CN114118792A - Scheduling prediction method and scheduling prediction device for centralized operation center - Google Patents
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Abstract
The invention discloses a scheduling prediction method and a scheduling prediction device for a centralized operation center, wherein the method comprises the following steps: acquiring first attribute information of a task and second attribute information of a resource; determining a task scope of the resource based on the first attribute information and the second attribute information; acquiring operation data; establishing a scheduling prediction model based on the operation data; and generating and issuing scheduling prediction information based on the scheduling prediction model and the task range. The work tasks and the workers are accurately defined, and the scheduling of each worker is predicted based on the operation data acquired in real time, so that the workload of managers in the scheduling management process is greatly reduced, and the work efficiency is improved; meanwhile, the shift arrangement management is carried out by combining all operation data of the whole centralized operation center, so that the use of manpower resources is optimized on the whole, the utilization efficiency of the manpower resources is improved, and the operation benefit of enterprises is improved.
Description
Technical Field
The invention relates to the technical field of human resource management, in particular to a scheduling prediction method and a scheduling prediction device for a centralized operation center.
Background
Along with the development of economy, the scale of banks is continuously developed, so that the banking business is continuously increased, and therefore the number of processing personnel and processing flows of banks is also continuously increased.
In the working process of the existing bank centralized operation center, the working efficiency is improved for the foreground service mechanism through streamlined operation, but the complicated service types and high-frequency service volumes of the bank have higher requirements on professional ability, operation efficiency, pressure resistance and the like of operators, so that the staff needs to be reasonably arranged to rest while the normal production operation of the service is ensured through the staff scheduling management system.
In the prior art, the shift arrangement management of the operators only realizes the online management of the approval process, the shift arrangement mode and the shift arrangement result of the operators are still manually arranged according to the historical experience of the managers, and with the continuous increase of the personnel at all bank outlets, a large amount of extra workload is brought to the managers, and the workload is increased. Meanwhile, because different business categories and management barriers exist in each current centralized operation center, the arrangement range of managers can be greatly limited, and the operators of a plurality of centralized operation centers can not be comprehensively and dynamically managed, so that the waste or shortage of human resources is caused, the operating benefit of enterprises is reduced, and the production and operation requirements of the existing enterprises can not be met.
Disclosure of Invention
In order to solve the technical problems in the prior art, embodiments of the present invention provide a scheduling prediction method for a centralized operation center, which defines tasks and human resources and performs scheduling prediction according to defined information and real-time acquired operation data, so as to reduce workload of managers, improve work efficiency, and improve accuracy of scheduling management.
In order to achieve the above object, an embodiment of the present invention provides a shift scheduling prediction method for a centralized operation center, where the method includes: acquiring first attribute information of a task and second attribute information of a resource; determining a task scope of the resource based on the first attribute information and the second attribute information; acquiring operation data; establishing a scheduling prediction model based on the operation data; and generating and issuing scheduling prediction information based on the scheduling prediction model and the task range.
Preferably, the method further comprises: before the shift scheduling prediction model is established, preprocessing the operation data based on the first attribute information and the second attribute information to obtain preprocessed data; acquiring a preset classification rule; classifying the preprocessed data based on the preset classification rule to obtain classified data; and establishing the scheduling prediction model based on the classified data.
Preferably, the 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; respectively establishing a corresponding relation between each classified resource and at least one task based on the first attribute information; and determining the task range of each classified resource based on the corresponding relation.
Preferably, the building of the shift scheduling prediction model based on the operation data includes: obtaining an initial prediction model; extracting the characteristics of the operation data to obtain corresponding characteristic information; performing format conversion on the characteristic information to obtain converted information; and acquiring a preset characteristic factor, and training the initial prediction model based on the converted information and the preset characteristic factor to obtain the scheduling prediction model.
Preferably, the method further comprises: acquiring limiting conditions, wherein the limiting conditions comprise the geographic position of the centralized operation center and the type of a network point; and generating and issuing scheduling prediction information based on the limiting conditions, the task range and the scheduling prediction model.
Preferably, the method further comprises: acquiring preset monitoring index information; calculating the operation data based on the preset monitoring index information to generate corresponding monitoring data, wherein the monitoring data comprises resource monitoring data corresponding to each resource; analyzing the resource monitoring data, and determining an analysis result corresponding to each resource; generating a visual monitoring view based on the analysis result.
Preferably, the method further comprises: before issuing the scheduling prediction information, acquiring 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 issuing the adjusted scheduling information.
Correspondingly, the embodiment of the invention also provides a shift arrangement prediction device of the centralized operation center, which comprises: a first acquiring unit configured to acquire first attribute information of a task and second attribute information of a 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 obtaining unit, configured to obtain operation data; a model establishing unit for establishing a shift scheduling prediction model based on the operation data; and the scheduling prediction unit is used for generating and issuing scheduling prediction information based on the scheduling prediction model and the task range.
Preferably, the apparatus further comprises a pre-processing unit for: before the shift scheduling prediction model is established, preprocessing the operation data based on the first attribute information and the second attribute information to obtain preprocessed data; acquiring a preset classification rule; classifying the preprocessed data based on the preset classification rule to obtain classified data; the model building unit is further configured to: and establishing the scheduling prediction model based on the classified data.
Preferably, the range determining unit includes: the classification module is used for classifying the resources based on the second attribute information to obtain classified resources; a corresponding module, configured to respectively establish a corresponding relationship between each classified resource and at least one task based on the first attribute information; and the range determining module is used for determining the task range of each classified resource based on the corresponding relation.
Preferably, the model building unit includes: the initial model obtaining module is used for obtaining an initial prediction model; the characteristic extraction module is used for extracting the characteristics of the operation data to obtain corresponding characteristic information; the conversion module is used for executing format conversion on the characteristic information to obtain converted information; and the model establishing module is used for acquiring a preset characteristic factor, training the initial prediction model based on the converted information and the preset characteristic factor and acquiring the scheduling prediction model.
Preferably, the apparatus further comprises a limiting unit for: acquiring limiting conditions, wherein the limiting conditions comprise the geographic position of the centralized operation center and the type of a network point; the shift scheduling prediction unit is further configured to: and generating and issuing scheduling prediction information based on the limiting conditions, the task range and the scheduling prediction model.
Preferably, the apparatus further comprises a visualization monitoring unit for: acquiring preset monitoring index information; calculating the operation data based on the preset monitoring index information to generate corresponding monitoring data, wherein the monitoring data comprises resource monitoring data corresponding to each resource; analyzing the resource monitoring data, and determining an analysis result corresponding to each resource; generating a visual monitoring view based on the analysis result.
Preferably, the apparatus further comprises an adjusting unit for: before issuing the scheduling prediction information, acquiring 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 issuing the adjusted scheduling information.
In another aspect, the embodiment of the present invention further provides a processor, where the processor is configured to execute the method provided by the embodiment of the present invention.
In another aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the method provided by the embodiment of the present invention.
In another aspect, an embodiment of the present invention further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the computer program implements the method provided by the embodiment of the present invention.
Through the technical scheme provided by the invention, the invention at least has the following technical effects:
the work tasks and the workers are accurately defined, and the scheduling of each worker is predicted based on the operation data acquired in real time, so that the workload of managers in the scheduling management process is greatly reduced, and the work efficiency is improved; meanwhile, the shift arrangement management is carried out by combining all operation data of the whole centralized operation center, so that the use of manpower resources is optimized on the whole, the utilization efficiency of the manpower resources is improved, and the operation benefit of enterprises is improved.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the embodiments of the invention without limiting the embodiments of the invention. In the drawings:
fig. 1 is a flowchart of a specific implementation of a shift scheduling prediction method for a centralized operation center according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating a specific implementation of determining a task range in a scheduling prediction method of a centralized operation center according to an embodiment of the present invention;
fig. 3 is a flowchart illustrating a specific implementation of establishing a shift scheduling prediction model in the shift scheduling prediction method of the centralized operation center according to the embodiment of the present invention;
fig. 4 is a schematic structural diagram of a shift schedule prediction apparatus of a centralized work center according to an embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating embodiments of the invention, are given by way of illustration and explanation only, not limitation.
The terms "system" and "network" in embodiments of the present invention may be used interchangeably. The "plurality" means two or more, and in view of this, the "plurality" may also be understood as "at least two" in the embodiments of the present invention. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" generally indicates that the preceding and following related objects are in an "or" relationship, unless otherwise specified. In addition, it should be understood that the terms first, second, etc. in the description of the embodiments of the invention are used for distinguishing between the descriptions and are not intended to indicate or imply relative importance or order to be construed.
Referring to fig. 1, an embodiment of the present invention provides a shift scheduling prediction method for a centralized operation center, including:
s10) acquiring first attribute information of the task and second attribute information of the resource;
s20) determining a task scope of the resource based on the first attribute information and the second attribute information;
s30) acquiring operation data;
s40) establishing a shift scheduling prediction model based on the operation data;
s50) generating and issuing scheduling prediction information based on the scheduling prediction model and the task range.
In order to accurately predict the shift arrangement of the personnel in the centralized operation center, firstly, the task content and the staff of the personnel need to be defined, and in a possible implementation mode, the shift arrangement prediction is carried out on the staff in a bank. First attribute information of a task required to be executed by a bank person and second attribute information of the bank person are obtained, for example, the first attribute information includes but is not limited to a service type of the task, a service scene of the task, timeliness requirements of the task and the like, the second attribute information includes but is not limited to a position, professional ability and the like of the bank person, and then a task range capable of being operated by the bank person can be determined according to the first attribute information of the task and the second attribute information of the bank person.
At this time, further, the operation data may be acquired, for example, by docking with each business system of the centralized operation center of the bank, and acquiring the operation data in a data transmission manner such as an online transaction calling manner and a batch data file, for example, under the condition that the production operation of the bank staff is not affected, acquiring real-time data of the occupation condition of the task/the bank staff and data of the vacation condition of the bank staff in the production operation process, and establishing a scheduling prediction model according to the operation data.
However, in the actual application process, since the operation data directly collected from the service system has too many data contents and various data formats, the data processing cannot be directly performed, and thus, the operation data directly obtained needs to be preprocessed.
In an embodiment of the present invention, the method further comprises: before the shift scheduling prediction model is established, preprocessing the operation data based on the first attribute information and the second attribute information to obtain preprocessed data; acquiring a preset classification rule; classifying the preprocessed data based on the preset classification rule to obtain classified data; and establishing the scheduling prediction model based on the classified data.
In a possible implementation manner, after the operation data is obtained, the operation data is preprocessed based on the first attribute information and the second attribute information, for example, the operation data is screened according to the previous task and the standard definition of the worker 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 determined based on the actual service type and qualification requirement, the classified data is obtained, so that the operation data with the available value is obtained, and on the basis, a scheduling prediction model is established.
In the embodiment of the invention, the directly acquired operation data is optimized and classified, so that the automatic analysis and processing of the data in the subsequent process of establishing the scheduling prediction model are facilitated, the available value of the data is improved, and the accuracy of the scheduling prediction is improved.
Referring to fig. 2, in the embodiment of the present invention, the determining the task range of the resource based on the first attribute information and the second attribute information includes:
s21) classifying the resources based on the second attribute information to obtain classified resources;
s22) respectively establishing the corresponding relation between each classified resource and at least one task based on the first attribute information;
s23) determining a task scope for each classified resource based on the correspondence.
When determining the task range in which the bank personnel can operate, firstly classifying the bank personnel according to the second attribute information, for example, classifying the bank personnel according to the positions of the bank personnel, the professional abilities of the bank personnel and the like to obtain the classified resources, then matching the second attribute information of the bank personnel according to the conditions of the service type, the attributive service scene, the task timeliness requirement and the like of each task to establish the corresponding relationship between each classified bank personnel and at least one task, and determining the task range of each classified bank personnel according to the corresponding relationship. After the task range of each bank worker is determined, a scheduling prediction model can be established according to the operation data.
Referring to fig. 3, in an embodiment of the present invention, the building a shift scheduling prediction model based on the operation data includes:
s41) obtaining an initial prediction model;
s42) extracting the characteristics of the operation data to obtain corresponding characteristic information;
s43) performing format conversion on the characteristic information to obtain converted information;
s44) obtaining a preset characteristic factor, training the initial prediction model based on the converted information and the preset characteristic factor, and obtaining the scheduling prediction model.
Because the operation data volume of the bank is very large, if a manual or semi-manual mode is adopted to check and schedule each operation data, the workload of the manager is greatly increased, the manager is greatly stressed, and an intelligent data analysis mode is adopted for the management. In a possible embodiment, an initial prediction model is first obtained, for example, the initial prediction model may be a prediction model based on big data, a prediction model based on a deep neural network, a prediction model based on a support vector machine, and the like, then feature extraction is performed on the operation data, and corresponding feature information is obtained, for example, in an embodiment of the present invention, the extracted feature information is stored in a vector manner, and then format conversion is performed on the feature information to obtain converted information, where the converted information is information with a uniform format, at this time, a preset feature factor is further obtained, for example, the preset feature factor may be manually input or preset based on experience by a manager, and then the initial prediction model is trained according to the converted information and the preset feature factor, for example, the converted information and the preset feature factor are input as training data into the initial prediction model for training, and obtaining a scheduling prediction model.
In the embodiment of the invention, the intelligent analysis and automatic scheduling prediction are carried out on the operation data by establishing the scheduling prediction model, and the current manual scheduling mode is replaced, so that the scheduling efficiency of managers can be greatly improved, the workload of the managers is reduced, and the working efficiency of the managers is improved; meanwhile, because the unified scheduling prediction is carried out on all the operation data of the whole centralized operation center, the scheduling of all the bank personnel in the centralized operation center can be optimized, the scheduling effectiveness of the bank personnel is effectively improved, the utilization rate of human resources is improved, and the operation benefit of an enterprise is improved.
In an embodiment of the present invention, the method further comprises: acquiring limiting conditions, wherein the limiting conditions comprise the geographic position of the centralized operation center and the type of a network point; and generating and issuing scheduling prediction information based on the limiting conditions, the task range and the scheduling prediction model.
In a possible embodiment, after the scheduling prediction model is established, the administrator may directly perform scheduling prediction through the scheduling prediction model, however, in order to further improve the accuracy of scheduling prediction, the administrator needs to limit the prediction process according to actual situations, for example, first obtain a limiting condition, where the limiting condition includes, but is not limited to, a geographical location of a centralized operation center, a network type where bank personnel work, and the like, and in the process of performing scheduling prediction on each bank personnel, the scheduling prediction model performs scheduling prediction according to the limiting condition, a task range of each bank personnel and operation data obtained in real time, and after generating scheduling prediction information, issues the scheduling prediction information.
In the embodiment of the invention, the intelligent scheduling prediction model is further limited in the scheduling prediction process, so that the generated prediction result can be ensured to better meet the actual requirement and the actual condition, the accuracy of the prediction result is further improved, the rapid, automatic and accurate scheduling management can be realized, and the working experience of bank personnel is improved.
In the actual application process, the model training and the analysis process of the operation data are both automatic, the automatic scheduling prediction still needs the management personnel to manually input information such as relevant limiting conditions, and the management personnel needs to input the information according to the actual working condition of each bank personnel, so that the workload of the management personnel is further reduced, the working experience of the management personnel is improved, the operation data can be visually displayed, and the management personnel can be assisted to perform better information input and scheduling prediction management.
In an embodiment of the present invention, the method further comprises: acquiring preset monitoring index information; calculating the operation data based on the preset monitoring index information to generate corresponding monitoring data, wherein the monitoring data comprises resource monitoring data corresponding to each resource; analyzing the resource monitoring data, and determining an analysis result corresponding to each resource; generating a visual monitoring view based on the analysis result.
Further, in an embodiment of the present invention, the method further includes: before issuing the scheduling prediction information, acquiring 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 issuing the adjusted scheduling information.
In a possible implementation manner, first, preset monitoring index information is obtained, for example, the preset monitoring index information is a related monitoring index determined by a manager according to actual requirements of manual monitoring, after operation data of a bank is obtained in real time, the operation data is calculated according to the preset monitoring index information, and corresponding monitoring data is generated. At this time, the resource monitoring data is analyzed, for example, the resource monitoring data may be analyzed according to information such as a mechanism, a task category, a post, and the like, so as to determine an analysis result corresponding to each bank person, for example, the analysis result includes, but is not limited to, information such as efficiency of processing a task, average time consumption of processing a task, occupation of the bank person, task access execution, and the like of each bank person, and then a visual monitoring view is generated according to the analysis result and displayed on a screen in a visual manner for a manager to directly view.
In the actual application process, before the scheduling prediction information is generated and then published, user operation information can be acquired, for example, the user operation information is an operation made by a manager based on the visual monitoring view, and the manager inevitably considers that the currently generated scheduling prediction information has some improper places in the visual monitoring process, so that the scheduling prediction information is manually adjusted, the adjusted scheduling information is generated, and then the adjusted scheduling information is published.
In the embodiment of the invention, the real-time and visual monitoring view is provided for the manager during the scheduling prediction process, so that the manager is assisted to accurately adjust and correct the scheduling prediction result, the issued scheduling management information is accurate and effective, the management accuracy of bank personnel is improved, the utilization rate of human resources is improved, and the operating benefit of an enterprise is improved.
The following describes a shift schedule prediction apparatus of a centralized operation center according to an embodiment of the present invention with reference to the drawings.
Referring to fig. 4, based on the same inventive concept, an embodiment of the present invention provides a shift schedule prediction apparatus for a centralized operation center, including: a first acquiring unit configured to acquire first attribute information of a task and second attribute information of a 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 obtaining unit, configured to obtain operation data; a model establishing unit for establishing a shift scheduling prediction model based on the operation data; and the scheduling prediction unit is used for generating and issuing scheduling prediction information based on the scheduling prediction model and the task range.
In an embodiment of the present invention, the apparatus further includes a preprocessing unit, and the preprocessing unit is configured to: before the shift scheduling prediction model is established, preprocessing the operation data based on the first attribute information and the second attribute information to obtain preprocessed data; acquiring a preset classification rule; classifying the preprocessed data based on the preset classification rule to obtain classified data; the model building unit is further configured to: and establishing the scheduling prediction model based on the classified data.
In an embodiment of the present invention, the range determining unit includes: the classification module is used for classifying the resources based on the second attribute information to obtain classified resources; a corresponding module, configured to respectively establish a corresponding relationship between each classified resource and at least one task based on the first attribute information; and the range determining module is used for determining the task range of each classified resource based on the corresponding relation.
In an embodiment of the present invention, the model establishing unit includes: the initial model obtaining module is used for obtaining an initial prediction model; the characteristic extraction module is used for extracting the characteristics of the operation data to obtain corresponding characteristic information; the conversion module is used for executing format conversion on the characteristic information to obtain converted information; and the model establishing module is used for acquiring a preset characteristic factor, training the initial prediction model based on the converted information and the preset characteristic factor and acquiring the scheduling prediction model.
In an embodiment of the present invention, the apparatus further includes a limiting unit, the limiting unit is configured to: acquiring limiting conditions, wherein the limiting conditions comprise the geographic position of the centralized operation center and the type of a network point; the shift scheduling prediction unit is further configured to: and generating and issuing scheduling prediction information based on the limiting conditions, the task range and the scheduling prediction model.
In an embodiment of the invention, the apparatus further comprises a visualization monitoring unit, the visualization monitoring unit being configured to: acquiring preset monitoring index information; calculating the operation data based on the preset monitoring index information to generate corresponding monitoring data, wherein the monitoring data comprises resource monitoring data corresponding to each resource; analyzing the resource monitoring data, and determining an analysis result corresponding to each resource; generating a visual monitoring view based on the analysis result.
In an embodiment of the present invention, the apparatus further includes an adjusting unit, where the adjusting unit is configured to: before issuing the scheduling prediction information, acquiring 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 issuing the adjusted scheduling information.
Further, the embodiment of the present invention also provides a processor, where the processor is configured to execute the method according to the embodiment of the present invention.
Further, the embodiment of the present invention also provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor implements the method according to the embodiment of the present invention.
Further, the embodiment of the present invention also provides a computer program product, which includes a computer program, and the computer program implements the method of the embodiment of the present invention when being executed by a processor.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
Computer-readable media, which include both non-transitory and non-transitory, removable and non-removable media, may implement the information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
According to the technical scheme, the data acquisition, storage, use, processing and the like meet relevant regulations of national laws and regulations.
Claims (17)
1. A scheduling prediction method of a centralized operation center is characterized by comprising the following steps:
acquiring first attribute information of a task and second attribute information of a resource;
determining a task scope of the resource based on the first attribute information and the second attribute information;
acquiring operation data;
establishing a scheduling prediction model based on the operation data;
and generating and issuing scheduling prediction information based on the scheduling prediction model and the task range.
2. The method of claim 1, further comprising:
before the shift scheduling prediction model is established, preprocessing the operation data based on the first attribute information and the second attribute information to obtain preprocessed data;
acquiring a preset classification rule;
classifying the preprocessed data based on the preset classification rule to obtain classified data;
and establishing the scheduling prediction model based on the classified data.
3. The method of claim 1, wherein determining the task scope of the resource based on the first attribute information and the second attribute information comprises:
classifying the resources based on the second attribute information to obtain classified resources;
respectively establishing a corresponding relation between each classified resource and at least one task based on the first attribute information;
and determining the task range of each classified resource based on the corresponding relation.
4. The method of claim 1, wherein building a shift prediction model based on the operational data comprises:
obtaining an initial prediction model;
extracting the characteristics of the operation data to obtain corresponding characteristic information;
performing format conversion on the characteristic information to obtain converted information;
and acquiring a preset characteristic factor, and training the initial prediction model based on the converted information and the preset characteristic factor to obtain the scheduling prediction model.
5. The method of claim 1, further comprising:
acquiring limiting conditions, wherein the limiting conditions comprise the geographic position of the centralized operation center and the type of a network point;
and generating and issuing scheduling prediction information based on the limiting conditions, the task range and the scheduling prediction model.
6. The method of claim 1, further comprising:
acquiring preset monitoring index information;
calculating the operation data based on the preset monitoring index information to generate corresponding monitoring data, wherein the monitoring data comprises resource monitoring data corresponding to each resource;
analyzing the resource monitoring data, and determining an analysis result corresponding to each resource;
generating a visual monitoring view based on the analysis result.
7. The method of claim 6, further comprising:
before issuing the scheduling prediction information, acquiring 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 issuing the adjusted scheduling information.
8. A shift schedule prediction apparatus for a centralized work center, the apparatus comprising:
a first acquiring unit configured to acquire first attribute information of a task and second attribute information of a 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 obtaining unit, configured to obtain operation data;
a model establishing unit for establishing a shift scheduling prediction model based on the operation data;
and the scheduling prediction unit is used for generating and issuing scheduling prediction information based on the scheduling prediction model and the task range.
9. The apparatus of claim 8, further comprising a pre-processing unit to:
before the shift scheduling prediction model is established, preprocessing the operation data based on the first attribute information and the second attribute information to obtain preprocessed data;
acquiring a preset classification rule;
classifying the preprocessed data based on the preset classification rule to obtain classified data;
the model building unit is further configured to: and establishing the scheduling prediction model based on the classified data.
10. The apparatus of claim 8, wherein the range determining unit comprises:
the classification module is used for classifying the resources based on the second attribute information to obtain classified resources;
a corresponding module, configured to respectively establish a corresponding relationship between each classified resource and at least one task based on the first attribute information;
and the range determining module is used for determining the task range of each classified resource based on the corresponding relation.
11. The apparatus of claim 8, wherein the model building unit comprises:
the initial model obtaining module is used for obtaining an initial prediction model;
the characteristic extraction module is used for extracting the characteristics of the operation data to obtain corresponding characteristic information;
the conversion module is used for executing format conversion on the characteristic information to obtain converted information;
and the model establishing module is used for acquiring a preset characteristic factor, training the initial prediction model based on the converted information and the preset characteristic factor and acquiring the scheduling prediction model.
12. The apparatus of claim 8, further comprising a restriction unit to:
acquiring limiting conditions, wherein the limiting conditions comprise the geographic position of the centralized operation center and the type of a network point;
the shift scheduling prediction unit is further configured to: and generating and issuing scheduling prediction information based on the limiting conditions, the task range and the scheduling prediction model.
13. The apparatus according to claim 8, further comprising a visualization monitoring unit for:
acquiring preset monitoring index information;
calculating the operation data based on the preset monitoring index information to generate corresponding monitoring data, wherein the monitoring data comprises resource monitoring data corresponding to each resource;
analyzing the resource monitoring data, and determining an analysis result corresponding to each resource;
generating a visual monitoring view based on the analysis result.
14. The apparatus of claim 13, further comprising an adjustment unit configured to:
before issuing the scheduling prediction information, acquiring 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 issuing the adjusted scheduling information.
15. A processor configured to perform the method of any one of claims 1-7.
16. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a method of cropping a live video according to any one of claims 1 to 7.
17. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the method of cropping a live video of any of claims 1-7.
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