CN109699020A - A kind of manufaturing data cognitive method optimizing sensor node deployment - Google Patents
A kind of manufaturing data cognitive method optimizing sensor node deployment Download PDFInfo
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- CN109699020A CN109699020A CN201811458083.3A CN201811458083A CN109699020A CN 109699020 A CN109699020 A CN 109699020A CN 201811458083 A CN201811458083 A CN 201811458083A CN 109699020 A CN109699020 A CN 109699020A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/18—Network planning tools
- H04W16/20—Network planning tools for indoor coverage or short range network deployment
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/22—Traffic simulation tools or models
- H04W16/225—Traffic simulation tools or models for indoor or short range network
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/02—Traffic management, e.g. flow control or congestion control
- H04W28/08—Load balancing or load distribution
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Abstract
The present invention relates to the data perception technical fields in manufacture Internet of Things, more particularly to a kind of manufaturing data cognitive method for optimizing sensor node deployment.Its multiple sensor is deployed in scene to be measured according to preset deployment index, scans in scene to be measured barrier to construct Disorder Model, and combine and obtain setting weight function.It is modeled after tentatively perceive to practical scene to be measured based on dynamic pattern recognition, and sensor node deployment is adjusted, then Three-Dimensional Dynamic model space model is established according to the physical space feature of scene to be measured, sensor node deployment adjusted is simulated in Three-Dimensional Dynamic model space model, judges that sensor node optimization level reaches preset value in step until tentatively perceiving.The problems such as manufaturing data cognitive method of the optimization sensor node deployment, the deployment being able to solve under the factors such as physical space constraints, electromagnetic environment are complicated, manufacturing recourses dynamic is changeable restrict is positioned, perceived, realize the sensor node deployment of rapid Optimum.
Description
Technical field
The present invention relates to the data perception technical fields in manufacture Internet of Things, more particularly to a kind of optimization sensor node portion
The manufaturing data cognitive method of administration.
Background technique
Manufacture Internet of Things is just causing industry to be attracted attention as the core technology of industry 4.0.It is by network, embedded, RFID, biography
The electronic information technologies such as sensor and actuator are blended with manufacturing technology, are realized to making in product design, manufacture and service process
Make dynamic sensing, Intelligent treatment and one kind of optimal control of resource and information resources novel manufacturing mode and information service mould
Formula.It will be by the distributed collaboration of awareness apparatus under manufacturing environment (RFID, sensor etc.), to needing to supervise in manufacturing shop
The various manufacture information progress automatic collection and complete perception in product, material, product of control, connection, interaction, so that manufacture vehicle
Between in have the ability of mutual perception, inquiry, monitoring, optimization product manufacturing and service process and full Life Cycle between a variety of entities
Phase manufacturing recourses and the dynamic sensing of information resources, Intelligent treatment and optimal control.
How to carry out reliable perception to the various materials in manufacturing shop is the major issue manufactured in Internet of Things.Wirelessly
Radio frequency identification (RFID) technology as the core key technology in Internet of Things be widely used at present including logistics with
Many fields such as track, warehousing management, intelligent transportation.RFID reader node can be in relatively large range in label
Data are written and read, and do not need to meet line-of-sight transmission between reader and label, it is thus possible to greatly be promoted and be read effect
Rate.Material in manufacturing shop can be identified connection in the flowing tracking of product, personnel by RFID.Then pass through vehicle
Between multiple RFID readers of middle deployment carry out communication positioning, to realize perception, tracking, positioning to these manufacturing recourses.
Using RFID reader (Reader) node disposed in advance, workshop manufacturing recourses are monitored and are managed.System
Making that physical space constraints in workshop, electromagnetic environment are complicated, manufacturing recourses dynamic is changeable, how to optimize deployment is one important
Problem.In manufacturing shop, manufacturing process is mostly focused on surface process, and Flow of Goods and Materials tracking is mostly focused on level ground.When
It is preceding numerous in the deployment research of two-dimensional space for RFID reader, the ecotopia or RFID of most of hypothesis deployment region
Reader model rule, is then arranged node by certain rule by the method for computational geometry in the case where determining deployment.
These methods are only applicable to solve general disposition optimization ideally, under interference environments numerous in manufacturing shop how
Rapid Optimum is simultaneously not suitable for.
Summary of the invention
It is of the existing technology the purpose of the present invention is overcoming the problems, such as, one kind is provided and is able to solve physical space constraints, electricity
The problems such as deployment under the factors restrictions such as magnetic environment is complicated, manufacturing recourses dynamic is changeable is positioned, perceived, realizes the biography of rapid Optimum
The manufaturing data cognitive method of sensor node deployment.
A kind of manufaturing data cognitive method optimizing sensor node deployment is provided, is included the following steps:
Disorder Model construction step is deployed in multiple sensors in scene to be measured according to preset deployment index, scanning
Barrier in scene to be measured, constructs Disorder Model accordingly；
Weight function establishment step is set, is referred to according to Disorder Model obtained in Disorder Model construction step and preset deployment
Mark establishes setting weight function；
Preliminary perception step, the Sensing model of sensor is carried out based on dynamic pattern recognition, obtains manufaturing data perception knot
Fruit judges that sensor node optimizes level according to manufaturing data sensing results；
Sensor node Optimization Steps, according to setting weight function corresponding to sensor node optimization level to sensor section
Point deployment is adjusted；
Three-dimensional modeling step establishes Three-Dimensional Dynamic model space model according to the physical space feature of scene to be measured, three
Sensor node deployment adjusted is simulated in dimension dynamic mode spatial model；
Verification step, circulation executes preliminary perception step, sensor node optimization in Three-Dimensional Dynamic model space model
Step and three-dimensional modeling step judge that sensor node optimization level reaches preset value until tentatively perceiving in step.
Wherein, the scene to be measured is a flat surface model of ellipse, elliptic coordinates equationPolar coordinates are public
Formula isWherein:Node coordinate is left or right side focus.
Wherein, in the Disorder Model construction step, barrier in scene to be measured is scanned, it is specific to construct Disorder Model accordingly
Are as follows: multiple barrier zone Ω are divided according to obstacle identity in scene to be measured and/or size and/or spatial distributionj, each obstacle
Region ΩjTake different electromagnetic interference weight σi。
Wherein, the deployment index is specially message capacity load balance index: being existed according to each area label to be measured
Probability weight β, it is assumed that certain sensor RiOverlay area si, then label desired amt are as follows:Overall expectation number of tags
Amount are as follows:
Wherein, the deployment index is specially to cover redundancy:
Wherein, Δ sijFor with other sensors overlapping region area, siFor sensor RiPractical covering surface
Product.
Wherein, the setting weight function are as follows: Fi=w1ρi+w2σi, the w1、w2For weight.
Wherein, the sensor node Optimization Steps include optimal location selecting step, are calculated according to setting weight function each
The weight size of a sensor node current coverage area carries out rotation calculating by step-length of angle A since initial value, record
The weight of each position, return to after initial value take the position of maximum weight in all positions as current sensor sensing node most
Excellent position, each sensor node successively execute optimal location selecting step, respectively obtain optimal value.
Wherein, each sensor node successively executes optimal location selection step according to ascending sequence itself is numbered
Suddenly.
Wherein, angle A value is 5 °.
Wherein, the sensor node optimization level refers to: the classification of perception accuracy from low to high.
Beneficial effects of the present invention:
The manufaturing data cognitive method of the optimization sensor node deployment, the different type according to barrier construct obstacle mould
Type is set with covering redundancy and message capacity load balance index as deployment index in conjunction with the two and providing according to application demand
Determine weight function, then each sensing node sequentially carries out the orientation optimization deployment of selection optimum choice maximum weight.Solves biography
Sensor in wide in range manufacture Internet of Things workshop to area monitoring coverage optimization the problem of, improve sensor using artificial deployment efficiency
Low and not high the degree of automation situation avoids and is easy to appear skip, and too many label with machine sowing using conventional method
The situations such as the methods of spreading causes existing node density uncontrollable, and sensing range and hot spot region covering cannot be adjusted.Also, benefit
In information technology and internet of things technology enhance manufacturing recourses physical space and information space merge and perception interdynamic so that
The driving method of workshop level production management and process control is driving to the driving transformation of information, response mode from original energy
From a passive one to an active one, control process turns to accurate type by extensive style.
Detailed description of the invention
The present invention will be further described with reference to the accompanying drawings, but the embodiment in attached drawing is not constituted to any limit of the invention
System, for those of ordinary skill in the art, without creative efforts, can also obtain according to the following drawings
Other attached drawings.
Fig. 1 is the flow diagram of the manufaturing data cognitive method of the optimization sensor node deployment.
Specific embodiment
The invention will be further described with the following Examples.
The manufaturing data cognitive method of the optimization sensor node deployment, includes the following steps.
Disorder Model construction step, according to preset deployment index multiple sensor deployments in scene to be measured, scanning
Barrier in scene to be measured, constructs Disorder Model accordingly.Specifically, according to obstacle identity in scene to be measured and/or size and/
Or spatial distribution divides multiple barrier zone Ωj, each barrier zone ΩjTake different electromagnetic interference weight σi.Wherein, deployment refers to
Mark is specially message capacity load balance index and covering redundancy.
Message capacity load balance index: each barrier zone Ω to be measured is presetjMiddle label existing probability weight β, it is false
Fixed certain sensor RiOverlay area si, then label desired amt are as follows:Overall expectation number of labels are as follows:
Cover redundancy:Wherein, Δ sijFor with other sensors overlapping region area, siFor sensor Ri
Practical area coverage.
Weight function establishment step is set, is referred to according to Disorder Model obtained in Disorder Model construction step and preset deployment
Mark establishes setting weight function.Wherein, weight function is set are as follows: Fi=w1ρi+w2σi, w1、w2For weight.
Preliminary perception step, the Sensing model of sensor is carried out based on general dynamic pattern recognition method, obtains manufacture
Data perception result (equipment running status that such as sensor detects), judges sensor node according to manufaturing data sensing results
Optimize level, the i.e. classification of perception accuracy from low to high.
Sensor node Optimization Steps, according to setting weight function corresponding to sensor node optimization level to sensor section
Point deployment is adjusted.Wherein, including optimal location selecting step, to calculate each sensor node according to setting weight function current
The weight size of overlay area with angle A (A can be with value for 5 °) is covering of the step-length by each sensor since initial value
Region is rotated by axis of itself position, and records the weight of each rotation position, is taken after rotating back to initial value again all
Optimal location of the position of maximum weight as current sensor sensing node in position.Each sensor node is compiled according to itself
Number ascending sequence successively executes optimal location selecting step, respectively obtains optimal value.
Three-dimensional modeling step establishes Three-Dimensional Dynamic model space model according to the physical space feature of scene to be measured, three
Sensor node deployment adjusted is simulated in dimension dynamic mode spatial model.
Verification step, circulation executes preliminary perception step, sensor node optimization in Three-Dimensional Dynamic model space model
Step and three-dimensional modeling step judge that sensor node optimization level reaches preset value until tentatively perceiving in step.
Wherein, scene to be measured is a flat surface model of ellipse, elliptic coordinates equationPolar coordinates formula isWherein:Node coordinate is left or right side focus.
The manufaturing data cognitive method of the optimization sensor node deployment, the different type according to barrier construct obstacle mould
Type is set with covering redundancy and message capacity load balance index as deployment index in conjunction with the two and providing according to application demand
Determine weight function, then each sensing node sequentially carries out the orientation optimization deployment of selection optimum choice maximum weight.Solves biography
Sensor in wide in range manufacture Internet of Things workshop to area monitoring coverage optimization the problem of, improve sensor using artificial deployment efficiency
Low and not high the degree of automation situation avoids and is easy to appear skip, and too many label with machine sowing using conventional method
The situations such as the methods of spreading causes existing node density uncontrollable, and sensing range and hot spot region covering cannot be adjusted.Also, benefit
In information technology and internet of things technology enhance manufacturing recourses physical space and information space merge and perception interdynamic so that
The driving method of workshop level production management and process control is driving to the driving transformation of information, response mode from original energy
From a passive one to an active one, control process turns to accurate type by extensive style.
Finally it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than the present invention is protected
The limitation of range is protected, although explaining in detail referring to preferred embodiment to the present invention, those skilled in the art are answered
Work as understanding, it can be with modification or equivalent replacement of the technical solution of the present invention are made, without departing from the reality of technical solution of the present invention
Matter and range.
Claims (10)
1. a kind of manufaturing data cognitive method for optimizing sensor node deployment, which comprises the steps of:
Disorder Model construction step is deployed in multiple sensors in scene to be measured according to preset deployment index, scans to be measured
Barrier in scene, constructs Disorder Model accordingly；
Weight function establishment step is set, is built according to Disorder Model obtained in Disorder Model construction step and preset deployment index
It erects and determines weight function；
Preliminary perception step, the Sensing model of sensor is carried out based on dynamic pattern recognition, obtains manufaturing data sensing results, root
Judge that sensor node optimizes level according to manufaturing data sensing results；
Sensor node Optimization Steps, according to setting weight function corresponding to sensor node optimization level to sensor node portion
Administration is adjusted；
Three-dimensional modeling step establishes Three-Dimensional Dynamic model space model according to the physical space feature of scene to be measured, dynamic in three-dimensional
Sensor node deployment adjusted is simulated in morphotype formula spatial model；
Verification step, circulation executes preliminary perception step, sensor node Optimization Steps in Three-Dimensional Dynamic model space model
With three-dimensional modeling step, judge that sensor node optimization level reaches preset value in step until tentatively perceiving.
2. a kind of manufaturing data cognitive method for optimizing sensor node deployment as described in claim 1, which is characterized in that institute
It states scene to be measured and is a flat surface model of ellipse, elliptic coordinates equationPolar coordinates formula is
Wherein:P=a (1-e2), node coordinate is left or right side focus.
3. a kind of manufaturing data cognitive method for optimizing sensor node deployment as described in claim 1, which is characterized in that institute
It states in Disorder Model construction step, scans barrier in scene to be measured, construct Disorder Model accordingly specifically: according to scene to be measured
Interior obstacle identity and/or size and/or spatial distribution divide multiple barrier zone Ωj, each barrier zone ΩjTake different electricity
Magnetic disturbance weight σi。
4. a kind of manufaturing data cognitive method for optimizing sensor node deployment as claimed in claim 3, which is characterized in that institute
Stating deployment index is specially message capacity load balance index: according to each area label existing probability weight β to be measured, it is assumed that certain
Sensor RiOverlay area si, then label desired amt are as follows:Overall expectation number of labels are as follows:
5. a kind of manufaturing data cognitive method for optimizing sensor node deployment as claimed in claim 3, which is characterized in that institute
Stating deployment index is specially to cover redundancy:
Wherein, Δ sijFor with other sensors overlapping region area, siFor sensor RiPractical area coverage.
6. a kind of manufaturing data cognitive method for optimizing sensor node deployment as claimed in claim 5, which is characterized in that institute
State setting weight function are as follows: Fi=w1ρi+w2σi, the w1、w2For weight.
7. a kind of manufaturing data cognitive method for optimizing sensor node deployment as claimed in claim 6, which is characterized in that institute
Stating sensor node Optimization Steps includes optimal location selecting step, and it is current to calculate each sensor node according to setting weight function
The weight size of overlay area, carries out rotation calculating by step-length of angle A since initial value, records the weight of each position, returns
Optimal location of the position of maximum weight in all positions as current sensor sensing node, each sensing are taken after to initial value
Device node successively executes optimal location selecting step, respectively obtains optimal value.
8. a kind of manufaturing data cognitive method for optimizing sensor node deployment as claimed in claim 7, which is characterized in that each
A sensor node successively executes optimal location selecting step according to ascending sequence itself is numbered.
9. a kind of manufaturing data cognitive method for optimizing sensor node deployment as claimed in claim 4, which is characterized in that angle
Spending A value is 5 °.
10. a kind of manufaturing data cognitive method for optimizing sensor node deployment as claimed in claim 4, which is characterized in that
The sensor node optimization level refers to: the classification of perception accuracy from low to high.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117991708A (en) * | 2024-04-03 | 2024-05-07 | 福建智联万物科技有限公司 | Industrial automatic early warning system based on Internet of things |
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Application publication date: 20190430 |