CN114077976A - Scheduling processing method, device, equipment and storage medium - Google Patents
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Abstract
The application relates to the technical field of resource scheduling, and provides a scheduling processing method, a device, equipment and a storage medium, wherein the method is used for acquiring network point data of a network point to be scheduled, which is sent by data acquisition equipment, wherein the network point data comprises network point business data and a historical scheduling scheme; carrying out data processing on the website data to obtain website characteristics and a scheduling label corresponding to the website to be scheduled; inputting the website characteristics and the scheduling labels into a preset classification model, wherein the preset classification model is obtained by training a plurality of website characteristic samples and a plurality of scheduling label samples; obtaining a scheduling scheme of a to-be-scheduled website according to the output of a preset classification model; through big data and artificial intelligence technology, accurate prediction and scientific modeling can be carried out on background mass services of the network points, so that a reasonable and fair scheduling scheme is obtained, manual scheduling is not needed, scheduling time is reduced, and scheduling efficiency is improved.
Description
Technical Field
The present invention relates to the field of resource scheduling technologies, and in particular, to a scheduling processing method, apparatus, device, and storage medium.
Background
The personnel scheduling is a practical problem in social life, and the arrangement of personnel from enterprise units to group organizations and classes can not be carried out without the personnel scheduling, for example, in the daily work of banks, a reasonable and fair bank outlet scheduling system is needed to ensure the normal operation of banking business.
Taking a bank system as an example, the current shift arrangement system is mainly operated by a bank outlet operation manager, and most of the bank outlet operation managers integrate a plurality of point data services and manually designate shift arrangement schemes for hundreds of outlet points according to previous experiences.
However, in the prior art, there are administrative factors in the manual scheduling scheme, which cannot ensure the effective allocation of resources and the fairness of scheduling, and it takes a lot of time and manpower, and the scheduling efficiency is low.
Disclosure of Invention
The application provides a scheduling processing method, a scheduling processing device, a scheduling processing apparatus and a storage medium, so that the technical problems that in the prior art, due to the fact that a master factor exists in a mode of manually making a scheduling scheme, effective allocation of resources and scheduling fairness cannot be guaranteed, a large amount of time and manpower are consumed, and scheduling efficiency is low are solved.
In a first aspect, the present application provides a shift arrangement processing method, including:
acquiring network point data of a network point to be scheduled, which is sent by data acquisition equipment, wherein the network point data comprises network point business data and a historical scheduling scheme;
performing data processing on the website data to obtain website features and a scheduling label corresponding to the website to be scheduled;
inputting the website features and the scheduling labels into a preset classification model, wherein the preset classification model is obtained by training a plurality of website feature samples and a plurality of scheduling label samples;
and obtaining the scheduling scheme of the to-be-scheduled website according to the output of the preset classification model.
The application provides a scheduling processing method, which combines the network operating data and the historical scheduling scheme of the network, extracts the characteristics and labels of the network, and realizes the intellectualization and automation of scheduling through a preset classification model, wherein the establishment of the preset classification model combines a plurality of network characteristic samples and a plurality of scheduling label samples, and through big data and artificial intelligence technology, the background massive business of the network can be accurately predicted and scientifically modeled, so that a reasonable and fair scheduling scheme is obtained, manual scheduling is not needed, the scheduling time is reduced, and the scheduling efficiency is improved.
Optionally, the performing data processing on the website data to obtain the website features and the shift scheduling labels corresponding to the to-be-scheduled website includes:
carrying out data preprocessing on the website data to obtain standardized data corresponding to the to-be-scheduled website;
and performing feature extraction and label generation processing on the standardized data to obtain the website features and the scheduling labels corresponding to the websites to be scheduled.
When the embodiment of the application performs data processing on the website data, firstly, the standardized data corresponding to the website to be scheduled is obtained through data preprocessing so as to reduce errors caused by abnormal values and missing values in original data, realize data standardization, improve the precision of model classification, and then, the standardized data is subjected to feature extraction and label generation processing so as to obtain an accurate model classification result, thereby further improving the accuracy and the rationality of scheduling.
Optionally, the performing feature extraction and label generation processing on the standardized data to obtain the website features and the shift scheduling labels corresponding to the website to be scheduled includes:
extracting the business features of the outlets according to the business data of the outlets;
extracting historical scheduling characteristics and the average weekend scheduling amount of peripheral network points according to the historical scheduling scheme;
determining the weekend shift value of the to-be-scheduled branch according to the branch shift scheduling scheme and/or the branch business data;
and generating a scheduling label according to the weekend scheduling value.
The method and the system can extract the network operating characteristics through network operating data, extract historical scheduling characteristics and the average weekend scheduling amount of peripheral networks through a historical scheduling scheme, combine the operating data of the network and the historical operating data and the historical scheduling of the network, consider the previous scheduling conditions of the peripheral networks, accurately reflect the characteristic data of the network, and comprehensively extract the characteristic data and the label data of the network to be scheduled through the characteristics, and further improve the rationality and the accuracy of scheduling.
Optionally, the outlet business features include traffic data, passenger flow data, queuing time data, key traffic data, and employee quantity data.
Here, the embodiment of the application comprehensively considers traffic data, passenger flow data, queuing time data, key traffic data and employee quantity data, and provides a basis for fairness and intelligence of scheduling.
Optionally, after obtaining the scheduling scheme of the to-be-scheduled website according to the output of the preset classification model, the method further includes:
and sending the scheduling scheme to a display device.
After the scheduling scheme is obtained, the scheduling scheme is sent to the display device so as to be checked, and user experience is improved.
Optionally, before the inputting the website features and the shift scheduling labels into a preset classification model, the method further includes:
acquiring a plurality of site feature samples and a plurality of shift label samples;
and inputting the plurality of net point feature samples and the plurality of scheduling label samples into a classification model for training to obtain a preset classification model.
Here, when scheduling is performed according to the embodiment of the application, model training is performed according to a plurality of website feature samples and a plurality of scheduling label samples to obtain an accurate preset classification model, so that a scheduling scheme can be accurately predicted according to the preset classification model.
Optionally, the obtaining a plurality of site feature samples and a plurality of shift label samples includes:
obtaining a plurality of network point data samples, wherein the network point data comprises network point business data samples and historical shift scheduling scheme samples;
and carrying out data processing on the dot data samples to obtain a plurality of dot characteristic samples and a plurality of shift scheduling label samples.
Optionally, the inputting the multiple dot feature samples and the multiple shift scheduling label samples into a classification model for training to obtain a preset classification model includes:
inputting the plurality of site feature samples and the plurality of shift scheduling label samples into a classification model by adopting a 5-fold cross validation method, and determining an optimal hyper-parameter;
obtaining a pre-training classification model according to the optimal hyper-parameter;
and inputting the plurality of net point feature samples and the plurality of shift scheduling label samples into the pre-training classification to obtain a preset classification model.
Here, in the embodiment of the present application, when training the preset classification model, after the website features and the scheduling labels thereof are input, first, 5-fold cross validation is adopted to select the optimal hyper-parameter, and the obtained optimal hyper-parameter C is used to retrain the entire training set to obtain the optimal model, so that the precision of the preset classification model is improved, and the accuracy of scheduling is further improved.
Optionally, the preset classification model is a support vector machine model.
The support vector machine model is simple in algorithm and has robustness, the scheduling steps are simplified, and the scheduling accuracy is improved.
In a second aspect, the present application provides a shift arrangement processing apparatus, including:
the system comprises a first acquisition module, a second acquisition module and a scheduling module, wherein the first acquisition module is used for acquiring the website data of the to-be-scheduled website sent by data acquisition equipment, and the website data comprises website business data and a historical scheduling scheme;
the data processing module is used for carrying out data processing on the website data to obtain website characteristics and a scheduling label corresponding to the website to be scheduled;
the input module is used for inputting the website features and the scheduling labels into a preset classification model;
and the determining module is used for obtaining the scheduling scheme of the to-be-scheduled website according to the output of the preset classification model.
Optionally, the data processing module specifically includes a preprocessing submodule and an extraction submodule;
the preprocessing submodule is used for preprocessing the data of the network point to obtain standardized data corresponding to the network point to be scheduled;
and the extraction submodule is used for carrying out feature extraction and label generation processing on the standardized data to obtain the website features and the scheduling labels corresponding to the websites to be scheduled.
Optionally, the extracting sub-module is specifically configured to:
extracting the business features of the outlets according to the business data of the outlets;
extracting historical scheduling characteristics and the average weekend scheduling amount of peripheral network points according to the historical scheduling scheme;
determining the weekend shift value of the to-be-scheduled branch according to the branch shift scheduling scheme and/or the branch business data;
and generating a scheduling label according to the weekend scheduling value.
Optionally, the outlet business features include traffic data, passenger flow data, queuing time data, key traffic data, and employee quantity data.
Optionally, the device further includes a sending module, configured to send the scheduling scheme to a display device.
Optionally, before the input module inputs the website features and the shift scheduling labels into a preset classification model, the apparatus further includes:
a training module to:
acquiring a plurality of site feature samples and a plurality of shift label samples; and inputting the plurality of net point feature samples and the plurality of scheduling label samples into a classification model for training to obtain a preset classification model.
Optionally, the training module is specifically configured to: obtaining a plurality of network point data samples, wherein the network point data comprises network point business data samples and historical shift scheduling scheme samples; and carrying out data processing on the dot data samples to obtain a plurality of dot characteristic samples and a plurality of shift scheduling label samples.
Optionally, the training module is further specifically configured to:
inputting the plurality of site feature samples and the plurality of shift scheduling label samples into a classification model by adopting a 5-fold cross validation method, and determining an optimal hyper-parameter;
obtaining a pre-training classification model according to the optimal hyper-parameter;
and inputting the plurality of net point feature samples and the plurality of shift scheduling label samples into the pre-training classification to obtain a preset classification model.
Optionally, the preset classification model is a support vector machine model.
In a third aspect, the present application provides a shift arrangement processing apparatus, including: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executes computer-executable instructions stored by the memory to cause the at least one processor to perform the shift processing method as set forth in the first aspect above and in various possible designs of the first aspect.
In a fourth aspect, the present invention provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, the shift scheduling processing method according to the first aspect and various possible designs of the first aspect is implemented.
In a fifth aspect, the present invention provides a computer program product comprising a computer program which, when executed by a processor, implements a shift scheduling method as set forth in the first aspect above and in various possible designs of the first aspect.
The scheduling processing method, the device, the equipment and the storage medium are provided by the application, wherein the method combines the website business data and the historical scheduling scheme of the website to extract the characteristics and the labels of the website, and realizes the intellectualization and automation of scheduling through the preset classification model, wherein the establishment of the preset classification model combines a plurality of website characteristic samples and a plurality of scheduling label samples, and through big data and artificial intelligence technology, the accurate prediction and scientific modeling can be carried out on the background mass business of the website, so that the user experience is improved, thereby intelligently and automatically obtaining a reasonable and fair scheduling scheme, needing no artificial scheduling, reducing the scheduling time and improving the scheduling efficiency.
Drawings
In order to more clearly illustrate the embodiments of the present application 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, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a schematic diagram of a shift scheduling processing system according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a shift scheduling processing method according to an embodiment of the present application;
fig. 3 is a schematic flow chart of another shift scheduling processing method according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a shift scheduling processing apparatus according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a shift scheduling processing apparatus according to an embodiment of the present application.
With the foregoing drawings in mind, certain embodiments of the disclosure have been shown and described in more detail below. These drawings and written description are not intended to limit the scope of the disclosed concepts in any way, but rather to illustrate the concepts of the disclosure to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
The terms "first," "second," "third," and "fourth," if any, in the description and claims of this application and the above-described figures are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
First, terms in the embodiments of the present application are explained:
and (3) supervised learning: in the case of existing data and tags, a model is learned that can be mapped from the data to the tags.
Is characterized in that: in machine learning and pattern recognition, a feature is an independent, measurable variable in observed phenomena. A simple machine learning item may use a single feature, while a more complex machine learning item may use millions of features.
Labeling: the labels are categories of things to predict.
Model: the model defines a mapping relationship between the features and the tags.
Support Vector Machines (SVMs): the method is a two-class classification model, and the main idea is to establish an optimal hyperplane in a sample space so as to maximize the interval between two classes of samples. The method mainly comprises a linear branching support vector machine for solving the linear branching of the samples and a nonlinear support vector machine for solving the linear inseparable of the samples by using a kernel function.
The bank branch scheduling system is operated by a bank branch operation manager, and relates to hundreds of branches. Most of scheduling schemes are that a scheduling staff integrates a plurality of network point data services and then makes the scheduling mode based on past experiences, the scheduling mode not only needs the staff to face a large number of operating staff and operating posts, but also has strong subjective will, manual scheduling not only consumes time and labor, but also is difficult to form effective resource allocation of staff and operation, therefore, network point resource waste is easily caused, and the overall efficiency of an operation center is directly reduced. In the prior art, due to the fact that a scheduling scheme is manually established, a supervisor factor exists, effective allocation of resources and scheduling fairness cannot be guaranteed, a large amount of time and manpower are consumed, and scheduling efficiency is low.
In order to solve the above problems, embodiments of the present application provide a scheduling processing method, an apparatus, a device, and a storage medium, where the method combines a big data technology and an artificial intelligence technology, performs accurate prediction and scientific modeling on mass background services, and performs intelligent scheduling through a preset classification model, so that an intelligent scheduling system improves the scheduling efficiency of an operation center, and improves the management refinement level.
Optionally, fig. 1 is a schematic diagram of a shift scheduling processing system according to an embodiment of the present disclosure. In fig. 1, the architecture includes at least one of a data acquisition device 101, a processing device 102, and a display device 103.
It is to be understood that the illustrated structure of the embodiment of the present application does not form a specific limitation on the architecture of the shift scheduling processing system. In other possible embodiments of the present application, the foregoing architecture may include more or less components than those shown in the drawings, or combine some components, or split some components, or arrange different components, which may be determined according to practical application scenarios, and is not limited herein. The components shown in fig. 1 may be implemented in hardware, software, or a combination of software and hardware.
In a specific implementation process, the data acquisition device 101 may include an input/output interface or a communication interface, and the data acquisition device 101 may establish a connection with a network management system of a network point to be acquired through the input/output interface or the communication interface, and acquire network point data of the network point to be scheduled.
The data acquisition device 101 may also be a network management system of a network point to be scheduled, and directly obtains the network point data of the network point to be scheduled.
The processing device 102 can combine a big data technology and an artificial intelligence technology to perform accurate prediction and scientific modeling on mass background services, and perform intelligent scheduling through a preset classification model, so that the intelligent scheduling system improves the scheduling efficiency of an operation center and improves the management refinement level.
The display device 103 may be any display device arranged at a network site to be scheduled, or may be a user terminal of a staff at the network site to be scheduled.
The display device 103 may also be a touch display screen for receiving user instructions while displaying the above-mentioned content to enable interaction with the user.
It should be understood that the above processing device may be implemented by a processor reading instructions in a memory and executing the instructions, or may be implemented by a chip circuit.
In addition, the network architecture and the service scenario described in the embodiment of the present application are for more clearly illustrating the technical solution of the embodiment of the present application, and do not constitute a limitation to the technical solution provided in the embodiment of the present application, and it can be known by a person skilled in the art that along with the evolution of the network architecture and the appearance of a new service scenario, the technical solution provided in the embodiment of the present application is also applicable to similar technical problems.
The technical scheme of the application is described in detail by combining specific embodiments as follows:
optionally, fig. 2 is a schematic flow chart of a shift scheduling processing method provided in an embodiment of the present application. The execution subject in the embodiment of the present application may be the processing device 102 in fig. 1, and the specific execution subject may be determined according to an actual application scenario. As shown in fig. 2, the method comprises the steps of:
s201: and acquiring the website data of the website to be scheduled, which is sent by the data acquisition equipment.
The network point data comprises network point business data and historical scheduling schemes.
Alternatively, the outlet business data may include traffic data, passenger flow data, queuing time data, spot traffic data, and employee quantity data.
Optionally, the website data is the website business data and the historical shift schedule acquired according to the time window of the preset first day.
It is understood that the preset first day number can be determined according to actual conditions, and the comparison of the embodiments of the present application is not particularly limited.
S202: and carrying out data processing on the website data to obtain website characteristics and a scheduling label corresponding to the website to be scheduled.
Optionally, the data processing is performed on the website data to obtain website features and a scheduling label corresponding to the website to be scheduled, and the method includes:
carrying out data preprocessing on the network point data to obtain standardized data corresponding to the network points to be scheduled; and performing feature extraction and label generation processing on the standardized data to obtain the website features and the scheduling labels corresponding to the websites to be scheduled.
In some embodiments, the data pre-processing includes an outlier and missing value processing portion and a data normalization portion.
The abnormal value and missing value processing part cuts off the abnormal values of the first preset percentage before and after the mesh point data, so that the influence of the abnormal values on the calculation result is avoided, and the mean value is adopted for filling the missing value. It is understood that the first predetermined percentage can be determined according to practical situations, and the comparison of the examples of the present application is not particularly limited.
Where data normalization is the scaling of the sample's attributes to some specified range. Normalizing the data by adopting a normalization pair, counting and calculating a minimum value and a maximum value for each attribute, and mapping the attribute value into an interval [0, 1], wherein the formula is as follows:
optionally, the step of performing feature extraction and label generation processing on the standardized data to obtain a website feature and a shift scheduling label corresponding to the website to be scheduled includes:
extracting the business features of the outlets according to the business data of the outlets; extracting historical scheduling characteristics and the average weekend scheduling amount of peripheral network points according to a historical scheduling scheme; determining the weekend scheduling value of the network point to be scheduled according to the network point scheduling scheme and/or the network point business data; and generating a shift arrangement label according to the weekend shift arrangement value.
Optionally, the outlet business characteristics include traffic data, passenger flow data, queuing time data, emphasis traffic data, and employee quantity data. The embodiment of the application comprehensively considers the traffic data, the passenger flow data, the queuing time data, the key traffic data and the staff number data, and provides a basis for fairness and intelligence of scheduling.
In some feasible implementation modes, the scheduling process of the network site is that a scheduling staff works out a scheme, and after the scheme is submitted to an auditing staff, the scheme is distributed to the business network site after the auditing is finished. And taking the business data of the network points in the time window of T days and the historical scheduling scheme thereof for feature extraction and label generation, selecting a specific day as an expiration date T, performing feature extraction on the network point operation data in the time period before T, and generating label data to be fitted according to the weekend scheduling conditions after T. It is understood that T and T can be determined according to practical situations, and the comparison of the examples in the present application is not particularly limited.
Firstly, for the extraction of the network operation data characteristics in the time period before t, the following three characteristics can be extracted before t:
the method is characterized in that: calculating historical average characteristics from the business data of the network points, and splicing the business data of the current day t as the business characteristics X of the network points1；
And (b) is as follows: determining the weekend and saturday shift before t as 1 and the non-shift as 0 to generate a sequence, and taking the sequence as the historical shift characteristic X2；
C: the peripheral network point scheduling characteristics-the scheduling staff also considers the previous scheduling conditions of the peripheral network points when performing network point scheduling, and the peripheral radius of the network point is the average weekend scheduling amount X of the network point set t-t1 weeks in the first preset range3. Here, the first preset range and t1 may be determined according to practical situations, and the embodiment of the present application is not particularly limited herein.
Will be X above1、X2And X3And splicing into a feature vector X which is used as the feature input of the model.
Assuming that T is 16 days, (including three complete weekends and 10 working days), and the last week are shift deadline T, extracting the features of business data of a certain network, knowing the following data, network traffic, passenger flow, queuing time, employee amount, key traffic, T-2 weeks saturday shift, and T-1 week weekend shift, the features are constructed as follows:
X1is characterized by comprising the following steps:
X2the historical shift arrangement is characterized in that:
X2＝[1，0,0,1]
X3build up the scheduling features of peripheral network pointsCharacterized in that:
optionally, the halftone dot feature X of the input model is X1、X2And X3Spliced into a 14-dimensional vector.
In some feasible implementation manners, the manner of generating the shift schedule label according to the weekend shift schedule value is as follows: the label value of scheduled weekend of the website for t + t2 days is set as 1, and the non-scheduled time is set as 0. It is understood that t2 can be determined according to practical situations, and is not particularly limited by the embodiments of the present application.
The method and the system can extract the network operating characteristics through network operating data, extract historical scheduling characteristics and the average weekend scheduling amount of peripheral networks through a historical scheduling scheme, combine the operating data of the network and the historical operating data and the historical scheduling of the network, consider the previous scheduling conditions of the peripheral networks, accurately reflect the characteristic data of the network, and comprehensively extract the characteristic data and the label data of the network to be scheduled through the characteristics, and further improve the rationality and the accuracy of scheduling.
When the data processing is carried out on the mesh point data, firstly, the standardized data corresponding to the mesh points to be scheduled are obtained through data preprocessing so as to reduce errors caused by abnormal values and missing values in original data, realize the standardization of the data, improve the precision of model classification, and then, the standardized data are subjected to feature extraction and label generation processing so as to obtain an accurate model classification result, and further improve the accuracy and the rationality of the scheduling.
S203: and inputting the website features and the shift scheduling labels into a preset classification model.
The preset classification model is obtained through training of a plurality of net point feature samples and a plurality of shift scheduling label samples.
S204: and obtaining a scheduling scheme of the to-be-scheduled website according to the output of the preset classification model.
Optionally, the output of the preset classification model may be a classification label corresponding to a website to be scheduled, and a scheduling scheme may be determined according to the classification label.
Alternatively, the output of the preset classification model may be a shift schedule.
The application provides a scheduling processing method, which combines the network operating data and the historical scheduling scheme of the network, extracts the characteristics and the labels of the network, and realizes the intellectualization and automation of scheduling through a preset classification model, wherein the establishment of the preset classification model combines a plurality of network characteristic samples and a plurality of scheduling label samples, and through big data and artificial intelligence technology, the accurate prediction and scientific modeling can be carried out on the background massive business of the network, so that the user experience is improved, the reasonable and fair scheduling scheme is obtained, the manual scheduling is not needed, the scheduling time is reduced, and the scheduling efficiency is improved.
In some embodiments, the classification model may also be trained in advance in the embodiment of the present application, so as to facilitate performing a shift scheduling according to the classification model, and accordingly, fig. 3 is a schematic flow diagram of another shift scheduling processing method provided in the embodiment of the present application, as shown in fig. 3, the method includes:
s301: and acquiring the website data of the website to be scheduled, which is sent by the data acquisition equipment.
The network point data comprises network point business data and historical scheduling schemes.
S302: and carrying out data processing on the website data to obtain website characteristics and a scheduling label corresponding to the website to be scheduled.
The implementation manners of steps S301 to S302 are similar to the implementation manners of steps S201 to S202, and are not described herein again.
S303: and acquiring a plurality of net point feature samples and a plurality of shift scheduling label samples.
Optionally, obtaining a plurality of site feature samples and a plurality of shift label samples includes:
obtaining a plurality of network point data samples, wherein the network point data comprises network point business data samples and historical shift scheduling scheme samples; and carrying out data processing on the dot data samples to obtain a plurality of dot characteristic samples and a plurality of shift scheduling label samples.
Here, the data processing manner of the dot data samples is similar to that of the dot data in step S202, and is not described herein again.
S304: and inputting the plurality of net point feature samples and the plurality of shift scheduling label samples into a classification model for training to obtain a preset classification model.
Optionally, the predetermined classification model is a support vector machine model.
The support vector machine model is simple in algorithm and has robustness, the steps of scheduling processing are simplified, and the accuracy of the scheduling processing is improved.
Optionally, inputting a plurality of dot feature samples and a plurality of shift label samples into the classification model for training, and obtaining a preset classification model, including:
inputting a plurality of site feature samples and a plurality of shift label samples into a classification model by adopting a 5-fold cross validation method, and determining an optimal hyper-parameter;
obtaining a pre-training classification model according to the optimal hyper-parameter;
and inputting the plurality of net point feature samples and the plurality of shift scheduling label samples into a pre-training classification to obtain a preset classification model.
Here, in the embodiment of the present application, when the preset classification model is trained, after the website features and the scheduling labels thereof are input, the optimal hyper-parameter is selected by using 5-fold cross validation, and the optimal model is obtained by retraining the entire training set with the obtained optimal hyper-parameter C, so that the precision of the preset classification model is improved, and the accuracy of the scheduling processing is further improved.
S305: and inputting the website features and the shift scheduling labels into a preset classification model.
The preset classification model is obtained through training of a plurality of net point feature samples and a plurality of shift scheduling label samples.
S306: and obtaining a scheduling scheme of the to-be-scheduled website according to the output of the preset classification model.
S307: and sending the scheduling scheme to the display device.
After the scheduling scheme is obtained, the scheduling scheme is sent to the display device so as to be checked, and user experience is improved.
Optionally, the display device may be any display device arranged at a network site to be scheduled, or may be a user terminal of a staff at the network site to be scheduled.
Optionally, the display device may also be a touch display screen for receiving a user instruction while displaying the above content to enable interaction with a user.
When the scheduling processing is carried out, firstly, model training is carried out according to a plurality of network point feature samples and a plurality of scheduling label samples, and an accurate preset classification model is obtained through supervised learning, so that the scheduling scheme can be accurately predicted according to the preset classification model.
Fig. 4 is a schematic structural diagram of a shift scheduling processing apparatus according to an embodiment of the present application, and as shown in fig. 4, the apparatus according to the embodiment of the present application includes: a first obtaining module 401, a data processing module 402, an input module 403 and a determining module 404. The shift register may be the processor itself, or a chip or an integrated circuit that implements the functions of the processor. It should be noted here that the division of the first obtaining module 401, the data processing module 402, the input module 403 and the determining module 404 is only a division of logical functions, and the two may be integrated or independent physically.
The system comprises a first acquisition module, a second acquisition module and a scheduling module, wherein the first acquisition module is used for acquiring the website data of the to-be-scheduled website sent by data acquisition equipment, and the website data comprises website business data and a historical scheduling scheme;
the data processing module is used for carrying out data processing on the website data to obtain website characteristics and a scheduling label corresponding to the website to be scheduled;
the input module is used for inputting the website characteristics and the scheduling labels into a preset classification model;
and the determining module is used for obtaining the scheduling scheme of the to-be-scheduled website according to the output of the preset classification model.
Optionally, the device further includes a sending module, configured to send the scheduling scheme to a display device.
Optionally, the data processing module specifically includes a preprocessing submodule and an extraction submodule;
the preprocessing submodule is used for preprocessing the data of the network point to obtain standardized data corresponding to the network point to be scheduled;
and the extraction submodule is used for carrying out feature extraction and label generation processing on the standardized data to obtain the website features and the scheduling labels corresponding to the websites to be scheduled.
Optionally, the extraction submodule is specifically configured to:
extracting the business features of the outlets according to the business data of the outlets;
extracting historical scheduling characteristics and the average weekend scheduling amount of peripheral network points according to a historical scheduling scheme;
determining the weekend scheduling value of the network point to be scheduled according to the network point scheduling scheme and/or the network point business data;
and generating a shift arrangement label according to the weekend shift arrangement value.
Optionally, the outlet business characteristics include traffic data, passenger flow data, queuing time data, emphasis traffic data, and employee quantity data.
Optionally, before the input module inputs the website features and the shift scheduling labels into the preset classification model, the apparatus further includes:
a training module to:
acquiring a plurality of site feature samples and a plurality of shift label samples; and inputting the plurality of net point feature samples and the plurality of shift scheduling label samples into a classification model for training to obtain a preset classification model.
Optionally, the training module is specifically configured to: obtaining a plurality of network point data samples, wherein the network point data comprises network point business data samples and historical shift scheduling scheme samples; and carrying out data processing on the dot data samples to obtain a plurality of dot characteristic samples and a plurality of shift scheduling label samples.
Optionally, the training module is further specifically configured to:
inputting a plurality of site feature samples and a plurality of shift label samples into a classification model by adopting a 5-fold cross validation method, and determining an optimal hyper-parameter;
obtaining a pre-training classification model according to the optimal hyper-parameter;
and inputting the plurality of net point feature samples and the plurality of shift scheduling label samples into a pre-training classification to obtain a preset classification model.
Optionally, the predetermined classification model is a support vector machine model.
Fig. 5 is a schematic structural diagram of a shift scheduling processing apparatus according to an embodiment of the present application, where the shift scheduling processing apparatus may be the processing apparatus 102 in fig. 1. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not limiting to the implementations of the present application described and/or claimed herein.
As shown in fig. 5, the shift arrangement processing apparatus includes: a processor 501 and a memory 502, the various components being interconnected using different buses, and may be mounted on a common motherboard or in other manners as desired. The processor 501 may process instructions executed within the shift processing apparatus, including instructions for graphical information stored in or on a memory for display on an external input/output device (such as a display device coupled to an interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. In fig. 5, one processor 501 is taken as an example.
The memory 502, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the method of the scheduling processing apparatus in the embodiment of the present application (for example, as shown in fig. 4, the first obtaining module 401, the data processing module 402, the input module 403, and the determining module 404). The processor 501 executes various functional applications and a scheduling processing method by running a non-transitory software program, instructions, and modules stored in the memory 502, that is, a method of implementing the scheduling processing apparatus in the above-described method embodiments.
The shift arrangement processing apparatus may further include: an input device 503 and an output device 504. The processor 501, the memory 502, the input device 503 and the output device 504 may be connected by a bus or other means, and fig. 5 illustrates the connection by a bus as an example.
The input device 503 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the shift processing apparatus, such as a touch screen, a keypad, a mouse, or a plurality of mouse buttons, a trackball, a joystick, or the like. The output device 504 may be an output device such as a display device of the shift processing apparatus. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.
The scheduling processing device in the embodiment of the present application may be configured to execute the technical solutions in the method embodiments of the present application, and the implementation principle and the technical effect are similar, which are not described herein again.
The embodiment of the application also provides a computer-readable storage medium, wherein computer-executable instructions are stored in the computer-readable storage medium, and when being executed by a processor, the computer-executable instructions are used for realizing the shift scheduling processing method of any one of the above items.
An embodiment of the present application further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the computer program is configured to implement any one of the above scheduling processing methods.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit is merely a logical division, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (14)
1. A shift arrangement processing method is characterized by comprising the following steps:
acquiring network point data of a network point to be scheduled, which is sent by data acquisition equipment, wherein the network point data comprises network point business data and a historical scheduling scheme;
performing data processing on the website data to obtain website features and a scheduling label corresponding to the website to be scheduled;
inputting the website features and the scheduling labels into a preset classification model, wherein the preset classification model is obtained by training a plurality of website feature samples and a plurality of scheduling label samples;
and obtaining the scheduling scheme of the to-be-scheduled website according to the output of the preset classification model.
2. The method as claimed in claim 1, wherein said performing data processing on said website data to obtain website features and scheduling labels corresponding to said to-be-scheduled website comprises:
carrying out data preprocessing on the website data to obtain standardized data corresponding to the to-be-scheduled website;
and performing feature extraction and label generation processing on the standardized data to obtain the website features and the scheduling labels corresponding to the websites to be scheduled.
3. The method according to claim 2, wherein the performing feature extraction and label generation processing on the standardized data to obtain the website feature and the shift scheduling label corresponding to the website to be scheduled comprises:
extracting the business features of the outlets according to the business data of the outlets;
extracting historical scheduling characteristics and the average weekend scheduling amount of peripheral network points according to the historical scheduling scheme;
determining the weekend shift value of the to-be-scheduled branch according to the branch shift scheduling scheme and/or the branch business data;
and generating a scheduling label according to the weekend scheduling value.
4. The method of claim 3, wherein the outlet business characteristics comprise traffic data, passenger flow data, queuing time data, spot traffic data, and employee quantity data.
5. The method according to any one of claims 1 to 4, wherein after obtaining the scheduling scheme of the to-be-scheduled website according to the output of the preset classification model, the method further comprises:
and sending the scheduling scheme to a display device.
6. The method according to any one of claims 1 to 4, wherein before inputting the site features and the shift labels into a preset classification model, further comprising:
acquiring a plurality of site feature samples and a plurality of shift label samples;
and inputting the plurality of net point feature samples and the plurality of scheduling label samples into a classification model for training to obtain a preset classification model.
7. The method of claim 6, wherein obtaining a plurality of site feature samples and a plurality of shift label samples comprises:
obtaining a plurality of network point data samples, wherein the network point data comprises network point business data samples and historical shift scheduling scheme samples;
and carrying out data processing on the dot data samples to obtain a plurality of dot characteristic samples and a plurality of shift scheduling label samples.
8. The method according to claim 6, wherein the inputting the plurality of dot feature samples and the plurality of shift-scheduling label samples into a classification model for training to obtain a preset classification model comprises:
inputting the plurality of site feature samples and the plurality of shift scheduling label samples into a classification model by adopting a 5-fold cross validation method, and determining an optimal hyper-parameter;
obtaining a pre-training classification model according to the optimal hyper-parameter;
and inputting the plurality of net point feature samples and the plurality of shift scheduling label samples into the pre-training classification to obtain a preset classification model.
9. The method according to any one of claims 1 to 4, wherein the preset classification model is a support vector machine model.
10. A shift arrangement processing apparatus, comprising:
the system comprises a first acquisition module, a second acquisition module and a scheduling module, wherein the first acquisition module is used for acquiring the website data of the to-be-scheduled website sent by data acquisition equipment, and the website data comprises website business data and a historical scheduling scheme;
the data processing module is used for carrying out data processing on the website data to obtain website characteristics and a scheduling label corresponding to the website to be scheduled;
the input module is used for inputting the website features and the scheduling labels into a preset classification model;
and the determining module is used for obtaining the scheduling scheme of the to-be-scheduled website according to the output of the preset classification model.
11. The shift arrangement processing apparatus according to claim 10, further comprising:
and the sending module is used for sending the scheduling scheme to display equipment.
12. A shift arrangement processing apparatus, characterized by comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the shift scheduling method of any one of claims 1 to 9.
13. A computer-readable storage medium having computer-executable instructions stored therein, which when executed by a processor, are configured to implement the shift scheduling processing method according to any one of claims 1 to 9.
14. A computer program product comprising a computer program, characterized in that the computer program realizes the method of any of claims 1 to 9 when executed by a processor.
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| CN117151672A (en) * | 2023-10-31 | 2023-12-01 | 江苏人加信息科技有限公司 | Intelligent scheduling method, equipment and storage medium for medicine enterprise sales personnel |
| CN117151672B (en) * | 2023-10-31 | 2024-01-26 | 江苏人加信息科技有限公司 | Intelligent scheduling method, equipment and storage medium for medicine enterprise sales personnel |
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