviation of the contact degree ranking and the item participation degree ranking, and calculating the deviation of the contact degree ranking and the item participation degree ranking; and collecting the contact relationship degree and ranking of each person and other persons, the item correlation degree and ranking, and calculating the deviation of the contact relationship ranking and the item correlation degree ranking.
Optionally, in step 1, the contact relationship and the contact duration between the persons are monitored and recorded in a person contact relationship map database, specifically: and monitoring contact activities among the personnel from a computer vision identity recognition system, a conference record and an online office chat record, calculating to obtain a contacter, a contactee and contact time, and storing the contact time into a personnel contact relation chart database.
Optionally, in step 1, the step of calculating the contact degree of each person, and ranking the persons according to the contact degree from high to low specifically includes:
calculating a touched PR _ P value of each person according to a person contact relation map database by using a PageRank algorithm, wherein PR _ P (i) represents the touched PR _ P value of the ith person, the sum of the touched time lengths of each person is recorded as TP _ P, the touched time length of the ith person is expressed as TP _ P (i), the touched degree WP _ P of each person is calculated, WP _ P (i) represents the touched degree of the ith person, and the calculation formula is as follows:
WP_P(i)＝PR_P(i)*TP_P(i)
ranking the persons according to the WP _ P value from high to low, and representing the WP _ P value ranking of the ith person by RP _ P (i).
Optionally, in step 1, the calculating the contact degree of each person, and ranking the persons according to the contact degree from high to low specifically includes:
reversing the pointing direction in the person contact map database, pointing the contactee at the contactee by using the PageRank algorithm to calculate a contact PR _ a value for each person, wherein PR _ a (i) represents the PR _ a value of the ith person contacting the other person; the sum of the time length of each person contacting other persons is recorded as TP _ A, wherein TP _ A (i) represents the time length of the ith person contacting other persons, and the contact degree WP _ A of each person is calculated, wherein WP _ A (i) represents the contact degree of the ith person, and the calculation formula is as follows:
WP_A(i)＝PR_A(i)*TP_A(i)
ranking the persons according to the WP _ A value from high to low, and representing the WP _ A value of the ith person by RP _ A (i).
Optionally, in step 1, the contact relationship degree between each two people is calculated, and the contact relationship between each person and other people is ranked according to the contact relationship degree from high to low, specifically:
summarizing the mutual contact time length between every two persons as the contact relation degree, recording the contact relation degree between the ith person and the jth person as WP _ R (i, j), ranking the contact relation between each person and other persons according to the WP _ R value from high to low, and representing the ranking of the jth person in the contact relation of the ith person by RP _ R (i, j).
Optionally, in step 1, the detecting the contact population of the persons according to the degree of contact relationship between each two persons specifically includes:
establishing an affinity list of each person, adding each person into the affinity list of the person, adding the top N-1 persons most closely contacted with the person into the affinity list of the person according to a contact relation degree WP _ R value and a ranking RP _ R value, wherein N represents the maximum number of the affinity lists, and obtaining a person contact group by using a frequent item mining algorithm according to the affinity list of each person.
Optionally, in step 2, the work daily newspaper is analyzed, the personnel item relationship and the item keyword relationship are extracted, and the personnel item relationship and the item keyword relationship are recorded in a personnel item relationship map database, specifically:
collecting the items filled in the daily work report, the duration of each item and specific work content, and extracting keywords from all the work content of each item to obtain the relationship between the keywords and the items; summarizing the working time of each person participating in each item to obtain the relationship among the person, the item and the working time; and storing the keywords, the item relation, the personnel, the items and the working time length relation into a personnel item map database.
Optionally, in step 2, the calculating the importance degree of each item specifically includes:
according to the personnel event map database, the PageRank algorithm is used for calculating the PR _ S value of each event, and the PR _ S (i) is used for expressing the importance PR _ S value of the ith event.
Optionally, in step 2, the calculating the item participation degree of each person specifically includes:
multiplying the importance PR _ S value of each matter participated by each person by the time of the matter participated by the person, and then summing all the products to obtain the matter participation WP _ S value of each person, wherein the calculation formula is as follows:
WP_S(i)＝SUMj(TP_S(i,j)*PR_S(j))
WP _ S (i) indicates the subject participation degree of the ith person, TP _ S (i, j) indicates the time length for the ith person to participate in the jth subject, PR _ S (j) indicates the importance degree of the jth subject, the subject is ranked according to the subject participation degree WP _ S value from high to low, and RP _ S (i) indicates the subject participation degree ranking of the ith person.
Optionally, in step 2, the degree of correlation between items of the computing personnel is specifically:
finding the matters commonly participated by every two persons, and calculating the value of the matter correlation degree WP _ SR between the two persons, wherein the calculation formula is as follows:
WP_SR(i,k)＝SUMj(TP_S(i,j)*PR_S(j)*TP_S(k,j))
WP _ SR (i, k) represents the item correlation degree of the ith person and the kth person, PR _ S (j) represents the importance degree of the jth item, TP _ S (i, j) represents the time length of the ith person participating in the jth item, and TP _ S (k, j) represents the time length of the kth person participating in the jth item;
and ranking the event correlation relations between each person and other persons according to the event correlation degree WP _ SR value from high to low, and then representing the ranking of the jth person in the event correlation relation of the ith person as RP _ SR (i, j).
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: according to the office staff relation analysis method provided by the invention, a staff contact relation chart database is constructed by monitoring the contact activities of office staff, the contact or contacted degree of the staff and the contact relation degree between every two staff can be calculated, the staff item chart database is constructed by analyzing the daily report of work and extracting the staff item relation, the item correlation degree between every two staff can be calculated, the deviation degree of the contact degree and the item correlation degree between the staff can be calculated through the calculation result, and the support of some data levels can be provided for the work evaluation, the abnormal contact relation monitoring and the improvement of the work efficiency of the office staff; a personnel contact relationship map database and a personnel item contact relationship map database are constructed, and various relationships can be directly obtained from the map, so that simplicity and clarity are realized; the calculated degree indexes are sorted, and the influence of the degree indexes can be visually observed.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a general flowchart of a method for analyzing office staff relationship according to an embodiment of the present invention;
FIG. 2 is an exemplary diagram of a person contact relationship map;
FIG. 3 is an exemplary human event map;
FIG. 4 is a flow chart for constructing a person contact relationship map;
FIG. 5 is a flow chart for constructing a personnel event map.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide an office staff relation analysis method, which analyzes the relevance of staff contact relation, actual staff activities and matters responsible for or participated by the staff, provides data level support for monitoring abnormal contact relation and improving working efficiency, and realizes comprehensive and objective evaluation on staff value.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
As shown in fig. 1 to 5, the method for analyzing office staff relationship according to the embodiment of the present invention includes the following steps:
step 1, analyzing the contact relationship of people, and establishing a people contact relationship chart database:
monitoring the contact relationship and the contact duration between the personnel and recording the contact relationship and the contact duration into a personnel contact relationship chart database; calculating the contact degree of each person based on a person contact relation chart database, ranking the persons according to the contact degree from high to low, calculating the contact degree of each person, and ranking the persons according to the contact degree from high to low; calculating the contact relationship degree between every two persons, ranking the contact relationship between each person and other persons according to the contact relationship degree from high to low, and detecting the person contact group according to the contact relationship degree between every two persons;
step 2, analyzing personnel item relationship, and establishing a personnel item map database:
analyzing the daily work report, extracting the personnel item relationship and the item keyword relationship, and recording the personnel item relationship and the item keyword relationship into a personnel item relationship map database; calculating the importance degree of each item and the item participation degree of each person according to the personnel item relation chart database, ranking the persons according to the item participation degree, calculating the item correlation degree between every two persons, and ranking the item correlation relation between the person and other persons according to the item correlation degree from high to low;
and 3, analyzing the deviation degree of the personnel contact relation and the personnel item relation:
collecting the contact degree and the ranking, and the item participation degree and the ranking of each person, calculating the deviation of the contact degree ranking and the item participation degree ranking, and calculating the deviation of the contact degree ranking and the item participation degree ranking; and collecting the contact relationship degree and ranking of each person and other persons, the item correlation degree and ranking, and calculating the deviation of the contact relationship ranking and the item correlation degree ranking.
In step 1, the contact relationship and the contact duration between the persons are monitored and recorded in a person contact relationship map database, specifically: and monitoring contact activities among the personnel from a computer vision identity recognition system, a conference record and an online office chat record, calculating to obtain a contacter, a contactee and contact time, and storing the contact time into a personnel contact relation chart database.
As shown in fig. 4, the specific process of constructing the person contact relationship map is as follows: monitoring contact activities among the personnel by using a computer vision identity recognition system, recognizing the contacted person according to station information of the personnel, and if the contacted person cannot be recognized, determining that each personnel in the contact activities is both the contacted person and the contacted person; for each meeting, recording participants and meeting duration as one-time contact activity, wherein each participant is both a contacted person and a contacter; monitoring online contact activities among the personnel according to chat records of online office software, wherein if the online contact activities are chatted by two persons, an initiator of the chat is a contacter, and if the online contact activities are chatted by multiple persons, each personnel is both a contactee and a contacter; recording all the contact persons, the contacted persons and the contact time lengths, summing the contact time lengths of each pair of the contact persons and the contacted persons to obtain (the contact persons, the contacted persons and the total contact time lengths) and storing the sum into a person contact relation chart database, wherein FIG. 2 is a person contact relation chart of 5 persons.
In step 1, the step of calculating the contact degree of each person and ranking the persons according to the contact degree from high to low specifically comprises the following steps:
calculating a touched PR _ P value of each person according to a person contact relation map database by using a PageRank algorithm, wherein PR _ P (i) represents the touched PR _ P value of the ith person, the sum of the touched time lengths of each person is recorded as TP _ P, the touched time length of the ith person is expressed as TP _ P (i), the touched degree WP _ P of each person is calculated, WP _ P (i) represents the touched degree of the ith person, and the calculation formula is as follows:
WP_P(i)＝PR_P(i)*TP_P(i)
ranking the persons according to the WP _ P value from high to low, and representing the WP _ P value ranking of the ith person by RP _ P (i).
In step 1, the contact degree of each person is calculated, and the person is ranked according to the contact degree from high to low, specifically:
reversing the pointing direction in the person contact map database, pointing the contactee at the contactee by using the PageRank algorithm to calculate a contact PR _ a value for each person, wherein PR _ a (i) represents the PR _ a value of the ith person contacting the other person; the sum of the time length of each person contacting other persons is recorded as TP _ A, wherein TP _ A (i) represents the time length of the ith person contacting other persons, and the contact degree WP _ A of each person is calculated, wherein WP _ A (i) represents the contact degree of the ith person, and the calculation formula is as follows:
WP_A(i)＝PR_A(i)*TP_A(i)
ranking the persons according to the WP _ A value from high to low, and representing the WP _ A value of the ith person by RP _ A (i).
In step 1, the contact relationship degree between each two people is calculated, and the contact relationship between each person and other people is ranked from high to low according to the contact relationship degree, specifically:
summarizing the mutual contact time length between every two persons as the contact relation degree, recording the contact relation degree between the ith person and the jth person as WP _ R (i, j), ranking the contact relation between each person and other persons according to the WP _ R value from high to low, and representing the ranking of the jth person in the contact relation of the ith person by RP _ R (i, j).
In the step 1, the detecting of the contact population of the persons through the contact relationship degree between every two persons specifically comprises the following steps:
establishing an affinity list of each person, adding each person into the affinity list of the person, adding the top N-1 persons most closely contacted with the person into the affinity list of the person according to a contact relation degree WP _ R value and a ranking RP _ R value, wherein N represents the maximum number of the affinity lists, and obtaining a person contact group by using a frequent item mining algorithm according to the affinity list of each person.
In step 2, analyzing the daily report, extracting the personnel item relationship and the item keyword relationship, and recording the personnel item relationship and the item keyword relationship into a personnel item relationship map database, wherein the method specifically comprises the following steps:
collecting the items filled in the daily work report, the duration of each item and specific work content, and extracting keywords from all the work content of each item to obtain the relationship between the keywords and the items; summarizing the working time of each person participating in each item to obtain the relationship among the person, the item and the working time; and storing the keywords, the item relation, the personnel, the items and the working time length relation into a personnel item map database.
As shown in fig. 5, the specific process of constructing the personnel and item relationship map is as follows: analyzing the daily work report to obtain items, the duration of each item and the specific work content, extracting keywords from all the work content of each item to obtain (keyword, item) relations, summarizing the work duration of each item participated by each person to obtain (person, item, work duration) relations, and storing the (keyword, item) relations and the (person, item, work duration) relations into a person and item relation graph database, for example, fig. 3 is a person and item relation graph, which comprises 5 persons, 3 times and 7 extracted keywords.
In step 2, the calculating the importance degree of each item specifically includes:
according to the personnel event map database, the PageRank algorithm is used for calculating the PR _ S value of each event, and the PR _ S (i) is used for expressing the importance PR _ S value of the ith event.
In step 2, the calculating of the item participation degree of each person specifically includes:
multiplying the importance PR _ S value of each matter participated by each person by the time of the matter participated by the person, and then summing all the products to obtain the matter participation WP _ S value of each person, wherein the calculation formula is as follows:
WP_S(i)＝SUMj(TP_S(i,j)*PR_S(j))
WP _ S (i) indicates the subject participation degree of the ith person, TP _ S (i, j) indicates the time length for the ith person to participate in the jth subject, PR _ S (j) indicates the importance degree of the jth subject, the subject is ranked according to the subject participation degree WP _ S value from high to low, and RP _ S (i) indicates the subject participation degree ranking of the ith person.
In step 2, the degree of correlation between items of the calculation personnel is specifically:
finding the matters commonly participated by every two persons, and calculating the value of the matter correlation degree WP _ SR between the two persons, wherein the calculation formula is as follows:
WP_SR(i,k)＝SUMj(TP_S(i,j)*PR_S(j)*TP_S(k,j))
WP _ SR (i, k) represents the item correlation degree of the ith person and the kth person, PR _ S (j) represents the importance degree of the jth item, TP _ S (i, j) represents the time length of the ith person participating in the jth item, and TP _ S (k, j) represents the time length of the kth person participating in the jth item;
and ranking the event correlation relations between each person and other persons according to the event correlation degree WP _ SR value from high to low, and then representing the ranking of the jth person in the event correlation relation of the ith person as RP _ SR (i, j).
According to the office staff relation analysis method provided by the invention, a staff contact relation chart database is constructed by monitoring the contact activities of office staff, the contact or contacted degree of the staff and the contact relation degree between every two staff can be calculated, the staff item chart database is constructed by analyzing the daily report of work and extracting the staff item relation, the item correlation degree between every two staff can be calculated, the deviation degree of the contact degree and the item correlation degree between the staff can be calculated through the calculation result, and the support of some data levels can be provided for the work evaluation, the abnormal contact relation monitoring and the improvement of the work efficiency of the office staff; a personnel contact relationship map database and a personnel item contact relationship map database are constructed, and various relationships can be directly obtained from the map, so that simplicity and clarity are realized; the calculated degree indexes are sorted, and the influence of the degree indexes can be visually observed.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (10)
1. An office staff relationship analysis method is characterized by comprising the following steps:
step 1, analyzing the contact relationship of people, and establishing a people contact relationship chart database:
monitoring the contact relationship and the contact duration between the personnel and recording the contact relationship and the contact duration into a personnel contact relationship chart database; calculating the contact degree of each person based on a person contact relation chart database, ranking the persons according to the contact degree from high to low, calculating the contact degree of each person, and ranking the persons according to the contact degree from high to low; calculating the contact relationship degree between every two persons, ranking the contact relationship between each person and other persons according to the contact relationship degree from high to low, and detecting the person contact group according to the contact relationship degree between every two persons;
step 2, analyzing personnel item relationship, and establishing a personnel item map database:
analyzing the daily work report, extracting the personnel item relationship and the item keyword relationship, and recording the personnel item relationship and the item keyword relationship into a personnel item relationship map database; calculating the importance degree of each item and the item participation degree of each person according to the personnel item relation chart database, ranking the persons according to the item participation degree, calculating the item correlation degree between every two persons, and ranking the item correlation relation between the person and other persons according to the item correlation degree from high to low;
and 3, analyzing the deviation degree of the personnel contact relation and the personnel item relation:
collecting the contact degree and the ranking, and the item participation degree and the ranking of each person, calculating the deviation of the contact degree ranking and the item participation degree ranking, and calculating the deviation of the contact degree ranking and the item participation degree ranking; and collecting the contact relationship degree and ranking of each person and other persons, the item correlation degree and ranking, and calculating the deviation of the contact relationship ranking and the item correlation degree ranking.
2. The office staff relationship analysis method according to claim 1, wherein in step 1, the contact relationship and the contact duration between the staff are monitored and recorded in a staff contact relationship chart database, specifically: and monitoring contact activities among the personnel from a computer vision identity recognition system, a conference record and an online office chat record, calculating to obtain a contacter, a contactee and contact time, and storing the contact time into a personnel contact relation chart database.
3. The office personnel relationship analysis method according to claim 1, wherein in step 1, the degree of contact of each personnel is calculated, and the personnel are ranked according to the degree of contact from high to low, specifically:
calculating a touched PR _ P value of each person according to a person contact relation map database by using a PageRank algorithm, wherein PR _ P (i) represents the touched PR _ P value of the ith person, the sum of the touched time lengths of each person is recorded as TP _ P, the touched time length of the ith person is expressed as TP _ P (i), the touched degree WP _ P of each person is calculated, WP _ P (i) represents the touched degree of the ith person, and the calculation formula is as follows:
WP_P(i)＝PR_P(i)*TP_P(i)
ranking the persons according to the WP _ P value from high to low, and representing the WP _ P value ranking of the ith person by RP _ P (i).
4. The office personnel relationship analysis method according to claim 3, wherein in step 1, said calculating the contact degree of each personnel and ranking the personnel according to the contact degree from high to low specifically comprises:
reversing the pointing direction in the person contact map database, pointing the contactee at the contactee by using the PageRank algorithm to calculate a contact PR _ a value for each person, wherein PR _ a (i) represents the PR _ a value of the ith person contacting the other person; the sum of the time length of each person contacting other persons is recorded as TP _ A, wherein TP _ A (i) represents the time length of the ith person contacting other persons, and the contact degree WP _ A of each person is calculated, wherein WP _ A (i) represents the contact degree of the ith person, and the calculation formula is as follows:
WP_A(i)＝PR_A(i)*TP_A(i)
ranking the persons according to the WP _ A value from high to low, and representing the WP _ A value of the ith person by RP _ A (i).
5. The office personnel relationship analysis method according to claim 1, wherein in step 1, the contact relationship degree between each two personnel is calculated, and the contact relationship between each personnel and other personnel is ranked according to the contact relationship degree from high to low, specifically:
summarizing the mutual contact time length between every two persons as the contact relation degree, recording the contact relation degree between the ith person and the jth person as WP _ R (i, j), ranking the contact relation between each person and other persons according to the WP _ R value from high to low, and representing the ranking of the jth person in the contact relation of the ith person by RP _ R (i, j).
6. The office staff relationship analysis method according to claim 5, wherein in step 1, the detecting of the staff contact population through the contact relationship degree between two staff members specifically comprises:
establishing an affinity list of each person, adding each person into the affinity list of the person, adding the top N-1 persons most closely contacted with the person into the affinity list of the person according to a contact relation degree WP _ R value and a ranking RP _ R value, wherein N represents the maximum number of the affinity lists, and obtaining a person contact group by using a frequent item mining algorithm according to the affinity list of each person.
7. The office staff relationship analysis method according to claim 1, wherein in step 2, the analysis work daily report extracts staff and item relationships and item keyword relationships, and records the staff and item relationships in a staff and item relationship map database, specifically:
collecting the items filled in the daily work report, the duration of each item and specific work content, and extracting keywords from all the work content of each item to obtain the relationship between the keywords and the items; summarizing the working time of each person participating in each item to obtain the relationship among the person, the item and the working time; and storing the keywords, the item relation, the personnel, the items and the working time length relation into a personnel item map database.
8. The office staff relationship analysis method according to claim 7, wherein in step 2, the calculating the importance degree of each item specifically comprises:
according to the personnel event map database, the PageRank algorithm is used for calculating the PR _ S value of each event, and the PR _ S (i) is used for expressing the importance PR _ S value of the ith event.
9. The office staff relation analysis method according to claim 8, wherein in step 2, said calculating the item participation degree of each staff specifically is:
multiplying the importance PR _ S value of each matter participated by each person by the time of the matter participated by the person, and then summing all the products to obtain the matter participation WP _ S value of each person, wherein the calculation formula is as follows:
WP_S(i)＝SUMj(TP_S(i,j)*PR_S(j))
WP _ S (i) indicates the subject participation degree of the ith person, TP _ S (i, j) indicates the time length for the ith person to participate in the jth subject, PR _ S (j) indicates the importance degree of the jth subject, the subject is ranked according to the subject participation degree WP _ S value from high to low, and RP _ S (i) indicates the subject participation degree ranking of the ith person.
10. The office staff relationship analysis method according to claim 9, wherein in step 2, the degree of event correlation between two staff is calculated, specifically:
finding the matters commonly participated by every two persons, and calculating the value of the matter correlation degree WP _ SR between the two persons, wherein the calculation formula is as follows:
WP_SR(i,k)＝SUMj(TP_S(i,j)*PR_S(j)*TP_S(k,j))
WP _ SR (i, k) represents the item correlation degree of the ith person and the kth person, PR _ S (j) represents the importance degree of the jth item, TP _ S (i, j) represents the time length of the ith person participating in the jth item, and TP _ S (k, j) represents the time length of the kth person participating in the jth item;
and ranking the event correlation relations between each person and other persons according to the event correlation degree WP _ SR value from high to low, and then representing the ranking of the jth person in the event correlation relation of the ith person as RP _ SR (i, j).
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| CN111694963A (en) * | 2020-05-11 | 2020-09-22 | 电子科技大学 | Key government affair flow identification method and device based on item association network |
| CN111695003A (en) * | 2020-05-11 | 2020-09-22 | 电子科技大学 | Government affair shared material identification method and system based on item association network |
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| WO2011056231A1 (en) * | 2009-11-06 | 2011-05-12 | Bryan Cave Llp | Systems and methods for providing business rankings |
| CN111694963A (en) * | 2020-05-11 | 2020-09-22 | 电子科技大学 | Key government affair flow identification method and device based on item association network |
| CN111695003A (en) * | 2020-05-11 | 2020-09-22 | 电子科技大学 | Government affair shared material identification method and system based on item association network |
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