CN107484196A - The quality of data ensuring method and computer-readable medium of sensor network - Google Patents
The quality of data ensuring method and computer-readable medium of sensor network Download PDFInfo
- Publication number
- CN107484196A CN107484196A CN201710693830.0A CN201710693830A CN107484196A CN 107484196 A CN107484196 A CN 107484196A CN 201710693830 A CN201710693830 A CN 201710693830A CN 107484196 A CN107484196 A CN 107484196A
- Authority
- CN
- China
- Prior art keywords
- detection data
- data
- sequence
- time
- node
- 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
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/04—Arrangements for maintaining operational condition
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W84/00—Network topologies
- H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Testing And Monitoring For Control Systems (AREA)
- Data Exchanges In Wide-Area Networks (AREA)
Abstract
Disclose the quality of data ensuring method and computer-readable medium of a kind of sensor network.Detected by the detection data sequence that acquisition is periodically detected to the node of sensor network, the Variation Features based on detection data sequence judge whether fault detection data, and fault detection data is modified based on history detection data.Thus, it is possible to effectively find and correct the wrong data that sensor network nodes sporadicly occur.And then by the detection data sequence of the different sensor network nodes of total road branch road Relationship Comparison so as to the node for positioning the period for being likely to occur mistake with being likely to occur failure.Thus, it is possible to carry out the amendment and malfunctioning node positioning of data in larger sensor network automatically, ensure the quality of data.
Description
Technical field
The present invention relates to network data diagnostic techniques, and in particular to the quality of data ensuring method of sensor network and calculating
Machine computer-readable recording medium.
Background technology
With the development of computer techno-stress technology, intelligence is carried out to the device data in building and ambient parameter
The technology of Internet of things of monitoring and control is widely used.Prior art can be monitored in entire building space by sensor network
The change of all electrical equipment and ambient parameters.But the node of sensor network is possible to because failure or environment become
Change the detection data for reporting mistake.Therefore, in order to ensure the quality of data of sensor network acquisition, event can be searched by needing one kind badly
Hinder the quality of data ensuring method of node and the sensor network of wrong data.
The content of the invention
In view of this, the present invention provides a kind of quality of data ensuring method and computer-readable medium of sensor network,
To search the wrong data that sensor network nodes record and the node for being likely to occur failure automatically, ensure the number of sensor network
According to quality.
According to the first aspect of the application, there is provided a kind of quality of data ensuring method of sensor network, it is characterised in that
Including：
The detection data sequence of each node in the sensor network is obtained, the detection data sequence includes corresponding node
The detection data periodically obtained；
Detection data sequence to each node detects, and obtains fault detection data；
Data are detected according to history the revised detection data sequence of acquisition is modified to the fault detection data；With
And
Detected according to predetermined total road branch road relation between revised detection data sequence and each nodal test data
Malfunctioning node, the malfunctioning node for detection data existing defects all the time node.
Preferably, obtaining the detection data sequence of each node in the sensor network includes：
Obtain the raw sensor data sequence of each node in the sensor network；And
Data sequence is detected according to the raw sensor data retrieval detection cycle identical.
Preferably, the detection data sequence to each node detects, and obtaining fault detection data includes：
Obtain the difference sequence of the detection data sequence；
History detection data bulk meet require when, according at least part history detect data acquisition predetermined amount of time or
The difference threshold range of predetermined point of time；
Fault detection data will be labeled as beyond the detection data corresponding to the differential data of the difference threshold range；
Wherein, when the detection data are cumulative amount, the difference sequence is the second differnce sequence of detection data sequence
Row, when the detection data are instantaneous flow, the difference sequence is the first-order difference sequence of detection data sequence.
Preferably, the difference threshold range includes the first threshold range and the second threshold range, wherein, described first
Limit scope and data statistics acquisition is detected according to the history of preceding predetermined amount of time, second threshold range is according to same type day
In the corresponding period history detection data statistics obtain.
Preferably, the detection data sequence to each node detects, and obtaining fault detection data also includes：
When two continuous and in opposite direction differential datas are detected in the difference sequence, in current point in time
The differential data with model identical is searched in the detection data sequence of the first length before；
When finding the differential data with model identical, corresponding detection data are labeled as fault detection data；
Wherein, the differential data with model identical refers to the continuous differential data of sign change order identical.
Preferably, when finding the differential data with reverse mode, the detection data of corresponding time is prompted and are obtained
The mark instructions of the corresponding detection data；
Wherein, the differential data with reverse mode refers to the opposite continuous differential data of sign change order.
Preferably, the detection data sequence to each node detects, and obtaining fault detection data also includes：
The length that zero is continuously in difference sequence is more than the second length, and corresponding difference is not present in history detection data
When data continue the detection data sequence that the second length is zero, prompt the detection data of corresponding time and obtain to the testing number
According to mark instructions.
Preferably, the detection data sequence to each node detects, and obtaining fault detection data also includes：
Obtain variation tendency of the difference sequence within the period of the 3rd length；
When the variation tendency is lasting increase or reduced, the detection data for corresponding to the time are prompted simultaneously to obtain to the inspection
Survey the mark instructions of data.
Preferably, data are detected according to history and the revised detection data of acquisition is modified to the fault detection data
Sequence includes：
The fault detection data is replaced using data were detected corresponding to a upper time cycle；
According to the detection data in predetermined amount of time before and after fault detection data to the detection data correction after replacement.
Preferably, the detection data are instantaneous value, according to the testing number in predetermined amount of time before and after fault detection data
Include according to the detection data correction after replacement：
Detection data after being replaced according to the detection data Serial regulation in predetermined amount of time before and after fault detection data are equal
Value and amplitude；
Or
The detection data are aggregate-value, according to the detection data in predetermined amount of time before and after fault detection data to replacing
Detection data correction afterwards includes：
According to the detection data before and after fault detection data in predetermined amount of time to wink corresponding to the detection data after replacement
Duration is adjusted；And
Detection data after being replaced based on the instantaneous value amendment after adjustment.
Preferably, closed according to predetermined total road branch road between revised detection data sequence and each nodal test data
System's detection malfunctioning node includes：
Calculate the acquisition of detection data sum and the sequence of all branch roads corresponding to total road；
Calculate the detection data sequence and described and sequence relative error and coefficient correlation on total road；
When the relative error and the coefficient correlation meet predetermined condition, prompt total road branch road imbalance be present, and
Search wrong time section；
Malfunctioning node is positioned according to the detection data sequence in wrong time section.
Preferably, searching wrong time section includes：
Slided by detection data sequence of the time window with predetermined window length to total road and described and sequence
Dynamic detection；
Bias data points in window are set to wrong time section more than the initial time of the time window of first threshold
At the beginning of between；
Bias data points in window are dropped into the end time of the time window of Second Threshold as wrong time
The end time of section；
Wherein, bias data points are more than the number of the data point of corresponding data in described and sequence for the detection data on total road
The detection data on Liang Huozong roads are less than the quantity of the data point of corresponding data in described and sequence.
Second aspect, there is provided a kind of computer-readable medium, for storing computer program instructions, its feature for fear of,
The computer program instructions realize method as described in relation to the first aspect when executed.
Detected by the detection data sequence that acquisition is periodically detected to the node of sensor network, examined based on history
The Variation Features for surveying data sequence judge whether fault detection data, and detect data to fault detection data based on history
It is modified.It can effectively find and correct the wrong data that sensor network nodes sporadicly occur.And then pass through total road branch
The detection data sequence of the different sensor network nodes of road Relationship Comparison so as to position be likely to occur mistake period and can
The node that can be broken down.Thus, it is possible to carry out amendment and the failure of data automatically in larger sensor network
Node locating, ensure the quality of data.
Brief description of the drawings
By the description to the embodiment of the present invention referring to the drawings, above-mentioned and other purpose of the invention, feature and
Advantage will be apparent from, in the accompanying drawings：
Fig. 1 is the flow chart of the quality of data ensuring method of the sensor network of the embodiment of the present invention；
Fig. 2 is the flow chart that the embodiment of the present invention detect data retrieval；
Fig. 3 is the flow chart of detection fault detection data of the embodiment of the present invention；
Fig. 4 is the flow chart of another implementation of detection fault detection data of the embodiment of the present invention；
Fig. 5 is the flow chart of another implementation of detection fault detection data of the embodiment of the present invention；
Fig. 6 is the flow chart that the embodiment of the present invention carries out fault detection data amendment；
Fig. 7 is the flow chart of positioning malfunctioning node of the embodiment of the present invention；
Fig. 8 is performed for the schematic diagram of the computer system of the method for the embodiment of the present invention.
Embodiment
Below based on embodiment, present invention is described, but the present invention is not restricted to these embodiments.Under
It is detailed to describe some specific detail sections in the literary detailed description to the present invention.Do not have for a person skilled in the art
The description of these detail sections can also understand the present invention completely.In order to avoid obscuring the essence of the present invention, known method, mistake
The not narration in detail of journey, flow, element and circuit.
In addition, it should be understood by one skilled in the art that provided herein accompanying drawing be provided to explanation purpose, and
What accompanying drawing was not necessarily drawn to scale.
Unless the context clearly requires otherwise, otherwise entire disclosure is similar with the " comprising " in claims, "comprising" etc.
Word should be construed to the implication included rather than exclusive or exhaustive implication；That is, it is containing for " including but is not limited to "
Justice.
In the description of the invention, it is to be understood that term " first ", " second " etc. are only used for describing purpose, without
It is understood that to indicate or implying relative importance.In addition, in the description of the invention, unless otherwise indicated, the implication of " multiple "
It is two or more.
Fig. 1 is the flow chart of the quality of data ensuring method of the sensor network of the embodiment of the present invention.As shown in figure 1, institute
The method of stating includes：
Step S100, the detection data sequence of each node in the sensor network is obtained.
Each detection data sequence includes the detection data that corresponding node periodically obtains.Due in sensor network
Node be probably respectively collocation in successive steps.According to the difference of the node type of configuration, the detection cycle of each node can phase
Together, can also be different.For example, a part of node can be to carry out data acquisition in the cycle with 3 minutes.Another part node can be with
5 minutes are to carry out data acquisition in the cycle.Thus, it is desirable to the raw sensor data sequence collected is standardized.Specifically,
As shown in Fig. 2 step S100 may include steps of：
Step S110, the raw sensor data sequence of each node in the sensor network is obtained.
Step S120, data sequence is detected according to the raw sensor data retrieval detection cycle identical.
For example, when being respectively 3 minutes and 5 minutes at the interval of raw sensor data sequence, can be from raw sensor data
Sequence was sampled according to the cycle of 15 minutes, so as to obtain the detection data sequence that detection cycle is 15 minutes.
Thus, it is possible to conveniently subsequently it is further processed.
Step S200, the detection data sequence to each node detects, and obtains fault detection data.
In this step, fault detection data can be obtained using a variety of different methods.
In an optional implementation of the present embodiment, as shown in figure 3, step S200 includes：
Step S210, the difference sequence of the detection data sequence is obtained.
Wherein, when the detection data are cumulative amount, the difference sequence is the second differnce sequence of detection data sequence
Row, when the detection data are instantaneous flow, the difference sequence is the first-order difference sequence of detection data sequence.Pass through second order
Difference or first-order difference can obtain the variation tendency of corresponding instance variable, so as to judge number by the monitoring to variation tendency
According to the presence or absence of mutation.
Step S220, when history detection data bulk meets to require, it is pre- that data acquisition is detected according at least part history
The difference threshold range of section of fixing time or predetermined point of time.
Specifically, if history detection data are less, accumulation data are kept.
Specifically, difference threshold range can include the first threshold range and the second threshold range, wherein, described first
Limit scope and data statistics acquisition is detected according to the history of preceding predetermined amount of time, second threshold range is according to same type day
In the corresponding period history detection data statistics obtain.That is, the first threshold range is consecutive hours anaplasia in a short time
The threshold range that change trend statistics obtains.For example, for the difference sequence of all 10 points of a whole mornings, according to going through for 2 hours before
History detects data and obtains the first threshold range to count.Second threshold range becomes for the data variation of the total same time of phase same date
The threshold range that gesture statistics obtains.For example, for the difference sequence of all 10 points of a whole mornings, according to 10 weeks before each all a whole mornings
10 points of history detection data obtain the second threshold range to count.
Step S230, defects detection will be labeled as beyond detection data corresponding to the differential data of the difference threshold range
Data.
For example, the average value and variance of the difference sequence of particular point in time can be asked for, and according to the average value and variance
To set a difference threshold range.When detection data exceed the difference threshold range, assert detection data exception, marked
It is designated as fault detection data.
On the premise of difference threshold range is set, the detection data of some sensor network nodes can be in difference thresholding model
Interior saltus step is enclosed, this saltus step can also be as the clue of detection wrong data.
In another optional embodiment of the present embodiment, as shown in figure 4, step S200 also includes：
Step S240, detect that two continuous in difference sequence and during difference that symbol is opposite, in current point in time
Detection data jump is searched in the history detection data sequence of the first length before.
In the present embodiment, the saltus step of detection data is detected by monitoring difference sequence.It is instantaneous value in detection data
When, it can judge whether it saltus step occurs by asking for its first-order difference sequence.First-order difference sequence can characterize testing number
According to variation tendency, be more than positive predetermined value once difference sequence value or less than negative predetermined value if illustrate that saltus step occurs for data.
The symbol of first-order difference value can characterize the direction of saltus step., can be by asking for secondly jump when it is aggregate-value to detect data
Divide to judge whether its corresponding instantaneous value saltus step occurs.Second differnce sequence can characterize instance variable corresponding to aggregate-value
Variation tendency.Therefore, the saltus step of detection data can equally be detected.It should be understood that it can also be detected using other manner
Saltus step.
In this step, if detecting two differential datas continuous and that symbol is opposite, mark goes out at the time of corresponding
The saltus step of detection data is showed.Data jump is detected from being searched at the time of there is saltus step into the difference sequence for returning the first length L1
(that is, searching whether continuous two differential data symbol differences be present), to judge identical saltus step whether occur preceding.
Step S250, when finding the differential data with model identical, corresponding detection data are labeled as defect
Detect data.
Wherein, the differential data with model identical refers to the continuous differential data of sign change order identical.For example,
If detect continuous two differential data symbols be respectively+and-, to preceding length be L1 difference sequences in search be
It is no continuous two differential data symbol differences to be present, its be also it is previous for+, also one for-, if it is, assert find
There is the differential data of model identical.
In this step, if the differential data with model identical is found, namely the first length before saltus step is detected
Degree history detection data sequence in detect identical saltus step (mutual saltus step is in opposite direction, and respectively with it is current
The direction of saltus step is identical), then data will be detected corresponding to current transition and be labeled as fault detection data.
If found the differential data with reverse mode, namely before saltus step is detected the first length history
Saltus step is detected in detection data sequence, but the pattern of the saltus step is different from current Hopping Pattern (that is, with working as front jumping
The direction of change differs), then the detection data that the differential data corresponds to the time will be prompted to manager by human-computer interaction interface,
And obtain mark instructions of the manager for the detection data.That is, for this kind of detection data, it is scarce that can only suspect it
Detection data are fallen into, therefore, it is necessary to is manually further judged and is marked.
Alternatively, if not detecting saltus step, then caused by may determine that the saltus step is due to more emat sensor, this
The mistake for detecting data will not generally be caused.
For example, by taking the active power for monitoring ammeter as an example, if in data sequence is detected, the instantaneous value of active power is first
Downward saltus step saltus step then up (accordingly, difference sequence is presented as the opposite difference value of continuous symbol).Then from current
Moment starts to search whether to exist into the detection data sequence for returning L1 length identical first downward saltus step saltus step then up
Situation (accordingly, difference sequence is presented as the opposite difference value of continuous symbol).If so, then by current detection data mark
It is designated as fault detection data.If detecting saltus step, but the mode of saltus step is that first saltus step is descended in saltus step backward upwards, then needs people
Work intervention judges whether it is defective data.
Meanwhile it can also judge whether it mistake occurs according to difference sequence tendency interior for a period of time.
In another optional implementation of the present embodiment, step S200 also includes：
Step S260, the length that zero is continuously in difference sequence is more than the second length L2, and corresponding to history detection data
When not continuing the difference sequence that the second length is zero in difference sequence, prompt the detection data and obtain to the testing number
According to mark instructions.
That is, if it find that difference sequence is continuously zero, and length is longer, but never there are this feelings preceding
Condition, then explanation detection data be likely to occur problem, it is necessary to prompt the user with and obtain user and further indicate.
In another optional embodiment of the present embodiment, as shown in figure 5, step S200 also includes：
Step S270, variation tendency of the difference sequence within the period of the 3rd length is obtained.
Step S280, when the variation tendency is persistently increases or reduced, prompt to detect corresponding to the differential data
Data and the mark instructions for obtaining the corresponding detection data.
The differential data sequence that detection data sequence pair is answered would generally fluctuate up and down according to the practical operation situation of equipment, and
It will not always increase or reduce always.So when detecting that difference sequence persistently increases or reduced substantially, can be as
Suspicious detection data are submitted to user and further confirmed that.
Thus, characteristic and history the detection data sequence can for the difference sequence itself answered by detecting data sequence pair
Detect the defects of occurring in most of short time range data.
Step S300, data are detected according to history and the revised testing number of acquisition is modified to the fault detection data
According to sequence.
In this step, data can be detected using history come to time point corresponding to fault detection data or period
Detection data are predicted, so as to be modified to fault detection data.
In an optional implementation of the present embodiment, as shown in fig. 6, step S300 is repaiied in the following way
Just：
Step S310, the fault detection data is replaced using detection data corresponding to upper a cycle.
Step S320, according to the detection data in predetermined amount of time before and after fault detection data to the detection data after replacement
Amendment.
This is actually that the characteristics of interrelated is presented substantially using detecting data sequence variation, come needs are corrected when
Between the detection data put be predicted.Aforesaid way detects data to carry out data correction and need not store substantial amounts of history,
As long as (such as one week or one month) does not detect fault detection data in a cycle, it is possible to by all inspections in the cycle
Survey data to preserve, used when giving over to follow-up be modified.
Meanwhile it can be that instantaneous value can also be aggregate-value to detect data.By taking the active power of ammeter measurement as an example, detection
Data may be instantaneous active power, it is also possible to accumulative quantity of power.For different types of detection data, it is necessary to using not
Same correcting mode.
When it is instantaneous value to detect data, step S320 includes：
Step S321, the inspection after being replaced according to the detection data Serial regulation in predetermined amount of time before and after fault detection data
Survey data mean value and amplitude.The generally linear change of instantaneous value, therefore, after being replaced using history detection data, enter
One step enters horizontal-linearity control by the data before and after the time point so that revised detection data and the testing number of front and rear record
According to continuous, so as to more approach actual value.
When it is aggregate-value to detect data, step S320 includes：
Step S322, according to the detection data in predetermined amount of time before and after fault detection data to the detection data after replacement
Corresponding instantaneous value is adjusted.
Adjustment principle to instantaneous value is similar with above-mentioned steps S321.
Step S323, the detection data after being replaced based on the instantaneous value amendment after adjustment.
Thus, it is possible to aggregate-value is corrected based on the instantaneous value that actual value is approached after adjustment.
By step S100-S300, can according to the detection data sequence of each sensor network nodes self record come
Detection defective data is simultaneously modified, and ensures the accuracy of data.But when node breaks down, its corresponding branch road
All the time there is problem in detection data sequence, then the detection data sequence only by self record is can not to find and correct this
Kind mistake.Therefore, it is necessary to based on the relation between different sensor network nodes detection objects, particularly total road branch road relation
Further to be diagnosed.
Step S400, according to predetermined total road branch road between revised detection data sequence and each nodal test data
Relation detects malfunctioning node, the malfunctioning node for detection data existing defects all the time node.
In the present embodiment, a Ge Zong roads correspond to multiple branch roads, and the detection data sums of multiple branch roads in theory should be with
The detection data on total road are equal.Include having for water detection, throughput detection or power supply in the presence of the application scenarios of total road and branch road
The plurality of application scenes such as work(power detection.Pass through total road according to corresponding to the configuration of the detection object of different sensor network nodes
Branch road relation, can be according to predetermined total road branch road relation between revised detection data sequence and each nodal test data
Detect malfunctioning node.
Specifically, as shown in fig. 7, step S400 can include：
Step S410, the acquisition of detection data sum and the sequence of all branch roads corresponding to total road are calculated.
In the present embodiment, the detection data that revised detection data were further processed as in units of day are carried out again
Subsequent treatment.
But it should be readily apparent to one skilled in the art that detection data interval or the cycle can be according to application scenarios or week
Phase selects.
Step S420, the detection data sequence and described and sequence relative error and coefficient correlation on total road are calculated.
Wherein, relative error refers to measuring caused absolute error and by the ratio between conventional true value.In the present embodiment,
Will be with sequence as conventional true value.Coefficient correlation is used for measuring the linear relationship between two variables, and it can pass through equation below
Calculate and obtain：
Wherein, r (x, y) is x and y coefficient correlation, and cov (x, y) is variable x and y covariance, and var (x) is variable x
Variance, var (y) be variable y variance.
Relative error can characterize the detection data sequence on total road and described and sequence departure degree, and coefficient correlation can be with
Characterize the degree of correlation of both variation tendencies.Therefore, whether the amount that can judge the two accordingly should synchronously change occurs
It is abnormal.
Step S430, when the relative error and the coefficient correlation meet predetermined condition, prompt total road branch road be present
Imbalance, and search wrong time section.
In the present embodiment, the detection data sequence on total road is assert when relative error is less than 5% and coefficient correlation is more than 0.9
Arrange and change with sequence basic synchronization, total road branch road relation balance.Otherwise, assert both not synchronous changes, total road branch road be present
It is uneven.Thus, being searched in entirely detection data sequence causes the unbalanced period, namely wrong time section.
In the present embodiment, by one have predetermined window length time window enter line slip detect, if when
Between in window, bias data points are more than first threshold, then the initial time of actual time window are set as into wrong time section
At the beginning of between.Wherein, bias data points refer to that the detection data on total road are more than the data of corresponding data in described and sequence
Points or the detection data on total road are less than the data points of corresponding data in described and sequence.
Window forward slip over time, when bias data points drop to Second Threshold, by actual time window
End time is set as the end time of wrong time section.
In the present embodiment, the length of time window is arranged to 30 days, and first threshold is arranged to 25 days, and Second Threshold is set
For 20 days.
Thus, it is possible to effectively find that mass data produces the period deviateed, so as to the Wrong localization period.
Step S440, according to the detection data in wrong time section, malfunctioning node is positioned.
In this step, malfunctioning node can be positioned in several ways.
On the one hand, can judge whether to lack by the detection data sequence of all total roads or branch road in wrong time section
Several nodes.
On the other hand, it can also determine whether that branch road goes out by the detection data sequence of all branch roads in wrong time section
Now change.
Thus, detected, be based on by being periodically detected the detection data sequence of acquisition to the node of sensor network
The Variation Features of history detection data sequence judge whether fault detection data, and defect is examined based on history detection data
Data are surveyed to be modified.It can effectively find and correct the wrong data that sensor network nodes sporadicly occur.And then pass through
The detection data sequence of the different sensor network nodes of total road branch road Relationship Comparison is likely to occur the time of mistake so as to position
Section and the node for being likely to occur failure.Thus, it is possible in larger sensor network automatically carry out data amendment and
Malfunctioning node positions.
Data structure and code described in above embodiment are generally stored inside on computer-readable recording medium,
It can be any equipment or medium that can store the code used for computer system and/or data.It is computer-readable to deposit
Storage media includes but is not limited to volatile memory, nonvolatile memory, magnetic and optical storage apparatus, such as disk drive
Device, tape, CD (CD), DVD (digital versatile disc or digital video disk) or currently known or develop later can
Other of store code and/or data medium.
Can by specific embodiment part describe method and process be embodied as code and/or data, the code and/
Or data are storable in computer-readable recording medium as described above.When computer system is read and is performed computer-readable
When the code and/or data that are stored in storage medium, computer system performs and is embodied as data structure and code and is stored in
Method and process in computer-readable recording medium.
Furthermore, it is possible to method described herein and process are included in hardware module or device.These modules or device
Application specific integrated circuit (ASIC) chip, field programmable gate array (FPGA) can be included but is not limited to, performed in special time
Specific software module or the special or shared processor of one section of code and/or other are currently known or that develops later programmable patrols
Collect equipment.When activating hardware module or device, they perform the method and process being included therein.
Fig. 8 is performed for the schematic diagram of the computer system of the method for the embodiment of the present invention.As shown in figure 8, the clothes
Business device 8 includes general computer hardware structure, and it comprises at least processor 81 and memory 82.Processor 81 and memory 82
Connected by bus 83.Memory 82 is suitable to the executable instruction of storage processor 81 or program.Processor 81 can be independent
Microprocessor or one or more microprocessor set.Thus, processor 81 is deposited by performing memory 82
The instruction of storage, so as to perform presentation of the method flow of the embodiment of the present invention as described above realization for webpage.Bus 88 will
Above-mentioned multiple components are linked together, while said modules are connected into display controller 84 and display device and input/defeated
Go out (I/O) device 85.Input/output (I/O) device 85 can be that mouse, keyboard, modem, network interface, touch-control are defeated
Enter device, body-sensing input unit, printer and other devices well known in the art.Typically, input/output device 85 passes through
Input/output (I/O) controller 86 is connected with system.
The preferred embodiments of the present invention are the foregoing is only, are not intended to limit the invention, for those skilled in the art
For, the present invention can have various changes and change.All any modifications made within spirit and principles of the present invention, it is equal
Replace, improve etc., it should be included in the scope of the protection.
Claims (13)
- A kind of 1. quality of data ensuring method of sensor network, it is characterised in that including：The detection data sequence of each node in the sensor network is obtained, the detection data sequence includes the corresponding node cycle Property obtain detection data；Detection data sequence to each node detects, and obtains fault detection data；Data are detected according to history the revised detection data sequence of acquisition is modified to the fault detection data；AndFailure is detected according to predetermined total road branch road relation between revised detection data sequence and each nodal test data Node, the malfunctioning node for detection data existing defects all the time node.
- 2. according to the method for claim 1, it is characterised in that obtain the detection data of each node in the sensor network Sequence includes：Obtain the raw sensor data sequence of each node in the sensor network；AndData sequence is detected according to the raw sensor data retrieval detection cycle identical.
- 3. according to the method for claim 1, it is characterised in that the detection data sequence to each node detects, and obtains Fault detection data is taken to include：Obtain the difference sequence of the detection data sequence；When history detection data bulk meets to require, data acquisition predetermined amount of time or predetermined is detected according at least part history The difference threshold range at time point；Fault detection data will be labeled as beyond the detection data corresponding to the differential data of the difference threshold range；Wherein, when the detection data are cumulative amount, the difference sequence is the second differnce sequence of detection data sequence, When the detection data are instantaneous flow, the difference sequence is the first-order difference sequence of detection data sequence.
- 4. according to the method for claim 3, it is characterised in that the difference threshold range includes the first threshold range and the Two threshold ranges, wherein, first threshold range detects data statistics according to the history of preceding predetermined amount of time and obtained, institute The history detection data statistics for stating corresponding period of second threshold range in same type day obtains.
- 5. according to the method for claim 3, it is characterised in that the detection data sequence to each node detects, and obtains Fault detection data is taken also to include：Detect that two continuous in the difference sequence and during differential data that symbol is opposite, before current point in time The first length detection data sequence in search with model identical differential data；When finding the differential data with model identical, corresponding detection data are labeled as fault detection data；Wherein, the differential data with model identical refers to the continuous differential data of sign change order identical.
- 6. according to the method for claim 4, it is characterised in that when finding the differential data with reverse mode, carry Show the detection data of corresponding time and obtain the mark instructions of the corresponding detection data；Wherein, the differential data with reverse mode refers to the opposite continuous differential data of sign change order.
- 7. according to the method for claim 3, it is characterised in that the detection data sequence to each node detects, and obtains Fault detection data is taken also to include：The length that zero is continuously in difference sequence is more than the second length, and corresponding differential data is not present in history detection data When continuing the detection data sequence that the second length is zero, prompt the detection data of corresponding time and obtain to the detection data Mark instructions.
- 8. according to the method for claim 3, it is characterised in that the detection data sequence to each node detects, and obtains Fault detection data is taken also to include：Obtain variation tendency of the difference sequence within the period of the 3rd length；When the variation tendency is lasting increase or reduced, the detection data for corresponding to the time are prompted simultaneously to obtain to the testing number According to mark instructions.
- 9. according to the method for claim 1, it is characterised in that data are detected according to history the fault detection data is entered Row amendment, which obtains revised detection data sequence, to be included：The fault detection data is replaced using data were detected corresponding to a upper time cycle；According to the detection data in predetermined amount of time before and after fault detection data to the detection data correction after replacement.
- 10. according to the method for claim 9, it is characterised in that the detection data are instantaneous value, wherein, according to defect Detection data before and after detection data in predetermined amount of time include to the detection data correction after replacement：According in predetermined amount of time before and after fault detection data detection data Serial regulation replace after detection data mean value and Amplitude；OrIt is described detection data be aggregate-value, wherein, according to the detection data in predetermined amount of time before and after fault detection data to for Detection data correction after changing includes：According to the detection data before and after fault detection data in predetermined amount of time to instantaneous value corresponding to the detection data after replacement It is adjusted；AndDetection data after being replaced based on the instantaneous value amendment after adjustment.
- 11. according to the method for claim 1, it is characterised in that examined according to revised detection data sequence and each node The predetermined total road branch road relation detection malfunctioning node surveyed between data includes：Calculate the acquisition of detection data sum and the sequence of all branch roads corresponding to total road；Calculate the detection data sequence and described and sequence relative error and coefficient correlation on total road；When the relative error and the coefficient correlation meet predetermined condition, prompt total road branch road imbalance be present, and search Wrong time section；AndMalfunctioning node is positioned according to the detection data sequence in wrong time section.
- 12. according to the method for claim 11, it is characterised in that searching wrong time section includes：Enter line slip by detection data sequence of the time window with predetermined window length to total road and described and sequence to examine Survey；Bias data points in window are set to opening for wrong time section more than the initial time of the time window of first threshold Begin the time；Bias data points in window are dropped into the end time of the time window of Second Threshold as wrong time section End time；Wherein, bias data points for the detection data on total road be more than it is described with the quantity of the data point of corresponding data in sequence or The detection data on total road are less than the quantity of the data point of corresponding data in described and sequence.
- 13. a kind of computer-readable medium, for storing computer program instructions, it is characterised in that the computer program refers to The method as any one of claim 1-12 is realized in order when executed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710693830.0A CN107484196B (en) | 2017-08-14 | 2017-08-14 | Data quality assurance method for sensor network and computer readable medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710693830.0A CN107484196B (en) | 2017-08-14 | 2017-08-14 | Data quality assurance method for sensor network and computer readable medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107484196A true CN107484196A (en) | 2017-12-15 |
| CN107484196B CN107484196B (en) | 2020-10-09 |
Family
ID=60600339
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710693830.0A Active CN107484196B (en) | 2017-08-14 | 2017-08-14 | Data quality assurance method for sensor network and computer readable medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107484196B (en) |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102011612A (en) * | 2010-11-20 | 2011-04-13 | 无锡泰立特科技有限公司 | Multifunctional tube bundle monitoring and emergency extinguishing prevention system |
| US20110283981A1 (en) * | 2010-05-20 | 2011-11-24 | Toyota Jidosha Kabushiki Kaisha | Apparatus for acquiring responsibility of oxygen concentration sensor |
| US20130223405A1 (en) * | 2012-02-28 | 2013-08-29 | Samsung Electronics Co., Ltd. | Apparatus and method of providing medical data based on a handover of a medical sensor |
| CN103439758A (en) * | 2013-02-08 | 2013-12-11 | 北京佳讯飞鸿电气股份有限公司 | Data processing method for rain sensor |
| CN104096303A (en) * | 2013-04-15 | 2014-10-15 | 深圳迈瑞生物医疗电子股份有限公司 | Electronic flow monitor, monitoring method and anesthesia machine |
| CN104202765A (en) * | 2014-08-26 | 2014-12-10 | 北京邮电大学 | Method and device for detecting sensor faults based on distributed network |
| CN105184394A (en) * | 2015-08-26 | 2015-12-23 | 中国电力科学研究院 | On-line data mining optimized control method based on cyber physical system (CPS) of power distribution network |
| CN105627528A (en) * | 2016-03-30 | 2016-06-01 | 北京博锐尚格节能技术股份有限公司 | Alarm method for cold station group control system |
| CN106371939A (en) * | 2016-09-12 | 2017-02-01 | 山东大学 | Time-series data exception detection method and system thereof |
| US20170103339A1 (en) * | 2015-10-09 | 2017-04-13 | International Business Machines Corporation | Data collection for predictive maintenance of networked assets leveraging mobile measurement devices |
| CN106709250A (en) * | 2016-12-22 | 2017-05-24 | 河海大学 | Data flow abnormality detection method based on parallel Kalman algorithm |
| CN106909664A (en) * | 2017-02-28 | 2017-06-30 | 国网福建省电力有限公司 | A kind of power equipment data stream failure recognition methods |
| US20170193395A1 (en) * | 2016-01-06 | 2017-07-06 | International Business Machines Corporation | Optimizing performance of event detection by sensor data analytics |
| CN107038338A (en) * | 2017-03-22 | 2017-08-11 | 西安交通大学 | A kind of intelligent monitoring device and method based on Rankine cycle |
-
2017
- 2017-08-14 CN CN201710693830.0A patent/CN107484196B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110283981A1 (en) * | 2010-05-20 | 2011-11-24 | Toyota Jidosha Kabushiki Kaisha | Apparatus for acquiring responsibility of oxygen concentration sensor |
| CN102011612A (en) * | 2010-11-20 | 2011-04-13 | 无锡泰立特科技有限公司 | Multifunctional tube bundle monitoring and emergency extinguishing prevention system |
| US9693223B2 (en) * | 2012-02-28 | 2017-06-27 | Samsung Electronics Co., Ltd | Apparatus and method of providing medical data based on a handover of a medical sensor |
| US20130223405A1 (en) * | 2012-02-28 | 2013-08-29 | Samsung Electronics Co., Ltd. | Apparatus and method of providing medical data based on a handover of a medical sensor |
| CN103439758A (en) * | 2013-02-08 | 2013-12-11 | 北京佳讯飞鸿电气股份有限公司 | Data processing method for rain sensor |
| CN104096303A (en) * | 2013-04-15 | 2014-10-15 | 深圳迈瑞生物医疗电子股份有限公司 | Electronic flow monitor, monitoring method and anesthesia machine |
| CN104202765A (en) * | 2014-08-26 | 2014-12-10 | 北京邮电大学 | Method and device for detecting sensor faults based on distributed network |
| CN105184394A (en) * | 2015-08-26 | 2015-12-23 | 中国电力科学研究院 | On-line data mining optimized control method based on cyber physical system (CPS) of power distribution network |
| US20170103339A1 (en) * | 2015-10-09 | 2017-04-13 | International Business Machines Corporation | Data collection for predictive maintenance of networked assets leveraging mobile measurement devices |
| US20170193395A1 (en) * | 2016-01-06 | 2017-07-06 | International Business Machines Corporation | Optimizing performance of event detection by sensor data analytics |
| CN105627528A (en) * | 2016-03-30 | 2016-06-01 | 北京博锐尚格节能技术股份有限公司 | Alarm method for cold station group control system |
| CN106371939A (en) * | 2016-09-12 | 2017-02-01 | 山东大学 | Time-series data exception detection method and system thereof |
| CN106709250A (en) * | 2016-12-22 | 2017-05-24 | 河海大学 | Data flow abnormality detection method based on parallel Kalman algorithm |
| CN106909664A (en) * | 2017-02-28 | 2017-06-30 | 国网福建省电力有限公司 | A kind of power equipment data stream failure recognition methods |
| CN107038338A (en) * | 2017-03-22 | 2017-08-11 | 西安交通大学 | A kind of intelligent monitoring device and method based on Rankine cycle |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107484196B (en) | 2020-10-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110008080B (en) | Business index anomaly detection method and device based on time sequence and electronic equipment | |
| CN106897178B (en) | Slow disk detection method and system based on extreme learning machine | |
| CN109558295A (en) | A kind of performance indicator method for detecting abnormality and device | |
| CN107644194A (en) | The system and method that monitoring data is provided | |
| JP2004150439A (en) | Method for determining failure phenomenon | |
| EP3795975B1 (en) | Abnormality sensing apparatus, abnormality sensing method, and abnormality sensing program | |
| CN109143094A (en) | A kind of abnormal deviation data examination method and device of power battery | |
| TW202211341A (en) | Predicting equipment fail mode from process trace | |
| Juddoo et al. | A qualitative assessment of machine learning support for detecting data completeness and accuracy issues to improve data analytics in big data for the healthcare industry | |
| US20180018570A1 (en) | Information processing system, change point detection method, and recording medium | |
| Song et al. | Novel application of deep learning for adaptive testing based on long short-term memory | |
| CN107301109B (en) | Hard disk quality screening method and device | |
| CN108459920A (en) | A kind of identification of test data outlier and modification method | |
| JP2009110262A (en) | Filter device, filtering method for filter device and filtering program | |
| CN107484196A (en) | The quality of data ensuring method and computer-readable medium of sensor network | |
| CN111459797A (en) | Method, system and medium for detecting abnormity of developer behaviors in open source community | |
| CN108229586B (en) | The detection method and system of a kind of exceptional data point in data | |
| KR20200033726A (en) | Data processing method, data processing device, and computer readable recording medium with data processing program thereon | |
| CN114781128A (en) | Chip life prediction system and method using artificial intelligence technology | |
| US20130030863A1 (en) | Trend-based target setting for process control | |
| WO2021031091A1 (en) | Method and apparatus for detecting operating status of device, and computer readable medium | |
| JPH09212387A (en) | Program development supporting device | |
| CN115599037B (en) | Automatic monitoring method for gene detection laboratory equipment | |
| CN117558307B (en) | Threshold voltage adjusting method, device and equipment of memory chip and storage medium | |
| TWI841020B (en) | Error factor estimation device, error factor estimation method, and computer readable medium |
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 | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20200907 Address after: Room 221, Building 5, 11 Deshengmen Avenue, Xicheng District, Beijing, 100120 Applicant after: Borui Shangge Technology Co.,Ltd. Address before: Office b-221-a046, 2nd floor, building 2, yard 1, Nongda South Road, Haidian District, Beijing 100084 Applicant before: BEIJING SAGA CLOUD TECHNOLOGY Co.,Ltd. |
|
| TA01 | Transfer of patent application right | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |