CN105933932B - The real-time fault diagnosis method and system of wireless sensor network under complex environment - Google Patents
The real-time fault diagnosis method and system of wireless sensor network under complex environment Download PDFInfo
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- CN105933932B CN105933932B CN201610416372.1A CN201610416372A CN105933932B CN 105933932 B CN105933932 B CN 105933932B CN 201610416372 A CN201610416372 A CN 201610416372A CN 105933932 B CN105933932 B CN 105933932B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/04—Arrangements for maintaining operational condition
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- H04W—WIRELESS COMMUNICATION NETWORKS
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- H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
Abstract
The invention discloses the real-time fault diagnosis method and system of wireless sensor network under a kind of complex environment, the diagnostic method include: S1, using improved k-medoids clustering algorithm in presumptive area sensor node carry out sub-clustering；The data that S2, basis are acquired to diagnostic sensor node and its neighbor node, calculate the Vote value to diagnostic sensor node, determine the health status to diagnostic sensor node according to Vote value；S3, the sensor entity that malfunctioning node is found according to sensor network, and repair.The present invention by the failure diagnostic processes of three phases, allow the sensor network nodes under complex environment experience sub-clustering, diagnosis, maintenance stage after, can timely and effectively complete the maintenance of network.
Description
Technical field
The present invention relates to wireless sensor network (WSN) technical field, more particularly to wirelessly being passed under a kind of complex environment
The real-time fault diagnosis method and system of sensor network.
Background technique
In the epoch that current information technology is developed by leaps and bounds, brought by the network of representative of Internet to people's lives
Huge variation.Technology of Internet of things was born from 1999, spreads within 2005 2009 and enters the rapid development stage, has become
Important component part in current information-based industry, wherein wireless sensor network technology is the key technique of Internet of Things.
Wireless sensor network be by a large amount of, communication and the limited sensor node of storage capacity by one jump or multi-hop communication mode from
A kind of no static infrastructure network organized the formation of, for perceiving, acquiring and the various information of real-time delivery monitoring object,
Overall structure principle is joined shown in Fig. 1.
Also from research, initially concentrating on military field has turned to civil field for the application of wireless sensor network technology, including
Smart grid, intelligent transportation, Intelligent logistics, intelligent medical, smart home etc., and play increasingly in these fields
Big effect.But under normal conditions, sensor node is all the equipment of very little, and is deployed in nobody adverse circumstances more
Under the conditions of, which results in, if there is failure has occurred in node, be difficult timely and effectively to carry out these malfunctioning nodes in network
Maintenance or replacement, this generates the demands for studying fault diagnosis technology in a kind of valid wireless sensor device network.
In existing some wireless sensor networks in the technology of Fault Node Diagnosis, set using temporal correlation principle
Meter technical solution is a kind of mode of suitable mainstream.But these technical solutions consider the deployed environment of network at the beginning of design
Only single, consistency environment, these diagnostic techniques can all have preferable application performance under environment herein.But in reality
In the network deployment on border, complex environment is only the most common situation, in this kind of environment, needs before being diagnosed in network
Sensor node effectively classified, this to solve complex environment under fault diagnosis have vital effect.
Therefore, in view of the above technical problems, it is necessary to a kind of the real-time of wireless sensor network under complex environment is provided therefore
Hinder diagnostic method and system.
Summary of the invention
In view of this, the purpose of the present invention is to provide a kind of real time fails of wireless sensor network under complex environment to examine
Disconnected method and system, with timely to the malfunctioning node that phase does not occur in the wireless sensor network being deployed under complicated adverse circumstances
Diagnosis.
To achieve the goals above, technical solution provided in an embodiment of the present invention is as follows:
The real-time fault diagnosis method of wireless sensor network, the diagnostic method include: under a kind of complex environment
S1, sub-clustering is carried out to the sensor node in presumptive area using the clustering algorithm of improved k-medoids；
The data that S2, basis are acquired to diagnostic sensor node and its neighbor node, calculate to diagnostic sensor node
Vote value determines the health status to diagnostic sensor node according to Vote value；
S3, the sensor entity that malfunctioning node is found according to sensor network, and repair.
As a further improvement of the present invention, the step S1 specifically:
The quantity K of cluster is determined according to actual environment；
K cluster centre is initialized, K sensor node is randomly choosed；
Sub-clustering is initialized, selects nearest node as cluster head according to criterion function E；
Calculate the sum of the criterion function value E of all the sensors node and its cluster central sensor node in K cluster after sub-clustering
For M value；
Judge whether M value is sufficiently small, if it is not, reselecting any node in each cluster as new cluster head, so that M value drops
It is low, and according to new leader cluster node again sub-clustering, if so, generation is cluster structured, and broadcasted to sensor network.
As a further improvement of the present invention, in the step S1, sub-clustering is initialized specifically:
Any 2 sensor node SiWith SjBetween use criterion functionIt is calculated, SjSelection
The smallest S of criterion functioniCenter as its sub-clustering；
Wherein, α, β indicate constant coefficient；dijFor the distance between two sensor nodes, dijUsing Euclidean distance calculate or
Using the intensity estimation signal between two sensors；pijIndicate nearest t phase between two sensor node historical datas
Off course degree,
As a further improvement of the present invention, in the step S1, the sum of criterion function value E is M value specifically:
Wherein, NK ' expression cluster center SK′Sensor node number in the place cluster of node,For the M value of an independent cluster.
As a further improvement of the present invention, the step S2 specifically:
Selection is to diagnostic sensor node；
It collects to diagnostic sensor node and its data of neighbours, calculates the Vote value to diagnostic sensor node；
If Vote value is less than or equal to first threshold, determine to diagnostic sensor node to be malfunctioning node；
If Vote value is greater than or equal to second threshold, determine to diagnostic sensor node to be fault-free node；
If Vote value is greater than first threshold and is less than second threshold, determines to diagnosis node to be nondeterministic statement, update
The reference state of neighbours is collected to diagnostic sensor node and its data of neighbours, Vote value is recalculated, if Vote value is less than
Or be equal to third threshold value, then determine to diagnostic sensor node to be malfunctioning node, if Vote value is greater than third threshold value, determine to
Diagnostic sensor node is fault-free node.
As a further improvement of the present invention, Neighbor (S is used to diagnostic sensor node in the step S2i) and
Neighborsame(Si) in sensor node participate in diagnosis process.
As a further improvement of the present invention, the Vote value in the step S2 to diagnostic sensor node specifically:
Or
Wherein, CijFor node SiWith node SjWhether consistent variate-value,θ1
For threshold value, weight is the weight coefficient value of ballot table.
As a further improvement of the present invention, in the step S2 to diagnostic sensor node SiState by 2 wheel
Voting process carries out diagnostic analysis.
Correspondingly, under a kind of complex environment wireless sensor network real-time fault diagnosis system, the diagnostic system packet
It includes:
Sensing unit, including several sensors and D/A converter module, several sensors form sensor network, and sensing is single
Member is for acquiring surrounding physical context information and information being carried out digital-to-analogue conversion；
Processing unit, including the processor for being handled sensing unit data and storage for storing data
Device；
Communication unit, for carrying out data transmission；
Power supply unit, for providing energy supply for sensing unit, processing unit and communication unit.
As a further improvement of the present invention, the processor is also used to:
Sub-clustering is carried out to the sensor node in presumptive area using the clustering algorithm of improved k-medoids；
And it according to the data to diagnostic sensor node and its neighbor node acquisition, calculates to diagnostic sensor node
Vote value, health status to diagnostic sensor node is determined according to Vote value.
The beneficial effects of the present invention are:
By the failure diagnostic process of three phases, the sensor network nodes under complex environment is allowed to undergo sub-clustering, examine
After disconnected, maintenance stage, the maintenance of network can be timely and effectively completed；
By the process of sub-clustering, the adverse effect that complex environment generates diagnostic result is avoided；
The Distributed-solution of use can reduce the energy consumption of nodes, avoid sensor node because
Excessive communication leads to the puzzlement for influencing network lifecycle.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
The some embodiments recorded in invention, for those of ordinary skill in the art, without creative efforts,
It is also possible to obtain other drawings based on these drawings.
Fig. 1 is the overall structure schematic diagram of wireless sensor network (WSN)；
Fig. 2 is the real-time fault diagnosis method of wireless sensor network under complex environment in the embodiment of the invention
Flow diagram；
Fig. 3 is the specific flow chart of sensor node sub-clustering in the embodiment of the invention；
Fig. 4 is the specific flow chart of sensor node diagnosis in the embodiment of the invention；
Fig. 5 is the real-time fault diagnosis system of wireless sensor network under complex environment in the embodiment of the invention
Module diagram.
Specific embodiment
Technical solution in order to enable those skilled in the art to better understand the present invention, below in conjunction with of the invention real
The attached drawing in example is applied, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described implementation
Example is only a part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, this field is common
Technical staff's every other embodiment obtained without making creative work, all should belong to protection of the present invention
Range.
The present invention allows the wireless sensor network node under complex environment passing through by the failure diagnostic process of three phases
After going through the stage of sub-clustering, diagnosis, maintenance, the maintenance of network can be timely and effectively completed, specifically includes following three step:
First according to the clustering algorithm of optimized k-mediods a kind of, to the node in wireless sensor network
Region division is carried out, each sensor node is enabled to find the node therewith under like environment, they, which collectively form, belongs to certainly
Oneself group of sensor node (or being cluster).It is adjacent according to the principle for the temporal correlation that wireless sensor network has
The data of node perceived should be in an acceptable range δ；
After node to be detected starts diagnosis process, the neighbor node request data with surrounding is begun to, after receiving data
It is compared with oneself data collected, then counts the consistency of neighbor node and data, using Vote value as weighing apparatus
Measure the parameter of consistency.During determining Vote value, the state of node surrounding neighbours is fully considered, include whether as failure
Whether state had been diagnosed recently, whether is being in similar environment etc. condition with node to be detected.In the state of 2 bouts
After decision process, the health status of node to be detected is obtained, finally sends the diagnostic result of node to base station, is recorded in base station
All node states in network；
Finally, after having found malfunctioning node in record corresponding sensing can be found according to the routing Map view of network
Device entity repairs the operation of replacement.
In the present invention during wireless sensor network is run, sensor node is required according to the time, is diagnosed
The information such as result adjust itself state in time, can be provided during participating in the breakdown judge of other nodes and meet reality
Reference value.
Specifically, join shown in Fig. 2, the real-time event of wireless sensor network under a preferred embodiment complex environment of the invention
Hinder diagnostic method, specifically include:
S1, sub-clustering is carried out to the sensor node in presumptive area using the clustering algorithm of improved k-medoids；
The data that S2, basis are acquired to diagnostic sensor node and its neighbor node, calculate to diagnostic sensor node
Vote value determines the health status to diagnostic sensor node according to Vote value；
S3, the sensor entity that malfunctioning node is found according to sensor network, and repair.
Wherein, as shown in connection with fig. 3, step S1 specifically includes the following steps:
1) the quantity K of cluster, is determined according to actual environment.
The operation for carrying out sub-clustering to the sensor node in specified region with the clustering algorithm of k-medoids, because of k-
The clustering algorithm of medoids needs to know the quantity K of cluster, can rule of thumb specify the sub-clustering quantity K of Experimental Area, example here
Such as teaching building can determine the quantity of sub-clustering according to classroom, corridor region quantity.
2) K cluster centre, is initialized, K sensor node is randomly choosed in region as initial cluster centre.
3) sub-clustering, is initialized, selects nearest node as cluster head according to criterion function E.
Any 2 sensor node SiWith SjBetween use criterion functionIt is calculated, SjSelection
The smallest S of criterion functioniCenter as its sub-clustering (or cluster)；
Wherein, α, β indicate constant coefficient, can be adjusted according to the actual situation；
dijFor the distance between two sensor nodes, dijEuclidean distance can be used and calculate or use two sensors
Between intensity estimation signal.Preferably, take the distance between 2 nodes of parameter Estimation of RSSI according to letter in present embodiment
Number intensity determines, such as: unlike signal intensity divides
pijIt indicates nearest t different degree (i.e. opposite with similarity) between two sensor node historical datas, uses
FormulaIt is calculated.
4), calculate after sub-clustering all the sensors node and its cluster central sensor node in K cluster criterion function value E it
With for M value.
M value is calculated as follows with formula:
Wherein, NK ' expression cluster center SK′Sensor node number in the place cluster of node,For the M value of an independent cluster.
5), judge whether M value is sufficiently small, if it is not, reselecting any node in each cluster as new cluster head, so that M
Value reduces, and according to new leader cluster node again sub-clustering, if so, generation is cluster structured, and broadcasts to sensor network.
For remaining N-K sensor node, one node of random unduplicated selection replaces the cluster center belonging to it
Node calculates the sum of criterion function value E of network M ' at this time, if M ' < M, jumps to step 3), otherwise N-K sensor section
M value can not further decrease after point is attempted, then terminate cluster process, export the result of current sub-clustering.Specific step is as follows:
For i=1 to N-K
■ Choose node SiReplace its affiliated cluster central node；
The sum of criterion function value E M ' in network after ■ calculating replacement；
■ If M ' < M
◆ jump procedure 3)；
The current sub-clustering result of Return
As shown in connection with fig. 4, step S2 specifically includes the following steps:
Selection is to diagnostic sensor node；
It collects to diagnostic sensor node and its data of neighbours, calculates the Vote value to diagnostic sensor node；
If Vote value is less than or equal to first threshold (threshold value 1), determine to diagnostic sensor node to be malfunctioning node；
If Vote value is greater than or equal to second threshold (threshold value 2), determine to diagnostic sensor node to be fault-free node；
If Vote value is greater than first threshold (threshold value 1) and is less than second threshold (threshold value 2), determine to diagnosis node to be not
Determine state, the reference state of more new neighbor is collected to diagnostic sensor node and its data of neighbours, recalculates Vote
Value determines to diagnostic sensor node to be malfunctioning node, if Vote if Vote value is less than or equal to third threshold value (threshold value 3)
Value is greater than third threshold value (threshold value 3), then determines to diagnostic sensor node to be fault-free node.
Specifically, Neighbor (S is used to diagnostic sensor node in the present embodimenti) (indicate node SiIt is all
Neighbor node) and/or Neighborsame(Si) (indicate SiThe neighbor node of all same clusters) in sensor node participate in
Diagnosis process, the Vote value to diagnostic sensor node specifically:
Or
Wherein, CijFor node SiWith node SjWhether consistent variate-value,θ1
For threshold value, weight is the weight coefficient value of ballot table.
In present embodiment step S2 to diagnostic sensor node SiState by 2 wheel voting processes carry out diagnosis point
Analysis, specific steps and code are as follows:
Correspondingly, join shown in Fig. 5, wireless sensor under a kind of complex environment is also disclosed in another embodiment of the present invention
The real-time fault diagnosis system of network, specifically includes:
Sensing unit 10, including several sensors 11 and D/A converter module 12, several sensors form sensor network,
Sensing unit is for acquiring surrounding physical context information and information being carried out digital-to-analogue conversion；
Processing unit 20, depositing including the processor 21 for being handled sensing unit data and for storing data
Reservoir 22；
Communication unit 30, for carrying out data transmission, it is preferable that communication unit 30 is radio receiving transmitting module 31, to carry out
Wireless communication；
Power supply unit 40, for providing energy supply for sensing unit 10, processing unit 20 and communication unit 30.
Wherein, processor 21 is specifically used for:
Sub-clustering is carried out to the sensor node in presumptive area using the clustering algorithm of k-medoids；
And it according to the data to diagnostic sensor node and its neighbor node acquisition, calculates to diagnostic sensor node
Vote value, health status to diagnostic sensor node is determined according to Vote value.
In a specific embodiment of the invention, use the wireless development board of Jennic as the section in wireless sensor network
Point, in addition temperature humidity sensor module, constructs WSN as shown in Figure 1, for monitoring the environment in specified region.
First by the end PC, process, completes the clustering process of sensor network in base station, then, when need according to Fig.3,
When diagnosing to nodes health status, the order of diagnosis is sent from base station to network by the end PC.Each net
Node in network is diagnosed according to the process of Fig. 4, and after node to be detected starts diagnosis process, the neighbor node with surrounding is asked
Temperature and humidity information is sought, is compared after receiving data with oneself temperature and humidity value collected, neighbor node and itself are then counted
The consistency of data, using Vote value as the parameter for measuring consistency.During determining Vote value, node week is fully considered
The state for enclosing neighbours, include whether for malfunction, whether be diagnosed recently, it is whether similar with node to be detected
Environment etc. condition.After the state decision process of 2 bouts, the health status of node to be detected is obtained, finally examining node
Disconnected result is sent to base station, all node states in the record network of base station.It, can be with after having found malfunctioning node in record
Corresponding sensor entity is found according to the routing Map view of network, repairs the operation of replacement.
As can be seen from the above technical solutions, the invention has the following advantages:
By the failure diagnostic process of three phases, the wireless sensor network node under complex environment is allowed to divide in experience
Cluster, diagnosis, maintenance stage after, can timely and effectively complete the maintenance of network；
By the process of sub-clustering, the adverse effect that complex environment generates diagnostic result is avoided；
The Distributed-solution of use can reduce the energy consumption of nodes, avoid sensor node because
Excessive communication leads to the puzzlement for influencing network lifecycle.
It is obvious to a person skilled in the art that invention is not limited to the details of the above exemplary embodiments, Er Qie
In the case where without departing substantially from spirit or essential attributes of the invention, the present invention can be realized in other specific forms.Therefore, no matter
From the point of view of which point, the present embodiments are to be considered as illustrative and not restrictive, and the scope of the present invention is by appended power
Benefit requires rather than above description limits, it is intended that all by what is fallen within the meaning and scope of the equivalent elements of the claims
Variation is included within the present invention.Any reference signs in the claims should not be construed as limiting the involved claims.
In addition, it should be understood that although this specification is described in terms of embodiments, but not each embodiment is only wrapped
Containing an independent technical solution, this description of the specification is merely for the sake of clarity, and those skilled in the art should
It considers the specification as a whole, the technical solutions in the various embodiments may also be suitably combined, forms those skilled in the art
The other embodiments being understood that.
Claims (6)
1. the real-time fault diagnosis method of wireless sensor network under a kind of complex environment, which is characterized in that the diagnostic method
Include:
S1, sub-clustering is carried out to the sensor node in presumptive area using the clustering algorithm of improved k-medoids；
The data that S2, basis are acquired to diagnostic sensor node and its neighbor node, calculate the Vote to diagnostic sensor node
Value determines the health status to diagnostic sensor node according to Vote value；
S3, the sensor entity that malfunctioning node is found according to sensor network, and repair；
The step S1 specifically:
The quantity K of cluster is determined according to actual environment；
K cluster centre is initialized, K sensor node is randomly choosed；
Sub-clustering is initialized, selects nearest node as cluster head according to criterion function E；
The sum of criterion function value E of all the sensors node and its cluster central sensor node is M in K cluster after calculating sub-clustering
Value；
Judge whether M value is sufficiently small, for remaining N-K sensor node, random one node of unduplicated selection replacement
Cluster central node belonging to it calculates the sum of criterion function value E of network M ' at this time, if M ' < M, reselects in each cluster
Any node is as new cluster head, so that M value reduces, and according to new leader cluster node again sub-clustering, if N-K sensor node carries out
M value can not further decrease after trial, and generation is cluster structured, and broadcast to sensor network；
In the step S1, sub-clustering is initialized specifically:
Any 2 sensor node SiWith SjBetween use criterion functionIt is calculated, SjSelection criterion
The smallest S of functioniCenter as its sub-clustering；
Wherein, α, β indicate constant coefficient；dijThe distance between two sensor nodes, dijTwo are calculated or used using Euclidean distance
Intensity estimation signal between a sensor；pijIndicate nearest t different degree between two sensor node historical datas,
In the step S1, the sum of criterion function value E is M value specifically:
Wherein, NK ' expression cluster center SK′Sensor node number in the place cluster of node,For
The M value of an independent cluster.
2. diagnostic method according to claim 1, which is characterized in that the step S2 specifically:
Selection is to diagnostic sensor node；
It collects to diagnostic sensor node and its data of neighbours, calculates the Vote value to diagnostic sensor node；
If Vote value is less than or equal to first threshold, determine to diagnostic sensor node to be malfunctioning node；
If Vote value is greater than or equal to second threshold, determine to diagnostic sensor node to be fault-free node；
If Vote value is greater than first threshold and is less than second threshold, determine to diagnosis node to be nondeterministic statement, more new neighbor
Reference state, collect to diagnostic sensor node and its data of neighbours, Vote value is recalculated, if Vote value is less than or waits
In third threshold value, then determine to diagnostic sensor node to be malfunctioning node, if Vote value is greater than third threshold value, determine wait diagnose
Sensor node is fault-free node.
3. diagnostic method according to claim 2, which is characterized in that used in the step S2 to diagnostic sensor node
Neighbor(Si) and Neighborsame(Si) in sensor node participate in diagnosis process.
4. diagnostic method according to claim 3, which is characterized in that diagnostic sensor node in the step S2
Vote value specifically:
Or
Wherein, CijFor node SiWith node SjWhether consistent variate-value,θ1For threshold
Value, weight are the weight coefficient value of ballot table.
5. diagnostic method according to claim 1, which is characterized in that in the step S2 to diagnostic sensor node Si
State by 2 wheel voting processes carry out diagnostic analysis.
6. the real-time fault diagnosis system of wireless sensor network under a kind of complex environment, which is characterized in that the diagnostic system
Include:
Sensing unit, including several sensors and D/A converter module, several sensors form sensor network, and sensing unit is used
In acquisition surrounding physical context information and information is subjected to digital-to-analogue conversion；
Processing unit, including the processor for being handled sensing unit data and memory for storing data；
Communication unit, for carrying out data transmission；
Power supply unit, for providing energy supply for sensing unit, processing unit and communication unit；
The processor is also used to:
Sub-clustering is carried out to the sensor node in presumptive area using the clustering algorithm of improved k-medoids；
And it according to the data to diagnostic sensor node and its neighbor node acquisition, calculates to diagnostic sensor node
Vote value determines the health status to diagnostic sensor node according to Vote value；
The processor is used for:
The quantity K of cluster is determined according to actual environment；
K cluster centre is initialized, K sensor node is randomly choosed；
Sub-clustering is initialized, selects nearest node as cluster head according to criterion function E；
The sum of criterion function value E of all the sensors node and its cluster central sensor node is M in K cluster after calculating sub-clustering
Value；
Judge whether M value is sufficiently small, for remaining N-K sensor node, random one node of unduplicated selection replacement
Cluster central node belonging to it calculates the sum of criterion function value E of network M ' at this time, if M ' < M, reselects in each cluster
Any node is as new cluster head, so that M value reduces, and according to new leader cluster node again sub-clustering, if N-K sensor node carries out
M value can not further decrease after trial, and generation is cluster structured, and broadcast to sensor network；
Wherein, sub-clustering is initialized specifically:
Any 2 sensor node SiWith SjBetween use criterion functionIt is calculated, SjSelection criterion
The smallest S of functioniCenter as its sub-clustering；
Wherein, α, β indicate constant coefficient；dijThe distance between two sensor nodes, dijTwo are calculated or used using Euclidean distance
Intensity estimation signal between a sensor；pijIndicate nearest t different degree between two sensor node historical datas,
Wherein, the sum of criterion function value E is M value specifically:
Wherein, NK ' expression cluster center SK′Sensor node number in the place cluster of node,For
The M value of an independent cluster.
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| CN106685731B (en) * | 2017-01-22 | 2019-05-31 | 苏州大学 | A kind of diagnostic method and system of real time fail |
| CN108449730A (en) * | 2018-02-10 | 2018-08-24 | 深圳万智联合科技有限公司 | A kind of sewage network intelligent monitor system based on big data |
| CN109714789B (en) * | 2019-02-28 | 2022-06-21 | 福建师范大学 | Wireless sensor node fault diagnosis algorithm based on multiple SINK nodes |
| CN109831336B (en) * | 2019-02-28 | 2022-02-11 | 福建师范大学 | MSD-based sensor node fault diagnosis method |
| CN110602716A (en) * | 2019-09-03 | 2019-12-20 | 沈阳化工大学 | Wireless sensor network node clustering optimization method based on fuzzy clustering algorithm |
| CN111093176B (en) * | 2020-03-25 | 2020-07-07 | 炬星科技（深圳）有限公司 | Robot network monitoring method, device and storage medium |
| CN114484732B (en) * | 2022-01-14 | 2023-06-02 | 南京信息工程大学 | Air conditioning unit sensor fault diagnosis method based on voting network |
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