CN117851055A - Task scheduling method, device, equipment and storage medium thereof - Google Patents
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Abstract
The embodiment of the application belongs to the technical field of financial science and technology, and relates to a task scheduling method, device, equipment and storage medium thereof, which are applied to a financial business multichannel scheduling scene.
Description
Technical Field
The present invention relates to the technical field of financial science and technology, and is applied to a financial business multichannel scheduling scenario, in particular, to a task scheduling method, device, equipment and storage medium thereof.
Background
With the rapid development of the internet, various industries seek industry breakthrough points by relying on the internet, and in recent years, the financial industry is expanding online business around the internet. Because the financial industry involves a large amount of business and data, with the increasing demand of users for products, financial products are continuously updated in category and created in business channels.
For example, different marketing channels, on-line self-service channels, agent handling channels, off-line handling channels, or different task handling channels, such as mail sending channels, short message sending channels, telephone voice channels, etc. when making service notifications. Thus, there is a need to implement the distribution of different task processing requests into different task channels in conjunction with a task scheduling framework. The current distribution method generally distributes different task processing requests to different marketing channels according to the attribute, the label and other information of the user, so as to improve the conversion rate and reduce the cost. However, this distribution method is not scientific and intelligent enough, and task scheduling cannot be performed scientifically and intelligently.
Disclosure of Invention
An object of the embodiments of the present application is to provide a task scheduling method, apparatus, device, and storage medium thereof, so as to solve the problem that in the prior art, different task processing requests are distributed to different marketing channels, and a distribution manner cannot scientifically and intelligently perform task scheduling.
In order to solve the above technical problems, the embodiments of the present application provide a task scheduling method, which adopts the following technical schemes:
a task scheduling method, comprising the steps of:
receiving a task processing request sent by a target user terminal;
analyzing real-time state information of all task channels according to a preset task channel monitoring analysis model and distinguishing identification information of all task channels;
screening all optional task channels based on the real-time state information;
analyzing the task processing request to acquire attribute information and label information of the target user;
inputting the attribute information and the label information of the target user into a preset task channel screening model, and determining an optimal task channel from all the selectable task channels according to the output result of the task channel screening model, wherein the preset task channel screening model is a learned task channel screening model, and the learned task channel screening model successfully learns the association relation between all the task channels and the attribute information and the label information of the user respectively;
identifying the distinguishing identification information of the optimal task channel, and pushing the task processing request to the optimal task channel for task processing through a preset task scheduling component and the distinguishing identification information of the optimal task channel.
Further, the step of analyzing real-time status information of all task channels according to the preset task channel monitoring analysis model and the distinguishing identification information of all task channels specifically includes:
counting the amount of the tasks being processed and the amount of the tasks in a waiting queuing state in all task channels at the current time point through the distinguishing identification information;
calculating the sum of the task quantity being processed and the task quantity in a waiting queuing state in each task channel;
according to a preset proportional algorithm formula:calculating the real-time usable proportion corresponding to all task channels, wherein a i A sum value representing the amount of tasks currently being processed and the amount of tasks waiting to be queued in the task channel i The maximum value of the sum value of the task quantity which is allowed in the current task channel and is in the waiting queuing state is represented, i represents the distinguishing identification information of the current task channel;
respectively acquiring real-time feedback information of all users in all task channels, wherein the real-time feedback information comprises task processing efficiency scoring information and task processing result feedback information;
and taking the real-time usable proportion, the task processing efficiency scoring information and the task processing result feedback information corresponding to all the task channels as comprehensive analysis indexes, inputting the comprehensive analysis indexes into the task channel monitoring analysis model for comprehensive analysis, and identifying the real-time state information of all the task channels according to the comprehensive analysis results of the task channel monitoring analysis model.
Further, the step of inputting the real-time usable proportion, the task processing efficiency scoring information and the task processing result feedback information corresponding to all the task channels as comprehensive analysis indexes to the task channel monitoring analysis model for comprehensive analysis, and identifying the real-time state information of all the task channels according to the comprehensive analysis result of the task channel monitoring analysis model specifically includes:
step 401, performing numeric processing on real-time usable proportion, task processing efficiency scoring information and task processing result feedback information corresponding to all task channels to obtain numeric processing results;
step 402, acquiring analysis weights respectively corresponding to real-time usable proportion, task processing efficiency scoring information and task processing result feedback information which are preset in the task channel monitoring analysis model;
step 403, according to the distinguishing identification information of all task channels, sequentially taking different task channels in all task channels as current task channels;
step 404, according to the analysis weights respectively corresponding to the real-time usable proportion, the task processing efficiency scoring information and the task processing result feedback information and the numerical processing results corresponding to the current task channel, accumulating and summing to obtain an accumulated and summed result as a comprehensive analysis result of the current task channel;
Step 405, identifying real-time state information of a current task channel according to a preset state identification strategy and the comprehensive analysis result, wherein the preset state identification strategy is to compare the comprehensive analysis result as a state characterization value with a preset analysis threshold value, and if the comprehensive analysis result is larger than the analysis threshold value, the real-time state information of the current task channel is better;
step 406, repeatedly executing step 403 to step 405, and identifying the real-time status information of all task channels.
Further, the step of screening all the selectable task channels based on the real-time status information specifically includes:
sequencing the comprehensive analysis results respectively corresponding to all the task channels by adopting a sequencing method from large to small to obtain a first sequencing sequence;
screening all comprehensive analysis results greater than the analysis threshold from the first ordered sequence by comparison;
and identifying the task channels respectively corresponding to all the comprehensive analysis results as all the selectable task channels.
Further, before the step of inputting the attribute information and the tag information of the target user into a preset task channel screening model and determining an optimal task channel from all the selectable task channels according to the output result of the task channel screening model, the method further includes:
Identifying task channels distributed by all users of a history according to a preset history distribution record assembly;
acquiring attribute information and label information of all historical users according to a preset acquisition component;
the task channels distributed by all the historical users, the attribute information and the label information of all the historical users are used as learning knowledge to be input into a task channel screening model to be learned, and the association relation between all the task channels and the attribute information and the label information of the users is learned in a machine learning mode;
and obtaining a learned task channel screening model as the preset task channel screening model.
Further, the step of inputting the task channels distributed by all the historical users, the attribute information and the label information of all the historical users as learning knowledge into a task channel screening model to be learned, and learning association relations between all the task channels and the attribute information and the label information of the users respectively in a machine learning mode specifically includes:
acquiring a Bayesian classifier constructed by taking all task channels as classification results in advance;
deploying the Bayesian classifier into the task channel screening model to be learned;
Taking the task channels distributed by all the historical users, the attribute information and the label information of all the historical users as priori knowledge, and carrying out classification probability learning on the Bayesian classifier to learn classification probability values corresponding to different attribute information and label information in different task channels respectively;
and taking the classification probability values corresponding to different attribute information and label information in different task channels as the association relations between all task channels and the attribute information and label information of the user respectively.
Further, the step of inputting the attribute information and the tag information of the target user into a preset task channel screening model, and determining an optimal task channel from all the selectable task channels according to an output result of the task channel screening model specifically includes:
screening the expected task channels with target quantity from the preset task channel screening model according to the attribute information and the label information of the target user and the association relation between all task channels and the attribute information and the label information of the user respectively;
performing association relation sorting from strong to weak on the target number of expected task channels to obtain a second sorting sequence as an output result of the task channel screening model;
Identifying whether intersection relations exist between all the selectable task channels and the target number of expected task channels or not through comparison;
if the intersection relation between all the selectable task channels and the target number of expected task channels does not exist, selecting the selectable task channel with the highest ranking from all the selectable task channels according to the first sequencing sequence as the optimal task channel;
if the intersection relation exists between all the selectable task channels and the target number of expected task channels, identifying whether the intersection result only contains a unique task channel or not;
if the intersection result only contains a unique task channel, directly taking the unique task channel as the optimal task channel;
and if the intersection result comprises a plurality of task channels, screening a task channel with the highest ranking from the plurality of task channels according to the second ordering sequence to serve as the optimal task channel.
In order to solve the above technical problems, the embodiments of the present application further provide a task scheduling device, which adopts the following technical scheme:
a task scheduling device, comprising:
the task processing request receiving module is used for receiving a task processing request sent by a target user terminal;
The real-time state information analysis module is used for analyzing the real-time state information of all the task channels according to a preset task channel monitoring analysis model and the distinguishing identification information of all the task channels;
the optional task channel screening module is used for screening all optional task channels based on the real-time state information;
the task processing request analysis module is used for analyzing the task processing request and acquiring attribute information and label information of the target user;
the optimal task channel determining module is used for inputting the attribute information and the label information of the target user into a preset task channel screening model, and determining an optimal task channel from all the selectable task channels according to the output result of the task channel screening model, wherein the preset task channel screening model is a learned task channel screening model, and the learned task channel screening model successfully learns the association relation between all task channels and the attribute information and the label information of the user respectively;
the task processing request scheduling module is used for identifying the distinguishing identification information of the optimal task channel and pushing the task processing request to the optimal task channel for task processing through a preset task scheduling component and the distinguishing identification information of the optimal task channel.
In order to solve the above technical problems, the embodiments of the present application further provide a computer device, which adopts the following technical schemes:
a computer device comprising a memory having stored therein computer readable instructions which when executed by a processor implement the steps of the task scheduling method described above.
In order to solve the above technical problems, embodiments of the present application further provide a computer readable storage medium, which adopts the following technical solutions:
a computer readable storage medium having stored thereon computer readable instructions which when executed by a processor implement the steps of a task scheduling method as described above.
Compared with the prior art, the embodiment of the application has the following main beneficial effects:
according to the task scheduling method, a task processing request sent by a target user terminal is received; analyzing real-time state information of all task channels according to a preset task channel monitoring analysis model and distinguishing identification information of all task channels; screening all optional task channels based on the real-time state information; analyzing the task processing request to acquire attribute information and label information of the target user; inputting the attribute information and the label information of the target user into a preset task channel screening model, and determining an optimal task channel from all the selectable task channels according to the output result of the task channel screening model; identifying the distinguishing identification information of the optimal task channel, and pushing the task processing request to the optimal task channel for task processing through a preset task scheduling component and the distinguishing identification information of the optimal task channel. The method comprises the steps of obtaining real-time state information of all task channels, carrying out primary screening of the task channels according to real-time feedback information of a user, removing all task channels in a processing saturation state, screening out all optional task channels, then selecting an optimal task channel from all optional task channels in real time according to attribute information and label information of a target user, and pushing a task processing request to the optimal task channel for task processing, so that task scheduling is facilitated scientifically and intelligently.
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For a clearer description of the solution in the present application, a brief description will be given below of the drawings that are needed in the description of the embodiments of the present application, it being obvious that the drawings in the following description are some embodiments of the present application, and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;
FIG. 2 is a flow chart of one embodiment of a task scheduling method according to the present application;
FIG. 3 is a flow chart of one embodiment of step 202 of FIG. 2;
FIG. 4 is a flow chart of one embodiment of step 305 shown in FIG. 3;
FIG. 5 is a flow chart of one embodiment of step 205 of FIG. 2;
FIG. 6 is a schematic diagram of a structure of one embodiment of a task scheduler according to the present application;
FIG. 7 is a schematic structural diagram of one embodiment of a computer device according to the present application.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the applications herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description and claims of the present application and in the description of the figures above are intended to cover non-exclusive inclusions. The terms first, second and the like in the description and in the claims or in the above-described figures, are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
In order to better understand the technical solutions of the present application, the following description will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings.
As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as a web browser application, a shopping class application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc., may be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablet computers, electronic book readers, MP3 players (Moving Picture ExpertsGroup Audio Layer III, dynamic video expert compression standard audio plane 3), MP4 (Moving PictureExperts Group Audio Layer IV, dynamic video expert compression standard audio plane 4) players, laptop and desktop computers, and the like.
The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.
It should be noted that, the task scheduling method provided in the embodiments of the present application is generally executed by a server/terminal device, and accordingly, the task scheduling device is generally disposed in the server/terminal device.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continued reference to FIG. 2, a flow chart of one embodiment of a task scheduling method according to the present application is shown. The task scheduling method comprises the following steps:
Step 201, receiving a task processing request sent by a target user terminal, wherein the task processing request contains attribute information and tag information of a target user.
In this embodiment, the target user terminal includes a financial service user terminal, and the task processing request includes a financial service processing request, for example, a insurance transaction request, an insurance claim settlement request, a credit card opening request, and the like.
Step 202, analyzing real-time state information of all task channels according to a preset task channel monitoring analysis model and distinguishing identification information of all task channels.
With continued reference to FIG. 3, FIG. 3 is a flow chart of one embodiment of step 202 shown in FIG. 2, comprising:
step 301, counting the task quantity in process and the task quantity in waiting queuing state in all task channels at the current time point through the distinguishing identification information;
step 302, calculating the sum of the task quantity being processed and the task quantity in a waiting queuing state in each task channel;
step 303, according to a preset proportional algorithm formula:calculating the real-time usable proportion corresponding to all task channels, wherein a i A sum value representing the amount of tasks currently being processed and the amount of tasks waiting to be queued in the task channel i The maximum value of the sum value of the task quantity which is allowed in the current task channel and is in the waiting queuing state is represented, i represents the distinguishing identification information of the current task channel;
the sum value of the task quantity being processed and the task quantity in the waiting queuing state in each task channel is calculated by counting the task quantity being processed and the task quantity in the waiting queuing state in all the task channels at the current time point, and the real-time usable proportion corresponding to all the task channels is calculated according to a preset proportion algorithm formula, so that the task processing request is prevented from being sent to the channel in the busy processing state, and the task scheduling is guaranteed to be scientifically and intelligently carried out.
Step 304, respectively acquiring real-time feedback information of all users in all task channels, wherein the real-time feedback information comprises task processing efficiency scoring information and task processing result feedback information;
and 305, inputting real-time usable proportion, task processing efficiency scoring information and task processing result feedback information corresponding to all task channels into the task channel monitoring analysis model as comprehensive analysis indexes to perform comprehensive analysis, and identifying real-time state information of all task channels according to the comprehensive analysis results of the task channel monitoring analysis model.
In this embodiment, the all task channels may refer to different marketing channels, for example, an online self-service channel, an agent service channel, and an offline service channel during a insurance service, or may refer to different processing channels, for example, a mail sending channel, a short message sending channel, a phone voice channel during a service notification, and so on.
With continued reference to fig. 4, fig. 4 is a flow chart of one embodiment of step 305 shown in fig. 3, comprising:
step 401, performing numeric processing on real-time usable proportion, task processing efficiency scoring information and task processing result feedback information corresponding to all task channels to obtain numeric processing results;
step 402, acquiring analysis weights respectively corresponding to real-time usable proportion, task processing efficiency scoring information and task processing result feedback information which are preset in the task channel monitoring analysis model;
step 403, according to the distinguishing identification information of all task channels, sequentially taking different task channels in all task channels as current task channels;
step 404, according to the analysis weights respectively corresponding to the real-time usable proportion, the task processing efficiency scoring information and the task processing result feedback information and the numerical processing results corresponding to the current task channel, accumulating and summing to obtain an accumulated and summed result as a comprehensive analysis result of the current task channel;
Step 405, identifying real-time state information of a current task channel according to a preset state identification strategy and the comprehensive analysis result, wherein the preset state identification strategy is to compare the comprehensive analysis result as a state characterization value with a preset analysis threshold value, and if the comprehensive analysis result is larger than the analysis threshold value, the real-time state information of the current task channel is better;
step 406, repeatedly executing step 403 to step 405, and identifying the real-time status information of all task channels.
The real-time state information of all the task channels is identified according to the comprehensive analysis result of the task channel monitoring analysis model by taking the real-time usable proportion, the task processing efficiency scoring information and the task processing result feedback information corresponding to all the task channels as comprehensive analysis indexes and inputting the comprehensive analysis indexes into the task channel monitoring analysis model, so that a target user terminal can be further helped to screen out the task channels with high real-time usable proportion, high task processing efficiency and satisfactory task processing results, and scientific and intelligent task scheduling is ensured.
And step 203, screening out all optional task channels based on the real-time state information.
In this embodiment, the step of screening all the selectable task channels based on the real-time status information specifically includes: sequencing the comprehensive analysis results respectively corresponding to all the task channels by adopting a sequencing method from large to small to obtain a first sequencing sequence; screening all comprehensive analysis results greater than the analysis threshold from the first ordered sequence by comparison; and identifying the task channels respectively corresponding to all the comprehensive analysis results as all the selectable task channels.
And screening all the optional task channels based on the real-time state information, so that the optimal task channels can be conveniently screened out from all the optional task channels, and the optimal task channels are screened out by adopting secondary screening, thereby ensuring scientific and intelligent task scheduling.
And 204, analyzing the task processing request to acquire attribute information and label information of the target user.
Step 205, inputting the attribute information and the label information of the target user into a preset task channel screening model, and determining an optimal task channel from all the selectable task channels according to the output result of the task channel screening model, wherein the preset task channel screening model is a learned task channel screening model, and the learned task channel screening model successfully learns the association relation between all the task channels and the attribute information and the label information of the user respectively.
In this embodiment, before the step of inputting the attribute information and the tag information of the target user into a preset task channel filtering model and determining an optimal task channel from all the selectable task channels according to an output result of the task channel filtering model, the method further includes: identifying task channels distributed by all users of a history according to a preset history distribution record assembly; acquiring attribute information and label information of all historical users according to a preset acquisition component; the task channels distributed by all the historical users, the attribute information and the label information of all the historical users are used as learning knowledge to be input into a task channel screening model to be learned, and the association relation between all the task channels and the attribute information and the label information of the users is learned in a machine learning mode; and obtaining a learned task channel screening model as the preset task channel screening model.
In this embodiment, the step of inputting the task channels distributed by all the historical users, the attribute information and the tag information of all the historical users as learning knowledge into a task channel filtering model to be learned, and learning association relations between all the task channels and the attribute information and the tag information of the users respectively in a machine learning manner specifically includes: acquiring a Bayesian classifier constructed by taking all task channels as classification results in advance; deploying the Bayesian classifier into the task channel screening model to be learned; taking the task channels distributed by all the historical users, the attribute information and the label information of all the historical users as priori knowledge, and carrying out classification probability learning on the Bayesian classifier to learn classification probability values corresponding to different attribute information and label information in different task channels respectively; and taking the classification probability values corresponding to different attribute information and label information in different task channels as the association relations between all task channels and the attribute information and label information of the user respectively.
Because the task channels distributed by all historical users and the attribute information and the label information of all historical users are known, a Bayesian classifier can be constructed, classification probability values corresponding to different attribute information and label information in different task channels are learned through classification probability learning of the Bayesian classifier, the classification probability values corresponding to different attribute information and label information in different task channels are used as the association relations between all task channels and the attribute information and the label information of the users respectively, so that when a task processing request is carried out by a target user subsequently, the expected task channels with target quantity can be screened out directly according to the attribute information and the label information of the target user, and scientific and intelligent task scheduling is ensured.
With continued reference to fig. 5, fig. 5 is a flow chart of one embodiment of step 205 shown in fig. 2, comprising:
step 501, screening a target number of expected task channels from the preset task channel screening model according to the attribute information and the label information of the target user and the association relation between all task channels and the attribute information and the label information of the user respectively;
Step 502, performing association relation from strong to weak sorting on the target number of expected task channels, and obtaining a second sorting sequence as an output result of the task channel screening model;
step 503, identifying whether intersection relationships exist between the all selectable task channels and the target number of expected task channels through comparison;
step 504, if the intersection relationship between all the selectable task channels and the target number of expected task channels does not exist, selecting a task channel with the highest ranking from all the selectable task channels according to the first ordering sequence as the optimal task channel;
step 505, if the intersection relationship exists between all the selectable task channels and the target number of expected task channels, identifying whether the intersection result only includes a unique task channel;
step 506, if the intersection result only includes a unique task channel, directly using the unique task channel as the optimal task channel;
step 507, if the intersection result includes a plurality of task channels, selecting a task channel with the highest ranking from the plurality of task channels according to the second ordering sequence as the optimal task channel.
By comparing, whether the intersection relation exists between all the selectable task channels and the expected task channels with the target number is identified, and the optimal task channels are screened out according to the intersection result, so that the task scheduling is ensured to be scientifically and intelligently carried out.
Step 206, identifying the distinguishing identification information of the optimal task channel, and pushing the task processing request to the optimal task channel for task processing through the distinguishing identification information of the preset task scheduling component and the optimal task channel.
The method comprises the steps of obtaining real-time state information of all task channels, carrying out task channel primary screening according to real-time feedback information of a user, removing all task channels in a processing saturation state, screening out all optional task channels, screening out target number of expected task channels from a preset task channel screening model according to attribute information and label information of a target user and association relations between all task channels and the attribute information and label information of the user respectively, and carrying out task processing by pushing a task processing request to the optimal task channels in real time by combining all the optional task channels and the target number of expected task channels.
The method comprises the steps of receiving a task processing request sent by a target user terminal; analyzing real-time state information of all task channels according to a preset task channel monitoring analysis model and distinguishing identification information of all task channels; screening all optional task channels based on the real-time state information; analyzing the task processing request to acquire attribute information and label information of the target user; inputting the attribute information and the label information of the target user into a preset task channel screening model, and determining an optimal task channel from all the selectable task channels according to the output result of the task channel screening model; identifying the distinguishing identification information of the optimal task channel, and pushing the task processing request to the optimal task channel for task processing through a preset task scheduling component and the distinguishing identification information of the optimal task channel. The method comprises the steps of obtaining real-time state information of all task channels, carrying out primary screening of the task channels according to real-time feedback information of a user, removing all task channels in a processing saturation state, screening out all optional task channels, then selecting an optimal task channel from all optional task channels in real time according to attribute information and label information of a target user, and pushing a task processing request to the optimal task channel for task processing, so that task scheduling is facilitated scientifically and intelligently.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, large task scheduling technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
In the embodiment of the application, by acquiring the real-time state information of all task channels, performing primary screening of the task channels according to the real-time feedback information of the user, removing all task channels in a processing saturation state, screening all optional task channels, and then screening out target number of expected task channels from the preset task channel screening model according to the attribute information and the label information of the target user and the association relation between the attribute information and the label information of all task channels respectively, combining all the optional task channels and the target number of expected task channels, selecting an optimal task channel in real time, pushing a task processing request to the optimal task channel for task processing, and facilitating scientific and intelligent task scheduling.
With further reference to fig. 6, as an implementation of the method shown in fig. 2, the present application provides an embodiment of a task scheduling device, where an embodiment of the device corresponds to the embodiment of the method shown in fig. 2, and the device may be specifically applied to various electronic devices.
As shown in fig. 6, the task scheduling device 600 according to the present embodiment includes: the system comprises a task processing request receiving module 601, a real-time state information analyzing module 602, an optional task channel screening module 603, a task processing request analyzing module 604, an optimal task channel determining module 605 and a task processing request scheduling module 606. Wherein:
a task processing request receiving module 601, configured to receive a task processing request sent by a target user terminal, where the task processing request includes attribute information and tag information of a target user;
the real-time state information analysis module 602 is configured to analyze real-time state information of all task channels according to a preset task channel monitoring analysis model and distinguishing identification information of all task channels;
the optional task channel screening module 603 is configured to screen all optional task channels based on the real-time status information;
a task processing request parsing module 604, configured to parse the task processing request, and obtain attribute information and tag information of the target user;
The optimal task channel determining module 605 is configured to input attribute information and tag information of the target user into a preset task channel screening model, and determine an optimal task channel from all the selectable task channels according to an output result of the task channel screening model, where the preset task channel screening model is a learned task channel screening model, and the learned task channel screening model successfully learns association relations between all task channels and attribute information and tag information of the user, respectively;
the task processing request scheduling module 606 is configured to identify the distinguishing identifier information of the optimal task channel, and push the task processing request to the optimal task channel for task processing through a preset task scheduling component and the distinguishing identifier information of the optimal task channel.
In some embodiments of the present application, the optimal task channel determining module 605 includes a desired task channel screening sub-module, a desired task channel sorting sub-module, a first judgment and identification sub-module, an optimal task channel first screening sub-module, a second judgment and identification sub-module, an optimal task channel second screening sub-module, and an optimal task channel third screening sub-module. Wherein:
The expected task channel screening sub-module is used for screening the expected task channels with target quantity from the preset task channel screening model according to the attribute information and the label information of the target user and the association relation between all task channels and the attribute information and the label information of the user respectively;
the expected task channel sorting sub-module is used for sorting the association relation of the expected task channels of the target number from strong to weak, and obtaining a second sorting sequence as an output result of the task channel screening model;
the first judging and identifying sub-module is used for identifying whether intersection relations exist between all the selectable task channels and the target number of expected task channels or not through comparison;
the first screening sub-module of the optimal task channel is used for screening the task channel which is the most top ranking and is selected from all the task channels according to the first sequencing sequence as the optimal task channel if the intersection relation between all the task channels which are selected and the target number of expected task channels does not exist;
the second judging and identifying sub-module is used for identifying whether the intersection result only contains a unique task channel or not if the intersection relation exists between all the selectable task channels and the expected task channels of the target number;
The optimal task channel second screening sub-module is used for directly taking the unique task channel as the optimal task channel if the intersection result only contains the unique task channel;
and the third screening sub-module of the optimal task channel is used for screening the task channel with the highest ranking from the plurality of task channels according to the second sequencing sequence to serve as the optimal task channel if the intersection result contains the plurality of task channels.
The method comprises the steps of receiving a task processing request sent by a target user terminal; analyzing real-time state information of all task channels according to a preset task channel monitoring analysis model and distinguishing identification information of all task channels; screening all optional task channels based on the real-time state information; analyzing the task processing request to acquire attribute information and label information of the target user; inputting the attribute information and the label information of the target user into a preset task channel screening model, and determining an optimal task channel from all the selectable task channels according to the output result of the task channel screening model; identifying the distinguishing identification information of the optimal task channel, and pushing the task processing request to the optimal task channel for task processing through a preset task scheduling component and the distinguishing identification information of the optimal task channel. The method comprises the steps of obtaining real-time state information of all task channels, carrying out primary screening of the task channels according to real-time feedback information of a user, removing all task channels in a processing saturation state, screening out all optional task channels, then selecting an optimal task channel from all optional task channels in real time according to attribute information and label information of a target user, and pushing a task processing request to the optimal task channel for task processing, so that task scheduling is facilitated scientifically and intelligently.
Those skilled in the art will appreciate that implementing all or part of the above described embodiment methods may be accomplished by computer readable instructions, stored on a computer readable storage medium, that the program when executed may comprise the steps of embodiments of the methods described above. The storage medium may be a nonvolatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a random access Memory (Random Access Memory, RAM).
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.
In order to solve the technical problems, the embodiment of the application also provides computer equipment. Referring specifically to fig. 7, fig. 7 is a basic structural block diagram of a computer device according to the present embodiment.
The computer device 7 comprises a memory 7a, a processor 7b, a network interface 7c communicatively connected to each other via a system bus. It should be noted that only a computer device 7 having components 7a-7c is shown in the figures, but it should be understood that not all of the illustrated components need be implemented, and that more or fewer components may alternatively be implemented. It will be appreciated by those skilled in the art that the computer device herein is a device capable of automatically performing numerical calculations and/or information processing in accordance with predetermined or stored instructions, the hardware of which includes, but is not limited to, microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASICs), programmable gate arrays (fields-Programmable Gate Array, FPGAs), digital processors (Digital Signal Processor, DSPs), embedded devices, etc.
The computer equipment can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing equipment. The computer equipment can perform man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad or voice control equipment and the like.
The memory 7a includes at least one type of readable storage medium including flash memory, hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), programmable Read Only Memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the storage 7a may be an internal storage unit of the computer device 7, such as a hard disk or a memory of the computer device 7. In other embodiments, the memory 7a may also be an external storage device of the computer device 7, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the computer device 7. Of course, the memory 7a may also comprise both an internal memory unit of the computer device 7 and an external memory device. In this embodiment, the memory 7a is typically used to store an operating system and various application software installed on the computer device 7, such as computer readable instructions of a task scheduling method. Further, the memory 7a may be used to temporarily store various types of data that have been output or are to be output.
The processor 7b may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other task scheduling chip in some embodiments. The processor 7b is typically used to control the overall operation of the computer device 7. In this embodiment, the processor 7b is configured to execute computer readable instructions stored in the memory 7a or process data, such as computer readable instructions for executing the task scheduling method.
The network interface 7c may comprise a wireless network interface or a wired network interface, which network interface 7c is typically used for establishing a communication connection between the computer device 7 and other electronic devices.
The computer equipment provided by the embodiment belongs to the technical field of financial science and technology, and is applied to a financial business multichannel scheduling scene. The method comprises the steps of receiving a task processing request sent by a target user terminal; analyzing real-time state information of all task channels according to a preset task channel monitoring analysis model and distinguishing identification information of all task channels; screening all optional task channels based on the real-time state information; analyzing the task processing request to acquire attribute information and label information of the target user; inputting the attribute information and the label information of the target user into a preset task channel screening model, and determining an optimal task channel from all the selectable task channels according to the output result of the task channel screening model; identifying the distinguishing identification information of the optimal task channel, and pushing the task processing request to the optimal task channel for task processing through a preset task scheduling component and the distinguishing identification information of the optimal task channel. The method comprises the steps of obtaining real-time state information of all task channels, carrying out primary screening of the task channels according to real-time feedback information of a user, removing all task channels in a processing saturation state, screening out all optional task channels, then selecting an optimal task channel from all optional task channels in real time according to attribute information and label information of a target user, and pushing a task processing request to the optimal task channel for task processing, so that task scheduling is facilitated scientifically and intelligently.
The present application also provides another embodiment, namely, a computer readable storage medium storing computer readable instructions executable by a processor to cause the processor to perform the steps of the task scheduling method as described above.
The computer readable storage medium provided by the embodiment belongs to the technical field of financial science and technology, and is applied to a financial business multichannel scheduling scene. The method comprises the steps of receiving a task processing request sent by a target user terminal; analyzing real-time state information of all task channels according to a preset task channel monitoring analysis model and distinguishing identification information of all task channels; screening all optional task channels based on the real-time state information; analyzing the task processing request to acquire attribute information and label information of the target user; inputting the attribute information and the label information of the target user into a preset task channel screening model, and determining an optimal task channel from all the selectable task channels according to the output result of the task channel screening model; identifying the distinguishing identification information of the optimal task channel, and pushing the task processing request to the optimal task channel for task processing through a preset task scheduling component and the distinguishing identification information of the optimal task channel. The method comprises the steps of obtaining real-time state information of all task channels, carrying out primary screening of the task channels according to real-time feedback information of a user, removing all task channels in a processing saturation state, screening out all optional task channels, then selecting an optimal task channel from all optional task channels in real time according to attribute information and label information of a target user, and pushing a task processing request to the optimal task channel for task processing, so that task scheduling is facilitated scientifically and intelligently.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk), comprising several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method described in the embodiments of the present application.
It is apparent that the embodiments described above are only some embodiments of the present application, but not all embodiments, the preferred embodiments of the present application are given in the drawings, but not limiting the patent scope of the present application. This application may be embodied in many different forms, but rather, embodiments are provided in order to provide a more thorough understanding of the present disclosure. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described in the foregoing, or equivalents may be substituted for elements thereof. All equivalent structures made by the specification and the drawings of the application are directly or indirectly applied to other related technical fields, and are also within the protection scope of the application.
Claims (10)
1. A task scheduling method, characterized by comprising the steps of:
receiving a task processing request sent by a target user terminal;
analyzing real-time state information of all task channels according to a preset task channel monitoring analysis model and distinguishing identification information of all task channels;
screening all optional task channels based on the real-time state information;
analyzing the task processing request to acquire attribute information and label information of the target user;
inputting the attribute information and the label information of the target user into a preset task channel screening model, and determining an optimal task channel from all the selectable task channels according to the output result of the task channel screening model, wherein the preset task channel screening model is a learned task channel screening model, and the learned task channel screening model successfully learns the association relation between all the task channels and the attribute information and the label information of the user respectively;
identifying the distinguishing identification information of the optimal task channel, and pushing the task processing request to the optimal task channel for task processing through a preset task scheduling component and the distinguishing identification information of the optimal task channel.
2. The task scheduling method according to claim 1, wherein the step of analyzing real-time status information of all task channels according to a preset task channel monitoring analysis model and distinguishing identification information of all task channels specifically includes:
counting the amount of the tasks being processed and the amount of the tasks in a waiting queuing state in all task channels at the current time point through the distinguishing identification information;
calculating the sum of the task quantity being processed and the task quantity in a waiting queuing state in each task channel;
according to a preset proportional algorithm formula:calculating the real-time usable proportion corresponding to all task channels, wherein a i A sum value representing the amount of tasks currently being processed and the amount of tasks waiting to be queued in the task channel i The maximum value of the sum value of the task quantity which is allowed in the current task channel and is in the waiting queuing state is represented, i represents the distinguishing identification information of the current task channel;
respectively acquiring real-time feedback information of all users in all task channels, wherein the real-time feedback information comprises task processing efficiency scoring information and task processing result feedback information;
And taking the real-time usable proportion, the task processing efficiency scoring information and the task processing result feedback information corresponding to all the task channels as comprehensive analysis indexes, inputting the comprehensive analysis indexes into the task channel monitoring analysis model for comprehensive analysis, and identifying the real-time state information of all the task channels according to the comprehensive analysis results of the task channel monitoring analysis model.
3. The task scheduling method according to claim 2, wherein the step of inputting the real-time usable proportion, the task processing efficiency scoring information and the task processing result feedback information corresponding to all the task channels as comprehensive analysis indexes to the task channel monitoring analysis model for comprehensive analysis, and identifying the real-time status information of all the task channels according to the comprehensive analysis result of the task channel monitoring analysis model specifically comprises:
step 401, performing numeric processing on real-time usable proportion, task processing efficiency scoring information and task processing result feedback information corresponding to all task channels to obtain numeric processing results;
step 402, acquiring analysis weights respectively corresponding to real-time usable proportion, task processing efficiency scoring information and task processing result feedback information which are preset in the task channel monitoring analysis model;
Step 403, according to the distinguishing identification information of all task channels, sequentially taking different task channels in all task channels as current task channels;
step 404, according to the analysis weights respectively corresponding to the real-time usable proportion, the task processing efficiency scoring information and the task processing result feedback information and the numerical processing results corresponding to the current task channel, accumulating and summing to obtain an accumulated and summed result as a comprehensive analysis result of the current task channel;
step 405, identifying real-time state information of a current task channel according to a preset state identification strategy and the comprehensive analysis result, wherein the preset state identification strategy is to compare the comprehensive analysis result as a state characterization value with a preset analysis threshold value, and if the comprehensive analysis result is larger than the analysis threshold value, the real-time state information of the current task channel is better;
step 406, repeatedly executing step 403 to step 405, and identifying the real-time status information of all task channels.
4. The task scheduling method according to claim 3, wherein the step of screening all the optional task channels based on the real-time status information specifically includes:
Sequencing the comprehensive analysis results respectively corresponding to all the task channels by adopting a sequencing method from large to small to obtain a first sequencing sequence;
screening all comprehensive analysis results greater than the analysis threshold from the first ordered sequence by comparison;
and identifying the task channels respectively corresponding to all the comprehensive analysis results as all the selectable task channels.
5. The task scheduling method according to claim 1, wherein before the step of inputting the attribute information and the tag information of the target user into a preset task channel filtering model and determining an optimal task channel from the all the selectable task channels according to an output result of the task channel filtering model, the method further comprises:
identifying task channels distributed by all users of a history according to a preset history distribution record assembly;
acquiring attribute information and label information of all historical users according to a preset acquisition component;
the task channels distributed by all the historical users, the attribute information and the label information of all the historical users are used as learning knowledge to be input into a task channel screening model to be learned, and the association relation between all the task channels and the attribute information and the label information of the users is learned in a machine learning mode;
And obtaining a learned task channel screening model as the preset task channel screening model.
6. The task scheduling method according to claim 5, wherein the step of inputting the task channels distributed by all the history users, the attribute information and the tag information of all the history users as learning knowledge into a task channel filtering model to be learned, and learning association relations between all the task channels and the attribute information and the tag information of the users respectively by using a machine learning manner specifically comprises:
acquiring a Bayesian classifier constructed by taking all task channels as classification results in advance;
deploying the Bayesian classifier into the task channel screening model to be learned;
taking the task channels distributed by all the historical users, the attribute information and the label information of all the historical users as priori knowledge, and carrying out classification probability learning on the Bayesian classifier to learn classification probability values corresponding to different attribute information and label information in different task channels respectively;
and taking the classification probability values corresponding to different attribute information and label information in different task channels as the association relations between all task channels and the attribute information and label information of the user respectively.
7. The task scheduling method according to claim 4, wherein the step of inputting the attribute information and the tag information of the target user into a preset task channel filtering model, and determining an optimal task channel from the all selectable task channels according to an output result of the task channel filtering model specifically includes:
screening the expected task channels with target quantity from the preset task channel screening model according to the attribute information and the label information of the target user and the association relation between all task channels and the attribute information and the label information of the user respectively;
performing association relation sorting from strong to weak on the target number of expected task channels to obtain a second sorting sequence as an output result of the task channel screening model;
identifying whether intersection relations exist between all the selectable task channels and the target number of expected task channels or not through comparison;
if the intersection relation between all the selectable task channels and the target number of expected task channels does not exist, selecting the selectable task channel with the highest ranking from all the selectable task channels according to the first sequencing sequence as the optimal task channel;
If the intersection relation exists between all the selectable task channels and the target number of expected task channels, identifying whether the intersection result only contains a unique task channel or not;
if the intersection result only contains a unique task channel, directly taking the unique task channel as the optimal task channel;
and if the intersection result comprises a plurality of task channels, screening a task channel with the highest ranking from the plurality of task channels according to the second ordering sequence to serve as the optimal task channel.
8. A task scheduling device, comprising:
the task processing request receiving module is used for receiving a task processing request sent by a target user terminal;
the real-time state information analysis module is used for analyzing the real-time state information of all the task channels according to a preset task channel monitoring analysis model and the distinguishing identification information of all the task channels;
the optional task channel screening module is used for screening all optional task channels based on the real-time state information;
the task processing request analysis module is used for analyzing the task processing request and acquiring attribute information and label information of the target user;
The optimal task channel determining module is used for inputting the attribute information and the label information of the target user into a preset task channel screening model, and determining an optimal task channel from all the selectable task channels according to the output result of the task channel screening model, wherein the preset task channel screening model is a learned task channel screening model, and the learned task channel screening model successfully learns the association relation between all task channels and the attribute information and the label information of the user respectively;
the task processing request scheduling module is used for identifying the distinguishing identification information of the optimal task channel and pushing the task processing request to the optimal task channel for task processing through a preset task scheduling component and the distinguishing identification information of the optimal task channel.
9. A computer device comprising a memory and a processor, the memory having stored therein computer readable instructions which when executed by the processor implement the steps of the task scheduling method of any one of claims 1 to 7.
10. A computer readable storage medium having stored thereon computer readable instructions which when executed by a processor implement the steps of the task scheduling method of any one of claims 1 to 7.
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