CN106469348A - A kind of method and system of dynamic adjustment sensor data acquisition algorithm - Google Patents
A kind of method and system of dynamic adjustment sensor data acquisition algorithm Download PDFInfo
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Abstract
The present invention relates to dynamically adjusting the method and system of sensor data acquisition algorithm, data based on related sensor harvester and prediction scheme, realize the drainage pattern to sensor acquisition device or recognizer enters Mobile state adjustment, realize sensor acquisition device can real-time matching external environmental condition, reach holding the accurate effect of data.The present invention carries out pattern recognition by sensor acquisition device local side to drainage pattern or recognizer, and carries out the larger optimization computing of load in remote port, then updates drainage pattern or recognizer by remote online, improves and updates efficiency.Only recognition result is sent to remote port, save the huge construction cost existing in the case that magnanimity disposes sensor acquisition device and difficulty.Additionally it is possible in case of need, batch changes drainage pattern or the recognizer of sensor acquisition device for the enforcement of the present invention, to meet the difference in functionality demand of the sensor acquisition device to setting it is adaptable to the different working condition requiring.
Description
Technical field
The present invention relates to sensing algorithm update method, more particularly, it relates to a kind of dynamic sensing data that adjusts is adopted
The method of set algorithm, and a kind of system of dynamic adjustment sensor data acquisition algorithm.
Background technology
Traditional sensor is to rely on Unified Algorithm to carry out data acquisition, then under any circumstance, all with changeless
Algorithm carries out data acquisition.If the condition deviation of the environment installed and place and generating algorithm, the data gathering with
Will there is more serious error in objective reality data, have a strong impact on the collection accuracy of sensor, and then can affect based on biography
The functional realiey of the apparatus system of sensor.
And the judgement whether adapting to current environmental condition to sensor is the result from sensor-based apparatus system
Counter pushed away, and then judge current sensor collection initial data whether can accurately embody objective reality data, and this
Process generally requires manually tentatively to be judged by personal experience, it is determined whether there is larger error, then traces back to biography again
Sensor, is acquired the renewal of pattern to sensor.And updating work is typically all the offline mode updating, not only need to dismount
Sensor, connection update the steps such as optimization, not only complex operation, and need larger time cost and human cost.
Content of the invention
It is an object of the invention to overcoming the deficiencies in the prior art, one kind is provided can real-time ensuring sensor to adapt to all the time work as
Front environment, and optimize the method easily dynamically adjusting sensor data acquisition algorithm, and dynamically adjust sensing data
The system of gathering algorithm.
Technical scheme is as follows：
A kind of method of dynamic adjustment sensor data acquisition algorithm, enters to the initial data of sensor acquisition device collection
Row mode identifies, is identified result；Recognition result is contrasted with default prediction scheme, is judged that recognition result whether there is pre-
Defect specified in case, if it is, according to the prioritization scheme corresponding with current defect of setting in prediction scheme, optimize current identification
The drainage pattern of result corresponding sensor acquisition device or recognizer.
Preferably, when the initial data that sensor acquisition device is gathered carries out pattern recognition, first obtaining part sensing
The identification nonce of device harvester, the identification nonce of current sensor harvester and other sensors harvester is entered
Row contrast, is identified result, recognition result includes the identification of current sensor harvester and other sensors harvester
The difference of nonce.
Preferably, prioritization scheme is：By machine learning algorithm, sensed with other based on current sensor harvester
The data of the collection of device harvester or the initial data passed back, the drainage pattern to current sensor harvester or identification are calculated
Method is optimized, and the drainage pattern of current sensor harvester is updated to the drainage pattern after optimization or recognizer.
Preferably, sending recognition result to remote port, after remote port is acquired pattern or recognizer optimizes,
Again the drainage pattern after optimizing or recognizer are carried out online updating to sensor acquisition device.
Preferably, when the rule of prediction scheme requires to be acquired the optimization of pattern or recognizer according to initial data
When, the initial data of sensor acquisition device collection is sent to remote port, carries out pattern recognition and drainage pattern in remote port
Or after recognizer optimizes, then the drainage pattern after optimizing or recognizer are carried out to sensor acquisition device online more
Newly.
Preferably, the method for the prediction scheme of storage sensor harvester collaborative work includes following manual type or half
Automated manner：
Manual type：Artificial setting prediction scheme simultaneously comes into force after examination ＆ approval；
Semiautomatic fashion：Automatically deduced according to existing prediction scheme and current recognition result and obtain optimizing prediction scheme, through manually examining
Come into force after batch.
A kind of system of dynamic adjustment sensor data acquisition algorithm, the dynamic adjustment sensing data described in perform claim
The method of gathering algorithm；Sensor acquisition device connects original data storage device, by the original data storage of collection original
Data storage device；Sensor acquisition device connection mode recognizer storage device, memory module recognizer；Initial data
Storage device and algorithm for pattern recognition storage device connection mode identifying device respectively, the identification of pattern recognition device invocation pattern is calculated
Method, carries out pattern recognition to the initial data of original data storage device storage, is identified result；Pattern recognition device passes through
Data source and sink sends recognition result to data storage device, and data storage device connects program management module, judges to know
Other result whether there is defect specified in prediction scheme；Data storage device connectionist learning deduces module, program management module and
Practise deduction module to be connected, according to the prioritization scheme corresponding with current defect of setting in prediction scheme, optimize current recognition result and correspond to
Sensor acquisition device drainage pattern；Study is deduced module and is connected data source and sink, will be excellent by data source and sink
Drainage pattern after change or recognizer are updated to sensor acquisition device.
Preferably, the recognition result of multiple sensor acquisition devices sends to same data storage device.
Preferably, when needing to optimize into line algorithm or drainage pattern, according to the rule of prediction scheme, by specific one
Or the initial data of multiple sensor acquisition device collection sends to data storage device, module optimization collection is deduced by study
Pattern, the drainage pattern after optimizing or recognizer are sent to the algorithm for pattern recognition being connected with current sensor harvester
Storage device.
Preferably, in the rule of prediction scheme, according to multiple sensors in front and back of current sensor harvester and its correlation
The initial data of harvester collection, is optimized to the drainage pattern or recognizer of current sensor harvester.
Preferably, module is deduced in study passes through machine learning algorithm or artificial modeling method to current sensor collection dress
The drainage pattern put or recognizer are optimized, and accordingly, it is machine learning module or artificial modeling that module is deduced in study
Transmission module on model.
Preferably, original data storage device is also connected with data source and sink, by data source and sink by sensor
The initial data of harvester collection sends to data storage device.
Beneficial effects of the present invention are as follows：
The method and system of dynamic adjustment sensor data acquisition algorithm of the present invention, based on related sensor collection
The data of device and prediction scheme, realize the drainage pattern to sensor acquisition device or recognizer enters Mobile state adjustment, realize passing
Sensor harvester can real-time matching external environmental condition, reach holding the accurate effect of data.The present invention passes through sensor
Harvester local side carries out pattern recognition to drainage pattern or recognizer, and carries out the larger optimization fortune of load in remote port
Calculate, then drainage pattern or recognizer are updated by remote online, improve and update efficiency.Meanwhile, only by recognition result send to
Remote port, saves the huge construction cost existing in the case that magnanimity disposes sensor acquisition device and difficulty.
The enforcement of the present invention additionally it is possible in case of need, batch change sensor acquisition device drainage pattern or
Recognizer, to meet the difference in functionality demand to the sensor acquisition device of setting it is adaptable to the different work feelings requiring
Condition.
Brief description
Fig. 1 is the theory diagram of the described system of dynamic adjustment sensor data acquisition algorithm.
Specific embodiment
Below in conjunction with drawings and Examples, the present invention is described in further detail.
The present invention in order to the sensor acquisition device algorithm that solves prior art single it is impossible to actively adapt to environment, cause
Cannot ensure that gathered data is accurately not enough in real time, a kind of method of dynamic adjustment sensor data acquisition algorithm, and dynamically
The system of adjustment sensor data acquisition algorithm.The data based on related sensor harvester for the present invention and prediction scheme, it is right to realize
The drainage pattern of sensor acquisition device or recognizer enter Mobile state adjustment, and realizing sensor acquisition device being capable of real-time matching
External environmental condition, reaches the holding accurate effect of data.
The method of dynamic adjustment sensor data acquisition algorithm of the present invention, key step is as follows：
1) pattern recognition is carried out to the initial data of sensor acquisition device collection, be identified result；
2) recognition result is contrasted with default prediction scheme, judged that recognition result whether there is defect specified in prediction scheme
(for example, the adjacent sensor acquisition device of current sensor harvester has all recognized certain car plate, and current sensor is always
Fail to accurately identify), if it is, according to the prioritization scheme corresponding with current defect of setting in prediction scheme, optimizing current identification
The drainage pattern of result corresponding sensor acquisition device or recognizer.
Based on the method for described dynamic adjustment sensor data acquisition algorithm, the present invention provides a kind of dynamic adjustment sensing
The system of device data acquisition algorithm, the method for executing described dynamic adjustment sensor data acquisition algorithm；As Fig. 1 institute
Show, sensor acquisition device connects original data storage device, by the original data storage of collection in original data storage device；
Sensor acquisition device connection mode recognizer storage device, memory module recognizer；Original data storage device and mould
Formula recognizer storage device connection mode identifying device respectively, pattern recognition device invocation pattern recognizer, to original number
Initial data according to storage device storage carries out pattern recognition, is identified result；Pattern recognition device is filled by data transmit-receive
Put and send recognition result to data storage device, data storage device connects program management module, whether judges recognition result
There is defect specified in prediction scheme；Data storage device connectionist learning deduces module, and program management module deduces module with study
It is connected, according to the prioritization scheme corresponding with current defect of setting in prediction scheme, optimize the corresponding sensor of current recognition result and adopt
The drainage pattern of acquisition means or recognizer；Study is deduced module and is connected data source and sink, will be excellent by data source and sink
Drainage pattern after change or recognizer are updated to sensor acquisition device.
In the present invention, sensor acquisition device (such as photographic head) is upper (for example to be used by least one data source and sink
4G communication module in radio communication).The algorithm for pattern recognition that algorithm for pattern recognition storage device is stored can be by data transmit-receive
Device is updated.
Step 1) in, to sensor acquisition device collection initial data carry out pattern recognition when, first obtain part (as institute
Have the majority of sensor acquisition device) the identification nonce of sensor acquisition device, by current sensor harvester and other
The identification nonce of sensor acquisition device contrasted, be identified result, recognition result includes current sensor collection
The difference of the identification nonce of device and other sensor acquisition devices.
Sensor acquisition device collection original data storage on the original data storage device of sensor acquisition device,
Generally, only send recognition result to remote port, gather not when judging that current sensor harvester exists according to prediction scheme
When accurately needing to optimize, just send initial data to remote port, such as when needing to collect evidence in detail, more for example need according to original
During data re-optimization recognizer.
In the present invention, recognition result is sent to remote port, after remote port is acquired pattern or recognizer optimizes,
Again the drainage pattern after optimizing or recognizer are carried out online updating to sensor acquisition device.I.e. implementation procedure of the present invention
In, only send recognition result, then do not need for initial data to be sent to data storage device, save magnanimity deployment sensor acquisition
The huge construction cost existing in the case of device and difficulty.And then realize sending out the recognition result of multiple sensor acquisition devices
Deliver to same data storage device, be also not in difficulty and the Cost Problems constructed with operating aspect.
The prioritization scheme of the present invention is：By machine learning algorithm or artificial model, or other optimization methods, based on working as
The data of the collection of front sensor harvester and other sensor acquisition devices, the collection to current sensor harvester
Pattern or recognizer are optimized, and the drainage pattern of current sensor harvester or recognizer are updated to after optimization
Drainage pattern or recognizer.
When execution optimizes, according to the rule of prediction scheme, specific one or the collection of multiple sensor acquisition device is original
Data is activation, to data storage device, is deduced module by study and is optimized drainage pattern or recognizer, by the collection after optimizing
Pattern or recognizer are sent to the algorithm for pattern recognition storage device being connected with current sensor harvester.
Which which during enforcement, specifically need sent initial data of sensor acquisition device, by the rule decision of prediction scheme.
As the basis optimizing, can be the original of the sensor acquisition device collection similar or identical with current sensor harvester
Data, that is, when implementing, as the data basis optimizing it is not necessary to be the initial data of identical sensor acquisition device, and only
If sensor acquisition device, its data certainly exists dependency, you can be optimized by machine learning.
As a kind of specific embodiment, in the rule of prediction scheme, according to current sensor harvester and its correlation
Initial data, the drainage pattern to current sensor harvester or the recognizer of multiple sensor acquisition device collections in front and back
It is optimized.
Store the prediction scheme of related sensor collaborative work with artificial, semi-automatic or automated manner in program management module,
The present invention, with artificial, semiautomatic fashion storage sensor harvester collaborative work prediction scheme, carries out prediction scheme using various ways
Collaborative work, manual intervention can be carried out in the case of running into and cannot be carried out being automatically performed prediction scheme contrast work, realize half
Automatic Optimal.According to enforcement demand it is also possible to prediction scheme contrast is carried out by manual type.
Wherein, manual type is artificial setting prediction scheme and comes into force after examination ＆ approval；Semiautomatic fashion be according to existing prediction scheme and
Current recognition result is automatically deduced and is obtained optimizing prediction scheme, comes into force after artificial examination ＆ approval.
When needing to mitigate the load in local side for the sensor acquisition device, the process of pattern recognition can be transferred to far
Cheng Duan is carried out, and such as carries out on data storage device.During enforcement, original data storage device is also connected with data source and sink, when
When the rule of prediction scheme requires the optimization being acquired pattern or recognizer according to initial data, sensor acquisition device is adopted
The initial data of collection sends to remote port (as the data storage device of remote port), is adopted sensor by data source and sink
The initial data of acquisition means collection sends to data storage device.Carry out pattern recognition in remote port to calculate with drainage pattern or identification
After method optimizes, then the drainage pattern after optimizing or recognizer are carried out online updating to sensor acquisition device.
Based on the thinking of the present invention, the present invention can also be implemented on batch change sensor acquisition device drainage pattern or
Recognizer, can be used for changing the functional realiey of all the sensors harvester in certain region.For example, in some special feelings
Under condition, by drainage pattern or the recognizer of some sensor acquisition devices of program management module batch updating, to increase sensing
Device harvester is directed to the disposing capacity of specific event.Specifically, for example need the function of sensor acquisition device from car plate
Identification is changed to the search of the unlicensed vehicle of certain feature, and the enforcement of the present invention can be within a very short time to being likely to occur this vehicle
Photographic head enter line algorithm modification, after finding this vehicle, other unrelated photographic head change back former algorithm (such as system-wide section covering, this
Process is Millisecond), related photographic head keeps algorithm, and this vehicle is kept track.
In the present invention, study is deduced module and can be passed through machine learning algorithm or artificial modeling method, and other optimizations
Method is optimized to the drainage pattern of current sensor harvester or recognizer, if adopting machine learning algorithm or people
Work modeling method, then accordingly, it is transmission module on machine learning module or the model of artificial modeling that module is deduced in study.
Above-described embodiment is intended merely to the present invention is described, and is not used as limitation of the invention.As long as according to this
Bright technical spirit, is changed to above-described embodiment, modification etc. all will fall in the range of the claim of the present invention.
Claims (12)
1. a kind of method of dynamic adjustment sensor data acquisition algorithm it is characterised in that to sensor acquisition device collection
Initial data carries out pattern recognition, is identified result；Recognition result is contrasted with default prediction scheme, is judged recognition result
With the presence or absence of defect specified in prediction scheme, if it is, according to the prioritization scheme corresponding with current defect of setting in prediction scheme, excellent
Change drainage pattern or the recognizer of current recognition result corresponding sensor acquisition device.
2. the method for dynamic adjustment sensor data acquisition algorithm according to claim 1 is it is characterised in that to sensor
When the initial data of harvester collection carries out pattern recognition, first obtain the identification nonce of operative sensor harvester, will
Current sensor harvester is contrasted with the identification nonce of other sensors harvester, is identified result, identification
Result includes the difference of current sensor harvester and the identification nonce of other sensors harvester.
3. the method for dynamic adjustment sensor data acquisition algorithm according to claim 2 is it is characterised in that prioritization scheme
For：By machine learning algorithm, data based on current sensor harvester and the collection of other sensors harvester or
The initial data passed back, is optimized to the drainage pattern or recognizer of current sensor harvester, and by current sensor
The drainage pattern of device harvester is updated to drainage pattern or recognizer after optimization.
4. the method for dynamic adjustment sensor data acquisition algorithm according to claim 1 is it is characterised in that tie identification
Fruit sends to remote port, after remote port is acquired pattern or recognizer optimizes, then by the drainage pattern after optimizing or knowledge
Other algorithm carries out online updating to sensor acquisition device.
5. the method for dynamic adjustment sensor data acquisition algorithm according to claim 1 is it is characterised in that work as prediction scheme
When rule requires the optimization being acquired pattern or recognizer according to initial data, the collection of sensor acquisition device is former
Beginning data is activation, to remote port, after remote port carries out pattern recognition and drainage pattern or recognizer optimizes, then will optimize
Drainage pattern afterwards or recognizer carry out online updating to sensor acquisition device.
6. the method for dynamic adjustment sensor data acquisition algorithm according to claim 1 is it is characterised in that storage senses
The method of the prediction scheme of device harvester collaborative work includes following manual type or semiautomatic fashion：
Manual type：Artificial setting prediction scheme simultaneously comes into force after examination ＆ approval；
Semiautomatic fashion：Automatically deduced according to existing prediction scheme and current recognition result and obtain optimizing prediction scheme, after artificial examination ＆ approval
Come into force.
7. a kind of system of dynamic adjustment sensor data acquisition algorithm is it is characterised in that perform claim requires 1 to 6 any one
The method of described dynamic adjustment sensor data acquisition algorithm；Sensor acquisition device connects original data storage device, will
The original data storage of collection is in original data storage device；Sensor acquisition device connection mode recognizer storage device,
Memory module recognizer；Original data storage device and algorithm for pattern recognition storage device connection mode identifying device respectively,
Pattern recognition device invocation pattern recognizer, carries out pattern recognition to the initial data of original data storage device storage, obtains
To recognition result；Pattern recognition device is sent recognition result to data storage device, data storage by data source and sink
Device connects program management module, judges that recognition result whether there is defect specified in prediction scheme；Data storage device connects to be learned
Practise and deduce module, program management module is deduced module with study and is connected, corresponding with current defect excellent according to arrange in prediction scheme
Change scheme, optimizes the drainage pattern of current recognition result corresponding sensor acquisition device；Study is deduced module and is connected data receipts
Drainage pattern after optimizing or recognizer are updated to sensor acquisition device by data source and sink by transmitting apparatus.
8. the system of dynamic adjustment sensor data acquisition algorithm according to claim 7 is it is characterised in that multiple sensing
The recognition result of device harvester sends to same data storage device.
9. dynamic adjustment sensor data acquisition algorithm according to claim 8 system it is characterised in that when need into
When line algorithm or drainage pattern optimize, according to the rule of prediction scheme, by specific one or the collection of multiple sensor acquisition device
Initial data send to data storage device, module is deduced by study and optimizes drainage pattern, by the drainage pattern after optimizing
Or recognizer is sent to the algorithm for pattern recognition storage device being connected with current sensor harvester.
10. the system of dynamic adjustment sensor data acquisition algorithm according to claim 9 is it is characterised in that prediction scheme
In rule, according to the initial data of current sensor harvester and its multiple sensor acquisition devices collection in front and back of correlation,
The drainage pattern or recognizer of current sensor harvester is optimized.
The system of 11. dynamic adjustment sensor data acquisition algorithms according to claim 9 is it is characterised in that study pushes away
Drill module and pass through machine learning algorithm or artificial modeling method to the drainage pattern of current sensor harvester or recognizer
It is optimized, accordingly, it is transmission module on machine learning module or the model of artificial modeling that module is deduced in study.
The system of 12. dynamic adjustment sensor data acquisition algorithms according to claim 7 is it is characterised in that original number
It is also connected with data source and sink according to storage device, by data source and sink, the initial data that sensor acquisition device gathers is sent out
Deliver to data storage device.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106972959A (en) * | 2017-03-15 | 2017-07-21 | 千寻位置网络有限公司 | A kind of online service method and system of sensing algorithm |
| CN111796979A (en) * | 2019-04-09 | 2020-10-20 | Oppo广东移动通信有限公司 | Data acquisition strategy determining method and device, storage medium and electronic equipment |
| WO2021217637A1 (en) * | 2020-04-30 | 2021-11-04 | 上海华东汽车信息技术有限公司 | Terminal policy configuration method and apparatus, and computer device and storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102175269A (en) * | 2011-01-24 | 2011-09-07 | 华东师范大学 | Sensor device capable of changing sampling frequency and control method thereof |
| CN102855460A (en) * | 2011-06-30 | 2013-01-02 | 陕西省公安厅 | Research on video server pattern recognition algorithm parameters based on automatic modification of linear sensors |
| CN102868859A (en) * | 2012-08-21 | 2013-01-09 | 中兴通讯股份有限公司 | Method and system for realizing media shooting by multiple-terminal networking, and terminals |
| WO2015183820A1 (en) * | 2014-05-30 | 2015-12-03 | Google Inc. | Dynamic authorization |
| CN105163061A (en) * | 2015-09-21 | 2015-12-16 | 深圳乐行天下科技有限公司 | Remote video interactive system |
| CN105604807A (en) * | 2015-12-31 | 2016-05-25 | 北京金风科创风电设备有限公司 | Wind turbine generator monitoring method and device |
-
2016
- 2016-08-31 CN CN201610784061.0A patent/CN106469348B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102175269A (en) * | 2011-01-24 | 2011-09-07 | 华东师范大学 | Sensor device capable of changing sampling frequency and control method thereof |
| CN102855460A (en) * | 2011-06-30 | 2013-01-02 | 陕西省公安厅 | Research on video server pattern recognition algorithm parameters based on automatic modification of linear sensors |
| CN102868859A (en) * | 2012-08-21 | 2013-01-09 | 中兴通讯股份有限公司 | Method and system for realizing media shooting by multiple-terminal networking, and terminals |
| WO2015183820A1 (en) * | 2014-05-30 | 2015-12-03 | Google Inc. | Dynamic authorization |
| CN105163061A (en) * | 2015-09-21 | 2015-12-16 | 深圳乐行天下科技有限公司 | Remote video interactive system |
| CN105604807A (en) * | 2015-12-31 | 2016-05-25 | 北京金风科创风电设备有限公司 | Wind turbine generator monitoring method and device |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106972959A (en) * | 2017-03-15 | 2017-07-21 | 千寻位置网络有限公司 | A kind of online service method and system of sensing algorithm |
| CN111796979A (en) * | 2019-04-09 | 2020-10-20 | Oppo广东移动通信有限公司 | Data acquisition strategy determining method and device, storage medium and electronic equipment |
| CN111796979B (en) * | 2019-04-09 | 2022-08-02 | Oppo广东移动通信有限公司 | Data acquisition strategy determining method and device, storage medium and electronic equipment |
| WO2021217637A1 (en) * | 2020-04-30 | 2021-11-04 | 上海华东汽车信息技术有限公司 | Terminal policy configuration method and apparatus, and computer device and storage medium |
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| CN106469348B (en) | 2020-09-29 |
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