CN104615122A - Industrial control signal detection system and detection method - Google Patents
Industrial control signal detection system and detection method Download PDFInfo
- Publication number
- CN104615122A CN104615122A CN201410753803.4A CN201410753803A CN104615122A CN 104615122 A CN104615122 A CN 104615122A CN 201410753803 A CN201410753803 A CN 201410753803A CN 104615122 A CN104615122 A CN 104615122A
- Authority
- CN
- China
- Prior art keywords
- signal
- data
- real
- output
- control signal
- 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
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0208—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the configuration of the monitoring system
- G05B23/0213—Modular or universal configuration of the monitoring system, e.g. monitoring system having modules that may be combined to build monitoring program; monitoring system that can be applied to legacy systems; adaptable monitoring system; using different communication protocols
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Train Traffic Observation, Control, And Security (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The invention discloses an industrial control signal detection system and a detection method; the detection system comprises a data collecting module which collects real-time status data of equipment electrical features, and a historical process database which is connected with the data collecting module and used for storing the real-time status data collected by the data collecting module; the detection system further comprises a signal process predicating model which is connected with the historical process database and establishes correspondence between signal input and output in the electrical features based on the data inside the historical process database; the signal process predicating model obtains a predicated output signal based on the real-time input signal collected by the data collecting module, and sends the predicated output signal to a signal filter; the signal filter receivers the predicated output signal from the signal process predicating model and the real-time output signal collected by the data collecting module, and compares the signals, if the feature of the corresponding real-time output signal accords with the feature of the predicated output signal, then the real-time output signal is judged to be an effective executable signal; otherwise, the real-time output signal is judged to be a negative non-executable signal.
Description
Technical field
The invention belongs to industrial control system information security field, specifically, relate to a kind of detection system of industry control signal, more precisely, relate to the detection system of the signal of the trains such as a kind of high ferro, subway; The invention still further relates to a kind of detection method of industry control signal.
Background technology
Train control system is the integrated of various signalling arrangement and subsystem and utility appliance thereof.The system integration is one of high ferro signal gordian technique, is directly connected to the security of system.Train control system functional requirement specification (FRS), system requirement specification (SRS) and System-Interface-Specification (FIS) are the Main Basiss carrying out the system integration.The integrated interfacing laid particular emphasis between distinct device or system of current train control system, and the signalling arrangement of unlike signal manufacturer interconnecting when construction system.Because these signalling arrangements and subsystem are that different times is developed and is added in train control system, so the train control system formed on this basis, non-once, according to system engineering theory global design, is difficult to the advantage playing overall system and system level.The integrated of this simple superposition formula lacks data sharing and fusion in system, lack between each subsystem and mutually test and checking, coordinated signals and logical constraint mechanism, lack the equipment complexs such as newly-increased radio communication and transponder to utilize, lack the overall planning realizing " fail-safe " in system level.
Current signal maintenance and fault handling work, Real-Time Monitoring is carried out in main dependence signal microcomputer monitoring system buildup networking, but signal microcomputer monitoring system side overweights results management, exist obviously not enough in process management, not by drawing regular conclusion to the collection of Monitoring Data, although signal microcomputer monitoring equipment is widespread use at the scene, but much still rely on the change manually checking data, in cases where an amount of data is large, manually cannot be competent to data correlation analysis and to historical data comparative analysis.Although summarize the various data of nearly all signalling arrangement, to the excavation of signal data and logic analysis, data sharing and fusion treatment inadequate.Exceed standard except warning except signal microcomputer monitoring system has simple Logic judgment part electric parameters, other side does not have real-time automatic alarm and the effect of pilot signal system cloud gray model.
Summary of the invention
The present invention, in order to solve problems of the prior art, provides a kind of industry control signal detection system.
In order to realize above-mentioned object, technical scheme of the present invention is: a kind of industry control signal detection system, comprising:
Data acquisition module, the real-time status data of collecting device electrical specification;
And the historical process data storehouse being used for the real-time status data that storage data acquisition module collects is connected with data acquisition module;
Also comprise be connected with historical process data storehouse and set up the signal process forecast model of signal input and output corresponding relation in electrical specification by the data in historical process data storehouse; Described signal process forecast model, according to the real-time input signal collected in data acquisition module, obtains prediction output signal, and this prediction output signal is sent to
Traffic filter, described traffic filter receive from the prediction output signal of signal process forecast model and data collecting module collected to real time output and compare, if corresponding real time output outputs signal feature conform to prediction, then regarding as effectively can executive signal; Otherwise then regard as invalid refusal executive signal.
Preferably, described data acquisition module comprises:
Digital data acquisition submodule, gathers the digital information of industry control signal;
Analog acquisition submodule, gathers the analog quantity information of industry control signal;
Logical signal gathers submodule, gathers the data message in industry control signal logic bus.
Preferably, the fault analysis judgment models be connected with traffic filter is also comprised.
Preferably, described signal process forecast model is according to neural network, support vector machine or PCA method establishment.
Present invention also offers a kind of detection method of industry control signal, comprise the following steps:
Step S1: the real-time status data of data collecting module collected equipment electrical specification; And stored in historical process data storehouse;
Step S2: with the data in historical process data storehouse for foundation, sets up signal process forecast model;
Step S3: input signal to be checked is in real time sent to signal process forecast model, and prediction of output output signal;
Step S4: prediction output signal feature and real time output feature are carried out consistance contrast by traffic filter; If corresponding real time output outputs signal feature conform to prediction, then regarding as effectively can executive signal; If it is inconsistent that corresponding real time output and prediction output signal feature, then regard as invalid refusal executive signal.
Preferably, also comprise after step s4:
Step S5: incorrect signal is uploaded to fault analysis judge module, fault analysis judges that diagnostic module failure judgement occurs and carries out fault analysis.
Preferably, in described step S1, the data message on the digital information in data collecting module collected equipment electrical specification, analog quantity information, logic bus.
Detection system of the present invention and detection method, the final output function detection of subsystems from each industrial control system, pass through off-line training, with prediction output signal, believable consistance contrast is carried out to the real time output of detected system, the system invasion that Timeliness coverage is caused by information security and fault.Fundamentally prevent information security to cause industrial control system safety, change the method for putting prevention first that industrial control information safety is traditional, with the method for the reverse backstepping of the result of function accuracy, fundamentally solve the information security issue of industrial control system.
Accompanying drawing explanation
Fig. 1 is the theory diagram of detection system of the present invention.
Fig. 2 is the workflow diagram of detection method.
Embodiment
The technical matters solved to make the present invention, the technical scheme of employing, the technique effect easy to understand obtained, below in conjunction with concrete accompanying drawing, be described further the specific embodiment of the present invention.
With reference to figure 1, the invention provides a kind of industry control signal detection system, comprising: data acquisition module 1, be used for the real-time status data of collecting device electrical specification; The real-time status of the electrical specification of hardware device in this data acquisition module acquisition system, mainly comprise the voltage of equipment, electric current, frequency etc., these values can be gathered by PLC or DCS system, only need train control system to provide acquisition interface just passable.Preferably, described data acquisition module 1 comprises digital data acquisition submodule 10, analog acquisition submodule 11 and logical signal and gathers submodule 12 in the present invention.Wherein, digital data acquisition submodule 10 gathers digital information, as switch closed condition etc.; Analog acquisition submodule 11 gathers analog quantity information, as voltage, electric current etc.; Logical signal gathers the data message etc. on submodule 12 data acquisition bus.
Industry control signal detection system of the present invention also comprises the historical process data storehouse 2 being connected the real-time status data arrived for storage data acquisition module collection 1 with data acquisition module 1; Foundation for following model provides data basis.Also comprise be connected with historical process data storehouse 2 and set up the signal process forecast model 3 of signal input and output corresponding relation in electrical specification by the data in historical process data storehouse 2.
These data collected and output have corresponding relation, in this case, as long as provide a large amount of historical datas, signal process prediction module carries out off-line training modeling to it, the relation wherein between input and output can be found out, represent with y=f (x), wherein y represents output quantity; X represents input variable.Y may have multiple output quantity y1, y2...ym; X also may have multiple input quantity x1, x2...xn.
By different types of historical data source, can rapid build model, analyze continuous, discrete, batch production process.Data encasement, visual check data can be carried out, can also rule model be set up.Draw the interact relation between parameters, predict the value of inconvenient measurement parameter, carry out off-line or online optimal control, the knowledge excavated from these models, help to process improve income assess, whole process also very simple and convenient, workload is little.Use this model system, the reason of production run fluctuation can be found out, and adjust, reach stabilized product quality, improve the target of output.
The signal process forecast model 3 of the corresponding relation of input and output can be built by existing technology.Such as according to neural network, support vector machine or PCA method.
Artificial neural network (Artificial Neural Networks, be abbreviated as ANNs) also referred to as neural network (NNs) or be called link model (Connection Model), it is a kind of imitation animal nerve network behavior feature, carries out the algorithm mathematics model of distributed parallel information processing.This network relies on the complexity of system, by adjusting interconnective relation between inner great deal of nodes, thus reaches the object of process information.
The algorithm that neural network is used is exactly vector multiplication, and extensive symbolization function and variously to approach.Parallel, fault-tolerant, can hardware implementing and self-teaching characteristic, being several principal advantages of neural network, is also the difference place of neural computing method and classic method.
Support vector machine method is that the VC being based upon Statistical Learning Theory ties up on theoretical and Structural risk minization basis, between the complicacy (namely to the study precision of specific training sample) and learning ability (namely identifying the ability of arbitrary sample error-free) of model, optimal compromise is sought, in the hope of obtaining best Generalization Ability according to limited sample information.
DUAL PROBLEMS OF VECTOR MAPPING in the space of a more higher-dimension, is set up and is had a largest interval lineoid by support vector machine in this space.Two lineoid parallel to each other are had on the both sides of the separately lineoid of data.Setting up the suitable separating hyperplane in direction makes the distance between two parallel with it lineoid maximize.It is assumed to, the distance between parallel lineoid or gap larger, the total error of sorter is less.
Principal component analysis (PCA) (Principal Component Analysis, PCA) or pivot analysis.Be a kind of statistical analysis technique grasping things principal contradiction, it can parse major influence factors from polynary things, discloses the essence of things, simplifies complicated problem.The object calculating major component is that high dimensional data is projected to comparatively lower dimensional space.M observed value of a given n variable, form the data matrix of a n ' m, n is usually larger.If the main aspect of things is just embodied on several primary variables, we only need by these separating variables out, to carry out labor.But, in the ordinary course of things, can not directly find out such key variables.At this moment we can represent the main aspect of things with the linear combination of original variable, and PCA is exactly so a kind of analytical approach.
Industry control signal detection system of the present invention, described signal process forecast model 3 is according to the real-time input signal collected in data acquisition module 1, obtain prediction output signal, and this prediction output signal is sent to traffic filter 4, described traffic filter 4 receives real time output that prediction output signal and data acquisition module 1 from signal process forecast model 3 collect and compares, if corresponding real time output outputs signal feature conform to prediction, then regarding as effectively can executive signal; Otherwise then regard as invalid refusal executive signal.
The present invention preferably, also comprise the fault analysis judgment models 5 be connected with traffic filter 4, when traffic filter 4 judges real time output and predict that outputing signal feature does not conform to, by this signal transmission in fault analysis judgment models 5, fault analysis judgment models 5 failure judgement occurs and carries out fault analysis.Fault analysis judgment models 5 pairs of data analysis, utilize neuron algorithm to set up operational model, find out the various factors relevant to fault.Fault can be reported to the police after occurring, and user defines the rank of warning according to the threshold value of data.Alert levels is divided into critical, senior, intermediate, rudimentary, normal condition and unknown accident.
Present invention also offers a kind of detection method of industry control signal, with reference to figure 2, comprise the following steps:
Step S1: the real-time status data of data collecting module collected equipment electrical specification; And stored in historical process data storehouse;
Step S2: with the data in historical process data storehouse for foundation, sets up signal process forecast model;
Step S3: input signal to be checked is in real time sent to signal process forecast model, and prediction of output output signal;
Step S4: prediction output signal feature and real time output feature are carried out consistance contrast by traffic filter; If corresponding real time output outputs signal feature conform to prediction, then regarding as effectively can executive signal; If it is inconsistent that corresponding real time output and prediction output signal feature, then regard as invalid refusal executive signal.
Preferably, also comprise after step s4:
Step S5: incorrect signal is uploaded to fault analysis judge module, fault analysis judges that diagnostic module failure judgement occurs and carries out fault analysis.
Preferably, in described step S1, the data message on the digital information in data collecting module collected equipment electrical specification, analog quantity information, logic bus.
Detection system of the present invention and detection method, the final output function detection of subsystems from each industrial control system, pass through off-line training, with prediction output signal, believable consistance contrast is carried out to the real time output of detected system, the system invasion that Timeliness coverage is caused by information security and fault.Fundamentally prevent information security to cause industrial control system safety, change the method for putting prevention first that industrial control information safety is traditional, with the method for the reverse backstepping of the result of function accuracy, fundamentally solve the information security issue of industrial control system.
The present invention is by preferred embodiment having carried out detailed explanation.But, by studying carefully above, concerning the change of each embodiment with to increase be apparent for one of ordinary skill in the art.Being intended that these changes all and increasing of applicant has all dropped in scope that the claims in the present invention protect.
Claims (7)
1. an industry control signal detection system, is characterized in that, comprising:
Data acquisition module, the real-time status data of collecting device electrical specification;
And the historical process data storehouse being used for the real-time status data that storage data acquisition module collects is connected with data acquisition module;
Also comprise be connected with historical process data storehouse and set up the signal process forecast model of signal input and output corresponding relation in electrical specification by the data in historical process data storehouse; Described signal process forecast model, according to the real-time input signal collected in data acquisition module, obtains prediction output signal, and this prediction output signal is sent to
Traffic filter, described traffic filter receive from the prediction output signal of signal process forecast model and data collecting module collected to real time output and compare, if corresponding real time output outputs signal feature conform to prediction, then regarding as effectively can executive signal; Otherwise then regard as invalid refusal executive signal.
2. industry control signal detection system according to claim 1, is characterized in that: described data acquisition module comprises:
Digital data acquisition submodule, gathers the digital information of industry control signal;
Analog acquisition submodule, gathers the analog quantity information of industry control signal;
Logical signal gathers submodule, gathers the data message in industry control signal logic bus.
3. industry control signal detection system according to claim 1, is characterized in that: also comprise the fault analysis judgment models be connected with traffic filter.
4. industry control signal detection system according to claim 1, is characterized in that: described signal process forecast model is according to neural network, support vector machine or PCA method establishment.
5. a detection method for industry control signal, is characterized in that, comprises the following steps:
Step S1: the real-time status data of data collecting module collected equipment electrical specification; And stored in historical process data storehouse;
Step S2: with the data in historical process data storehouse for foundation, sets up signal process forecast model;
Step S3: input signal to be checked is in real time sent to signal process forecast model, and prediction of output output signal;
Step S4: prediction output signal feature and real time output feature are carried out consistance contrast by traffic filter; If corresponding real time output outputs signal feature conform to prediction, then regarding as effectively can executive signal; If it is inconsistent that corresponding real time output and prediction output signal feature, then regard as invalid refusal executive signal.
6. detection method according to claim 5, is characterized in that: also comprise after step s4:
Step S5: incorrect signal is uploaded to fault analysis judge module, fault analysis judges that diagnostic module failure judgement occurs and carries out fault analysis.
7. detection method according to claim 5, is characterized in that: in described step S1, the data message on the digital information in data collecting module collected equipment electrical specification, analog quantity information, logic bus.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410753803.4A CN104615122B (en) | 2014-12-11 | 2014-12-11 | A kind of industry control signal detection system and detection method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410753803.4A CN104615122B (en) | 2014-12-11 | 2014-12-11 | A kind of industry control signal detection system and detection method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104615122A true CN104615122A (en) | 2015-05-13 |
| CN104615122B CN104615122B (en) | 2018-04-06 |
Family
ID=53149621
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410753803.4A Active CN104615122B (en) | 2014-12-11 | 2014-12-11 | A kind of industry control signal detection system and detection method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104615122B (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106096789A (en) * | 2016-06-22 | 2016-11-09 | 华东师范大学 | A kind of based on machine learning techniques can be from the abnormal industry control security protection of perception and warning system |
| CN106202769A (en) * | 2016-07-15 | 2016-12-07 | 深圳市永达电子信息股份有限公司 | The industrial system detection method that a kind of on-line checking and simulation modeling checking combine |
| CN107728599A (en) * | 2017-09-01 | 2018-02-23 | 北京中燕信息技术有限公司 | A kind of method and apparatus for determining refinery device valve state |
| CN108900538A (en) * | 2018-08-09 | 2018-11-27 | 深圳市永达电子信息股份有限公司 | A kind of industry control signal detecting method and device |
| CN110456272A (en) * | 2019-09-12 | 2019-11-15 | 国电联合动力技术有限公司 | A kind of test macro and test method of generating set complete machine security system |
| CN110579367A (en) * | 2019-09-23 | 2019-12-17 | 北京国电龙源环保工程有限公司 | Fault self-diagnosis system and method for drying bed |
| TWI709054B (en) * | 2019-12-05 | 2020-11-01 | 財團法人資訊工業策進會 | Building device and building method of prediction model and monitoring system for product quality |
| CN115208703A (en) * | 2022-09-16 | 2022-10-18 | 北京安帝科技有限公司 | Industrial control equipment intrusion detection method and system of fragment parallelization mechanism |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1109101A2 (en) * | 1999-12-16 | 2001-06-20 | Xerox Corporation | Systems and methods for failure prediction, diagnosis and remediation using data acquisition and feedback for a distributed electronic system |
| CN1472671A (en) * | 2003-06-19 | 2004-02-04 | 上海交通大学 | Method for apparatus status monitoring and performance degenerate fore casting based on network |
| CN201017232Y (en) * | 2006-11-23 | 2008-02-06 | 浙江大学 | Industry process non-linearity failure diagnosis device based on fisher |
| CN102063109A (en) * | 2010-11-29 | 2011-05-18 | 株洲南车时代电气股份有限公司 | Neural network-based subway train fault diagnosis device and method |
| CN102692537A (en) * | 2012-05-15 | 2012-09-26 | 辽宁省电力有限公司抚顺供电公司 | Electric quantity test instrument resetting method |
| CN103197634A (en) * | 2013-03-15 | 2013-07-10 | 上海大学 | Generating system and generating method of on-line prediction and on-line processing plan for automatic manufacturing and processing system |
-
2014
- 2014-12-11 CN CN201410753803.4A patent/CN104615122B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1109101A2 (en) * | 1999-12-16 | 2001-06-20 | Xerox Corporation | Systems and methods for failure prediction, diagnosis and remediation using data acquisition and feedback for a distributed electronic system |
| CN1472671A (en) * | 2003-06-19 | 2004-02-04 | 上海交通大学 | Method for apparatus status monitoring and performance degenerate fore casting based on network |
| CN201017232Y (en) * | 2006-11-23 | 2008-02-06 | 浙江大学 | Industry process non-linearity failure diagnosis device based on fisher |
| CN102063109A (en) * | 2010-11-29 | 2011-05-18 | 株洲南车时代电气股份有限公司 | Neural network-based subway train fault diagnosis device and method |
| CN102692537A (en) * | 2012-05-15 | 2012-09-26 | 辽宁省电力有限公司抚顺供电公司 | Electric quantity test instrument resetting method |
| CN103197634A (en) * | 2013-03-15 | 2013-07-10 | 上海大学 | Generating system and generating method of on-line prediction and on-line processing plan for automatic manufacturing and processing system |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106096789A (en) * | 2016-06-22 | 2016-11-09 | 华东师范大学 | A kind of based on machine learning techniques can be from the abnormal industry control security protection of perception and warning system |
| CN106202769A (en) * | 2016-07-15 | 2016-12-07 | 深圳市永达电子信息股份有限公司 | The industrial system detection method that a kind of on-line checking and simulation modeling checking combine |
| CN107728599A (en) * | 2017-09-01 | 2018-02-23 | 北京中燕信息技术有限公司 | A kind of method and apparatus for determining refinery device valve state |
| CN108900538A (en) * | 2018-08-09 | 2018-11-27 | 深圳市永达电子信息股份有限公司 | A kind of industry control signal detecting method and device |
| CN108900538B (en) * | 2018-08-09 | 2021-03-23 | 深圳市永达电子信息股份有限公司 | Industrial control signal detection method and device |
| CN110456272A (en) * | 2019-09-12 | 2019-11-15 | 国电联合动力技术有限公司 | A kind of test macro and test method of generating set complete machine security system |
| CN110579367A (en) * | 2019-09-23 | 2019-12-17 | 北京国电龙源环保工程有限公司 | Fault self-diagnosis system and method for drying bed |
| TWI709054B (en) * | 2019-12-05 | 2020-11-01 | 財團法人資訊工業策進會 | Building device and building method of prediction model and monitoring system for product quality |
| CN115208703A (en) * | 2022-09-16 | 2022-10-18 | 北京安帝科技有限公司 | Industrial control equipment intrusion detection method and system of fragment parallelization mechanism |
| CN115208703B (en) * | 2022-09-16 | 2022-12-13 | 北京安帝科技有限公司 | Industrial control equipment intrusion detection method and system of fragment parallelization mechanism |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104615122B (en) | 2018-04-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104615122A (en) | Industrial control signal detection system and detection method | |
| CN108418841B (en) | Next-generation key message infrastructure network Security Situation Awareness Systems based on AI | |
| CN106125714B (en) | In conjunction with the Failure Rate Forecasting Method of BP neural network and two parameters of Weibull | |
| US9778629B2 (en) | Situational awareness / situational intelligence system and method for analyzing, monitoring, predicting and controlling electric power systems | |
| CN105515184B (en) | Multisensor many reference amounts distribution synergic monitoring system based on wireless sensor network | |
| CN103439933B (en) | A kind of production run adaptive process monitoring system and method applying OCSVM | |
| CN104615121B (en) | A kind of train fault diagnostic method and system | |
| CN103793853A (en) | Overhead power transmission line running state assessment method based on bidirectional Bayesian network | |
| CN107423414A (en) | A kind of process industry complex electromechanical systems fault source tracing method based on information transmission model | |
| CN112817280A (en) | Implementation method for intelligent monitoring alarm system of thermal power plant | |
| CN113887846B (en) | Out-of-tolerance risk early warning method for capacitive voltage transformer | |
| CN108830335A (en) | Photovoltaic plant fault early warning method and system | |
| CN106096789A (en) | A kind of based on machine learning techniques can be from the abnormal industry control security protection of perception and warning system | |
| CN105634796A (en) | Network device failure prediction and diagnosis method | |
| CN106160218A (en) | The real-time monitoring analysis system of electrical network distribution line | |
| CN113033772A (en) | Multi-equipment state monitoring method based on federal learning | |
| CN106227127B (en) | Generating equipment intelligent monitoring and controlling device and monitoring method | |
| CN115642706A (en) | Power distribution load monitoring system in power grid | |
| CN108199891A (en) | A kind of cps network attack discrimination methods based on artificial neural network multi-angle comprehensive decision | |
| CN117014471A (en) | Engineering thing networking safety monitoring system based on artificial intelligence | |
| CN112991096A (en) | Monitoring and managing device and method for configuration type bridge cluster structure | |
| CN112734977A (en) | Equipment risk early warning system and algorithm based on Internet of things | |
| CN116455060B (en) | Intelligent monitoring and early warning method and system for working condition of power grid equipment | |
| CN117216481A (en) | Remote monitoring method and system for electric appliance | |
| US20140052425A1 (en) | Method and apparatus for evaluating a model of an industrial plant process |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C53 | Correction of patent of invention or patent application | ||
| CB02 | Change of applicant information |
Address after: Three D301-309 room, building 518000, building D, Shenzhen Institute of Aerospace Science and technology, No. 6 South ten road, Nanshan District, Shenzhen, Guangdong, China Applicant after: Shenzhen Yongda electronic Touchplus information Corp Address before: Three D301-309 room, building 518000, building D, Shenzhen Institute of Aerospace Science and technology, No. 6 South ten road, Nanshan District, Shenzhen, Guangdong, China Applicant before: Shenzhen Rongda Electronics Co., Ltd. |
|
| COR | Change of bibliographic data |
Free format text: CORRECT: APPLICANT; FROM: SHENZHEN RONGDA ELECTRONICS CO., LTD. TO: SHENZHEN YONGDA ELECTRONIC INFORMATION CO., LTD. |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |