CN113223705B - Offline prediction method suitable for privacy computing platform - Google Patents
Offline prediction method suitable for privacy computing platform Download PDFInfo
- Publication number
- CN113223705B CN113223705B CN202110561364.7A CN202110561364A CN113223705B CN 113223705 B CN113223705 B CN 113223705B CN 202110561364 A CN202110561364 A CN 202110561364A CN 113223705 B CN113223705 B CN 113223705B
- Authority
- CN
- China
- Prior art keywords
- data
- offline
- prediction
- computing platform
- prediction mode
- 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.)
- Active
Links
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
-
- 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
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Data Mining & Analysis (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Medical Informatics (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Computing Systems (AREA)
- Databases & Information Systems (AREA)
- Molecular Biology (AREA)
- Computational Linguistics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Biophysics (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Pathology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Epidemiology (AREA)
- Primary Health Care (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application discloses an offline prediction method suitable for a privacy computing platform, which comprises the following steps: before data prediction is performed by using an artificial neural network model, an operation interface is provided for a user to select a data prediction mode, wherein the data prediction mode comprises: an online prediction mode and an offline prediction mode; after the user selects the offline prediction mode, an operation window for uploading offline data required by the offline prediction mode is provided through an operation interface; after uploading the offline data through the operation window, the user judges whether the offline data accords with a preset data verification standard; if the data verification standard meets the preset data verification standard, the uploaded offline data is input into an artificial neural network model for data prediction; and enabling the artificial neural network model to output the result data of the data prediction and display or/and store the result data. The method has the advantages of effectively solving the problem that data prediction fails and data is asynchronous caused by network or database abnormality, and being suitable for the offline prediction method of the privacy computing platform.
Description
Technical Field
The application relates to an offline prediction method suitable for a privacy computing platform.
Background
In the near future, the medical industry will incorporate more high technologies such as artificial intelligence and sensing technology, so that the medical service is truly intelligentized, and the prosperous development of medical industry is promoted. In the great background of new medical improvement in China, intelligent medical treatment is going into the lives of ordinary people.
There is a need for privacy protection of medical industry data, so when applying artificial intelligence to research, model training and data prediction in the medical field, multiple medical institutions are often required to perform in a networking and data collaboration manner.
When the existing privacy computing platform uses the artificial neural network model to predict data, the data prediction is often failed due to network or database abnormality.
Disclosure of Invention
In order to solve the defects of the prior art, the application provides an offline prediction method suitable for a privacy computing platform, which comprises the following steps: before data prediction is performed by using an artificial neural network model, an operation interface is provided for a user to select a data prediction mode, and the data prediction mode comprises: an online prediction mode and an offline prediction mode; after the user selects the offline prediction mode, an operation window for uploading offline data required by the offline prediction mode is provided through the operation interface; after uploading the offline data through the operation window, a user judges whether the offline data accords with a preset data verification standard; if the data verification standard meets the preset data verification standard, inputting the uploaded offline data into the artificial neural network model for data prediction; and outputting the result data of the data prediction by the artificial neural network model, and displaying or/and storing the result data.
Further, the offline prediction method suitable for the privacy computing platform further comprises the following steps: before data prediction is performed by adopting an artificial neural network model, detecting whether network connection is abnormal, and if so, prompting a user to only adopt the offline prediction mode.
Further, the offline prediction method suitable for the privacy computing platform further comprises the following steps: before data prediction is performed by adopting an artificial neural network model, detecting whether the database connection is abnormal, and if so, prompting a user to only adopt the offline prediction mode.
Further, the offline prediction method suitable for the privacy computing platform further comprises the following steps: and if the data verification standard does not meet the preset data verification standard, displaying the specific data which does not meet the data verification standard.
Further, the offline data is table data.
Further, if the preset data verification criteria are not met, a specific row, column or cell is displayed that does not meet the data verification criteria.
Further, the offline prediction method suitable for the privacy computing platform further comprises the following steps: after uploading the offline data through the operation window, the user detects whether the network connection is restored, and if the network connection is restored, the user inquires the data required by training on line according to the uploaded offline data.
Further, the offline prediction method suitable for the privacy computing platform further comprises the following steps: comparing the offline data uploaded by the user in the offline prediction mode with the online data obtained through online inquiry after network connection reply, judging whether the offline data and the online data have differences, and if the differences do not exist, taking the offline data as input data for training the artificial neural network model.
Further, the offline prediction method suitable for the privacy computing platform further comprises the following steps: if the offline data and the online data have differences, judging which data integrity of the offline data and the online data is higher, and taking the one with higher data integrity as input data for training the artificial neural network model.
Further, the offline prediction method suitable for the privacy computing platform further comprises the following steps: if the offline data and the online data are different, judging whether the offline data have the data which are not possessed by the online data, and if so, uploading the data which are not possessed by the online data to a database on line.
The application has the advantages that: the offline prediction method applicable to the privacy computing platform can effectively solve the problems of data prediction failure and data asynchronization caused by network or database abnormality.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application, are incorporated in and constitute a part of this specification. The drawings and their description are illustrative of the application and are not to be construed as unduly limiting the application. In the drawings:
FIG. 1 is a block diagram illustrating steps of an offline prediction method suitable for use with a privacy computing platform in accordance with one embodiment of the present application;
fig. 2 is a schematic diagram of an operation interface of an offline prediction method suitable for a privacy computing platform according to an embodiment of the present application.
Detailed Description
In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate in order to describe the embodiments of the application herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other. The application will be described in detail below with reference to the drawings in connection with embodiments.
As shown in fig. 1, the offline prediction method suitable for a privacy computing platform of the present application includes the following steps: before data prediction is performed by using an artificial neural network model, an operation interface is provided for a user to select a data prediction mode, wherein the data prediction mode comprises: an online prediction mode and an offline prediction mode; after the user selects the offline prediction mode, an operation window for uploading offline data required by the offline prediction mode is provided through an operation interface; after uploading the offline data through the operation window, the user judges whether the offline data accords with a preset data verification standard; if the data verification standard meets the preset data verification standard, the uploaded offline data is input into an artificial neural network model for data prediction; and enabling the artificial neural network model to output the result data of the data prediction and display or/and store the result data.
Specifically, the offline prediction method suitable for the privacy computing platform further comprises the following steps: before data prediction is performed by adopting an artificial neural network model, detecting whether network connection is abnormal or not, and if the network connection is abnormal, prompting a user to only adopt an offline prediction mode.
Specifically, the offline prediction method suitable for the privacy computing platform further comprises the following steps: before data prediction is performed by adopting an artificial neural network model, detecting whether the database connection is abnormal, and if so, prompting a user to only adopt an offline prediction mode.
Specifically, the offline prediction method suitable for the privacy computing platform further comprises the following steps: and if the data verification standard does not meet the preset data verification standard, displaying the specific data which does not meet the data verification standard.
Specifically, the offline data is tabular data.
Specifically, if the preset data verification criteria are not met, a specific row, column or cell is displayed that does not meet the data verification criteria.
Specifically, the offline prediction method suitable for the privacy computing platform further comprises the following steps: after uploading the offline data through the operation window, the user detects whether the network connection is restored, and if the network connection is restored, the user inquires the data required by training on line according to the uploaded offline data.
Specifically, the offline prediction method suitable for the privacy computing platform further comprises the following steps: comparing the offline data uploaded by the user in the offline prediction mode with the online data obtained through online inquiry after network connection reply, judging whether the offline data and the online data have differences, and if the differences do not exist, taking the offline data as input data for training an artificial neural network model.
Specifically, the offline prediction method suitable for the privacy computing platform further comprises the following steps: if the offline data and the online data have differences, judging which data integrity of the offline data and the online data is higher, and taking the one with higher data integrity as input data for training the artificial neural network model.
Specifically, the offline prediction method suitable for the privacy computing platform further comprises the following steps: if the offline data and the online data have differences, judging whether the offline data have data which are not possessed by the online data, and if so, uploading the data which are not possessed by the online data to a database on line.
By adopting the method, an offline prediction mode is provided during data prediction, and meanwhile, database data and local data can be effectively synchronized, so that a model prediction result is more accurate.
Referring to fig. 2, corresponding data can be obtained online from a server database by means of query during model training and prediction, or corresponding data documents can be uploaded without a network.
The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (1)
1. An offline prediction method suitable for a privacy computing platform is characterized in that:
the offline prediction method suitable for the privacy computing platform comprises the following steps:
before data prediction is performed by using an artificial neural network model, an operation interface is provided for a user to select a data prediction mode, and the data prediction mode comprises: an online prediction mode and an offline prediction mode;
after the user selects the offline prediction mode, an operation window for uploading offline data required by the offline prediction mode is provided through the operation interface;
after uploading the offline data through the operation window, a user judges whether the offline data accords with a preset data verification standard;
if the data verification standard meets the preset data verification standard, inputting the uploaded offline data into the artificial neural network model for data prediction;
outputting result data of data prediction by the artificial neural network model, and displaying or/and storing the result data;
the offline prediction method suitable for the privacy computing platform further comprises the following steps:
before data prediction is carried out by adopting an artificial neural network model, detecting whether network connection is abnormal or not, and if the network connection is abnormal, prompting a user to only adopt the offline prediction mode;
the offline prediction method suitable for the privacy computing platform further comprises the following steps:
before data prediction is carried out by adopting an artificial neural network model, detecting whether the database connection is abnormal, and if so, prompting a user to only adopt the offline prediction mode;
the offline prediction method suitable for the privacy computing platform further comprises the following steps:
if the data verification standard does not accord with the preset data verification standard, specific data which does not accord with the data verification standard is displayed;
the offline data are table data;
if the data verification standard does not meet the preset data verification standard, displaying specific rows, columns or cells which do not meet the data verification standard;
the offline prediction method suitable for the privacy computing platform further comprises the following steps:
after uploading the offline data through the operation window, the user detects whether the network connection is restored, and if the network connection is restored, the user inquires the data required by training on line according to the uploaded offline data;
the offline prediction method suitable for the privacy computing platform further comprises the following steps:
comparing the offline data uploaded by the user in the offline prediction mode with the online data obtained through online inquiry after network connection reply, judging whether the offline data and the online data have differences, and taking the offline data as input data for training the artificial neural network model if the offline data and the online data have no differences;
the offline prediction method suitable for the privacy computing platform further comprises the following steps:
if the offline data and the online data have differences, judging which data integrity of the offline data and the online data is higher, and taking the one with higher data integrity as input data for training the artificial neural network model;
the offline prediction method suitable for the privacy computing platform further comprises the following steps:
if the offline data and the online data are different, judging whether the offline data have the data which are not possessed by the online data, and if so, uploading the data which are not possessed by the online data to a database on line.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110561364.7A CN113223705B (en) | 2021-05-22 | 2021-05-22 | Offline prediction method suitable for privacy computing platform |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110561364.7A CN113223705B (en) | 2021-05-22 | 2021-05-22 | Offline prediction method suitable for privacy computing platform |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113223705A CN113223705A (en) | 2021-08-06 |
| CN113223705B true CN113223705B (en) | 2023-08-29 |
Family
ID=77099343
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110561364.7A Active CN113223705B (en) | 2021-05-22 | 2021-05-22 | Offline prediction method suitable for privacy computing platform |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113223705B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114925934B (en) * | 2022-06-17 | 2025-03-21 | 西藏众陶联供应链服务有限公司 | A prediction model validation method for architectural ceramics production |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5659667A (en) * | 1995-01-17 | 1997-08-19 | The Regents Of The University Of California Office Of Technology Transfer | Adaptive model predictive process control using neural networks |
| WO2016191313A1 (en) * | 2015-05-27 | 2016-12-01 | Google Inc. | Dynamically updatable offline grammar model for resource-constrained offline device |
| CN112070226A (en) * | 2020-09-02 | 2020-12-11 | 北京百度网讯科技有限公司 | Training method, device and equipment of online prediction model and storage medium |
| KR102239163B1 (en) * | 2020-11-13 | 2021-04-12 | 황덕현 | Method and apparatus for predicting the presence or absence of diseases using artificial neural networks |
| CN112786188A (en) * | 2021-02-05 | 2021-05-11 | 北京致医健康信息技术有限公司 | Offline working method and device of auxiliary diagnosis system, terminal equipment and medium |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102468989B (en) * | 2010-11-11 | 2016-04-13 | 腾讯科技（深圳）有限公司 | The method and system of network data |
| US10762444B2 (en) * | 2018-09-06 | 2020-09-01 | Quickpath, Inc. | Real-time drift detection in machine learning systems and applications |
| US10558823B2 (en) * | 2018-06-11 | 2020-02-11 | Grey Market Labs, PBC | Systems and methods for controlling data exposure using artificial-intelligence-based modeling |
| CN111047085B (en) * | 2019-12-06 | 2022-09-06 | 北京理工大学 | Hybrid vehicle working condition prediction method based on meta-learning |
-
2021
- 2021-05-22 CN CN202110561364.7A patent/CN113223705B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5659667A (en) * | 1995-01-17 | 1997-08-19 | The Regents Of The University Of California Office Of Technology Transfer | Adaptive model predictive process control using neural networks |
| WO2016191313A1 (en) * | 2015-05-27 | 2016-12-01 | Google Inc. | Dynamically updatable offline grammar model for resource-constrained offline device |
| CN112070226A (en) * | 2020-09-02 | 2020-12-11 | 北京百度网讯科技有限公司 | Training method, device and equipment of online prediction model and storage medium |
| KR102239163B1 (en) * | 2020-11-13 | 2021-04-12 | 황덕현 | Method and apparatus for predicting the presence or absence of diseases using artificial neural networks |
| CN112786188A (en) * | 2021-02-05 | 2021-05-11 | 北京致医健康信息技术有限公司 | Offline working method and device of auxiliary diagnosis system, terminal equipment and medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113223705A (en) | 2021-08-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ellahi et al. | Redesigning Curriculum in line with Industry 4.0 | |
| US20190124202A1 (en) | Call center system having reduced communication latency | |
| US10992486B2 (en) | Collaboration synchronization | |
| US20090037378A1 (en) | Automatic generation of forms based on activity | |
| Faraz et al. | Multiobjective genetic algorithm approach to the economic statistical design of control charts with an application to bar and s2 charts | |
| WO2015013436A2 (en) | Methods of identifying relevant content and subject matter expertise for online communities | |
| US20200042947A1 (en) | Intelligent sending of an automatic event invite based on identified candidate content | |
| JP2019510320A (en) | Problem prediction method and system | |
| WO2015131125A1 (en) | Tag-based performance framework for contact center | |
| US20200201293A1 (en) | Industrial data service, data modeling, and data application platform | |
| CN108470071B (en) | Data processing method and device | |
| US10970648B2 (en) | Machine learning for time series using semantic and time series data | |
| KR20150101363A (en) | A method and system for synchronizing, organizing and ranking contacts in an electronic device | |
| US20160154900A1 (en) | 3d model component comparing system and method thereof | |
| CN113223705B (en) | Offline prediction method suitable for privacy computing platform | |
| US20210250443A1 (en) | Systems for detecting harassing communication | |
| CN112036161A (en) | Requirement document processing method, device, equipment and storage medium | |
| CN110574056A (en) | Participant-specific meeting attributes | |
| EP3674988A1 (en) | Method and system for prediction of correct discrete sensor data based on temporal uncertainty | |
| US20180247213A1 (en) | Query and ranking prediction using network action | |
| US9117187B2 (en) | Presenting a filtered list of work items | |
| CN106294058A (en) | The target strategy processing problems of operation document determines method and device | |
| CN108764607A (en) | User month data reinspection method, apparatus, equipment and storage medium | |
| CN109005246A (en) | A kind of synchronous method of data, apparatus and system | |
| Betterton et al. | Production rate of synchronous transfer lines using Monte Carlo simulation |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |