CN117709691A - Intelligent sub-packaging management method and system based on cloud service - Google Patents
Intelligent sub-packaging management method and system based on cloud service Download PDFInfo
- Publication number
- CN117709691A CN117709691A CN202410162438.3A CN202410162438A CN117709691A CN 117709691 A CN117709691 A CN 117709691A CN 202410162438 A CN202410162438 A CN 202410162438A CN 117709691 A CN117709691 A CN 117709691A
- Authority
- CN
- China
- Prior art keywords
- sub
- risk
- service
- package
- business
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
- 238000007726 management method Methods 0.000 title claims abstract description 71
- 238000004806 packaging method and process Methods 0.000 title claims abstract description 27
- 238000012544 monitoring process Methods 0.000 claims abstract description 34
- 238000013210 evaluation model Methods 0.000 claims abstract description 16
- 238000011156 evaluation Methods 0.000 claims abstract description 14
- 230000000007 visual effect Effects 0.000 claims abstract description 12
- 238000000354 decomposition reaction Methods 0.000 claims abstract description 11
- 238000005457 optimization Methods 0.000 claims abstract description 11
- 239000013598 vector Substances 0.000 claims description 72
- 238000000034 method Methods 0.000 claims description 45
- 238000007781 pre-processing Methods 0.000 claims description 14
- 230000008447 perception Effects 0.000 claims description 13
- 230000002776 aggregation Effects 0.000 claims description 10
- 238000004220 aggregation Methods 0.000 claims description 10
- 238000012937 correction Methods 0.000 claims description 10
- 230000007246 mechanism Effects 0.000 claims description 10
- 239000011159 matrix material Substances 0.000 claims description 9
- 238000012545 processing Methods 0.000 claims description 9
- 230000003993 interaction Effects 0.000 claims description 8
- 230000002159 abnormal effect Effects 0.000 claims description 5
- 230000004931 aggregating effect Effects 0.000 claims description 5
- 238000004458 analytical method Methods 0.000 claims description 5
- 238000011176 pooling Methods 0.000 claims description 5
- 230000002457 bidirectional effect Effects 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 4
- 239000003795 chemical substances by application Substances 0.000 claims description 4
- 238000005520 cutting process Methods 0.000 claims description 4
- 238000000605 extraction Methods 0.000 claims description 4
- 238000005516 engineering process Methods 0.000 abstract description 8
- 238000003860 storage Methods 0.000 description 9
- 230000008569 process Effects 0.000 description 5
- 238000012216 screening Methods 0.000 description 4
- 238000011161 development Methods 0.000 description 3
- 230000018109 developmental process Effects 0.000 description 3
- 238000012549 training Methods 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000012512 characterization method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000008451 emotion Effects 0.000 description 2
- BTCSSZJGUNDROE-UHFFFAOYSA-N gamma-aminobutyric acid Chemical compound NCCCC(O)=O BTCSSZJGUNDROE-UHFFFAOYSA-N 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000011218 segmentation Effects 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000008713 feedback mechanism Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 210000001503 joint Anatomy 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000003908 quality control method Methods 0.000 description 1
- 238000005096 rolling process Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06313—Resource planning in a project environment
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
- G06N3/0442—Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Data Mining & Analysis (AREA)
- Entrepreneurship & Innovation (AREA)
- Life Sciences & Earth Sciences (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Marketing (AREA)
- General Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Game Theory and Decision Science (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- Biophysics (AREA)
- Mathematical Physics (AREA)
- Computing Systems (AREA)
- Molecular Biology (AREA)
- General Health & Medical Sciences (AREA)
- Computational Linguistics (AREA)
- Software Systems (AREA)
- Biomedical Technology (AREA)
- Educational Administration (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Biodiversity & Conservation Biology (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses an intelligent sub-packaging management method and system based on cloud service, comprising the following steps: acquiring the package sending requirement and the item package information of a target item, and dividing the package sending requirement and the item package information into a plurality of sub-items; sensing execution data of each sub-item by using the data, uploading the execution data to a cloud platform, acquiring service progress and service quality of the sub-item, and performing risk identification by risk decomposition; constructing a subcontracting service evaluation model, evaluating service risk results of subcontracting services of the sub-projects according to the risk quantity, and generating multi-dimensional early warning of the subcontracting services; and monitoring the execution parameters corresponding to the subpackaging services based on the early warning information, acquiring the similarity of the subpackaging services, and performing scheduling optimization among the subpackaging services according to the similarity. The invention combines the information technology and the cloud service technology to realize the global visual supervision of the sub-packet service, and the risk factors are comprehensively identified to carry out evaluation and early warning, thereby ensuring the service progress and the service quality of the project and improving the management efficiency of the sub-packet service of the project.
Description
Technical Field
The invention relates to the technical field of sub-packet management, in particular to an intelligent sub-packet management method and system based on cloud services.
Background
With the rapid development of society, the requirements on business projects are higher and higher, and division of labor is becoming finer. In order to effectively reduce the management cost and the operation cost of enterprises, corresponding tasks are subcontracted to matched subcontracting units, and quality monitoring is conducted on the subcontracting units. The business sub-packaging mode is widely applied by various large enterprises, and not only can the sub-packaging mode achieve the sub-division cooperation effect with sub-packaging units, but also the risk can be effectively avoided, the risk of the business sub-packaging mode is reduced, and the benefit of the business sub-packaging mode is prevented from being lost.
The management mode of the current subcontracting is continuously explored, and the current subcontracting is gradually entered into a development stable area to form a fixed mode. There are many problems and bottlenecks in subcontracting management, such as the fact that subcontracting is not standard in selection, the development of subcontracting management is delayed, and the management strength of business process is insufficient. The problems of technology, management, monitoring and the like in the actual process of project on-site subcontracting management and quality monitoring limit the implementation of project on-site subcontracting management and quality monitoring, so that various uncontrollable risk problems of projects occur. Therefore, how to improve the sub-package management based on the cloud service, and by evaluating the business risk, the sub-package management is enhanced, and the management efficiency is improved.
Disclosure of Invention
In order to solve the technical problems, the invention provides an intelligent sub-package management method and system based on cloud services.
The first aspect of the invention provides an intelligent sub-package management method based on cloud service, which comprises the following steps:
acquiring the package sending requirement and the item sub-package information of a target item, dividing the target item to generate a plurality of sub-items, and matching the sub-items with the item sub-package information;
acquiring execution data of each sub-item by utilizing data perception, uploading the execution data to a cloud platform for preprocessing and aggregation processing, acquiring service progress and service quality of the sub-item, and carrying out risk identification by combining the service progress and the service quality through risk decomposition;
determining the risk quantity of risk factors by combining the execution data of the cloud platform, constructing a sub-package service evaluation model, evaluating the service risk result of sub-project sub-package service according to the risk quantity, and generating multi-dimensional early warning of the sub-package service;
and monitoring the execution parameters corresponding to the subpackaging services based on the early warning information, acquiring the similarity of the subpackaging services, and performing scheduling optimization among the subpackaging services according to the similarity.
In the scheme, the target item is divided to generate a plurality of sub-items, and the sub-items are matched with the item sub-packaging information, specifically:
Dividing the package demand of the target item into words, matching the word division result with a preset word stock to obtain package demand keywords, and dividing the target item into packages according to the package demand keywords to obtain sub-items corresponding to different packages;
acquiring item sub-package information according to sub-package bidding information, matching the item sub-package information with the sub-item, acquiring a historical sub-package record of a sub-package corresponding to the item sub-package information, and acquiring a sub-package credit image through the historical sub-package record;
obtaining similar items in the historical subcontracting records by utilizing similarity calculation based on the business content of the sub-items, extracting satisfaction evaluation of the similar items on subcontracting merchants, and determining initial credit weight by combining the satisfaction evaluation with subcontracting merchant credit figures;
and constructing a visual management model of the target item in the cloud platform, importing each sub-item, the corresponding subcontractor and the initial credit weight into the visual management model, and carrying out visual marking by combining business logic of different sub-items.
In the scheme, execution data of each sub-item is acquired by utilizing data perception and is uploaded to a cloud platform for preprocessing and aggregation, and the service progress and service quality of the sub-item are acquired specifically as follows:
Acquiring full-flow monitoring and management data of sub-project corresponding sub-package business according to distributed data sensing equipment, matching the full-flow monitoring and management data with business results to generate execution data, preprocessing the execution data in a cloud platform, and eliminating abnormal sensing data;
the method comprises the steps of obtaining sub-packaging business workflow of different sub-items, constructing an execution data sequence according to execution data corresponding to a preset time step, cutting the execution data sequence to generate a plurality of execution sub-sequences, clustering the execution sub-sequences, calculating the mahalanobis distance between the different execution sub-sequences, and obtaining a clustering result;
acquiring service execution categories of the execution sub-sequences according to the clustering result, positioning the execution data sequences with service execution category labels in the sub-package service workflow, and respectively importing the execution sub-sequences of different service execution categories into a Bi-GRU network;
extracting features corresponding to the execution subsequences from the Bi-GRU network by utilizing a bidirectional gating unit, acquiring time correlation of the execution subsequences, importing the features into association between learning features of the graph-annotation-force network, constructing a feature graph according to the association, and acquiring attention weights among the features through an attention mechanism;
Constructing an adjacency matrix to represent the feature graph by taking the attention weight as the weight of the feature graph edge structure, multiplying the adjacency matrix by the output of the Bi-GRU network to obtain execution characteristics of different service execution categories, and predicting the service progress according to the execution characteristics;
in addition, according to the sub-sequence extraction operation procedure, fine granularity analysis is carried out on the operation procedure to obtain operation procedure key points, point-by-point matching is carried out according to the operation procedure key points and the procedure corresponding to the sub-package business workflow, and the business quality is obtained according to the matching result.
In the scheme, the risk identification is carried out by combining the business progress and the business quality through risk decomposition, and specifically comprises the following steps:
acquiring the business progress and the business quality of each sub-item, comparing the business progress and the business quality with preset standards, and judging whether the sub-package business corresponding to the sub-item has business progress or business quality risk;
acquiring a risk event by utilizing a big data means, performing embedding operation on a text corresponding to the risk event to acquire a corresponding word vector, constructing a semantic model according to a Bi-LSTM model, and importing the word vector into the semantic set model to acquire a forward hidden vector and a backward hidden vector containing semantic information;
Splicing the forward hidden vector and the backward hidden vector to obtain an output vector, determining semantic information of a word vector through the output vector, obtaining risk factor classification according to the semantic information, and obtaining correlation coefficients of a risk factor entity and a risk event entity by adopting a pearson correlation coefficient;
sorting according to the correlation coefficient to obtain a preset number of risk factors, and generating a risk factor set corresponding to the business progress risk and the business quality risk;
and when the sub-projects have business progress risks or business quality risks, carrying out risk identification in the whole-flow monitoring and management data corresponding to each sub-package business according to the risk factor set, and determining risk factors corresponding to different sub-projects.
In the scheme, a sub-package service evaluation model is constructed, service risk results of sub-project sub-package services are evaluated according to the risk amount, and multi-dimensional early warning of the sub-package services is generated, specifically:
acquiring a historical interaction relation among risk factors according to a risk event, generating an undirected graph corresponding to the risk factors through the historical interaction relation, learning the undirected graph through a graph convolution network, and constructing a subcontracting service evaluation model;
Assigning values to risk factors according to a preset mode to the whole-flow monitoring and management data of sub-package business corresponding to different sub-projects, obtaining the risk quantity of the risk factors, interpolating an undirected graph according to the risk quantity, and extracting a graph structure corresponding to the sub-projects;
generating self-attention weights among the risk factors by using a self-attention mechanism in the graph structure, aggregating the risk factors by using the self-attention weights, and acquiring association vectors by using an average pooling operation;
fusing the association vector with the initial vector representation of the risk factor node to obtain a final vector representation, and combining the final vector representation with a preset factor weight to obtain the initial business risk degree of the sub-item;
and combining the initial business risk degree of the sub-item with the initial credit weight of the subcontractor to obtain a business risk result of the subcontractor, and generating multi-dimensional early warning of the subcontractor according to the Markov distance between the final vector representation of the risk factor and the reference vector in the low-dimensional space when the business risk result is larger than a preset threshold.
In the scheme, the execution parameters corresponding to the subpackaging services are monitored based on the early warning information, the similarity of the subpackaging services is obtained, and the scheduling optimization among the subpackaging services is performed according to the similarity, specifically:
Acquiring risk factors corresponding to early warning information, extracting relevant data from the whole-flow monitoring and management data of the subcontracting service by utilizing the risk factors, and sending the early warning information to corresponding subcontracting agents by combining the relevant data to carry out supervision, punishment and rectification of field parameters;
updating the credit profile of the SCE based on the supervision, penalty and correction results;
the method comprises the steps of obtaining sub-package services with service progress risks or service quality risks, marking, calculating the similarity between the marked sub-package services and other sub-package services, preselecting other sub-package services according to the similarity, and guiding the correction of the marked sub-package services by utilizing the service execution data in the preselected other sub-package services;
and if the service risk result after the correction is still greater than a preset threshold value, performing priority re-bidding in the subcontracting business corresponding to the other pre-selected subcontracting business, and replacing the subcontracting business.
The second aspect of the present invention also provides an intelligent subcontracting management system based on cloud service, the system comprising: the intelligent sub-package management system comprises a memory and a processor, wherein the memory comprises an intelligent sub-package management method program based on cloud service, and the intelligent sub-package management method program based on cloud service realizes the following steps when being executed by the processor:
Acquiring the package sending requirement and the item sub-package information of a target item, dividing the target item to generate a plurality of sub-items, and matching the sub-items with the item sub-package information;
acquiring execution data of each sub-item by utilizing data perception, uploading the execution data to a cloud platform for preprocessing and aggregation processing, acquiring service progress and service quality of the sub-item, and carrying out risk identification by combining the service progress and the service quality through risk decomposition;
determining the risk quantity of risk factors by combining the execution data of the cloud platform, constructing a sub-package service evaluation model, evaluating the service risk result of sub-project sub-package service according to the risk quantity, and generating multi-dimensional early warning of the sub-package service;
and monitoring the execution parameters corresponding to the subpackaging services based on the early warning information, acquiring the similarity of the subpackaging services, and performing scheduling optimization among the subpackaging services according to the similarity.
The invention discloses an intelligent sub-packaging management method and system based on cloud service, comprising the following steps: acquiring the package sending requirement and the item package information of a target item, and dividing the package sending requirement and the item package information into a plurality of sub-items; sensing execution data of each sub-item by using the data, uploading the execution data to a cloud platform, acquiring service progress and service quality of the sub-item, and performing risk identification by risk decomposition; constructing a subcontracting service evaluation model, evaluating service risk results of subcontracting services of the sub-projects according to the risk quantity, and generating multi-dimensional early warning of the subcontracting services; and monitoring the execution parameters corresponding to the subpackaging services based on the early warning information, acquiring the similarity of the subpackaging services, and performing scheduling optimization among the subpackaging services according to the similarity. The invention combines the information technology and the cloud service technology to realize the global visual supervision of the sub-packet service, and the risk factors are comprehensively identified to carry out evaluation and early warning, thereby ensuring the service progress and the service quality of the project and improving the management efficiency of the sub-packet service of the project.
Drawings
FIG. 1 shows a flow chart of the intelligent subcontracting management method based on cloud services of the present invention;
FIG. 2 is a flow chart of a method for acquiring the business progress and the business quality of a sub-project according to the present invention;
FIG. 3 is a flow chart of a method of evaluating the business risk results of a subcontracting business in accordance with the present invention;
fig. 4 shows a block diagram of the intelligent subcontracting management system based on cloud services of the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
Fig. 1 shows a flowchart of the intelligent subcontracting management method based on the cloud service.
As shown in fig. 1, a first aspect of the present invention provides an intelligent subcontracting management method based on cloud services, including:
S102, acquiring the package sending requirement and the item sub-package information of a target item, dividing the target item to generate a plurality of sub-items, and matching the sub-items with the item sub-package information;
s104, acquiring execution data of each sub-item by utilizing data perception, uploading the execution data to a cloud platform for preprocessing and aggregation processing, acquiring service progress and service quality of the sub-item, and carrying out risk identification by combining the service progress and the service quality through risk decomposition;
s106, determining the risk quantity of the risk factors by combining the execution data of the cloud platform, constructing a sub-package service evaluation model, evaluating the service risk result of sub-project sub-package service according to the risk quantity, and generating multi-dimensional early warning of the sub-package service;
s108, monitoring the execution parameters corresponding to the sub-package services based on the early warning information, acquiring the similarity of the sub-package services, and performing scheduling optimization among the sub-package services according to the similarity.
It should be noted that, the package issuing party provides a project package issuing requirement, which relates to a labor service, a device lease, a material use, a policy financial service and the like, the package issuing requirement of a target project is segmented, a word segmentation result is matched with a preset word stock to obtain package issuing requirement keywords, data cleaning, data segmentation and training models are carried out on the keywords based on machine learning, a paraphrasing is analyzed, the result is used as a corresponding package issuing requirement keyword, the target project is subjected to package marking division according to the package issuing requirement keywords, and sub projects corresponding to different package marks are obtained; acquiring item sub-package information according to sub-package bidding information, matching the item sub-package information with the sub-item, acquiring a historical sub-package record of a sub-package corresponding to the item sub-package information, and acquiring a sub-package credit image through the historical sub-package record; obtaining similar items in a history subcontracting record by utilizing similarity calculation based on service contents of the sub-items, extracting satisfaction evaluation of the similar items for subcontracting agents, wherein the satisfaction evaluation comprises equipment resource conditions, service management team, service item quality, pricing and the like, and determining initial credit weight by combining the satisfaction evaluation with subcontracting agent credit figures; the method comprises the steps of constructing a visual management model of a target item in the cloud platform, importing each sub-item, a corresponding subcontractor and initial credit weight into the visual management model, providing a flexible service integration and management mechanism, enabling clients (a package sender), a management party (a system operator) and suppliers (a package receiver) to efficiently cooperate, realizing seamless butt joint and optimization of services, carrying out visual marking by combining service logic of different sub-items, and providing a service execution progress tracking, quality control and feedback mechanism, so that the services can be completed in high quality on time.
Fig. 2 shows a flow chart of a method of the invention for obtaining the business progress and the business quality of a sub-project.
According to the embodiment of the invention, the execution data of each sub-item is acquired by utilizing data perception and is uploaded to a cloud platform for preprocessing and aggregation, and the service progress and the service quality of the sub-item are acquired, specifically:
s202, acquiring full-flow monitoring and management data of sub-project corresponding sub-package business according to distributed data sensing equipment, matching the full-flow monitoring and management data with business results to generate execution data, preprocessing the execution data in a cloud platform, and eliminating abnormal sensing data;
s204, acquiring sub-package business workflow of different sub-items, constructing an execution data sequence according to execution data corresponding to a preset time step, cutting the execution data sequence to generate a plurality of execution sub-sequences, clustering the execution sub-sequences, calculating the mahalanobis distance between the different execution sub-sequences, and acquiring a clustering result;
s206, acquiring the service execution category of the execution sub-sequence according to the clustering result, positioning the execution data sequence with the service execution category label in the sub-package service workflow, and respectively importing the execution sub-sequences of different service execution categories into the Bi-GRU network;
S208, extracting features corresponding to the execution subsequences by utilizing a bidirectional gating unit in the Bi-GRU network, acquiring time correlation of the execution subsequences, importing the features into association between learning features of the graph annotation force network, constructing a feature graph according to the association, and acquiring attention weights among the features through an attention mechanism;
s210, constructing an adjacency matrix to represent the feature graph by using the attention weight as the weight of the feature graph edge structure, multiplying the adjacency matrix by the output of the Bi-GRU network to obtain the execution characteristics of different service execution categories, and predicting the service progress according to the execution characteristics;
and S212, carrying out fine granularity analysis on the operation procedure according to the execution of the subsequence extraction operation procedure to obtain operation procedure key points, carrying out point-by-point matching according to the operation procedure key points and the procedure corresponding to the sub-packet service workflow, and obtaining the service quality according to a matching result.
The method is characterized in that the data such as basic information and transaction records of clients and suppliers are acquired through a user interface or a data interface by using distributed data perception by using information technology, cloud service, data transmission, internet of things and other technologies, and the data is processed according to business logic to support data visual display. The whole process real-time monitoring is carried out on the business process, and the abnormal or illegal data are found to be required to store related perception data and are used as evidence of rectification and illegal; in addition, the security technologies such as HTTPS protocol, data encryption and access control are adopted to protect the privacy and data security of the service. And meanwhile, the local backup and the encrypted remote backup are provided, so that the influence of hardware faults on data is reduced to the minimum.
The Bi-GRU network is utilized on the time level, so that the service progress prediction process can fully utilize the state of the historical time, and the graph annotation network is utilized on the time sequence characteristics to learn the action relationship between the time sequence characteristics, so that the service progress prediction result is more accurate. In addition, fine granularity analysis is performed on the operation procedure of executing the sub-sequence, preferably sequence matching is performed by using algorithms such as dynamic time warping and the like, whether the operation or the data logic of executing the sub-sequence is out of specification or whether the execution result does not reach the expected condition or the like and does not meet the preset standard is judged, and the service quality is represented by the judgment result.
It should be noted that, acquiring the service progress and the service quality of each sub-item, comparing the service progress and the service quality with preset standards, and judging whether the sub-package service corresponding to the sub-item has the service progress or the service quality risk; acquiring a risk event by utilizing a big data means, performing embedding operation on a text corresponding to the risk event to acquire a corresponding word vector, constructing a semantic model according to a Bi-LSTM model, and importing the word vector into the semantic set model to acquire a forward hidden vector and a backward hidden vector containing semantic information; splicing the forward hidden vector and the backward hidden vector to obtain an output vector, determining semantic information of a word vector through the output vector, obtaining risk factor classification according to the semantic information, and obtaining correlation coefficients of a risk factor entity and a risk event entity by adopting a pearson correlation coefficient; sorting according to the correlation coefficient to obtain a preset number of risk factors, and generating a risk factor set corresponding to the business progress risk and the business quality risk; and when the sub-projects have business progress risks or business quality risks, carrying out risk identification in the whole-flow monitoring and management data corresponding to each sub-package business according to the risk factor set, and determining risk factors corresponding to different sub-projects.
Fig. 3 shows a flow chart of a method of evaluating the business risk results of a subcontracting business according to the present invention.
According to the embodiment of the invention, a sub-package service evaluation model is constructed, service risk results of sub-project sub-package services are evaluated according to the risk amount, and multi-dimensional early warning of the sub-package services is generated, specifically:
s302, acquiring a historical interaction relation between risk factors according to a risk event, generating an undirected graph corresponding to the risk factors through the historical interaction relation, learning the undirected graph through a graph rolling network, and constructing a subcontracting service evaluation model;
s304, assigning values to risk factors according to a preset mode to the whole-flow monitoring and management data of the sub-package business corresponding to different sub-projects, obtaining the risk quantity of the risk factors, interpolating an undirected graph according to the risk quantity, and extracting a graph structure corresponding to the sub-projects;
s306, generating self-attention weights among risk factors by using a self-attention mechanism in the graph structure, aggregating the risk factors by using the self-attention weights, and acquiring association vectors by using an average pooling operation;
s308, fusing the association vector and the initial vector representation of the risk factor node to obtain a final vector representation, and combining the final vector representation with a preset factor weight to obtain the initial business risk degree of the sub-item;
S310, acquiring a business risk result of the sub-package business by combining the initial business risk degree of the sub-item with the initial credit weight of the sub-package business, and generating multi-dimensional early warning of the sub-package business according to the Markov distance between the final vector representation of the risk factors and the reference vector in the low-dimensional space when the business risk result is larger than a preset threshold.
It should be noted that, the risk score is obtained by expert evaluation scoring for the historical whole-flow monitoring and management data of the sub-package business corresponding to different sub-projects, and the risk factors and the corresponding wind in the historical whole-flow monitoring and management data are utilizedAnd training a valuation rule by the risk score, and performing valuation through the valuation rule to obtain the risk quantity of the risk factor. Generating self-attention weights between risk factors in a graph structure by using a self-attention mechanism, and calculating similarity between key vector keys and query vector queries as the attention weights,/>Wherein T represents the matrix transpose, +.>Query vector representing risk factor i +.>A key vector representing a risk factor j; aggregating risk factors by self-attention weights to obtain an aggregated representation +.> ,/>Value vector representing risk factor i, pooling the aggregated representation to obtain a correlation vector +. >Association vector->Initial vector characterization with Risk factor node->Fusion is carried out to obtain final vector characterization->,,/>Is a trainable parameter.
When the business risk result is larger than a preset threshold, a corresponding business progress risk or business quality risk is generated, multidimensional early warning of perception data is carried out under the business progress risk or business quality risk, for example, when business progress early warning occurs, specific factors of manpower, equipment or service affecting the business progress are extracted, and multidimensional early warning is generated according to the specific factors affecting the business progress. And the anomaly detection is characterized as distance judgment in a low-dimensional space, early warning information is generated according to the comparison between the mahalanobis distance and a preset distance threshold, and the early warning efficiency is improved while the judgment difficulty is simplified.
The risk factors corresponding to the early warning information are obtained, relevant data are extracted from the whole-flow monitoring and management data of the sub-packaging business by utilizing the risk factors, and the early warning information is combined with the relevant data and sent to the corresponding sub-packaging business to carry out supervision, punishment and correction of the field parameters; updating the credit profile of the SCE based on the supervision, penalty and correction results; the method comprises the steps of obtaining sub-package services with service progress risks or service quality risks, marking, calculating the similarity between the marked sub-package services and other sub-package services, preselecting other sub-package services according to the similarity, and guiding the correction of the marked sub-package services by utilizing the service execution data in the preselected other sub-package services; and if the service risk result after the correction is still greater than a preset threshold value, performing priority re-bidding in the subcontracting business corresponding to the other pre-selected subcontracting business, and replacing the subcontracting business.
The method comprises the steps of constructing a dedicated database of the SCE, constructing a SCE portrait according to SCE resources, SCE performance, items related to the SCE and SCE credit, storing the SCE portrait in the database, and determining dominant SCE business of each SCE according to the SCE portrait; acquiring project package sending requirements according to current project package bidding information, utilizing similarity calculation to acquire similar historical package sending services in the database by utilizing the project package sending requirements, extracting package separators of the similar historical package sending services, screening package separators taking the current package sending services as dominant package sending services from the extracted package separators as pre-screening package separators, reading historical package sending service evaluation corresponding to the pre-screening package separators, reading corresponding emotion information based on evaluation data, training emotion information characterization rules, acquiring evaluation scores, and acquiring the pre-screening package separators with the highest evaluation scores for recommendation; and obtaining the business risk results of the SCRs in different SCRs, and updating SCRs 'images of each SCR in the SCRs' exclusive database.
Fig. 4 shows a block diagram of a cloud service-based intelligent subcontracting management system of the present invention.
The second aspect of the present invention also provides an intelligent subcontracting management system 4 based on cloud services, the system comprising: the memory 41 and the processor 42, wherein the memory includes a cloud service-based intelligent subcontracting management method program, and the cloud service-based intelligent subcontracting management method program realizes the following steps when being executed by the processor:
acquiring the package sending requirement and the item sub-package information of a target item, dividing the target item to generate a plurality of sub-items, and matching the sub-items with the item sub-package information;
acquiring execution data of each sub-item by utilizing data perception, uploading the execution data to a cloud platform for preprocessing and aggregation processing, acquiring service progress and service quality of the sub-item, and carrying out risk identification by combining the service progress and the service quality through risk decomposition;
determining the risk quantity of risk factors by combining the execution data of the cloud platform, constructing a sub-package service evaluation model, evaluating the service risk result of sub-project sub-package service according to the risk quantity, and generating multi-dimensional early warning of the sub-package service;
and monitoring the execution parameters corresponding to the subpackaging services based on the early warning information, acquiring the similarity of the subpackaging services, and performing scheduling optimization among the subpackaging services according to the similarity.
The third aspect of the present invention also provides a computer readable storage medium, where the computer readable storage medium includes a cloud service-based intelligent subcontracting management method program, where the cloud service-based intelligent subcontracting management method program, when executed by a processor, implements the steps of the cloud service-based intelligent subcontracting management method described above.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-On Memory (ROM), a random access Memory (RAM, ran dom Access Memory), a magnetic disk or an optical disk, or other various media capable of storing program codes.
Alternatively, the above-described integrated units of the present invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. The intelligent sub-packaging management method based on the cloud service is characterized by comprising the following steps of:
acquiring the package sending requirement and the item sub-package information of a target item, dividing the target item to generate a plurality of sub-items, and matching the sub-items with the item sub-package information;
acquiring execution data of each sub-item by utilizing data perception, uploading the execution data to a cloud platform for preprocessing and aggregation processing, acquiring service progress and service quality of the sub-item, and carrying out risk identification by combining the service progress and the service quality through risk decomposition;
determining the risk quantity of risk factors by combining the execution data of the cloud platform, constructing a sub-package service evaluation model, evaluating the service risk result of sub-project sub-package service according to the risk quantity, and generating multi-dimensional early warning of the sub-package service;
And monitoring the execution parameters corresponding to the subpackaging services based on the early warning information, acquiring the similarity of the subpackaging services, and performing scheduling optimization among the subpackaging services according to the similarity.
2. The intelligent sub-packaging management method based on the cloud service according to claim 1, wherein the target item is divided to generate a plurality of sub-items, and the sub-items are matched with the item sub-packaging information, specifically:
dividing the package demand of the target item into words, matching the word division result with a preset word stock to obtain package demand keywords, and dividing the target item into packages according to the package demand keywords to obtain sub-items corresponding to different packages;
acquiring item sub-package information according to sub-package bidding information, matching the item sub-package information with the sub-item, acquiring a historical sub-package record of a sub-package corresponding to the item sub-package information, and acquiring a sub-package credit image through the historical sub-package record;
obtaining similar items in the historical subcontracting records by utilizing similarity calculation based on the business content of the sub-items, extracting satisfaction evaluation of the similar items on subcontracting merchants, and determining initial credit weight by combining the satisfaction evaluation with subcontracting merchant credit figures;
And constructing a visual management model of the target item in the cloud platform, importing each sub-item, the corresponding subcontractor and the initial credit weight into the visual management model, and carrying out visual marking by combining business logic of different sub-items.
3. The intelligent sub-package management method based on cloud service according to claim 1, wherein the method is characterized in that execution data of each sub-item is obtained by utilizing data perception and uploaded to a cloud platform for preprocessing and aggregation processing, and the business progress and the business quality of the sub-item are obtained specifically as follows:
acquiring full-flow monitoring and management data of sub-project corresponding sub-package business according to distributed data sensing equipment, matching the full-flow monitoring and management data with business results to generate execution data, preprocessing the execution data in a cloud platform, and eliminating abnormal sensing data;
the method comprises the steps of obtaining sub-packaging business workflow of different sub-items, constructing an execution data sequence according to execution data corresponding to a preset time step, cutting the execution data sequence to generate a plurality of execution sub-sequences, clustering the execution sub-sequences, calculating the mahalanobis distance between the different execution sub-sequences, and obtaining a clustering result;
Acquiring service execution categories of the execution sub-sequences according to the clustering result, positioning the execution data sequences with service execution category labels in the sub-package service workflow, and respectively importing the execution sub-sequences of different service execution categories into a Bi-GRU network;
extracting features corresponding to the execution subsequences from the Bi-GRU network by utilizing a bidirectional gating unit, acquiring time correlation of the execution subsequences, importing the features into association between learning features of the graph-annotation-force network, constructing a feature graph according to the association, and acquiring attention weights among the features through an attention mechanism;
constructing an adjacency matrix to represent the feature graph by taking the attention weight as the weight of the feature graph edge structure, multiplying the adjacency matrix by the output of the Bi-GRU network to obtain execution characteristics of different service execution categories, and predicting the service progress according to the execution characteristics;
in addition, according to the sub-sequence extraction operation procedure, fine granularity analysis is carried out on the operation procedure to obtain operation procedure key points, point-by-point matching is carried out according to the operation procedure key points and the procedure corresponding to the sub-package business workflow, and the business quality is obtained according to the matching result.
4. The intelligent sub-packaging management method based on cloud service according to claim 1, wherein risk identification is performed by risk decomposition in combination with business progress and business quality, specifically:
acquiring the business progress and the business quality of each sub-item, comparing the business progress and the business quality with preset standards, and judging whether the sub-package business corresponding to the sub-item has business progress or business quality risk;
acquiring a risk event by utilizing a big data means, performing embedding operation on a text corresponding to the risk event to acquire a corresponding word vector, constructing a semantic model according to a Bi-LSTM model, and importing the word vector into the semantic set model to acquire a forward hidden vector and a backward hidden vector containing semantic information;
splicing the forward hidden vector and the backward hidden vector to obtain an output vector, determining semantic information of a word vector through the output vector, obtaining risk factor classification according to the semantic information, and obtaining correlation coefficients of a risk factor entity and a risk event entity by adopting a pearson correlation coefficient;
sorting according to the correlation coefficient to obtain a preset number of risk factors, and generating a risk factor set corresponding to the business progress risk and the business quality risk;
And when the sub-projects have business progress risks or business quality risks, carrying out risk identification in the whole-flow monitoring and management data corresponding to each sub-package business according to the risk factor set, and determining risk factors corresponding to different sub-projects.
5. The cloud service-based intelligent sub-packaging management method according to claim 1, wherein a sub-packaging service evaluation model is constructed, service risk results of sub-project sub-packaging services are evaluated according to the risk amount, and multi-dimensional early warning of the sub-packaging services is generated, specifically:
acquiring a historical interaction relation among risk factors according to a risk event, generating an undirected graph corresponding to the risk factors through the historical interaction relation, learning the undirected graph through a graph convolution network, and constructing a subcontracting service evaluation model;
assigning values to risk factors according to a preset mode to the whole-flow monitoring and management data of sub-package business corresponding to different sub-projects, obtaining the risk quantity of the risk factors, interpolating an undirected graph according to the risk quantity, and extracting a graph structure corresponding to the sub-projects;
generating self-attention weights among the risk factors by using a self-attention mechanism in the graph structure, aggregating the risk factors by using the self-attention weights, and acquiring association vectors by using an average pooling operation;
Fusing the association vector with the initial vector representation of the risk factor node to obtain a final vector representation, and combining the final vector representation with a preset factor weight to obtain the initial business risk degree of the sub-item;
and combining the initial business risk degree of the sub-item with the initial credit weight of the subcontractor to obtain a business risk result of the subcontractor, and generating multi-dimensional early warning of the subcontractor according to the Markov distance between the final vector representation of the risk factor and the reference vector in the low-dimensional space when the business risk result is larger than a preset threshold.
6. The cloud service-based intelligent sub-packaging management method according to claim 1, wherein the method is characterized in that the execution parameters corresponding to sub-packaging services are supervised based on early warning information, the similarity of the sub-packaging services is obtained, and the scheduling optimization between the sub-packaging services is performed according to the similarity, specifically:
acquiring risk factors corresponding to early warning information, extracting relevant data from the whole-flow monitoring and management data of the subcontracting service by utilizing the risk factors, and sending the early warning information to corresponding subcontracting agents by combining the relevant data to carry out supervision, punishment and rectification of field parameters;
updating the credit profile of the SCE based on the supervision, penalty and correction results;
The method comprises the steps of obtaining sub-package services with service progress risks or service quality risks, marking, calculating the similarity between the marked sub-package services and other sub-package services, preselecting other sub-package services according to the similarity, and guiding the correction of the marked sub-package services by utilizing the service execution data in the preselected other sub-package services;
and if the service risk result after the correction is still greater than a preset threshold value, performing priority re-bidding in the subcontracting business corresponding to the other pre-selected subcontracting business, and replacing the subcontracting business.
7. An intelligent subcontracting management system based on cloud service, which is characterized by comprising: the intelligent sub-package management system comprises a memory and a processor, wherein the memory comprises an intelligent sub-package management method program based on cloud service, and the intelligent sub-package management method program based on cloud service realizes the following steps when being executed by the processor:
acquiring the package sending requirement and the item sub-package information of a target item, dividing the target item to generate a plurality of sub-items, and matching the sub-items with the item sub-package information;
acquiring execution data of each sub-item by utilizing data perception, uploading the execution data to a cloud platform for preprocessing and aggregation processing, acquiring service progress and service quality of the sub-item, and carrying out risk identification by combining the service progress and the service quality through risk decomposition;
Determining the risk quantity of risk factors by combining the execution data of the cloud platform, constructing a sub-package service evaluation model, evaluating the service risk result of sub-project sub-package service according to the risk quantity, and generating multi-dimensional early warning of the sub-package service;
and monitoring the execution parameters corresponding to the subpackaging services based on the early warning information, acquiring the similarity of the subpackaging services, and performing scheduling optimization among the subpackaging services according to the similarity.
8. The intelligent sub-package management system based on cloud service according to claim 7, wherein the execution data of each sub-item is obtained by data perception and uploaded to a cloud platform for preprocessing and aggregation processing, and the business progress and the business quality of the sub-item are obtained specifically as follows:
acquiring full-flow monitoring and management data of sub-project corresponding sub-package business according to distributed data sensing equipment, matching the full-flow monitoring and management data with business results to generate execution data, preprocessing the execution data in a cloud platform, and eliminating abnormal sensing data;
the method comprises the steps of obtaining sub-packaging business workflow of different sub-items, constructing an execution data sequence according to execution data corresponding to a preset time step, cutting the execution data sequence to generate a plurality of execution sub-sequences, clustering the execution sub-sequences, calculating the mahalanobis distance between the different execution sub-sequences, and obtaining a clustering result;
Acquiring service execution categories of the execution sub-sequences according to the clustering result, positioning the execution data sequences with service execution category labels in the sub-package service workflow, and respectively importing the execution sub-sequences of different service execution categories into a Bi-GRU network;
extracting features corresponding to the execution subsequences from the Bi-GRU network by utilizing a bidirectional gating unit, acquiring time correlation of the execution subsequences, importing the features into association between learning features of the graph-annotation-force network, constructing a feature graph according to the association, and acquiring attention weights among the features through an attention mechanism;
constructing an adjacency matrix to represent the feature graph by taking the attention weight as the weight of the feature graph edge structure, multiplying the adjacency matrix by the output of the Bi-GRU network to obtain execution characteristics of different service execution categories, and predicting the service progress according to the execution characteristics;
in addition, according to the sub-sequence extraction operation procedure, fine granularity analysis is carried out on the operation procedure to obtain operation procedure key points, point-by-point matching is carried out according to the operation procedure key points and the procedure corresponding to the sub-package business workflow, and the business quality is obtained according to the matching result.
9. The cloud service-based intelligent subcontracting management system of claim 7, wherein risk identification is performed by risk decomposition in combination with business progress and business quality, and specifically comprises:
acquiring the business progress and the business quality of each sub-item, comparing the business progress and the business quality with preset standards, and judging whether the sub-package business corresponding to the sub-item has business progress or business quality risk;
acquiring a risk event by utilizing a big data means, performing embedding operation on a text corresponding to the risk event to acquire a corresponding word vector, constructing a semantic model according to a Bi-LSTM model, and importing the word vector into the semantic set model to acquire a forward hidden vector and a backward hidden vector containing semantic information;
splicing the forward hidden vector and the backward hidden vector to obtain an output vector, determining semantic information of a word vector through the output vector, obtaining risk factor classification according to the semantic information, and obtaining correlation coefficients of a risk factor entity and a risk event entity by adopting a pearson correlation coefficient;
sorting according to the correlation coefficient to obtain a preset number of risk factors, and generating a risk factor set corresponding to the business progress risk and the business quality risk;
And when the sub-projects have business progress risks or business quality risks, carrying out risk identification in the whole-flow monitoring and management data corresponding to each sub-package business according to the risk factor set, and determining risk factors corresponding to different sub-projects.
10. The cloud service-based intelligent subcontracting management system according to claim 7, wherein a subcontracting service evaluation model is constructed, service risk results of subcontracting services of the sub-projects are evaluated according to the risk amount, and multidimensional early warning of the subcontracting services is generated, specifically:
acquiring a historical interaction relation among risk factors according to a risk event, generating an undirected graph corresponding to the risk factors through the historical interaction relation, learning the undirected graph through a graph convolution network, and constructing a subcontracting service evaluation model;
assigning values to risk factors according to a preset mode to the whole-flow monitoring and management data of sub-package business corresponding to different sub-projects, obtaining the risk quantity of the risk factors, interpolating an undirected graph according to the risk quantity, and extracting a graph structure corresponding to the sub-projects;
generating self-attention weights among the risk factors by using a self-attention mechanism in the graph structure, aggregating the risk factors by using the self-attention weights, and acquiring association vectors by using an average pooling operation;
Fusing the association vector with the initial vector representation of the risk factor node to obtain a final vector representation, and combining the final vector representation with a preset factor weight to obtain the initial business risk degree of the sub-item;
and combining the initial business risk degree of the sub-item with the initial credit weight of the subcontractor to obtain a business risk result of the subcontractor, and generating multi-dimensional early warning of the subcontractor according to the Markov distance between the final vector representation of the risk factor and the reference vector in the low-dimensional space when the business risk result is larger than a preset threshold.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202410162438.3A CN117709691A (en) | 2024-02-05 | 2024-02-05 | Intelligent sub-packaging management method and system based on cloud service |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202410162438.3A CN117709691A (en) | 2024-02-05 | 2024-02-05 | Intelligent sub-packaging management method and system based on cloud service |
Publications (1)
Publication Number | Publication Date |
---|---|
CN117709691A true CN117709691A (en) | 2024-03-15 |
Family
ID=90162774
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202410162438.3A Withdrawn CN117709691A (en) | 2024-02-05 | 2024-02-05 | Intelligent sub-packaging management method and system based on cloud service |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN117709691A (en) |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN117952572A (en) * | 2024-03-27 | 2024-04-30 | 广东电网有限责任公司 | A method for generating construction operation file information based on big data analysis |
CN118536853A (en) * | 2024-04-10 | 2024-08-23 | 苏州朗力文化发展有限公司 | Quality risk management method and system for building decoration engineering |
CN118735444A (en) * | 2024-06-24 | 2024-10-01 | 中国建筑工程(香港)有限公司 | A construction project subcontract management system and method |
-
2024
- 2024-02-05 CN CN202410162438.3A patent/CN117709691A/en not_active Withdrawn
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN117952572A (en) * | 2024-03-27 | 2024-04-30 | 广东电网有限责任公司 | A method for generating construction operation file information based on big data analysis |
CN118536853A (en) * | 2024-04-10 | 2024-08-23 | 苏州朗力文化发展有限公司 | Quality risk management method and system for building decoration engineering |
CN118735444A (en) * | 2024-06-24 | 2024-10-01 | 中国建筑工程(香港)有限公司 | A construction project subcontract management system and method |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN117709691A (en) | Intelligent sub-packaging management method and system based on cloud service | |
CN113610366B (en) | Risk warning generation method and device and electronic equipment | |
CN111145009A (en) | User post-loan risk assessment method, device and electronic equipment | |
CN116664306A (en) | Intelligent recommendation method and device for wind control rules, electronic equipment and medium | |
CN112765003B (en) | Risk prediction method based on APP behavior log | |
CN119151629A (en) | User operation behavior wind control method and device, equipment and medium thereof | |
CN116976935A (en) | Electronic commerce accurate marketing system based on internet | |
CN106997371B (en) | Method for constructing single-user intelligent map | |
CN110097250B (en) | Product risk prediction method, device, computer equipment and storage medium | |
CN119090542B (en) | An e-commerce platform order analysis method and system based on data mining | |
CN116402334A (en) | Multimode data compliance analysis and intelligent evaluation method and device | |
CA2607477A1 (en) | Test mining systems and methods for early detection and warning | |
CN118967200A (en) | A sales big data analysis method and system based on cross-border e-commerce ERP | |
Lo et al. | An emperical study on application of big data analytics to automate service desk business process | |
CN114528908B (en) | Network request data classification model training method, classification method and storage medium | |
CN113706207B (en) | Order success rate analysis method, device, equipment and medium based on semantic analysis | |
CN117132383A (en) | Credit data processing method, device, equipment and readable storage medium | |
CN111400567B (en) | AI-based user data processing method, device and system | |
CN112818235A (en) | Violation user identification method and device based on associated features and computer equipment | |
CN112668796A (en) | Money return prediction method and system | |
CN111861493A (en) | Information processing method, information processing device, electronic equipment and storage medium | |
Du et al. | Stock volatility forecast base on comparative learning and autoencoder framework | |
CN118886945B (en) | Enterprise information management method and system based on big data | |
Chopra et al. | Enhancing Customer Lifetime Value Prediction Using Ensemble Learning and Long Short-Term Memory Networks | |
Tokhtakhunov et al. | Optimizing Similar Audience Search in Targeted Advertising: Effectiveness of Siamese Networks for Autoencoder-based User Embeddings |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
WW01 | Invention patent application withdrawn after publication |
Application publication date: 20240315 |
|
WW01 | Invention patent application withdrawn after publication |