CN117252362A

CN117252362A - Scheduling method and device based on artificial intelligence, computer equipment and storage medium

Info

Publication number: CN117252362A
Application number: CN202311177511.6A
Authority: CN
Inventors: 田文艳
Original assignee: Ping An Health Insurance Company of China Ltd
Current assignee: Ping An Health Insurance Company of China Ltd
Priority date: 2023-09-12
Filing date: 2023-09-12
Publication date: 2023-12-19

Abstract

The application belongs to the field of artificial intelligence and the field of digital medical treatment, and relates to a scheduling method based on artificial intelligence, which comprises the following steps: if a service application request triggered by a target client is received, extracting client grade information and service demand information from the service application request; personnel matching is carried out on the client grade information and the service demand information based on the scheduling rule to obtain personnel matching results; if the personnel matching result is an empty result, a scheduling prediction model is called to perform scheduling prediction processing on the service demand information to obtain a scheduling prediction result; and executing personnel scheduling processing corresponding to the service application request based on the scheduling prediction result. The application also provides a scheduling device, computer equipment and a storage medium based on the artificial intelligence. In addition, the present application relates to blockchain techniques in which scheduling prediction results may be stored. The method and the device can be applied to personnel distribution scenes in the financial field, effectively improve the accuracy of service personnel distribution, and are favorable for improving customer satisfaction.

Description

Scheduling method and device based on artificial intelligence, computer equipment and storage medium

Technical Field

The present application relates to the field of artificial intelligence development and digital medical technology, and in particular, to an artificial intelligence-based scheduling method, apparatus, computer device, and storage medium.

Background

In the current assisted medical service scenario, in order to facilitate centralized management of clients, an enterprise generally collects client information and client service information, and establishes an information management client system, through which operations such as adding, viewing, recording or service personnel allocation are performed on the client information and the client service information. The traditional service personnel distribution processing mode is that customer information of customers is matched with personnel information to be distributed through a customer system, and based on experience through a manual screening mode, the service personnel are familiar with which hospitals, which service personnel have time, the service personnel are contacted by telephone, and the service personnel are manually arranged to serve patients, so that the efficiency of the service personnel distribution processing mode is low, the service effect is not guaranteed, and the customer satisfaction cannot be guaranteed: for example, there is no consideration as to whether the attendant is properly servicing the patient, such as a young, preferably homosexual, and strong, elderly person with constant mobility; whether the service quality is stable or not, for example, service personnel who are complained for many times should strengthen training and management and forbid service; whether the service cost is optimal, such as the service personnel being arranged nearby, etc., results in that the clients cannot be accurately allocated to the adapted service personnel, and the accuracy of the allocation of the service personnel is low.

Disclosure of Invention

An aim of the embodiment of the application is to provide a scheduling method, a scheduling device, computer equipment and a storage medium based on artificial intelligence, so as to solve the technical problems that the existing service personnel allocation processing mode is low in efficiency, the service effect is not guaranteed, clients cannot be accurately allocated to the adaptive service personnel, and the allocation accuracy of the service personnel is low.

In order to solve the above technical problems, the embodiments of the present application provide an artificial intelligence based scheduling method, which adopts the following technical scheme:

judging whether a service application request triggered by a target client is received or not; wherein, the service application request carries the client grade information and the service demand information of the target client;

if yes, extracting the client grade information and the service demand information from the service application request;

performing personnel matching processing on the client grade information and the service demand information based on a preset scheduling rule to obtain a corresponding personnel matching result;

judging whether the personnel matching result is an empty result or not;

if the personnel matching result is a null result, a preset scheduling prediction model is called to perform scheduling prediction processing on the service demand information, and a corresponding scheduling prediction result is obtained;

And executing personnel scheduling processing corresponding to the service application request based on the scheduling prediction result.

Further, the step of performing personnel matching processing on the client level information and the service requirement information based on a preset scheduling rule to obtain a corresponding personnel matching result specifically includes:

calling a preset rule engine;

executing the scheduling rule through the rule engine, and screening service personnel matched with the client grade information and the service demand information from a preset personnel pool;

acquiring the number of service personnel;

and if the number of the service personnel is 1, generating the personnel matching result based on the personnel information of the service personnel.

Further, after the step of obtaining the number of service personnel, the method further includes:

if the number of the service personnel comprises a plurality of service personnel, acquiring service type information from the service demand information;

acquiring operation attribute information of each service person;

performing prediction processing on the service type information and the job attribute information based on a preset processing prediction model, and generating estimated processing time length for each service person to complete the business service task corresponding to the service type information;

Data analysis is carried out on all the estimated processing time lengths, and target service personnel are determined from all the service personnel;

and generating the personnel matching result based on the personnel information of the target service personnel.

Further, the step of analyzing the data of all the estimated processing time periods and determining the target service person from all the service persons specifically includes:

comparing the values of all the estimated processing time periods, and screening out the appointed estimated processing time period with the smallest value from all the estimated processing time periods;

determining a designated service person corresponding to the designated estimated processing time length from all the service persons;

and taking the designated service personnel as the target service personnel.

Further, before the step of predicting the service type information and the job attribute information based on the preset processing prediction model to generate the estimated processing duration of each service person for completing the business service task corresponding to the service type information, the method further includes:

acquiring task sample data acquired in advance; the task sample data comprises service type information of a service task and job attribute information of a service person who completes the service task;

Calling a preset initial learning model;

training the initial learning model based on the task sample data to construct the process prediction model.

Further, before the step of calling a preset scheduling prediction model to perform scheduling prediction processing on the service demand information to obtain a corresponding scheduling prediction result, the method further includes:

acquiring historical scheduling sample data of predictive acquisition; the history scheduling sample data comprises service demand information of a history scheduling task, personnel identity information of history service personnel completing the history scheduling task, and satisfaction degree of the history service personnel completing the history scheduling task and the service demand information;

calling a preset initial neural network model;

training the initial neural network model based on the historical scheduling sample data to construct the scheduling prediction model.

Further, the step of executing the personnel scheduling process corresponding to the service application request based on the scheduling prediction result specifically includes:

determining a scheduling service personnel corresponding to the scheduling prediction result;

acquiring communication information of the dispatching service personnel;

Generating a target service task corresponding to the service application request;

and distributing the target service task to the dispatch service personnel based on the communication information.

In order to solve the above technical problems, the embodiments of the present application further provide an artificial intelligence-based scheduling device, which adopts the following technical scheme:

the first judging module is used for judging whether a service application request triggered by a target client is received or not; wherein, the service application request carries the client grade information and the service demand information of the target client;

the extraction module is used for extracting the client grade information and the service demand information from the service application request if yes;

the first processing module is used for carrying out personnel matching processing on the client grade information and the service demand information based on a preset scheduling rule to obtain a corresponding personnel matching result;

the second judging module is used for judging whether the personnel matching result is an empty result or not;

the second processing module is used for calling a preset scheduling prediction model to perform scheduling prediction processing on the service demand information if the personnel matching result is a null result, so as to obtain a corresponding scheduling prediction result;

And the third processing module is used for executing personnel scheduling processing corresponding to the service application request based on the scheduling prediction result.

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:

judging whether the personnel matching result is an empty result or not;

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:

judging whether the personnel matching result is an empty result or not;

Compared with the prior art, the embodiment of the application has the following main beneficial effects:

the embodiment of the application firstly judges whether a service application request triggered by a target client is received or not; if yes, extracting the client grade information and the service demand information from the service application request; then, carrying out personnel matching processing on the client grade information and the service demand information based on a preset scheduling rule to obtain a corresponding personnel matching result; subsequently judging whether the personnel matching result is an empty result or not; if the personnel matching result is a null result, a preset scheduling prediction model is called to perform scheduling prediction processing on the service demand information, and a corresponding scheduling prediction result is obtained; and finally, executing personnel scheduling processing corresponding to the service application request based on the scheduling prediction result. According to the intelligent scheduling distribution method based on the combination of the scheduling rules and the scheduling prediction model, the problem of expanding the service application request triggered by the target client into multi-dimensional decision is solved, and the service personnel scheduling processing of the service application request can be automatically and rapidly completed by carrying out personnel matching processing on the client grade information and the service demand information carried in the service application request, so that service personnel distribution is carried out according to the client information of the target client, the accuracy of service personnel distribution is effectively improved, and the customer satisfaction is improved.

Drawings

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 an artificial intelligence based scheduling method according to the present application;

FIG. 3 is a schematic diagram of one embodiment of an artificial intelligence based scheduler according to the present application;

FIG. 4 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 Experts Group Audio Layer III, dynamic video expert compression standard audio plane 3), MP4 (Moving Picture Experts 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 scheduling method based on artificial intelligence provided in the embodiments of the present application is generally executed by a server/terminal device, and accordingly, the scheduling device based on artificial intelligence is generally disposed in the server/terminal device.

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, big data processing 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.

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 an artificial intelligence based scheduling method according to the present application is shown. The order of the steps in the flowchart may be changed and some steps may be omitted according to various needs. The scheduling method based on the artificial intelligence can be applied to any scene needing personnel scheduling, and can be applied to products of the scenes, such as assisted medical scenes in the field of digital medical treatment. The scheduling method based on artificial intelligence comprises the following steps:

Step S201, determining whether a service application request triggered by the target client is received.

In this embodiment, the electronic device (for example, the server/terminal device shown in fig. 1) on which the scheduling method based on artificial intelligence operates may acquire the image to be checked through a wired connection manner or a wireless connection manner. It should be noted that the wireless connection may include, but is not limited to, 3G/4G/5G connection, wiFi connection, bluetooth connection, wiMAX connection, zigbee connection, UWB (ultra wideband) connection, and other now known or later developed wireless connection. The scheduling method based on artificial intelligence can be applied to application scenes for assisting medical treatment, and the service application request is a service application request which is triggered by a target client and is used for applying for service personnel to provide medical treatment assistance. The service application request carries the client grade information and the service demand information of the target client. The customer level information may include high-level, medium-level, and low-level. The service demand information may include a service type, a region, and may also include medical resources (direct resources, collaborative resources, etc.).

Step S202, if yes, extracting the client level information and the service requirement information from the service application request.

In this embodiment, the information extraction may be performed on the service application request to extract the client level information and the service requirement information from the service application request.

And step 203, performing personnel matching processing on the client level information and the service demand information based on a preset scheduling rule to obtain a corresponding personnel matching result.

In this embodiment, the foregoing specific implementation process of performing personnel matching processing on the client level information and the service requirement information based on the preset scheduling rule to obtain the corresponding personnel matching result will be described in further detail in the subsequent specific embodiments, which will not be described herein.

Step S204, judging whether the personnel matching result is an empty result.

In this embodiment, the above-described person matching result may include a null result or a result with matching person information. The number of matching personnel information may include one or more.

Step S205, if the personnel matching result is a null result, a preset scheduling prediction model is called to perform scheduling prediction processing on the service demand information, and a corresponding scheduling prediction result is obtained.

In this embodiment, the service demand information may be input into a scheduling prediction model, so that the scheduling prediction model performs scheduling prediction processing on the service demand information to obtain a corresponding scheduling prediction result. The training generation process of the scheduling prediction result will be described in further detail in the following embodiments, which will not be described herein.

And step S206, executing personnel scheduling processing corresponding to the service application request based on the scheduling prediction result.

In this embodiment, the above specific implementation procedure of performing the personnel scheduling process corresponding to the service application request based on the scheduling prediction result will be described in further detail in the following specific embodiments, which will not be described herein.

Firstly, judging whether a service application request triggered by a target client is received or not; if yes, extracting the client grade information and the service demand information from the service application request; then, carrying out personnel matching processing on the client grade information and the service demand information based on a preset scheduling rule to obtain a corresponding personnel matching result; subsequently judging whether the personnel matching result is an empty result or not; if the personnel matching result is a null result, a preset scheduling prediction model is called to perform scheduling prediction processing on the service demand information, and a corresponding scheduling prediction result is obtained; and finally, executing personnel scheduling processing corresponding to the service application request based on the scheduling prediction result. According to the intelligent scheduling distribution method based on the combination of the scheduling rules and the scheduling prediction model, the problem that the service application request triggered by the target client is expanded into multi-dimensional decisions is solved, and the service personnel scheduling processing of the service application request can be automatically and rapidly completed by carrying out personnel matching processing on the client grade information and the service demand information carried in the service application request, so that service personnel distribution is carried out according to the client information of the target client, the accuracy of service personnel distribution is effectively improved, and the customer satisfaction is improved.

In some alternative implementations, step S203 includes the steps of:

and calling a preset rule engine.

In this embodiment, the rule engine is a pre-built processing engine for providing flexible configuration according to personal needs or business needs for the operation and maintenance user.

And executing the scheduling rule through the rule engine, and screening service personnel matched with the client grade information and the service demand information from a preset personnel pool.

In this embodiment, in the business scenario for assisting in medical treatment, the personnel attribute information of a plurality of service personnel for providing the service for assisting in medical treatment is stored in the personnel pool. The scheduling rules are service scheduling rules matched according to different client types and service types. The setup dimension of the service dispatch rules may include the following dimensions, customer level; customer requirements: service type, region; medical resource: direct connection resources, cooperative resources, etc.; service personnel: class, capability, quality of service, etc. The content of the service scheduling rules may include: the general scheduling rule is that when a specific client type, a specific service area and a specific service type of a client are obtained, a service class needs to be assigned to a service personnel with a preset capacity and a price in a preset interval to reach a preset class. The service scheduling rule is internally configured with personnel attribute information of service personnel having a mapping relation with the client grade information and the service demand information, and by executing the scheduling rule, the personnel attribute information matched with the client grade information and the service demand information can be screened out from a preset personnel pool, and then the service personnel matched with the personnel attribute information can be screened out from the personnel pool. In addition, the preset level, the preset capability and the preset interval are not limited, and may be set according to actual service allocation requirements.

And acquiring the number of the service personnel.

In this embodiment, after the service personnel are obtained, the number of the service personnel may be counted.

In this embodiment, if the number of service personnel is 1, the personnel information of the service personnel is directly used as the personnel matching result.

The method comprises the steps of calling a preset rule engine; then executing the scheduling rule through the rule engine, and screening service personnel matched with the client grade information and the service demand information from a preset personnel pool; then, the number of the service personnel is obtained; and if the number of the service personnel is 1, generating the personnel matching result based on the personnel information of the service personnel. The method and the device execute the regulation rules based on the use of the rule engine, so that the customer grade information and the service demand information can be automatically and quickly subjected to personnel matching processing to obtain the corresponding personnel matching result, and the target customers can be subjected to service personnel distribution according to the personnel matching result, so that the service personnel distribution is carried out according to the customer information of the target customers, and the accuracy of the service personnel distribution is effectively improved.

In some optional implementations of this embodiment, after the step of obtaining the number of service personnel, the electronic device may further perform the following steps:

and if the number of the service personnel comprises a plurality of service personnel, acquiring service type information from the service demand information.

In this embodiment, the service type information may be extracted from the service by information extraction of the service requirement information. In a medical services scenario, the service type information may include a type of assisted medical care, a type of patient care, and so on.

And acquiring the operation attribute information of each service person.

In this embodiment, the job attribute information may include personal identification information of the service person, quality information of the service person completing the history service task, and time information of the service person completing the history service task. The personal identity information may include the name of the attendant, the field of tampering, the age of service.

And carrying out prediction processing on the service type information and the job attribute information based on a preset processing prediction model, and generating the estimated processing time length for each service person to complete the business service task corresponding to the service type information.

In this embodiment, the service type information and the job attribute information may be input into the processing prediction model, and the processing prediction model predicts the service type information and the job attribute information, so as to output the estimated processing duration of each service person for completing the business service task corresponding to the service type information. The foregoing process of generating the training prediction model will be described in further detail in the following embodiments, which will not be described herein.

And carrying out data analysis on all the estimated processing time lengths, and determining a target service person from all the service persons.

In this embodiment, the number of the target service personnel is 1. The data analysis is performed on all the estimated processing durations, and the specific implementation process of the target service person is determined from all the service persons, which will be described in further detail in the following specific embodiments, and will not be described herein.

In this embodiment, the person information of the target service person may be used as the person matching result.

If the number of the service personnel is detected to be multiple, acquiring service type information from the service demand information; then acquiring the operation attribute information of each service person; then, based on a preset processing prediction model, predicting the service type information and the operation attribute information to generate the estimated processing time length for each service person to complete the business service task corresponding to the service type information; subsequently, data analysis is carried out on all the estimated processing time lengths, and target service personnel are determined from all the service personnel; and finally, generating the personnel matching result based on the personnel information of the target service personnel. According to the method and the system, the regulation rules are executed based on the use of the rule engine, when the customer grade information and the service demand information are automatically and rapidly subjected to personnel matching processing to obtain corresponding service personnel, if the number of the service personnel is detected to comprise a plurality of service personnel, the service type information of the target customer and the operation attribute information of each service personnel are further subjected to prediction processing based on the use of the processing prediction model, the estimated processing time of each service personnel for completing the business service task corresponding to the service type information is generated, the data analysis is carried out on the estimated processing time, the target service personnel are determined in all the service personnel, the personnel matching result is generated, and the accuracy of the generated personnel matching result is improved. And the service personnel distribution is carried out on the target clients according to the personnel matching result, so that the service personnel distribution is carried out not only singly according to the client information of the target clients, and the accuracy of the service personnel distribution is effectively improved.

In some optional implementations, the data analysis is performed on all the estimated processing durations, and a target service person is determined from all the service persons, including the following steps:

and comparing the values of all the estimated processing time periods, and screening out the appointed estimated processing time period with the smallest value from all the estimated processing time periods.

In this embodiment, the value comparison may be performed on all the estimated processing durations, and then all the estimated processing durations are ranked in order from smaller value to larger value to obtain a ranking result, and then the first estimated processing duration ranked in the ranking result is used as the specified estimated processing duration.

And determining a designated service person corresponding to the designated estimated processing time length from all the service persons.

And taking the designated service personnel as the target service personnel.

The method comprises the steps of comparing the values of all the estimated processing time periods, and screening out the appointed estimated processing time period with the smallest value from all the estimated processing time periods; then determining a designated service person corresponding to the designated estimated processing time length from all the service persons; and taking the designated service personnel as the target service personnel. According to the method and the device, the estimated processing time length of each service person generated by the processing prediction model is compared in numerical value, and then the designated service person corresponding to the designated estimated processing time length with the minimum value is selected from the estimated processing time length to serve as the final target service person, so that the determination accuracy of the target service person is improved, the subsequent service is provided for the target customer service by the target service person, the service cost time can be effectively prolonged, and the service use experience of the target customer is further improved.

In some optional implementations, before the step of predicting the service type information and the job attribute information based on the preset processing prediction model to generate the estimated processing duration of each service person for completing the business service task corresponding to the service type information, the electronic device may further execute the following steps:

acquiring task sample data acquired in advance; the task sample data comprises service type information of a service task and job attribute information of a service person who completes the service task.

In this embodiment, in a medical service scenario, the service type information may include a type of assisted medical care, a type of patient care, and so on. The job attribute information may include personal identification information of the service person, quality information of the service person completing the history service task, and time information of the service person completing the history service task. The personal identity information may include the name of the attendant, the field of tampering, the age of service.

And calling a preset initial learning model.

In this embodiment, the model type of the initial learning model is not limited, and for example, a deep learning model and a machine learning model can be used. The deep learning model can be a decision tree model, a logistic regression model and the like. The machine learning model may be a convolutional neural network model, a cyclic neural network model, or the like.

In this embodiment, iterative training is performed on the initial learning model by using task sample data, so that the initial learning model can learn a corresponding relationship between service type information of a service task and time information of a service person completing a historical service task until the initial learning model meets preset model training times and model accuracy conditions, thereby obtaining a required processing prediction model. The number of model training times and the numerical value of the model accuracy condition are not limited, and can be set according to actual model training requirements.

The method comprises the steps of obtaining task sample data collected in advance; then calling a preset initial learning model; and training the initial learning model based on the task sample data to construct the processing prediction model. According to the method and the device, the initial learning model is trained by using the task sample data acquired in advance, so that the processing prediction model applied to the estimated processing time length of the task can be quickly trained and generated, the construction efficiency of the processing prediction model is improved, the subsequent prediction processing of the service type information and the operation attribute information can be conveniently performed based on the processing prediction model, and the estimated processing time length of each service person for completing the service task corresponding to the service type information can be quickly and accurately generated.

In some optional implementations of this embodiment, before step S205, the electronic device may further perform the following steps:

and acquiring historical scheduling sample data of predictive acquisition.

In this embodiment, the history scheduling sample data includes service requirement information of a history scheduling task, personnel identity information of a history service personnel who completes the history scheduling task, and satisfaction degree of the history service personnel who completes the history scheduling task and the service requirement information.

And calling a preset initial neural network model.

In this embodiment, the selection of the initial neural network model is not limited, and may be set according to actual service usage requirements. For example, a deep learning model and a machine learning model can be used.

In this embodiment, iterative training is performed on the initial neural network model by using the historical scheduling sample data, so that the initial neural network model can learn service requirement information of a historical scheduling task, personnel identity information of a historical service personnel completing the historical scheduling task, and a data association relationship between the satisfaction degree of the historical service personnel completing the historical scheduling task and the service requirement information, until the initial neural network model meets preset training times and accuracy conditions, thereby obtaining a required processing prediction model. The number of training times and the numerical value of the accuracy condition are not limited, and can be set according to actual model training requirements. In addition, in the model application process, a model algorithm for processing the prediction model can be optimized, and if customer complaints are caused after service personnel based on system recommendation are dispatched, the model algorithm is brought into a negative sample for model tuning. To generate a model processing prediction model with more stable model effect.

The method comprises the steps of obtaining historical scheduling sample data collected through prediction; then calling a preset initial neural network model; and training the initial neural network model based on the historical scheduling sample data to construct the scheduling prediction model. According to the method and the device, the initial neural network model is trained by using the pre-collected historical scheduling sample data, so that the scheduling prediction model applied to the scheduling prediction processing of the service demand information can be quickly trained and generated, the construction efficiency of the scheduling prediction model is improved, the subsequent scheduling prediction processing of the service demand information can be conveniently performed based on the scheduling prediction model, and the corresponding scheduling prediction result can be quickly and accurately generated.

In some alternative implementations of the present embodiment, step S206 includes the steps of:

and determining a scheduling service personnel corresponding to the scheduling prediction result.

In this embodiment, the scheduling prediction result may include personnel information of a scheduling service personnel.

And acquiring communication information of the dispatching service personnel.

In this embodiment, information inquiry may be performed based on personnel information of the dispatch service personnel to obtain communication information of the dispatch service personnel. The communication information may include a telephone number or a mail address.

And generating a target service task corresponding to the service application request.

In this embodiment, the corresponding target service task may be generated by acquiring the client information of the target client, acquiring the service requirement information, and filling the client information and the service requirement information into a preset service task template. The service task template is a template file which is constructed in advance according to service requirements generated by the service task.

The scheduling service personnel corresponding to the scheduling prediction result are determined; then, acquiring communication information of the dispatching service personnel; then generating a target service task corresponding to the service application request; and distributing the target service task to the dispatching service personnel based on the communication information, so that personnel dispatching processing of the target service task is rapidly completed, and the processing efficiency and the processing intelligence of personnel dispatching are improved.

It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.

It should be emphasized that, to further ensure the privacy and security of the scheduling prediction result, the scheduling prediction result may also be stored in a node of a blockchain.

The blockchain referred to in the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by computer readable instructions stored in a computer readable storage medium that, when executed, may comprise the steps of the 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.

With further reference to fig. 3, as an implementation of the method shown in fig. 2, the present application provides an embodiment of an artificial intelligence-based scheduling apparatus, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be specifically applied to various electronic devices.

As shown in fig. 3, the scheduling apparatus 300 based on artificial intelligence according to the present embodiment includes: a loading module 301, a calling module 302, a collecting module 303, a generating module 304 and a processing module 305. Wherein:

a first determining module 301, configured to determine whether a service application request triggered by a target client is received; wherein, the service application request carries the client grade information and the service demand information of the target client;

an extracting module 302, configured to extract the client level information and the service requirement information from the service application request if the client level information and the service requirement information are available;

the first processing module 303 is configured to perform personnel matching processing on the client level information and the service requirement information based on a preset scheduling rule, so as to obtain a corresponding personnel matching result;

a second judging module 304, configured to judge whether the person matching result is an empty result;

the second processing module 305 is configured to invoke a preset scheduling prediction model to perform scheduling prediction processing on the service demand information if the personnel matching result is a null result, so as to obtain a corresponding scheduling prediction result;

And a third processing module 306, configured to execute a personnel scheduling process corresponding to the service application request based on the scheduling prediction result.

In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the scheduling method based on artificial intelligence in the foregoing embodiment one by one, which is not described herein again.

In some alternative implementations of the present embodiment, the first processing module 303 includes:

the first calling sub-module is used for calling a preset rule engine;

the screening sub-module is used for executing the scheduling rules through the rule engine and screening service personnel matched with the client grade information and the service demand information from a preset personnel pool;

the first acquisition sub-module is used for acquiring the number of the service personnel;

and the first generation sub-module is used for generating the personnel matching result based on the personnel information of the service personnel if the number of the service personnel is 1.

In some alternative implementations of the present embodiment, the first processing module 303 further includes:

The second obtaining sub-module is used for obtaining service type information from the service requirement information if the number of the service personnel comprises a plurality of service personnel;

a third obtaining sub-module, configured to obtain job attribute information of each service person;

the second generation sub-module is used for carrying out prediction processing on the service type information and the operation attribute information based on a preset processing prediction model, and generating estimated processing time length for each service person to complete the business service task corresponding to the service type information;

the first determining submodule is used for carrying out data analysis on all the estimated processing time lengths and determining target service personnel from all the service personnel;

and the third generation sub-module is used for generating the personnel matching result based on the personnel information of the target service personnel.

In some optional implementations of this embodiment, the first determining submodule includes:

the screening unit is used for carrying out numerical comparison on all the estimated processing time lengths, and screening out the appointed estimated processing time length with the minimum numerical value from all the estimated processing time lengths;

The first determining unit is used for determining a designated attendant corresponding to the designated estimated processing time length from all the attendant;

and the second determining unit is used for taking the designated service personnel as the target service personnel.

a fourth acquisition sub-module, configured to acquire task sample data acquired in advance; the task sample data comprises service type information of a service task and job attribute information of a service person who completes the service task;

the second calling sub-module is used for calling a preset initial learning model;

and the construction submodule is used for training the initial learning model based on the task sample data so as to construct the processing prediction model.

In some optional implementations of this embodiment, the artificial intelligence based scheduling apparatus further includes:

the acquisition module is used for acquiring the historical scheduling sample data of the prediction acquisition; the history scheduling sample data comprises service demand information of a history scheduling task, personnel identity information of history service personnel completing the history scheduling task, and satisfaction degree of the history service personnel completing the history scheduling task and the service demand information;

the calling module is used for calling a preset initial neural network model;

and the construction module is used for training the initial neural network model based on the historical scheduling sample data so as to construct the scheduling prediction model.

In some alternative implementations of the present embodiment, the third processing module 306 includes:

the second determining submodule is used for determining a scheduling service person corresponding to the scheduling prediction result;

a fifth obtaining sub-module, configured to obtain communication information of the dispatch service personnel;

A fourth generation sub-module, configured to generate a target service task corresponding to the service application request;

and the allocation sub-module is used for allocating the target service task to the dispatch service personnel based on the communication information.

In order to solve the technical problems, the embodiment of the application also provides computer equipment. Referring specifically to fig. 4, fig. 4 is a basic structural block diagram of a computer device according to the present embodiment.

The computer device 4 comprises a memory 41, a processor 42, a network interface 43 communicatively connected to each other via a system bus. It should be noted that only computer device 4 having components 41-43 is shown in the figures, but it should be understood that not all of the illustrated components are required to be implemented and that more or fewer components may be implemented instead. 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 41 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 41 may be an internal storage unit of the computer device 4, such as a hard disk or a memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, 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 4. Of course, the memory 41 may also comprise both an internal memory unit of the computer device 4 and an external memory device. In this embodiment, the memory 41 is typically used to store an operating system and various application software installed on the computer device 4, such as computer readable instructions of an artificial intelligence based scheduling method. Further, the memory 41 may be used to temporarily store various types of data that have been output or are to be output.

The processor 42 may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is configured to execute computer readable instructions stored in the memory 41 or process data, such as computer readable instructions for executing the artificial intelligence based scheduling method.

The network interface 43 may comprise a wireless network interface or a wired network interface, which network interface 43 is typically used for establishing a communication connection between the computer device 4 and other electronic devices.

in the embodiment of the application, firstly, whether a service application request triggered by a target client is received is judged; if yes, extracting the client grade information and the service demand information from the service application request; then, carrying out personnel matching processing on the client grade information and the service demand information based on a preset scheduling rule to obtain a corresponding personnel matching result; subsequently judging whether the personnel matching result is an empty result or not; if the personnel matching result is a null result, a preset scheduling prediction model is called to perform scheduling prediction processing on the service demand information, and a corresponding scheduling prediction result is obtained; and finally, executing personnel scheduling processing corresponding to the service application request based on the scheduling prediction result. According to the intelligent scheduling distribution method based on the combination of the scheduling rules and the scheduling prediction model, the problem of expanding the service application request triggered by the target client into multi-dimensional decision is solved, and the service personnel scheduling processing of the service application request can be automatically and rapidly completed by carrying out personnel matching processing on the client grade information and the service demand information carried in the service application request, so that service personnel distribution is carried out according to the client information of the target client, the accuracy of service personnel distribution is effectively improved, and the customer satisfaction is improved.

The present application also provides another embodiment, namely, a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of an artificial intelligence-based scheduling method as described above.

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

1. An artificial intelligence-based scheduling method is characterized by comprising the following steps:

judging whether the personnel matching result is an empty result or not;

2. The scheduling method based on artificial intelligence according to claim 1, wherein the step of performing personnel matching processing on the client level information and the service requirement information based on a preset scheduling rule to obtain a corresponding personnel matching result specifically comprises:

Calling a preset rule engine;

acquiring the number of service personnel;

3. The artificial intelligence based scheduling method of claim 2, further comprising, after the step of obtaining the number of service personnel:

acquiring operation attribute information of each service person;

4. The scheduling method based on artificial intelligence according to claim 3, wherein the step of performing data analysis on all the estimated processing time periods to determine a target service person from all the service persons specifically comprises:

and taking the designated service personnel as the target service personnel.

5. The scheduling method based on artificial intelligence according to claim 3, further comprising, before the step of predicting the service type information and the job attribute information based on the preset process prediction model to generate a predicted process duration for each service person to complete a business service task corresponding to the service type information:

Calling a preset initial learning model;

6. The scheduling method based on artificial intelligence according to claim 1, wherein before the step of calling a preset scheduling prediction model to perform scheduling prediction processing on the service demand information to obtain a corresponding scheduling prediction result, the scheduling method further comprises:

calling a preset initial neural network model;

7. The scheduling method based on artificial intelligence according to claim 1, wherein the step of performing a person scheduling process corresponding to the service application request based on the scheduling prediction result specifically comprises:

acquiring communication information of the dispatching service personnel;

8. An artificial intelligence based scheduling apparatus, comprising:

9. A computer device comprising a memory having stored therein computer readable instructions which when executed implement the steps of the artificial intelligence based 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 artificial intelligence based scheduling method of any one of claims 1 to 7.