CN113392291A - Service recommendation method and system based on data center - Google Patents
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Abstract
The utility model provides a service recommendation method and system based on a data center, which is used for acquiring employee behavior data in a project center; obtaining an employee behavior map according to the behavior data, establishing a connection between the constructed structured behavior map and each service project, and clustering user preference; obtaining a service recommendation result according to the clustering result and a preset random forest regression model; the staff life service recommendation method and device based on the staff behavior data achieve staff life service recommendation based on the staff plan tasks and the project parameter data, and greatly improve work efficiency on the premise of improving staff construction site life quality.
Description
Technical Field
The disclosure relates to the technical field of data processing, and in particular relates to a service recommendation method and system based on a data center.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
With the global vigorous development of the data center industry and the rapid growth of social economy, the development and construction of the data center are in a high-speed period, and the development of the data center industry is greatly favored by the strong support of emerging industries by government departments in various regions. With the rapid development of the data center industry, a large development space is available in many cities in the future, and a large data center is required to be constructed in the construction and operation processes of domestic conventional thermal power generation projects.
However, the inventor finds that in the management process of engineering projects, the application of the data center still remains on the simple data query, and the data is not effectively processed and integrated to improve the working efficiency; with the continuous development of intelligent construction sites, the working environment and working conditions of employees are improved more and more, and the better life service of the employees cannot be realized by directly processing data of a data center at present; most of the existing task allocation modes are field arrangement, effective planning management is lacked, and task execution efficiency is low due to parallel implementation of tasks or lack of task executors for some urgent work tasks related to a flow form, such as financial reimbursement tasks of construction site financial personnel, supervision tasks of construction site supervision personnel and approval tasks of various chatless applications.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a service recommendation method and system based on a data center, which realize staff life service recommendation based on staff behavior data, simultaneously realize work task recommendation based on staff planning tasks and project parameter data, and greatly improve the work efficiency on the premise of improving the quality of staff construction site life.
In order to achieve the purpose, the following technical scheme is adopted in the disclosure:
the first aspect of the disclosure provides a service recommendation method based on a data center.
A service recommendation method based on a data center comprises the following processes:
acquiring employee behavior data in a project center;
obtaining an employee behavior map according to the behavior data, establishing a connection between the constructed structured behavior map and each service project, and clustering user preference;
and obtaining a service recommendation result according to the clustering result and a preset random forest regression model.
Further, the construction of the employee behavior map comprises the following steps:
and extracting attributes, relations and entities of the collected structured service data, carrying out knowledge fusion on scattered data through reference resolution, entity disambiguation and entity linkage to obtain knowledge expression, and obtaining a user interest map through quality evaluation.
Further, training of the random forest regression model comprises:
assuming that the divided training set data samples are N, extracting samples with the same capacity by adopting a Bootstrap sampling method to form a training subset;
assuming that the training subset has M characteristics, randomly extracting M characteristics from the training subset as a splitting characteristic subset, and splitting without pruning by adopting a CART regression algorithm;
repeating the previous two steps for n times to generate n sub regression trees and predict the result to form an RF regression prediction recommendation model;
and obtaining a final recommendation result according to the output average value of the n child regression trees.
Further, acquiring project parameter data;
obtaining scores of all plan tasks according to plan task data of the staff to be recommended and project parameter data related to the plan tasks;
and sequencing the planning tasks according to the sequence of scores from large to small, and recommending the planning tasks according to the sequencing result.
Further, the project parameter data includes at least: the system comprises planned task data of each employee in a preset time period, current on-duty data of each employee, current task data of each employee and physical data required by task execution.
Further, the physical data required for task execution includes at least: personnel quantity, material quantity, equipment state and environmental parameters.
Furthermore, when a task needing to be executed emergently is received, the emergency task is executed, after the emergency task is executed, scoring calculation of the remaining planning tasks is carried out again, and task recommendation is carried out again according to a ranking result of scoring.
Furthermore, obtaining the scores of all the plan tasks according to the plan task data of the staff to be recommended and the project parameter data related to the staff tasks to be recommended, which comprises the following steps:
acquiring historical completion data of a planned task;
obtaining flow data required by task completion according to the acquired historical completion data;
matching the obtained flow data with the obtained project parameter data, calculating the time required by task completion, and comparing the obtained time required by task completion with the historical completion time of the planned task to obtain the score of the planned task;
when the time required for completing the planning task is less than the historical completion time, the score is larger when the difference is larger, and when the time required for completing the planning task is greater than the historical completion time, the score is smaller when the difference is larger.
A second aspect of the present disclosure provides a data center-based service recommendation system.
A data center-based service recommendation system, comprising:
a data acquisition module configured to: acquiring employee behavior data in a project center;
a preference clustering module configured to: obtaining an employee behavior map according to the behavior data, establishing a connection between the constructed structured behavior map and each service project, and clustering user preference;
a service recommendation module configured to: and obtaining a service recommendation result according to the clustering result and a preset random forest regression model.
A third aspect of the present disclosure provides a computer-readable storage medium, on which a program is stored, which when executed by a processor, performs the steps in the data center-based service recommendation method according to the first aspect of the present disclosure.
A fourth aspect of the present disclosure provides an electronic device, including a memory, a processor, and a program stored on the memory and executable on the processor, where the processor executes the program to implement the steps in the data center-based service recommendation method according to the first aspect of the present disclosure.
Compared with the prior art, the beneficial effect of this disclosure is:
1. according to the method, the system, the medium or the electronic equipment, the employee behavior graph is obtained according to the behavior data, the established structured behavior graph is connected with each service project, and user preference clustering is carried out; and obtaining a service recommendation result according to the clustering result and a preset random forest regression model, realizing the life and work preference of the staff through the analysis of the staff behavior data, and better recommending the staff service.
2. The method, the system, the medium or the electronic equipment disclosed by the invention can be used for sequencing tasks by combining project parameter data and staff planning task data, so that the completion efficiency of the planning tasks is greatly improved, and the interruption of the execution of the planning tasks can be avoided to the greatest extent.
3. According to the method, the system, the medium or the electronic equipment, when the temporary important task is received, the temporary important task is preferentially executed, after the important task is executed, the rest planned tasks are reordered, and the task recommendation is carried out according to the result of the reordering.
Advantages of additional aspects of the disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the disclosure.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
Fig. 1 is a schematic flowchart of a data center-based service recommendation method provided in embodiment 1 of the present disclosure.
Detailed Description
The present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
Example 1:
as shown in fig. 1, an embodiment 1 of the present disclosure provides a data center-based service recommendation method, including the following processes:
acquiring employee behavior data in a project center;
obtaining an employee behavior map according to the behavior data, establishing a connection between the constructed structured behavior map and each service project, and clustering user preference;
and obtaining a service recommendation result according to the clustering result and a preset random forest regression model.
Specifically, the method comprises the following steps:
acquiring behavior data and user attributes of all employees, wherein the behavior data at least comprises building site diet data, building site sleep data, building site entertainment data and the like, and the user attributes at least comprise common workers, supervisors, project managers and the like;
the method comprises the steps of extracting attributes, relations and entities of collected structured user information, carrying out knowledge fusion on scattered information through key technologies such as reference resolution, entity disambiguation, entity linking and the like to obtain a series of knowledge expressions, and obtaining a user behavior map through quality evaluation.
Establishing a relation between the constructed structured user interest map and energy service products, clustering user preference, associating employee attributes with service types, and clustering according to scores of various services of employees.
The random forest regression model is composed of multiple cart (classification and regression tree) regression trees, the regression trees correspond to a partition of the input space (feature space) and output values on the partition units, and can be represented by a set, that is: { h (X, Ψ k) | k ═ 1, 2, …, N }, X denotes an input vector matrix, Ψ k denotes generation of k sub-regression trees, the sub-regression trees grown in the set are independent samples extracted based on a boottrap method and have the same distribution, and finally, a final recommendation result is obtained through statistics, wherein the specific training comprises:
(1) and assuming that the divided training set data samples are N, extracting samples with the same capacity from the divided training set data samples by adopting a Bootstrap sampling method to form a training subset.
(2) Assuming that the training subset has M features, randomly extracting M features from the training subset as splitting feature subsets (M is less than or equal to M), and then splitting without pruning by adopting a CART regression algorithm.
(3) Repeating the steps (1) to (2) n times, so that a corresponding number of sub regression trees are generated and result prediction is carried out, and the RF regression prediction recommendation model is formed.
(4) And verifying the reliability of the model by using the divided test set, and obtaining a final recommendation result by using the output average value of the n sub regression trees.
Adopting Forest-RI form, if training set has M dimension, randomly selecting F (F is less than or equal to M) characteristic vectors to train, if F is obtained small enough, then between subtrees
The correlation of (a) tends to be weak; meanwhile, the effect of subtree integration is improved along with the increase of F. Taken together, the F value typically needs to be determined according to empirical formula (1):
F＝1+log2M (1)
and constructing a personalized recommendation system based on the contents of the spectral clustering and random forest algorithm model and the real grading data of the employees on the construction site services. Selecting 80% of user original data for processing, constructing a user behavior map, importing the map into a spectral clustering model for segmentation, and clustering into Nc(Nc6) cluster, NcAnd after normalization processing of the cluster data, introducing a random forest regression model, obtaining model hyperparameters by using an empirical formula, obtaining the predicted values of the components of the subsequences after the FR model training is finished, carrying out reverse normalization processing, and overlapping the predicted values of all the subsequences to obtain a final recommendation result.
The following service recommendations are also included:
s1: application function recommendation
The application function management can manage and maintain an application function list recommended by the intelligent portal, maintain modules commonly used by users in enterprises, add icons to the function links, and print labels, so that the application functions can be recommended to the users through employee behavior logs and preference information, and meanwhile, the users are also supported to search, and an administrator can popularize the application according to a certain application.
S1.1: application management
S1.1.1: application maintenance: and adding, modifying and deleting maintenance for the recommended application manually, adding and modifying a recommendable application list, setting an application icon, a character description and an entry link between the PC and the mobile terminal, marking a corresponding label for the application, and recommending original data for the application function.
S1.1.2: application interface import: the list of applications that the user finds is imported EKP as recommendable applications in support of a data dictionary interface provided by the EKP to reduce maintenance efforts by administrative maintenance personnel.
S1.1.3: application log importing: and automatically importing the module entrance with high-frequency access and the newly-built functional link into a recommendable application list through a user access log, wherein the imported application list can be recommended to the user after editing and processing are performed to perfect information such as pictures and the like.
S1.2: application recommendation
S1.2.1: an application recommendation interface: and providing an interface capable of acquiring the application recommendation list of the current user, and returning the recommendation list data recommended to the current user and calculated by the system.
S1.2.2: and (3) application recommendation showing: and the user provides a portal presentation component capable of presenting the application recommendation list information and presents the presentable list.
S1.2.3: personal application collection: the user can collect the required application list of the user, and the collected application list is preferentially returned in the recommendation interface return list
S2: related work recommendations
The work recommendation pushes recent related work tasks in respective fields to users of different departments, posts and labels, so that the users can reasonably process the work tasks according to priority and deadline, follow up the progress and state of the tasks at any time and any place, and ensure that the tasks are completed on time.
S2.1: event management
And newly establishing and maintaining event configuration, including event name, event type, detection interface address, message keyword, enabling and disabling, and the like, wherein the event sends a request to the detection interface address, determines whether to generate or update a corresponding task according to the returned data, and records an event execution log.
S2.2: task management
And newly establishing and maintaining task related configuration, including task name, task type, task link, task description, priority, effective period, generation and update event, update event execution frequency and the like. When a rule executes, the generating task is configured accordingly.
And viewing and searching all generated task related information, task names, executives, states, priorities, creation time, deadline and the like.
S2.3: rule management
And establishing and updating rules of the tasks, configuring related tasks for labels or departments, posts, groups and personnel, and executing related events to generate or update work tasks for configured people under the configured frequency. The rules can be started and stopped at regular time or immediately, and flexible control is provided.
S2.4: client push
And (3) working and pushing: the user can check and process the current task to be processed at the portal, reasonably arrange work according to the priority and the deadline, and follow up the progress and the state of the task.
Specifically, for a related non-urgent planning task which needs cooperation of multiple persons or is performed by multiple subtasks in parallel or in a flow manner, the following task pushing is performed:
acquiring project parameter data;
obtaining scores of all plan tasks according to plan task data of the staff to be recommended and project parameter data related to the plan tasks;
and sequencing the planning tasks according to the sequence of scores from large to small, and recommending the planning tasks according to the sequencing result.
The project parameter data includes at least: the system comprises planned task data of each employee in a preset time period, current on-duty data of each employee, current task data of each employee and physical data required by task execution.
The physical data required for task execution include at least: personnel quantity, material quantity, equipment state and environmental parameters.
And when receiving a task needing to be executed emergently, executing the emergency task, after the emergency task is executed, re-performing grading calculation on the rest planned tasks, and re-performing task recommendation according to a grading sequencing result.
Obtaining the scores of all the plan tasks according to the plan task data of the staff to be recommended and the project parameter data related to the staff tasks to be recommended, wherein the scores comprise:
acquiring historical completion data of a planned task;
obtaining flow data required by task completion according to the acquired historical completion data;
matching the obtained flow data with the obtained project parameter data, calculating the time required by task completion, and comparing the obtained time required by task completion with the historical completion time of the planned task to obtain the score of the planned task;
when the time required for completing the planning task is less than the historical completion time, the score is larger when the difference is larger, and when the time required for completing the planning task is greater than the historical completion time, the score is smaller when the difference is larger.
S3: content tag management
The content tag management provides a favorite recommendation support of knowledge content recommendation, provides a uniform tagging service for knowledge to be recommended, supports the processing of portrait favorite tags of users by defining and marking uniform tags, and recommends knowledge related to the favorite tags for the users.
S3.1.: content tag modification
Smart label: the system recommends the use of related labels according to the knowledge content text, helps the user to carry out rapid classified marking management on the knowledge content, and reduces the burden of a plurality of knowledge content creators on document classified management
Common labels: the intelligent label recommended by the system does not accurately reflect knowledge content information, and a user can add a common label to classify knowledge
S3.2: and (3) content tag query: the content label management can be carried out screening through the content source system and the corresponding content label, and the label information marked by the content can be checked and managed
Example 2:
an embodiment 2 of the present disclosure provides a service recommendation system based on a data center, including:
a data acquisition module configured to: acquiring employee behavior data in a project center;
a preference clustering module configured to: obtaining an employee behavior map according to the behavior data, establishing a connection between the constructed structured behavior map and each service project, and clustering user preference;
a service recommendation module configured to: and obtaining a service recommendation result according to the clustering result and a preset random forest regression model.
The working method of the system is the same as the data center-based service recommendation method provided in embodiment 1, and is not described herein again.
Example 3:
the embodiment 3 of the present disclosure provides a computer-readable storage medium, on which a program is stored, which when executed by a processor, implements the steps in the data center-based service recommendation method according to the embodiment 1 of the present disclosure.
Example 4:
the embodiment 4 of the present disclosure provides an electronic device, which includes a memory, a processor, and a program stored in the memory and executable on the processor, where the processor executes the program to implement the steps in the data center-based service recommendation method according to embodiment 1 of the present disclosure.
As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure 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, optical storage, and the like) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. 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.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Claims (10)
1. A service recommendation method based on a data center is characterized in that: the method comprises the following steps:
acquiring employee behavior data in a project center;
obtaining an employee behavior map according to the behavior data, establishing a connection between the constructed structured behavior map and each service project, and clustering user preference;
and obtaining a service recommendation result according to the clustering result and a preset random forest regression model.
2. The data center-based service recommendation method of claim 1, wherein:
the method comprises the following steps of constructing an employee behavior map, including:
and extracting attributes, relations and entities of the collected structured service data, carrying out knowledge fusion on scattered data through reference resolution, entity disambiguation and entity linkage to obtain knowledge expression, and obtaining a user interest map through quality evaluation.
3. The data center-based service recommendation method of claim 1, wherein:
training a random forest regression model, comprising:
assuming that the divided training set data samples are N, extracting samples with the same capacity by adopting a Bootstrap sampling method to form a training subset;
assuming that the training subset has M characteristics, randomly extracting M characteristics from the training subset as a splitting characteristic subset, and splitting without pruning by adopting a CART regression algorithm;
repeating the previous two steps for n times to generate n sub regression trees and predict the result to form an RF regression prediction recommendation model;
and obtaining a final recommendation result according to the output average value of the n child regression trees.
4. The data center-based service recommendation method of claim 1, wherein:
acquiring project parameter data;
obtaining scores of all plan tasks according to plan task data of the staff to be recommended and project parameter data related to the plan tasks;
and sequencing the planning tasks according to the sequence of scores from large to small, and recommending the planning tasks according to the sequencing result.
5. The data center-based service recommendation method of claim 4, wherein:
the project parameter data includes at least: the system comprises planning task data of each employee in a preset time period, current on-duty data of each employee, current task data of each employee and physical data required by task execution;
alternatively, the first and second electrodes may be,
the physical data required for task execution include at least: personnel quantity, material quantity, equipment state and environmental parameters.
6. The data center-based service recommendation method of claim 4, wherein:
and when receiving a task needing to be executed emergently, executing the emergency task, after the emergency task is executed, re-performing grading calculation on the rest planned tasks, and re-performing task recommendation according to a grading sequencing result.
7. The data center-based service recommendation method of claim 4, wherein:
obtaining the scores of all the plan tasks according to the plan task data of the staff to be recommended and the project parameter data related to the staff tasks to be recommended, wherein the scores comprise:
acquiring historical completion data of a planned task;
obtaining flow data required by task completion according to the acquired historical completion data;
matching the obtained flow data with the obtained project parameter data, calculating the time required by task completion, and comparing the obtained time required by task completion with the historical completion time of the planned task to obtain the score of the planned task;
when the time required for completing the planning task is less than the historical completion time, the score is larger when the difference is larger, and when the time required for completing the planning task is greater than the historical completion time, the score is smaller when the difference is larger.
8. A service recommendation system based on a data center is characterized in that: the method comprises the following steps:
a data acquisition module configured to: acquiring employee behavior data in a project center;
a preference clustering module configured to: obtaining an employee behavior map according to the behavior data, establishing a connection between the constructed structured behavior map and each service project, and clustering user preference;
a service recommendation module configured to: and obtaining a service recommendation result according to the clustering result and a preset random forest regression model.
9. A computer-readable storage medium, on which a program is stored, which, when being executed by a processor, carries out the steps of the data center based service recommendation method according to any one of claims 1-7.
10. An electronic device comprising a memory, a processor, and a program stored on the memory and executable on the processor, wherein the processor implements the steps of the data center-based service recommendation method of any one of claims 1-7 when executing the program.
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