A data processing method, system and storage medium based on personnel portrait

Abstract

The present invention relates to the field of data processing technology, and more specifically to a data processing method, system and storage medium based on personnel portrait. The method comprises: step S1: establishing a personnel portrait database; step S2: obtaining target personnel information from an internal network service through an information collection unit, and searching for messages related to the target personnel information on the internal network service based on the keywords through a query unit; step S3: obtaining the target personnel type according to the target information and its personnel portrait; step S4: obtaining the attendance type according to the type of the target personnel; step S5: obtaining attendance information matching the target personnel type. The present invention solves the problem of inaccurate personnel attendance data and improves the accuracy of personnel attendance information.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and more specifically to a data processing method, system and storage medium based on personnel portrait. Background Art

[0002] With the development of data processing technology, the application field of this technology is constantly being broadened. For example, in the field of public security, the management of personnel will adopt big data technology based on this technology, that is, by collecting various information of the managed personnel, and assigning labels to the corresponding personnel based on the information, and then, the personnel portrait data of the personnel can be formed based on the label. Personnel information management is carried out through personnel portraits. For example: Chinese invention patent CN110992105A, the personnel portrait processing method and device, electronic equipment and storage medium provided by the invention application, relate to the field of data processing technology. In this application, first, the personnel portrait data to be processed is obtained, wherein the personnel portrait data includes at least one identification information. Secondly, based on the target identification information in at least one identification information, the target processing unit is determined in the preset multiple processing units; then, the labeling operation is performed on the personnel portrait data based on the target processing unit. Based on the above scheme, the problem of poor data consistency that is prone to occur when the existing portrait processing technology is used to process the personnel portrait data can be improved. Another example is the Chinese invention patent CN112801821A, which proposes a construction team portrait method and system based on big data analysis. The method includes the following steps: obtaining the characteristic information of the construction team members and the attribute information of the historical projects undertaken by the construction team members; constructing a data matrix of the attributes of the historical projects undertaken by the construction team members, and clustering the data matrix; then forming a similarity matrix by calculating the similarity between the objects in the data matrix, and normalizing the similarity matrix; after normalizing the data matrix, using a network segmentation algorithm to segment the data, and drawing a portrait of the construction team members in combination with the characteristic information. With the increasing number of employees in the enterprise, the division of labor and types of employees are also increasing. In actual attendance, it is necessary to conduct comprehensive attendance for different employees or managers of different levels, resulting in more and more data, and there are also differences in the types of judgment, making it difficult to obtain comprehensive evaluation information and often making mistakes. Moreover, the attendance methods of the same employee are different at different stages, and the attendance type needs to be updated in a timely manner. Especially for cadre management, the authority is relatively strict. Different releases require different authorities to ensure the reliability and accuracy of the messages. If there are errors in the authority of the released messages, it may lead to adverse effects. Summary of the invention

[0003] In order to better solve the above problems, the present invention provides a data processing method based on personnel portrait, which comprises the following steps:

[0004] Step S1: Establishing a personnel portrait database, including inputting personnel-related information through an information collection unit, generating personnel portraits based on the relevant information, and storing the personnel portraits in a storage unit;

[0005] Step S2: obtaining target person information from an internal network service through an information collection unit, obtaining keywords related to the target person information through a keyword generation unit based on the target person information and the portrait information of the target person, and searching for messages related to the target person information on the internal network service based on the keywords through a query unit;

[0006] Step S3: obtaining the target person type according to the message related to the target person information; and determining whether the target person type is the same as the type stored in the storage unit, and if different, further determining whether to update;

[0007] Step S4: Obtain attendance type according to the type of target personnel;

[0008] Step S5: importing the attendance data of the target person through the punching unit, and acquiring normal attendance data and abnormal attendance data based on the attendance data, and also analyzing the normal attendance data and the abnormal attendance data based on the attendance type of the target person to acquire attendance data matching the target person type.

[0009] As a preferred technical solution of the present invention, in step S1, storing the personnel portrait in a storage unit includes the following steps:

[0010] Step S11: allocating an address space to a first storage area in the storage unit for a person through an allocation unit, and allocating a first index address and a second index address in the address space, and searching the storage unit for person information that coincides with the person portrait through the query unit, and obtaining a search result, wherein the number of the second index addresses is N, and N is greater than or equal to 1;

[0011] Step S12: storing the portrait information of each person in the second storage area of ​​the storage unit, wherein the second index address points to each type of information in the person portrait in the second storage area;

[0012] Among them, the storage addresses in the first storage area are allocated starting from the minimum storage address of the storage unit, and the storage addresses in the second storage area are allocated starting from the maximum storage address of the storage unit. When the storage addresses of the second storage area are used up and the addresses of the first storage area are not used up, the unsaved information in the second storage area is saved in the first storage area starting from the maximum storage address.

[0013] As a preferred technical solution of the present invention, step S5 comprises the following steps:

[0014] Step S51: extracting abnormal attendance data and normal attendance data from the attendance data, and dividing the abnormal attendance data and the normal attendance data into a normal attendance data group and an abnormal attendance data group according to time periods through a grouping unit; wherein the time period corresponds to the attendance time range of the last attendance type of the target personnel saved in the punching unit, and the number of groups is equal to the number of attendance times per day in the attendance type;

[0015] Step S52: analyzing the normal attendance data group and the abnormal attendance data group by an analysis unit, and obtaining difference factors between the normal attendance data group and the abnormal attendance data group;

[0016] Step S53: By comparing the attendance type of the target person with the difference factor, it is determined whether the data of the difference factor in the normal attendance data group and the abnormal attendance data group conform to the normal range and abnormal range of the difference factor in the attendance type of the target person;

[0017] When the difference factors of the normal attendance data group and the abnormal attendance data group are both within the normal range and the abnormal range of the difference factors of the attendance type of the target person, the attendance mode of the target person matches the attendance data;

[0018] When at least one of the difference factors of the normal attendance data group and the abnormal attendance data group is not within the normal range and the abnormal range corresponding to the target personnel attendance type, the attendance type of the target personnel does not match the attendance data, the last target personnel attendance type stored in the punching unit is obtained, and the target personnel attendance type is compared with the last target personnel attendance type. When the two are consistent, the content corresponding to the target personnel attendance type is updated to the corresponding content of the target personnel attendance type stored in the punching unit, and the attendance data is regenerated through the punching unit; when the two are inconsistent, the target personnel attendance type is updated to the punching unit again, and the attendance data is regenerated through the punching unit.

[0019] As a preferred technical solution of the present invention, step S3 includes: when the message posted by the target person changes, judging the number of times the target person posts the changed message, and when the cumulative number is greater than a second threshold within a preset time, obtaining the type of the target person through the changed message posted by the target person, and updating the type of the target person to the storage unit, otherwise not updating; when the target person has not posted a message, judging the type of the target person through the message that the target person is paying attention to, and after the message that the target person is paying attention to changes and the cumulative time is greater than the first time, obtaining the type of the target person through the message that the target person is paying attention to, and updating the type of the target person to the storage unit, otherwise not updating.

[0020] As a preferred technical solution of the present invention, the content corresponding to the attendance type of the target person at least includes: attendance time range, attendance method and attendance address.

[0021] As a preferred technical solution of the present invention, the relevant information of the personnel includes at least: basic information of the personnel, work resume, job details, family members, assessment and training records, wherein the basic information of the personnel includes: name, gender, age, place of origin and educational background.

[0022] The present invention also provides a data processing system based on personnel portraits, the system is used to implement the above-mentioned data processing method based on personnel portraits, the system comprises:

[0023] A creation unit, used to establish a personnel portrait database, including inputting personnel-related information through an information collection unit, and generating personnel portraits based on the relevant information;

[0024] A storage unit, used to store the personnel portrait;

[0025] An information collection unit, used to input personnel-related information and also used to obtain target personnel information from the internal network service;

[0026] A keyword generating unit, configured to acquire keywords related to the target person information based on the target person information and the portrait information of the target person;

[0027] A query unit, configured to search for messages related to target personnel information on an internal network service based on the keywords;

[0028] A personnel type acquisition unit, used to acquire the target personnel type according to the message related to the target personnel information; and to determine whether the target personnel type is the same as the type stored in the storage unit, and if different, to further determine whether to update;

[0029] An attendance type acquisition unit, used to acquire the attendance type according to the type of the target person, and update the attendance type corresponding to the target person to the punching unit;

[0030] A clock-in unit, used to import the attendance data of the target personnel and obtain normal attendance data and abnormal attendance data;

[0031] An analyzing unit is used to analyze the normal attendance data and the abnormal attendance data based on the attendance type of the target personnel to obtain attendance information matching the type of the target personnel.

[0032] The present invention also provides a storage medium, which stores program instructions, wherein when the program instructions are executed, the device where the storage medium is located is controlled to execute the above-mentioned data processing method based on personnel portrait.

[0033] Compared with the prior art, the beneficial effects of the present invention are at least as follows:

[0034] The present invention obtains target personnel information from an internal network service through an acquisition unit, searches for messages related to the target personnel information on the internal network service based on the above keywords through a query unit to obtain the target personnel type, and also obtains the attendance type based on the above target personnel type, by judging whether the data of the difference factor in the normal attendance data and the abnormal attendance data conform to the normal range and abnormal range of the above difference factor in the attendance type of the above target personnel, and by judging whether the normal attendance data and the abnormal attendance data corresponding to the attendance time are within the normal attendance time and abnormal attendance time of the attendance type of the above target personnel. If it is within the time range, it means that the above attendance data matches the attendance method of the above target personnel and can accurately reflect the attendance information of the above target personnel. If it is not, it is determined whether the last attendance type of the target personnel in the punch card unit is consistent with the attendance method of the above target personnel. If they are consistent, it may be that the content corresponding to the current target personnel attendance type has changed. It is necessary to update the corresponding content of the target personnel attendance type to the punch card unit, and regenerate the attendance data based on the punch card information of the target personnel through the punch card unit, thereby ensuring the accuracy of the attendance data. If they are inconsistent, it means that the attendance type of the above target personnel has changed. It is not updated to the punch card unit in time, therefore, it is necessary to update the attendance type of the target personnel to the punch card unit, and regenerate the attendance data through the punch card unit, thereby improving the accuracy of the attendance data. When the message posted by the target personnel queried changes, when the cumulative number of times within the preset time is greater than the second threshold, it is considered that the position of the target personnel has changed, so the type of the target personnel is updated to the storage unit, otherwise, it does not need to be updated; it is also possible that after the position of the target personnel changes, there is no message that needs to be posted for a period of time, but because the target personnel are in different positions and pay attention to different messages, in order to accurately obtain the type of the target personnel, it is also possible to judge whether the type of the target personnel has changed through the message that the target personnel pays attention to. After the message that the target personnel pays attention to changes and the cumulative time is greater than the first time, it is considered that the position of the target personnel has changed, and it is necessary to update the type of the target personnel to the storage unit, otherwise, it is considered that the target personnel may have only temporarily paid attention to other messages, thereby improving the accuracy of judging the type of the target personnel, thereby improving the accuracy of the attendance method of the target personnel, and further improving the accuracy of the attendance information of the target personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a flow chart of a data processing method based on personnel portraits of the present invention;

[0036] Figure 2 This is a structural diagram of a data processing system based on personnel portraits in the present invention. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0038] The present invention provides a data processing method based on personnel portraits, such as Figure 1 As shown, the method comprises the following steps:

[0039] Step S1: Establishing a personnel portrait database, including inputting personnel-related information through an information collection unit, generating personnel portraits based on the relevant information, and storing the personnel portraits in a storage unit;

[0040] Specifically, the relevant information of the personnel is entered through the information collection unit, and the relevant information of the above personnel can also be imported in batches through the information collection unit, and the relevant information of the above personnel is fused to generate a personnel portrait, wherein the above relevant information includes: the basic information of the personnel, work resume, job details, family members, assessment and training records, wherein the basic information of the personnel includes: name, gender, age, place of origin and educational background, and based on the relevant information of the above personnel, the above relevant information and the personnel are associated through information fusion technology to generate a personnel portrait, wherein the information fusion technology is a prior art and will not be repeated here.

[0041] Step S2: obtaining target person information from an internal network service through an information collection unit, obtaining keywords related to the target person information through a keyword generation unit based on the target person information and the portrait information of the target person, and searching for messages related to the target person information on the internal network service based on the keywords through a query unit;

[0042] Specifically, the target person information may be, for example, the name, title or other personnel identification information of the target person. The target person information is obtained from an internal network service through an information collection unit. The target person information may identify the target person. The internal network service may be a service interface that provides message sharing and sending and receiving. The occupation and work content of the target person are obtained through the target person information and the portrait information corresponding to the target person. The keyword generation unit also generates keywords related to the target person information based on the occupation and work content of the target person. For example, if the target person's occupation is an engineer and his work content is project development, the keywords generated may be words related to engineering project development. For example, the development progress of a certain project, etc., the query unit searches for messages related to the target personnel information on the internal network service based on the above keywords, and the message extraction unit extracts the target information through the message content. The target information can reflect the level of the target personnel and the type of current attendance. For example, the content of the message can be project planning, project schedule and project meeting, and the publishing objects are multiple personnel, then the target personnel may be the project manager, and the working method of the target personnel can also be obtained according to the project meeting address or meeting method and project planning, such as business trips, working from home, etc. Through the above scheme, the information of the target personnel can be obtained, thereby providing a reference for obtaining the type of the target personnel.

[0043] Step S3: obtaining the target person type according to the message related to the target person information; and determining whether the target person type is the same as the type stored in the storage unit, and if different, further determining whether to update;

[0044] Specifically, the type of the target personnel is determined based on the messages posted or followed by the target personnel. The type of the target personnel can be a position level. For example, if the target personnel is sales, the position is sales manager, and the working method is field work, the position level of the target personnel can be determined to be level three, if the position is sales team leader, it is level two, and if the position is sales director, it is level one. Different levels correspond to different personnel types and corresponding attendance types are also different.

[0045] Step S4: Obtain attendance type according to target personnel type;

[0046] Specifically, the target personnel type includes the target personnel's level, position and working method. The attendance type is obtained through the above target personnel type. The content corresponding to the above attendance type at least includes the target personnel's attendance time range, attendance method and attendance address, where different attendance types correspond to different attendance times, attendance methods and attendance addresses.

[0047] Step S5: importing the attendance data of the target person through the punching unit, and acquiring normal attendance data and abnormal attendance data based on the attendance data, and also analyzing the normal attendance data and the abnormal attendance data based on the attendance type of the target person to acquire attendance data matching the target person type.

[0048] Specifically, the attendance data of the target personnel is imported by reading the storage module in the punch card unit. The storage module can be a memory or a memory card, and can only obtain the attendance result, but cannot obtain the content of the attendance type corresponding to each person. The attendance type corresponding to each person can only be viewed and modified by the administrator through the punch card unit, wherein the attendance data is obtained based on the attendance type of the target personnel last saved in the storage module of the punch card unit, and the attendance data that meets the attendance type of the target personnel last saved belongs to normal attendance data, and the attendance data outside the attendance type of the target personnel belongs to abnormal attendance data, wherein the abnormal attendance data and abnormal data are the attendance data of the target personnel judged based on the attendance type of the target personnel saved in the punch card unit, wherein the abnormal attendance data at least includes Including lateness, early leave, card absence, location not in the punching range, etc., the normal attendance data and abnormal attendance data of the target personnel obtained through the above steps are judged whether they meet the attendance type of the current target personnel, that is, the attendance type of the target personnel obtained in step S4, and through the attendance time, attendance method and attendance location corresponding to the attendance type of the existing target personnel, it is judged whether the above abnormal attendance data and the above normal attendance data are respectively within the abnormal range and normal range of the attendance time, attendance method and attendance location corresponding to the attendance type of the current target personnel. If so, it means that the attendance type of the target personnel saved in the punching unit is consistent with the attendance type of the current target personnel, and no update is required. If not, it means that the attendance type of the target personnel saved in the punching unit is inconsistent with the attendance type of the current target personnel, and needs to be updated.

[0049] Furthermore, in the step S1, storing the personnel portrait in a storage unit includes the following steps:

[0050] Step S11: allocating an address space to a first storage area in the storage unit for a person through an allocation unit, and allocating a first index address and a second index address in the address space, and searching the storage unit for person information that coincides with the person portrait through the query unit, and obtaining a search result, wherein the number of the second index addresses is N, and N is greater than or equal to 1;

[0051] Specifically, the first index address is the label address of each person. For example, starting from address X1, the storage address corresponding to the portrait information of the first person is stored. The address space is used to save the storage address of each person portrait. When reading, the person is identified by the first index address, and the address of each type of information content in the portrait information is obtained through the second index address in the address space. The query unit is also used to check for duplicates. When there are duplicate person portraits, there is no need to divide the address space, thereby saving storage resources.

[0052] Step S12: storing the portrait information of each person in the second storage area of ​​the storage unit, wherein the second index address points to each type of information in the person portrait in the second storage area;

[0053] Among them, the storage addresses in the first storage area are allocated starting from the minimum storage address of the storage unit, and the storage addresses in the second storage area are allocated starting from the maximum storage address of the storage unit. When the storage addresses of the second storage area are used up and the addresses of the first storage area are not used up, the unsaved information in the second storage area is saved in the first storage area starting from the maximum storage address.

[0054] Specifically, the portrait information content of each person pointed to by the above-mentioned second index address is stored in the second storage area. The utilization rate of storage resources is improved through the above-mentioned storage address allocation, which ensures that more person portraits can be stored in a limited storage space, and realizes dynamic allocation of addresses.

[0055] Furthermore, the step S5 comprises the following steps:

[0056] Step S51: extracting abnormal attendance data and normal attendance data from the attendance data, and dividing the abnormal attendance data and the normal attendance data into a normal attendance data group and an abnormal attendance data group according to time periods through a grouping unit; wherein the time period corresponds to the attendance time range of the last attendance type of the target personnel saved in the punching unit, and the number of groups is equal to the number of attendance times per day in the attendance type;

[0057] Specifically, the attendance data is divided into a normal attendance data group and an abnormal attendance data group according to the attendance time range in the attendance type saved in the punching unit through the grouping unit. For example, the attendance time period corresponding to the attendance type of the target personnel saved in the above-mentioned punching unit is: the morning work attendance time is 6:00-8:30, the afternoon work attendance time is 12:30-14:30, and the afternoon off-get off work attendance time is 18:00-20:00. Since different personnel in the same company have different times, places and methods, but the number of attendances is the same, the above-mentioned normal attendance data and the above-mentioned abnormal attendance data can be divided into 3 normal attendance data groups and 3 abnormal attendance data groups according to the above-mentioned three time periods, respectively, to facilitate the analysis of the difference factors between the above-mentioned normal attendance data groupings and the above-mentioned abnormal attendance data groupings.

[0058] Step S52: analyzing the normal attendance data group and the abnormal attendance data group by an analysis unit, and obtaining difference factors between the normal attendance data group and the abnormal attendance data group;

[0059] Specifically, by analyzing the normal attendance data group and the abnormal attendance data group corresponding to the same time period, and analyzing the abnormal reasons of the abnormal attendance data group relative to the normal attendance data group, and obtaining the corresponding difference factors through the above abnormal reasons, the above difference factors may be the difference in punching time, the difference in punching location, and the difference in punching method, thereby laying a foundation for further judging whether the above attendance data matches the above target personnel based on the above difference factors and the attendance type of the above target personnel.

[0060] Step S53: By comparing the attendance type of the target person with the difference factor, it is determined whether the data of the difference factor in the normal attendance data group and the abnormal attendance data group conform to the normal range and abnormal range of the difference factor in the attendance type of the target person;

[0061] When the difference factors of the normal attendance data group and the abnormal attendance data group are both within the normal range and the abnormal range of the difference factors of the attendance type of the target person, the attendance mode of the target person matches the attendance data;

[0062] Specifically, for example, the above-mentioned difference factor is attendance time, and the first normal punching time and the first abnormal punching time are obtained through the current attendance type of the above-mentioned target person, and according to the second normal punching time in the above-mentioned normal attendance data and the second abnormal punching time of the above-mentioned abnormal attendance data exported by the punching unit, the above-mentioned first normal punching time and the above-mentioned second normal punching time are compared to determine whether they are consistent, and the above-mentioned first abnormal punching time and the above-mentioned second abnormal punching time are compared to determine whether they are consistent. When the two comparison results are consistent, the attendance method of the above-mentioned target person matches the above-mentioned attendance data, otherwise, they do not match.

[0063] When at least one of the difference factors of the normal attendance data group and the abnormal attendance data group is not within the normal range and the abnormal range corresponding to the target personnel attendance type, the attendance type of the target personnel does not match the attendance data, the last target personnel attendance type stored in the punching unit is obtained, and the target personnel attendance type is compared with the last target personnel attendance type. When the two are consistent, the content corresponding to the target personnel attendance type is updated to the corresponding content of the target personnel attendance type stored in the punching unit, and the attendance data is regenerated through the punching unit; when the two are inconsistent, the target personnel attendance type is updated to the punching unit again, and the attendance data is regenerated through the punching unit.

[0064] Specifically, by judging whether the data of the difference factor in the normal attendance data and the abnormal attendance data conform to the normal range and abnormal range of the difference factor in the attendance type of the target person, for example, the difference factor is the punch-in time, by judging whether the normal attendance data and the abnormal attendance data corresponding to the attendance time are within the normal attendance time and abnormal attendance time range of the attendance type of the target person, if so, it means that the attendance data matches the attendance method of the target person and can accurately reflect the attendance information of the target person, if not, it is judged whether the last attendance type of the target person in the punch-in unit is consistent with the current attendance type of the target person, wherein, the system can prompt the management personnel to The attendance type corresponding to the target person in the time clock is input into the system through the information collection unit. When they are consistent, it is possible that the content corresponding to the current target person's attendance type has changed, while the time clock unit has not been updated. Therefore, the administrator needs to update the content corresponding to the above target person's attendance type to the time clock, and regenerate the attendance data through the time clock unit according to the time clock information and the updated content corresponding to the target person's attendance type, thereby ensuring the accuracy of the attendance data. When they are inconsistent, it means that the above target person's attendance type has not been updated to the time clock unit in time when it changes. Therefore, the administrator needs to update the above target person's attendance type to the time clock unit, and regenerate the attendance data through the time clock unit according to the time clock information.

[0065] Whether the attendance type saved in the punching unit needs to be updated. If not, the attendance type of the target person is still the attendance type saved in the punching unit. If it needs to be updated, the attendance type of the target person is the attendance type obtained in step S5.

[0066] Further, the step S3 includes: when the message posted by the target person changes, judging the number of times the target person posts messages, and when the cumulative number within a preset time is greater than a second threshold, obtaining the type of the target person through the changed message posted by the target person, and updating the type of the target person to the storage unit, otherwise not updating; when the target person has not posted a message, judging the type of the target person through the message that the target person pays attention to, after the message that the target person pays attention to changes and the cumulative time is greater than the first time, obtaining the type of the target person through the message that the target person pays attention to, and updating the type of the target person to the storage unit, otherwise, not updating.

[0067] Specifically, when the message posted by the above-mentioned target person obtained through the query changes, the message posted by the above-mentioned target person can be obtained from the service interface that provides message sharing and sending and receiving from the internal network service. For example, the message posted by the above-mentioned target person can be searched in the above-mentioned service interface according to the name of the target person. It is possible that the position of the above-mentioned target person has changed. For example, the original message posted by the target person is project planning, project schedule and project meeting. The message posted by the target person currently queried is the company's development strategy or industry development layout. The position of the target person may be promoted from project manager to director or general manager. It may also be a message posted by the person who temporarily replaces the above-mentioned message. When the number of times accumulated within the preset time is greater than the second threshold, the above-mentioned preset time can be one week, and the value of the above-mentioned second threshold can be 3 or 5. For example, if it appears more than 3 times within a week, it is considered that the position of the above-mentioned target person has changed, so the type of the above-mentioned target person is updated to the storage unit, otherwise, no update is required; it is also possible that after the position of the above-mentioned target person changes, there is no message that needs to be released for a period of time, but because the above-mentioned target person is in different positions and pays attention to different messages, in order to accurately obtain the type of the above-mentioned target person, it is also possible to judge whether the type of the above-mentioned target person has changed through the messages that the above-mentioned target person pays attention to. After the message that the above-mentioned target person pays attention to changes and the continuous cumulative time is greater than the first time, for example, the continuous cumulative time after the message that the target person pays attention to changes is greater than 5 days, and the value of the above-mentioned first time can be 5 days, it is considered that the position of the above-mentioned target person has changed, and the type of the above-mentioned target person needs to be updated to the storage unit, otherwise, it is considered that the above-mentioned target person may have only temporarily paid attention to other messages, thereby improving the accuracy of judging the type of the target person, thereby improving the accuracy of the target person's attendance method, and further improving the accuracy of the attendance information of the above-mentioned target person.

[0068] Furthermore, the content corresponding to the attendance type of the target personnel at least includes: attendance time range, attendance method and attendance address.

[0069] Furthermore, the relevant information of the personnel includes at least: basic information of the personnel, work resume, job details, family members, assessment and training records, wherein the basic information of the personnel includes: name, gender, age, place of origin and educational background.

[0070] The present invention also provides a data processing system based on personnel portraits, the system is used to implement the above-mentioned data processing method based on personnel portraits, such as Figure 2 As shown, the system comprises:

[0071] A creation unit, used to establish a personnel portrait database, including inputting personnel-related information through an information collection unit, and generating personnel portraits based on the relevant information;

[0072] A storage unit, used to store the personnel portrait;

[0073] An information collection unit, used to input personnel-related information and also used to obtain target personnel information from the internal network service;

[0074] A keyword generating unit, configured to acquire keywords related to the target person information based on the target person information and the portrait information of the target person;

[0075] A query unit, configured to search for messages related to target personnel information on an internal network service based on the keywords;

[0076] A personnel type acquisition unit, used to acquire the target personnel type according to the message related to the target personnel information; and to determine whether the target personnel type is the same as the type stored in the storage unit, and if different, to further determine whether to update;

[0077] An attendance type acquisition unit, used to acquire the attendance type according to the type of the target person;

[0078] A clock-in unit, used to import the attendance data of the target personnel and obtain normal attendance data and abnormal attendance data;

[0079] An analyzing unit is used to analyze the normal attendance data and the abnormal attendance data based on the attendance type of the target personnel to obtain attendance information matching the type of the target personnel.

[0080] The present invention also provides a storage medium, which stores program instructions, wherein when the program instructions are executed, the device where the storage medium is located is controlled to execute the above-mentioned data processing method based on personnel portrait.

[0081] In summary, the present invention obtains target personnel information from an internal network service through an acquisition unit, searches and queries messages related to the target personnel information on the internal network service based on the above keywords through a query unit to obtain the target personnel type, and also obtains the attendance type based on the above target personnel type, by judging whether the data of the difference factor in the normal attendance data and the abnormal attendance data conform to the normal range and abnormal range of the above difference factor in the attendance type of the above target personnel, and by judging whether the normal attendance data and the abnormal attendance data corresponding to the attendance time are within the normal attendance time and abnormal range of the attendance type of the above target personnel. If it is within the abnormal attendance time range, it means that the above attendance data matches the attendance method of the above target personnel and can accurately reflect the attendance information of the above target personnel. If it is not, it is determined whether the last attendance type of the target personnel in the punch card unit is consistent with the attendance method of the above target personnel. If they are consistent, it may be that the content corresponding to the current target personnel attendance type has changed. It is necessary to update the corresponding content of the target personnel attendance type to the punch card unit, and regenerate the attendance data based on the punch card information of the target personnel through the punch card unit, thereby ensuring the accuracy of the attendance data. If they are inconsistent, it means that the attendance type of the above target personnel has changed. When the target person's position changes, it is not updated to the punching unit in time. Therefore, it is necessary to update the attendance type of the target person to the punching unit, and regenerate the attendance data through the punching unit, thereby improving the accuracy of the attendance data. When the message posted by the target person queried changes, when the cumulative number of times within the preset time is greater than the second threshold, it is considered that the position of the target person has changed, so the target person type is updated to the storage unit, otherwise, it does not need to be updated; it is also possible that after the position of the target person changes, there is no message that needs to be posted for a period of time, but because the target person is in different positions and the messages he pays attention to are also different, in order to accurately obtain the type of the target person, it is also possible to judge whether the type of the target person has changed through the message that the target person pays attention to. After the message that the target person pays attention to changes and the cumulative time is greater than the first time, it is considered that the position of the target person has changed, and it is necessary to update the target person type to the storage unit, otherwise, it is considered that the target person may have only temporarily paid attention to other messages, thereby improving the accuracy of judging the type of the target person, thereby improving the accuracy of the attendance method of the target person, and further improving the accuracy of the attendance information of the target person.

[0082] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0083] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The above-mentioned program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0084] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0085] The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for those of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0086] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A data processing method based on personnel portrait, characterized in that: The method comprises the following steps: Step S1: Establishing a personnel portrait database, including inputting personnel-related information through an information collection unit, generating personnel portraits based on the relevant information, and storing the personnel portraits in a storage unit; Step S2: further obtaining target person information from the internal network service through the information collection unit, obtaining keywords related to the target person information through the keyword generation unit based on the target person information and the target person portrait, and further searching for messages related to the target person information on the internal network service based on the keywords through the query unit; Step S3: obtaining the target person type according to the message related to the target person information; and determining whether the target person type is the same as the type stored in the storage unit, and if different, further determining whether to update; Step S4: obtaining the attendance type according to the target person type, and updating the attendance type corresponding to the target person to the punching unit; Step S5: importing the attendance data of the target person through the punching unit, and acquiring normal attendance data and abnormal attendance data based on the attendance data, and also analyzing the normal attendance data and the abnormal attendance data based on the attendance type of the target person to acquire attendance data matching the type of the target person; Wherein, the step S5 comprises the following steps: Step S51: extracting abnormal attendance data and normal attendance data from the attendance data, and dividing the abnormal attendance data and the normal attendance data into a normal attendance data group and an abnormal attendance data group according to time periods through a grouping unit; wherein the time period corresponds to the attendance time range of the last attendance type of the target personnel saved in the punching unit, and the number of groups is equal to the number of attendance times per day in the attendance type; Step S52: analyzing the normal attendance data group and the abnormal attendance data group by an analysis unit, and obtaining difference factors between the normal attendance data group and the abnormal attendance data group; Step S53: By comparing the attendance type of the target person with the difference factor, it is determined whether the data of the difference factor in the normal attendance data group and the abnormal attendance data group conform to the normal range and abnormal range of the difference factor in the attendance type of the target person; When the difference factors of the normal attendance data group and the abnormal attendance data group are both within the normal range and the abnormal range of the difference factors of the attendance type of the target person, the attendance mode of the target person matches the attendance data; When at least one of the difference factors of the normal attendance data group and the abnormal attendance data group is not within the normal range and the abnormal range corresponding to the target personnel attendance type, the attendance type of the target personnel does not match the attendance data, the last target personnel attendance type stored in the punching unit is obtained, and the target personnel attendance type is compared with the last target personnel attendance type. When the two are consistent, the content corresponding to the target personnel attendance type is updated to the corresponding content of the target personnel attendance type stored in the punching unit, and the attendance data is regenerated through the punching unit; when the two are inconsistent, the target personnel attendance type is updated to the punching unit again, and the attendance data is regenerated through the punching unit.

2. According to claim 1, a data processing method based on personnel portrait is characterized in that: In the step S1, storing the person portrait in a storage unit includes the following steps: Step S11: allocating an address space to a first storage area in the storage unit for a person through an allocation unit, and allocating a first index address and a second index address in the address space, and searching the storage unit for person information that coincides with the person portrait through the query unit, and obtaining a search result, wherein the number of the second index addresses is N, and N is greater than or equal to 1; Step S12: storing the portrait information of each person in the second storage area of ​​the storage unit, wherein the second index address points to each type of information in the person portrait in the second storage area; Among them, the storage addresses in the first storage area are allocated starting from the minimum storage address of the storage unit, and the storage addresses in the second storage area are allocated starting from the maximum storage address of the storage unit. When the storage addresses of the second storage area are used up and the addresses of the first storage area are not used up, the unsaved information in the second storage area is saved in the first storage area starting from the maximum storage address.

3. According to claim 1, a data processing method based on personnel portrait, characterized in that: The step S3 includes: when the message posted by the target person changes, the number of times the target person posts the changed message is determined, and when the number is accumulated to be greater than a second threshold within a preset time, the type of the target person is obtained through the changed message posted by the target person, and the type of the target person is updated to the storage unit, otherwise it is not updated; when the target person has not posted a message, the type of the target person is also determined through the message that the target person is paying attention to, and when the message that the target person is paying attention to changes and the accumulated time is greater than the first time, the type of the target person is obtained through the message that the target person is paying attention to, and the type of the target person is updated to the storage unit, otherwise it is not updated.

4. The data processing method based on personnel portrait according to claim 1 is characterized in that: The attendance type corresponding content of the target personnel at least includes: attendance time, attendance method and attendance address.

5. The data processing method based on personnel portrait according to claim 1 is characterized in that: The relevant information of the personnel includes at least: basic information of the personnel, work resume, job details, family members, assessment and training records, among which the basic information of the personnel includes: name, gender, age, place of origin and educational background.

6. A data processing system based on personnel portrait, characterized in that: The system is used to implement the data processing method based on personnel portrait according to any one of claims 1 to 5, and the system includes: A creation unit, used to establish a personnel portrait database, including inputting personnel-related information through an information collection unit, and generating personnel portraits based on the relevant information; A storage unit, used for storing the personnel portrait; An information collection unit, used to input personnel-related information and also used to obtain target personnel information from the internal network service; A keyword generating unit, configured to acquire keywords related to the target person information based on the target person information and the portrait information of the target person; A query unit, configured to search for messages related to target personnel information on an internal network service based on the keywords; A personnel type acquisition unit, used to acquire the target personnel type according to the message related to the target personnel information; and to determine whether the target personnel type is the same as the type stored in the storage unit, and if different, to further determine whether to update; An attendance type acquisition unit, used to acquire the attendance type according to the type of the target person; A clock-in unit, used to import the attendance data of the target personnel and obtain normal attendance data and abnormal attendance data; An analyzing unit is used to analyze the normal attendance data and the abnormal attendance data based on the attendance type of the target personnel to obtain attendance information matching the type of the target personnel.

7. A storage medium, characterized in that: The storage medium stores program instructions, wherein when the program instructions are executed, the device where the storage medium is located is controlled to execute a data processing method based on personnel portrait as described in any one of claims 1 to 5.

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