CN113807597A - Network scheduling method, device, equipment and storage medium - Google Patents

Network scheduling method, device, equipment and storage medium Download PDF

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CN113807597A
CN113807597A CN202111113277.1A CN202111113277A CN113807597A CN 113807597 A CN113807597 A CN 113807597A CN 202111113277 A CN202111113277 A CN 202111113277A CN 113807597 A CN113807597 A CN 113807597A
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胡晓航
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Industrial and Commercial Bank of China Ltd ICBC
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Abstract

The embodiment of the specification relates to the technical field of big data processing, and provides a network point scheduling method, a device, equipment and a storage medium, wherein the method comprises the following steps: selecting a target mesh point from the mesh point set; acquiring historical traffic of the target network point and a scheduling influence factor of the target network point in a target time period; taking the historical traffic and the scheduling influence factor as input, and calling an autoregressive moving average model to predict the predicted traffic of the target network point in the target time period; acquiring scheduling constraint conditions of the target network points; constructing a shift scheduling objective function according to the predicted traffic and the shift scheduling constraint condition; and determining the scheduling data of the target network points by optimizing the scheduling objective function. The embodiment of the specification can improve the scheduling efficiency of the network points and improve the matching degree of the scheduling result and the actual traffic demand.

Description

Network scheduling method, device, equipment and storage medium
Technical Field
The present disclosure relates to the field of big data processing technologies, and in particular, to a method, an apparatus, a device, and a storage medium for scheduling a website.
Background
Currently, the scheduling of the network points is mostly finished manually. For example, taking a human resource shift (hereinafter referred to as shift) of a bank outlet as an example, at the present stage, the shift of the bank outlet is generally manually planned by a manager according to experience and actual rest requirements of employees. However, the manual planning method is not only inefficient, but also easily results in mismatching of the scheduling result with the actual traffic demand, thereby easily causing the problems of excessive or insufficient manpower resources at the network points.
Disclosure of Invention
An object of the embodiments of the present specification is to provide a method, an apparatus, a device, and a storage medium for scheduling a website, so as to improve the efficiency of scheduling the website and improve the matching degree between the scheduling result and the actual traffic demand.
In order to achieve the above object, in one aspect, an embodiment of the present specification provides a network scheduling method, including:
selecting a target mesh point from the mesh point set;
acquiring historical traffic of the target network point and a scheduling influence factor of the target network point in a target time period;
taking the historical traffic and the scheduling influence factor as input, and calling an autoregressive moving average model to predict the predicted traffic of the target network point in the target time period;
acquiring scheduling constraint conditions of the target network points;
constructing a shift scheduling objective function according to the predicted traffic and the shift scheduling constraint condition;
and determining the scheduling data of the target network points by optimizing the scheduling objective function.
In an embodiment of the present specification, the autoregressive moving average model includes any one of the following models:
an ARMA model;
ARIMA model.
In an embodiment of the present specification, the shift scheduling objective function includes:
Figure BDA0003274456930000021
in, min represents the minimum of shift objective function, tjIndicating that the j-th day is within n days,
Figure BDA0003274456930000022
representing the predicted traffic volume of the net in the j day of n days, M representing the number of the net in post, I representing the number of the net post,
Figure BDA0003274456930000023
denotes for arbitrary, PmiRepresenting the average traffic handling of the m-th person on the ith position, ciCoefficient of labor cost, x, required to represent the ith positioniNumber of persons on Shift, x, representing ith ShiftiaDenotes xiRole a in, xibDenotes xiB in (1), and s.t. represents a shift scheduling constraint condition.
In an embodiment of the present specification, the shift scheduling constraint includes:
Figure BDA0003274456930000024
wherein, PkDenotes the kth person, XkRepresents PkQualification of, TkRepresents PkOn a workable day of RiDenotes xiThe value range of (a); s represents a specified plurality of xiSum of RsAnd the value range of S is shown.
In an embodiment of the present specification, the determining the shift data of the target website by optimizing the shift objective function includes:
and optimizing the shift scheduling objective function through a nonlinear programming algorithm to determine the shift scheduling data of the target mesh point.
In the embodiments of the present specification, the shift schedule influence factor includes any one or more of the following combinations:
weather conditions;
holidays;
the mesh point level.
In the embodiments of the present specification, the shift scheduling constraint includes any one or more of the following combinations:
qualification of personnel;
the number of people;
information on vacations of personnel;
a human priority.
On the other hand, an embodiment of the present specification further provides a network point shift scheduling device, including:
a selection module for selecting a target mesh point from the mesh point set;
the first acquisition module is used for acquiring the historical traffic of the target network point and the scheduling influence factor of the target network point in a target time period;
the calling module is used for calling an autoregressive moving average model to predict the predicted traffic of the target network point in the target time period by taking the historical traffic and the scheduling influence factor as input;
the second acquisition module is used for acquiring the scheduling constraint conditions of the target network points;
the construction module is used for constructing a scheduling objective function according to the predicted traffic and the scheduling constraint condition;
and the optimization module is used for determining the shift scheduling data of the target network points by optimizing the shift scheduling objective function.
In another aspect, the embodiments of the present specification further provide a computer device, which includes a memory, a processor, and a computer program stored on the memory, and when the computer program is executed by the processor, the computer program executes the instructions of the above method.
In another aspect, the present specification further provides a computer storage medium, on which a computer program is stored, and the computer program is executed by a processor of a computer device to execute the instructions of the method.
According to the technical scheme provided by the embodiment of the specification, the historical traffic of the target network point and the scheduling influence factor of the target network point in the target time period are taken as input, an autoregressive moving average model is called to predict the predicted traffic of the target network point in the target time period, then a scheduling objective function is automatically constructed according to the predicted traffic and the scheduling constraint condition, and then the scheduling data of the target network point is determined by optimizing the scheduling objective function, so that the automatic scheduling of the network point is realized, and compared with manual scheduling, the embodiment of the specification greatly improves the scheduling efficiency; in addition, in the embodiment of the description, various factors such as the historical service volume of a network point, the scheduling influence factor of the network point in a target time period, the scheduling constraint condition and the like are comprehensively considered when automatic scheduling is carried out; therefore, the scheduling result is more matched with the actual traffic demand, namely, the matching degree of the scheduling result and the actual traffic demand is improved, and the problems of excessive manpower resources or insufficient manpower resources of the network points are reduced or avoided.
Drawings
In order to more clearly illustrate the embodiments of the present specification or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present specification, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort. In the drawings:
FIG. 1 illustrates a schematic diagram of a website shift scheduling system in some embodiments of the present description;
FIG. 2 illustrates a flow diagram of a method for scheduling a website in some embodiments of the present description;
FIG. 3 is a block diagram of a website shift device in some embodiments of the present disclosure;
FIG. 4 shows a block diagram of a computing device in some embodiments of the present description.
[ description of reference ]
1. A shift arrangement system;
2. a scheduling client;
3. a service database;
4. a weather forecast system;
5. a human management system;
6. a channel management system;
31. a selection module;
32. a first acquisition module;
33. calling a module;
34. a second acquisition module;
35. building a module;
36. an optimization module;
402. a computer device;
404. a processor;
406. a memory;
408. a drive mechanism;
410. an input/output interface;
412. an input device;
414. an output device;
416. a presentation device;
418. a graphical user interface;
420. a network interface;
422. a communication link;
424. a communication bus.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only a part of the embodiments of the present specification, and not all of the embodiments. All other embodiments obtained by a person skilled in the art based on the embodiments in the present specification without any inventive step should fall within the scope of protection of the present specification.
Embodiments of the present description relate to automatic (or intelligent) scheduling techniques for a website. The network node is generally referred to as a business node (e.g., a bank business node). Scheduling generally refers to planning the taking turns of the employees of the enterprise. In view of the problems that in the prior art, the efficiency of manual scheduling is low, and the scheduling of a network point is easily mismatched with the actual traffic demand, so that the network point is easily surplus in human resources or insufficient in human resources, the embodiments of the present specification provide an automated (or intelligent) network point scheduling technical scheme.
The network shift scheduling system of some embodiments of the present description is shown in fig. 1 and includes a shift scheduling system 1 and a shift scheduling client 2; the shift scheduling client 2 may provide user login or perform other human-computer interaction (e.g., send a shift scheduling request to the shift scheduling system 1, etc.); the shift scheduling system 1 can communicate with the shift scheduling client 2 and other systems. Specifically, the shift scheduling system 1 may select a target website from the website set based on a timing task or when receiving a shift scheduling request sent by the shift scheduling client 2; acquiring historical traffic of the target network point from a traffic database 3, and acquiring a scheduling influence factor of the target network point in a target time period from a weather forecast system 4, a manpower management system 5, a channel management system 6 and the like; then, taking the historical traffic and the scheduling influence factor as input, and calling an autoregressive moving average model to predict the predicted traffic of the target network point in the target time period; acquiring the scheduling constraint conditions of the target network points from a human management system 5; constructing a shift scheduling objective function according to the predicted traffic and the shift scheduling constraint condition; determining the scheduling data of the target network points by optimizing the scheduling objective function; and outputting the scheduling data of the target network point (for example, returning a scheduling response carrying the scheduling data of the target network point to the scheduling client 2); thereby realizing automatic scheduling of the network points.
In some embodiments, the shift scheduling system may be an electronic device with computing and network interaction functions; software that runs in the electronic device and provides business logic for data processing and network interaction is also possible. The shift client can be a mobile terminal (i.e., a smart phone), a display, a desktop computer, a tablet computer, a notebook computer, a digital assistant, or an intelligent wearable device. Wherein, wearable equipment of intelligence can include intelligent bracelet, intelligent wrist-watch, intelligent glasses or intelligent helmet etc.. Of course, the user end is not limited to the electronic device with a certain entity, and may also be software running in the electronic device.
The embodiment of the specification further provides a network node scheduling method, and the network node scheduling method can be applied to the scheduling system side. Referring to fig. 2, in some embodiments, the website shift scheduling method may include the following steps:
s201, selecting a target mesh point from the mesh point set.
S202, acquiring historical traffic of the target network point and a scheduling influence factor of the target network point in a target time period.
S203, taking the historical traffic and the scheduling influence factor as input, and calling an autoregressive moving average model to predict the predicted traffic of the target network point in the target time period.
And S204, acquiring the scheduling constraint condition of the target network point.
S205, a scheduling objective function is constructed according to the predicted traffic and the scheduling constraint condition.
And S206, determining the scheduling data of the target network point by optimizing the scheduling objective function.
In the embodiment of the specification, the historical traffic of a target network point and a scheduling influence factor of the target network point in a target time interval are taken as input, an autoregressive sliding average model is called to predict the predicted traffic of the target network point in the target time interval, then a scheduling objective function is automatically constructed according to the predicted traffic and scheduling constraint conditions, and then the scheduling data of the target network point is determined by optimizing the scheduling objective function, so that automatic scheduling of the network point is realized, and compared with manual scheduling, the embodiment of the specification greatly improves the scheduling efficiency; in addition, in the embodiment of the description, various factors such as the historical service volume of a network point, the scheduling influence factor of the network point in a target time period, the scheduling constraint condition and the like are comprehensively considered when automatic scheduling is carried out; therefore, the scheduling result is more matched with the actual traffic demand, namely, the matching degree of the scheduling result and the actual traffic demand is improved, and the problems of excessive manpower resources or insufficient manpower resources of the network points are reduced or avoided.
In some embodiments, the scheduling system may periodically execute the scheduling method of the website based on the timed task, that is, whenever the timed time comes, the scheduling system may automatically execute the scheduling method of the website to obtain the scheduling result. For example, in an exemplary embodiment, the shift scheduling system may be executed once per day to obtain shift scheduling results for 7 days in the future (here, 7 days are taken as the predicted duration, i.e., 7 days are taken as the target period). In this scenario, the scheduling system may select a target site from the set of sites each time a timed time arrives.
In other embodiments, the scheduling system may also execute the network point scheduling method when triggered by a specific random event, that is, when the specific random event occurs, the scheduling system may execute the network point scheduling method once to obtain the scheduling result. For example, in an exemplary embodiment, the specific random event may be that a scheduling request sent by the client is received, that is, when the scheduling system receives the scheduling request sent by the client, a website scheduling method is executed once to obtain a scheduling result.
The network point set is a set of network points which need to be subjected to network point scheduling; the range of the mesh points in the mesh point set can be selected according to actual needs. For example, taking XX bank MM city branch as an example, when each website of XX bank MM city branch needs to uniformly schedule websites, the website range of the website set may include all websites of XX bank MM city branch. When the XX bank MM city branch allows each branch to carry out network point scheduling, for the NN branch of the XX bank MM city branch, the network point range of the network point set may be all network points of the NN branch of the XX bank MM city branch. Of course, this is merely an example, and the range of the mesh points in the mesh point set may be larger or smaller according to actual needs, which is not specifically limited in this specification.
In this embodiment, the target mesh point is the currently selected mesh point. When there are multiple nodes in the node set, in order to implement automatic scheduling of each node in the node set, in some embodiments, scheduling may be performed one by one in a random selection manner or a sequential selection manner. Of course, in other embodiments, the selection of the target website from the website set may also be completed under manual intervention, that is, a website option page is provided to the shift scheduling client, so that the user can select the target website.
In some embodiments, the historical traffic volume of the target site may be obtained from a traffic database or the like. Wherein, the historical traffic of the target network point can be the historical traffic of the target network point in a specified time range. For example, historical traffic volume for the last half year, last three years, etc. of a target site. The historical traffic may include historical traffic of all services (e.g., an account opening/selling service, a depositing service, a loan service, a financing service, a fund service, etc.) or a designated part of services of the target network point.
In some embodiments, the shift-scheduling impact factor of the target website in the target time period may be obtained from a weather forecast system, a human management system 5, a channel management system 6, and/or the like. The target time interval is a set prediction duration, namely, the scheduling of the target network point in the future time range can be predicted at one time. For example, in an exemplary embodiment, the target period may be a future day, a future week, a future month, etc., as may be selected as desired. The shift scheduling influence factors are influence factors which have important influence on the shift scheduling result, and if the shift scheduling influence factors are not considered, the shift scheduling result has larger deviation from the actual business volume requirement.
In some embodiments, the shift impact factors may include, but are not limited to, weather conditions, holidays, and website levels, etc. (described in more detail below).
Weather conditions
Statistical studies have shown that weather conditions are one of the important factors affecting network traffic. In order to make the shift result more matched with the actual traffic demand, the influence degree of different weather on the number of the clients in the store can be obtained by comparing the change of the number of the clients in the store in the same period (namely the number of the clients handling the service in the branch) in different weather. For example, in an exemplary embodiment, historical contemporaneous weather data may be compared against recent one month weather forecast data. If the predicted weather is severe weather (such as weather with temperature higher than 35 ℃, freezing, rainstorm, typhoon, sand storm and the like), the scheduling system properly reduces the predicted traffic (such as by 10-30%); if the predicted weather is normal weather (i.e., weather other than severe weather), there is no need to reduce traffic accordingly. The weather conditions may be obtained from a weather forecast system or the like. The weather forecast system is a system for providing weather forecast information.
Holidays of festival II
Holidays are also one of the important factors affecting network point traffic. For example, for most users, there may not be time to go to a website to transact business due to work on a weekday, and the user may wait until the weekend to transact business. Thus, the mesh point traffic demand on weekends may be higher relative to the mesh point traffic demand during normal weekdays; in addition, when a holiday comes (especially a relatively long holiday such as a national day, a spring festival, etc.), many customers may go out of the house or go to a vacation, etc. Thus, the mesh point traffic demand during this period may be low relative to the mesh point traffic demand during normal weekdays. Therefore, the scheduling system can increase or decrease the predicted traffic appropriately according to the holiday condition corresponding to the target time period. The holiday situation can be obtained from a human resource management system, an external system providing holiday information service, and the like.
(III) mesh point rank
The rank of a website is one of the important factors influencing the scheduling of the website. The mesh point level is mainly determined according to the mesh point scale, the mesh point category and the like of the mesh point. The network node scale can be determined by information such as service range and resource allocation. The resource configuration may include: the intelligent bank note management system comprises self-service terminals, an intelligent counter and the like, and a counter is arranged (namely, the counter relates to real bank notes in real objects) and a cashless counter (generally, the consultation and sale work of financial products is carried out, and the executed work content does not relate to the real bank notes in real objects). The network point type is whether the network point is an accounting network point or not; the accounting network points refer to: besides executing the related business functions of the ordinary network points, the network points also can undertake the work of accounting, statistics, collection and the like of all the network points in the district where the network points are located. Obviously, the size of the network point scale has direct influence on the number and proportion of the staff in the scheduling; in view of the fact that the accounting network points need to bear extra services, the labor investment is more than that of the ordinary network points when the shifts are arranged; therefore, the website category is one of the important factors influencing the website shift. The website level may be obtained from a channel management system or the like. The channel management system is a system for maintaining resource configuration information of network points.
The shift schedule impact factors described above are merely exemplary; however, this should not be construed as a limitation on the shift schedule influence factor in the embodiments of the present specification; considering that there are many shift scheduling influence factors, in other embodiments of the present specification, the shift scheduling influence factor of the target site in the target time period may be more or less according to actual needs.
In some embodiments, an Autoregressive Moving Average model (ARMA) is an algorithm that studies time series. The core idea is as follows: and learning a time-varying rule from the historical time series data, and predicting a future time series according to the rule. The autoregressive moving average model must meet the requirement of stationarity. Wherein, the stationarity means: the fitted curve obtained through the time series of samples will continue inertially following the existing morphology for a future period of time. Specifically, whether the time series is stationary can be evaluated by whether the mean and variance of the time series change significantly. In the application scenario of scheduling for a website in the embodiment of the present specification, since the traffic of the website generally satisfies the stationarity requirement, the website scheduling can be predicted by using an autoregressive moving average model. Obviously, in the embodiments of the present specification, when an autoregressive moving average model is used to predict a shift of a website, both the input historical traffic and the output predicted traffic are also time series data.
In other embodiments, the above ARMA model may be replaced with ARIMA (Autoregressive Integrated Moving Average model, Integrated Moving Average Autoregressive model). Unlike the ARMA model which applies to a stationary raw data time sequence, the ARIMA model is a stationary time sequence after applying to the raw data difference. In other embodiments, the ARMA model may be replaced with any other suitable time series prediction model. It can be seen that, in the embodiments of the present specification, the ARMA model is only an illustrative example, not a unique limitation, and can be selected according to the needs in practical implementation.
In order to further improve the accuracy of the prediction result, on the basis of considering the shift scheduling influence factors, the limiting conditions of personnel qualification, personnel quantity, personnel vacation information, personnel priority and the like of a website can be considered. The personnel qualification reflects the service range, the service level, the work efficiency and the like of personnel, the vacation information of personnel reflects the actual day of rest arrangement of the personnel, and obviously, the restrictive conditions have influence on scheduling, so the restrictive conditions can be called scheduling constraint conditions. In some embodiments, the shift scheduling constraints may be obtained from a human resources management system.
In some embodiments, based on determining the predicted traffic volume, the constructed shift objective function may be expressed as:
Figure BDA0003274456930000091
the meaning of the shift target function is as follows: predicting traffic volume for the j day within n days in the future
Figure BDA0003274456930000092
The difference between the average business processing amount of all the staff on duty in the shift is minimum; therefore, the method can meet the traffic demand of the j day and does not waste human resources. Wherein min represents the minimum of the shift target function, tjIndicating that the j-th day is within n days,
Figure BDA0003274456930000093
representing the predicted traffic volume of the net in the j day of n days, M representing the number of the net in post, I representing the number of the net post,
Figure BDA0003274456930000094
denotes for arbitrary, PmiRepresenting the average traffic handling of the m-th person on the ith position, ciCoefficient of labor cost, x, required to represent the ith positioniNumber of persons on Shift, x, representing ith ShiftiaDenotes xiRole a in, xibDenotes xiB in (1), and s.t. represents a shift scheduling constraint condition.
In some embodiments, the shift scheduling constraints may include
Figure BDA0003274456930000101
Wherein, PkDenotes the kth person, XkRepresents PkQualification of, TkRepresents PkOn a workable day of RiDenotes xiThe value range of (a); s represents a specified plurality of xiSum of RsAnd the value range of S is shown.
For example, in an exemplary embodiment, the shift scheduling constraint may be:
Figure BDA0003274456930000102
in the above-mentioned scheduling constraints, p1～p5Representing different employees (e.g. p)1Is small, p2Floret, etc.), x1,x2,x3,x4Representing different positions of a target site (e.g. x)1For cash business, x2For fund services, etc.), x1aAnd x1bRepresents a position x1May be composed of two or more different roles a and b (not necessarily for each position), i.e. x1aDenotes x1Role a in, x1bDenotes x1B role in (1); others may be explained with reference to the drawings. Wherein (p)1∈{x1b,x2a,x3},p2∈{x3,x4},p3∈{x2,x3,x4},p4∈{x2,x5},p5∈{x1a}) reflects the qualification of the person, which is equivalent to the person who can be on any post: x is the number of1＝{p1,p5}，x2＝{p1,p3,p4}，x3＝{p1,p2,p3}，x4＝{p2,p3}，x5＝{p4}。p1∈{t1,t2,t3,t4,t5},p2∈{t1,t2,t4,t5,t6},p3∈{1,t2,t3,t4,t5,t6,t7},p4∈{1,t2,t3,t4,t5,t6},p5∈{t1,t2,t3,t4,t5Reflects the workday of each employee. For example, with p1∈{t1,t2,t3,t4,t5As an example, p1∈{t1,t2,t3,t4,t5Denotes employee p1The possible working days of (a) are from day 1 to day 5 in the future. x is the number of1≥1,x2＝1,x3The constraint condition of the number of the personnel at each post is represented by less than or equal to 2; for example, in x3X is not more than 232 or less represents x3The number of people on duty on day can not exceed 2. (x)1+x2+x4) Less than or equal to 3 reflects the priority of the staff in the post.
In some embodiments, for a scene with relatively few scheduling influence factors and relatively simple scheduling constraint conditions, the scheduling objective function may be solved through a deterministic algorithm such as a benders algorithm, a D-W algorithm, a cut plane method, a branch-and-bound method, a branch pricing method, or a lagrangian relaxation algorithm, so as to determine the scheduling data of the target mesh point. And because the algorithms are all deterministic algorithms, the method has better convergence and is easy to obtain the global optimal solution.
In contrast, for a scene with relatively more shift scheduling influence factors and relatively complex shift scheduling constraint conditions, the service demand or personnel needs to be reasonably predicted or simplified, and the feasible scheme and the cost evaluation thereof need to be assisted by a computer program. Therefore, the shift scheduling objective function can be solved by using a non-linear programming algorithm such as a column generation algorithm, a genetic algorithm, a simulated annealing algorithm and a tabu algorithm, so that shift scheduling data of the target mesh point can be determined.
In some cases, when solving or optimizing the shift objective function may be difficult to guarantee global optimality, the solution or optimization may be performed again by appropriately modifying the shift constraints (e.g., discarding some less important shift constraints) or replacing the optimization algorithm, etc. If the global optimum is still difficult to ensure, the scheduling data of the target network point can be selectively determined according to the solving result corresponding to the local optimum. Under special conditions, if solving or optimizing the shift scheduling objective function is difficult to obtain local optimization, an error report can be thrown.
In addition, in some embodiments, the scheduling system may further provide an emergency maintenance interface, so as to modify the scheduling constraint condition when optimizing the scheduling objective function, or manually input or adjust the scheduling influence factor parameter or the like in the case that traffic volume is suddenly changed due to some reason (for example, preferential activity, sudden weather, or the like), thereby enabling the scheduling system to have greater flexibility.
While the process flows described above include operations that occur in a particular order, it should be appreciated that the processes may include more or less operations that are performed sequentially or in parallel (e.g., using parallel processors or a multi-threaded environment).
Corresponding to the above scheduling method, an embodiment of the present disclosure further provides a scheduling apparatus for a network node, which can be configured in the above scheduling system. Referring to fig. 3, in some embodiments, the website shift scheduling apparatus may include a selection module 31, a first obtaining module 32, a calling module 33, a second obtaining module 34, a construction module 35, and an optimization module 36. Wherein:
a selection module 31, operable to select a target mesh point from the set of mesh points;
a first obtaining module 32, configured to obtain a historical traffic volume of the target node and a shift scheduling influence factor of the target node in a target time period;
the calling module 33 may be configured to call an autoregressive moving average model to predict the predicted traffic volume of the target node in the target time period, with the historical traffic volume and the shift scheduling influence factor as inputs;
a second obtaining module 34, configured to obtain a shift scheduling constraint condition of the target website;
the construction module 35 may be configured to construct a shift scheduling objective function according to the predicted traffic volume and the shift scheduling constraint condition;
an optimization module 36 may be configured to determine shift data of the target website by optimizing the shift objective function.
In some embodiments, the autoregressive moving average model comprises any one of the following models:
an ARMA model;
ARIMA model.
In an embodiment of the present specification, the shift scheduling objective function includes:
Figure BDA0003274456930000121
wherein min represents the minimum of the shift target function, tjIndicating that the j-th day is within n days,
Figure BDA0003274456930000122
the predicted traffic of the net point on the j th day in n days, M represents the number of the net points on duty, I represents the number of the net points on duty,
Figure BDA0003274456930000123
denotes for arbitrary, PmiRepresenting the average traffic handling of the m-th person on the ith position, ciCoefficient of labor cost, x, required to represent the ith positioniNumber of persons on Shift, x, representing ith ShiftiaDenotes xiRole a in, xibDenotes xiB in (1), and s.t. represents a shift scheduling constraint condition.
In some embodiments, the shift scheduling constraints include:
Figure BDA0003274456930000124
wherein, PkDenotes the kth person, XkRepresents PkQualification of, TkRepresents PkOn a workable day of RiDenotes xiThe value range of (a); s represents a specified plurality of xiSum of RsAnd the value range of S is shown.
In some embodiments, the determining the shift data of the target website by optimizing the shift objective function may include:
and optimizing the shift scheduling objective function through a nonlinear programming algorithm to determine the shift scheduling data of the target mesh point.
In some embodiments, the shift schedule impact factor comprises any one or more of the following in combination:
weather conditions;
holidays;
the mesh point level.
In some embodiments, the shift scheduling constraints include any one or more of the following in combination:
qualification of personnel;
the number of people;
information on vacations of personnel;
a human priority.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functions of the various elements may be implemented in the same one or more software and/or hardware implementations of the present description.
Embodiments of the present description also provide a computer device. As shown in FIG. 4, in some embodiments of the present description, the computer device 402 may include one or more processors 404, such as one or more Central Processing Units (CPUs) or Graphics Processors (GPUs), each of which may implement one or more hardware threads. The computer device 402 may also include any memory 406 for storing any kind of information such as code, settings, data, etc., and in a particular embodiment, a computer program that is executed on the memory 406 and on the processor 404, and when executed by the processor 404, may perform the instructions of the network site scheduling method described in any of the above embodiments. For example, and without limitation, memory 406 may include any one or more of the following in combination: any type of RAM, any type of ROM, flash memory devices, hard disks, optical disks, etc. More generally, any memory may use any technology to store information. Further, any memory may provide volatile or non-volatile retention of information. Further, any memory may represent fixed or removable components of computer device 402. In one case, when the processor 404 executes the associated instructions, which are stored in any memory or combination of memories, the computer device 402 can perform any of the operations of the associated instructions. The computer device 402 also includes one or more drive mechanisms 408, such as a hard disk drive mechanism, an optical disk drive mechanism, etc., for interacting with any memory.
Computer device 402 may also include input/output interface 410(I/O) for receiving various inputs (via input device 412) and for providing various outputs (via output device 414). One particular output mechanism may include a presentation device 416 and an associated graphical user interface 418 (GUI). In other embodiments, input/output interfaces 410(I/O), input devices 412, and output devices 414 may also be excluded, as just one computer device in a network. Computer device 402 can also include one or more network interfaces 420 for exchanging data with other devices via one or more communication links 422. One or more communication buses 424 couple the above-described components together.
Communication link 422 may be implemented in any manner, such as over a local area network, a wide area network (e.g., the Internet), a point-to-point connection, etc., or any combination thereof. Communication link 422 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc., governed by any protocol or combination of protocols.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products of some embodiments of the specification. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processor to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processor, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processor to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processor to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computer device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium which can be used to store information that can be accessed by a computer device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
The embodiments of this specification may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The described embodiments may also be practiced in distributed computing environments where tasks are performed by remote processors that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
It should also be understood that, in the embodiment of the present specification, the term "and/or" is only one kind of association relation describing an associated object, and means that three kinds of relations may exist. For example, a and/or B, may represent: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment. In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of an embodiment of the specification. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims (10)

1. A network scheduling method is characterized by comprising the following steps:
selecting a target mesh point from the mesh point set;
acquiring historical traffic of the target network point and a scheduling influence factor of the target network point in a target time period;
taking the historical traffic and the scheduling influence factor as input, and calling an autoregressive moving average model to predict the predicted traffic of the target network point in the target time period;
acquiring scheduling constraint conditions of the target network points;
constructing a shift scheduling objective function according to the predicted traffic and the shift scheduling constraint condition;
and determining the scheduling data of the target network points by optimizing the scheduling objective function.
2. A spot scheduling method according to claim 1 wherein the autoregressive moving average model comprises any one of the following models:
an ARMA model;
ARIMA model.
3. A method for scheduling a website as recited in claim 1, wherein the scheduling objective function comprises:
Figure FDA0003274456920000011
wherein min represents the minimum of the shift target function, tjIndicating that the j-th day is within n days,
Figure FDA0003274456920000012
representing the predicted traffic volume of the net in the j day of n days, M representing the number of the net in post, I representing the number of the net post,
Figure FDA0003274456920000013
denotes for arbitrary, PmiRepresenting the average traffic handling of the m-th person on the ith position, ciCoefficient of labor cost, x, required to represent the ith positioniNumber of persons on Shift, x, representing ith ShiftiaDenotes xiRole a in, xibDenotes xiB in (1), and s.t. represents a shift scheduling constraint condition.
4. A method for scheduling of claim 3 wherein said scheduling constraints comprise:
Figure FDA0003274456920000014
wherein, PkDenotes the kth person, XkRepresents PkQualification of, TkRepresents PkOn a workable day of RiDenotes xiThe value range of (a); s represents a specified plurality of xiSum of RsAnd the value range of S is shown.
5. A method of scheduling for a mesh point as recited in claim 1 wherein said determining scheduling data for said target mesh point by optimizing said scheduling objective function comprises:
and optimizing the shift scheduling objective function through a nonlinear programming algorithm to determine the shift scheduling data of the target mesh point.
6. A website shift scheduling method according to claim 1, wherein the shift scheduling influence factor comprises any one or more of the following:
weather conditions;
holidays;
the mesh point level.
7. A method for scheduling of network points as claimed in claim 1, wherein the scheduling constraints comprise any one or more of the following in combination:
qualification of personnel;
the number of people;
information on vacations of personnel;
a human priority.
8. An outlet scheduling apparatus, comprising:
a selection module for selecting a target mesh point from the mesh point set;
the first acquisition module is used for acquiring the historical traffic of the target network point and the scheduling influence factor of the target network point in a target time period;
the calling module is used for calling an autoregressive moving average model to predict the predicted traffic of the target network point in the target time period by taking the historical traffic and the scheduling influence factor as input;
the second acquisition module is used for acquiring the scheduling constraint conditions of the target network points;
the construction module is used for constructing a scheduling objective function according to the predicted traffic and the scheduling constraint condition;
and the optimization module is used for determining the shift scheduling data of the target network points by optimizing the shift scheduling objective function.
9. A computer device comprising a memory, a processor, and a computer program stored on the memory, wherein the computer program, when executed by the processor, performs the instructions of the method of any one of claims 1-7.
10. A computer storage medium on which a computer program is stored, characterized in that the computer program, when being executed by a processor of a computer device, executes instructions of a method according to any one of claims 1-7.
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