CN118211800A - Personnel allocation method, apparatus, computer device, storage medium and product - Google Patents
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Abstract
The application relates to a personnel allocation method, a personnel allocation device, computer equipment, a storage medium and a computer program product, which are applied to the technical field of big data. The method comprises the following steps: dividing the plurality of mesh points to be distributed into a plurality of areas according to the position information of the plurality of mesh points to be distributed; constructing a position relation diagram corresponding to the plurality of areas; each node in the position relation diagram corresponds to one region, and each line is connected with two adjacent regions; constructing a distributed constraint optimization model based on the position relation diagram; the distributed constraint optimization model takes the number of personnel distributed by each region as a variable; and solving the distributed constraint optimization model to obtain the number of people distributed for each region. By adopting the method, the efficiency of personnel distribution to the network points can be improved.
Description
Technical Field
The present application relates to the field of big data technology, and in particular, to a personnel allocation method, apparatus, computer device, storage medium, and computer program product.
Background
In the general technical support service management of the financial industry, technical support staff usually stay at branches or branch bases, and the technical support staff has the advantages of being convenient for unified scheduling management and convenient for storehouse access equipment unified from the branches at ordinary times. However, the distances between the branch line base and the rest of the net points are different, so that the ordinary technical staff can go to each net point to deal with faults on site, the way is possibly far, and the time spent on the way is relatively long. As during major event warranties or major project advances, a need to respond more quickly to site demands may arrange for technical service support personnel to stay on site support. However, because technicians are fewer, but the number of the network points is large, and because of the reasons of geography, population number and the like, the network points are not uniformly distributed, and the problem of low overall working efficiency of the technicians for serving all the network points exists.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a staff allocation method, apparatus, computer device, computer readable storage medium, and computer program product that can improve the overall work efficiency of a technician servicing each website.
In a first aspect, the present application provides a person allocation method, comprising:
dividing the plurality of mesh points to be distributed into a plurality of areas according to the position information of the plurality of mesh points to be distributed;
Constructing a position relation diagram corresponding to the plurality of areas; each node in the position relation diagram corresponds to one region, and each line is connected with two adjacent regions;
constructing a distributed constraint optimization model based on the position relation diagram; the distributed constraint optimization model takes the number of personnel distributed by each region as a variable;
And solving the distributed constraint optimization model to obtain the number of people distributed for each region.
In one embodiment, dividing the plurality of mesh points to be allocated into a plurality of areas according to the position information of the plurality of mesh points to be allocated includes:
Dividing the plurality of mesh points to be distributed into a plurality of rule areas according to the position information of the plurality of mesh points to be distributed and the preset area number;
Or alternatively
Dividing the plurality of mesh points to be distributed into a plurality of irregular areas according to the position information of the plurality of mesh points to be distributed, the geographic information of the plurality of mesh points to be distributed and the preset area number.
In one embodiment, the constructing a distributed constraint optimization model based on the position relation graph includes:
Determining a constraint cost calculation model of each region by taking each region in the position relation diagram as an agent, taking the selectable personnel number of each region as a variable, and taking the total personnel number configured for the plurality of network points to be distributed as a value range;
and obtaining the distributed constraint optimization model based on the set of agents, the set of variables, the set of value ranges and the constraint cost calculation model.
In one embodiment, the determining the constraint cost calculation model for each region includes:
determining a time calculation model of the total service time of the personnel in each area and the emergency degree of the personnel requirement of each area respectively;
and constructing a constraint cost calculation model corresponding to each region according to the time calculation model and the emergency degree.
In one embodiment, the time calculation model for determining the total service time of the personnel in each area includes:
respectively determining the number of the net points in each area, the average distance between the net points, the average spending time of each net point and the average moving speed of personnel between the net points;
And constructing a time calculation model of the total service time of the personnel in each area by taking the number of the personnel distributed in each area as a variable.
In one embodiment, the determining the emergency degree of the personnel requirement of each area includes:
Determining the relative population density and the number of dots of each region respectively;
Determining the emergency degree of the personnel requirement of each area according to the relative population density and the number of the dots of each area.
In one embodiment, the solving the distributed constraint optimization model to obtain the number of people allocated for each of the areas includes:
adopting a distributed constraint algorithm to search each region and adjacent regions in parallel, and calculating the minimum value of the sum of constraint costs corresponding to all the regions;
And calculating the number of people allocated to each area according to the minimum value of the sum of the constraint costs.
In a second aspect, the present application also provides a person distribution device, comprising:
the regional division module is used for dividing the plurality of mesh points to be distributed into a plurality of regions according to the position information of the plurality of mesh points to be distributed;
The diagram construction module is used for constructing a position relation diagram corresponding to the plurality of areas; each node in the position relation diagram corresponds to one region, and each line is connected with two adjacent regions;
The model construction module is used for constructing a distributed constraint optimization model based on the position relation diagram; the distributed constraint optimization model takes the number of personnel distributed by each region as a variable;
and the personnel calculation module is used for solving the distributed constraint optimization model to obtain the personnel number distributed for each region.
In a third aspect, the present application also provides a computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
dividing the plurality of mesh points to be distributed into a plurality of areas according to the position information of the plurality of mesh points to be distributed;
Constructing a position relation diagram corresponding to the plurality of areas; each node in the position relation diagram corresponds to one region, and each line is connected with two adjacent regions;
constructing a distributed constraint optimization model based on the position relation diagram; the distributed constraint optimization model takes the number of personnel distributed by each region as a variable;
And solving the distributed constraint optimization model to obtain the number of people distributed for each region.
In a fourth aspect, the present application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
dividing the plurality of mesh points to be distributed into a plurality of areas according to the position information of the plurality of mesh points to be distributed;
Constructing a position relation diagram corresponding to the plurality of areas; each node in the position relation diagram corresponds to one region, and each line is connected with two adjacent regions;
constructing a distributed constraint optimization model based on the position relation diagram; the distributed constraint optimization model takes the number of personnel distributed by each region as a variable;
And solving the distributed constraint optimization model to obtain the number of people distributed for each region.
In a fifth aspect, the application also provides a computer program product comprising a computer program which, when executed by a processor, performs the steps of:
dividing the plurality of mesh points to be distributed into a plurality of areas according to the position information of the plurality of mesh points to be distributed;
Constructing a position relation diagram corresponding to the plurality of areas; each node in the position relation diagram corresponds to one region, and each line is connected with two adjacent regions;
constructing a distributed constraint optimization model based on the position relation diagram; the distributed constraint optimization model takes the number of personnel distributed by each region as a variable;
And solving the distributed constraint optimization model to obtain the number of people distributed for each region.
According to the personnel allocation method, the device, the computer equipment, the storage medium and the computer program product, the mesh points to be allocated are divided into the plurality of areas, the areas corresponding to the nodes are constructed, each line is connected with the position relation diagrams of the adjacent two areas for the plurality of areas, the position constraint among different areas is better reflected, the distributed constraint optimization model is constructed according to the position relation diagrams, the personnel number allocated to each area is solved according to the distributed constraint optimization model, the personnel allocation problem is solved in each area, the distributed calculation is realized, the calculation efficiency and the feasibility are improved, the constraint conditions of personnel allocation to each area mesh point are considered, the optimal personnel allocation scheme meeting the constraint conditions of each area is obtained, and therefore the overall working efficiency of personnel allocation to each area for service is improved.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the related art, the drawings that are required to be used in the embodiments or the related technical descriptions will be briefly described, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to the drawings without inventive effort for those skilled in the art.
FIG. 1 is a diagram of an application environment for a human distribution method in one embodiment;
FIG. 2 is a flow diagram of a human assignment method in one embodiment;
FIG. 3 is a flow diagram of a distributed constraint optimization model determination step in one embodiment;
FIG. 4 is a flow diagram of a constraint cost model determination step in one embodiment;
FIG. 5 is a flow chart of an intelligent recommendation method based on a distributed constraint optimization problem for a scientist to go to a site residence of a website in another embodiment;
FIG. 6 is a schematic diagram of a map region division structure in one embodiment;
FIG. 7 is a schematic diagram of the structure of a constraint map in one embodiment;
FIG. 8 is a block diagram of an exemplary embodiment of a human dispensing device;
fig. 9 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The personnel allocation method provided by the embodiment of the application can be applied to an application environment shown in figure 1. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104 or may be located on a cloud or other network server. The server 104 divides the plurality of mesh points to be distributed into a plurality of areas according to the position information of the plurality of mesh points to be distributed from the terminal 102, and then the server 104 constructs a position relation diagram corresponding to the plurality of areas; each node in the position relation diagram corresponds to one area, and each line is connected with two adjacent areas; further, the server 104 builds a distributed constraint optimization model based on the position relation diagram; the distributed constraint optimization model takes the number of personnel distributed in each area as a variable; finally, server 104 solves the distributed constraint optimization model to obtain the number of people assigned for each region. The terminal 102 may be, but is not limited to, various personal computers, notebook computers, smart phones, and internet of things devices. The server 104 may be implemented as a stand-alone server or as a server cluster of multiple servers.
In an exemplary embodiment, as shown in fig. 2, a personnel allocation method is provided, and the method is applied to the server 104 in fig. 1 for illustration, and includes the following steps 202 to 206. Wherein:
Step S202, dividing the plurality of mesh points to be allocated into a plurality of areas according to the position information of the plurality of mesh points to be allocated.
The network site refers to a place where the bank is open to the outside, and is generally divided into branch, deposit house, twenty-four hour self-service bank and the like; the multiple mesh points to be distributed can be mesh points needing personnel support, such as mesh points needing technical support; the location information may be the distribution of the dots on the map.
Optionally, the plurality of mesh points to be allocated are divided into a plurality of areas according to the position information of the plurality of mesh points to be allocated, which is supported by the personnel, for example, the plurality of mesh points to be allocated are divided into a plurality of areas according to the preset rule according to the position information of the plurality of mesh points to be allocated, which is distributed on the map.
Step S204, constructing a position relation diagram corresponding to the plurality of areas.
The position relation diagram can be a diagram structure comprising nodes and edges, each node in the position relation diagram corresponds to one region, and each line is connected with two adjacent regions.
Optionally, each area is taken as a node, and the relationship between every two adjacent areas is taken as an edge, so that a position relationship diagram corresponding to a plurality of areas is constructed.
And S206, constructing a distributed constraint optimization model based on the position relation diagram.
The distributed constraint optimization model takes the number of personnel distributed in each area as a variable.
Optionally, a distributed constraint optimization model is constructed based on relationships between regions in the positional relationship diagram and other relevant parameters.
And step S208, solving the distributed constraint optimization model to obtain the number of people allocated to each region.
Optionally, carrying out calculation by taking preset parameters into a distributed constraint optimization model to obtain the number of people allocated to each area, and obtaining an optimal scheme of the number of people allocated to different areas.
In the personnel allocation method, the to-be-allocated mesh points are divided into the areas, the position relation diagrams for the areas are constructed, corresponding to the nodes, of each line connecting two adjacent areas are constructed, the position constraint among different areas is better reflected, the distributed constraint optimization model is constructed according to the position relation diagrams, the personnel number allocated to each area is solved according to the distributed constraint optimization model, the personnel allocation problem is solved in each area, the distributed calculation is realized, the calculation efficiency and the feasibility are improved, each constraint condition of personnel allocation to each area mesh point is considered, the optimal personnel allocation scheme meeting the constraint condition of each area is obtained, and therefore the overall working efficiency of personnel allocation to each area for service is improved.
In an exemplary embodiment, step S202 includes dividing the plurality of dots to be allocated into a plurality of areas according to the position information of the plurality of dots to be allocated:
Dividing the plurality of mesh points to be distributed into a plurality of regular areas according to the position information of the plurality of mesh points to be distributed and the preset area number; or dividing the plurality of mesh points to be distributed into a plurality of irregular areas according to the position information of the plurality of mesh points to be distributed, the geographic information of the plurality of mesh points to be distributed and the preset area number.
Wherein the regular and irregular areas may be regular and irregular shaped areas on the map; the geographic information may be geographic environment information of the location of the network point, such as topography, road distribution, etc.
Optionally, according to the position information of the plurality of dots to be allocated and the preset area number, dividing the map where the plurality of dots are located into a plurality of regular areas, for example, if the preset area number is 9, dividing the distribution map of the plurality of dots to be allocated into 9 regular-shaped areas. Or dividing the plurality of the mesh points to be distributed into a plurality of irregularly-shaped areas according to the terrain according to the geographic information of the position of the mesh points to be distributed and the preset area number.
In this embodiment, the plurality of dots to be allocated are divided into the plurality of areas according to the position information of the plurality of dots to be allocated, so that subsequent calculation for each dot is avoided, and the calculation efficiency is improved.
In an exemplary embodiment, as shown in fig. 3, step S206 builds a distributed constraint optimization model based on the positional relationship diagram, including the following steps S302 to S304. Wherein:
Step S302, determining a constraint cost calculation model of each area by taking each area in the position relation diagram as an agent, the selectable personnel number of each area as a variable, and the total personnel number configured for a plurality of network points to be allocated as a value range.
The intelligent agent refers to an individual or an agent participating in problem solving, has certain intelligence and autonomy, and can make decisions and actions according to local information and constraint conditions so as to achieve the goal of global optimal solution or near optimal solution; the value range refers to the value range of each variable from the value range; constraint costs define relationships between variables, e.g., the values of certain variables must satisfy certain conditions. Constraints may be unitary (involving one variable), binary (involving two variables), or multi-dimensional (involving multiple variables).
Optionally, each area in the position relation diagram is taken as an agent to form an agent set, the number of people selectable in each area is taken as a variable set, each agent controls one variable, the total number of people configured for a plurality of network points to be distributed is taken as a value field, each variable takes a value from the value field, and a constraint cost calculation model of each area is determined.
And step S304, obtaining a distributed constraint optimization model based on the set of agents, the set of variables, the set of value ranges and the constraint cost calculation model.
Optionally, a quaternary module is formed based on the agent set, the variable set, the value range set and the constraint cost calculation model, and a distributed constraint optimization model is obtained.
In this embodiment, according to the relation between the areas, a distributed optimization constraint model related to the area parameters is established, and the optimal solution scheme with highest overall completion efficiency is calculated for subsequent calculation.
In an exemplary embodiment, as shown in fig. 4, step S302 determines a constraint cost calculation model for each region, including the following steps S402 to S404. Wherein:
step S402, determining a time calculation model of the total service time of the personnel in each area and the emergency degree of the personnel requirement of each area, respectively.
The total service time comprises time spent in the way of the personnel going to the network point and time spent in the service of the network point, and the emergency degree of the personnel requirement is the priority degree of the personnel required to be distributed to each area.
Optionally, the time calculation model of the total service time of the personnel in each area and the emergency degree of the personnel requirement of each area are respectively determined according to a plurality of preset parameters.
And step S404, constructing a constraint cost calculation model corresponding to each region according to the time calculation model and the emergency degree.
Optionally, constructing a constraint cost calculation model corresponding to each region by taking the time calculation model as a base and taking the emergency degree as an index.
In the embodiment, the total service time of the personnel in each area and the emergency degree of the personnel requirement are combined to construct the constraint cost calculation model, and the spending time and the requirement of each area are comprehensively considered, so that under the condition of giving priority to the requirement, the spending time is reduced, and the overall working efficiency of the personnel for service is improved.
In an exemplary embodiment, step S402 of determining a time calculation model of total service time of personnel in each area, respectively, includes:
Respectively determining the number of the net points in each area, the average distance between the net points, the average spending time of each net point and the average moving speed of personnel between the net points; and constructing a time calculation model of the total service time of the personnel in each area by taking the number of the personnel distributed in each area as a variable.
Optionally, determining the number of dots in each area, the average distance between dots, the average spending time of each dot and the average moving speed of personnel between dots respectively; with the number of people allocated per area as a variable, a time calculation model of the total service time of the people in each area is constructed, and the time calculation model can be expressed as:
Wherein, For the number of dots in the region,/>For the average distance between points in the area, x represents the number of people allocated to each area,/>Representing the average speed of movement of a person between dots,/>Representing the average time spent per dot.
In the embodiment, a time calculation model corresponding to the total service time of the personnel in each area is constructed, the number of the personnel, the number of the network points, the average moving speed of the personnel, the average spent time and the like are considered, and the time is taken as a constraint of a scheme, so that the efficiency of personnel distribution is improved.
In an exemplary embodiment, step S402 determines the emergency level of the personnel requirement of each area, respectively, including:
determining the relative population density and the number of dots of each region respectively; the degree of urgency of the personnel needs of each zone is determined based on the relative population density and number of dots per zone.
Alternatively, the relative population density and number of dots for each region are determined separately, e.g.,For the relative population density of the region, expressed as:
Wherein, For the area,/>Is the population number.
Further, determining the urgency of personnel demand for each zone based on the relative population density and number of dots for each zoneExpressed as:
in this embodiment, the relative population density and the number of dots in each area are determined, so that the emergency degree of population demand is determined, and the priority of personnel allocation is considered, so that the working cost is reduced.
In an exemplary embodiment, step S208 solves the distributed constraint optimization model to obtain the number of people assigned for each region includes:
Adopting a distributed constraint algorithm to search each region and adjacent regions in parallel, and calculating the minimum value of the sum of constraint costs corresponding to all the regions; and calculating the number of people allocated to each area according to the minimum value of the constraint cost sum.
Optionally, a distributed constraint algorithm is adopted to search each region and adjacent regions in parallel, and the minimum value of the sum F of constraint costs corresponding to all the regions is calculated through the following formula:
according to the minimum value of the constraint cost sum, the number of people allocated for each area is calculated by the following formula:
in this embodiment, the sum of the minimum constraint costs calculated by the distributed constraint algorithm may be used as a basis for optimizing the personnel allocation scheme. Personnel can be reasonably allocated according to the sum of constraint costs, so that the service efficiency is improved, and the cost is reduced.
In an exemplary embodiment, as shown in fig. 5, an intelligent recommendation method based on a distributed constraint optimization problem is provided, wherein the intelligent recommendation method specifically includes the following steps:
step S502, dividing the dot area graph.
As shown in fig. 6, taking the dot distribution map of the main city partial area in fig. 6 as an example, dots and numerical identifiers in the map represent one dot, the map rule is divided into 9 areas, and modeling is performed based on the divided areas. The division may be regular, irregular or according to the actual terrain.
Step S504, defining the relevant parameters.
The method is characterized in that a certain new system of the whole area is required to be put into production, deployed and debugged, the full-coverage test operation of the area is required to be completed as soon as possible, and meanwhile, the influence on the normal operation of the area in the deployment process is required to be as small as possible. The following parameters will be defined for this problem: currently, IT personnel: m; total number of dots in map: n; dividing the area A into; Average speed of movement of IT personnel between sites: /(I); Distance between dots: s; average time IT is for an IT person to deal with a problem at a website: /(I); Emergency of regional demand: /(I)。
Step S506, a constraint map is established.
Wherein, as shown in fig. 7, a constraint graph established for each region is provided. When the number of people allocated in one area is more and the number of people allocated in the other area is less, the number of IT people in the adjacent areas can directly influence the time spent for completing projects in the areas and the adjacent areas and the degree of relieving emergency conditions of the areas, but the system is required to complete the new system in the shortest time instead of the highest completion efficiency of the single area.
In step S508, a mathematical model is built.
According to the requirements, a mathematical model of a distributed constraint optimization problem can be established; can be expressed as
Is a four-tuple model of:
: a is a collection of agents, here denoted as9 regions of division;
: x represents a set of variables, here represented as selectable demand numbers (1~k-1) for different regions, each Agent controlling one of the variables, that is, here selecting 9 of the k-1 numbers for use;
representing a set of value ranges, each variable/> From its value range/>The value of the total IT personnel number m is the value of the total IT personnel number m;
representing a set of constraint costs, where constraint costs are expressed as:
Wherein: The number of the dots in the area; /(I) The average distance between the points in the area; /(I)Emergency degree required for the area; /(I)Is the average speed of movement of IT personnel between the dots.
Wherein: For the relative population density of the region,
Wherein,For the area,/>Is the population number.
The above formulas are explained here separately: relative population densityDesigned as/>Will ensure that the population density ofThe value range of (1) is (0, 10), thereby ensuring/>As/>The index of (3) is not too large, and the existence quantity/>, of the net points can be shown under the population density of the areaThe impact on the area, namely the urgency/>, of the area requirement；/>Represents/>, under the regionThe individual dots will/>The IT personnel go to deploy the debugging, and the average time spent by each website is/>，/>Representing the/>, of the regionThere are/>, among the individual dotsPath of segments, there will be/>Individual IT personnel drive up, eventually/>The method comprises the following steps: the total time spent in each area is taken as a base, and the emergency degree of the area demands is the sum of indexes to be taken as the constraint cost value.
The final our solution targets are:
Finally, the number of IT personnel needed by each Agent (region) can be obtained when the global constraint cost is minimum The optimal recommendation scheme for selecting the number of IT personnel in different areas is achieved.
Step S510, solving the mathematical model.
And solving by adopting an incomplete algorithm in the distributed constraint optimization problem solving algorithm. The complete algorithm is that an optimal solution of a problem can be found through the algorithm, but the complete algorithm is only applicable to a small-scale problem and takes a long time, and the incomplete algorithm is that a large-scale problem is solved through a suboptimal solution of the problem which is solved through the algorithm rapidly. In order to meet the current practical problem, the method is high in efficiency, high in speed and large in scale, and is applicable to an incomplete algorithm. Here, a distributed constraint algorithm (LPOS) of local parallel search is adopted, and the algorithm adopts parallel search between Agent and neighbor to obtain a local optimal solution.
In the embodiment, the distributed constraint optimization problem model is adopted to solve the optimal solution of the dispatching number of the website personnel in each area, so that the personnel allocation scheme is rapidly provided, the IT service support efficiency is improved, and the labor cost is reduced.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a personnel allocation device for realizing the personnel allocation method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiment of one or more personnel allocation devices provided below may be referred to the limitation of the personnel allocation method hereinabove, and will not be repeated here.
In one exemplary embodiment, as shown in fig. 8, a people dispensing device 800 is provided, comprising: a region partitioning module 802, a graph construction module 804, a model construction module 806, and a person calculation module 808, wherein:
The area dividing module 802 is configured to divide the plurality of dots to be allocated into a plurality of areas according to the position information of the plurality of dots to be allocated.
A diagram construction module 804, configured to construct a positional relationship diagram corresponding to the plurality of regions; each node in the position relation diagram corresponds to one region, and each line is connected with two adjacent regions.
The model construction module 806 is configured to construct a distributed constraint optimization model based on the location relationship graph; the distributed constraint optimization model takes the number of people allocated per region as a variable.
And the personnel calculation module 808 is used for solving the distributed constraint optimization model to obtain the personnel number allocated to each area.
Further, in one embodiment, the area dividing module 802 is further configured to divide the plurality of dots to be allocated into a plurality of rule areas according to the position information of the plurality of dots to be allocated and the preset area number; or dividing the plurality of mesh points to be distributed into a plurality of irregular areas according to the position information of the plurality of mesh points to be distributed, the geographic information of the plurality of mesh points to be distributed and the preset area number.
Further, in one embodiment, the model building module 806 is further configured to determine a constraint cost calculation model of each area with each area in the location relationship diagram as an agent, with the number of people selectable for each area as a variable, and with the total number of people configured for the plurality of dots to be allocated as a value range; and obtaining a distributed constraint optimization model based on the set of the intelligent agents, the set of the variables, the set of the value ranges and the constraint cost calculation model.
Further, in one embodiment, the model building module 806 is further configured to determine a time calculation model of the total service time of the personnel in each area and the urgency of the personnel requirement of each area, respectively; and constructing a constraint cost calculation model corresponding to each region according to the time calculation model and the emergency degree.
Further, in one embodiment, the model building module 806 is further configured to determine the number of dots in each area, the average distance between dots, the average time spent by each dot, and the average speed of movement of the person between dots, respectively; and constructing a time calculation model of the total service time of the personnel in each area by taking the number of the personnel distributed in each area as a variable.
Further, in one embodiment, the model building module 806 is further configured to determine the relative population density and number of dots for each region, respectively; the degree of urgency of the personnel needs of each zone is determined based on the relative population density and number of dots per zone.
Further, in one embodiment, the personnel computing module 808 is further configured to perform parallel search between each region and an adjacent region by using a distributed constraint algorithm, and calculate a minimum value of a sum of constraint costs corresponding to all regions; and calculating the number of people allocated to each area according to the minimum value of the constraint cost sum.
The various modules in the people mover 800 described above may be implemented in whole or in part in software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one exemplary embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 9. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is for storing data related to the distributed constraint optimization model. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a personnel allocation method.
It will be appreciated by persons skilled in the art that the architecture shown in fig. 9 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements are applicable, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In an embodiment, there is also provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, carries out the steps of the method embodiments described above.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are both information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data are required to meet the related regulations.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magneto-resistive random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (PHASE CHANGE Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in various forms such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.
Claims (11)
1. A method of personnel allocation, the method comprising:
dividing the plurality of mesh points to be distributed into a plurality of areas according to the position information of the plurality of mesh points to be distributed;
Constructing a position relation diagram corresponding to the plurality of areas; each node in the position relation diagram corresponds to one region, and each line is connected with two adjacent regions;
constructing a distributed constraint optimization model based on the position relation diagram; the distributed constraint optimization model takes the number of personnel distributed by each region as a variable;
And solving the distributed constraint optimization model to obtain the number of people distributed for each region.
2. The method of claim 1, wherein the dividing the plurality of dots to be allocated into a plurality of areas according to the position information of the plurality of dots to be allocated comprises:
Dividing the plurality of mesh points to be distributed into a plurality of rule areas according to the position information of the plurality of mesh points to be distributed and the preset area number;
Or alternatively
Dividing the plurality of mesh points to be distributed into a plurality of irregular areas according to the position information of the plurality of mesh points to be distributed, the geographic information of the plurality of mesh points to be distributed and the preset area number.
3. The method of claim 1, wherein the constructing a distributed constraint optimization model based on the location relationship graph comprises:
Determining a constraint cost calculation model of each region by taking each region in the position relation diagram as an agent, taking the selectable personnel number of each region as a variable, and taking the total personnel number configured for the plurality of network points to be distributed as a value range;
and obtaining the distributed constraint optimization model based on the set of agents, the set of variables, the set of value ranges and the constraint cost calculation model.
4. A method according to claim 3, wherein said determining a constraint cost calculation model for each region comprises:
determining a time calculation model of the total service time of the personnel in each area and the emergency degree of the personnel requirement of each area respectively;
and constructing a constraint cost calculation model corresponding to each region according to the time calculation model and the emergency degree.
5. The method of claim 4, wherein the time calculation model for determining the total service time of the personnel in each area, respectively, comprises:
respectively determining the number of the net points in each area, the average distance between the net points, the average spending time of each net point and the average moving speed of personnel between the net points;
And constructing a time calculation model of the total service time of the personnel in each area by taking the number of the personnel distributed in each area as a variable.
6. The method of claim 4, wherein the determining the emergency level of the personnel requirement of each area separately comprises:
Determining the relative population density and the number of dots of each region respectively;
Determining the emergency degree of the personnel requirement of each area according to the relative population density and the number of the dots of each area.
7. The method of any one of claims 1 to 6, wherein said solving the distributed constraint optimization model to obtain the number of people assigned for each of the regions comprises:
adopting a distributed constraint algorithm to search each region and adjacent regions in parallel, and calculating the minimum value of the sum of constraint costs corresponding to all the regions;
And calculating the number of people allocated to each area according to the minimum value of the sum of the constraint costs.
8. A personnel dispensing apparatus, the apparatus comprising:
the regional division module is used for dividing the plurality of mesh points to be distributed into a plurality of regions according to the position information of the plurality of mesh points to be distributed;
The diagram construction module is used for constructing a position relation diagram corresponding to the plurality of areas; each node in the position relation diagram corresponds to one region, and each line is connected with two adjacent regions;
The model construction module is used for constructing a distributed constraint optimization model based on the position relation diagram; the distributed constraint optimization model takes the number of personnel distributed by each region as a variable;
and the personnel calculation module is used for solving the distributed constraint optimization model to obtain the personnel number distributed for each region.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
11. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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