CN104809205B - A kind of online network of waterways space-time accident detection method - Google Patents
A kind of online network of waterways space-time accident detection method Download PDFInfo
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- CN104809205B CN104809205B CN201510206245.4A CN201510206245A CN104809205B CN 104809205 B CN104809205 B CN 104809205B CN 201510206245 A CN201510206245 A CN 201510206245A CN 104809205 B CN104809205 B CN 104809205B
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- A kind of 1. online network of waterways space-time accident detection method, it is characterised in that comprise the following steps:(1) connected dominating set algorithms selection backbone node is utilized, monitors time and the spatiality of backbone node on-line, instead of prison Survey whole nodes;(2) Markov Chain simulated time process is used, the backbone node selected according to step (1) individually passes through Markov Chain, utilize transition probability predicted events development tendency between state;When by Markov Chain, if the backbone of prediction Junction sensor stateThe sequence τ and sensor states X (s of actual measurement for some timei, t+ τ) it is inconsistent when, it is believed that There occurs time anomaly;(3) spatial distribution of network of waterways sensor is represented with Bayesian network, and each sensor is regarded as one of Bayesian network Node, the flow direction in river regard the direction on Bayesian network side as;Sensor network can be regarded as the shellfish of a directed acyclic This network of leaf, speculate that the probability of occurrence of spatial event judges whether that free air anomaly occurs with Bayesian network;(4) see whether having time anomalous event occurs backbone node in the network of waterways according to Markov state conversion, then according to shellfish Whether this neural network forecast backbone node of leaf has space anomalous event, finally determines whether there is the generation of space-time anomalous event, when Between and spatial coherence model will coordinate mutually to detect space-time anomalous event.
- 2. online network of waterways space-time accident detection method according to claim 1, it is characterised in that the step (1) Concretely comprise the following steps:(1.1) labeling process:During initialization, all nodes are all non-dominant nodes;The each node u and R in its communication radiusu Neighbours exchange the information of respective neighbours, now node u is according to neighbor information, judge whether two can not directly phase intercommunication The neighbor node v and w of letter, if dominating node as candidate in the presence of, node u participates in structure CDS, otherwise show that node u can be with By other node controls, not as CDS nodes;(1.2) prune rule:The candidate's domination number of nodes obtained by labeling process is excessive, and candidate, which dominates node, needs basis Prune rule, reduce the quantity for dominating set of node;If the K neighbor node that candidate dominates node u can be by other dominations Node is communicated, then the set of node that deletion of node u is obtained from CDS is still CDS, therefore dominates in set of node and delete from candidate Except node u, node u turns into non-CDS nodes;(1.3) node in connected dominating set is as key contact, it is only necessary to which the data flow for monitoring backbone node replaces monitoring institute There is node;This strategy can guarantee that non-selected node can find at least one backbone node from its neighbor node, when Anomalous event occurs in non-selected non-key near nodal, can travel to key contact as soon as possible, can quickly detect Abnormal generation, reduce loss.
- 3. online network of waterways space-time accident detection method according to claim 1, it is characterised in that the step (2) With Markov Chain simulated time process, by comparison prediction value and measured value, judge whether time of origin exception;Detailed process It is as follows:(2.1) Markov Chain is a limited or denumerable sequence of events (E on discrete time point1,E2...) set, At any time, following behavior of process is dependent only on current state, unrelated with state before;With Markov Chain mould Pseudotime process, the backbone node selected according to step (1) individually pass through Markov Chain, utilize transition probability between state The state that predicted events occur;(2.2) free chloro concentration is quantized into the sensor stream of three state and individually passes through Markov in the Sensor monitoring network of waterways Chain, current state is defined first, then predict the state of next step;(2.3) sensor deviates normal time behavior and can be regarded as noise or be used as a time anomaly event;Due to inclined From frequently occurring, so rejecting noise this possibility, therefore, the deviation continuously occurred can regard time anomaly thing as Part, when by Markov Chain, if the backbone node sensor states of predictionFor some time sequence τ with Sensor states X (the s of actual measurementi, t+ τ) it is inconsistent when, it is believed that there occurs time anomaly.
- 4. online network of waterways space-time accident detection method according to claim 1, it is characterised in that the step (3) It is as follows with the spatial coherence between Bayesian network analog sensor, detailed process:(3.1) carry out the spatial relationship of analog sensor node and its neighbor node for network of waterways structure Bayesian network G, passed each Sensor regards a node of Bayesian network as, and the flow direction in river regards the direction on Bayesian network side as, it is possible to sensing Device network regards the Bayesian network of a directed acyclic as;(3.2) training data of Bayesian network is by S tuplesComposition,Representative sensor State;It can be concentrated from historical data and produce training data, then can learn to obtain shellfish with maximum likelihood estimation algorithm The parameter of this network of leaf;(3.3) after the training stage, to each sensor, a conditional probability table can be obtained, is predicted and passed according to conditional probability table The state of sensor;Predicted state and true observer state are contrasted, if sensor SiObservation be not belonging to that state, it is believed that Actual value deviate from the spatial relationship that study obtains, and have free air anomaly.
- 5. online network of waterways space-time accident detection method according to claim 1, it is characterised in that the step (4) Time and spatial coherence model will be coordinated mutually to detect space-time anomalous event, and detailed process is as follows:(4.1) backbone node selected according to step (1), see backbone node whether send out by having time anomalous event according to step (2) It is raw;If backbone node is there occurs time anomaly, using the child nodes of the abnormal backbone node of time of origin as Candidate Set;Such as Fruit does not have time of origin abnormal, continues to detect the next timestamp state of backbone node;(4.2) node of the abnormal backbone node of time of origin and its Candidate Set is passed through by Bayesian network according to step (3) Practise obtain expecting state compared with observer state, see whether sensor SiIt deviate from the spatial relationship that study obtains;(4.3) if backbone node SE is different from SI, free air anomaly occurs, due to time anomaly, the value of space-time anomalous counts device Add 1, wherein SE is sensor node predicted value, and SI is observation;(4.4) if the node in Candidate Set has been detected free air anomaly, then the child nodes of both candidate nodes are also added to Candidate Set;Detect simultaneously and candidate's centralized node of free air anomaly occurs whether having time is abnormal, if so, space-time anomalous counts device Add 1, similarly, continue to detect whether candidate's centralized node has free air anomaly, if so, detection time is abnormal, time of origin is abnormal, Space-time anomalous counts device adds 1;When the number of backbone node and its Candidate Set space-time exception reaches threshold value Q, just think to occur Space-time anomalous event.
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CN110969556B (en) * | 2019-09-30 | 2023-11-21 | 上海仪电(集团)有限公司中央研究院 | Machine learning multidimensional multi-model fusion river channel water quality anomaly detection method and device |
CN111858712A (en) * | 2020-07-20 | 2020-10-30 | 上海仪电(集团)有限公司中央研究院 | In-situ water quality inspection data time-space analysis and anomaly detection method and system |
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Inventor after: Wang Longbao Inventor after: Mao Yingchi Inventor after: Jie Qing Inventor after: Jia Bicong Inventor after: Ping Ping Inventor after: Xu Feng Inventor after: Zhou Xiaofeng Inventor before: Mao Yingchi Inventor before: Jie Qing Inventor before: Jia Bicong Inventor before: Ping Ping Inventor before: Wang Longbao Inventor before: Xu Feng Inventor before: Zhou Xiaofeng |