Shift prediction method and shift prediction device for centralized operation center

Abstract

The present invention discloses a scheduling prediction method and a scheduling prediction device for a centralized operation center, the method comprising: obtaining first attribute information of a task and second attribute information of a resource; determining the task scope of the resource based on the first attribute information and the second attribute information; obtaining operation data; establishing a scheduling prediction model based on the operation data; generating and publishing scheduling prediction information based on the scheduling prediction model and the task scope. By accurately defining work tasks and staff members, and predicting the schedule of each staff member based on real-time collected operation data, the workload of managers in the scheduling management process is greatly reduced and work efficiency is improved; at the same time, by combining all operation data of the entire centralized operation center for scheduling management, the use of human resources is optimized as a whole, the utilization efficiency of human resources is improved, and the operating efficiency of the enterprise is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of human resource management, and in particular to a shift scheduling prediction method and a shift scheduling prediction device for a centralized operation center. Background Art

[0002] With the development of economy, the scale of banks is also growing, which brings about the continuous increase of banking business, and therefore the number of bank processing personnel and processing procedures is also increasing.

[0003] In the existing working process of the bank's centralized operation center, streamlined operations are used to improve the work efficiency of front-end service agencies. However, the bank's complex business types and high-frequency business volume have high requirements on the professional ability, work efficiency, and stress resistance of the operators. Therefore, it is necessary to use a personnel scheduling management system to reasonably arrange employee rest while ensuring the normal production and operation of the business.

[0004] In the existing technology, the scheduling management of operators only realizes the online management approval process, while the scheduling method and scheduling results of operators are still manually arranged based on the historical experience of managers. With the continuous increase of personnel in various bank branches, a large amount of additional workload has been brought to managers, increasing their workload. At the same time, due to the different business scopes and management barriers of various centralized operation centers, the scope of managers' scheduling is greatly limited, and it is impossible to comprehensively and dynamically manage the personnel in multiple centralized operation centers, resulting in waste or shortage of human resources, reducing the operating efficiency of enterprises, and failing to meet the production and operation needs of existing enterprises. Summary of the invention

[0005] In order to overcome the above-mentioned technical problems existing in the prior art, an embodiment of the present invention provides a scheduling prediction method for a centralized operation center, which defines tasks and human resources, and performs scheduling prediction based on the definition information combined with real-time operational data, thereby reducing the workload of management personnel, improving work efficiency, and improving the accuracy of scheduling management.

[0006] In order to achieve the above-mentioned purpose, an embodiment of the present invention provides a scheduling prediction method for a centralized operation center, the method comprising: obtaining first attribute information of a task and second attribute information of a resource; determining the task scope of the resource based on the first attribute information and the second attribute information; obtaining operation data; establishing a scheduling prediction model based on the operation data; generating and publishing scheduling prediction information based on the scheduling prediction model and the task scope.

[0007] Preferably, the method also includes: before establishing the scheduling prediction model, preprocessing the operating data based on the first attribute information and the second attribute information to obtain preprocessed data; obtaining preset classification rules; classifying the preprocessed data based on the preset classification rules to obtain classified data; and establishing the scheduling prediction model based on the classified data.

[0008] Preferably, determining the task scope of the resource based on the first attribute information and the second attribute information includes: classifying the resources based on the second attribute information to obtain classified resources; establishing a corresponding relationship between each classified resource and at least one task based on the first attribute information; and determining the task scope of each classified resource based on the corresponding relationship.

[0009] Preferably, establishing a scheduling prediction model based on the operating data includes: obtaining an initial prediction model; performing feature extraction on the operating data to obtain corresponding feature information; performing format conversion on the feature information to obtain converted information; obtaining preset feature factors, and training the initial prediction model based on the converted information and the preset feature factors to obtain the scheduling prediction model.

[0010] Preferably, the method further includes: obtaining restriction conditions, wherein the restriction conditions include the geographical location and the type of outlets of the centralized operation center; and generating and publishing the scheduling prediction information based on the restriction conditions, the task scope and the scheduling prediction model.

[0011] Preferably, the method also includes: obtaining preset monitoring indicator information; calculating the operation data based on the preset monitoring indicator information to generate corresponding monitoring data, wherein the monitoring data includes resource monitoring data corresponding to each resource; analyzing the resource monitoring data to determine the analysis results corresponding to each resource; and generating a visual monitoring view based on the analysis results.

[0012] Preferably, the method further includes: before publishing the scheduling prediction information, obtaining user operation information, wherein the user operation information is associated with the visual monitoring view; adjusting the scheduling prediction information based on the user operation information to obtain adjusted scheduling information; and publishing the adjusted scheduling information.

[0013] Correspondingly, an embodiment of the present invention also provides a scheduling prediction device for a centralized operation center, the device comprising: a first acquisition unit, used to acquire first attribute information of a task and second attribute information of a resource; a scope determination unit, used to determine the task scope of the resource based on the first attribute information and the second attribute information; a second acquisition unit, used to acquire operation data; a model building unit, used to build a scheduling prediction model based on the operation data; a scheduling prediction unit, used to generate and publish scheduling prediction information based on the scheduling prediction model and the task scope.

[0014] Preferably, the device also includes a preprocessing unit, which is used to: before establishing the scheduling prediction model, preprocess the operating data based on the first attribute information and the second attribute information to obtain preprocessed data; obtain preset classification rules; classify the preprocessed data based on the preset classification rules to obtain classified data; the model building unit is also used to: establish the scheduling prediction model based on the classified data.

[0015] Preferably, the scope determination unit includes: a classification module, used to classify the resources based on the second attribute information to obtain classified resources; a corresponding module, used to establish a corresponding relationship between each classified resource and at least one task based on the first attribute information; and a scope determination module, used to determine the task scope of each classified resource based on the corresponding relationship.

[0016] Preferably, the model building unit includes: an initial model acquisition module, used to obtain an initial prediction model; a feature extraction module, used to extract features from the operation data to obtain corresponding feature information; a conversion module, used to perform format conversion on the feature information to obtain converted information; a model building module, used to obtain preset feature factors, train the initial prediction model based on the converted information and the preset feature factors, and obtain the scheduling prediction model.

[0017] Preferably, the device also includes a restriction unit, which is used to: obtain restriction conditions, wherein the restriction conditions include the geographical location and network type of the centralized operation center; the scheduling prediction unit is also used to: generate and publish the scheduling prediction information based on the restriction conditions, the task scope and the scheduling prediction model.

[0018] Preferably, the device also includes a visual monitoring unit, which is used to: obtain preset monitoring indicator information; calculate the operation data based on the preset monitoring indicator information to generate corresponding monitoring data, and the monitoring data includes resource monitoring data corresponding to each resource; analyze the resource monitoring data to determine the analysis results corresponding to each resource; and generate a visual monitoring view based on the analysis results.

[0019] Preferably, the device also includes an adjustment unit, which is used to: before publishing the scheduling prediction information, obtain user operation information, wherein the user operation information is associated with the visual monitoring view; adjust the scheduling prediction information based on the user operation information to obtain adjusted scheduling information; and publish the adjusted scheduling information.

[0020] On the other hand, an embodiment of the present invention further provides a processor, which is configured to execute the method provided by the embodiment of the present invention.

[0021] On the other hand, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, and when the program is executed by a processor, the method provided by the embodiment of the present invention is implemented.

[0022] On the other hand, an embodiment of the present invention further provides a computer program product, including a computer program, and when the computer program is executed by a processor, the method provided by the embodiment of the present invention is implemented.

[0023] Through the technical solution provided by the present invention, the present invention has at least the following technical effects:

[0024] By accurately defining work tasks and staff, and predicting the schedule of each staff member based on real-time collected operational data, the workload of managers in the scheduling process is greatly reduced and work efficiency is improved. At the same time, by combining all operational data of the entire centralized operation center for scheduling management, the overall use of human resources is optimized, the utilization efficiency of human resources is improved, and the operating benefits of the enterprise are improved.

[0025] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0027] Figure 1This is a specific implementation flow chart of the scheduling prediction method for a centralized operation center provided by an embodiment of the present invention;

[0028] Figure 2 It is a specific implementation flow chart of determining the task scope in the scheduling prediction method of the centralized operation center provided by the embodiment of the present invention;

[0029] Figure 3 This is a specific implementation flow chart of establishing a shift prediction model in the shift prediction method for a centralized operation center provided by an embodiment of the present invention;

[0030] Figure 4 It is a structural diagram of a shift scheduling prediction device for a centralized operation center provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0032] The terms "system" and "network" in the embodiments of the present invention can be used interchangeably. "Multiple" means two or more than two. In view of this, "multiple" can also be understood as "at least two" in the embodiments of the present invention. "And / or" describes the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / ", unless otherwise specified, generally indicates that the previous and subsequent associated objects are in an "or" relationship. In addition, it should be understood that in the description of the embodiments of the present invention, the words "first", "second", etc. are only used to distinguish the purpose of description, and cannot be understood as indicating or implying relative importance, nor can they be understood as indicating or implying order.

[0033] See also Figure 1 The embodiment of the present invention provides a method for scheduling prediction of a centralized operation center, characterized in that the method comprises:

[0034] S10) obtaining first attribute information of the task and second attribute information of the resource;

[0035] S20) determining the task scope of the resource based on the first attribute information and the second attribute information;

[0036] S30) obtaining operation data;

[0037] S40) establishing a scheduling prediction model based on the operation data;

[0038] S50) Generate and publish scheduling prediction information based on the scheduling prediction model and the task scope.

[0039] In order to accurately predict the shift schedule of the personnel in the centralized operation center, it is first necessary to define the task content and staff of the personnel. In a possible implementation, the shift schedule prediction is performed for the staff in the bank. First, the first attribute information of the tasks that the bank personnel need to perform and the second attribute information of the bank personnel are obtained. For example, the first attribute information includes but is not limited to the business type of the task, the business scenario to which it belongs, the timeliness requirement of the task, etc., and the second attribute information includes but is not limited to the position and professional ability of the bank personnel, etc. Then, based on the first attribute information of the task and the second attribute information of the bank personnel, the scope of the tasks that the bank personnel can perform can be determined.

[0040] At this point, further operational data can be obtained, for example, by connecting with the various business systems of the bank's centralized operation center and obtaining operational data through online transaction calls, batch data files and other data transmission methods. For example, without affecting the production operations of bank personnel, real-time data on the occupancy of tasks / bank personnel in the production operation process and data on bank personnel's leave can be collected, and a scheduling prediction model can be established based on the operational data.

[0041] However, in actual application, due to the excessive data content and diverse data formats of the operational data collected directly from the business system, it is often impossible to directly process the data. Therefore, it is necessary to pre-process the directly obtained operational data.

[0042] In an embodiment of the present invention, the method further includes: before establishing the scheduling prediction model, preprocessing the operating data based on the first attribute information and the second attribute information to obtain preprocessed data; obtaining preset classification rules; classifying the preprocessed data based on the preset classification rules to obtain classified data; and establishing the scheduling prediction model based on the classified data.

[0043] In a possible implementation, after obtaining the above-mentioned operation data, the operation data is first preprocessed based on the above-mentioned first attribute information and second attribute information. For example, the operation data is screened according to the standard definition of previous tasks and staff to obtain screened data (i.e., preprocessed data). Then, a preset classification rule is obtained. For example, the classification rule is a rule for classifying the preprocessed data based on the actual business type and qualification requirements. The classified data is obtained, thereby obtaining operation data with usable value. On this basis, a scheduling prediction model is established.

[0044] In the embodiment of the present invention, by optimizing and classifying the directly collected operational data, automatic analysis and processing of the data in the subsequent process of establishing a scheduling prediction model is facilitated, thereby increasing the available value of the data and improving the accuracy of the scheduling prediction.

[0045] See also Figure 2 In an embodiment of the present invention, determining the task scope of the resource based on the first attribute information and the second attribute information includes:

[0046] S21) classifying the resources based on the second attribute information to obtain classified resources;

[0047] S22) establishing a corresponding relationship between each classified resource and at least one task based on the first attribute information;

[0048] S23) Determine the task scope of each classified resource based on the corresponding relationship.

[0049] When determining the scope of tasks that bank personnel can perform, first classify bank personnel according to the second attribute information, for example, classify bank personnel according to their positions, professional capabilities, etc., to obtain classified resources, and then match the second attribute information of bank personnel according to conditions such as the business type, business scenario, and task timeliness requirements of each task, so as to establish a corresponding relationship between each classified bank personnel and at least one task, and determine the task scope of each classified bank personnel according to the above corresponding relationship. After determining the task scope of each bank personnel, a scheduling prediction model can be established based on operational data.

[0050] See also Figure 3 In an embodiment of the present invention, the step of establishing a scheduling prediction model based on the operation data includes:

[0051] S41) obtaining an initial prediction model;

[0052] S42) extracting features from the operation data to obtain corresponding feature information;

[0053] S43) performing format conversion on the characteristic information to obtain converted information;

[0054] S44) Obtain preset characteristic factors, and train the initial prediction model based on the converted information and the preset characteristic factors to obtain the scheduling prediction model.

[0055] Since the amount of operational data of a bank is very large, if each operational data is viewed and scheduled manually or semi-manually, it will greatly increase the workload of the management personnel and cause great work pressure for the management personnel. Therefore, an intelligent data analysis method is used. In a possible implementation, an initial prediction model is first obtained, for example, the initial prediction model can be a prediction model based on big data, a prediction model based on a deep neural network, and a prediction model based on a support vector machine, etc., and then the operating data is feature extracted, and the corresponding feature information is obtained. For example, in an embodiment of the present invention, the extracted feature information is stored in the form of a vector, and then the format of the above feature information is converted to obtain converted information. The above converted information is information with a unified format. At this time, a preset feature factor is further obtained. For example, the preset feature factor can be manually input or pre-set by the management personnel based on experience, and then the initial prediction model is trained according to the above converted information and the preset feature factor. For example, the above converted information and the above preset feature factor are input as training data into the initial prediction model for training, and a scheduling prediction model is obtained.

[0056] In the embodiment of the present invention, by establishing a scheduling prediction model to perform intelligent analysis of operational data and automatically perform scheduling prediction, the current manual scheduling method is replaced, which can greatly improve the scheduling efficiency of management personnel, reduce the workload of management personnel, and improve the work efficiency of management personnel; at the same time, because a unified scheduling prediction is performed based on all operational data of the entire centralized operation center, the scheduling of all bank personnel in the centralized operation center can be optimized, effectively improving the effectiveness of bank personnel scheduling, improving the utilization rate of human resources, and improving the operating efficiency of the enterprise.

[0057] In an embodiment of the present invention, the method further includes: obtaining restriction conditions, the restriction conditions including the geographical location and the type of outlets of the centralized operation center; generating and publishing the scheduling prediction information based on the restriction conditions, the task scope and the scheduling prediction model.

[0058] In one possible implementation, after establishing a scheduling prediction model, managers can directly perform scheduling predictions through the scheduling prediction model. However, in order to further improve the accuracy of scheduling predictions, it is also necessary to restrict the prediction process according to actual conditions. For example, first obtain restriction conditions, which include but are not limited to the geographical location of the centralized operation center, the type of branch where bank personnel work, etc. In the process of scheduling predictions for each bank employee, the scheduling prediction model performs scheduling predictions based on the above-mentioned restriction conditions, the task scope of each bank employee, and the real-time acquired operational data. After generating the scheduling prediction information, the scheduling prediction information is published.

[0059] In an embodiment of the present invention, by further restricting the intelligent scheduling prediction model during the scheduling prediction process, it is possible to ensure that the generated prediction results are more in line with actual needs and actual conditions, thereby further improving the accuracy of the prediction results, and realizing fast, automatic and accurate scheduling management, thereby improving the work experience of bank personnel.

[0060] In actual application, since the above-mentioned model training and operation data analysis processes are automated, the above-mentioned automatic scheduling prediction still requires managers to manually input relevant restrictions and other information, and managers need to input information based on the actual work situation of each bank employee. Therefore, in order to further reduce the workload of managers and improve their work experience, the operation data can be visualized to assist managers in better information input and scheduling prediction management.

[0061] In an embodiment of the present invention, the method also includes: obtaining preset monitoring indicator information; calculating the operation data based on the preset monitoring indicator information to generate corresponding monitoring data, wherein the monitoring data includes resource monitoring data corresponding to each resource; analyzing the resource monitoring data to determine the analysis results corresponding to each resource; and generating a visual monitoring view based on the analysis results.

[0062] Furthermore, in an embodiment of the present invention, the method also includes: before publishing the scheduling prediction information, obtaining user operation information, wherein the user operation information is associated with the visual monitoring view; adjusting the scheduling prediction information based on the user operation information to obtain adjusted scheduling information; and publishing the adjusted scheduling information.

[0063] In a possible implementation, first obtain preset monitoring index information, for example, the preset monitoring index information is the relevant monitoring index determined by the management personnel according to the actual needs of manual monitoring, and after obtaining the bank's operating data in real time, calculate the operating data according to the preset monitoring index information, and generate corresponding monitoring data. At this time, analyze the resource monitoring data, for example, analyze the resource monitoring data according to the information such as the organization, task type, and position, so as to determine the analysis results corresponding to each bank staff, for example, the analysis results include but are not limited to the efficiency of each bank staff in processing tasks, the average time spent in processing tasks, the occupancy of bank staff, task access execution, and other information, and then generate a visual monitoring view based on the above analysis results, and display it on the screen in a visual way for the management personnel to view directly.

[0064] In actual application, user operation information can also be obtained before the scheduling forecast information is released after it is generated. For example, the user operation information is the operation performed by the management personnel based on the above-mentioned visual monitoring view. During the visual monitoring process, the management personnel must believe that there are some inappropriate aspects in the currently generated scheduling forecast information, so they manually adjust the above-mentioned scheduling forecast information, generate the adjusted scheduling information, and then release the adjusted scheduling information.

[0065] In the embodiment of the present invention, by providing real-time, visual monitoring views to management personnel during the scheduling forecast process, management personnel are assisted in making accurate adjustments and corrections to the scheduling forecast results, thereby ensuring that the published scheduling management information is accurate and effective scheduling management information, thereby improving the accuracy of management of bank personnel, improving the utilization rate of human resources, and improving the operating efficiency of the enterprise.

[0066] The shift scheduling prediction device for a centralized operation center provided by an embodiment of the present invention is described below with reference to the accompanying drawings.

[0067] See also Figure 4 Based on the same inventive concept, an embodiment of the present invention provides a scheduling prediction device for a centralized operation center, the device comprising: a first acquisition unit, used to acquire first attribute information of a task and second attribute information of a resource; a scope determination unit, used to determine the task scope of the resource based on the first attribute information and the second attribute information; a second acquisition unit, used to acquire operation data; a model building unit, used to build a scheduling prediction model based on the operation data; a scheduling prediction unit, used to generate and publish scheduling prediction information based on the scheduling prediction model and the task scope.

[0068] In an embodiment of the present invention, the device also includes a preprocessing unit, which is used to: before establishing the scheduling prediction model, preprocess the operating data based on the first attribute information and the second attribute information to obtain preprocessed data; obtain preset classification rules; classify the preprocessed data based on the preset classification rules to obtain classified data; the model building unit is also used to: establish the scheduling prediction model based on the classified data.

[0069] In an embodiment of the present invention, the scope determination unit includes: a classification module, used to classify the resources based on the second attribute information to obtain classified resources; a corresponding module, used to establish a corresponding relationship between each classified resource and at least one task based on the first attribute information; and a scope determination module, used to determine the task scope of each classified resource based on the corresponding relationship.

[0070] In an embodiment of the present invention, the model building unit includes: an initial model acquisition module, used to obtain an initial prediction model; a feature extraction module, used to extract features from the operation data to obtain corresponding feature information; a conversion module, used to perform format conversion on the feature information to obtain converted information; a model building module, used to obtain preset feature factors, train the initial prediction model based on the converted information and the preset feature factors, and obtain the scheduling prediction model.

[0071] In an embodiment of the present invention, the device also includes a restriction unit, which is used to: obtain restriction conditions, wherein the restriction conditions include the geographical location and network type of the centralized operation center; the scheduling prediction unit is also used to: generate and publish the scheduling prediction information based on the restriction conditions, the task scope and the scheduling prediction model.

[0072] In an embodiment of the present invention, the device also includes a visual monitoring unit, which is used to: obtain preset monitoring indicator information; calculate the operation data based on the preset monitoring indicator information to generate corresponding monitoring data, and the monitoring data includes resource monitoring data corresponding to each resource; analyze the resource monitoring data to determine the analysis results corresponding to each resource; and generate a visual monitoring view based on the analysis results.

[0073] In an embodiment of the present invention, the device also includes an adjustment unit, which is used to: before publishing the scheduling prediction information, obtain user operation information, wherein the user operation information is associated with the visual monitoring view; adjust the scheduling prediction information based on the user operation information to obtain adjusted scheduling information; and publish the adjusted scheduling information.

[0074] Furthermore, an embodiment of the present invention also provides a processor, which is configured to execute the method described in the embodiment of the present invention.

[0075] Furthermore, an embodiment of the present invention also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method described in the embodiment of the present invention is implemented.

[0076] Furthermore, an embodiment of the present invention also provides a computer program product, including a computer program, and when the computer program is executed by a processor, the method described in the embodiment of the present invention is implemented.

[0077] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0078] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0079] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0080] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0081] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0082] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0083] Computer readable media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0084] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0085] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

[0086] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws and regulations.

Claims

1. A method for scheduling prediction of a centralized operation center, characterized in that: The method comprises: Obtaining first attribute information of the task and second attribute information of the resource; Determine the task scope of the resource based on the first attribute information and the second attribute information; Obtain operational data; Establishing a scheduling prediction model based on the operational data; Generate and publish shift forecast information based on the shift forecast model and the task scope; The resource includes bank personnel, the first attribute information includes the business type of each task, the business scenario to which it belongs, and the timeliness requirement of the task, the second attribute information includes the position of the bank personnel and the professional ability of the bank personnel, and the determining of the task scope of the resource based on the first attribute information and the second attribute information includes: Classify bank personnel according to their positions and professional capabilities and obtain classified resources; Matching the second attribute information of each task with the business type, the business scenario to which it belongs, and the timeliness requirement of the task, so as to establish a corresponding relationship between each classified bank employee and at least one task; The task scope of each classified resource is determined based on the corresponding relationship.

2. The method according to claim 1, characterized in that The method further comprises: Before establishing the shift scheduling prediction model, preprocessing the operation data based on the first attribute information and the second attribute information to obtain preprocessed data; Get preset classification rules; Classify the preprocessed data based on the preset classification rule to obtain classified data; The shift scheduling prediction model is established based on the classified data.

3. The method according to claim 1, characterized in that The establishing of a scheduling prediction model based on the operation data includes: Obtaining an initial prediction model; Extracting features from the operation data to obtain corresponding feature information; Performing format conversion on the characteristic information to obtain converted information; Acquire preset characteristic factors, train the initial prediction model based on the converted information and the preset characteristic factors, and obtain the shift scheduling prediction model.

4. The method according to claim 1, characterized in that: The method further comprises: Obtaining restriction conditions, wherein the restriction conditions include the geographical location and type of outlets of the centralized operation center; The shift scheduling prediction information is generated and published based on the restriction condition, the task scope and the shift scheduling prediction model.

5. The method according to claim 1, characterized in that The method further comprises: Get preset monitoring indicator information; Calculate the operation data based on the preset monitoring indicator information to generate corresponding monitoring data, wherein the monitoring data includes resource monitoring data corresponding to each resource; Analyze the resource monitoring data to determine the analysis result corresponding to each resource; A visual monitoring view is generated based on the analysis result.

6. The method according to claim 5, characterized in that The method further comprises: Before publishing the shift forecast information, obtaining user operation information, wherein the user operation information is associated with the visual monitoring view; Adjusting the shift scheduling prediction information based on the user operation information to obtain adjusted shift scheduling information; Release the adjusted shift information.

7. A shift scheduling prediction device for a centralized operation center, characterized in that: The device comprises: A first acquisition unit, used for acquiring first attribute information of the task and second attribute information of the resource; a scope determination unit, configured to determine a task scope of the resource based on the first attribute information and the second attribute information; A second acquisition unit, used for acquiring operation data; A model building unit, used to build a scheduling prediction model based on the operation data; A shift prediction unit, configured to generate and publish shift prediction information based on the shift prediction model and the task scope; The resource includes bank personnel, the first attribute information includes the business type of each task, the business scenario to which it belongs, and the timeliness requirement of the task, the second attribute information includes the position of the bank personnel and the professional ability of the bank personnel, and the scope determination unit includes: The classification module is used to classify bank personnel according to their positions and professional capabilities and obtain classified resources; A corresponding module, used for matching the second attribute information of the bank personnel according to the business type, the business scenario and the timeliness requirement of each task, so as to establish a corresponding relationship between each classified bank personnel and at least one task; The scope determination module is used to determine the task scope of each classified resource based on the corresponding relationship.

8. The device according to claim 7, characterized in that The device further comprises a preprocessing unit, which is used for: Before establishing the shift scheduling prediction model, preprocessing the operation data based on the first attribute information and the second attribute information to obtain preprocessed data; Get preset classification rules; Classify the preprocessed data based on the preset classification rule to obtain classified data; The model building unit is also used to: build the scheduling prediction model based on the classified data.

9. The device according to claim 7, characterized in that The model building unit comprises: An initial model acquisition module, used to acquire an initial prediction model; A feature extraction module, used to extract features from the operation data to obtain corresponding feature information; A conversion module, used to perform format conversion on the characteristic information to obtain converted information; The model building module is used to obtain preset characteristic factors, train the initial prediction model based on the converted information and the preset characteristic factors, and obtain the scheduling prediction model.

10. The device according to claim 7, characterized in that The device further comprises a limiting unit, which is used for: Obtaining restriction conditions, wherein the restriction conditions include the geographical location and type of outlets of the centralized operation center; The shift scheduling prediction unit is further used to generate and publish the shift scheduling prediction information based on the restriction condition, the task scope and the shift scheduling prediction model.

11. The device according to claim 7, characterized in that The device further comprises a visual monitoring unit, which is used for: Get preset monitoring indicator information; Calculate the operation data based on the preset monitoring indicator information to generate corresponding monitoring data, wherein the monitoring data includes resource monitoring data corresponding to each resource; Analyze the resource monitoring data to determine the analysis result corresponding to each resource; A visual monitoring view is generated based on the analysis result.

12. The device according to claim 11, characterized in that The device further comprises an adjustment unit, which is used for: Before publishing the shift forecast information, obtaining user operation information, wherein the user operation information is associated with the visual monitoring view; Adjusting the shift scheduling prediction information based on the user operation information to obtain adjusted shift scheduling information; Release the adjusted shift information.

13. A processor, characterized in that: The method is configured to perform the method as claimed in any one of claims 1 to 6.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the scheduling prediction method for a centralized operation center described in any one of claims 1 to 6 is implemented.

15. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the computer program implements the scheduling prediction method for a centralized operation center as described in any one of claims 1 to 6.

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