CN114971366B - Talent flow evaluation method based on area analysis, storage medium and electronic equipment - Google Patents
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Abstract
The invention discloses a talent flow evaluation method based on regional analysis, and relates to the technical field of talent analysis. The method comprises the following steps: s1, dividing a local evaluation period; s2, generating a social security inquiry request and inquiring a social security payment record of the evaluation period; s3, generating a record comparison request, and counting a first talent list flowing into the local and a second talent list flowing out of the local in the evaluation period; s4, generating talent flow evaluation requests, and carrying out weighted summation on weights of different academies and corresponding people to obtain talent inflow scores and talent outflow scores; calculating talent flow index by dividing the difference value of talent inflow score and talent outflow score by the sum value; and S5, evaluating the local talent flow condition according to the talent flow index. The method and the system enable the evaluation result of talent flow conditions to be more accurate, more accord with local requirements, and facilitate the establishment of talent introduction guidelines in later stages.
Description
Technical Field
The present invention relates to the field of talent analysis technologies, and in particular, to a talent flow evaluation method based on regional analysis, a storage medium, and an electronic device.
Background
Talent introduction is an important ring in the development strategy of China, and if accurate analysis of talent migration factors can be provided, the talent introduction can help party and government and enterprises to introduce proper talents in less time with lower cost, and the economic and social development of local industry is promoted.
Therefore, the analysis and the treatment of the related talent information have important reference significance for the construction of local economy and society. The original talent information processing method generally includes: 1. collecting personal information and building a warehouse; 2. counting the flow direction of talents and issuing a questionnaire; 3. the results of the questionnaire are analyzed in combination to give an analysis report. This method requires manual analysis, takes a long time and depends on the experience of the staff, and cannot guarantee an accurate analysis result.
The patent of the invention with the publication number of CN110610267B, namely a talent information processing method and device, a computer storage medium and electronic equipment, provides a method capable of improving prediction efficiency and prediction accuracy, but the method does not actually analyze talents, but the talents are taken as important credentials of talents, and are greatly related to the capability of the talents. Therefore, in order to improve the scientificity of talent flow analysis, how to develop a system for evaluating talent flow in combination with academic history is one of the challenges to be solved.
Disclosure of Invention
In order to solve at least one technical problem mentioned in the background art, the invention aims to provide a talent flow evaluation method, a storage medium and electronic equipment based on area analysis, which can evaluate and analyze local talent flow conditions in combination with talent learning, so that the evaluation result of the talent flow conditions is more accurate, more accords with local requirements, and is convenient for formulating post talent introduction guidelines.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a talent flow evaluation method based on regional analysis comprises the following steps:
s1, dividing a local evaluation period;
s2, generating a social security inquiry request when each evaluation period is finished, and inquiring a social security payment record of the evaluation period;
s3, generating a record comparison request, comparing the social security payment records of the evaluation period and the previous evaluation period, and counting a first talent list flowing into the local and a second talent list flowing out of the local in the evaluation period;
s4, generating talent flow evaluation requests, counting the number of each academic in the first talent list and the second talent list, and carrying out weighted summation on the weights of different academies and the corresponding number of people to obtain talent inflow scores and talent outflow scores; calculating talent flow index by dividing the difference value of talent inflow score and talent outflow score by the sum value;
and S5, evaluating the local talent flow condition according to the talent flow index.
Further, the statistical methods of the first talent list and the second talent list are as follows:
comparing the social security payment records of the evaluation period with the social security payment records of the previous evaluation period;
if the same talents exist in the social security payment records of the previous evaluation period and the current evaluation period at the same time, the talents are regarded as not flowing;
if the same talents only have social security payment records of the evaluation period and do not have social security payment records of the previous evaluation period, the talents are considered to flow into the local area in the evaluation period and are listed in a first talent list;
if the same talents only have the social security payment record of the previous evaluation period and do not have the social security payment record of the present evaluation period, the talents are regarded as flowing out of the local area in the present evaluation period and are listed in a second talent list.
Further, the initial value of the weights of the different academies is doctor: filling the person: an optical fiber of the family: big special: the university is: 3:1:0.5:0.35.
further, in the step S4, before the weighted summation, step adjustment is further performed on the weights, specifically:
counting the inflow weighted values of the weights of the various schools and the corresponding people when the talent inflow score is calculated in a previous evaluation period, and the outflow weighted values of the weights of the various schools and the corresponding people when the talent outflow score is calculated;
and for the same academy, making a difference between the inflow weighting value and the outflow weighting value;
if the obtained difference is larger than zero, the weight of the corresponding academy is reduced by one step when the talent inflow score is calculated, and/or the weight of the corresponding academy is increased by one step when the talent outflow score is calculated;
if the obtained difference value is smaller than zero, the weight of the corresponding academy is increased by one step when the talent inflow score is calculated, and/or the weight of the corresponding academy is decreased by one step when the talent outflow score is calculated;
if the obtained difference is equal to zero, the weight of the corresponding learning is unchanged when the talent inflow score is calculated and the talent outflow score is calculated.
Further, the step size is 0.05 times of the corresponding weight.
Further, when processing social security inquiry requests, record comparison requests and talent flow evaluation requests, distributing the requests to the execution clients in proportion according to the processing speed of the execution clients; the method comprises the following steps:
FP1, placing each request in a message queue;
FP2, initially distributing, namely sequentially polling and distributing the requests to be processed in the message queue to each execution client;
FP3, after finishing distributing, insert the task of calculating the processing speed of the execution client before every request that the execution client last k waits to process;
FP4, each executing client is provided with a counter, and each time a request is processed, the counter counts up a count;
FP5, when any executing client processes the request to be processed from the last k, triggering a task for calculating the processing speed, calculating the processing speed of all executing clients, and deleting the task for calculating the processing speed in other executing clients;
and FP6, when any executing client finishes processing all the requests, the requests to be processed in the message queue at the moment are distributed in proportion according to the proportion of the speed of processing the requests of all the executing clients, and return to FP3.
Further, the method for executing the speed of the client processing the request is as follows: the time point allocated last time is a period so far, and the total number of processing requests of the execution client in the period is counted, and the total number is divided by the duration of the period, namely the speed of executing the processing requests of the client.
Further, the evaluation method of S5 is as follows:
if the talent flow index is greater than zero, in the evaluation period, the talent flow index is in a local talent inflow state;
if the talent flow index is smaller than zero, in the evaluation period, the talent flow index is in a local talent outflow state;
if the talent flow index is equal to zero, the talent flow index is in a talent balance state locally in the evaluation period.
A terminal device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements a talent flow assessment method based on regional analysis as described above when executing the computer program.
A computer storage medium having stored thereon a computer program, characterized in that the program, when executed by a processor, implements a talent flow assessment method based on regional analysis as described above.
Compared with the prior art, the invention has the beneficial effects that: according to the talent flow condition evaluation method, the talent flow condition is evaluated and analyzed by combining the talents' academia, the weight of different academia and the corresponding number of people are weighted and summed to obtain the talent inflow score and the talent outflow score, and the talent flow index is calculated by dividing the difference value of the talent inflow score and the talent outflow score by the sum value to represent the local talent flow condition, so that the talent flow condition evaluation result is more accurate, the local requirement is met, and the later talent introduction policy is formulated conveniently.
Drawings
Fig. 1 is an overall flow chart of the present invention.
FIG. 2 is a flow chart of the partial area allocation of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment one:
referring to fig. 1, the embodiment provides a talent flow evaluation method based on regional analysis, which includes the following steps:
s1, dividing a local evaluation period; it may be 1 month as one evaluation period.
S2, generating a social security inquiry request when each evaluation period is finished, and inquiring a social security payment record of the evaluation period;
s3, generating a record comparison request, comparing the social security payment records of the evaluation period and the previous evaluation period, and counting a first talent list flowing into the local and a second talent list flowing out of the local in the evaluation period; specifically, the statistical methods of the first talent list and the second talent list are as follows:
comparing the social security payment records of the evaluation period with the social security payment records of the previous evaluation period;
if the same talents exist in the social security payment records of the previous evaluation period and the current evaluation period at the same time, the talents are regarded as not flowing;
if the same talents only have social security payment records of the evaluation period and do not have social security payment records of the previous evaluation period, the talents are considered to flow into the local area in the evaluation period and are listed in a first talent list;
if the same talents only have the social security payment record of the previous evaluation period and do not have the social security payment record of the present evaluation period, the talents are regarded as flowing out of the local area in the present evaluation period and are listed in a second talent list.
S4, generating talent flow evaluation requests, counting the number of each academic in the first talent list and the second talent list, and carrying out weighted summation on the weights of different academies and the corresponding number of people to obtain talent inflow scores and talent outflow scores; the talent flow index is calculated by dividing the difference between the talent inflow score and the talent outflow score by the sum. The specific calculation formula is as follows:
talent inflow score = Σ (number of people flowing into the local corresponding to academic weight);
talent outflow score = Σ (number of people who outflow the local area corresponding to the academic weight);
talent flow index= (talent inflow score-talent outflow score)/(talent inflow score+talent outflow score) ×100;
generally, the talents of the high school are more difficult to obtain and the number of the talents is smaller, so that the talents of the high school are more important to watch, the weight of the talents of the high school is larger, and the weight reflects the demands of the talents of the corresponding school. Preferably, the initial value of the weights of the different academia is doctor: filling the person: an optical fiber of the family: big special: the university is: 3:1:0.5:0.35.
as the number of talents in different local schools changes over time, the overall social architecture also changes, and thus the demands for the talents in different schools also change. In order to adapt to the change of the social architecture, step length adjustment is further carried out on the weights before weighted summation is carried out, specifically:
counting the inflow weighted value of the weights of each academic and the corresponding people when the inflow score of the talents is calculated in a previous evaluation period: x= (number of persons flowing into the local corresponding to the academic weight);
and the weight of each academic and the outflow weighted value of the corresponding number of people when calculating the talent outflow score: y= (number of people flowing out of the local corresponding to the academic weight)
And for the same academy, taking the difference (X-Y) between the inflow weighting value and the outflow weighting value;
then, according to the magnitude of the difference, the following three steps can be performed:
first kind:
if the obtained difference value is larger than zero, reducing the weight of the corresponding academic by one step length when calculating talent inflow scores;
if the obtained difference value is smaller than zero, when the talent inflow score is calculated, the weight of the corresponding academic is increased by one step;
if the obtained difference is equal to zero, the weight of the corresponding learning is unchanged when the talent inflow score is calculated and the talent outflow score is calculated.
Second kind:
if the obtained difference value is larger than zero, increasing the weight of the corresponding academic by one step when calculating talent outflow scores;
if the obtained difference value is smaller than zero, reducing the weight of the corresponding academic by one step length when calculating talent outflow scores;
if the obtained difference is equal to zero, the weight of the corresponding learning is unchanged when the talent inflow score is calculated and the talent outflow score is calculated.
Third kind:
if the obtained difference is greater than zero, the weight of the corresponding school is reduced by one step when the talent inflow score is calculated, and the weight of the corresponding school is increased by one step when the talent outflow score is calculated;
if the obtained difference is smaller than zero, the weight of the corresponding academy is increased by one step when the talent inflow score is calculated, and the weight of the corresponding academy is reduced by one step when the talent outflow score is calculated;
if the obtained difference is equal to zero, the weight of the corresponding learning is unchanged when the talent inflow score is calculated and the talent outflow score is calculated.
Through the real-time step length adjustment, the calculated talent flow index is more close to the requirements of a social architecture changing at any time for different talents, and the step length is preferably 0.05 times of the corresponding weight.
S5, evaluating the local talent flow condition according to the talent flow index; the specific evaluation method is as follows:
if the talent flow index is greater than zero, in the evaluation period, the talent flow index is in a local talent inflow state;
if the talent flow index is smaller than zero, in the evaluation period, the talent flow index is in a local talent outflow state;
if the talent flow index is equal to zero, the talent flow index is in a talent balance state locally in the evaluation period.
The different talent flowing conditions can introduce guidelines at the following talents, and a certain reference meaning is given to staff in the process of optimizing the talent structure, so that the introduced guidelines of the talents are more fit with local demands.
It is worth mentioning that the invention also adjusts the evaluation period in real time according to talent flow index. Specifically, two adjustment thresholds are set first, for example, a first threshold is 10, and a second threshold is 30; then calculating the absolute value of the talent flow index, wherein the absolute value of the talent flow index reflects the local talent stability condition, and the smaller the absolute value is, the more stable the local talents are, and otherwise, the more the talents flow out of or flow into the local; the following adjustments were made: when the absolute value of the talent flow index is smaller than a first threshold value, the evaluation period is prolonged; when the absolute value of the talent flow index is between the first threshold value and the second threshold value, the evaluation period is unchanged; and when the absolute value of the talent flow index is larger than the second threshold value, shortening the evaluation period. The step length for the extension and shortening may be 5 days or 10 days depending on the actual situation, and a shortest evaluation period is set, and the shortest evaluation period is shortened to the shortest. By the adjustment, the evaluation frequency can be reduced under the condition that talents are relatively stable, and the load brought by a large number of request tasks to the client is reduced; on the contrary, the frequency of evaluation needs to be increased so as to timely respond to the instability of talents.
When processing social security inquiry requests, record comparison requests and talent flow evaluation requests, a plurality of execution clients are needed to complete, but because the performances and the efficiencies of different execution clients are different, when the requests are distributed to the execution clients, the requests are required to be distributed to the execution clients in proportion according to the processing speeds of the execution clients, so that the loads of the different execution clients are relatively balanced. Referring to fig. 2, the method specifically includes the following steps:
FP1, placing each request to be processed in a message queue;
FP2, initially distributing, namely sequentially polling and distributing the requests to be processed in the message queue to each execution client; if the 1 st to 5 th execution clients are included, the pushed 1 st request is distributed to the 1 st execution client, the 2 nd request is distributed to the 2 nd execution client, and so on; the 6 th request is assigned to the 1 st execution client and so on. The method of allocation is such that the number of requests to be processed in each executing client is substantially leveled.
FP3, after finishing distributing, insert the task of calculating the processing speed of the execution client before every request that the execution client last k waits to process; in order to leave sufficient time for the task of calculating the processing speed and to make the sample data of the processing speed as large as possible, k is preferably 5.
FP4, each executing client is provided with a counter, and each time a request is processed, the counter counts up by one count, and when each allocation is performed, the counter returns to 0;
and FP5, when any execution client processes the request to be processed from the last k, triggering a task for calculating the processing speed, and calculating the speed of all the execution clients for processing the request, wherein the method for processing the request by the execution clients is as follows: the time point allocated last time is a period so far, and the total number of processing requests of the execution client in the period is counted, and the total number is divided by the duration of the period, namely the speed of executing the processing requests of the client. Meanwhile, deleting other tasks for executing the calculation processing speed in the client, so that the tasks for calculating the processing speed are prevented from being triggered for multiple times, and resources of the client are wasted; meanwhile, if other tasks for executing the calculation speed in the client are not deleted, the time interval between two adjacent tasks is short in the same allocation period, and even insufficient for processing one request, the calculation speed is not contributed.
And FP6, when any executing client finishes processing all the requests, according to the ratio of the speeds of all the executing clients for processing the requests, the requests to be processed in the message queue at the moment are distributed proportionally, and return to FP3 for the next round of distribution.
In the allocation method described above, it can be approximately ensured that the times at which the execution clients complete the requests for each allocation from the second allocation are approximately equal. However, this is an ideal situation, but the time difference actually completed by the last allocated request for each executing client cannot be eliminated, if the time differences are overlapped with multiple allocations, the time difference may be eventually increased, and the load balancing of the executing client is thoroughly broken. To solve the above problem, the allocation method may also use another policy based on a filling mechanism:
FP1, placing each request to be processed in a message queue;
FP2, initially distributing, namely sequentially polling and distributing the requests to be processed in the message queue to each execution client; if the 1 st to 5 th execution clients are included, the pushed 1 st request is distributed to the 1 st execution client, the 2 nd request is distributed to the 2 nd execution client, and so on; the 6 th request is assigned to the 1 st execution client and so on. The method of allocation is such that the number of requests to be processed in each executing client is substantially leveled.
FP3, after the allocation is finished, inserting a timer before each 1 st request to be processed of the execution client, and sequentially inserting a timing task and a task for calculating the processing speed in the middle of all requests to be processed; the insertion position is chosen in the middle here in order to ensure that the timing tasks and the tasks of calculating the processing speed occur at each allocation node and can be performed.
FP4, each executing client is provided with a counter, and each time a request is processed, the counter counts up by one count, and when each allocation is performed, the counter returns to 0;
FP5, when each executing client processes the request to be processed in the middle, triggering a timing task, and recording the timing duration of each executing client for executing half of the request to be processed in the period; each executing client then triggers the task of calculating the processing speed, which is equal to the counter count of each executing client divided by the time duration, of all executing clients processing the request so far at the last distribution node.
FP6, when any executing client finishes processing all the requests, firstly filling according to the timing duration of half of the requests to be processed in the period of executing the executing client of each executing client, specifically, selecting the maximum value of the timing durations of the executing clients as a reference value, and then calculating the difference between the timing duration of each executing client and the reference value multiplied by the speed of the processing request of the corresponding executing client as the number of the executing server to be filled; then, the corresponding number of requests are captured preferentially from the message queue and distributed to each execution client to realize filling; and finally, according to the proportion of the speeds of all the processing requests of the execution clients, the requests to be processed which are remained in the message queue at the moment are distributed proportionally, and the request returns to FP3 to be distributed in the next round.
By the filling mechanism, the time difference actually completed by the last allocated request of each execution client can be eliminated, and the phenomenon that the balance is broken due to the fact that the accumulation of the time difference is finally achieved when the execution client specifically executes after each allocation is avoided.
Embodiment two:
a terminal device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the regional analysis-based talent flow assessment method of embodiment one when executing the computer program.
Embodiment III:
a computer storage medium having a computer program stored thereon, wherein the program when executed by a processor implements the talent flow assessment method based on regional analysis as described in embodiment two.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Claims (8)
1. The talent flow evaluation method based on the regional analysis is characterized by comprising the following steps of:
s1, dividing a local evaluation period;
s2, generating a social security inquiry request when each evaluation period is finished, and inquiring a social security payment record of the evaluation period;
s3, generating a record comparison request, comparing the social security payment records of the evaluation period and the previous evaluation period, and counting a first talent list flowing into the local and a second talent list flowing out of the local in the evaluation period;
s4, generating talent flow evaluation requests, counting the number of each academic in the first talent list and the second talent list, and carrying out weighted summation on the weights of different academies and the corresponding number of people to obtain talent inflow scores and talent outflow scores; calculating talent flow index by dividing the difference value of talent inflow score and talent outflow score by the sum value;
s5, evaluating the local talent flow condition according to the talent flow index;
when processing social security inquiry requests, record comparison requests and talent flow evaluation requests, distributing each request to each execution client in proportion based on a filling mechanism according to the processing speed of the execution client;
FP1, placing each request in a message queue;
FP2, initially distributing, namely sequentially polling and distributing the requests to be processed in the message queue to each execution client;
FP3, after the allocation is finished, inserting a timer before each 1 st request to be processed of the execution client, and sequentially inserting a timing task and a task for calculating the processing speed in the middle of all requests to be processed;
FP4, each executing client is provided with a counter, and each time a request is processed, the counter counts up a count;
FP5, when each executing client processes the request to be processed in the middle, triggering a timing task, and recording the timing duration of each executing client for executing half of the request to be processed in the period; then each executing client triggers a task of calculating the processing speed, and the speed of processing the request by all the executing clients at the last distributing node is calculated, wherein the speed is equal to the count of a counter of each executing client divided by the time duration;
FP6, when any executing client finishes processing all the requests, firstly filling according to the timing duration of half of the requests to be processed in the period executed by each executing client; selecting the maximum value of the timing time lengths of a plurality of execution clients as a reference value, and then calculating the difference value between the timing time length of each execution client and the reference value multiplied by the processing request speed of the corresponding execution client as the number of the execution server to be filled; then grabbing a corresponding number of requests from the message queue and distributing the requests to each execution client to realize filling; and finally, according to the proportion of the speeds of all the processing requests of the execution clients, the requests to be processed which are remained in the message queue at the moment are distributed proportionally, and the request returns to FP3 to be distributed in the next round.
2. The talent flow assessment method based on regional analysis of claim 1, wherein the statistical methods of the first talent list and the second talent list are as follows:
comparing the social security payment records of the evaluation period with the social security payment records of the previous evaluation period;
if the same talents exist in the social security payment records of the previous evaluation period and the current evaluation period at the same time, the talents are regarded as not flowing;
if the same talents only have social security payment records of the evaluation period and do not have social security payment records of the previous evaluation period, the talents are considered to flow into the local area in the evaluation period and are listed in a first talent list;
if the same talents only have the social security payment record of the previous evaluation period and do not have the social security payment record of the present evaluation period, the talents are regarded as flowing out of the local area in the present evaluation period and are listed in a second talent list.
3. The talent flow evaluation method based on regional analysis according to claim 1, wherein the initial value of the weights of the different academies is doctor: filling the person: an optical fiber of the family: big special: the university is: 3:1:0.5:0.35.
4. the talent flow evaluation method based on regional analysis according to claim 1 or 2, wherein in S4, before the weighted summation, step adjustment is further performed on the weights, specifically:
counting the inflow weighted values of the weights of the various schools and the corresponding people when the talent inflow score is calculated in a previous evaluation period, and the outflow weighted values of the weights of the various schools and the corresponding people when the talent outflow score is calculated;
and for the same academy, making a difference between the inflow weighting value and the outflow weighting value;
if the obtained difference is larger than zero, the weight of the corresponding academy is reduced by one step when the talent inflow score is calculated, and/or the weight of the corresponding academy is increased by one step when the talent outflow score is calculated;
if the obtained difference value is smaller than zero, increasing the weight of the corresponding academy by one step when calculating the talent inflow score, and/or decreasing the weight of the corresponding academy by one step when calculating the talent outflow score;
if the obtained difference is equal to zero, the weight of the corresponding learning is unchanged when the talent inflow score is calculated and the talent outflow score is calculated.
5. The regional analysis based talent flow assessment method of claim 4, wherein said step size is 0.05 times the corresponding weight.
6. The talent flow evaluation method based on regional analysis according to claim 1, wherein the evaluation method of S5 is as follows:
if the talent flow index is greater than zero, in the evaluation period, the talent flow index is in a local talent inflow state;
if the talent flow index is smaller than zero, in the evaluation period, the talent flow index is in a local talent outflow state;
if the talent flow index is equal to zero, the talent flow index is in a talent balance state locally in the evaluation period.
7. A terminal device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the talent flow assessment method based on regional analysis according to any one of claims 1 to 6 when executing the computer program.
8. A computer storage medium having stored thereon a computer program, which when executed by a processor implements the talent flow assessment method based on regional analysis as claimed in any one of claims 1 to 6.
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| CN105787619A (en) * | 2014-12-25 | 2016-07-20 | 阿里巴巴集团控股有限公司 | Data processing method, data processing device, and data processing system |
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| CN112966966A (en) * | 2021-03-25 | 2021-06-15 | 上海柏观数据科技有限公司 | Talent introduction index control method for introduced talent matching |
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