CN113223705A - Offline prediction method suitable for privacy computing platform - Google Patents
Offline prediction method suitable for privacy computing platform Download PDFInfo
- Publication number
- CN113223705A CN113223705A CN202110561364.7A CN202110561364A CN113223705A CN 113223705 A CN113223705 A CN 113223705A CN 202110561364 A CN202110561364 A CN 202110561364A CN 113223705 A CN113223705 A CN 113223705A
- Authority
- CN
- China
- Prior art keywords
- data
- offline
- computing platform
- privacy computing
- prediction method
- 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.)
- Granted
Links
Images
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
Abstract
The application discloses an offline prediction method suitable for a privacy computing platform, which comprises the following steps: before data prediction is carried out by adopting an artificial neural network model, providing an operation interface for a user to select a data prediction mode, wherein the data prediction mode comprises the following steps: an online prediction mode and an offline prediction mode; after a user selects an offline prediction mode, providing an operation window for uploading offline data required by the offline prediction mode through an operation interface; after the user uploads the offline data through the operation window, judging whether the offline data meets a preset data verification standard or not; if the data accords with the preset data verification standard, the uploaded offline data is input into the artificial neural network model for data prediction; and enabling the artificial neural network model to output data prediction result data and display or/and store the result data. The method has the beneficial effects that the problem that data prediction fails and data is not synchronous due to network or database abnormity can be effectively solved, and the method is 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, sensing technology and the like, so that the medical service is made to be intelligent in real sense, and the prosperity and development of the medical industry are promoted. Under the background of new Chinese medical improvement, intelligent medical treatment is going to live in the lives of common people.
The data of the medical industry has the need of privacy protection, so that when artificial intelligence is applied to the research, model training and data prediction in the medical field, a plurality of medical institutions are often required to perform the research, model training and data prediction in a networking and data collaboration mode.
When the existing privacy computing platform uses an 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 overcome the defects in 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 carried out by adopting an artificial neural network model, providing an operation interface for a user to select a data prediction mode, wherein the data prediction mode comprises the following steps: an online prediction mode and an offline prediction mode; after the user selects the offline prediction mode, providing an operation window for uploading offline data required by the offline prediction mode through the operation interface; after the user uploads the offline data through the operation window, judging whether the offline data meets a preset data verification standard or not; if the offline data meet the preset data verification standard, inputting the uploaded offline data into the artificial neural network model for data prediction; and enabling the artificial neural network model to output data prediction result data and display or/and store the result data.
Further, the offline prediction method applied to the privacy computing platform further comprises the following steps: before data prediction is carried out by adopting an artificial neural network model, whether network connection is abnormal or not is detected, and if the network connection is abnormal, a user is prompted to adopt the offline prediction mode only.
Further, the offline prediction method applied to the privacy computing platform further comprises the following steps: before data prediction is carried out by adopting an artificial neural network model, whether the connection of the database is abnormal or not is detected, and if the connection of the database is abnormal, a user is prompted to adopt the offline prediction mode only.
Further, the offline prediction method applied to the privacy computing platform further comprises the following steps: and if the data 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 standard is not met, displaying the specific row, column or cell which does not meet the data verification standard.
Further, the offline prediction method applied to the privacy computing platform further comprises the following steps: and after the user uploads the offline data through the operation window, detecting whether the network connection is recovered, and if the network connection is recovered, inquiring data required by training on line according to the uploaded offline data.
Further, the offline prediction method applied to the privacy computing platform further comprises the following steps: and comparing offline data uploaded by a user in an offline prediction mode with online data acquired by online inquiry after network connection reply, judging whether the offline data and the online data are different, and if not, taking the offline data as input data for artificial neural network model training.
Further, the offline prediction method applied to the privacy computing platform further comprises the following steps: and if the offline data and the online data are different, judging which data of the offline data and the online data has higher integrity, and taking the data with higher integrity as input data for training the artificial neural network model.
Further, the offline prediction method applied to the privacy computing platform further comprises the following steps: and if the offline data and the online data have differences, judging whether the offline data has data which is not possessed by the online data, and if so, uploading the data which is not possessed by the online data to a database on line.
The application has the advantages that: the off-line prediction method suitable for the privacy computing platform can effectively solve data prediction failure and data asynchronism caused by network or database abnormality.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, serve to provide a further understanding of the application and to enable other features, objects, and advantages of the application to be more apparent. The drawings and their description illustrate the embodiments of the invention and do not limit it. In the drawings:
FIG. 1 is a block diagram illustrating steps of an offline prediction method applicable to a private computing platform according to an 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 application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be used. 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 the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
As shown in fig. 1, the offline prediction method applied to the privacy computing platform of the present application includes the following steps: before data prediction is carried out by adopting an artificial neural network model, providing an operation interface for a user to select a data prediction mode, wherein the data prediction mode comprises the following steps: an online prediction mode and an offline prediction mode; after a user selects an offline prediction mode, providing an operation window for uploading offline data required by the offline prediction mode through an operation interface; after the user uploads the offline data through the operation window, judging whether the offline data meets a preset data verification standard or not; if the data accords with the preset data verification standard, the uploaded offline data is input into the artificial neural network model for data prediction; and enabling the artificial neural network model to output data prediction result data and display or/and store the result data.
Specifically, the offline prediction method applied to the privacy computing platform further comprises the following steps: before data prediction is carried out by adopting an artificial neural network model, whether network connection is abnormal or not is detected, and if the network connection is abnormal, a user is prompted to adopt an offline prediction mode only.
Specifically, the offline prediction method applied to the privacy computing platform further comprises the following steps: before data prediction is carried out by adopting an artificial neural network model, whether the connection of the database is abnormal or not is detected, and if the connection of the database is abnormal, a user is prompted to adopt an offline prediction mode only.
Specifically, the offline prediction method applied to the privacy computing platform further comprises the following steps: and if the data 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 criterion is not met, a specific row, column or cell that does not meet the data verification criterion is displayed.
Specifically, the offline prediction method applied to the privacy computing platform further comprises the following steps: after the user uploads the off-line data through the operation window, whether the network connection is recovered or not is detected, and if the network connection is recovered, the data required by training is inquired on line according to the uploaded off-line data.
Specifically, the offline prediction method applied to the privacy computing platform further comprises the following steps: and comparing the offline data uploaded by the user in the offline prediction mode with the online data acquired by online inquiry after network connection reply, judging whether the offline data and the online data have difference, and if not, taking the offline data as input data for artificial neural network model training.
Specifically, the offline prediction method applied to the privacy computing platform further comprises the following steps: and if the offline data and the online data are different, judging which data of the offline data and the online data has higher integrity, and taking the data with higher integrity as input data for training the artificial neural network model.
Specifically, the offline prediction method applied to the privacy computing platform further comprises the following steps: and if the offline data and the online data have differences, judging whether the offline data has data which is not possessed by the online data, and if so, uploading the data which is not possessed by the online data to a database on line.
By adopting the method, an off-line prediction mode is provided during data prediction, and database data and local data can be effectively synchronized, so that the model prediction result is more accurate.
Referring to fig. 2, when model training and prediction are performed, corresponding data may be obtained from a server database on line in a query manner, or corresponding data documents may be uploaded without a network.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. An off-line prediction method suitable for a privacy computing platform is characterized by comprising the following steps:
the off-line prediction method suitable for the privacy computing platform comprises the following steps:
before data prediction is carried out by adopting an artificial neural network model, providing an operation interface for a user to select a data prediction mode, wherein the data prediction mode comprises the following steps: an online prediction mode and an offline prediction mode;
after the user selects the offline prediction mode, providing an operation window for uploading offline data required by the offline prediction mode through the operation interface;
after the user uploads the offline data through the operation window, judging whether the offline data meets a preset data verification standard or not;
if the offline data meet the preset data verification standard, inputting the uploaded offline data into the artificial neural network model for data prediction;
and enabling the artificial neural network model to output data prediction result data and display or/and store the result data.
2. The off-line prediction method for a privacy computing platform of claim 1, wherein:
the off-line 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, whether network connection is abnormal or not is detected, and if the network connection is abnormal, a user is prompted to adopt the offline prediction mode only.
3. The off-line prediction method for a privacy computing platform of claim 1, wherein:
the off-line 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, whether the connection of the database is abnormal or not is detected, and if the connection of the database is abnormal, a user is prompted to adopt the offline prediction mode only.
4. The off-line prediction method for a privacy computing platform of claim 1, wherein:
the off-line prediction method suitable for the privacy computing platform further comprises the following steps:
and if the data does not meet the preset data verification standard, displaying the specific data which does not meet the data verification standard.
5. The offline prediction method applicable to the privacy computing platform according to claim 4, wherein:
the off-line data is table data.
6. The off-line prediction method for a privacy computing platform of claim 5, wherein:
and if the data does not meet the preset data verification standard, displaying the specific row, column or cell which does not meet the data verification standard.
7. The off-line prediction method for a privacy computing platform of claim 1, wherein:
the off-line prediction method suitable for the privacy computing platform further comprises the following steps:
and after the user uploads the offline data through the operation window, detecting whether the network connection is recovered, and if the network connection is recovered, inquiring data required by training on line according to the uploaded offline data.
8. The off-line prediction method for a privacy computing platform of claim 7, wherein:
the off-line prediction method suitable for the privacy computing platform further comprises the following steps:
and comparing offline data uploaded by a user in an offline prediction mode with online data acquired by online inquiry after network connection reply, judging whether the offline data and the online data are different, and if not, taking the offline data as input data for artificial neural network model training.
9. The offline prediction method for use in a privacy computing platform of claim 8, wherein:
the off-line prediction method suitable for the privacy computing platform further comprises the following steps:
and if the offline data and the online data are different, judging which data of the offline data and the online data has higher integrity, and taking the data with higher integrity as input data for training the artificial neural network model.
10. The offline prediction method for use in a privacy computing platform of claim 9, wherein:
the off-line prediction method suitable for the privacy computing platform further comprises the following steps:
and if the offline data and the online data have differences, judging whether the offline data has data which is not possessed by the online data, and if so, uploading the data which is 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 true CN113223705A (en) | 2021-08-06 |
| CN113223705B 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) |
Citations (9)
| 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 |
| US20130246617A1 (en) * | 2010-11-11 | 2013-09-19 | Tencent Technology (Shenzhen) Company Limited | Method and system for processing network data |
| WO2016191313A1 (en) * | 2015-05-27 | 2016-12-01 | Google Inc. | Dynamically updatable offline grammar model for resource-constrained offline device |
| US20190377902A1 (en) * | 2018-06-11 | 2019-12-12 | Grey Market Labs, PBC | Systems and Methods for Controlling Data Exposure Using Artificial-Intelligence-Based Modeling |
| US20200082296A1 (en) * | 2018-09-06 | 2020-03-12 | Quickpath Analytics, Inc. | Real-time drift detection in machine learning systems and applications |
| 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 |
| WO2021109644A1 (en) * | 2019-12-06 | 2021-06-10 | 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 (10)
| 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 |
| US20130246617A1 (en) * | 2010-11-11 | 2013-09-19 | Tencent Technology (Shenzhen) Company Limited | Method and system for processing network data |
| WO2016191313A1 (en) * | 2015-05-27 | 2016-12-01 | Google Inc. | Dynamically updatable offline grammar model for resource-constrained offline device |
| US20190377902A1 (en) * | 2018-06-11 | 2019-12-12 | Grey Market Labs, PBC | Systems and Methods for Controlling Data Exposure Using Artificial-Intelligence-Based Modeling |
| US20200082296A1 (en) * | 2018-09-06 | 2020-03-12 | Quickpath Analytics, Inc. | Real-time drift detection in machine learning systems and applications |
| WO2021109644A1 (en) * | 2019-12-06 | 2021-06-10 | 北京理工大学 | Hybrid vehicle working condition prediction method based on meta-learning |
| 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 |
| US20210248513A1 (en) * | 2020-09-02 | 2021-08-12 | Beijing Baidu Netcom Science And Technology Co., Ltd. | Method and apparatus for training online prediction model, device 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 |
|---|---|
| CN113223705B (en) | 2023-08-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11323568B2 (en) | Remote distance assistance system and method | |
| Khatun et al. | Design and implementation of iris recognition based attendance management system | |
| US20190034937A1 (en) | Problem Prediction Method and System | |
| WO2015013436A2 (en) | Methods of identifying relevant content and subject matter expertise for online communities | |
| AU2020201883A1 (en) | Call center system having reduced communication latency | |
| CN111814775B (en) | Target object abnormal behavior identification method, device, terminal and storage medium | |
| US20190342146A1 (en) | System and method for autonomus data center operation and healing | |
| US20160154900A1 (en) | 3d model component comparing system and method thereof | |
| CN107181630A (en) | The treating method and apparatus of service fault in cloud system | |
| CN114742485A (en) | Information processing method and system for IT system maintenance service | |
| CN110232458A (en) | A kind of vaccine retrospect reservation system based on big data | |
| US20180336477A1 (en) | Information processing apparatus and non-transitory computer readable medium | |
| CN111612088B (en) | Method and device for detecting images of superimposed characters | |
| CN113223705A (en) | Offline prediction method suitable for privacy computing platform | |
| CN115686280A (en) | Deep learning model management system, method, computer device and storage medium | |
| Generosi et al. | Smart retrofitting for human factors: a face recognition-based system proposal | |
| Tancredi et al. | Analysis and testing of an online solution to monitor and solve safety issues for industrial systems | |
| Silva et al. | Defining context in a business process collaborative elicitation approach | |
| CN115904916A (en) | Hard disk failure prediction method and device, electronic equipment and storage medium | |
| CN113840040A (en) | Man-machine cooperation outbound method, device, equipment and storage medium | |
| CN110716101B (en) | Power line fault positioning method and device, computer and storage medium | |
| CN114245204A (en) | Video surface signing method and device based on artificial intelligence, electronic equipment and medium | |
| CN109165877B (en) | Method and system for improving network marking speed | |
| CN112968941B (en) | Data acquisition and man-machine collaborative annotation method based on edge calculation | |
| CN111105216B (en) | Cable statistical method based on local area network and application |
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 |