CN117151671A - Scheduling management method, device and storage medium - Google Patents

Scheduling management method, device and storage medium Download PDF

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Publication number
CN117151671A
CN117151671A CN202311121341.XA CN202311121341A CN117151671A CN 117151671 A CN117151671 A CN 117151671A CN 202311121341 A CN202311121341 A CN 202311121341A CN 117151671 A CN117151671 A CN 117151671A
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time period
business
visitor
service
predicted value
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李家明
尤誉龙
王楷波
李斯哲
王健
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China United Network Communications Group Co Ltd
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China United Network Communications Group Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation; Time management
    • G06Q10/109—Time management, e.g. calendars, reminders, meetings or time accounting
    • G06Q10/1093—Calendar-based scheduling for persons or groups
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06311—Scheduling, planning or task assignment for a person or group

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Abstract

The application discloses a scheduling management method, a scheduling management device and a storage medium, and relates to the technical field of information processing. The method comprises the following steps: the visitor quantity and the business acceptance quantity in the first time period are obtained; the first time period is a time period before the current time; inputting visitor quantity into a visitor quantity prediction model to obtain a predicted value of visitor quantity in a second time period, and inputting business acceptance quantity into a business acceptance quantity prediction model to obtain a predicted value of business acceptance quantity in the second time period; the second time period is a time period after the current time; and determining a scheduling plan of the second time period according to the predicted value of the visitor volume and the predicted value of the business acceptance volume in the second time period, wherein the scheduling plan comprises the number of business personnel in the second time period.

Description

Scheduling management method, device and storage medium
Technical Field
The present application relates to the field of information processing technologies, and in particular, to a shift management method, apparatus, and storage medium.
Background
The business hall is an important guarantee of the user marketing service and is closely related to the personal interests of the clients. In order to better establish a good brand image and provide a better service experience for users, business halls need to optimize business hall resources in advance under the condition of ensuring normal operation of business services, business handling efficiency is improved, and queuing waiting time of clients is reduced as much as possible. Therefore, how to predict the reasonable salesman scheduling combination of the salesman in the future is a problem to be solved.
Disclosure of Invention
The application provides a scheduling management method, a scheduling management device and a storage medium, which are used for providing a reasonable and accurate scheduling management method.
In order to achieve the above purpose, the application adopts the following technical scheme:
in a first aspect, a shift management method is provided, the method including: the visitor quantity and the business acceptance quantity in the first time period are obtained; the first time period is a time period before the current time; inputting visitor quantity into a visitor quantity prediction model to obtain a predicted value of visitor quantity in a second time period, and inputting business acceptance quantity into a business acceptance quantity prediction model to obtain a predicted value of business acceptance quantity in the second time period; the second time period is a time period after the current time; and determining a scheduling plan of the second time period according to the predicted value of the visitor volume and the predicted value of the business acceptance volume in the second time period, wherein the scheduling plan comprises the number of business personnel in the second time period.
In a possible implementation manner, the method further includes: acquiring a scheduling plan of the current time; determining a scheduling plan for the second time period based on the predicted values of the amount of visitors and the predicted values of the traffic acceptance amount in the second time period, comprising: if the service acceptance quantity predicted value is larger than the visitor quantity predicted value, adopting a scheduling plan of the current time; if the acceptance quantity predicted value is smaller than the visitor quantity predicted value, determining a scheduling plan of a second time period according to the capability information of a plurality of business personnel; the capability information of the service personnel is used for representing the service acceptance amount of the service personnel in unit time.
In a possible implementation manner, the determining the scheduling plan of the second time period according to the capability information of the plurality of service personnel includes: determining the service level of the service personnel according to the capability information of the service personnel; and determining a scheduling plan of a second time period according to the service levels of the plurality of service personnel, wherein the scheduling plan comprises the service personnel of the plurality of levels, and the predicted value of the service acceptance amount of the service personnel of the plurality of levels in the second time period is larger than the predicted value of the visitor amount.
In a possible implementation manner, the method further includes: acquiring service acceptance amount of each service person in a plurality of service persons in a first time period; the business acceptance amount of each business person in the first time period is input into a business acceptance amount prediction model, and a predicted value of the business acceptance amount in the second time period is obtained.
In a possible implementation manner, the method further includes: classifying and preprocessing the data of the visitor volume and the business acceptance volume according to the time sequence to obtain first target data; the first target data includes numeric data and non-numeric data; sequentially performing linear dimension reduction processing and nonlinear dimension reduction processing on the first target data to obtain second target data; training the visitor volume in the second target data according to a first preset algorithm to obtain a visitor volume prediction model; and training the business acceptance amount in the second target data according to a second preset algorithm to obtain a business acceptance amount prediction model.
Based on the method, the visitor quantity and the business acceptance quantity in the historical time period are obtained, and the visitor quantity and the business acceptance quantity in the historical time period are trained according to a preset algorithm to obtain a business acceptance quantity prediction model and a visitor quantity prediction model, so that the predicted value of the visitor quantity in the current time period and the predicted values of the business acceptance quantity of a plurality of business persons are obtained, and the scheduling plan in the current time period is determined. The method provided by the application can solve the problem that the accuracy of the time sequence analysis scene is not high enough, and can obtain reasonable scheduling. Compared with the prior art, the method provided by the application solves the problems that the existing model has too large parameter, gradient disappearance easily occurs in the training process, the training convergence speed is low, and the like. And a reasonable scheduling plan is formulated according to visitor quantity and business acceptance quantity in different time periods, so that waiting time of a user can be reduced, and good experience of the user is ensured.
In a second aspect, a shift management device is provided, which is applied to a chip or a system on a chip in the shift management device, and may be a functional module in the shift management device for implementing the method of the first aspect or any of the possible designs of the first aspect. The device can realize the functions executed by the shift management device in the aspects or the possible designs, and the functions can be realized by hardware executing corresponding software. The hardware or software comprises one or more modules corresponding to the functions. Such as: the device comprises an acquisition unit, a determination unit and a processing unit.
The acquisition unit is used for acquiring visitor quantity and service acceptance quantity in a first time period; the first time period is a time period before the current time; the processing unit is used for inputting the visitor quantity into the visitor quantity prediction model to obtain a predicted value of the visitor quantity in the second time period, and inputting the service acceptance quantity into the service acceptance quantity prediction model to obtain a predicted value of the service acceptance quantity in the second time period; the second time period is a time period after the current time; and the determining unit is used for determining a scheduling plan of the second time period according to the predicted value of the visitor volume and the predicted value of the business acceptance volume in the second time period, wherein the scheduling plan comprises the number of business personnel in the second time period.
In a possible implementation manner, the obtaining unit is further configured to obtain a scheduling plan of the current time; the processing unit is specifically configured to adopt a scheduling plan of the current time if the service acceptance amount predicted value is greater than the visitor amount predicted value; if the acceptance quantity predicted value is smaller than the visitor quantity predicted value, determining a scheduling plan of a second time period according to the capability information of a plurality of business personnel; the capability information of the service personnel is used for representing the service acceptance amount of the service personnel in unit time.
In a possible implementation manner, the determining unit is specifically configured to: determining the service level of the service personnel according to the capability information of the service personnel; and determining a scheduling plan of a second time period according to the service levels of the plurality of service personnel, wherein the scheduling plan comprises the service personnel of the plurality of levels, and the predicted value of the service acceptance amount of the service personnel of the plurality of levels in the second time period is larger than the predicted value of the visitor amount.
In a possible implementation manner, the obtaining unit is further configured to obtain a service acceptance amount of each service person in the plurality of service persons in the first period of time; the processing unit is further used for inputting the business acceptance amount of each business person in the first time period into the business acceptance amount prediction model to obtain the predicted value of the business acceptance amount in the second time period. In a possible implementation manner, the determining unit is specifically configured to: calculating the similarity between the carried article and the target article; and under the condition that the similarity is greater than or equal to the threshold value, determining the object carried by the person as the target object.
In a possible implementation, the processing unit is further configured to: classifying and preprocessing the data of the visitor volume and the business acceptance volume according to the time sequence to obtain first target data; the first target data includes numeric data and non-numeric data; sequentially performing linear dimension reduction processing and nonlinear dimension reduction processing on the first target data to obtain second target data; training the visitor volume in the second target data according to a first preset algorithm to obtain a visitor volume prediction model; and training the business acceptance amount in the second target data according to a second preset algorithm to obtain a business acceptance amount prediction model.
In a third aspect, a shift management device is provided, which may be a shift management device or a chip or a system on chip in a shift management device. The device may implement the functions performed by the shift management device in the aspects or in each possible design, where the functions may be implemented by hardware, for example: in one possible design, the apparatus may include: a processor and a communication interface, the processor being operable to support the shift management device to implement the functions involved in the first aspect or any of the possible designs of the first aspect.
In yet another possible design, the shift management device may further include a memory for holding computer-executable instructions and data necessary for the shift management device. When the device is running, the processor executes the computer-executable instructions stored in the memory to cause the device to perform the scheduling management method of the first aspect or any one of the possible designs of the first aspect.
In a fourth aspect, a computer readable storage medium is provided, which may be a readable non-volatile storage medium, storing computer instructions or a program which, when run on a computer, cause the computer to perform the scheduling management method of the first aspect or any one of the possible designs of the above aspects.
In a fifth aspect, there is provided a computer program product comprising instructions which, when run on a computer, cause the computer to perform the scheduling management method of the first aspect or any of the possible designs of the aspects.
In a sixth aspect, a shift management device is provided, which may be a shift management device or a chip or a system on a chip in a shift management device, the device comprising one or more processors and one or more memories. The one or more memories are coupled with the one or more processors, the one or more memories being for storing computer program code comprising computer instructions which, when executed by the one or more processors, cause the scheduling management apparatus to perform the scheduling management method as described above in the first aspect or any of the possible designs of the first aspect.
In a seventh aspect, a chip system is provided, the chip system comprising a processor and a communication interface, the chip system being operable to implement the functions performed by the shift management device in any of the above-mentioned first aspects or any of the possible designs of the first aspect. In one possible design, the chip system further includes a memory for holding program instructions and/or data. The chip system may be composed of a chip, or may include a chip and other discrete devices, without limitation.
Drawings
Fig. 1 is a schematic structural diagram of a shift management device 100 according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a scheduling management method according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a shift management device 30 according to an embodiment of the present application.
Detailed Description
In order to enable those skilled in the art to better understand the technical solutions of the present disclosure, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the disclosure described herein may be capable of operation in sequences other than those illustrated or described herein. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with aspects of embodiments of the application as detailed in the accompanying claims.
It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, and/or components.
Problems and thing development are related to time development changes in many research fields, such as market potential prediction, sales prediction, yield change, etc., which need to obtain a law of development itself by studying a history of past development of a thing. The causal relation model is not required to be established in the time sequence model, and only the data of the variable itself is required to be modeled. The time series model analysis algorithm commonly used in the deep learning field is RNN, LSTM, GRU.
The RNN model has the advantages that the problem of gradient disappearance occurs when the sequence is too long, so that parameters can only capture local relations and cannot learn long-term association, word memory capacity on the front part of the sequence is weak, and the LSTM model utilizes a memory channel to relieve the problems of gradient disappearance of the RNN model and cannot capture long-term association to a certain extent. The LSTM parameter is too large, and the problems of gradient disappearance, slow training convergence speed and the like easily occur in the training process. Compared with the LSTM model, the GRU model has the advantages that the internal structure is simplified, the gradient disappearance problem is relieved by using fewer parameters to a certain extent under the condition that the data size is not very large, the training convergence speed is faster, and meanwhile, the accuracy is improved.
In view of this, the present application provides a scheduling management method, which obtains a visitor amount and a service acceptance amount in a historical time period, trains the visitor amount and the service acceptance amount in the historical time period according to a preset algorithm to obtain a service acceptance amount prediction model and a visitor amount prediction model, and further obtains a predicted value of the visitor amount in a current time period and predicted values of service acceptance amounts of a plurality of service staff, thereby determining a scheduling plan in the current time period. The method provided by the application can solve the problem that the accuracy of the time sequence analysis scene is not high enough. Compared with the prior art, the method provided by the application solves the problems that the existing model has too large parameter, gradient disappearance easily occurs in the training process, the training convergence speed is low, and the like.
It will be appreciated that the above-described method may be performed by any computing-capable device. For example, the execution may be performed by a server, a computer, a computing device, or the like (hereinafter referred to as a shift schedule management apparatus). Wherein the server includes, but is not limited to: tower servers, blade servers, rack servers, physical servers, virtual hosts, virtual private servers (Virtual Private Server, VPS), cloud servers, home servers, enterprise servers, and the like.
In the embodiment of the application, the server can adopt a heterogeneous architecture server with a CPU and GPU combined, and supports a plurality of deployment modes such as local deployment, cloud deployment, edge computing and the like.
In some embodiments, the server may be a single server, or may be a server cluster formed by a plurality of servers. In some implementations, the server cluster may also be a distributed cluster. The present disclosure is not limited to a specific implementation of the server.
When the server is configured with a database, the database can store the acquired visitor amount and acceptance amount data and the data after analysis and processing by the server.
The execution subject of the scheduling management method provided in the present disclosure is a scheduling management device, and may be a chip, a system on a chip, or the like in the scheduling management device, without limitation.
The embodiment of the application does not limit the application scene of the scheduling management system. The system architecture and the service scenario described in the embodiments of the present application are for more clearly describing the technical solution provided in the embodiments of the present application, and do not constitute a limitation on the technical solution provided in the embodiments of the present application, and those skilled in the art can know that, with the evolution of the network architecture and the appearance of a new service scenario, the technical solution provided in the embodiments of the present application is equally applicable to similar technical problems.
In one example, an embodiment of the present application provides a shift management device that may be used to perform a method of the embodiment of the present application.
For example, as shown in fig. 1, a schematic structural diagram of a shift management device 100 according to an embodiment of the present application is provided. The shift management device 100 may include a processor 101, a communication interface 102, and a communication line 103.
Further, the shift management device 100 may further include a memory 104. The processor 101, the memory 104 and the communication interface 102 may be connected through a communication line 103.
The processor 101 is a CPU, general-purpose processor, network processor (network processor, NP), digital signal processor (digital signal processing, DSP), microprocessor, microcontroller, programmable logic device (programmable logic device, PLD), or any combination thereof. The processor 101 may also be any other device having processing functions, such as, without limitation, a circuit, a device, or a software module.
A communication interface 102 for communicating with other devices or other communication networks. The communication interface 102 may be a module, a circuit, a communication interface, or any device capable of enabling communication.
A communication line 103 for transmitting information between the respective components included in the shift management device 100.
Memory 104 for storing instructions. Wherein the instructions may be computer programs.
The memory 104 may be, but is not limited to, a read-only memory (ROM) or other type of static storage device capable of storing static information and/or instructions, a random access memory (random access memory, RAM) or other type of dynamic storage device capable of storing information and/or instructions, an EEPROM, a CD-ROM (compact disc read-only memory) or other optical disk storage, an optical disk storage (including compact disk, laser disk, optical disk, digital versatile disk, blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, etc.
It should be noted that the memory 104 may exist separately from the processor 101 or may be integrated with the processor 101. The memory 104 may be used to store instructions or program code or some data, etc. The memory 104 may be located in the shift management device 100 or may be located outside the shift management device 100, and is not limited. The processor 101 is configured to execute instructions stored in the memory 104 to implement a scheduling management method according to the following embodiments of the present application.
In one example, processor 101 may include one or more CPUs, such as CPU0 and CPU1 in fig. 1.
As an alternative implementation, the shift management device 100 includes a plurality of processors, for example, the processor 107 may be included in addition to the processor 101 in fig. 1.
As an alternative implementation, the shift management apparatus 100 further comprises an output device 105 and an input device 106. Illustratively, the input device 106 is a keyboard, mouse, microphone, or joystick device, and the output device 105 is a display screen, speaker (spaker), or the like.
It should be noted that the shift management apparatus 100 may be a desktop computer, a portable computer, a web server, a mobile phone, a tablet computer, a wireless terminal, an embedded device, a chip system, or a device having a similar structure as in fig. 1. Further, the constituent structure shown in fig. 1 is not limited, and may include more or less components than those shown in fig. 1, or may combine some components, or may be arranged differently, in addition to those shown in fig. 1.
In the embodiment of the application, the chip system can be composed of chips, and can also comprise chips and other discrete devices.
Further, actions, terms, and the like, which are referred to between embodiments of the present application, are not limited thereto. The message names of interactions between the devices or parameter names in the messages in the embodiments of the present application are just an example, and other names may be used in specific implementations without limitation.
In order to clearly describe the technical solution of the embodiments of the present application, in the embodiments of the present application, the words "first", "second", etc. are used to distinguish the same item or similar items having substantially the same function and effect. It will be appreciated by those of skill in the art that the words "first," "second," and the like do not limit the amount and order of execution, and that the words "first," "second," and the like do not necessarily differ.
In the present application, the words "exemplary" or "such as" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "for example" should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion.
In the present application, "at least one" means one or more, and "a plurality" means two or more. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a alone, a and B together, and B alone, wherein a, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b, or c may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or plural.
The following describes a scheduling management method provided by the embodiment of the present application with reference to the scheduling management apparatus shown in fig. 1. In which the terms and the like related to the actions of the embodiments of the present application are mutually referred to, without limitation. The message names of interactions between the devices or parameter names in the messages in the embodiments of the present application are just an example, and other names may be used in specific implementations without limitation. The actions involved in the embodiments of the present application are just an example, and other names may be adopted in the specific implementation, for example: the "included" of the embodiments of the present application may also be replaced by "carried on" or the like.
As shown in fig. 2, the method for scheduling management provided by the embodiment of the application includes:
s201, the visitor quantity and the business acceptance quantity in the first time period are obtained.
Wherein the first time period is a time period prior to the current time. For example, the first time period may be a historical time period.
In one example, the guest volume may include historical time series data that affects guest volume related factors. For example, the data may include historic lobby visitor volume, historic lobby flow, lobby location territory level, visitor traffic volume, lobby distance, lobby location regional environment, lobby volume, whether on weekdays and holidays, etc. The traffic volume may include data affecting traffic volume related factors. For example, the data may include a sales person's historical daily business acceptance, sales person performance, sales person's operational years, sales person's college, sales person's post, and the like.
For example, the amount of guests and traffic acceptance can be expressed as follows in Table 1:
TABLE 1
In table 1, the data types of the guest amount and the traffic acceptance amount may include integer type, boolean type, character string, floating point type, and the like. For example, business hall history call volume 100 is artificial integer data, business office bank is character string data, and whether on weekdays and holidays true is boolean data.
It should be noted that the data in table 1 are exemplary, and the present application is not limited to the data in the table.
S202, inputting visitor quantity into a visitor quantity prediction model to obtain a predicted value of visitor quantity in a second time period, and inputting business acceptance quantity into a business processing quantity prediction model to obtain a predicted value of business acceptance quantity in the second time period.
Wherein the second time period is a time period after the current time. For example, the second time period may be a future time period.
In one possible implementation manner, the server performs model training on the acquired visitor volume and service acceptance volume in the first time period according to the time sequence to obtain a visitor volume prediction model and an acceptance volume prediction model.
Specifically, the server classifies the acquired visitor volume and service acceptance volume in the first time period to obtain first target data. The first target data may include numerical feature data and non-numerical feature data. And then the server scales the numerical characteristic data to 0-1, and codes the non-numerical characteristic data by using a label coding (labelencoder) mode to obtain preprocessed data. For example, the above-mentioned salesman's calendar benke may be encoded as 001, the calendar dazhuan may be encoded as 010, and the calendar shoshi may be encoded as 100.
For data other than the preprocessed data, the server inputs the data into a preset mapping function for reducing the calculation amount of the subsequent preprocessing.
The server performs dimension reduction processing on the preprocessed data, and selects linear combinations of the first k eigenvectors with the largest variance through principal component analysis (principal component analysis, PCA) to realize linear dimension reduction. Nonlinear dimension reduction is achieved by t-distribution random neighborhood embedding (t-distributed stochastic neighbor embedding, t-SNE) to minimize the divergence between probability distributions between the original high-dimensional space and the low-dimensional space. And obtaining second target data through linear dimension reduction and nonlinear dimension reduction of the preprocessed data. The second target data includes a guest volume and a traffic volume.
In one example, the server divides the amount of guests in the second target data into a training set and a testing set and inputs the training set into a first preset algorithm, which may be a gated loop unit (gated recurrent unit, GRU) algorithm. And the server sets the number of layers of the neural network, the number of nodes at each layer, the selection of an optimizer, the selection of a loss function and model parameters in a first preset algorithm according to the data size, and starts model training. And then the server outputs the test set data to a first preset algorithm for cyclic iterative training, and inputs the test set data to the first preset algorithm for evaluating the performance of the model. And the server restores the training data and the predicted data into a time sequence according to the numerical value during data preprocessing, calculates the errors of the training data and the predicted value, and adjusts the model parameters of the first preset algorithm to train again if the errors of the training data and the predicted value are larger than a preset threshold value. If the errors of the training data and the predicted values are smaller than the preset threshold, the predicted model after training is saved, and the visitor quantity predicted model is obtained.
In yet another example, the server divides the traffic acceptance amount in the second target data into a training set and a test set, and inputs the training set and the test set into the second preset algorithm. For example, the second preset algorithm may be a random forest algorithm. And the server reasonably sets the optimizer, the loss function and the model parameters in the second preset algorithm according to the data size, and selects the optimal super parameters through grid search or random search. And training a second preset algorithm by using the optimal super-parameters, and when the error between the predicted value and the training value of the second preset algorithm is smaller than a preset threshold value, storing the trained predicted model to obtain the acceptance quantity predicted model.
Further, the server inputs the visitor quantity in the first time period into a visitor quantity prediction model to obtain a predicted value of the visitor quantity in the second time period, and inputs the service acceptance quantity in the first time period into a service acceptance quantity prediction model to obtain a predicted value of the service acceptance quantity in the second time period.
S203, determining a scheduling plan of the second time period according to the predicted value of the visitor volume and the predicted value of the business acceptance volume in the second time period.
Wherein the scheduling of the second time period includes the number of staff members in the second time period. Business personnel may include business personnel of three levels, high, medium, and low. The capacity information corresponding to different service staff is different, and the capacity information of the service staff is used for representing the service acceptance amount of the service staff in unit time.
In one possible implementation, the server compares the predicted value of the amount of visitors in the second time period with the predicted value of the traffic acceptance amount to determine the scheduling plan in the second time period.
Specifically, if the predicted value of the traffic handling capacity in the second time period is greater than the predicted value of the visitor quantity, the current scheduling plan is adopted in the second time period, and the current scheduling plan can be a historical scheduling plan. If the predicted acceptance amount value in the second time period is smaller than the predicted visitor amount value, a scheduling plan is formulated according to the capability information of a plurality of business personnel. The server firstly determines the service level of the service personnel according to the capability information of the service personnel, and then makes a scheduling plan according to the service levels of a plurality of service personnel. The scheduling plan is made to include a plurality of classes of business personnel, and the sum of business acceptance amounts of the plurality of classes of business personnel in the second time period is larger than a predicted value of the business acceptance amount in the second time period.
In one example, if the historical schedule for a day in the second time period = a high level business person + a medium level business person + a low level business person. The sum of the business acceptances of the schedule on one day is 100, and the server predicts that the business acceptances of the schedule on one day in the second time period are 90 and 100>90 according to the business acceptances prediction model, and the schedule can be used as the schedule on the same day.
If the historical schedule of a certain day in the second time period is a high-level service person, a medium-level service person and a low-level service person, the sum of the service acceptance amount of the schedule in one day is 100, and the server predicts that the service acceptance amount predicted value of the certain day in the second time period is 150, 100<150 according to the service acceptance amount prediction model, the schedule can be made again. For example, the number of high-level or low-level business people is increased until the sum of business people's business acceptances in the second time period is greater than the business acceptances predicted value in the second time period.
Based on the technical scheme of fig. 2, the embodiment of the application provides a scheduling management method, which is characterized in that visitor quantity and business acceptance quantity in a historical time period are obtained, and the visitor quantity and business acceptance quantity in the historical time period are trained according to a preset algorithm to obtain a business acceptance quantity prediction model and a visitor quantity prediction model, so that a predicted value of the visitor quantity in a current time period and predicted values of business acceptance quantities of a plurality of business persons are obtained, and a scheduling plan in the current time period is determined. The method provided by the application can solve the problem that the accuracy of the time sequence analysis scene is not high enough, and can obtain reasonable scheduling. Compared with the prior art, the method provided by the application solves the problems that the existing model has too large parameter, gradient disappearance easily occurs in the training process, the training convergence speed is low, and the like. And a reasonable scheduling plan is formulated according to visitor quantity and business acceptance quantity in different time periods, so that waiting time of a user can be reduced, and good experience of the user is ensured.
The embodiment of the application can divide the functional modules or functional units of the shift management device according to the method example, for example, each functional module or functional unit can be divided corresponding to each function, and two or more functions can be integrated in one processing module. The integrated modules may be implemented in hardware, or in software functional modules or functional units. The division of the modules or units in the embodiment of the present application is schematic, which is merely a logic function division, and other division manners may be implemented in practice.
In the case of dividing the respective functional modules with the respective functions, fig. 3 shows a schematic configuration of a shift management device 30, which shift management device 30 can be used to perform the functions involved in the above-described embodiments. The shift management device 30 shown in fig. 3 may include: the apparatus comprises an acquisition unit 301, a determination unit 302 and a processing unit 303.
An obtaining unit 301, configured to obtain a visitor volume and a service acceptance volume in a first period; the first time period is a time period before the current time; the processing unit 303 is configured to input the visitor volume into a visitor volume prediction model to obtain a predicted value of the visitor volume in the second time period, and input the service acceptance volume into a service acceptance volume prediction model to obtain a predicted value of the service acceptance volume in the second time period; the second time period is a time period after the current time; the determining unit 302 is configured to determine a scheduling plan for the second time period according to the predicted value of the visitor volume and the predicted value of the traffic acceptance volume in the second time period, where the scheduling plan includes the number of the traffic personnel in the second time period.
In a possible implementation manner, the obtaining unit 301 is further configured to obtain a scheduling plan of the current time; the processing unit 303 is specifically configured to, if the predicted value of the traffic handling capacity is greater than the predicted value of the visitor capacity, adopt a scheduling plan of the current time; if the acceptance quantity predicted value is smaller than the visitor quantity predicted value, determining a scheduling plan of a second time period according to the capability information of a plurality of business personnel; the capability information of the service personnel is used for representing the service acceptance amount of the service personnel in unit time.
In a possible implementation manner, the determining unit 302 is specifically configured to: determining the service level of the service personnel according to the capability information of the service personnel; and determining a scheduling plan of a second time period according to the service levels of the plurality of service personnel, wherein the scheduling plan comprises the service personnel of the plurality of levels, and the predicted value of the service acceptance amount of the service personnel of the plurality of levels in the second time period is larger than the predicted value of the visitor amount.
In a possible implementation manner, the obtaining unit 301 is further configured to obtain a service acceptance amount of each of the plurality of service personnel in the first period of time; the processing unit 303 is further configured to input the traffic handling capacity of each traffic person in the first period into the traffic handling capacity prediction model, and obtain a predicted value of the traffic handling capacity in the second period.
In a possible implementation manner, the processing unit 303 is further configured to: classifying and preprocessing the data of the visitor volume and the business acceptance volume according to the time sequence to obtain first target data; the first target data includes numeric data and non-numeric data; sequentially performing linear dimension reduction processing and nonlinear dimension reduction processing on the first target data to obtain second target data; training the visitor volume in the second target data according to a first preset algorithm to obtain a visitor volume prediction model; and training the business acceptance amount in the second target data according to a second preset algorithm to obtain a business acceptance amount prediction model.
As yet another possible implementation, the processing unit 303 in fig. 3 may be replaced by a processor, which may integrate the functions of the processing unit 303.
Further, when the processing unit 303 is replaced by a processor, the shift management device 30 according to the embodiment of the present application may be the shift management device 100 shown in fig. 1.
The embodiment of the application also provides a computer readable storage medium. All or part of the flow in the above method embodiments may be implemented by a computer program to instruct related hardware, where the program may be stored in the above computer readable storage medium, and when the program is executed, the program may include the flow in the above method embodiments. The computer readable storage medium may be an internal storage unit of the communication device (including the data transmitting end and/or the data receiving end) of any of the foregoing embodiments, for example, a hard disk or a memory of the communication device. The computer readable storage medium may be an external storage device of the terminal apparatus, for example, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash card (flash card), or the like, which are provided in the terminal apparatus. Further, the computer readable storage medium may further include both an internal storage unit and an external storage device of the communication apparatus. The computer-readable storage medium is used to store the computer program and other programs and data required by the communication device. The above-described computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.
It should be noted that the terms "first" and "second" and the like in the description, the claims and the drawings of the present application are used for distinguishing between different objects and not for describing a particular sequential order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.
From the foregoing description of the embodiments, it will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of functional modules is illustrated, and in practical application, the above-described functional allocation may be implemented by different functional modules according to needs, i.e. the internal structure of the apparatus is divided into different functional modules to implement all or part of the functions described above.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical functional division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another apparatus, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and the parts displayed as units may be one physical unit or a plurality of physical units, may be located in one place, or may be distributed in a plurality of different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a readable storage medium. Based on such understanding, the technical solution of the embodiments of the present application may be essentially or a part contributing to the prior art or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium, including several instructions for causing a device (may be a single-chip microcomputer, a chip or the like) or a processor (processor) to perform all or part of the steps of the method described in the embodiments of the present application. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk, etc.
The foregoing is merely illustrative of specific embodiments of the present application, and the scope of the present application is not limited thereto, but any changes or substitutions within the technical scope of the present application should be covered by the scope of the present application. Therefore, the protection scope of the application is subject to the protection scope of the claims.

Claims (12)

1. A shift schedule determining method, the method comprising:
the visitor quantity and the business acceptance quantity in the first time period are obtained; the first time period is a time period before the current time;
inputting the visitor quantity into a visitor quantity prediction model to obtain a predicted value of the visitor quantity in a second time period, and inputting the service acceptance quantity into a service acceptance quantity prediction model to obtain a predicted value of the service acceptance quantity in the second time period; the second time period is a time period after the current time;
and determining a scheduling plan of the second time period according to the predicted value of the visitor volume and the predicted value of the business acceptance volume in the second time period, wherein the scheduling plan comprises the number of business personnel in the second time period.
2. The method according to claim 1, wherein the method further comprises:
Acquiring a scheduling plan of the current time;
the determining a scheduling plan of the second time period according to the predicted value of the visitor volume and the predicted value of the business acceptance volume in the second time period comprises the following steps:
if the service acceptance quantity predicted value is larger than the visitor quantity predicted value, adopting a scheduling plan of the current time;
if the acceptance quantity predicted value is smaller than the visitor quantity predicted value, determining a scheduling plan of the second time period according to capability information of a plurality of business personnel; the capability information of the business personnel is used for representing the business acceptance amount of the business personnel in unit time.
3. The method of claim 2, wherein determining the scheduling plan for the second time period based on the capability information of the plurality of business people comprises:
determining the service grade of the service personnel according to the capability information of the service personnel;
and determining a scheduling plan of the second time period according to the service levels of the plurality of service personnel, wherein the scheduling plan comprises the service personnel of the plurality of levels, and the predicted value of the service acceptance amount of the service personnel of the plurality of levels in the second time period is larger than the predicted value of the visitor amount.
4. A method according to claim 3, characterized in that the method further comprises:
acquiring service acceptance amount of each service person in the plurality of service persons in the first time period;
and inputting the business acceptance amount of each business person in the first time period into the business acceptance amount prediction model to obtain the predicted value of the business acceptance amount in the second time period.
5. The method according to claim 1, wherein the method further comprises:
classifying and preprocessing the data of the visitor volume and the business acceptance volume according to a time sequence to obtain first target data; the first target data includes numerical data and non-numerical data;
sequentially performing linear dimension reduction processing and nonlinear dimension reduction processing on the first target data to obtain second target data;
training the visitor volume in the second target data according to a first preset algorithm to obtain the visitor volume prediction model;
and training the business acceptance amount in the second target data according to a second preset algorithm to obtain the business acceptance amount prediction model.
6. A shift management device, the device comprising:
The acquisition unit is used for acquiring visitor quantity and service acceptance quantity in a first time period; the first time period is a time period before the current time;
the processing unit is used for inputting the visitor quantity into a visitor quantity prediction model to obtain a predicted value of the visitor quantity in a second time period, and inputting the service acceptance quantity into a service acceptance quantity prediction model to obtain a predicted value of the service acceptance quantity in the second time period; the second time period is a time period after the current time;
and the determining unit is used for determining a scheduling plan of the second time period according to the predicted value of the visitor volume and the predicted value of the business acceptance volume in the second time period, wherein the scheduling plan comprises the number of business personnel in the second time period.
7. The apparatus of claim 6, wherein the obtaining unit is further configured to obtain a scheduling plan for a current time;
the processing unit is specifically configured to adopt a scheduling plan of a current time if the service acceptance amount predicted value is greater than the visitor amount predicted value; if the acceptance quantity predicted value is smaller than the visitor quantity predicted value, determining a scheduling plan of the second time period according to capability information of a plurality of business personnel; the capability information of the business personnel is used for representing the business acceptance amount of the business personnel in unit time.
8. The apparatus according to claim 7, wherein the determining unit is specifically configured to:
determining the service grade of the service personnel according to the capability information of the service personnel;
and determining a scheduling plan of the second time period according to the service levels of the plurality of service personnel, wherein the scheduling plan comprises the service personnel of the plurality of levels, and the predicted value of the service acceptance amount of the service personnel of the plurality of levels in the second time period is larger than the predicted value of the visitor amount.
9. The apparatus of claim 8, wherein the obtaining unit is further configured to obtain a traffic acceptance amount for each of the plurality of traffic personnel during the first time period;
the processing unit is further configured to input the business acceptance amount of each business person in the first time period into the business acceptance amount prediction model, and obtain predicted values of the business acceptance amounts of the business persons in the second time period.
10. The apparatus of claim 6, wherein the processing unit is further configured to:
classifying and preprocessing the data of the visitor volume and the business acceptance volume according to a time sequence to obtain first target data; the first target data includes numerical data and non-numerical data;
Sequentially performing linear dimension reduction processing and nonlinear dimension reduction processing on the first target data to obtain second target data;
training the visitor volume in the second target data according to a first preset algorithm to obtain the visitor volume prediction model;
and training the business acceptance amount in the second target data according to a second preset algorithm to obtain the business acceptance amount prediction model.
11. A computer readable storage medium having instructions stored therein which, when executed, implement the method of any of claims 1-5.
12. A shift management device, comprising: a processor coupled to a memory for storing one or more programs, the one or more programs comprising computer-executable instructions, which when executed by the apparatus, cause the apparatus to perform the method of any of claims 1-5.
CN202311121341.XA 2023-08-31 2023-08-31 Scheduling management method, device and storage medium Pending CN117151671A (en)

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