Personnel allocation method and device, computer readable storage medium and electronic equipment

Abstract

The invention discloses a personnel allocation method and device, a computer readable storage medium and electronic equipment. The method comprises the steps of obtaining feature attributes of candidate members, and constructing a candidate member portrait model according to the feature attributes; extracting a target activity keyword, matching feature attributes in the candidate person portrait model according to the target activity keyword to obtain target feature attributes, and screening candidate persons according to the target feature attributes to obtain adaptive persons; and determining a quantized value and a weight of each target feature attribute, calculating a comprehensive score of each adaptive person according to the quantized value and the weight, generating a person distribution strategy according to the comprehensive score, and performing person distribution according to the person distribution strategy. The technical problems that in the prior art, workers of promotion activities mainly depend on experience selection or random selection, so that manpower resources are wasted, worker configuration schemes are not adaptive enough, and configuration schemes meeting actual requirements cannot be rapidly generated are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of personnel allocation, and in particular to a personnel allocation method, device, computer-readable storage medium and electronic device. Background Art

[0002] In some promotion activities, different activities may be distributed in different regions, facing different customers, and have multiple promotion methods. Therefore, as the main body of promoting activities to customers, the ability characteristics of staff are the key to determining the effectiveness of promotion. At present, the staff of most target activities mainly rely on superiors to select or randomly select based on industry experience. The main disadvantages of this method are: there is no objective quantitative standard for the comprehensive quality or ability characteristics of the selected staff; the ability characteristics of the selected staff are not intuitive enough; the selection range is small, the limitations are large, and it is more dependent on the subjectivity of superiors. These problems will affect the human resource allocation of staff in the target activities, and then affect the effect of the target activities. At the same time, with the promotion of target activities, the staff group will continue to expand, the demand for target activities will be dynamically updated, and other factors, the adaptability requirements for staff and target activities will become higher and higher. The existing target activity evaluation mainly considers the impact of customer portraits and work forms on the effect of activities, but almost no one considers the impact of the human resource allocation strategy of target activities on target activities from the perspective of staff allocation.

[0003] At present, the portrait models used in the target activity scenarios are almost all for the portrait modeling of the activity consumers to generate activity plans, and no consideration is given to using portrait technology to configure human resources for the activity from the perspective of the activity provider. The prior art discloses a method for constructing portraits of professional and technical employees within an enterprise. The invention uses portrait technology to establish portraits for Internet technical employees as an information library. First, the applicable scenario of the invention is Internet professional technology. Secondly, the invention only establishes an employee tag library and makes a primary application of the portrait technology, but does not consider the matching of keywords between employee portraits and their job changes; finally, the invention only outputs a method for establishing an employee tag library.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present invention provide a personnel allocation method, device, computer-readable storage medium and electronic device to at least solve the technical problems in the prior art that the staff of promotion activities mainly rely on experience selection or random selection, resulting in waste of human resources, insufficient adaptation of staff allocation plans, and inability to quickly generate configuration plans that meet actual needs.

[0006] According to one aspect of an embodiment of the present invention, there is provided a method for allocating personnel, comprising: acquiring characteristic attributes of candidate personnel, constructing a candidate personnel portrait model based on the characteristic attributes, wherein the characteristic attributes represent the characteristics of the candidate personnel, and the candidate personnel portrait model at least includes the characteristic attributes and attribute values ​​corresponding to the characteristic attributes; extracting target activity keywords, matching the characteristic attributes in the candidate personnel portrait model according to the target activity keywords to obtain target characteristic attributes, screening the candidate personnel according to the target characteristic attributes to obtain suitable personnel; determining a quantitative value and a weight of each of the target characteristic attributes, calculating a comprehensive score of each of the suitable personnel according to the quantitative value and the weight, generating a personnel allocation strategy according to the comprehensive score, and allocating personnel according to the personnel allocation strategy, wherein the quantitative value represents the importance of the target characteristic attribute.

[0007] Optionally, constructing a candidate person portrait model based on the feature attributes includes: respectively determining the attribute values ​​corresponding to the feature attributes of each candidate person; taking the feature attributes as third-level labels, and classifying the third-level labels to obtain multiple second-level labels, wherein each of the second-level labels corresponds to one or more of the feature attributes; classifying the multiple second-level labels to obtain multiple first-level labels, thereby constructing the candidate person portrait model, wherein each of the first-level labels corresponds to one or more second-level labels.

[0008] Optionally, the candidate personnel are screened according to the target feature attributes to obtain suitable personnel, including: determining multiple scoring levels of the target feature attributes; taking the target feature attributes of each candidate personnel as a sample, taking the scoring level as a cluster category, taking the sum of the squares of the distances from the sample to the cluster centroid of the cluster category as the clustering objective function, and performing cluster analysis on the candidate personnel; and screening the suitable personnel according to the cluster analysis results.

[0009] Optionally, determining the quantitative value and weight of each of the target feature attributes includes: determining multiple scoring levels of the target feature attributes, and determining the quantitative value corresponding to each scoring level according to the scoring level; determining the weight corresponding to the target feature attribute according to the importance of each of the target feature attributes.

[0010] Optionally, extracting the target activity keywords includes: acquiring a historical keyword library and a text scheme of the target activity, matching the text scheme with the historical keyword library, and obtaining the target activity keywords corresponding to the target activity.

[0011] Optionally, the comprehensive score of each of the suitable persons is calculated according to the quantitative value and the weight, including: calculating the product of the quantitative value of each of the target feature attributes and the weight, and calculating the sum of the scores of multiple target feature attributes to obtain the comprehensive score of each of the suitable persons.

[0012] Optionally, generating a personnel allocation strategy according to the comprehensive score includes: sorting the comprehensive scores to generate a fitness ranking; and generating the personnel allocation strategy according to the fitness ranking.

[0013] According to another aspect of an embodiment of the present invention, a personnel allocation device is also provided, comprising: a construction unit, used to obtain characteristic attributes of candidate personnel, and to construct a candidate personnel portrait model based on the characteristic attributes, wherein the characteristic attributes represent the characteristics of the candidate personnel, and the candidate personnel portrait model at least includes the characteristic attributes and the attribute values ​​corresponding to the characteristic attributes; a screening unit, used to extract target activity keywords, match the characteristic attributes in the candidate personnel portrait model according to the target activity keywords to obtain target characteristic attributes, and screen the candidate personnel according to the target characteristic attributes to obtain suitable personnel; an allocation unit, used to determine a quantitative value and a weight of each of the target characteristic attributes, calculate a comprehensive score of each of the suitable personnel according to the quantitative value and the weight, generate a personnel allocation strategy according to the comprehensive score, and allocate personnel according to the personnel allocation strategy, wherein the quantitative value represents the importance of the target characteristic attribute.

[0014] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is further provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is run, the device where the computer-readable storage medium is located is controlled to execute any one of the personnel allocation methods described above.

[0015] According to another aspect of an embodiment of the present invention, there is also provided an electronic device, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a method for allocating personnel for executing any one of the above-described methods.

[0016] In an embodiment of the present invention, the characteristic attributes of the candidate are obtained, and a candidate portrait model is constructed according to the characteristic attributes, wherein the characteristic attributes represent the characteristics of the candidate, and the candidate portrait model at least includes the characteristic attributes and the attribute values ​​corresponding to the characteristic attributes; the target activity keywords are extracted, and the characteristic attributes in the candidate portrait model are matched according to the target activity keywords to obtain the target characteristic attributes, and the candidate is screened according to the target characteristic attributes to obtain the suitable personnel; the quantitative value and weight of each target characteristic attribute are determined, and the comprehensive score of each suitable personnel is calculated according to the quantitative value and weight, and the personnel allocation strategy is generated according to the comprehensive score, and the personnel are allocated according to the personnel allocation strategy, wherein the quantitative value represents the importance of the target characteristic attribute. Through the technical scheme provided by the present invention, the purpose of using the portrait technology to process the various data of the candidate personnel, establishing the candidate personnel label model, and outputting the corresponding configuration strategy according to the activity keyword information, and at the same time, performing manual evaluation to confirm the configuration scheme is achieved, thereby achieving the technical effect of quickly generating the candidate personnel configuration scheme while ensuring adaptability, improving the efficiency and adaptability of the configuration, and thus solving the technical problem that the staff of the promotion activities in the prior art mainly rely on experience selection or random selection, resulting in waste of human resources, the staff configuration scheme is not adaptable enough, and the configuration scheme that meets the actual needs cannot be quickly generated. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0018] Figure 1 It is a hardware structure block diagram of a mobile terminal of a personnel allocation method according to an embodiment of the present invention;

[0019] Figure 2 is a flow chart of a method for allocating personnel according to an embodiment of the present invention;

[0020] Figure 3 is a flow chart of an optional personnel allocation method according to an embodiment of the present invention;

[0021] Figure 4 is a schematic diagram of a personnel allocation device according to an embodiment of the present invention.

[0022] The above drawings include the following reference numerals:

[0023] 102, processor; 104, memory; 106, transmission device; 108, input and output devices. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] As introduced in the background technology, in the prior art, the staff of promotion activities are mainly selected based on experience or random selection, resulting in a waste of human resources, insufficient adaptation of staff allocation schemes, and inability to quickly generate a configuration scheme that meets actual needs. In view of the above defects, a method, device, computer-readable storage medium, and electronic device for allocating personnel are provided in the embodiments of the present invention.

[0027] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0028] The method embodiments provided in the embodiments of the present invention can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 FIG. 1 is a hardware structure block diagram of a mobile terminal of a personnel allocation method according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is for illustration only and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0029] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the personnel allocation method in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific examples of the network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0030] According to an embodiment of the present invention, a method embodiment of a personnel allocation method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0031] Figure 2 is a flow chart of a method for allocating personnel according to an embodiment of the present invention. Figure 2 As shown, the method comprises the following steps:

[0032] Step S202, obtaining characteristic attributes of the candidate, and constructing a candidate portrait model based on the characteristic attributes, wherein the characteristic attributes represent the characteristics of the candidate, and the candidate portrait model at least includes the characteristic attributes and attribute values ​​corresponding to the characteristic attributes.

[0033] It should be noted that the above-mentioned portrait is a labeling model built by abstracting the attributes, behaviors and other information of real-world objects in order to understand the operation of the objects.

[0034] Optionally, the above-mentioned characteristic attributes may include: gender, age, psychological condition, physical condition, job level, years of work experience, permanent location, certificates held, training results, promotion experience, special contributions, management ability, communication ability, copywriting ability and team ability, etc.

[0035] In this embodiment, the characteristic attributes of the candidate can be obtained, and the characteristic attributes of the candidate can be used as a data source to build a candidate portrait label model, which can better evaluate the adaptability and matching degree of the candidate.

[0036] Step S204, extract the target activity keywords, match the characteristic attributes in the candidate person portrait model according to the target activity keywords, obtain the target characteristic attributes, screen the candidate persons according to the target characteristic attributes, and obtain suitable persons.

[0037] Figure 3 is a flow chart of an optional personnel allocation method according to an embodiment of the present invention, such as Figure 3 As shown in the figure, S1: build candidate profiles; S2: extract keywords for recommended activities; S3: select recommended personnel who are suitable for recommended activities; S4: assign weights to recommended personnel labels and calculate comprehensive scores; S5: generate recommended personnel suitability rankings and recommended personnel profiles. Recommended activities refer to activities that recommend suitable financial products to users.

[0038] Step S206, determine the quantitative value and weight of each target feature attribute, calculate the comprehensive score of each suitable person according to the quantitative value and weight, generate a personnel allocation strategy according to the comprehensive score, and allocate personnel according to the personnel allocation strategy, wherein the quantitative value represents the importance of the target feature attribute.

[0039] In this embodiment, quantitative rules can be formulated and weights can be assigned based on label attributes to calculate the comprehensive score of suitable personnel. Different target feature attributes can be assigned different weights according to their importance, so that the influence of each feature attribute can be better considered in the personnel allocation process. A personnel allocation strategy is generated based on the comprehensive score, and personnel are allocated according to the personnel allocation strategy, which can quickly and accurately evaluate the overall degree of adaptation of each suitable personnel.

[0040] As can be seen from the above, in an embodiment of the present invention, characteristic attributes of candidate candidates are obtained, and a candidate candidate portrait model is constructed based on the characteristic attributes, wherein the characteristic attributes represent the characteristics of the candidate candidates, and the candidate candidate portrait model at least includes characteristic attributes and attribute values ​​corresponding to the characteristic attributes; target activity keywords are extracted, and characteristic attributes in the candidate candidate portrait model are matched according to the target activity keywords to obtain target characteristic attributes, and candidate candidates are screened according to the target characteristic attributes to obtain suitable personnel; the quantitative value and weight of each target characteristic attribute are determined, and the comprehensive score of each suitable personnel is calculated according to the quantitative value and weight, and a personnel allocation strategy is generated according to the comprehensive score, and personnel are allocated according to the personnel allocation strategy, wherein the quantitative value represents the importance of the target characteristic attribute, and the purpose of using portrait technology to process various data of candidate candidates, establish a candidate candidate label model, and output corresponding configuration strategies according to activity keyword information, and at the same time, perform manual evaluation to confirm the configuration plan, thereby achieving the technical effect of quickly generating candidate personnel configuration plans while ensuring adaptability, and improving the efficiency and adaptability of configuration.

[0041] The above-mentioned technical solution provided by the embodiment of the present invention solves the technical problems in the prior art that the staff of the promotion activities mainly rely on experience selection or random selection, resulting in a waste of human resources, the staff configuration plan is not adaptable enough, and the configuration plan that meets the actual needs cannot be quickly generated.

[0042] According to the above embodiment of the present invention, a candidate person portrait model is constructed based on feature attributes, including: respectively determining the attribute values ​​corresponding to the feature attributes of each candidate person; taking the feature attributes as third-level labels, and classifying the third-level labels to obtain multiple second-level labels, wherein each second-level label corresponds to one or more feature attributes; classifying the multiple second-level labels to obtain multiple first-level labels, thereby constructing a candidate person portrait model, wherein each first-level label corresponds to one or more second-level labels.

[0043] It should be noted that labels are formed by analyzing and refining the information of objects to form some highly generalized, distinctive, and easy-to-understand features in order to describe the objects. The use of object labels also facilitates processing using tools such as computers.

[0044] In this embodiment, after obtaining the above-mentioned characteristic data of the candidate, a three-level candidate portrait label model can be formed, which can better understand the potential strengths and weaknesses of the candidate and more accurately match the candidate with the job requirements.

[0045] Optionally, the above-mentioned secondary tags may include: basic information, physical and mental condition, occupational information, training experience, promotion experience, work ability and special contributions, etc.; the above-mentioned first-level tags may include: static class, dynamic class and characteristic class.

[0046] In addition, after obtaining all the characteristic attributes of the candidate, we establish quantitative specifications based on the focus of the promotion activities and the industry experience of the superiors, and use these specifications to process all the data attributes of the candidate and perform min-max normalization on the data: X' = x-x mi n , where x is the original data,

[0047] x max -x min X' is the normalized data, x min is the minimum value in the original data dimension, x max It is the maximum value in the original data dimension.

[0048] Finally, a radar chart is used to present the normalized candidate data to show the portrait characteristics of each candidate.

[0049] It should be noted that the above-mentioned min-max normalization is a commonly used method for data preprocessing. Its main function is to eliminate the dimensional influence between data features. By linearly transforming the original data, the normalization of data features is completed to make different indicators comparable.

[0050] According to the above embodiment of the present invention, candidate personnel are screened according to target feature attributes to obtain suitable personnel, including: determining multiple scoring levels of the target feature attributes; taking the target feature attributes of each candidate as a sample, taking the scoring level as a clustering category, taking the sum of the squares of the distances from the sample to the cluster centroid of the clustering category as the clustering objective function, and performing cluster analysis on the candidate personnel; and obtaining suitable personnel by screening according to the cluster analysis results.

[0051] In this embodiment, target feature attributes are extracted from the data source as sample data, which are divided into multiple scoring levels and used as cluster categories. The sum of the squares of the distances from the samples to the cluster centroids of the cluster categories is used as the clustering objective function for cluster analysis to obtain screened suitable personnel. The similarity between each sample data point and the cluster center can be more accurately evaluated, thereby finding the most suitable personnel.

[0052] Specifically, the K-means algorithm is used to extract the data required for application from the acquired source data as the sample data for clustering. The number of cluster centroids (i.e., the number of classification categories) is determined according to the application requirements, which is called the K value (1≤K≤20). Finally, the sample data is classified based on the Euclidean distance from the K value.

[0053] The following example takes the extraction of “training results” and “activity experience” (i.e., target feature attributes) from the data source as sample data and divides them into three levels (i.e., scoring levels): excellent, good, and qualified. The square error and e SSE As the objective function of clustering and the indicator for measuring different clustering effects, the sample data in Euclidean space is represented as the distance from the sample point x to the cluster category C. i The cluster centroid μ i The sum of the squares of the distances between Among them, e SSE The smaller the value, the more similar the samples within the cluster category are, so it is necessary to make e SSE Reach the minimum value to obtain the best clustering result. This can help to make a preliminary classification of the candidate data. If the data is at different orders of magnitude, it needs to be normalized to limit its range to [0,1].

[0054] According to the above embodiment of the present invention, the quantitative value and weight of each target feature attribute are determined, including: determining multiple scoring levels of the target feature attribute, and determining the quantitative value corresponding to each scoring level according to the level of the scoring level; determining the weight corresponding to the target feature attribute according to the importance of each target feature attribute.

[0055] In this embodiment, weights can be assigned to candidate labels and comprehensive scores can be calculated. First, a quantification rule is formulated and weights are assigned to each rating level according to the rating level. This enables a clearer understanding of the performance of each feature attribute, facilitates comparison and evaluation of the importance and advantages and disadvantages of each attribute, and more accurately determines the contribution of each feature attribute to the overall goal, thereby making decisions and planning more effectively.

[0056] The above technical solution provided in the embodiment of the present invention is described below in conjunction with Table 1, wherein Table 1 shows the quantization rules and weights corresponding to the target feature attributes:

[0057] Table 1

[0058]

[0059]

[0060] As shown in Table 1 above, the quantification rules and weights corresponding to each label attribute are recorded. Gender, psychological status, permanent residence, training results and promotion experience are taken as key labels, and their quantification rules are formulated and weights are assigned. Among them, N1, N2, ..., N 14 It is a natural number, and the weight is P1+P2+P3+P4+P5=1.

[0061] According to the above embodiment of the present invention, extracting target activity keywords includes: acquiring a historical keyword library and a text scheme of the target activity, matching the text scheme with the historical keyword library, and obtaining target activity keywords corresponding to the target activity.

[0062] Optionally, the above-mentioned target activity keywords may include but are not limited to: new customers, financial management, funds, stocks, insurance, loans, credit cards, savings cards, digital RMB and payment, etc.

[0063] In this embodiment, the target activity keywords can be refined by string matching, which can save manpower and time costs and improve work efficiency. First, a vocabulary library (i.e., a historical keyword library) is constructed based on the above-mentioned target activity keywords, and activity keywords can be continuously accumulated and updated to improve the accuracy and comprehensiveness of extracting key information. Then, the target activity plan is stored in text form and matched with the data in the keyword library to extract the key information of the corresponding target activity. The key information in the target activity plan can be extracted quickly and accurately to more effectively understand and identify the key content of the target activity.

[0064] According to the above embodiment of the present invention, the comprehensive score of each suitable person is calculated based on the quantitative value and the weight, including: calculating the product of the quantitative value of each target feature attribute and the weight, and calculating the sum of the scores of multiple target feature attributes to obtain the comprehensive score of each suitable person.

[0065] In this embodiment, the product of the quantization value and the weight of each target label attribute can be calculated according to the quantization rules and weights, and then the scores of multiple target label attributes can be added to obtain the personal comprehensive score. That is, the personal comprehensive score of each suitable person is calculated by weighted summation based on the data in the above table, which helps to evaluate the performance of each suitable person on each target label attribute, so as to have a more comprehensive understanding of their abilities and potentials, and can more objectively evaluate the comprehensive quality of each suitable person.

[0066] According to the above embodiment of the present invention, generating a personnel allocation strategy based on comprehensive scores includes: sorting the comprehensive scores to generate a fitness ranking; and generating a personnel allocation strategy based on the fitness ranking.

[0067] In this embodiment, a candidate suitability ranking and a profile of the suitable candidates can be generated to provide a strategy for the human resource allocation of the target activity, which helps to better understand the degree of match between each candidate and the target activity, thereby making more targeted human resource allocation, and better understand the candidates' abilities, experience, characteristics and potential contributions, thereby more effectively selecting the most suitable candidates to participate in the target activity and improving the success rate and benefits of the activity.

[0068] Specifically, the evaluation method for candidates is to comprehensively consider the secondary labels of the above-mentioned portrait label model. In the evaluation, for candidates with normal physical and mental conditions, only other information is considered; for the evaluation of professional information, attention is paid to their rank, years of work, and permanent place of residence; in terms of training experience, training results are selected; in terms of work ability, attention is paid to management ability, communication ability, copywriting ability, team ability and other abilities. The evaluation of each aspect is combined with the three-level labels considered or selected for comprehensive sorting. If an aspect has multiple labels, the average value of these labels needs to be calculated. However, since the quantitative standards of these labels are different, the labels with values ​​greater than 1 need to be normalized first to ensure that they are compared under the same standard. Finally, the candidate suitability ranking and the portrait of the suitable personnel are generated to provide strategies for the human resource allocation of promotion activities.

[0069] The technical solution provided by the above embodiment of the present invention solves the following problems: 1) There is no objective quantitative standard for the comprehensive quality or ability characteristics of the selected candidates; 2) The ability characteristics of the selected candidates are not intuitive enough; 3) The selection range is small, the limitations are large, and it is more dependent on the subjectivity of the superiors. All of these will affect the human resource allocation of candidates in the promotion activities, resulting in waste of human resources and increased promotion costs, which in turn affects the effect of the promotion activities. At the same time, with the promotion of the target activities, the candidate group will continue to expand, the demand for promotion activities will also be dynamically updated, and other factors will increase the compatibility requirements for candidates and promotion activities. Traditional methods cannot quickly generate candidate configuration plans while ensuring adaptability. At present, there is no solution for human resource allocation for promotion activities by establishing a portrait model for candidates. It also has the following beneficial effects: by using portrait technology, various data of candidate personnel are quantified, analyzed, and pre-processed, the various quality levels and ability characteristics of candidate personnel are extracted, and a candidate personnel label model is established; then, based on the keyword information of the promotion activity, the candidate personnel label model is applied to output the configuration strategy of the candidate personnel in the corresponding promotion activity; the next step is to manually evaluate the generated strategy, adjust it with reference to the actual situation, and finally confirm the configuration plan of the candidate personnel in the promotion activity. By jointly applying the candidate personnel portrait technology and keyword matching technology, a complete set of promotion activity human resource configuration strategies is output, which can optimize the configuration of candidate personnel in the promotion activity according to the ability characteristics of the candidate personnel.

[0070] In addition, the current personnel portrait model is mainly used in related scenarios that require data mining of big data. Through the classification algorithm of machine learning, known label types are extracted, and by establishing a mapping relationship between user information and label types, the model is finally established. Based on this model, it can be applied in scenarios such as generating targeted promotion activities based on customer portraits, personalized product recommendations, and push advertising.

[0071] As can be seen from the above, in the embodiments of the present invention, since the existing promotion activity evaluation mainly considers the impact of customer portraits and promotion forms on the activity effect, but almost does not consider the impact of the promotion activity human resource allocation strategy on the promotion activity from the perspective of personnel allocation. The present invention applies portrait technology to provide a new promotion activity human resource allocation strategy; by using portrait technology, various data of the staff are quantified, analyzed and pre-processed, and the various quality levels and ability characteristics of the staff are refined to establish a candidate label model. Then, according to the keyword information of the promotion activity, the candidate label model is applied to output the configuration strategy of the candidate in the corresponding promotion activity. In the next step, the generated strategy is manually evaluated, adjusted with reference to the actual situation, and finally the configuration plan of the candidate in the promotion activity is confirmed; an objective quantitative evaluation of the quality and ability of the candidate is achieved, the ability characteristics of the staff are reflected in an intuitive form, the selection scope of the staff is broadened, and a method for human resource allocation for target activities based on portrait technology is established by combining the objectivity of data and the subjectivity of manpower.

[0072] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0073] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0074] According to an embodiment of the present invention, a personnel allocation device for implementing the above personnel allocation method is also provided. Figure 4 is a schematic diagram of a personnel allocation device according to an embodiment of the present invention, such as Figure 4 As shown, the device comprises: a construction unit 401, a screening unit 403 and an allocation unit 405. The personnel allocation device is described below.

[0075] The construction unit 401 is used to obtain characteristic attributes of the candidate and construct a candidate portrait model based on the characteristic attributes, wherein the characteristic attributes represent the characteristics of the candidate, and the candidate portrait model at least includes the characteristic attributes and attribute values ​​corresponding to the characteristic attributes.

[0076] The screening unit 403 is used to extract target activity keywords, match the characteristic attributes in the candidate person portrait model according to the target activity keywords, obtain the target characteristic attributes, and screen the candidate persons according to the target characteristic attributes to obtain suitable persons.

[0077] The allocation unit 405 is used to determine the quantitative value and weight of each target feature attribute, calculate the comprehensive score of each suitable person according to the quantitative value and weight, generate a personnel allocation strategy according to the comprehensive score, and allocate personnel according to the personnel allocation strategy, wherein the quantitative value represents the importance of the target feature attribute.

[0078] It should be noted here that the above-mentioned construction unit 401, screening unit 403 and allocation unit 405 correspond to steps S202 to S206 in the above-mentioned embodiment, and the three modules have the same instances and application scenarios implemented by the corresponding steps, but are not limited to the contents disclosed in the above-mentioned embodiment.

[0079] As can be seen from the above, in the scheme recorded in the above-mentioned embodiment of the present invention, first, the construction unit can be used to obtain the characteristic attributes of the candidate personnel, and a candidate personnel portrait model can be constructed according to the characteristic attributes, wherein the characteristic attributes represent the characteristics of the candidate personnel, and the candidate personnel portrait model at least includes the characteristic attributes and the attribute values ​​corresponding to the characteristic attributes; then, the screening unit can be used to extract the target activity keywords, and the characteristic attributes in the candidate personnel portrait model are matched according to the target activity keywords to obtain the target characteristic attributes, and the candidate personnel are screened according to the target characteristic attributes to obtain suitable personnel; finally, the allocation unit can be used to determine the quantitative value and weight of each target characteristic attribute, and the comprehensive score of each suitable personnel is calculated according to the quantitative value and weight, and a personnel allocation strategy is generated according to the comprehensive score, and personnel are allocated according to the personnel allocation strategy, wherein the quantitative value represents the importance of the target characteristic attribute, and the purpose of using the portrait technology to process various data of the candidate personnel, establish a candidate personnel label model, and output the corresponding configuration strategy according to the activity keyword information, and at the same time, the configuration plan is confirmed by manual evaluation, thereby achieving the technical effect of quickly generating the candidate personnel configuration plan while ensuring adaptability, and improving the efficiency and adaptability of the configuration.

[0080] The above-mentioned technical solution provided by the embodiment of the present invention solves the technical problems in the prior art that the staff of the promotion activities mainly rely on experience selection or random selection, resulting in a waste of human resources, the staff configuration plan is not adaptable enough, and the configuration plan that meets the actual needs cannot be quickly generated.

[0081] In an optional embodiment, the construction unit includes: a first determination subunit, used to respectively determine the attribute values ​​corresponding to the characteristic attributes of each candidate; a first classification subunit, used to take the characteristic attributes as third-level labels, and classify the third-level labels to obtain multiple second-level labels, wherein each second-level label corresponds to one or more characteristic attributes; a second classification subunit, used to classify the multiple second-level labels to obtain multiple first-level labels, thereby constructing a candidate portrait model, wherein each first-level label corresponds to one or more second-level labels.

[0082] In an optional embodiment, the screening unit includes: a second determination subunit, used to determine multiple scoring levels of target feature attributes; a cluster analysis subunit, used to perform cluster analysis on the candidate personnel by taking the target feature attributes of each candidate as a sample, the scoring level as a cluster category, and the sum of the squares of the distances from the sample to the cluster centroid of the cluster category as the cluster objective function; and a screening subunit, used to screen suitable personnel according to the cluster analysis results.

[0083] In an optional embodiment, the allocation unit includes: a third determination subunit, used to determine multiple scoring levels of the target feature attributes, and determine the quantitative value corresponding to each scoring level according to the scoring level; a fourth determination unit, used to determine the weight corresponding to the target feature attribute according to the importance of each target feature attribute.

[0084] In an optional embodiment, the screening unit includes: a matching subunit, which is used to obtain the historical keyword library and the text scheme of the target activity, match the text scheme with the historical keyword library, and obtain the target activity keyword corresponding to the target activity.

[0085] In an optional embodiment, the allocation unit includes: a calculation subunit, which is used to calculate the product of the quantized value of each target feature attribute and the weight, and calculate the sum of the scores of multiple target feature attributes to obtain a comprehensive score for each matching person.

[0086] In an optional embodiment, the allocation unit includes: a sorting subunit for sorting the comprehensive scores to generate a fitness ranking; and a generating unit for generating a personnel allocation strategy according to the fitness ranking.

[0087] According to another aspect of an embodiment of the present invention, there is also provided an electronic device, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a method for allocating personnel for executing any of the above.

[0088] According to another aspect of an embodiment of the present invention, a processor is further provided, and the processor is used to run a program, wherein when the program is run, any one of the above-mentioned personnel allocation methods is executed.

[0089] According to another aspect of an embodiment of the present invention, a computer program product is provided, including computer instructions, and when the computer instructions are executed by a processor, any one of the above-mentioned personnel allocation methods is executed.

[0090] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned personnel allocation methods.

[0091] Optionally, in this embodiment, the computer-readable storage medium may be located in any one of the computer terminals in a computer terminal group in a computer network, or in any one of the communication devices in a communication device group.

[0092] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: obtaining characteristic attributes of the candidate personnel, constructing a candidate personnel portrait model based on the characteristic attributes, wherein the characteristic attributes represent the characteristics of the candidate personnel, and the candidate personnel portrait model at least includes the characteristic attributes and attribute values ​​corresponding to the characteristic attributes; extracting target activity keywords, matching the characteristic attributes in the candidate personnel portrait model according to the target activity keywords to obtain target characteristic attributes, screening the candidate personnel according to the target characteristic attributes to obtain suitable personnel; determining the quantitative value and weight of each target characteristic attribute, calculating the comprehensive score of each suitable personnel according to the quantitative value and weight, generating a personnel allocation strategy according to the comprehensive score, and allocating personnel according to the personnel allocation strategy, wherein the quantitative value represents the importance of the target characteristic attribute.

[0093] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: respectively determining the attribute values ​​corresponding to the characteristic attributes of each candidate; taking the characteristic attributes as third-level labels, and classifying the third-level labels to obtain multiple second-level labels, wherein each second-level label corresponds to one or more characteristic attributes; classifying the multiple second-level labels to obtain multiple first-level labels, thereby constructing a candidate portrait model, wherein each first-level label corresponds to one or more second-level labels.

[0094] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: determining multiple scoring levels for target feature attributes; taking the target feature attributes of each candidate as a sample, the scoring level as a clustering category, and the sum of the squares of the distances from the sample to the cluster centroid of the clustering category as the clustering objective function, and performing cluster analysis on the candidate personnel; and screening suitable personnel based on the cluster analysis results.

[0095] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: determining multiple scoring levels for the target feature attributes, and determining a quantitative value corresponding to each scoring level based on the scoring level; determining a weight corresponding to the target feature attribute based on the importance of each target feature attribute.

[0096] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: obtaining a historical keyword library and a text scheme of a target activity, matching the text scheme with the historical keyword library, and obtaining a target activity keyword corresponding to the target activity.

[0097] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: calculating the product of the quantized value of each target feature attribute and the weight, and calculating the sum of the scores of multiple target feature attributes to obtain a comprehensive score for each suitable person.

[0098] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: sorting the comprehensive scores to generate a fitness ranking; and generating a personnel allocation strategy according to the fitness ranking.

[0099] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0100] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0101] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the above-mentioned units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0102] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0103] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0104] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the above-mentioned methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0105] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A personnel allocation method, characterized in that: include: Acquire characteristic attributes of the candidate, and construct a candidate portrait model according to the characteristic attributes, wherein the characteristic attributes represent the characteristics of the candidate, and the candidate portrait model at least includes the characteristic attributes and attribute values ​​corresponding to the characteristic attributes; Extract target activity keywords, match the feature attributes in the candidate person portrait model according to the target activity keywords to obtain target feature attributes, and screen the candidate persons according to the target feature attributes to obtain suitable persons; Determine the quantitative value and weight of each of the target feature attributes, calculate the comprehensive score of each of the suitable personnel based on the quantitative value and weight, generate a personnel allocation strategy based on the comprehensive score, and allocate personnel according to the personnel allocation strategy, wherein the quantitative value represents the importance of the target feature attribute.

2. The personnel allocation method according to claim 1, characterized in that: Constructing a candidate profile model according to the characteristic attributes includes: Respectively determining the attribute value corresponding to the characteristic attribute of each candidate; The characteristic attributes are used as third-level labels, and the third-level labels are classified to obtain a plurality of second-level labels, wherein each of the second-level labels corresponds to one or more of the characteristic attributes; The multiple secondary labels are classified to obtain multiple primary labels, thereby constructing the candidate person portrait model, wherein each of the primary labels corresponds to one or more secondary labels.

3. The personnel allocation method according to claim 1, characterized in that: Screening the candidate personnel according to the target characteristic attributes to obtain suitable personnel includes: Determining a plurality of scoring levels for the target feature attributes; Taking the target feature attribute of each candidate as a sample, taking the score level as a cluster category, taking the sum of squares of the distances from the sample to the cluster centroid of the cluster category as a cluster objective function, and performing cluster analysis on the candidate; The suitable personnel are obtained by screening according to the cluster analysis results.

4. The personnel allocation method according to claim 1, characterized in that: Determining the quantitative value and weight of each of the target feature attributes, including: Determine a plurality of scoring levels of the target feature attribute, and determine the quantitative value corresponding to each scoring level according to the level of the scoring level; The weight corresponding to the target feature attribute is determined according to the importance of each target feature attribute.

5. The personnel allocation method according to claim 1, characterized in that: Extract target activity keywords, including: A historical keyword library and a text scheme of the target activity are obtained, and the text scheme is matched with the historical keyword library to obtain the target activity keyword corresponding to the target activity.

6. The personnel allocation method according to claim 1, characterized in that: Calculating a comprehensive score for each of the matching persons according to the quantified value and the weight includes: The product of the quantized value of each target feature attribute and the weight is calculated, and the sum of the scores of multiple target feature attributes is calculated to obtain a comprehensive score for each of the matching personnel.

7. The personnel allocation method according to claim 1, characterized in that: Generating a personnel allocation strategy according to the comprehensive score, including: Sorting the comprehensive scores to generate a fitness ranking; The personnel allocation strategy is generated according to the fitness ranking.

8. A personnel allocation device, characterized in that: include: A construction unit, configured to obtain characteristic attributes of a candidate, and to construct a candidate portrait model according to the characteristic attributes, wherein the characteristic attributes represent the characteristics of the candidate, and the candidate portrait model at least includes the characteristic attributes and attribute values ​​corresponding to the characteristic attributes; A screening unit, used to extract target activity keywords, match the feature attributes in the candidate person portrait model according to the target activity keywords to obtain target feature attributes, and screen the candidate persons according to the target feature attributes to obtain suitable persons; An allocation unit is used to determine the quantitative value and weight of each of the target feature attributes, calculate the comprehensive score of each of the suitable personnel according to the quantitative value and weight, generate a personnel allocation strategy according to the comprehensive score, and allocate personnel according to the personnel allocation strategy, wherein the quantitative value represents the importance of the target feature attribute.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the personnel allocation method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a method for executing the personnel allocation method described in any one of claims 1 to 7.

Images