CN108521636A - A kind of air pollution data processing system based on block chain technology - Google Patents
A kind of air pollution data processing system based on block chain technology Download PDFInfo
- Publication number
- CN108521636A CN108521636A CN201810285050.7A CN201810285050A CN108521636A CN 108521636 A CN108521636 A CN 108521636A CN 201810285050 A CN201810285050 A CN 201810285050A CN 108521636 A CN108521636 A CN 108521636A
- Authority
- CN
- China
- Prior art keywords
- air pollution
- data
- pollution concentration
- node
- concentration data
- 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.)
- Pending
Links
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
- H04W12/02—Protecting privacy or anonymity, e.g. protecting personally identifiable information [PII]
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
- H04W12/06—Authentication
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
- H04W12/10—Integrity
Abstract
The present invention provides a kind of air pollution data processing systems based on block chain technology, including headend, wireless sensor network and the data check platform being made of multiple data check nodes；Wireless sensor network obtains air pollution concentration Data Concurrent and send to headend for being monitored to air pollution；Headend is forwarded to the data check platform after the air pollution concentration data that receive that treated；Multiple data check nodes in data check platform are responsible for that the air pollution concentration data that the headend forwards are verified and preserved；Each data check node is block chain node, and multiple data check nodes form a distributed data base；Further include data processing platform (DPP), data processing platform (DPP) for pre-processing the air pollution concentration data after verification successively, clustering processing and outlier detection are handled, and database is stored in after the abnormal point detected is marked.
Description
Technical field
The present invention relates to air monitering technical fields, and in particular at a kind of air pollution data based on block chain technology
Reason system.
Background technology
In the related technology, mainly have to the method for urban air pollution monitoring：
(1) conventional method, the i.e. method of manual sampling lab analysis.This method can only obtain air pollution monitoring area
Monitor value in domain in certain time can not be monitored in real time, monitoring result by it is artificial influenced it is very big, meanwhile, work as air
The health of meeting grievous injury monitoring personnel when pollution monitoring region harmful gas concentration is very high；
(2) on-line monitoring popular at present, the automation air environment monitoring equipment for mostly using external import carry out
Monitoring, this monitoring method, although real-time monitoring can be realized, device therefor is complicated, it is expensive, be difficult to safeguard,
Operation cost is high and its working environment is harsh.
Internet of Things needs the various needs of object of monitoring, connection, interaction by various information sensing devices, in real time acquisition
Information is combined the huge network to be formed with internet.The purpose is to realize object and object, object and people, all article and net
The connection of network facilitates identification, management and control.Operation related with data all is being carried out all the time in Internet of things system,
Including links such as data acquisition, data transmission and data storages.Data are easy to be by malicious attack and non-in each link
Method distorts operation.Link is acquired in data, illegal node can pretend to be or attack legitimate node to carry out illegal sensing data
It uploads；In data transmission link, on the one hand it may cause error code since channel quality is bad, on the other hand since data are being transmitted
The data for being easy to be eventually led to by malicious modification upload in the process are illegal etc..Therefore, advance row data check is stored in data
It is essential operation.The purpose of data check be in order to prevent illegal node pretend to be with attack, prevent data and exist
Be maliciously tampered in transmission process, ensure authenticity, legitimacy and the integrality of data.
Invention content
In view of the above-mentioned problems, the present invention provides a kind of air pollution data processing system based on block chain technology.
The purpose of the present invention is realized using following technical scheme：
Provide a kind of air pollution data processing system based on block chain technology, including headend, wireless biography
Sensor network and the data check platform being made of multiple data check nodes；The wireless sensor network is used for sky
Gas pollution is monitored, and is obtained air pollution concentration Data Concurrent and is sent to headend；The headend is receiving
After the air pollution concentration data that treated, it is forwarded to the data check platform；It is more in the data check platform
A data check-node is responsible for that the air pollution concentration data that the headend forwards are verified and preserved；Respectively
The data check node is block chain node, and multiple data check nodes form a distributed data base；Further include
The data processing platform (DPP) being connect with data check platform, the data processing platform (DPP) are used for the air pollution concentration number after verification
According to pre-processed successively, clustering processing and outlier detection processing, be stored in data after the abnormal point detected is marked
Library.
Preferably, the wireless sensor network includes aggregation node and multiple sensor nodes, and sensor node is used
In acquisition air pollution concentration data, and the air pollution concentration data of acquisition are transmitted to remittance by parallel multipath routing mode
Poly- node；Aggregation node receives and the air pollution concentration data of processing sensor node output, and air is dirty by treated
Dye concentration data is transmitted to headend.
Further, system further includes authentication center, authentication center to the headend, the sensor node,
Aggregation node and the data check node carry out authorization identifying.
Preferably, sensor node becomes legal sensor node after the authorization identifying by authentication center, and obtains
Unique sensor node digital certificate and symmetric key；Aggregation node becomes legal after the authorization identifying by authentication center
Aggregation node, and obtain unique aggregation node digital certificate and symmetric key；The headend is passing through authentication center
Authorization identifying after, obtain unique headend digital certificate and symmetric key；The data check node is by recognizing
Become valid data check-node after the authorization identifying at card center, and obtains unique public key, private key, data check node number
Certificate and symmetric key；The public key, for by the headend to the data check platform and the wireless biography
Sensor Web broadcast；The private key, for by artificially being shared between the data check node.
Beneficial effects of the present invention are：Air pollution concentration data are obtained using wireless sensor network technology, are not necessarily to cloth
Line uses manpower and material resources sparingly, and scalability is good, is suitble to the large-scale monitoring system of structure, is suitble to promote and apply；Utilize data check
Node completes data check task, and verifying work is distributed to check-node from headend, can overcome due to verification
Task is excessively concentrated and the problems such as the verification efficiency brought is low, speed is slow, propagation delay time is high, vulnerable.
Description of the drawings
Using attached drawing, the invention will be further described, but the embodiment in attached drawing does not constitute any limit to the present invention
System, for those of ordinary skill in the art, without creative efforts, can also obtain according to the following drawings
Other attached drawings.
Fig. 1 is the system structure connection block diagram of an illustrative embodiment of the invention；
Fig. 2 is the structural schematic diagram of the data processing platform (DPP) of an illustrative embodiment of the invention.
Reference numeral：
Headend 1, wireless sensor network 2, data check platform 3, data processing platform (DPP) 4, preprocessing module 10,
Clustering processing module 20, outlier detection module 30, database 40.
Specific implementation mode
The invention will be further described with the following Examples.
Referring to Fig. 1, a kind of air pollution data processing system based on block chain technology, including number are present embodiments provided
According to transponder 1, wireless sensor network 2 and the data check platform 3 being made of multiple data check nodes；Described is wireless
Sensor network 2 obtains air pollution concentration Data Concurrent and send to headend 1 for being monitored to air pollution；Institute
Headend 1 is stated after the air pollution concentration data that receive that treated, is forwarded to the data check platform 3；
Multiple data check nodes in the data check platform 3 are responsible for the air pollution forwarded to the headend 1
Concentration data is verified and is preserved；Each data check node is block chain node, multiple data check node groups
At a distributed data base.
Wherein, the wireless sensor network 2 includes that aggregation node and multiple sensor nodes, sensor node are used for
Air pollution concentration data are acquired, and the air pollution concentration data of acquisition are transmitted to convergence by parallel multipath routing mode
Node；Aggregation node receives and the air pollution concentration data of processing sensor node output, and will treated air pollution
Concentration data is transmitted to headend 1.
Further, as shown in Figure 1 and Figure 2, system further includes the data processing platform (DPP) 4 being connect with data check platform 3,
The data processing platform (DPP) 4 for being pre-processed successively to the air pollution concentration data after verification, clustering processing and exception
Point detection process, database is stored in after the abnormal point detected is marked.The data processing platform (DPP) 4 includes being sequentially connected
Preprocessing module 10, clustering processing module 20, outlier detection module 30 and database 40.
Further, system further includes authentication center, authentication center to the headend 1, the sensor node,
Aggregation node and the data check node carry out authorization identifying.In one embodiment, sensor node is in by certification
Become legal sensor node after the authorization identifying of the heart, and obtains unique sensor node digital certificate and symmetric key；It converges
Poly- node becomes legal aggregation node after the authorization identifying by authentication center, and obtains unique aggregation node digital certificate
And symmetric key；The headend 1 obtains the number of unique headend 1 after the authorization identifying by authentication center
Word certificate and symmetric key；The data check node becomes valid data verification section after the authorization identifying by authentication center
Point, and obtain unique public key, private key, data check node digital certificate and symmetric key；The public key, for by described
Headend 1 is broadcasted to the data check platform 3 and the wireless sensor network 2；The private key, for by artificial
It is shared between the data check node.
The above embodiment of the present invention obtains air pollution concentration data using wireless sensor network technology, without connecting up,
It uses manpower and material resources sparingly, scalability is good, is suitble to the large-scale monitoring system of structure, is suitble to promote and apply；Utilize data check section
It puts to complete data check task, verifying work is distributed to check-node from headend 1, can overcome since verification is appointed
Business is excessively concentrated and the problems such as the verification efficiency brought is low, speed is slow, propagation delay time is high, vulnerable.
In one embodiment, preprocessing module 10 is for pre-processing air pollution concentration data, specially：It is right
There are the air pollution concentration data of 0 value or negative value to be pre-processed, and 0 value or negative value are replaced with preset substitution value.
The present embodiment can prevent 0 value in air pollution concentration data or negative value to subsequent air pollution concentration data
Clustering processing impacts.
In one embodiment, clustering processing module 20 is for gathering pretreated air pollution concentration data
Class specifically includes：
(1) the air pollution concentration data of the set period of time of extraction sample air pollution concentration data are as an air
Pollution concentration data set, is set as S；
(2) in first time iteration, a unlabelled air pollution in air pollution concentration data set S is randomly choosed
Concentration data is as first cluster central point D1, calculate remaining air pollution concentration data and cluster central point D1Between it is similar
Degree, if air pollution concentration data siWith cluster central point D1Between similarity be more than setting similarity threshold, then by air dirt
Contaminate concentration data siIt is assigned to cluster central point D1, and be marked；
(3) iterations x is enabled to add 1, a unlabelled air pollution in random selection air pollution concentration data set S
Concentration data is as another cluster central point Dx+1, calculate remaining air pollution concentration data and cluster central point Dx+1Between phase
Like degree；
If air pollution concentration data sjUnmarked and and Dx+1Between similarity be more than setting similarity threshold, then
By air pollution concentration data sjIt is assigned to cluster central point Dx+1, and be marked；
If air pollution concentration data sjIt is marked and meet reallocation condition, then by air pollution concentration data sjDistribution
To cluster central point Dx+1, and be marked, otherwise to air pollution concentration data sjAny operation is not made；
(4) repeat (3) until iterations x reach setting threshold value or all air pollution concentration data all by
Label executes (5)；
(5) the cluster central point for updating each cluster is the mean value of all air pollution concentration data in the cluster, and distribution is each non-
Cluster of the cluster central point to where with the highest cluster central point of its similarity, when all cluster central points all no longer update, algorithm
Stop；
Wherein, set reallocation condition as：
[λL(sy,Dx+1)-L(sy,Dy0)]×R[L(sy,Dx+1)-LT]>0
In formula, L (sy,Dx+1) indicate air pollution concentration data syWith cluster central point Dx+1Between similarity, LTIt is described
The similarity threshold of setting, R [L (sy,Dx+1)-LT] it is the judgement value function set, as L (sy,Dx+1)-LT>When 0, R [L (sy,
Dx+1)-LT]=1, as L (sy,Dx+1)-LTWhen≤0, R [L (sy,Dx+1)-LT]=0；L(sy,Dy0) it is air pollution concentration data sy
With the similarity between its allocated cluster central point, λ is the adjustment factor of setting, and value range is (0.8,1).
The present embodiment is set carries out clustering processing to the pretreated air pollution concentration data of data pre-processing unit
Specific mechanism, which can quickly and easily complete the cluster of air pollution concentration data, need not preassign cluster
Number.
The present embodiment innovatively sets reallocation condition, by the air pollution concentration number that will meet reallocation condition
According to being re-assigned in new cluster central point, enable to each air pollution concentration data that can distribute to and its most phase
As cluster, cluster mode compared to traditional k-means, better Clustering Effect can be obtained.
Wherein, the similarity between air pollution concentration data and cluster central point may be used existing similarity function into
Row calculates, and is measured for example, by using cosine similarity, Pearson correlation coefficient etc..
In a preferred embodiment, the calculating of the similarity between setting air pollution concentration data and cluster central point is public
Formula is：
In formula, L (sy,Dk) indicate air pollution concentration data syWith cluster central point DkBetween similarity, syαIndicate air
Pollution concentration data syα dimension attribute values, DkαIndicate cluster central point Dkα dimension attribute values, β be air pollution concentration data
Dimension, min expression be minimized, max expression be maximized, Z (syα,Dkα) it is the comparison value function set, work as syα=Dkα
When, Z (syα,Dkα)=0, works as syα≠DkαWhen, Z (syα,Dkα)=1.
The present embodiment innovatively sets the calculation formula of similarity, it is proposed that a kind of new measuring similarity mechanism,
The similarity obtained by the calculation formula weighs the similitude between two air pollution concentration data, enables to similar
The calculating of degree is not influenced by the dimension of air pollution concentration data, to avoid any unnecessary data conversion so that right
The cluster of air pollution concentration data is simpler quick, improves the efficiency of air pollution concentration data processing system.
In one embodiment, outlier detection module 30 is used to carry out the air pollution concentration data after clustering processing
Outlier detection specifically includes：
It (1), should if there are the number threshold value that the air pollution concentration data amount check of a cluster is less than setting after cluster
Cluster is considered as abnormal clusters, and all air pollution concentration data in abnormal clusters are considered as abnormal air pollution concentration data；
(2) similarity between the cluster central point of other normal clusters and the cluster central point of abnormal clusters is calculated；
(3) if there are the similarities between the cluster central point and the cluster central point of normal clusters of an abnormal clusters to be more than setting
Cluster similarity threshold then using the normal clusters as cluster to be detected, and is detected using the air pollution concentration data of the abnormal clusters
Air pollution concentration data in cluster to be detected, if the air pollution concentration data acquisition system of the abnormal clusters is Sv={ s1,s2,..,
sv, take { s1,s2,..,svIn median sr, as the air pollution concentration data s in cluster to be detectedbWhen meeting exceptional condition,
By air pollution concentration data sbIt is considered as abnormal air pollution concentration data.
Wherein, set exceptional condition as：
In formula, sbαIndicate air pollution concentration data sbα dimension attribute values, srαIndicate median srα dimension attributes
Value, sr∈Sv, β is the dimension of air pollution concentration data, LtFor the similarity threshold of another setting, C1(sbα,s4α) it is setting
Get the small value function, works as sbα-s4αWhen≤0, C1(sbα,s4α)=sbα, work as s5α-s4α>When 0, C1(sbα,s4α)=srα, C2(sbα,srα)
For the function that takes large values of setting, work as sbα-s4αWhen≤0, C2(sbα,s4α)=s4α, work as sbα-srα>When 0, C2(sbα,s4α)=sbα。
Due to comparatively loose between the air pollution concentration data in the smaller cluster of scale, and relative to other
Air pollution concentration data are more isolated, therefore the data in the cluster of scale is smaller are usually considered as abnormal number in the prior art
According to.Based on this, the present embodiment carries out outlier detection to the air pollution concentration data after clustering processing, therefrom innovatively carries
Go out for detecting whether air pollution concentration data are abnormal exceptional condition, the exceptional condition is according to air pollution concentration number
According to the similarity threshold between the median of the highest abnormal clusters of similarity come judge the air pollution concentration data whether be
Abnormal air pollution concentration data enable to detection not influenced by dimension, and detection mode is simple and effective, has certain
Accuracy of detection, to improve the data-handling efficiency and precision of air pollution concentration data processing system on the whole.
Finally it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than the present invention is protected
The limitation of range is protected, although being explained in detail to the present invention with reference to preferred embodiment, those skilled in the art answer
Work as understanding, technical scheme of the present invention can be modified or replaced equivalently, without departing from the reality of technical solution of the present invention
Matter and range.
Claims (6)
1. a kind of air pollution data processing system based on block chain technology, characterized in that including headend, wireless biography
Sensor network and the data check platform being made of multiple data check nodes；The wireless sensor network is used for sky
Gas pollution is monitored, and is obtained air pollution concentration Data Concurrent and is sent to headend；The headend is receiving
After the air pollution concentration data that treated, it is forwarded to the data check platform；It is more in the data check platform
A data check-node is responsible for that the air pollution concentration data that the headend forwards are verified and preserved；Respectively
The data check node is block chain node, and multiple data check nodes form a distributed data base；Further include
The data processing platform (DPP) being connect with data check platform, the data processing platform (DPP) are used for the air pollution concentration number after verification
According to pre-processed successively, clustering processing and outlier detection processing, be stored in data after the abnormal point detected is marked
Library.
2. a kind of air pollution data processing system based on block chain technology according to claim 1, characterized in that institute
The wireless sensor network stated includes aggregation node and multiple sensor nodes, and sensor node is for acquiring air pollution concentration
Data, and the air pollution concentration data of acquisition are transmitted to aggregation node by parallel multipath routing mode；Aggregation node connects
Receive and the air pollution concentration data of processing sensor node output, and will treated air pollution concentration data transmission to number
According to transponder.
3. a kind of air pollution data processing system based on block chain technology according to claim 2, characterized in that also
Including authentication center, authentication center is to the headend, the sensor node, aggregation node and the data check section
Point carries out authorization identifying.
4. a kind of air pollution data processing system based on block chain technology according to claim 3, characterized in that pass
Sensor node becomes legal sensor node after the authorization identifying by authentication center, and obtains unique sensor node number
Word certificate and symmetric key；Aggregation node becomes legal aggregation node after the authorization identifying by authentication center, and obtains only
One aggregation node digital certificate and symmetric key；The headend obtains after the authorization identifying by authentication center
Unique headend digital certificate and symmetric key；The data check node is after the authorization identifying by authentication center
As valid data check-node, and obtain unique public key, private key, data check node digital certificate and symmetric key；Institute
Public key is stated, for being broadcasted to the data check platform and the wireless sensor network by the headend；It is described
Private key, for by artificially being shared between the data check node.
5. a kind of air pollution data processing system based on block chain technology according to claim 1-4, characterized in that
The data processing platform (DPP) includes sequentially connected preprocessing module, clustering processing module, outlier detection module and database.
6. special according to a kind of air pollution data processing system based on block chain technology of claim 5 any one of them
Sign is that outlier detection module is used to carry out outlier detection to the air pollution concentration data after clustering processing, specifically includes：
(1) if there are the number threshold value that the air pollution concentration data amount check of a cluster is less than setting after cluster, which is regarded
For abnormal clusters, all air pollution concentration data in abnormal clusters are considered as abnormal air pollution concentration data；
(2) similarity between the cluster central point of other normal clusters and the cluster central point of abnormal clusters is calculated；
(3) if there are the cluster phases that the similarity between the cluster central point of an abnormal clusters and the cluster central point of normal clusters is more than setting
Like degree threshold value, then using the normal clusters as cluster to be detected, and detected using the air pollution concentration data of the abnormal clusters to be checked
The air pollution concentration data in cluster are surveyed, if the air pollution concentration data acquisition system of the abnormal clusters is Sv={ s1, s2.., sv,
Take { s1, s2.., svIn median sr, as the air pollution concentration data s in cluster to be detectedbIt, will be empty when meeting exceptional condition
Gas pollution concentration data sbIt is considered as abnormal air pollution concentration data；
Wherein, set exceptional condition as：
In formula, sbαIndicate air pollution concentration data sbα dimension attribute values, srαIndicate median srα dimension attribute values, sr
∈Sv, β is the dimension of air pollution concentration data, LtFor the similarity threshold of another setting, C1(sbα, srα) it is that taking for setting is small
Value function works as sbα-srαWhen≤0, C1(sbα, srα)=sbα, work as sbα-srαWhen ＞ 0, C1(sbα, srα)=srα, C2(sbα, srα) it is to set
The fixed function that takes large values, works as sbα-srαWhen≤0, C2(sbα, srα)=srα, work as sbα-srαWhen ＞ 0, C2(sbα, srα)=sbα。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810285050.7A CN108521636A (en) | 2018-04-02 | 2018-04-02 | A kind of air pollution data processing system based on block chain technology |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810285050.7A CN108521636A (en) | 2018-04-02 | 2018-04-02 | A kind of air pollution data processing system based on block chain technology |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108521636A true CN108521636A (en) | 2018-09-11 |
Family
ID=63431671
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810285050.7A Pending CN108521636A (en) | 2018-04-02 | 2018-04-02 | A kind of air pollution data processing system based on block chain technology |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108521636A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108600370A (en) * | 2018-04-25 | 2018-09-28 | 深圳大图科创技术开发有限公司 | A kind of air pollution data processing system based on block chain technology |
| CN111157682A (en) * | 2020-01-06 | 2020-05-15 | 上海应用技术大学 | Air quality monitoring and predicting system and method |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2007019388A3 (en) * | 2005-08-08 | 2010-09-02 | Honeywell International Inc. | Data compression and abnormal situation detection in a wireless sensor network |
| CN102802158A (en) * | 2012-08-07 | 2012-11-28 | 湖南大学 | Method for detecting network anomaly of wireless sensor based on trust evaluation |
| CN103020642A (en) * | 2012-10-08 | 2013-04-03 | 江苏省环境监测中心 | Water environment monitoring and quality-control data analysis method |
| CN105574547A (en) * | 2015-12-22 | 2016-05-11 | 北京奇虎科技有限公司 | Integrated learning method and device adapted to weight of dynamically adjustable base classifier |
| CN107249009A (en) * | 2017-08-02 | 2017-10-13 | 广东工业大学 | A kind of data verification method and system based on block chain |
-
2018
- 2018-04-02 CN CN201810285050.7A patent/CN108521636A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2007019388A3 (en) * | 2005-08-08 | 2010-09-02 | Honeywell International Inc. | Data compression and abnormal situation detection in a wireless sensor network |
| CN102802158A (en) * | 2012-08-07 | 2012-11-28 | 湖南大学 | Method for detecting network anomaly of wireless sensor based on trust evaluation |
| CN103020642A (en) * | 2012-10-08 | 2013-04-03 | 江苏省环境监测中心 | Water environment monitoring and quality-control data analysis method |
| CN105574547A (en) * | 2015-12-22 | 2016-05-11 | 北京奇虎科技有限公司 | Integrated learning method and device adapted to weight of dynamically adjustable base classifier |
| CN107249009A (en) * | 2017-08-02 | 2017-10-13 | 广东工业大学 | A kind of data verification method and system based on block chain |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108600370A (en) * | 2018-04-25 | 2018-09-28 | 深圳大图科创技术开发有限公司 | A kind of air pollution data processing system based on block chain technology |
| CN111157682A (en) * | 2020-01-06 | 2020-05-15 | 上海应用技术大学 | Air quality monitoring and predicting system and method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104317681B (en) | For the behavioral abnormal automatic detection method and detecting system of computer system | |
| CN108600332A (en) | A kind of pollution of waterhead data processing system based on block chain technology | |
| US8797901B2 (en) | Method and its devices of network TCP traffic online identification using features in the head of the data flow | |
| CN109117634A (en) | Malware detection method and system based on network flow multi-view integration | |
| CN107733851A (en) | DNS tunnels Trojan detecting method based on communication behavior analysis | |
| CN104036139B (en) | A kind of mobile object track monitoring method | |
| CN107645503A (en) | A kind of detection method of the affiliated DGA families of rule-based malice domain name | |
| CN110519128A (en) | A kind of operating system recognition methods based on random forest | |
| Zhang et al. | Distributed intrusion detection based on clustering | |
| CN109284296A (en) | A kind of big data PB grades of distributed informationm storage and retrieval platforms | |
| CN107392016A (en) | A kind of web data storehouse attack detecting system based on agency | |
| CN111157682A (en) | Air quality monitoring and predicting system and method | |
| CN102222187A (en) | Domain name structural feature-based hang horse web page detection method | |
| CN108600985A (en) | A kind of air pollution data acquisition and processing system based on block chain technology | |
| CN106973047A (en) | A kind of anomalous traffic detection method and device | |
| CN108737512A (en) | A kind of pollution of waterhead monitoring system based on block chain technology | |
| CN110120936A (en) | Distributed network attack detecting and security measurement system and method based on block chain | |
| WO2018011802A1 (en) | A system and method for providing a secure data monitoring system implemented within factory or plant | |
| CN105574593A (en) | Track state static-state detection and control system and method based on cloud computing and big data | |
| CN108521636A (en) | A kind of air pollution data processing system based on block chain technology | |
| CN108737511A (en) | A kind of air pollution surveillance system based on block chain | |
| CN109391624A (en) | A kind of terminal access data exception detection method and device based on machine learning | |
| CN102158486A (en) | Method for rapidly detecting network invasion | |
| CN107145779A (en) | A kind of recognition methods of offline Malware daily record and device | |
| CN105959328A (en) | Evidence graph and vulnerability reasoning combined network evidence collection method and system |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20180911 |