CN113360733A - Talent data tag classification method and system based on artificial intelligence and cloud platform - Google Patents
Talent data tag classification method and system based on artificial intelligence and cloud platform Download PDFInfo
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Abstract
The application relates to the technical field of big data and artificial intelligence, in particular to a talent data tag classification method and system based on artificial intelligence and a cloud platform. According to the method and the device, the target talent data simulation track included in the key strategy data is subjected to state matching through the talent data training model, so that the target talent data simulation track can be matched, the diversity of classification is added, the classification content is more accurate, a user can match the target talent data simulation track according to the actual premise, and the classification operation is more rapid.
Description
Technical Field
The disclosure relates to the technical field of big data and artificial intelligence, in particular to a talent data tag classification method and system based on artificial intelligence and a cloud platform.
Background
Currently, for the classification of structured data in a database, matching classification is mainly performed on the basis of semantic analysis on field names and annotations, rules on field contents, and the like.
For example, based on semantic information of the field, names and field comments with classified fields are extracted, and sensitive information possibly existing in the field is matched through a regular expression, for example, if the field is matched with "card", the field can be classified into the category of "bank card". Alternatively, the field content is sampled based on the field content, and then the content is analyzed by using past experience, such as an identity card number, so that the extracted field content can be checked by using a regular matching and check bit mode.
However, the above classification method has a problem of low classification accuracy.
Disclosure of Invention
In order to solve the technical problems existing in the background technology in the related art, the disclosure provides a talent data tag classification method and system based on artificial intelligence and a cloud platform.
The application provides a talent data tag classification method based on artificial intelligence, which comprises the following steps:
acquiring key strategy data of real-time talent data, wherein the key strategy data comprise a first talent data dividing range and at least one talent data simulation track for the boundary of the first talent data dividing range;
responding to the calculation operation aiming at a talent data training model, controlling the first talent data division range to calculate the talent data training model, wherein the talent data training model is used for changing the description track content of the talent data simulation track contained in the key strategy data;
according to the range description data of the talent data training model, matching the description track content of the target talent data simulation track contained in the key strategy data to obtain the matched target talent data simulation track;
and controlling the first-person data division range to classify on the matched target-person data simulation track based on the classification control operation aiming at the first-person data division range.
Preferably, the matching, according to the range description data of the talent data training model, the description track content of the target talent data simulation track included in the key strategy data to obtain the matched target talent data simulation track includes:
on the premise that the talent data training model has first range description data, converting the target talent data simulation track from a non-calculable state to a calculable state to obtain the matched target talent data simulation track; on the premise that the target talent data simulation track is in the non-calculable state, the first talent data division range cannot be classified on the target talent data simulation track;
and on the premise that the target talent data simulation track is in the calculable state, the first talent data division range can be classified on the target talent data simulation track.
Preferably, the converting the target talent data simulation track from a non-calculable state to a calculable state to obtain the matched target talent data simulation track on the premise that the talent data training model has the first range description data includes:
on the premise that the first range description data is the disordered data, eliminating disordered data used for eliminating the target talent data simulation track from the target talent data simulation track;
or, on the premise that the first range description data is the trajectory, adding a calculable talent data simulation trajectory into the target talent data simulation trajectory;
or, on the premise that the first range description data is a replacement trajectory, replacing the non-calculable talent data simulation trajectory with a calculable talent data simulation trajectory in the target displacement trajectory.
Preferably, the matching, according to the range description data of the talent data training model, the description track content of the target talent data simulation track included in the key strategy data to obtain the matched target talent data simulation track includes:
on the premise that the talent data training model has second range description data, converting the target talent data simulation track from a calculable state to a non-calculable state to obtain the matched target talent data simulation track; on the premise that the target talent data simulation track is in the calculable state, the second talent data division range in the key strategy data can be classified on the target talent data simulation track;
and on the premise that the target talent data simulation track is in the non-calculable state, the second talent data division range in the key strategy data cannot be classified on the target talent data simulation track, and the second talent data division range and the first talent data division range have a mapping relation.
Preferably, on the premise that the talent data training model has the second range description data, the step of converting the target talent data simulation track from a calculable state to a non-calculable state to obtain the matched target talent data simulation track includes:
on the premise that the second range description data is added with disorder data, adding disorder data for removing the target talent data simulation track into the target talent data simulation track;
or, on the premise that the second range description data is a replacement track, replacing a calculable talent data simulation track with an uncalculated talent data simulation track in the target talent data simulation track;
or, on the premise that the second range description data is a rejection track, removing a calculable talent data simulation track from the target displacement track.
Preferably, after the controlling the first-person data division range to calculate the talent data training model in response to the calculation operation on the talent data training model, the method further comprises:
and updating the content characteristics of talent data corresponding to the target talent data simulation track in talent data contents acquired by the key strategy data based on the matched target talent data simulation track.
Preferably, in the talent data content acquired by the key policy data, updating the content characteristics of talent data corresponding to the target talent data simulation track based on the matched target talent data simulation track includes:
acquiring the talent data training model serving as disorder data in talent data contents acquired by the key strategy data based on the matched target talent data simulation track;
or based on the matched target talent data simulation track, canceling acquiring disordered data for removing the target talent data simulation track from talent data contents acquired by the key strategy data;
or acquiring the action effect of the talent data training model on the target talent data simulation track in the talent data content acquired by the key strategy data based on the matched target talent data simulation track.
Preferably, after the controlling the first-person data division range to calculate the talent data training model in response to the calculation operation on the talent data training model, the method further comprises:
generating a plurality of reference track changing schemes based on the range description data of the talent data training model, wherein different track changing schemes correspond to different track matching modes;
acquiring a trajectory change scheme of the multiple references; and responding to the selection operation of a target track changing scheme in the multiple reference track changing schemes, matching the description track content of the target talent data simulation track contained in the key strategy data based on the target track changing scheme, and obtaining the matched target talent data simulation track.
The application provides talent data tag classification system based on artificial intelligence, including talent data acquisition equipment and cloud platform, talent data acquisition equipment with cloud platform interconnect, the cloud platform specifically is used for:
acquiring key strategy data of real-time talent data, wherein the key strategy data comprise a first talent data dividing range and at least one talent data simulation track for the boundary of the first talent data dividing range;
responding to the calculation operation aiming at a talent data training model, controlling the first talent data division range to calculate the talent data training model, wherein the talent data training model is used for changing the description track content of the talent data simulation track contained in the key strategy data;
according to the range description data of the talent data training model, matching the description track content of the target talent data simulation track contained in the key strategy data to obtain the matched target talent data simulation track;
and controlling the first-person data division range to classify on the matched target-person data simulation track based on the classification control operation aiming at the first-person data division range.
The application provides a cloud platform, which comprises a processor and a memory which are communicated with each other, wherein the processor is used for calling a computer program from the memory and realizing the method of any one of the above items by running the computer program.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects.
The talent data simulation tracks in key strategy data are subjected to state matching through a talent data training model, so that the target talent data simulation tracks can be matched, classified diversity is added, classified contents are more accurate, a user can match the target talent data simulation tracks according to actual premises, and classification operation is more rapid.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
FIG. 1 is a schematic diagram of an architecture of a talent data tag classification system based on artificial intelligence according to an embodiment of the present application;
FIG. 2 is a flowchart of a talent data tag classification method based on artificial intelligence according to an embodiment of the present application;
fig. 3 is a functional block diagram of a talent data tag classification device based on artificial intelligence according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
In order to facilitate the explanation of the artificial intelligence based talent data tag classification method, system and cloud platform, please refer to fig. 1, which provides a schematic diagram of a communication architecture of an artificial intelligence based talent data tag classification system 100 disclosed in the embodiments of the present application. The talent data tag classification system 100 based on artificial intelligence can comprise talent data acquisition equipment 200 and a cloud platform 300, wherein the talent data acquisition equipment 200 is in communication connection with the cloud platform 300.
In a specific embodiment, the cloud platform 300 may be a desktop computer, a tablet computer, a notebook computer, a mobile phone, or another cloud platform capable of implementing data processing and data communication, which is not limited herein.
On the basis, please refer to fig. 2 in combination, which is a schematic flow chart of a talent data tag classification method based on artificial intelligence according to an embodiment of the present application, where the talent data tag classification method based on artificial intelligence may be applied to the cloud platform 300 in fig. 1, and further, the talent data tag classification method based on artificial intelligence may specifically include the contents described in the following steps S21 to S24.
And step S21, acquiring key strategy data of the real-time talent data.
For example, the key policy data includes a first-person data partition range and at least one person data simulation track for the first-person data partition range boundary.
Step S22, in response to a calculation operation on a talent data training model, controlling the first talent data division range to calculate the talent data training model, where the talent data training model is used to change the description track content of the talent data simulation track included in the key strategy data.
In terms of distance, the description track content represents a feature vector of the talent data simulation track in the key strategy data of the real-time talent data.
Step S23, according to the range description data of the talent data training model, matching the description track content of the target talent data simulation track contained in the key strategy data to obtain the matched target talent data simulation track
And step S24, controlling the first-person data division range to classify on the matched target-person data simulation track based on the classification control operation aiming at the first-person data division range.
It can be understood that, when the contents described in the above steps S21 to S24 are executed, the talent data training model is used to perform state matching on the target talent data simulation track included in the key strategy data, so that the target talent data simulation track is matchable, diversity of classification is added, the classification content is more accurate, and the user can match the target talent data simulation track according to the actual premise, so that the classification operation is faster.
In an alternative embodiment, the step of matching the description track content of the target talent data simulation track included in the key strategy data according to the range description data of the talent data training model may specifically include the following steps S231 and S232, in order to improve the above technical problem, the step of matching the description track content of the target talent data simulation track included in the key strategy data according to the range description data of the talent data training model described in step S23 may specifically include the following contents described in steps S231 and S232.
And S231, on the premise that the talent data training model has the first range description data, converting the target talent data simulation track from a non-calculable state to a calculable state to obtain the matched target talent data simulation track.
For example, on the premise that the target talent data simulation track is in the non-calculable state, the first talent data division range cannot be classified on the target talent data simulation track.
Step S232, under the premise that the target talent data simulation track is in the calculable state, the first talent data division range can be classified on the target talent data simulation track
It can be understood that, when the contents described in the above steps S231 and S232 are executed, the description track contents of the target talent data simulation track included in the key strategy data are matched according to the range description data of the talent data training model, so as to avoid the problem of inaccurate description track contents, and thus, the matched target talent data simulation track can be accurately obtained.
In another alternative embodiment, on the premise that the talent data training model has the first range description data, the target talent data simulation trajectory is converted from a non-calculable state to a calculable state, and there is a problem of inaccurate calculation, so that it is difficult to accurately obtain the matched target talent data simulation trajectory, in order to improve the above technical problem, the step of converting the target talent data simulation trajectory from the non-calculable state to the calculable state to obtain the matched target talent data simulation trajectory on the premise that the talent data training model has the first range description data, which is described in step S231, may specifically include the contents described in the following step Q1 to step Q3.
And step Q1, on the premise that the first range description data is the eliminated disordered data, eliminating the disordered data used for eliminating the target talent data simulation track from the target talent data simulation track.
Step Q2, or on the premise that the first range description data is an added trajectory, adding a calculable talent data simulation trajectory to the target talent data simulation trajectory.
Step Q3, or, on the premise that the first range description data is a replacement trajectory, replacing the non-calculable talent data simulation trajectory with a calculable talent data simulation trajectory in the target displacement trajectory.
It can be understood that, when the contents described in the steps Q1 to Q3 are executed, on the premise that the talent data training model has the first range description data, the target talent data simulation track is converted from the non-calculable state to the calculable state, so as to avoid the problem of inaccurate calculation, and thus the matched target talent data simulation track can be accurately obtained.
In an alternative embodiment, the step of matching the description track content of the target talent data simulation track included in the key strategy data according to the range description data of the talent data training model may specifically include the following steps a1 and a2, in order to improve the above technical problem, the step of matching the description track content of the target talent data simulation track included in the key strategy data according to the range description data of the talent data training model described in step S23 may specifically include the following steps.
Step A1, on the premise that the talent data training model has second range description data, converting the target talent data simulation track from a calculable state to a non-calculable state to obtain the matched target talent data simulation track.
For example, on the premise that the target talent data simulation track is in the calculable state, the second talent data division range in the key strategy data can be classified on the target talent data simulation track.
Step a2, on the premise that the target talent data simulation track is in the non-calculable state, the second talent data partition range in the key strategy data cannot be classified on the target talent data simulation track, and the second talent data partition range and the first talent data partition range have a mapping relationship.
It can be understood that, when the contents described in the above steps a1 and a2 are executed, the description track contents of the target talent data simulation track included in the key strategy data are matched according to the range description data of the talent data training model, so as to avoid the problem of inaccurate description track contents, and thus, the matched target talent data simulation track can be accurately obtained.
In an alternative embodiment, on the premise that the talent data training model has the second range description data, the target talent data simulation track is converted from the calculable state to the non-calculable state, which has a problem of inaccurate conversion, so that it is difficult to accurately obtain the matched target talent data simulation track, and in order to improve the above technical problem, the step of converting the target talent data simulation track from the calculable state to the non-calculable state on the premise that the talent data training model has the second range description data, which is described in step a1, to obtain the matched target talent data simulation track may specifically include the contents described in the following step Z1 to step Z3.
Step Z1, on the premise that the second range description data is added with disorder data, adding disorder data for removing the target talent data simulation track into the target talent data simulation track.
Step Z2, or, on the premise that the second range description data is a replacement trajectory, replacing a calculable talent data simulation trajectory with an incalculable talent data simulation trajectory in the target talent data simulation trajectory.
Step Z3, or on the premise that the second range description data is a rejection track, removing a calculable talent data simulation track from the target displacement track.
It can be understood that, when the contents described in the above steps Z1-Z3 are executed, on the premise that the talent data training model has the second range description data, the target talent data simulation track is converted from the calculable state to the non-calculable state, so as to avoid the problem of inaccurate conversion, and thus the matched target talent data simulation track can be accurately obtained.
Based on the above basis, after controlling the first-person data division range to calculate the talent data training model in response to the calculation operation on the talent data training model, the following steps M1 are included.
And step M1, in the talent data content obtained by the key policy data, updating the content characteristics of talent data corresponding to the target talent data simulation track based on the matched target talent data simulation track.
It can be understood that, when the content described in the above step M1 is executed, the efficiency of updating the content characteristics of the talent data can be effectively improved by simulating the trajectory with the target talent data.
In an alternative embodiment, in the talent data content obtained by the key policy data, based on the matched target talent data simulation track, there is a problem that related data do not match, so that it is difficult to accurately update the talent data content features corresponding to the target talent data simulation track, in order to improve the above technical problem, the step of updating the talent data content features corresponding to the target talent data simulation track based on the matched target talent data simulation track in the talent data content obtained by the key policy data described in step M1 may specifically include the following steps described in T1-T3.
And T1, acquiring the talent data training model as disorder data in talent data content acquired by the key strategy data based on the matched target talent data simulation track.
Step T2, or based on the matched target talent data simulation trajectory, canceling obtaining the disordered data for removing the target talent data simulation trajectory from the talent data content obtained by the key policy data.
Step T3, or based on the matched target talent data simulation trajectory, obtaining an effect of the talent data training model on the target talent data simulation trajectory in the talent data content obtained by the key strategy data.
It can be understood that, when the contents described in the above steps T1-T3 are executed, in the talent data content obtained by the key policy data, a trajectory is simulated based on the matched target talent data, so as to avoid the problem of mismatch of related data, and thus, the content characteristics of talent data corresponding to the target talent data simulation trajectory can be accurately updated.
Based on the above basis, after the first-person data division range is controlled to calculate the talent data training model in response to the calculation operation for the talent data training model, the following steps J1 and J2 are also included.
And step J1, generating a plurality of reference track change schemes based on the range description data of the talent data training model.
Illustratively, different trajectory modification schemes correspond to different trajectory matching manners.
Step J2, obtaining the track change schemes of the multiple references; and responding to the selection operation of a target track changing scheme in the multiple reference track changing schemes, matching the description track content of the target talent data simulation track contained in the key strategy data based on the target track changing scheme, and obtaining the matched target talent data simulation track.
It can be understood that, when the contents described in the above steps J1 and J2 are executed, the accuracy of the target talent data simulation track can be effectively improved by the range description data.
In an alternative embodiment, the step of acquiring the trajectory change schemes of multiple references described in step J2 may specifically include the following steps D1 and D2, in order to improve the above technical problem, which has a problem of trajectory disorder and thus is difficult to accurately determine the trajectory change schemes of multiple references.
And D1, acquiring the action range of the talent data training model in the key strategy data based on the multiple reference track change schemes.
And D2, wherein the acquisition patterns of the action ranges of the talent data training models corresponding to different trajectory change schemes are different.
It can be understood that, when the contents described in the above steps D1 and D2 are performed, the trajectory change schemes of the multiple references are obtained, and the problem of trajectory disorder is avoided, so that the trajectory change schemes of the multiple references can be accurately determined.
Based on the above, the following descriptions of step F1 and step F2 are also included.
Step F1, obtaining range description data of the first-person data division range.
Step F2, acquiring a calculation control of the talent data training model from the key strategy data on the premise that the range description data of the first talent data division range meets the conditions.
Illustratively, the calculation control is used for triggering a calculation operation of training a model for the talent data.
It can be appreciated that the computation control of the talent data training model can be effectively improved by the range description data when the above-mentioned contents of steps F1 and F2 are executed.
Based on the same inventive concept, the system comprises talent data acquisition equipment and a cloud platform, wherein the talent data acquisition equipment is in communication connection with the cloud platform, and the cloud platform is specifically used for:
acquiring key strategy data of real-time talent data, wherein the key strategy data comprise a first talent data dividing range and at least one talent data simulation track for the boundary of the first talent data dividing range;
responding to the calculation operation aiming at a talent data training model, controlling the first talent data division range to calculate the talent data training model, wherein the talent data training model is used for changing the description track content of the talent data simulation track contained in the key strategy data;
according to the range description data of the talent data training model, matching the description track content of the target talent data simulation track contained in the key strategy data to obtain the matched target talent data simulation track;
and controlling the first-person data division range to classify on the matched target-person data simulation track based on the classification control operation aiming at the first-person data division range.
Further, the cloud platform is specifically configured to:
on the premise that the talent data training model has first range description data, converting the target talent data simulation track from a non-calculable state to a calculable state to obtain the matched target talent data simulation track; on the premise that the target talent data simulation track is in the non-calculable state, the first talent data division range cannot be classified on the target talent data simulation track;
and on the premise that the target talent data simulation track is in the calculable state, the first talent data division range can be classified on the target talent data simulation track.
Further, the cloud platform is specifically configured to:
on the premise that the first range description data is the disordered data, eliminating disordered data used for eliminating the target talent data simulation track from the target talent data simulation track;
or, on the premise that the first range description data is the trajectory, adding a calculable talent data simulation trajectory into the target talent data simulation trajectory;
or, on the premise that the first range description data is a replacement trajectory, replacing the non-calculable talent data simulation trajectory with a calculable talent data simulation trajectory in the target displacement trajectory.
Further, the cloud platform is specifically configured to:
on the premise that the talent data training model has second range description data, converting the target talent data simulation track from a calculable state to a non-calculable state to obtain the matched target talent data simulation track; on the premise that the target talent data simulation track is in the calculable state, the second talent data division range in the key strategy data can be classified on the target talent data simulation track;
and on the premise that the target talent data simulation track is in the non-calculable state, the second talent data division range in the key strategy data cannot be classified on the target talent data simulation track, and the second talent data division range and the first talent data division range have a mapping relation.
Further, the cloud platform is specifically configured to:
on the premise that the second range description data is added with disorder data, adding disorder data for removing the target talent data simulation track into the target talent data simulation track;
or, on the premise that the second range description data is a replacement track, replacing a calculable talent data simulation track with an uncalculated talent data simulation track in the target talent data simulation track;
or, on the premise that the second range description data is a rejection track, removing a calculable talent data simulation track from the target displacement track.
Further, the cloud platform is specifically configured to:
and updating the content characteristics of talent data corresponding to the target talent data simulation track in talent data contents acquired by the key strategy data based on the matched target talent data simulation track.
Further, the cloud platform is specifically configured to:
acquiring the talent data training model serving as disorder data in talent data contents acquired by the key strategy data based on the matched target talent data simulation track;
or based on the matched target talent data simulation track, canceling acquiring disordered data for removing the target talent data simulation track from talent data contents acquired by the key strategy data;
or acquiring the action effect of the talent data training model on the target talent data simulation track in the talent data content acquired by the key strategy data based on the matched target talent data simulation track.
Further, the cloud platform is specifically configured to:
generating a plurality of reference track changing schemes based on the range description data of the talent data training model, wherein different track changing schemes correspond to different track matching modes;
acquiring a trajectory change scheme of the multiple references; and responding to the selection operation of a target track changing scheme in the multiple reference track changing schemes, matching the description track content of the target talent data simulation track contained in the key strategy data based on the target track changing scheme, and obtaining the matched target talent data simulation track.
Further, the cloud platform is specifically configured to:
acquiring the action range of the talent data training model in the key strategy data based on the track change schemes of the multiple references;
and acquiring styles of action ranges of the talent data training models corresponding to different track changing schemes are different.
Further, the cloud platform is specifically configured to:
obtaining range description data of the first-person data division range;
acquiring a calculation control of the talent data training model from the key strategy data on the premise that the range description data of the first talent data division range meets the condition; wherein the calculation control is used for triggering calculation operation of the talent data training model.
Based on the same inventive concept as above, please refer to fig. 3, which also provides a functional block diagram of an artificial intelligence based talent data tag classification apparatus 500, and the detailed description about the artificial intelligence based talent data tag classification apparatus 500 is as follows.
An artificial intelligence-based talent data tag classification device 500 applied to a cloud platform, the device 500 comprising:
a key data obtaining module 510, configured to obtain key policy data of the real-time talent data, where the key policy data includes a first talent data partition range and at least one talent data simulation track for a boundary of the first talent data partition range;
a trajectory content calculation module 520, configured to control the first-person data partition range to calculate the talent data training model in response to a calculation operation on a talent data training model, where the talent data training model is used to change the trajectory description content of the talent data simulation trajectory included in the key strategy data;
a simulation trajectory determining module 530, configured to match, according to the range description data of the talent data training model, the description trajectory content of the target talent data simulation trajectory included in the key strategy data to obtain a matched target talent data simulation trajectory;
and the simulated trajectory classification model 540 is configured to control the first-person data partition range to classify on the matched target-person data simulated trajectory based on a classification control operation for the first-person data partition range.
A cloud platform comprising a processor and a memory in communication with each other, the processor being configured to retrieve a computer program from the memory and to implement the method of any of figure 2 by running the computer program.
To sum up, a talent data label classification method, a system and a cloud platform based on artificial intelligence carry out state matching on target talent data simulation tracks in key strategy data through a talent data training model, so that the target talent data simulation tracks can be matched, classified diversity is added, classified contents are more accurate, a user can match the target talent data simulation tracks according to actual premises, and classification operation is faster.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (10)
1. An artificial intelligence-based talent data tag classification method, characterized in that the method comprises:
acquiring key strategy data of real-time talent data, wherein the key strategy data comprise a first talent data dividing range and at least one talent data simulation track for the boundary of the first talent data dividing range;
responding to the calculation operation aiming at a talent data training model, controlling the first talent data division range to calculate the talent data training model, wherein the talent data training model is used for changing the description track content of the talent data simulation track contained in the key strategy data;
according to the range description data of the talent data training model, matching the description track content of the target talent data simulation track contained in the key strategy data to obtain the matched target talent data simulation track;
and controlling the first-person data division range to classify on the matched target-person data simulation track based on the classification control operation aiming at the first-person data division range.
2. The method according to claim 1, wherein the matching, according to the range description data of the talent data training model, the description track content of the target talent data simulation track included in the key strategy data to obtain the matched target talent data simulation track comprises:
on the premise that the talent data training model has first range description data, converting the target talent data simulation track from a non-calculable state to a calculable state to obtain the matched target talent data simulation track; on the premise that the target talent data simulation track is in the non-calculable state, the first talent data division range cannot be classified on the target talent data simulation track;
and on the premise that the target talent data simulation track is in the calculable state, the first talent data division range can be classified on the target talent data simulation track.
3. The method of claim 2, wherein the step of converting the target talent data simulation track from a non-calculable state to a calculable state to obtain the matched target talent data simulation track on the premise that the talent data training model has the first range description data comprises:
on the premise that the first range description data is the disordered data, eliminating disordered data used for eliminating the target talent data simulation track from the target talent data simulation track;
or, on the premise that the first range description data is the trajectory, adding a calculable talent data simulation trajectory into the target talent data simulation trajectory;
or, on the premise that the first range description data is a replacement trajectory, replacing the non-calculable talent data simulation trajectory with a calculable talent data simulation trajectory in the target displacement trajectory.
4. The method according to claim 1, wherein the matching, according to the range description data of the talent data training model, the description track content of the target talent data simulation track included in the key strategy data to obtain the matched target talent data simulation track comprises:
on the premise that the talent data training model has second range description data, converting the target talent data simulation track from a calculable state to a non-calculable state to obtain the matched target talent data simulation track; on the premise that the target talent data simulation track is in the calculable state, the second talent data division range in the key strategy data can be classified on the target talent data simulation track;
and on the premise that the target talent data simulation track is in the non-calculable state, the second talent data division range in the key strategy data cannot be classified on the target talent data simulation track, and the second talent data division range and the first talent data division range have a mapping relation.
5. The method of claim 4, wherein the converting the target talent data simulation trajectory from a calculable state to a non-calculable state to obtain the matched target talent data simulation trajectory on the premise that the talent data training model has the second range description data comprises:
on the premise that the second range description data is added with disorder data, adding disorder data for removing the target talent data simulation track into the target talent data simulation track;
or, on the premise that the second range description data is a replacement track, replacing a calculable talent data simulation track with an uncalculated talent data simulation track in the target talent data simulation track;
or, on the premise that the second range description data is a rejection track, removing a calculable talent data simulation track from the target displacement track.
6. The method of claim 1, wherein after controlling the first-person data scoping to compute the talent data training model in response to the computing operation on the talent data training model, further comprising:
and updating the content characteristics of talent data corresponding to the target talent data simulation track in talent data contents acquired by the key strategy data based on the matched target talent data simulation track.
7. The method according to claim 6, wherein the updating, in the talent data content obtained by the key policy data, the content characteristics of talent data corresponding to the target talent data simulation track based on the matched target talent data simulation track comprises:
acquiring the talent data training model serving as disorder data in talent data contents acquired by the key strategy data based on the matched target talent data simulation track;
or based on the matched target talent data simulation track, canceling acquiring disordered data for removing the target talent data simulation track from talent data contents acquired by the key strategy data;
or acquiring the action effect of the talent data training model on the target talent data simulation track in the talent data content acquired by the key strategy data based on the matched target talent data simulation track.
8. The method of claim 1, wherein after controlling the first-person data scoping to compute the talent data training model in response to the computing operation on the talent data training model, further comprising:
generating a plurality of reference track changing schemes based on the range description data of the talent data training model, wherein different track changing schemes correspond to different track matching modes;
acquiring a trajectory change scheme of the multiple references; and responding to the selection operation of a target track changing scheme in the multiple reference track changing schemes, matching the description track content of the target talent data simulation track contained in the key strategy data based on the target track changing scheme, and obtaining the matched target talent data simulation track.
9. The utility model provides a talent data label classification system based on artificial intelligence, its characterized in that, includes talent data acquisition equipment and cloud platform, talent data acquisition equipment with cloud platform interconnect, the cloud platform includes:
the key data acquisition module is used for acquiring key strategy data of the real-time talent data, wherein the key strategy data comprise a first talent data division range and at least one talent data simulation track for the boundary line of the first talent data division range;
the trajectory content calculation module is used for responding to calculation operation aiming at a talent data training model, controlling the first talent data division range to calculate the talent data training model, and the talent data training model is used for changing the description trajectory content of the talent data simulation trajectory contained in the key strategy data;
the simulation track determining module is used for matching the description track content of the target talent data simulation track contained in the key strategy data according to the range description data of the talent data training model to obtain the matched target talent data simulation track;
and the simulated track classification module is used for controlling the first-person data division range to classify on the matched target-person data simulated track based on the classification control operation aiming at the first-person data division range.
10. A cloud platform comprising a processor and a memory in communication with each other, the processor being configured to retrieve a computer program from the memory and to implement the method of any one of claims 1 to 8 by running the computer program.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060136259A1 (en) * | 2004-12-17 | 2006-06-22 | General Electric Company | Multi-dimensional analysis of medical data |
| US20150032589A1 (en) * | 2014-08-08 | 2015-01-29 | Brighterion, Inc. | Artificial intelligence fraud management solution |
| CN108628908A (en) * | 2017-03-24 | 2018-10-09 | 北京京东尚科信息技术有限公司 | The method, apparatus and electronic equipment of sorted users challenge-response boundary |
| CN110223104A (en) * | 2019-05-11 | 2019-09-10 | 浙江华坤道威数据科技有限公司 | A kind of client model building system based on big data |
| CN110399476A (en) * | 2019-06-18 | 2019-11-01 | 平安科技（深圳）有限公司 | Generation method, device, equipment and the storage medium of talent's portrait |
| CN112883018A (en) * | 2021-01-28 | 2021-06-01 | 中国建设银行股份有限公司 | Banking talent information prediction method and device based on big data analysis |
-
2021
- 2021-06-17 CN CN202110674183.5A patent/CN113360733A/en not_active Withdrawn
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060136259A1 (en) * | 2004-12-17 | 2006-06-22 | General Electric Company | Multi-dimensional analysis of medical data |
| US20150032589A1 (en) * | 2014-08-08 | 2015-01-29 | Brighterion, Inc. | Artificial intelligence fraud management solution |
| CN108628908A (en) * | 2017-03-24 | 2018-10-09 | 北京京东尚科信息技术有限公司 | The method, apparatus and electronic equipment of sorted users challenge-response boundary |
| CN110223104A (en) * | 2019-05-11 | 2019-09-10 | 浙江华坤道威数据科技有限公司 | A kind of client model building system based on big data |
| CN110399476A (en) * | 2019-06-18 | 2019-11-01 | 平安科技（深圳）有限公司 | Generation method, device, equipment and the storage medium of talent's portrait |
| CN112883018A (en) * | 2021-01-28 | 2021-06-01 | 中国建设银行股份有限公司 | Banking talent information prediction method and device based on big data analysis |
Non-Patent Citations (3)
| Title |
|---|
| SIYU TANG ET AL.: ""Multiple People Tracking by Lifted Multicut and Person Re-identification"", 《2017 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)》 * |
| 姚迪等: ""时空数据语义理解:技术与应用"", 《软件学报》 * |
| 梁珺秀等: ""基于时空标签轨迹的范围模式匹配查询"", 《计算机与现代化》 * |
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