Method, device and computer readable storage medium for orienting personnel to posts

Abstract

Embodiments of the present disclosure provide a method and device for directional personnel-post assignment, which comprises: constructing a weight matrix of personnel and posts according to ranking information of the personnel and volunteer application information of the posts; performing a weighted optimal solution calculation on the weight matrix based on a KM algorithm to determine a weighted matching scheme; and verifying the weighted matching scheme according to the volunteer application information of the posts, and outputting a maximum weight matching result after verification.

Description

TECHNICAL FIELD

[0001] Embodiments of the present disclosure relate to the field of big data analysis, and in particular, to a targeted personnel-post assignment method and device and a computer readable storage medium. BACKGROUND

[0002] The post assignment problem is usually a typical combinatorial optimization problem, which contains multiple objectives and constraints. Even if the evaluation criteria are clear and can be represented by a mathematical model, finding the optimal solution is usually very time-consuming and computationally intensive. For large-scale problems, using traditional exact algorithms (such as enumeration method, backtracking method, etc.) will face the problem of time complexity explosion.

[0003] Currently, the main technologies for solving personnel-post assignment problems include heuristic algorithms, greedy algorithms, and Hungarian algorithms, each of which has its own advantages and disadvantages and is suitable for different scenarios. For example, heuristic algorithms such as simulated annealing algorithm and genetic algorithm are suitable for problems with large solution space and are not easy to solve accurately. Their advantages lie in easy modification and adaptation to different needs, but they require higher parameter settings, have slow convergence speed, and do not guarantee to find the global optimal solution. The greedy algorithm makes the best choice at each step, and the final result may not be globally optimal, but it can quickly obtain an approximate solution. This algorithm lacks backtracking properties, which may miss a better solution. The Hungarian algorithm is a classic algorithm for solving the maximum matching problem of bipartite graphs, which can find the optimal matching between multiple tasks and multiple artificial workers. However, if multiple complex factors (such as weights, rankings, etc.) need to be considered, the algorithm needs to be extended or combined with other techniques. SUMMARY

[0004] To solve the problem of targeted assignment in specific industries that need to consider personnel rankings and volunteer weights, the embodiments described herein provide a targeted personnel-post assignment method, device, and computer readable storage medium storing a computer program.

[0005] According to a first aspect of the present disclosure, a targeted personnel-post assignment method is provided, characterized by: constructing a weight matrix of personnel and posts according to the ranking information of personnel and the volunteer application information of posts; performing a weighted optimal solution calculation on the personnel-post weight matrix based on the KM algorithm to determine a weighted matching scheme; and verifying the weighted matching scheme according to the volunteer application information of the posts, and outputting the maximum weight matching result after verification.

[0006] In some embodiments of the present disclosure, constructing the weight matrix of the personnel and the post according to the ranking information of the personnel and the post application information of the post includes: obtaining basic information of the personnel to be oriented and allocated and the post to be allocated, the basic information of the personnel including the ranking information, and the basic information of the post including the post application information of the post; determining a ranking weight coefficient according to the ranking information, and determining a post weight coefficient according to the application information of the post; and combining the ranking weight coefficient and the post weight coefficient to determine a matching relationship between the personnel and the post, and generating the weight matrix of the personnel and the post.

[0007] In some embodiments of the present disclosure, combining the ranking weight coefficient and the post weight coefficient to determine the matching relationship between the personnel and the post, and generating the weight matrix of the personnel and the post includes: dividing the personnel to be allocated and the post into two sets, and according to the rule that one person corresponds to one post and each student applies for at most three posts, allocating n posts to n students, and generating the weight matrix as G = {gij}, i, j = 1, 2, …, n; when gij = -∞, it indicates that the personnel ranked i does not apply for the jth post; when gij = p·(n-i)·v(3-e), it indicates that the personnel ranked i takes the post j as the e-th volunteer, n is the total number of personnel, i is the ranking of the personnel, p is the ranking weight coefficient, and v indicates the volunteer weight coefficient.

[0008] In some embodiments of the present disclosure, constructing the weight matrix of the personnel and the post according to the ranking information of the personnel and the post application information of the post further includes: determining a target function of the personnel post allocation as wherein wij is a decision variable, wij = 1 indicates that the personnel i is allocated to the post j, wij = 0 indicates that the personnel i is not allocated to the post j, x and y are vertex sets of the personnel and post sets respectively, g ij is an edge weight value between the vertex i of the x set and the vertex j of the y set.

[0009] In some embodiments of the present disclosure, the constraint condition of the target function is: indicates that each personnel i is allocated to one post; indicates that each post j is allocated to one personnel.

[0010] In some embodiments of the present disclosure, the weighted optimal solution calculation of the personnel post weight matrix based on the KM algorithm includes: setting the initialization vertex label of each vertex of the personnel set as p·(n-i)·v(3-e), and setting the initialization vertex label of each vertex of the post set as 0; iteratively searching for an augmented path, and when an augmented path is found, flipping the matching on the path; calculating a path on the augmented path that contains all points in the bipartite graph and the edges between the pairs of points satisfying l x + y = G ewherein lx represents the top index of vertex x in the personnel set, ly represents the top index of vertex y in the post set, G e represents the weight of the edge between any two points in the two sets; and when all nodes in the two sets are matched into pairs, and the sum of the weights between the paired nodes is maximum, the equal subgraph is determined as the maximum weight perfect matching, and the process ends, and the weighted matching scheme is output.

[0011] In some embodiments of the present disclosure, the weighted matching scheme is verified according to the post volunteer application information, and the maximum weight matching result is output after the verification is passed, including: checking whether the personnel-post mismatch or the post volunteer mismatch occurs according to the post volunteer application information; when the personnel-post mismatch or the post volunteer mismatch occurs, prompting the result to be abnormal, and adjusting the weight coefficients of the personnel-post weight matrix based on the manual input interface; and recalculating the weighted matching scheme based on the KM algorithm based on the adjusted weight coefficients until the maximum weight matching result is output after the verification is passed.

[0012] In some embodiments of the present disclosure, checking whether the personnel-post mismatch or the post volunteer mismatch occurs according to the post volunteer application information includes: checking whether each personnel is assigned a post; checking whether there is a post that is left vacant; and checking whether the post of each personnel meets the volunteer order thereof.

[0013] According to a second aspect of the present disclosure, a personnel-post allocation device is provided. The device includes at least one processor; and at least one memory storing a computer program. When the computer program is executed by the at least one processor, the device is caused to: construct a weight matrix of personnel and posts according to ranking information of the personnel and volunteer application information of the posts; determine a weighted matching scheme by performing a weighted optimal solution calculation on the personnel-post weight matrix based on the KM algorithm; and verify the weighted matching scheme according to the volunteer application information of the posts, and output a maximum weight matching result after the verification is passed.

[0014] In some embodiments of the present disclosure, the computer program, when executed by the at least one processor, causes the device to construct the weight matrix of the personnel and the posts by: obtaining basic information of personnel to be allocated and posts to be allocated, the basic information of the personnel including the ranking information, and the basic information of the posts including the post volunteer application information; determining a ranking weight coefficient according to the ranking information, and determining a post weight coefficient according to the volunteer application information; and combining the ranking weight coefficient and the post weight coefficient to determine a matching relationship between the personnel and the posts, and generating the weight matrix of the personnel and the posts.

[0015] In some embodiments of the present disclosure, the computer program, when executed by the at least one processor, causes the apparatus to construct a weight matrix of personnel and posts by: dividing the personnel to be allocated and the posts into two sets, assigning n posts to n students according to the rule that one person one post and each student applies for at most three posts, and generating a weight matrix G = {g ij}, i, j = 1, 2,..., n; when g ij = -∞, it means that the personnel ranked i does not apply for the jth post; when g ij = p·(n-i)·v(3-e), it means that the personnel ranked i takes the jth post as the e th volunteer, n is the total number of personnel, i is the ranking of the personnel, p is the ranking weight coefficient, and v represents the volunteer weight coefficient.

[0016] In some embodiments of the present disclosure, the computer program, when executed by the at least one processor, causes the apparatus to determine a target function of personnel post allocation as where w ij is a decision variable, w ij = 1 means that the personnel i is allocated to the post j, w ij = 0 means that the personnel i is not allocated to the post j, x and y are vertex sets of the personnel set and the post set respectively, and g ij is the edge weight value between vertex i of the x set and vertex j of the y set. The constraint condition of the target function is: each personnel i is allocated to one post; each post j is allocated to one personnel.

[0017] In some embodiments of the present disclosure, the computer program, when executed by the at least one processor, causes the apparatus to determine a weighted matching scheme by: setting the initial label of each vertex of the personnel set to p·(n-i)·v(3-e), and setting the initial label of each vertex of the post set to 0; iteratively searching for an augmenting path, and when an augmenting path is found, flipping the matching on the path; calculating an equal subgraph on the augmenting path that contains all points in the bipartite graph and the edges between the paired points satisfy l x +l y = G e , where lx represents the label of vertex x in the personnel set, ly represents the label of vertex y in the post set, and G e represents the weight of the edge between any two points in the two sets; and when all nodes in the two sets are matched in pairs and the sum of the weights between the paired nodes is maximum, confirming the equal subgraph as the maximum weight perfect matching, ending the calculation process, and outputting the weighted matching scheme.

[0018] In some embodiments of the present disclosure, the computer program, when executed by the at least one processor, causes the apparatus to output the maximum weight matching result by: checking whether the person-post mismatch or post-volunteer mismatch occurs according to the volunteer application information of the posts; when the person-post mismatch or post-volunteer mismatch occurs, prompting the result to be abnormal, and adjusting the weight coefficients of the personnel-post weight matrix based on a manual input interface; and recalculating the weighted matching scheme based on the KM algorithm based on the adjusted weight coefficients until the maximum weight matching result is output after being checked.

[0019] In some embodiments of the present disclosure, the computer program, when executed by the at least one processor, causes the apparatus to check whether the person-post mismatch or post-volunteer mismatch occurs by: checking whether each personnel is assigned to a post; checking whether there is a post that is left vacant; and checking whether the post of each personnel meets the volunteer order thereof.

[0020] According to a third aspect of the present disclosure, a computer readable storage medium storing a computer program is provided, wherein the computer program, when executed by a processor, implements the steps of the method for targeted personnel-post allocation according to the first aspect of the present disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments will be briefly described below. It should be known that the drawings described below only relate to some embodiments of the present disclosure, rather than limiting the present disclosure, wherein:

[0022] Figure 1 An exemplary flowchart of the method for targeted personnel-post allocation 100 according to the embodiments of the present disclosure is shown;

[0023] Figure 2 An exemplary flowchart of calculating the maximum weight matching scheme of personnel-post based on the KM algorithm according to the embodiments of the present disclosure is shown;

[0024] Figure 3 is a schematic block diagram of the apparatus for targeted personnel-post allocation 300 according to the embodiments of the present disclosure.

[0025] It should be noted that the elements in the drawings are schematic and not drawn to scale. DETAILED DESCRIPTION

[0026] In order to make the objects, technical solutions and advantages of the embodiments of the present disclosure clearer, the technical solutions of the embodiments of the present disclosure will be described clearly and completely below with reference to the drawings. Obviously, the described embodiments are part of the embodiments of the present disclosure, rather than all the embodiments. Based on the described embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without any creative effort also belong to the scope of protection of the present disclosure.

[0027] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this subject matter belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the specification and relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein. Additionally, terms such as "first" and "second" are used merely as label to distinguish one component (or portion of a component) from another component (or portion of a component).

[0028] For certain targeted personnel post allocation tasks, it is necessary to consider the personnel ranking and volunteer weight problems comprehensively, and the present disclosure proposes a targeted personnel post allocation method and device, which utilizes the efficiency advantage of Kuhn-Munkres algorithm (KM algorithm) in weighted calculation to optimize the post allocation problem, and can better adapt to the timeliness and accuracy requirements of personnel-post allocation work in the case of weighted calculation, so that the post allocation is more in line with the actual needs and expectations of personnel. Figure 1 An exemplary flowchart of a targeted personnel post allocation method 100 according to an embodiment of the present disclosure is shown.

[0029] At Figure 1 At block S102, a weight matrix of personnel and posts is constructed according to the ranking information of personnel and the volunteer application information of posts.

[0030] According to one embodiment of the present disclosure, the basic information of personnel to be allocated can be collected and maintained, which usually includes the information of personnel's name, education, experience, skills, professional qualification, personal volunteer, ranking, etc. The ranking information of personnel is usually sorted according to the factors of performance, ability, etc. The basic information of the post to be allocated is defined and maintained, such as the required skills, experience, education requirement, work content, post responsibility, number limit, etc. Each personnel can apply for a post according to personal will, and each student can apply for three posts, i.e. first volunteer, second volunteer and third volunteer, and the post volunteer application information can be obtained.

[0031] Then, the ranking weight coefficient is determined according to the ranking information, and the post weight coefficient is determined according to the volunteer application information. The ranking weight coefficient p is determined according to the ranking of personnel, that is, the personnel with higher ranking has higher priority for the post. Since the volunteer weight of each personnel for the post is different, it is necessary to adjust the weight of the post according to the personal volunteer situation of the personnel, and the volunteer weight coefficient v is calculated based on the volunteer order of the personnel (such as first volunteer, second volunteer, etc.), and the volunteer weight coefficient is larger when the volunteer order is earlier.

[0032] The allocation of personnel and positions can be converted into a bipartite graph problem, and the final goal is to achieve the best allocation of personnel and positions by maximizing the weight value of the matching edges.

[0033] Let gij represent the weight between the i-th ranked personnel i and the j-th position. e represents the e-th preference of the position (e = 1, 2, 3) of the personnel. In an embodiment of the present disclosure, for each personnel and position pair, the weight value gij is constructed as follows:

[0034] When g ij = -∞, it means that the personnel does not apply for the position, so the possibility of this pairing is not considered. When g ij = p·(n-i)·v(3-e), it means that the personnel i takes the position j as the e-th preference (the weight is calculated according to the ranking and preference of the personnel), n is the total number of personnel, i is the ranking of the personnel, the larger n-i, the greater the weight, v(3-e) represents the weight of the preference, which gradually decreases with the increase of the preference order. In this way, a weighted matching matrix is established between each personnel and each position. The weight matrix G is used to represent the matching relationship between the personnel and the position, and each element g ij of the weight matrix G represents the matching value of the personnel and the position. Combined with the ranking and preference priority of the personnel, the allocation of each position is not just a simple matching problem, but an allocation problem with certain priority and preference.

[0035] Suppose there are personnel A, B, C, positions 1, 2, 3, and the ranking and preference information of the personnel is as follows:

[0036] Ranking of personnel A: position 1 ranks 1, position 2 ranks 2, position 3 ranks 3, preference order: position 1 > position 2 > position 3

[0037] Ranking of personnel B: position 1 ranks 2, position 2 ranks 3, position 3 ranks 1, preference order: position 3 > position 1 > position 2

[0038] Ranking of personnel C: position 1 ranks 3, position 2 ranks 1, position 3 ranks 2, preference order: position 2 > position 3 > position 1

[0039] Then the maximum weight matrix can be: The personnel-position weight matrix reflects the ranking and preference priority of the personnel.

[0040] According to an embodiment of the present disclosure, the personnel-position allocation task is converted into a bipartite graph calculated by the KM algorithm, and the personnel to be allocated and the positions are divided into two sets, and the objective function of personnel-position allocation is determined as that is, maximizing the total weight of the matching between the personnel and the positions, that is, maximizing the sum of the edge weight values w ijis the decision variable, where w ij =1 means that person i is assigned to position j, w ij = 0 means that person i is not assigned to position j, x and y are the vertex sets of people and positions respectively, g ij is the edge weight between vertex i in set x and vertex in set y; the constraint of the objective function is that each person i must be assigned to a position: Each position j must be assigned to a person:

[0041] Then, refer to Figure 1 As shown, in block S104 , the weighted optimal solution of the personnel position weight matrix is ​​calculated based on the KM algorithm to determine a weighted matching solution.

[0042] Figure 2 An exemplary flow chart of calculating the maximum weight matching scheme of personnel positions based on the KM algorithm according to an embodiment of the present disclosure is shown. Figure 2 As shown in the figure, in a bipartite graph, the personnel and positions to be assigned are divided into two sets: personnel set X and position set Y. The KM algorithm first sets the initial top label of each vertex in personnel set X to p·(ni)·v(3-e), where p is the ranking weight coefficient, (ni) is the inverse of the personnel ranking, ensuring that the higher-ranked personnel have a higher top label, and v is the volunteer weight coefficient, indicating the priority of the e-th volunteer, with 1 being the first choice, 2 being the second choice, and 3 being the third choice. The initial top label of each vertex in the position set is set to 0, meaning that all positions are initially unpreferred. The top label represents the potential value of a vertex, that is, the relative importance of a person to the position.

[0043] The KM algorithm gradually approaches the optimal matching by continuously adjusting the top labels and searching for augmenting paths. The key idea is that during the matching process, the top labels are adjusted so that the difference between the sum of the top labels and the edge weights gradually decreases, matching all points in set X with points in set Y in pairs, and maximizing the sum of the weights between each pair.

[0044] Iteratively search for augmenting paths. Once an augmenting path is found, flip the matching on the path. An augmenting path is a path that starts at a node in X, travels through a series of unmatched edges (alternating edges) to an unmatched node in Y, and finally flips the matching via the reverse path. Flipping a matching improves the quality of the current matching. Flipping means that all matching edges on the path have their status reversed: if an edge was previously matched, it becomes unmatched; if an edge was previously unmatched, it becomes matched.

[0045] On the augmenting path, adjust the top marks of the personnel set and the position set so that l x +ly ≥G e where lx represents the top index of vertex x in the personnel set, ly represents the top index of vertex y in the post set, G e represents the weight of the edge between any two points in the two sets. By constantly adjusting the top index of the nodes in the X and Y sets, iteratively finding the augmenting path and flipping, the subgraph containing all points in the bipartite graph and the edges between the pairs of points satisfying l x +l y =G e is calculated, so as to constantly approach the maximum weight matching. Finally, when all the nodes in the two sets are matched in pairs, and the sum of the weights between the pairs of nodes is maximum, the weight matching scheme is determined.

[0046] The time complexity of the KM algorithm is O(n 3 ), where n is the number of personnel (or posts), and the space complexity is O(n 2 ), mainly for storing the weight matrix and top index. By adjusting the top index and constantly finding the augmenting path, the KM algorithm can effectively solve the maximum weight matching problem.

[0047] Although the KM algorithm will select an optimal post for each personnel, it does not mean that it will perfectly match the order of each personnel's preferences. In this case, there may be a situation where the post allocation does not match the preferences, so it is necessary to check whether the personnel and post are not matched or the post preferences are not matched after the algorithm is run according to the post preference information.

[0048] Returning to the block S106 of Figure 1 , the weight matching scheme is verified according to the post preference information, and the maximum weight matching result is output after the verification is passed.

[0049] Referring to Figure 2 , the weight matching scheme is verified according to the post preference, and the specific verification steps include: checking whether each personnel is assigned a post; checking whether there is a post that is empty (not assigned); checking whether the post of each personnel meets its preference order (i.e. first preference, second preference, etc.). When the personnel and post are not matched or the post preferences are not matched, the warning mechanism is triggered to prompt the abnormality, and the output of the algorithm is affected by adjusting the weighting coefficients in the weight matrix (such as adjusting the priority of the preference order, or re-evaluating the post matching degree). The adjusted weight matrix can be input into the KM algorithm again to recalculate the post allocation scheme until all the constraints are met.

[0050] Taking 100 persons and 100 to-be-assigned posts as an example, when the personnel-post assignment is performed by using the embodiment of the present disclosure, the time complexity is O(n^3), n is the number of vertices. The parameters only need to be configured with the ranking and the volunteer order weight, and the optimal solution can be obtained in milliseconds to seconds.

[0051] Figure 3 is a schematic block diagram of a directional personnel-post assignment apparatus 300 according to an embodiment of the present disclosure. As shown in the figure, the apparatus 300 can include a processor 310 and a memory 320 storing a computer program. When the computer program is executed by the processor 310, the apparatus 300 can perform the steps of the directional personnel-post assignment method 100 as shown in Figure 3 Figure 1 In one example, the apparatus 300 can be a computer device or a cloud computing node, so that the apparatus 300 can construct a weight matrix of personnel and posts according to the ranking information of the personnel and the volunteer application information of the posts; then, the apparatus 300 can perform a weighted optimal solution calculation on the personnel-post weight matrix based on the KM algorithm to determine a weighted matching scheme; and verify the weighted matching scheme according to the volunteer application information of the posts, and output the maximum weight matching result after the verification is passed.

[0052] In some embodiments of the present disclosure, the apparatus 300 can obtain basic information of to-be-assigned personnel and to-be-assigned posts, the basic information of the personnel including ranking information, and the basic information of the posts including post volunteer application information; determine a ranking weight coefficient according to the ranking information, determine a post weight coefficient according to the volunteer application information; and combine the ranking weight coefficient and the post weight coefficient to determine a matching relationship between the personnel and the posts, and generate a weight matrix of the personnel and the posts.

[0053] In some embodiments of the present disclosure, the apparatus 300 can divide the to-be-assigned personnel and the posts into two sets, and according to the rule that one person corresponds to one post and each student applies for at most three posts, assign n posts to n students, and generate a weight matrix as follows: G={g ij}, i, j = 1, 2,..., n; when g ij =-∞, it indicates that the personnel ranked i has not applied for the post j; when g ij =p·(n-i)·v(3-e), it indicates that the personnel ranked i takes the post j as the e-th volunteer, n is the total number of personnel, i is the ranking of the personnel, p is the ranking weight coefficient, and v indicates the volunteer weight coefficient.

[0054] In some embodiments of the present disclosure, the apparatus 300 can set a target function of the personnel-post assignment as where w ij is a decision variable, and w ij ​= 1 indicates that the person i is assigned to the post j, w ij = 0 indicates that the person i is not assigned to the post j, x, y are vertex sets of the person and post sets respectively, g ij is the edge weight between vertex i of the x set and vertex j of the y set; the constraint condition of the objective function is: indicates that each person i is assigned to a post; indicates that each post j is assigned to a person.

[0055] In some embodiments of the present disclosure, in order to determine the weighted assignment scheme, the device 300 can set the initial top index of each vertex of the person set to p·(n-i)·v(3-e), and set the initial top index of each vertex of the post set to 0; iteratively search for an augmented path, and after finding an augmented path, flip the matching on the path; calculate an equal subgraph containing all points in the bipartite graph and satisfying l x + y = G e , where lx indicates the top index of vertex x in the person set, ly indicates the top index of vertex y in the post set, G e indicates the weight of the edge between any two points in the two sets; when all nodes in the two sets are matched in pairs, and the weight sum of each pair of nodes is maximum, the equal subgraph is confirmed as the maximum weight perfect matching, and the calculation process ends, and the weighted matching scheme is output.

[0056] In some embodiments of the present disclosure, the device 300 can check whether the person-post mismatch or post volunteer mismatch occurs according to the post volunteer information; when the person-post mismatch or post volunteer mismatch occurs, prompt the result to be abnormal, and adjust the weight coefficients of the person-post weight matrix based on the manual input interface; and calculate the weighted matching scheme based on the KM algorithm based on the adjusted weight coefficients until the maximum weight matching result is output after passing the verification.

[0057] In some embodiments of the present disclosure, the device 300 checks whether each person is assigned to a post; checks whether there is a post that is left empty; and checks whether the post of each person meets the volunteer order, so as to determine whether the person-post mismatch or post volunteer mismatch occurs.

[0058] In embodiments of the present disclosure, the processor 310 can be, for example, a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a processor based on a multi-core processor architecture, etc. The memory 320 can be any type of memory implemented using data storage technology, including but not limited to random access memory, read only memory, semiconductor-based memory, flash memory, disk storage, etc.

[0059] In addition, in the embodiment of the present disclosure, the device 300 can also include an input device 330, such as a keyboard, a mouse, etc., for inputting basic information of personnel and basic information of posts. In addition, the device 300 can also include an output device 340, such as a display, etc., for outputting the maximum weight matching result.

[0060] In other embodiments of the present disclosure, a computer readable storage medium storing a computer program is also provided, wherein the computer program can implement the steps of the method for assigning personnel to posts according to the present disclosure when executed by a processor. Figure 1 as shown.

[0061] In summary, according to the method and device for assigning personnel to posts according to the embodiments of the present disclosure, the efficiency advantage of Kuhn-Munkres algorithm in solving the optimal solution with weights is utilized, and in the case of comprehensively considering the information of ranking order of personnel and priority of post volunteer, the problems of complex parameters, difficult backtracking, and difficult optimal solution determination in processing the personnel-post assignment are solved by constructing the permission matrix, and finally the assignment scheme can meet the double high matching requirements of ranking and volunteer order, can effectively improve the satisfaction of personnel to the assignment result, and can realize the efficient and accurate assignment of personnel to posts.

[0062] The flowcharts and block diagrams in the drawings show the architectural, functional and operational aspects of possible implementations of apparatuses and methods according to embodiments of the present disclosure. In this regard, each block in the flowcharts or block diagrams can represent a module, a program segment or a portion of instructions that contain one or more executable instructions for implementing the specified logical functions. In some alternative implementations, the functions noted in the blocks can occur in different orders than that shown in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and they can also be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and the combination of blocks in the block diagrams and / or flowcharts, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0063] Unless the context clearly indicates otherwise, as used herein and in the appended claims, the singular form of a word includes the plural and vice versa. Thus, the use of the singular will include its meaning of the plural whenever the articles "a" or "an" are used preceding the singular number form of the word. Similarly, the words "comprise", "comprises" and "comprising" are to be interpreted inclusively rather than exclusively. Likewise, the terms "include", "including" and "or" should be construed as inclusive rather than exclusive, unless otherwise indicated herein. Where the term "example" is used occurring in this document, particularly with respect to a term that is a member of a set of terms, the "example" is merely an example of the term and should not be considered to be exclusive or exhaustive unless otherwise indicated herein.

[0064] Further aspects and ranges of adaptation will become apparent from the description provided herein. It should be understood that various aspects of the application can be implemented alone or in combination with one or more other aspects. It should also be understood that the description and specific examples herein are intended to be illustrative only and are not intended to limit the scope of the present application.

[0065] The above detailed description of several embodiments of the disclosure has been presented for the purposes of illustration and description. It is apparent to those skilled in the art that various modifications and variations can be made to the embodiments of the disclosure without departing from the spirit and scope of the disclosure. The scope of the disclosure is defined by the appended claims.

Claims

1. A method for allocating targeted personnel positions, characterized in that: include: According to the ranking information of personnel and the voluntary application information of positions, a weight matrix of personnel and positions is constructed, which includes: obtaining basic information of personnel to be assigned and positions to be assigned, wherein the basic information of personnel includes ranking information, and the basic information of positions includes voluntary application information of positions; determining a ranking weight coefficient according to the ranking information, and determining a position weight coefficient according to the voluntary application information; combining the ranking weight coefficient and the position weight coefficient to determine the matching relationship between personnel and positions, and generating a weight matrix of personnel and positions, which includes: dividing the personnel to be assigned and positions into two sets, and allocating n positions to n trainees according to the rule of one person to one position and each trainee applying for a maximum of three positions, and generating a weight matrix as follows: G={g ij },i,j=1,2......n; when g ij =−∞, indicating that the person ranked i did not apply for the jth position; when , means that the person ranked i chooses position j as his / her e-th choice, n is the total number of people, i is the ranking of the person, p is the ranking weight coefficient, and v is the volunteer weight coefficient; Based on the KM algorithm, the weighted optimal solution of the personnel position weight matrix is ​​calculated and the weighted matching scheme is determined, which includes: setting the initial top mark of each vertex of the personnel set to , the initial top mark of each vertex in the job set is set to 0; iteratively search the augmenting path, and when an augmenting path is found, flip the matching on the path; calculate on the augmenting path all the points in the bipartite graph and the edges between pairs of points satisfy The equal subgraph of , where Indicates the top label of vertex x in the person set, l y Indicates the top label of vertex y in the job set, represents the weight of the edge between any two points in the two sets; when all nodes in the two sets are matched in pairs and the sum of the weights between each pair of nodes is the largest, the equal subgraph is confirmed to be a maximum weight perfect match, the calculation process ends, and a weighted matching solution is output; and The weighted matching scheme is verified according to the voluntary application information of the position, and the maximum weight matching result is output after the verification is passed.

2. The method for allocating targeted personnel positions according to claim 1, characterized in that: The weight matrix of personnel and positions constructed based on the personnel ranking information and the voluntary application information of the positions also includes: The objective function for determining personnel position allocation is , where w ij is the decision variable, where w ij =1 means that person i is assigned to position j, w ij =0 means that person i is not assigned to position j, x and y are the vertex sets of person and position sets respectively, is the edge weight between vertex i of set x and vertex j of set y.

3. The method for allocating targeted personnel positions according to claim 2, characterized in that: The constraints of the objective function are: , indicating that each person i is assigned to a position; , indicating that each position j is assigned to one person.

4. The method for allocating targeted personnel positions according to claim 1, characterized in that: The weighted matching scheme is verified based on the voluntary application information of the position, and the maximum weight matching result is output after the verification is passed, including: Check whether there is a mismatch between people and positions or whether the positions do not match the volunteers based on the volunteer application information of the positions; When there is a mismatch between people and positions or the positions do not match the volunteers, an abnormal result is prompted, and the weight coefficient of the personnel position weight matrix is ​​adjusted based on the manual input interface; and The weighted matching scheme is recalculated based on the KM algorithm based on the adjusted weight coefficient until the maximum weight matching result is output after passing the verification.

5. The method for allocating targeted personnel positions according to claim 4, characterized in that: The aforementioned checks based on the voluntary application information for positions to determine whether there is a mismatch between people and positions or a mismatch between job applications and voluntary applications include: Check whether each person has been assigned a position; check whether any positions are vacant; and Check whether each person's position is in the order of their preferences.

6. A device for allocating personnel positions, characterized in that: The device comprises: at least one processor; and at least one memory storing a computer program; Wherein, when the computer program is executed by the at least one processor, the device is caused to perform the steps of the method for targeted personnel position allocation according to any one of claims 1 to 5.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the computer program implements the steps of the method for targeted personnel position allocation according to any one of claims 1 to 5.

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