CN110345913A - Height detection method, device and electronic equipment - Google Patents
Height detection method, device and electronic equipment Download PDFInfo
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- CN110345913A CN110345913A CN201910562059.2A CN201910562059A CN110345913A CN 110345913 A CN110345913 A CN 110345913A CN 201910562059 A CN201910562059 A CN 201910562059A CN 110345913 A CN110345913 A CN 110345913A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C5/00—Measuring height; Measuring distances transverse to line of sight; Levelling between separated points; Surveyors' levels
- G01C5/06—Measuring height; Measuring distances transverse to line of sight; Levelling between separated points; Surveyors' levels by using barometric means
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Abstract
The present invention relates to a kind of height detection method, device and electronic equipments.This method comprises: obtaining the current vector value that position to be measured corresponds to the environmental characteristic vector of setting, environmental characteristic vector includes Humidity Features, temperature profile and gas pressure characteristic；According to the mapping relations of environmental characteristic vector and height, height value of the height value of corresponding current vector value as position to be measured is obtained.
Description
Technical field
The present invention relates to fields of measurement, more particularly, to a kind of height detection method, a kind of height detecting device and one
Kind electronic equipment.
Background technique
At present more accurately in height detection method, it usually needs according to the atmospheric pressure value of current time current location and work as
The atmospheric pressure value on preceding moment sea level determines height above sea level.If can not obtain and work as in the case where no networking or poor signal
The atmospheric pressure value on preceding moment sea level, that can not just obtain the height above sea level for accurately calculating current location.
Therefore, it is necessary to propose that one kind can be with offline inspection and the higher height detection method of precision.
Summary of the invention
One purpose of the embodiment of the present invention is to provide a kind of new technical solution of height detection.
According to the first aspect of the invention, a kind of height detection method is provided, comprising:
The current vector value that position to be measured corresponds to the environmental characteristic vector of setting is obtained, the environmental characteristic vector includes
Humidity Features, temperature profile and gas pressure characteristic；
According to the mapping relations of the environmental characteristic vector and height, the height value for obtaining the corresponding current vector value is made
For the height value of the position to be measured.
Optionally, the mapping relations are least square method supporting vector machine model.
Optionally, the method also includes obtaining the mapping relations, comprising:
Obtain the training sample set being made of multiple training samples, wherein each training sample includes matching
The environmental characteristic vector sample vector value and sample height value；
According to the training sample set, the mapping relations are obtained.
Optionally, the mapping relations are mapping function, described according to the training sample set, obtain the mapping and close
System, comprising:
According to the training sample set, the optimal value of the parameter group of the undetermined parameter of the Height Prediction expression formula of training setting
It closes, obtains the mapping function.
Optionally, described according to the training sample set, train the undetermined parameter of the Height Prediction expression formula of setting
Optimal value of the parameter combination, comprising:
Initial population is established, each of described initial population body corresponds to a seed ginseng of the Height Prediction expression formula
Combinations of values；
The initial population is selected, intersected and made a variation, population of new generation is obtained；
The selection is continued to the population of new generation, intersects and makes a variation, until the individual adaptation degree of the population
Meet preset condition or evolutionary generation reaches default algebra, according to the corresponding parameter value group of the individual in population at this time
Conjunction obtains the optimal value of the parameter combination.
Optionally, the method also includes obtaining individual adaptation degree, comprising:
Obtain the corresponding Height Prediction expression formula of the individual, wherein the undetermined parameter of the Height Prediction expression formula is
The corresponding combining parameter values of the individual；
It is corresponding to the sample vector value in the training sample set by the corresponding Height Prediction expression formula of the individual
Height predicted, obtain Height Prediction value；
The individual adaptation degree is obtained according to the Height Prediction value and the sample height value.
Optionally, the combining parameter values include the penalty factor and kernel functional parameter of least square method supporting vector machine.
It is optionally, described to obtain the training sample set being made of multiple training samples, comprising:
For each training sample, humidity under the conditions of preset temperature, preset pressure, preset height, root are measured
The training sample is obtained according to the preset temperature, preset pressure, preset height and the humidity measured.
According to the second aspect of the invention, a kind of height detecting device is also provided, comprising:
Sensor unit, the current vector value for corresponding to the environmental characteristic vector of setting for obtaining position to be measured are described
Environmental characteristic vector includes Humidity Features, temperature profile and gas pressure characteristic；
Data processing unit, for the mapping relations according to the environmental characteristic vector and height, corresponded to described in work as
Height value of the height value of preceding vector value as the position to be measured.
According to the third aspect of the invention we, a kind of electronic equipment is also provided, including height as described in respect of the second aspect of the invention
Spend detection device；Alternatively, the electronic equipment includes:
Memory, for storing executable command；
Processor, under the control of the executable command, executing as first aspect present invention is described in any item
Method.
A beneficial effect of the invention is: the height detection method in the present embodiment, by measuring position to be measured
Environmental characteristic vector, and the mapping relations between combining environmental feature vector and height, can be under off-line state more precisely
Determination position to be measured height above sea level.
By referring to the drawings to the detailed description of exemplary embodiment of the present invention, other feature of the invention and its
Advantage will become apparent.
Detailed description of the invention
It is combined in the description and the attached drawing for constituting part of specification shows the embodiment of the present invention, and even
With its explanation together principle for explaining the present invention.
Fig. 1 is the schematic diagram that can be used for realizing the electronic equipment of the embodiment of the present invention.
Fig. 2 is the flow chart for the height detection method that the embodiment of the present invention one provides.
Fig. 3 is the flow chart for the specific example that the embodiment of the present invention one provides.
Fig. 4 is the schematic diagram of height detecting device provided by Embodiment 2 of the present invention.
Fig. 5 is the schematic diagram for the electronic equipment that the embodiment of the present invention three provides.
Specific embodiment
Carry out the various exemplary embodiments of detailed description of the present invention now with reference to attached drawing.It should also be noted that unless in addition having
Body explanation, the unlimited system of component and the positioned opposite of step, numerical expression and the numerical value otherwise illustrated in these embodiments is originally
The range of invention.
Be to the description only actually of at least one exemplary embodiment below it is illustrative, never as to the present invention
And its application or any restrictions used.
Technology known to related fields ordinary skill personage, method and apparatus may be not discussed in detail, but suitable
In the case of, the technology, method and apparatus should be considered as part of specification.
It is shown here and discuss all examples in, any occurrence should be construed as merely illustratively, without
It is as limitation.Therefore, other examples of exemplary embodiment can have different values.
It should also be noted that similar label and letter indicate similar terms in following attached drawing, therefore, once a certain Xiang Yi
It is defined in a attached drawing, then in subsequent attached drawing does not need that it is further discussed.
<general plotting>
There are mapping relations between air pressure, air humidity, air themperature and height above sea level, the gas set by treating location
Pressure, humidity and temperature measure, and combine the mapping relations, and position to be measured just more can be accurately determined under off-line state
Height above sea level.
<hardware configuration>
Fig. 1 shows the schematic diagram that can be used for realizing the electronic equipment of the embodiment of the present invention.As shown in Figure 1, electronic equipment
100 include processor 101, memory 102, communication device 103, display device 104, input unit 105 and sensor 106.
Processor 101 is, for example, central processor CPU, Micro-processor MCV etc..Memory 102 is (read-only for example including ROM
Memory), RAM (random access memory), the nonvolatile memory of hard disk etc..Communication device 103 is for example for electricity
Sub- equipment 100 carries out wire communication or wireless communication, e.g. bluetooth module with other equipment.Display device 104 for example with
In display reminding information etc., e.g. liquid crystal display.Input unit 105 is for example, existing to be set for what is input operation instruction
It is standby, e.g. keyboard, can Touch Screen etc..
Sensor 106 is for example for measuring the humidity value, temperature value and atmospheric pressure value of current location, for example including humidity sensor
Device, temperature sensor and baroceptor.Humidity sensor is, for example, lithium chloride humidity sensor, carbon dew cell, aluminium oxide
Humidity sensor, Ceramic Humidity Sensor etc..Temperature sensor is, for example, thermocouple temperature sensor, thermistor temp sensing
Device, infrared temperature sensor, semiconductor temperature sensor etc..Pressure sensor is, for example, silicon semiconductor sensor, ambrose alloy conjunction
Golden sensor, ceramic sensor element etc..
Electronic equipment 100 shown in FIG. 1 is only explanatory, and never be intended to limitation the present invention, its application or
Purposes.
<embodiment one>
A kind of height detection method is present embodiments provided, this method is for example implemented by the electronic equipment 100 in Fig. 1.Such as
Shown in Fig. 2, this approach includes the following steps S1100-S1200:
Step S1100, obtain position to be measured correspond to setting environmental characteristic vector current vector value, environmental characteristic to
Amount includes Humidity Features, temperature profile and gas pressure characteristic.
In the present embodiment, it is environmental characteristic vector that the essential environmental factors that will affect air pressure, which extracts, which includes humidity
Three feature, temperature profile and gas pressure characteristic dimensions.
The mode for obtaining the current vector value in position to be measured is, for example, to pass through humidity sensor, temperature sensor and air pressure sensing
Device measures the physical quantity of response respectively.
Step S1200 obtains the height value of corresponding current vector value according to the mapping relations of environmental characteristic vector and height
Height value as position to be measured.
The mapping relations of environmental characteristic vector and height, such as determined by empirical equation, in another example passing through machine learning
Mode obtain.
In one example, the mapping relations between environmental characteristic vector and height can be stored in the data table of comparisons,
A part of the data table of comparisons is as shown in the table:
| Height above sea level H (m) | Temperature T (DEG C) | Air pressure P (hPa) | Relative humidity RH |
| 200 | 13<T≤14 | 950<P≤960 | 40% < RH≤50% |
| 400 | 12<T≤13 | 940<P≤950 | 40% < RH≤50% |
| 600 | 11<T≤12 | 930<P≤940 | 40% < RH≤50% |
| 800 | 10<T≤11 | 920<P≤930 | 40% < RH≤50% |
In other example, the mapping relations between environmental characteristic vector and height be can store as mapping function H=f
(T, P, RH), wherein H represents height above sea level, and T represents temperature, and P represents air pressure, and RH represents relative humidity, and f indicates Height Prediction
Expression formula.Height value is determined by mapping function, can obtain higher accuracy.
In one specific example of the present embodiment, side that the mapping relations of environmental characteristic vector sum height pass through machine learning
Formula obtains, and describes the mapping relations by least square method supporting vector machine model.
Support vector machines (Support Vector Machine, SVM) is a kind of method that can be used for classifying or returning,
Processing Small Sample Database embodies stronger ability, and the theoretical basis of this method is structural risk minimization principle, passes through maximum
Change the solution of the interval acquisition problem between two classes.Support vector machines actually corresponds to a quadratic programming problem, which can
To obtain the solution of problem using Dual Method, which can also be solved using alternative manner.
Least square method supporting vector machine (Least Squares Support Vector Machine, LSSVM) is to branch
The improvement for holding vector machine model simplifies a large amount of operation, and still on the basis of holding support vector machines original advantage
Possess good performance.
In one specific example of the present embodiment, which further includes obtaining environmental characteristic vector sum height
The step of mapping relations, the step further comprise the steps S1010-S1020:
Step S1010 obtains the training sample set that is made of multiple training samples, wherein each training sample includes
The sample vector value and sample height value of the environmental characteristic vector to match.
In step S1010, the method for obtaining training sample set is, for example: for each training sample, measuring pre-
If the humidity under the conditions of temperature, preset pressure, preset height, according to preset temperature, preset pressure, preset height and measure wet
Degree obtains training sample.
The acquisition process of above-mentioned training sample is by temperature, air pressure and height as independent variable, using humidity as dependent variable
Carry out data acquisition.In this way, being conducive to rationally and effectively obtain training sample set.
Step S1020 obtains mapping relations according to training sample set.
In the present embodiment, mapping relations will be obtained by way of sample training and machine learning.
In a specific example of the present embodiment, which is the form of mapping function, the acquisition of mapping relations
Include:
According to training sample set, the optimal value of the parameter of the undetermined parameter of the Height Prediction expression formula of training setting is combined,
Obtain mapping function.
In this example, Height Prediction expression formula is the expression formula of the mapping function.Pass through sample set training Height Prediction table
Up to formula parameter to finding best parameter group, also just obtained the mapping function.
When the mapping relations of environmental characteristic vector and height are least square method supporting vector machine model, Height Prediction expression
Penalty factor and kernel functional parameter of the undetermined parameter of formula for example including least square method supporting vector machine.
In one example, the best parameter group of Height Prediction expression formula is found by genetic algorithm.Genetic algorithm
(Genetic Algorithm, GA) is a kind of using biological evolution and hereditary variation as the searching algorithm of theoretical basis.In nature
In, the only good individual of adaptation environment could survive, and good characteristic trait is passed to lower generation by heredity by individual.And in heredity
In the process, group generates the excellent individual of adaptability gradually by evolving to adapt to environmental change, and biocenose obtains constantly
Develop and perfect.The evolution of genetic algorithm simulation biology and genetic mechanism by using selection (duplication), intersect (recombination), change
Genetic manipulations such as different (mutation) derive follow-on individual in population, until obtaining satisfactory population.
In one example, the process for finding best parameter group includes the following steps S2100-S2300:
Step S2100, establishes initial population, and each of initial population body corresponds to one kind of Height Prediction expression formula
Combining parameter values.
Step S2200 selects initial population, intersected and is made a variation, and population of new generation is obtained.
Step S2300 continues selection to population of new generation, intersects and make a variation, until the individual adaptation degree of population is full
Sufficient preset condition or evolutionary generation reach default algebra, are obtained according to the corresponding combining parameter values of individual in population at this time optimal
Combining parameter values.
In one example, gaussian kernel function K (x is selectedi,xj)=exp (- γ | | xi-xj||2) as in support vector machines
Kernel function, the undetermined parameter of Height Prediction expression formula is (γ, C) at this time, wherein γ is kernel functional parameter, C be punishment because
Son.The purpose of genetic algorithm optimizing is to find the optimum parameter value combination of (γ, C).
In searching process, initial population is first established, such as obtains in initial population by way of random value
Body A, individual B and individual C, wherein the corresponding combining parameter values of individual A are (0.25,10), the corresponding combining parameter values of individual B are
(0.5,20), the corresponding combining parameter values of individual C are (1,30).The shape that above-mentioned parameter value Combination conversion is encoded at binary string
Formula converts Parametric optimization problem to the form of gene coding.
Fitness function is arranged are as follows: h (γ, C)=accuracy.Wherein, accuracy is testing on training sample set
Demonstrate,prove accuracy rate.Fitness evaluation is carried out to population primary, such as the verifying accuracy rate of individual A is 0.5, the verifying of individual B is accurate
Rate is 0.6, and the verifying accuracy rate of individual C is 0.7, then individual C is fitness optimum individual.By the verifying accuracy rate of individual C with
Target accuracy rate is compared, and target accuracy rate is, for example, 0.85, then the verifying accuracy rate of individual C is unsatisfactory for required precision, is needed
Optimal value of the parameter combination is found by evolving.
When evolving, the select probability of individual is determined according to fitness value individual in initial population first, for example, by
Selection operator based on roulette wheel method carries out selection operation, obtains the individual for duplication.
In a replication process, setting crossover probability value is, for example, [0.3,0.4], and mutation probability is, for example, 0.03.Duplication
When the fitness optimum individual C of previous generation is retained.Intersect assuming that gene has occurred in duplication in individual A and individual B, hands over
Individual A ' and individual B ' is obtained after fork, the corresponding combining parameter values of individual A ' are (0.25,20), the corresponding parameter value of individual B '
Combination is (0.5,10).
The individual adaptation degree of population of new generation is calculated, show that the verifying accuracy rate of individual A ' is 0.9, individual B's '
Verifying accuracy rate is 0.8, and the verifying accuracy rate of individual C is 0.7.The verifying accuracy rate of fitness optimum individual A ' meets essence at this time
Degree requires, and the corresponding combining parameter values (0.25,20) of individual A ' are optimal value of the parameter combination.
Height detection method in the present embodiment, by measuring the environmental characteristic vector of position to be measured, and combining environmental is special
The mapping relations between vector sum height are levied, the height above sea level of position to be measured more can be accurately determined under off-line state.
<example>
Illustrate the embodiment of height detection method provided in this embodiment with a specific example below.
As shown in figure 3, Integrated Humidity Sensor, temperature sensor and baroceptor in the electronic device first.The electricity
Sub- equipment is, for example, wearable device.Meanwhile GA-LSSVM algorithm model is constructed, and carry out to the parameter of model reasonable initial
Change operation, population scale, initial population, individual lengths, crossover probability, mutation probability, maximum evolution generation including genetic algorithm
Several and fitness function, the penalty factor optimizing section and kernel functional parameter optimizing section of least square method supporting vector machine, and
The training precision of algorithm model.
Later, under known environment, multiple groups temperature under sensor module based on wearable device acquisition different condition,
Air pressure, humidity, altitude information, as training sample set.
After obtaining training sample, training sample data are normalized, and by the number of training after normalization
According to being input in initial GA-LSSVM model, it is based on fitness function, calculates the fitness of initial population, and carry out fitness and comment
Valence.Using optimum maintaining strategy, the optimal individual of fitness is preserved, and judges whether the optimal individual of the fitness is full
Sufficient required precision is directly combined if meeting the requirements according to the optimal value of the parameter that the individual obtains GA-LSSVM model, if not
Meet and then enters evolutional operation.
In evolutional operation, population of new generation (previous generation population is obtained by selection operation, crossover operation and mutation operation
Fitness optimum individual also remain into population of new generation).To population of new generation, continue above-mentioned fitness evaluation and into
Change operation, until the fitness optimum individual of certain generation population meets required precision or evolutionary generation has reached default algebra.
At this point, being combined according to the optimal value of the parameter that fitness optimum individual in group obtains GA-LSSVM model, complete to GA-LSSVM
The optimization of model.
GA-LSSVM model after optimization is stored in the memory of wearable device.When needing to carry out height detection,
It is detected based on air humidity, temperature gentle pressure of the sensor in wearable device to current location, and it is defeated to will test value
Enter into GA-LSSVM model, the height above sea level of current location is calculated.
<embodiment two>
The present embodiment provides a kind of height detecting devices.As shown in figure 4, height detecting device 400 includes sensor unit
410 and data processing unit 420.
Sensor unit 410, the current vector value for corresponding to the environmental characteristic vector of setting for obtaining position to be measured, ring
Border feature vector includes Humidity Features, temperature profile and gas pressure characteristic；
Data processing unit 420 obtains corresponding current vector for the mapping relations according to environmental characteristic vector and height
Height value of the height value of value as position to be measured.
In a specific example of the present embodiment, sensor unit 410 is gentle including humidity sensor, temperature sensor
Pressure sensor.
In a specific example of the present embodiment, mapping relations are least square method supporting vector machine model.
In a specific example of the present embodiment, height detecting device 400 further includes that mapping relations obtain module, this is reflected
It penetrates Relation acquisition module to be used for: obtaining the training sample set that is made of multiple training samples, wherein each training sample includes
The sample vector value and sample height value of the environmental characteristic vector to match；According to training sample set, mapping relations are obtained.
In a specific example of the present embodiment, mapping relations obtain module and are also used to: according to training sample set, instruction
The optimal value of the parameter combination for practicing the undetermined parameter of the Height Prediction expression formula of setting, obtains mapping function.
In a specific example of the present embodiment, mapping relations obtain module and are also used to: establish initial population, initial kind
Each of group body corresponds to a kind of combining parameter values of Height Prediction expression formula；Initial population is selected, intersect and
Variation, obtains population of new generation；Selection is continued to population of new generation, intersects and makes a variation, until the individual adaptation degree of population
Meet preset condition or evolutionary generation reaches default algebra, is obtained most according to the corresponding combining parameter values of individual in population at this time
Excellent combining parameter values.
In a specific example of the present embodiment, mapping relations obtain module and are also used to: obtaining the corresponding height of individual
Prediction expression, wherein the undetermined parameter of Height Prediction expression formula is the corresponding combining parameter values of individual；It is corresponding by individual
Height Prediction expression formula predicts the corresponding height of sample vector value in training sample set, obtains Height Prediction value；
Individual adaptation degree is obtained according to Height Prediction value and sample height value.
In a specific example of the present embodiment, combining parameter values include the penalty factor of least square method supporting vector machine
And kernel functional parameter.
In a specific example of the present embodiment, height detecting device 400 further includes sample acquisition unit, which obtains
It takes unit to be used for: for each training sample, measuring humidity under the conditions of preset temperature, preset pressure, preset height, according to
Preset temperature, preset pressure, preset height and the humidity measured obtain training sample.
<embodiment three>
The present embodiment provides a kind of electronic equipment, e.g. wearable device.The electronic equipment includes institute in embodiment two
The height detecting device stated, alternatively, the electronic equipment is as shown in Figure 5, comprising:
Memory 510, for storing executable command；
Processor 520, for executing such as institute in embodiment one under the control for the executable command that memory 510 stores
The method stated.
The present invention can be system, method and/or computer program product.Computer program product may include computer
Readable storage medium storing program for executing, containing for making processor realize the computer-readable program instructions of various aspects of the invention.
Computer readable storage medium, which can be, can keep and store the tangible of the instruction used by instruction execution equipment
Equipment.Computer readable storage medium for example can be-- but it is not limited to-- storage device electric, magnetic storage apparatus, optical storage
Equipment, electric magnetic storage apparatus, semiconductor memory apparatus or above-mentioned any appropriate combination.Computer readable storage medium
More specific example (non exhaustive list) includes: portable computer diskette, hard disk, random access memory (RAM), read-only deposits
It is reservoir (ROM), erasable programmable read only memory (EPROM or flash memory), static random access memory (SRAM), portable
Compact disk read-only memory (CD-ROM), digital versatile disc (DVD), memory stick, floppy disk, mechanical coding equipment, for example thereon
It is stored with punch card or groove internal projection structure and the above-mentioned any appropriate combination of instruction.Calculating used herein above
Machine readable storage medium storing program for executing is not interpreted that instantaneous signal itself, the electromagnetic wave of such as radio wave or other Free propagations lead to
It crosses the electromagnetic wave (for example, the light pulse for passing through fiber optic cables) of waveguide or the propagation of other transmission mediums or is transmitted by electric wire
Electric signal.
Computer-readable program instructions as described herein can be downloaded to from computer readable storage medium it is each calculate/
Processing equipment, or outer computer or outer is downloaded to by network, such as internet, local area network, wide area network and/or wireless network
Portion stores equipment.Network may include copper transmission cable, optical fiber transmission, wireless transmission, router, firewall, interchanger, gateway
Computer and/or Edge Server.Adapter or network interface in each calculating/processing equipment are received from network to be counted
Calculation machine readable program instructions, and the computer-readable program instructions are forwarded, for the meter being stored in each calculating/processing equipment
In calculation machine readable storage medium storing program for executing.
Computer program instructions for executing operation of the present invention can be assembly instruction, instruction set architecture (ISA) instructs,
Machine instruction, machine-dependent instructions, microcode, firmware instructions, condition setup data or with one or more programming languages
The source code or object code that any combination is write, the programming language include the programming language-of object-oriented such as
Smalltalk, C++ etc., and conventional procedural programming languages-such as " C " language or similar programming language.Computer
Readable program instructions can be executed fully on the user computer, partly execute on the user computer, be only as one
Vertical software package executes, part executes on the remote computer or completely in remote computer on the user computer for part
Or it is executed on server.In situations involving remote computers, remote computer can pass through network-packet of any kind
It includes local area network (LAN) or wide area network (WAN)-is connected to subscriber computer, or, it may be connected to outer computer (such as benefit
It is connected with ISP by internet).In some embodiments, by utilizing computer-readable program instructions
Status information carry out personalized customization electronic circuit, such as programmable logic circuit, field programmable gate array (FPGA) or can
Programmed logic array (PLA) (PLA), the electronic circuit can execute computer-readable program instructions, to realize each side of the invention
Face.
Referring herein to according to the method for the embodiment of the present invention, the flow chart of device (system) and computer program product and/
Or block diagram describes various aspects of the invention.It should be appreciated that flowchart and or block diagram each box and flow chart and/
Or in block diagram each box combination, can be realized by computer-readable program instructions.
These computer-readable program instructions can be supplied to general purpose computer, special purpose computer or other programmable datas
The processor of processing unit, so that a kind of machine is produced, so that these instructions are passing through computer or other programmable datas
When the processor of processing unit executes, function specified in one or more boxes in implementation flow chart and/or block diagram is produced
The device of energy/movement.These computer-readable program instructions can also be stored in a computer-readable storage medium, these refer to
It enables so that computer, programmable data processing unit and/or other equipment work in a specific way, thus, it is stored with instruction
Computer-readable medium then includes a manufacture comprising in one or more boxes in implementation flow chart and/or block diagram
The instruction of the various aspects of defined function action.
Computer-readable program instructions can also be loaded into computer, other programmable data processing units or other
In equipment, so that series of operation steps are executed in computer, other programmable data processing units or other equipment, to produce
Raw computer implemented process, so that executed in computer, other programmable data processing units or other equipment
Instruct function action specified in one or more boxes in implementation flow chart and/or block diagram.
The flow chart and block diagram in the drawings show the system of multiple embodiments according to the present invention, method and computer journeys
The architecture, function and operation in the cards of sequence product.In this regard, each box in flowchart or block diagram can generation
One module of table, program segment or a part of instruction, the module, program segment or a part of instruction include one or more use
The executable instruction of the logic function as defined in realizing.In some implementations as replacements, function marked in the box
It can occur in a different order than that indicated in the drawings.For example, two continuous boxes can actually be held substantially in parallel
Row, they can also be executed in the opposite order sometimes, and this depends on the function involved.It is also noted that block diagram and/or
The combination of each box in flow chart and the box in block diagram and or flow chart, can the function as defined in executing or dynamic
The dedicated hardware based system made is realized, or can be realized using a combination of dedicated hardware and computer instructions.It is right
For art technology personage it is well known that, by hardware mode realize, by software mode realize and pass through software and
It is all of equal value that the mode of combination of hardware, which is realized,.
Various embodiments of the present invention are described above, above description is exemplary, and non-exclusive, and
It is not limited to disclosed each embodiment.Without departing from the scope and spirit of illustrated each embodiment, for this skill
Many modifications and changes are obvious for the ordinary skill personage in art field.The selection of term used herein, purport
In principle, the practical application or to the technological improvement in market for best explaining each embodiment, or make the art its
Its ordinary skill personage can understand each embodiment disclosed herein.The scope of the present invention is defined by the appended claims.
Claims (10)
1. a kind of height detection method, comprising:
The current vector value that position to be measured corresponds to the environmental characteristic vector of setting is obtained, the environmental characteristic vector includes humidity
Feature, temperature profile and gas pressure characteristic；
According to the mapping relations of the environmental characteristic vector and height, the height value of the corresponding current vector value is obtained as institute
State the height value of position to be measured.
2. according to the method described in claim 1, wherein, the mapping relations are least square method supporting vector machine model.
3. method according to claim 1 or 2, wherein the method also includes obtaining the mapping relations, packet
It includes:
Obtain the training sample set being made of multiple training samples, wherein each training sample includes the institute to match
State the sample vector value and sample height value of environmental characteristic vector；
According to the training sample set, the mapping relations are obtained.
4. according to the method described in claim 3, wherein, the mapping relations are mapping function, described according to the trained sample
This set obtains the mapping relations, comprising:
According to the training sample set, the optimal value of the parameter of the undetermined parameter of the Height Prediction expression formula of training setting is combined,
Obtain the mapping function.
5. according to the method described in claim 4, wherein, described according to the training sample set, the height of training setting is pre-
Survey the optimal value of the parameter combination of the undetermined parameter of expression formula, comprising:
Initial population is established, each of described initial population body corresponds to a kind of parameter value of the Height Prediction expression formula
Combination；
The initial population is selected, intersected and made a variation, population of new generation is obtained；
The selection is continued to the population of new generation, intersects and makes a variation, until the individual adaptation degree of the population meets
Preset condition or evolutionary generation reach default algebra, are obtained according to the corresponding combining parameter values of the individual in population at this time
It is combined to the optimal value of the parameter.
6. according to the method described in claim 5, wherein, the method also includes obtaining individual adaptation degree, comprising:
Obtain the corresponding Height Prediction expression formula of the individual, wherein the undetermined parameter of the Height Prediction expression formula is described
The corresponding combining parameter values of individual；
By the corresponding Height Prediction expression formula of the individual to the corresponding height of sample vector value in the training sample set
Degree is predicted, Height Prediction value is obtained；
The individual adaptation degree is obtained according to the Height Prediction value and the sample height value.
7. according to the method described in claim 4, wherein, the combining parameter values include the punishment of least square method supporting vector machine
The factor and kernel functional parameter.
8. it is described to obtain the training sample set being made of multiple training samples according to the method described in claim 3, wherein,
Include:
For each training sample, humidity under the conditions of preset temperature, preset pressure, preset height is measured, according to institute
It states preset temperature, preset pressure, preset height and the humidity measured and obtains the training sample.
9. a kind of height detecting device, comprising:
Sensor unit, the current vector value for corresponding to the environmental characteristic vector of setting for obtaining position to be measured, the environment
Feature vector includes Humidity Features, temperature profile and gas pressure characteristic；
Data processing unit, for the mapping relations according to the environmental characteristic vector and height, obtain it is corresponding it is described when it is preceding to
Height value of the height value of magnitude as the position to be measured.
10. a kind of electronic equipment, including height detecting device as claimed in claim 8；Alternatively, the electronic equipment includes:
Memory, for storing executable command；
Processor, for executing such as method of any of claims 1-8 under the control of the executable command.
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